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AI in machine tools advanced rapidly between 21 and 28 November 2025. Several major industrial vendors demonstrated real applications of artificial intelligence inside CNC engineering, maintenance, welding inspection and factory operations. Siemens showed autonomous engineering at SPS 2025. IFS released AI digital workers that automate maintenance and materials. Honeywell confirmed the use of generative AI assistants in live industrial environments. New research in weld defect detection reached accuracy levels suitable for production. These updates strengthened the position of AI in machine tools and signaled that adoption is accelerating. 1. Siemens demonstrated AI in machine tools at SPS 2025 Siemens delivered the most important update for AI in machine tools during SPS 2025 in Nuremberg. The company demonstrated its Engineering Copilot TIA. This system used generative AI to automate engineering tasks and modify automation project elements without coding. Key outcomes: Automated creation of components inside TIA Portal AI generated improvements to engineering workflows Faster configuration of automation projects Integrated AI support across engineering, commissioning and production Source:https://press.siemens.com/global/en/pressrelease/siemens-showcases-future-autonomous-production-sps-2025 Why this matters for AI in machine toolsThe demonstration showed practical gains for CNC programming, CAM logic and cell automation. Siemens confirmed that AI driven engineering is now operational rather than experimental. This will influence how machine shops adopt automation and digital workflows in 2026. 2. IFS released AI digital workers that support machine tool environments On 27 November IFS launched IFS Cloud 25R2. The update introduced several digital workers powered by AI. These workers automate maintenance, materials management and operations tasks that directly affect machine tool uptime. Digital worker capabilities: Automatic completion of work order fields Conversion of operator notes and speech into structured data AI supported reliability analysis Inventory and material flow optimisation Source:https://www.ifs.com/news/product/ifs-cloud-25r2 Why this matters for AI in machine toolsMachine shops lose time to maintenance administration and material delays. IFS stated that invisible admin tasks can consume forty to sixty percent of technician time. AI digital workers reduce this overhead and increase CNC availability. 3. Honeywell advanced AI in factory operations with generative operator assistants During the Honeywell User Group Europe event this week, Honeywell confirmed that generative AI assistants were supporting operators inside live industrial environments. The AI helped explain alarms, retrieve historical incident data and suggest best practice responses. Capabilities included: Clear natural language explanations for alarms Retrieval of past corrective actions AI driven recommendations for resolving issues Identification of cyber and operational anomalies Source:https://timesofindia.indiatimes.com/business/international-business/honeywell-expands-ai-driven-industrial-capabilities/articleshow/114392298.cms Why this matters for AI in machine toolsThis approach will extend into CNC cells. Operator copilots can reduce downtime by improving decision making. Honeywell proved that generative AI can run safely inside real operational technology systems. 4. New AI research improved weld defect detection for manufacturing A peer reviewed study published this week demonstrated progress in AI based weld inspection. Researchers trained a YOLOv7 deep learning model on three thousand weld seam images to detect cracks, porosity, slag inclusion and undercut. Results included: Precision of approximately ninety seven percent mAP of approximately ninety four percent Strong generalisation on external datasets Source:https://www.frontiersin.org/articles/10.3389/frobt.2025.1564092/full Why this matters for machine tools and fabricationWelding accuracy is central to sheet metal manufacturing. AI based weld inspection can reduce rework, increase consistency and improve quality standards. This research showed that AI is becoming capable of reliable production performance. Key Takeaways for AI in Machine Tools Siemens demonstrated autonomous engineering powered by AI IFS launched digital workers that automate maintenance and materials Honeywell confirmed generative AI deployment inside industrial control rooms New research delivered near industrial accuracy for weld defect detection Adoption of AI in machine tools continued to accelerate across core workflows FAQ About AI in Machine Tools What was the most important AI in machine tools update this week The Siemens demonstration at SPS 2025 was the most impactful. It showed autonomous engineering workflows created by generative AI. How does AI in machine tools help small and mid sized shops It reduces programming time, cuts maintenance delays, improves materials flow and strengthens quality control. These improvements increase productivity without new machine purchases. Are factories already using AI in machine tools Yes. Honeywell confirmed live deployments of generative AI assistants. IFS also released AI digital workers designed for daily use. Where is AI in machine tools creating value right now Programming speed, maintenance automation, materials management, operator support and weld inspection are the leading areas. AI in welding automation CNC programming and CAM automation ### Nomagic and Zalando Expand Robotic Warehouse Capabilities Across Europe Canonical URL: https://machinetoolnews.ai/nomagic-zalando-ai-robotic-automation/ Published: 2025-10-30T09:37:03+00:00 Modified: 2025-11-04T10:08:00+00:00 Author: Publisher Categories: Germany, Poland, Robotics Featured image: https://machinetoolnews.ai/wp-content/uploads/2025/10/Zalando-SE_2017_Convenience_Logistics_Fulfillment-centers_Szczecin_Architecture_21-16_9.jpg Featured image alt: nomagic zalando ai robotics AI-driven robotics specialist Nomagic has been selected by Zalando, Europe’s leading online destination for fashion and lifestyle, to expand robotic automation across its fulfilment network and further strengthen operational excellence. The partnership builds on a proven track record: Nomagic’s intelligent robotic systems have already delivered measurable efficiency gains and reliability in existing Zalando facilities, supporting employees with repetitive daily tasks. Scaling AI-Powered Picking Robots Nomagic’s robots – known internally as Richard – perform complex warehouse processes such as item-level picking, scanning, and induction into automated pocket sorters. Following a successful pilot that achieved industry-leading performance with an average of 10,000 picks per day, Zalando will now expand deployment. A total of nine robots will be operational by the end of 2025, with double-digit numbers planned for 2026. As part of the collaboration, Zalando has also made a minority investment in Nomagic, participating in its previously announced $44 million Series B funding round. “At Zalando, we are continuously developing and testing the automation of work processes in logistics, both to make the work of employees easier and to provide customers with first-class service,” said Marcus Daute, Vice President Logistics Network at Zalando. “Nomagic’s technology has demonstrated clear, measurable value in our operations. Their AI-driven solutions allow us to scale automation quickly while maintaining the high service levels our customers expect.” Continuous Learning for Complex Inventories Nomagic’s computer-vision and machine-learning systems enable its robots to handle a vast and ever-changing assortment of products. The technology continuously learns and adapts — a vital capability for processing the wide variety of fashion and lifestyle items managed within Zalando’s fulfilment centres. About Nomagic Founded eight years ago by a team of former Google engineers, leading academics, and warehouse automation specialists, Nomagic’s mission is to use AI to understand and solve real-world physical problems. Its intelligent automation solutions are designed to take on repetitive warehouse tasks while integrating seamlessly into existing workflows. The company collaborates closely with customers to develop tailored, scalable systems that transform warehouse operations and support long-term efficiency and sustainability goals. Kacper Nowicki, CEO of Nomagic, concluded: “The partnership between Nomagic and Zalando will bring our technology to more fulfilment centres across Europe. Together, we’re proving that AI and robotics can drive real operational improvements at scale.” ### China’s AI Car Factories: How They Work – and Whether They’re Coming West Canonical URL: https://machinetoolnews.ai/ai-car-factories/ Published: 2025-10-15T09:30:57+00:00 Modified: 2025-10-16T13:23:35+00:00 Author: Publisher Categories: automotive, Hero - Home, Robotics, Top Picks Tags: Editors Pick, Popular, popular topics, Strategy Featured image: https://machinetoolnews.ai/wp-content/uploads/2025/10/ai-car-factories.png Featured image alt: ai car factories China’s leading EV makers have transformed vehicle plants into software-defined, sensor-rich “AI car factories,” combining robotics, computer vision, digital twins, and real-time optimization. Beyond the hype, these plants are delivering measurable gains in throughput, quality, and cost. The key question now is not if this model works – but where and how fast it will spread beyond China. What makes China’s “AI car factories” different? Automation + AI at scale. Chinese EV plants deploy robots, AGVs, and in-line AI inspection to reduce manual intervention. Vision and in-line analytics. Advanced machine vision systems catch micro-defects early in assembly, improving yield. Software-defined production. Factories are often planned via digital twins before physical buildout; manufacturing execution systems (MES), data pipelines, and vehicle software feed into one continuous feedback loop. These traits have allowed newer Chinese EV makers to leapfrog legacy manufacturers by building with full integration of AI, automation, and data orchestration from day one. Why China pulled ahead Greenfield advantage. Many of China’s EV entrants built factories from scratch, embedding advanced automation and modular processes rather than retrofitting old ones. Policy support. Industrial strategy, subsidies, and regional clustering aligned manufacturers and suppliers around smart manufacturing. Software-hardware feedback loop. Vehicle software, autonomy stacks, and factory operations are treated as an integrated system, enabling fast iteration and tuning. Are these AI factories coming to the West? Europe: Yes – already underway At IAA Mobility in September 2025, Stella Li, BYD’s Executive Vice President, told Reuters: “We are training ourselves to be more European in production … Give us like two to three years.” – Stella Li, Reuters, Sept. 2025 (reuters.com) BYD is building a plant in Hungary (startup by end-2025) and another in Turkey (2026) to support this shift. (reuters.com) Alfredo Altavilla, BYD’s European adviser, added: “It does not make sense to invest in car assembly (in Europe) but bring batteries from China.” – Altavilla, Reuters, Sept. 2025 (reuters.com) Moreover, European expansion is gaining steam: BYD plans for all Europe-destined EVs to be produced locally by 2028. (reuters.com) Spain is also emerging as a frontrunner for BYD’s third European plant. (reuters.com) Self-driving push via Europe: Chinese autonomous driving firms are focusing on Europe because the U.S. market remains blocked. Reuters reports: “Blocked from the U.S. market, Chinese self-driving technology firms are accelerating their push into Europe …” – Reuters, Oct. 6, 2025 (reuters.com) United States: Highly constrained In October 2025, Donald Trump declared massive new tariffs and export control plans: “Starting November 1st … the United States of America will impose a Tariff of 100% on China, over and above any tariff that they are currently paying.” – Donald Trump via Truth Social, Reuters, Oct. 10, 2025 (reuters.com) He also announced new export controls on U.S. critical software. (Reuters) The heightened stance makes Chinese factory deployment in the U.S. a very high-risk strategy. Representative quotes (2025) “We are training ourselves to be more European in production … Give us like two to three years.” – Stella Li, Reuters, Sept. 2025 “It does not make sense to invest in car assembly (in Europe) but bring batteries from China.” – Alfredo Altavilla, Reuters, Sept. 2025 “Starting November 1st … the United States of America will impose a Tariff of 100% on China, over and above any tariff that they are currently paying.” – Donald Trump, Reuters, Oct. 2025 What to watch next (12–24 months) Execution of BYD’s European plants and whether a Spain facility is confirmed. European regulation on foreign investment, IP protection, and data controls. Whether Trump follows through on the 100% tariff and software controls. Acceleration of AI car factories upgrades among European and U.S. OEMs. Localization of battery, semiconductor, sensor, and rare earth supply to support AI factories. Takeaway (2025 perspective) China’s EV makers have built a powerful AI factory model-robotics, digital twins, data orchestration-and now aim to export it. Europe is already seeing early signs via BYD’s localization and self-driving initiatives. The U.S., however, presents steep barriers: massive tariffs and software export controls make Chinese factory entry extremely challenging. The more likely path is for Western automakers to adopt the AI factory methods internally, while Chinese firms expand in more hospitable regions. ### CloudNC’s CAM Assist 2.0 Brings AI-Powered CNC Programming to 1,000+ Machine Shops Worldwide Canonical URL: https://machinetoolnews.ai/cloudnc-cam-assist-2-ai-cnc-programming/ Published: 2025-09-23T10:14:01+00:00 Modified: 2025-09-23T10:14:04+00:00 Author: Publisher Categories: AI in CNC, Featured, Hero - Home, Software, Top Picks Tags: Editors Pick, Featured, Popular Featured image: https://machinetoolnews.ai/wp-content/uploads/2025/09/CAM-Assist-Insistu-light-Binocular-Part.png Featured image alt: CloudNC CAM Assist 2.0 AI CNC programming interface with strategy analysis CloudNC has announced a major milestone for its flagship CAM automation solution. More than 1,000 machine shops across the globe are now using CAM Assist to accelerate CNC programming with artificial intelligence. Since its launch in 2024, CAM Assist has been helping machinists overcome one of the industry’s biggest bottlenecks: the time and expertise required to convert CAD models into machining strategies and reliable toolpaths. By automating the most repetitive and time-consuming programming steps, the solution allows engineers to spend less time on setup and more time on higher-value work. Now, CloudNC is rolling out CAM Assist 2.0, a significant upgrade that blends the speed of AI with greater transparency and human oversight. The result is a workflow that not only reduces programming time but also builds user confidence in every decision the AI makes. AI and Human Expertise Combined CAM Assist 2.0 introduces step-by-step guidance, enabling users to review and adjust strategies at each stage before they are finalised. Programmers can configure machines, materials, and tool assemblies in minutes, see AI-driven recommendations in context, and then approve or edit them before export. This “human-in-the-loop” design ensures that shop standards are never compromised while still saving hours on programming. “AI and human expertise can work side by side,” explained Dr Andy Cheadle, CTO at CloudNC. “By letting machinists see – and influence – the AI’s decisions, CAM Assist 2.0 helps shops program faster and with greater confidence.” For users, the benefits are immediate. Anthony Stephenson, prototype machinist at Avalanche Energy, commented: “CAM Assist automates the most time-consuming programming work in just a few clicks. The new 2.0 interface is clean and intuitive, making programming easier and faster—and helping us get more parts out the door.” Key Features of CAM Assist 2.0 One-click configuration: Intelligent defaults streamline setup so programming can begin within minutes. AI guidance at every step: Recommendations adapt to part geometry, machine limits, tools, and fixtures. Human-in-the-loop oversight: Every strategy can be reviewed, adjusted, and approved before committing. Seamless integration: Approved toolpaths are pushed directly back into Autodesk Fusion, Mastercam, or Siemens NX. By combining AI speed with hands-on control, CloudNC is positioning CAM Assist as a practical tool for the modern shop floor. With machine, material, and tool data stored in the cloud, shops can also standardise output across teams, ensuring consistent results and reducing variation between programmers. Impact on Manufacturing Productivity The growth of CAM Assist reflects a wider trend in advanced manufacturing: using AI to address skills shortages and improve throughput. Machine shops are under increasing pressure to deliver parts faster, with fewer skilled programmers available. By embedding intelligence directly into the CAM workflow, CloudNC believes CAM Assist offers a scalable way for manufacturers to close that gap. CloudNC, founded in 2015, continues to invest in AI solutions for manufacturing. Backed by leading investors including Atomico, Episode 1 Ventures, Autodesk, and Lockheed Martin, the company is headquartered in London with its own factory in Chelmsford. Its mission remains clear—to transform how parts are made by bringing automation and intelligence to every stage of CNC machining. With CAM Assist 2.0 now available to all existing customers and further platform integrations on the horizon, CloudNC is set to play a central role in the future of AI-driven manufacturing. For further information on CloudNC please visit: https://www.cloudnc.com ### Inside the Limitless Labs AI CAM Agent: CEO David Priev on the Next Shift in CNC Programming Canonical URL: https://machinetoolnews.ai/cam-agent-limitless-labs/ Published: 2026-09-04T10:46:24+00:00 Modified: 2026-09-04T10:46:26+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, Events, General, Software, Software / CAM / IIoT, USA Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/09/ChatGPT-Image-Sep-4-2026-08_39_15-AM.png Featured image alt: AI CAM Agent from Limitless Labs with CEO David Priev and CNC programming interface AI is moving rapidly into CNC programming, but Limitless Labs is pushing the technology considerably further than the familiar manufacturing chatbot or software copilot. The company is building an AI CAM Agent designed to work directly inside the CAD/CAM systems programmers already use, understand the physical realities of machining, learn how an individual machine shop works and generate native CAM operations that remain completely editable by the programmer. With support for Siemens NX, Mastercam now moving through pilot installations ahead of general availability at IMTS 2026, and PTC Creo on the roadmap, Limitless Labs is aiming directly at one of manufacturing’s biggest bottlenecks: the shortage of experienced CAM programming capacity. MachineToolNews.ai sat down with David Priev, CEO & Co-Founder of Limitless Labs, to find out how the technology works, why the company has moved into Mastercam, how proprietary manufacturing knowledge is protected and why Priev believes AI will fundamentally change the role of the CNC programmer. The conversation comes at a pivotal moment for AI in CAM. MachineToolNews.ai recently reported on the Cimatron CAM Agent, developed with Limitless Labs and set to make its debut at IMTS 2026. Limitless Labs believes the next step goes much deeper. At the centre of its approach is what the company calls a Physical AI Foundation Model for precision manufacturing, designed around CAD geometry, machining physics and the operational constraints of real CNC machines rather than relying purely on general-purpose language models. For machine shops, the proposition is significant: give the AI access to the tools, machines, fixtures, parameters and selected manufacturing knowledge already used by the business, allow it to generate a proposed machining process, then put the experienced programmer in control of reviewing and approving the result. Here is what David Priev told MachineToolNews.ai. MTN: Limitless Labs recently expanded to support Mastercam. What drove the decision, and what does it mean for current users? David Priev: Mastercam is where most North American job shops actually work. We started with Siemens NX because that’s where our first customers were, but if you want to reach the shops making the bulk of precision parts in the US, you have to be inside Mastercam. So that’s what we’re doing. To be precise about where it stands: it’s running with pilot customers right now, and will be GA at IMTS. That Mastercam move significantly expands the potential reach of the Limitless technology. Rather than trying to persuade programmers to move to a new CAM ecosystem, Limitless is taking its AI into the software environments manufacturers have already invested years building their workflows around. The company will demonstrate its CAM Agent at IMTS 2026, including workflows running alongside both Siemens NX and Mastercam. MTN: How does the CAM Agent fit into a programmer’s existing CAD/CAM environment without forcing them into a new software suite? David Priev: The programmer opens CAM the same way they did yesterday. The agent works inside it automatically. There’s no export, no intermediate file format, no “Limitless workflow” to learn. That matters more than it sounds. A shop’s CAM install isn’t stock software, it’s years of accumulated setup: their post processor, their tool library, their operation templates, their naming conventions. Anything that asks them to leave that behind is asking them to throw away real work. So we use their fixtures, their tools, their templates. What comes out is native CAM operations. The programmer can click into any one of them and change it, the same as if a colleague had programmed the part. Mastercam is in pilot on the same principle, and Creo is on the roadmap. This could be one of the most commercially important aspects of the Limitless approach. Machine shops do not operate generic versions of CAM software. Over years of production they accumulate tool libraries, post processors, preferred strategies, operation templates, feeds and speeds and programming conventions that become part of the company’s manufacturing capability. Limitless is effectively attempting to place an AI programmer on top of that accumulated knowledge rather than asking the manufacturer to start again. The result is intended to be native CAM operations that can still be opened, inspected, changed and approved by the programmer. MTN: How does AWS GovCloud ensure sensitive operational data stays protected? David Priev: We run two deployments, because our customers don’t have one threat model. ITAR and Aerospace and Defence are deployed in GovCloud, with the access controls and personnel restrictions that the environment requires. Commercial industrial customers run on our standard secured AWS regional cloud. A shop cutting aerospace hardware under export control and a shop cutting industrial machinery components have genuinely different obligations. It is important to say, no infrastructure choice makes data “fully protected” on its own. GovCloud is a boundary, and what matters is what you put inside it and who can reach in. Customer data is isolated per tenant, and part geometry doesn’t move between customers. For aerospace and defence manufacturers, this is likely to be fundamental to whether AI CAM technology gets through the door at all. Limitless uses AWS GovCloud for relevant sensitive deployments. The environment is designed by AWS for organisations handling controlled and regulated workloads, including workloads subject to ITAR requirements. The important point from Limitless is that security does not end with the infrastructure. How customer information is segregated, who is permitted access and what happens to manufacturing data after it enters the AI system are increasingly becoming part of the buying decision. MTN: How does the CAM Agent keep proprietary data isolated, unlike public AI models trained on internet data? David Priev: Two things get mixed together here, and they’re worth separating. One is what a model learned before it ever met you. The other is where your data goes once you start using it. The second one is what actually keeps shop owners up at night. Your geometry does not train anything shared. If the agent learns something from your parts, your setups, your feeds and speeds, that stays yours. It doesn’t show up in another shop’s session, and it doesn’t show up in a competitor’s quote. We put that in writing, because the question we get from every serious customer is the same: if I show you my hardest part, does it help my competitor? The answer has to be no, and it has to be contractual, not just architectural. This gets straight to one of the biggest questions surrounding AI adoption in precision manufacturing. A company’s competitive advantage is often sitting inside its CAM data. It can be the way an experienced programmer machines a difficult pocket, the cutting parameters that have been refined ove... ### Hexagon Brings AI-Assisted Metrology to OPTIV S Ahead of IMTS 2026 Canonical URL: https://machinetoolnews.ai/hhexagon-ai-optiv-s-imts/ Published: 2026-09-03T07:59:57+00:00 Modified: 2026-09-03T07:59:59+00:00 Author: Jelena Radojcic Categories: AI in Machining, Metrology & Vision, News, USA Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/09/ChatGPT-Image-Sep-3-2026-08_46_44-AM.png Featured image alt: Hexagon OPTIV S optical CMM with AI-assisted metrology technology ahead of IMTS 2026 Hexagon is bringing artificial intelligence deeper into industrial quality inspection with the launch of its next-generation OPTIV S optical coordinate measuring machine, combining faster measurement performance with a new generation of AI-assisted capabilities. Announced on 2 September ahead of IMTS 2026, the redesigned Hexagon OPTIV S is aimed at manufacturers inspecting complex, high-value components across electronics, medical devices, automotive and precision engineering. The launch is significant beyond the introduction of another CMM. Hexagon is beginning to use AI to automate some of the decisions traditionally made by experienced metrology programmers, particularly around edge detection, lighting and optical measurement setup. It also adds to a wider shift towards AI-driven quality control that MachineToolNews.ai has been tracking across metrology and machine vision, including Hexagon’s APOLLO predictive monitoring technology and the recent MVTec MERLIC deployment at Endress+Hauser. AI Edge Detection Comes to PC-DMIS At the centre of the AI story is AI Edge Detection, which will be commercially released with PC-DMIS 2026.2. The technology uses AI-assisted camera and light calibration to improve edge detection during optical inspection. According to Hexagon, this is designed to reduce programming time while producing more consistent measurements across different machines and changing part conditions. This tackles an important challenge in machine vision-based metrology. Reliable optical measurement depends heavily on correctly configuring lighting and identifying component edges, with results potentially influenced by part finish, geometry and environmental conditions. Automating more of that process could make advanced optical inspection less dependent on individual operator expertise. AI Illumination Will Automate Lighting Setup Hexagon is also developing AI Illumination, scheduled for commercial release during 2027. Rather than requiring programmers to manually determine suitable lighting parameters, the technology will automatically optimise illumination to improve edge detection. Hexagon says this should reduce the trial-and-error involved in optical measurement programming. A further feature, Fast Auto-Focus, is expected to deliver autofocus performance up to 50% faster when it becomes commercially available in 2027. Together, the technologies point towards an OPTIV platform where more of the optical inspection setup is handled intelligently by the measurement system itself. Faster Inspection Before the AI Is Added The new machine also delivers substantial hardware improvements. Hexagon says OPTIV S provides approximately 30% higher machine dynamics through increased travel speeds and acceleration. Initial application testing has produced approximately 15% shorter inspection cycle times. A larger field-of-view vision sensor allows more features to be measured from fewer images, while tactile accuracy has been improved to E0, MPEE = (1.6 + L/200) μm. This combination is important because the AI functionality is being introduced alongside measurable improvements to the underlying inspection platform. Existing OPTIV M Users Will Get the AI Technology Too The AI strategy is not restricted to buyers of the new machine. Hexagon says the PC-DMIS capabilities being developed for OPTIV S, including AI Edge Detection, AI Illumination and Fast Auto-Focus, will also become available for its existing OPTIV M platform. That potentially gives current users access to similar programming and inspection consistency improvements through upgrades rather than requiring replacement hardware. Hexagon Pushes Towards Autonomous Quality Control The bigger story is Hexagon’s stated ambition to move industrial measurement towards intelligent and increasingly autonomous quality control. The company says OPTIV S forms part of its wider strategy to embed AI and automation across its measurement technology portfolio, reducing dependence on specialist operator expertise and making high-precision inspection accessible to a broader range of manufacturers. Capabilities such as automated lighting optimisation and AI-assisted edge detection shift repetitive setup decisions towards the software, allowing programmers to concentrate on the inspection strategy and manufacturing problem. For manufacturers struggling with skills shortages while being asked to inspect more components and collect more quality data, that direction could be significant. What Manufacturers Need to Know AI Edge Detection will arrive with PC-DMIS 2026.2 and uses AI-assisted camera and light calibration to improve optical edge detection. AI Illumination is planned for 2027 and will automatically optimise lighting parameters. Fast Auto-Focus is designed to provide up to 50% faster autofocus performance and is also scheduled for 2027. Hexagon reports approximately 30% higher machine dynamics and around 15% shorter inspection cycles in initial application tests. Existing OPTIV M users are also expected to gain access to the new PC-DMIS AI-assisted capabilities. The first OPTIV S 4.4.3 will become commercially available on 14 September 2026 at IMTS and AMB. MTN Analysis There has been no shortage of AI announcements across manufacturing, but metrology presents a particularly interesting opportunity because inspection still relies heavily on specialist knowledge. Hexagon’s approach with OPTIV S is practical. AI is being applied to specific tasks where variability and manual setup can consume programming time. The most interesting technology could ultimately be AI Illumination. Automatically determining how a component should be illuminated before extracting reliable dimensional information addresses one of the fundamental challenges of optical inspection. Combine that with AI Edge Detection and faster autofocus, and the direction becomes clearer: an optical measurement system capable of making more of its own decisions about how a component should be inspected. That is a meaningful step towards the autonomous quality control Hexagon is describing. It also fits a broader trend MachineToolNews.ai is seeing across industrial inspection. AI is increasingly moving into the measurement workflow itself, from predictive monitoring of metrology equipment to AI machine vision deployed directly on production and assembly lines. OPTIV S Makes Its Debut at IMTS and AMB The first OPTIV S configuration, the 4.4.3, becomes commercially available on 14 September 2026, coinciding with IMTS in Chicago and AMB in Stuttgart. Additional machine sizes, configurations and AI-powered capabilities will then be introduced through 2027. For anyone attending IMTS, this makes the OPTIV S one of the more interesting AI-enabled metrology launches to see on the show floor. Manufacturers can read Hexagon’s full OPTIV S announcement or learn more about the OPTIV S platform. FAQ What is the Hexagon OPTIV S? OPTIV S is Hexagon’s next-generation optical coordin... ### IDS Brings AI Cameras and Edge Vision to VISION 2026 Canonical URL: https://machinetoolnews.ai/ids-ai-cameras-vision-2026/ Published: 2026-09-02T13:08:01+00:00 Modified: 2026-09-02T13:08:04+00:00 Author: Jelena Radojcic Categories: Events, General, Germany, Metrology & Vision Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/09/ids-vision-booth-1600x900-1.webp Featured image alt: IDS Imaging booth at VISION 2026 showcasing industrial camera and machine vision technology IDS Imaging Development Systems will use VISION 2026 to show how more intelligence is moving directly into industrial cameras and vision hardware, with new AI-capable uEye Live cameras, event-based imaging, embedded vision systems and upgraded 3D technologies all heading to Stuttgart. The company will exhibit in Hall 8, Booth 8C60 at Messe Stuttgart from 6 to 8 October 2026, under the theme “Trends. Innovations. Future.” While IDS has a broad range of new camera technologies planned for the show, the development that stands out for manufacturers is the expansion of its uEye Live platform with AI-enabled camera models capable of running neural networks at the edge. These systems are being designed to perform tasks including object and position detection, parcel handling and code reading, bringing AI processing closer to the point where the image is actually captured. For machine builders, automation specialists and manufacturers looking at machine vision, robotics or automated inspection, that makes the IDS stand particularly relevant. Visitors can find further information on the official IDS VISION 2026 page. AI Moves Into the uEye Live Camera One of the headline developments is the expansion of the IDS uEye Live monitoring camera series. The platform already combines industrial monitoring with streaming and recording functionality. IDS is now adding AI-capable models designed to enable AI-powered image processing directly at the edge. Neural networks from IDS partner DENKweit will support applications including fast object detection, position recognition, parcel handling and code reading. This is an important direction for industrial vision. Rather than every image needing to travel through a larger external processing architecture before a decision can be made, more of the intelligence can sit close to the camera and the production process. MachineToolNews.ai has already examined how this approach is developing in manufacturing in our guide to Edge AI in Manufacturing. We have also previously looked at IDS technology being used within an AI-supported inspection system combining an IDS uEye camera, edge computing and no-code AI software for shop-floor quality control. Read our IDS AI visual inspection article. The VISION 2026 announcement shows IDS continuing further along that path by bringing AI capability directly into its own camera portfolio. Jan Hartmann, Managing Partner at IDS, explains: “Computer vision must do more today than just reliably capture images. It is crucial how quickly and easily image processing moves from an idea to a concrete application – and how well cameras, software and AI functions can be integrated into existing systems.” That emphasis on integration is significant. Manufacturers already have access to increasingly capable AI models. The commercial challenge is making those models straightforward to deploy within cameras, machines, robots and production systems without creating another highly specialised integration project. New uEye Platforms Will Form the Basis of Future Industrial Cameras IDS will also present new uEye vision camera platforms that are intended to form the technological foundation for upcoming generations of 2D industrial cameras. The company says the new platforms are being developed around higher performance, straightforward integration and long-term availability. That last point matters in industrial manufacturing. Machine builders and system integrators typically need components that remain available and supportable over long machine lifecycles. A computer vision system has to deliver more than headline processing performance if it is going to become part of a production machine that could remain in service for many years. IDS says its wider strategy is therefore centred on modular hardware and software that customers can combine into their own solutions, with support for customer-specific adaptations where required. Event-Based Vision Targets Drones, AGVs and Autonomous Systems Another major part of the IDS exhibition will focus on event-based image processing. IDS is continuing its work with Prophesee and will demonstrate further developments in event-based camera technology aimed at highly dynamic applications including drones, automated guided vehicles and autonomous systems. Event-based cameras operate differently from conventional frame-based cameras. Instead of continuously recording complete images at fixed intervals, individual pixels respond to changes within the scene. That can dramatically reduce unnecessary visual data while allowing extremely fast motion to be detected, making the technology attractive for high-speed tracking and autonomous applications. IDS sees applications including object detection, tracking and safe navigation. The two companies have already commercialised IDS uEye EVS cameras using Prophesee event-based sensing technology and expanded their cooperation earlier this year around next-generation industrial vision systems. At VISION 2026, visitors should get a clearer look at where that technology is heading next. IDS Expands Its 3D Vision Portfolio Artificial intelligence is only part of the IDS exhibition. The company will also show developments across its 3D vision technology. The Nion 3D Time-of-Flight camera is gaining a new RGB set designed for synchronised image acquisition, allowing colour information and depth data to be combined. This can provide a richer representation of the scene for applications where both spatial information and conventional colour imaging are useful. IDS will also present a new version of its Ensenso N series active stereo vision system, aimed at applications including robotics, bin picking and industrial measurement. These applications are becoming increasingly connected with AI because intelligent robotic systems need reliable spatial information before they can understand, select or manipulate objects in changing environments. GMSL Multi-Camera System Moves Processing Closer to the Device One of the more interesting innovation projects on the IDS stand will be its GMSL Multi-Camera Vision System. The concept demonstrates how images from several cameras can be processed and combined directly within an embedded vision system using integrated image signal processors and systems-on-chip. The resulting data is then transmitted through a single GMSL connection. IDS says this approach reduces latency and data load and could provide a foundation for projects using distributed cameras, particularly in mobile applications. This fits closely with the wider trend visible throughout the company’s VISION 2026 announcement. Cameras are increasingly becoming active computing devices within the automation architecture rather than simply image acquisition components. IDS will also demonstrate active focus control integrated directly into the camera, with control and feedback handled through software. Target applications include logistics, autonomous systems and... ### Cimatron CAM Agent Brings Agentic AI Into CNC Programming Ahead of IMTS 2026 Canonical URL: https://machinetoolnews.ai/cimatron-cam-agent-agentic-ai-cnc-programming/ Published: 2026-08-27T07:34:16+00:00 Modified: 2026-08-27T07:34:18+00:00 Author: Jelena Radojcic Categories: Events, General, Software / CAM / IIoT, USA Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/Cimatron_CAM_Agent_IMTS_2026_1600x900.jpg Featured image alt: CNC milling operation illustrating Cimatron CAM Agent and AI-powered CAM programming ahead of IMTS 2026 Cimatron is bringing agentic AI directly into CNC programming with the launch of Cimatron CAM Agent, a new fully integrated machining strategy agent developed with Limitless Labs and set to make its debut at IMTS 2026 in Chicago. For CNC manufacturers and CAM programmers, the important part of the announcement is what the software is being asked to do inside the programming workflow. Cimatron CAM Agent analyses part geometry and available tool libraries, identifies machining requirements, recommends machining strategies and generates complete 3-axis CNC toolpaths. That moves AI deeper into one of the most expertise-heavy stages of CNC production: deciding how a component should actually be machined. Cimatron CAM Agent Brings Agentic AI Into the CAM Workflow Agentic AI is becoming an increasingly important development in manufacturing software because the technology can work through a sequence of tasks and decisions rather than responding to a single isolated request. Within Cimatron CAM Agent, that means analysing the component and the manufacturing resources available to the programmer before building a machining approach around them. Cimatron says the system can automatically recognise machining requirements, recommend suitable strategies and handle repetitive programming tasks. The resulting toolpaths remain inside the Cimatron environment, giving programmers a starting point that can be reviewed and refined before production. The significance for machine shops is programming capacity. CAM programming remains a major bottleneck for manufacturers running complex or highly varied components. A machining centre can only generate revenue once a reliable program reaches the machine, and expanding programming capacity normally means recruiting experienced people, training additional programmers or finding ways to remove repetitive work from the existing team. Cimatron CAM Agent introduces another option. More of the initial programming process can be generated automatically from the geometry, available tooling and machining requirements of the job. Cimatron Global Product Director Simone Bonino says the aim is to help manufacturers create faster and more consistent CNC programs, while allowing experienced programmers to spend more of their time on complex work and helping less experienced employees become productive sooner. Why the Limitless Labs Collaboration Matters The technology has been developed in collaboration with Limitless Labs, which is focused specifically on AI agents for precision manufacturing and CAM. Limitless Labs describes its CAM technology as an AI agent capable of understanding manufacturing context, recommending CAM operations, selecting tools from a manufacturer’s existing library and generating editable toolpaths inside the CAM environment. This is an important distinction for manufacturers assessing AI CAM systems. The value increasingly comes from how much manufacturing context the AI can use when making decisions. Choosing a machining strategy requires knowledge of geometry, tooling, machine limitations, material, cutting conditions and the sequence in which operations should take place. Moving AI into these decisions is what begins to turn a general-purpose assistant into a manufacturing-specific programming system. We have already seen similar movement across the CAM sector. MachineToolNews.ai has covered CloudNC bringing AI-generated machining strategies directly into GibbsCAM, while Siemens, Hexagon and other CAM developers are also embedding AI deeper into established programming workflows. Cimatron CAM Agent adds another major CAM platform to that shift. From Part Geometry to Complete 3-Axis Toolpaths The most significant capability announced by Cimatron is the generation of complete 3-axis toolpaths. The CAM Agent starts by analysing the part geometry and the tool libraries available to the manufacturer. It then identifies the machining requirements, recommends appropriate strategies and builds the toolpaths. For programmers, this changes where a new job can begin. Instead of manually working through every feature and building each machining operation from the beginning, the programmer can increasingly start with an AI-generated machining plan and concentrate on checking, modifying and optimising the decisions that have already been proposed. That is particularly relevant for manufacturers dealing with high part variation, short lead times or a large queue of components waiting to be programmed. MachineToolNews.ai has previously examined how AI toolpath optimisation is changing CAM programming. Cimatron CAM Agent pushes the discussion further because the AI is being applied earlier in the decision chain, from understanding the component through to creating the machining strategy and generating the resulting toolpaths. Cimatron 2027 Adds SuperFinish and FeedOptimizer CAM Agent will be demonstrated alongside new capabilities in Cimatron 2027, with Cimatron using IMTS to showcase wider improvements across mold and die design, CNC programming, machine simulation, electrode production, wire EDM and manufacturing data workflows. Two machining technologies stand out alongside the AI announcement: SuperFinish and FeedOptimizer. SuperFinish Cimatron SuperFinish has been developed with ModuleWorks and is designed to calculate finishing toolpaths using exact surface geometry. According to Cimatron, using the actual surface geometry rather than relying solely on a mesh approximation enables highly uniform, mirror-like milled surfaces without increasing machining time. ModuleWorks has also highlighted SuperFinish on NURBS surfaces in its latest technology release, combining surface-based toolpath accuracy with the reliability required for demanding mold and die finishing applications. For moldmakers in particular, this matters because finishing quality affects far more than appearance. Better surface consistency can reduce subsequent polishing requirements and shorten the route from machining to a finished mold or die. FeedOptimizer FeedOptimizer addresses another critical part of machining productivity. The technology uses tool, machine and material data to determine the appropriate feed rate throughout the toolpath. Rather than applying the same cutting conditions regardless of how tool engagement changes, FeedOptimizer adjusts feed rates according to the cutting conditions encountered during the operation. Cimatron says this can help reduce machining cycle times, extend tool life and protect machinery from excessive cutting loads. Together, CAM Agent, SuperFinish and FeedOptimizer show where Cimatron is concentrating its development effort: reducing programming work, improving the quality of generated toolpaths and optimising what happens once those toolpaths reach the machine. MTN Analysis: Cimatron CAM Agent is one of the more important AI CAM announcements ahead of IMTS because it moves the autonomy boundary further into the actual creation... ### How AI within software can introduce manufacturing production efficiencies Canonical URL: https://machinetoolnews.ai/ai-within-software-manufacturing-efficiency-lantek/ Published: 2026-08-21T07:09:30+00:00 Modified: 2026-08-21T07:09:32+00:00 Author: Jelena Radojcic Categories: AI in Sheet Metal, News, Software Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/even-lantek-ai-manufacturing-1600x900-1.jpg Featured image alt: Lantek AI manufacturing software dashboard showing production analytics, quoting data and shop floor performance Artificial intelligence (AI) is becoming recognised as a powerful tool that can enhance decision-making, automate complex tasks and enable organisations to respond more effectively to changing production demands. Adam Ball, Commercial Director at Lantek, looks at how AI is shaping manufacturing processes. Facing the challenges of modern manufacturing “Today’s manufacturers face increasing pressure to improve productivity and deliver shorter lead times and faster quotations. One of the earliest opportunities for AI integration lies within the quoting process. Material selection, machining times, tooling requirements, production routing, labour estimates and overhead calculations must all be considered before a competitive price can be produced. Traditionally, this process relied heavily on manual decision-making from experienced estimators and can become a bottleneck when enquiry volumes increase. This brings additional challenges when producing quotes, such as human error leading to inaccurate pricing and manual methods introducing inconsistency between quotes. Frequent discrepancies between quoted and actual costs can also make it harder to maintain reliable pricing. As production complexity increases, traditional manual methods struggle to keep up with demand and respond quickly to customer orders. This is where AI can make a significant difference, offering opportunities to improve efficiency across the entire production. Faster and more accurate quoting By learning from previous quotations, outcomes and actual costs, AI can assist estimators in generating highly accurate quotations faster than manual methods. With smart quotation systems, AI can identify similar historical jobs, recommend manufacturing methods, estimate production times and highlight potential bottlenecks before a quotation is issued. Smart quotation is a part of Lantek iQuoting, a cloud-based quoting solution that can estimate material wastage during production and calculate manufacturing times and costs. Digital workflows can record how parts are nested, the material yield achieved and the real machine times generated in production. The real challenge then is using this data to generate a quote before production processes are defined. Advanced AI-integrated quoting systems, like Lantek iQuoting, help manufacturers to digitalise the quoting process by connecting pricing, materials and production data in a single platform. This enables faster, more accurate quotes based on real manufacturing inputs rather than estimations. By using process-based calculation models and machine learning algorithms trained on real manufacturing and sales data that learn from historical production outcomes, manufacturers can improve the accuracy of early-stage cost estimation over time. If historical data is limited, advanced quoting systems will rely on parameterised models and workshop criteria while new operational data is collected. This results in faster response times and improved pricing consistency, providing greater confidence that quoted margins can be achieved during production. Therefore, AI can assist estimators in providing recommendations while allowing teams to validate and refine the final quotation based on their expertise. Smarter production planning AI integration also supports manufacturers by identifying patterns that would otherwise be difficult to detect manually, providing recommendations that improve both speed and accuracy. Traditionally, nesting systems focused on material usage and part placement, but manufacturing environments today require much more visibility. Machine availability, operators, energy consumption, incidents on the shop floor and changing commercial priorities all influence production decisions and schedules. AI-integrated smart production systems can monitor machine utilisation, work in progress, maintenance schedules and priorities to recommend schedule adjustments as jobs change. With smart production systems, operators can manage all those variables and adjust planning according to what is happening in real time, creating a production environment that is more responsive and resilient. A great advantage of AI is its ability to connect production planning with real-time shop floor conditions. Production schedules can change if machines experience downtime, materials are delayed, or there is a shortage of workers. As systems with AI monitor actual production performance and compare it against planned schedules, they can automatically recommend a revised production sequence that minimises disruption and delivers on projected outcomes. The future of AI AI is still in its early stages of development and adoption in manufacturing, yet it has great potential. As AI models become more sophisticated and connected, intelligent systems will play a greater role in supporting operational and production decision-making. AI becomes useful in manufacturing when it is a part of the real workflow. The most successful integrations will begin by addressing business needs. Manufacturers need to identify processes that consume significant time, involve repetitive decision-making or generate inconsistency. When focusing on quoting generation, production schedule, predictive maintenance, or inventory optimisation, AI-integrated systems can be effective in delivering meaningful recommendations that reflect actual manufacturing conditions.” For more details, visit www.lantek.co.uk About Lantek Founded in 1986, Lantek is the global leader in software solutions for the sheet metal industry. Its products enable companies to optimise manufacturing processes with cutting-edge technologies for CAD/CAM, MES, ERP, and analytics. With a presence in over 100 countries, Lantek empowers its customers to embrace digital transformation and achieve their goals with confidence. An FA Service team, comprising over 25 professionals, was fully integrated into Lantek Japan in January 2025. Lantek’s software systems are designed to manage the entire production process, from initial design through to final assembly. By integrating various functions into a single platform, Lantek enables manufacturers to streamline operations and improve efficiency. FAQ How can AI within software improve manufacturing production efficiency? AI within software can analyse historical and real-time manufacturing data to support decisions around quoting, production planning, machine utilisation and scheduling. Systems can identify patterns and recommend actions that would be difficult or time-consuming to calculate manually. How does Lantek use AI for manufacturing quotations? Lantek uses AI and machine-learning capabilities within its quoting technology to support faster and more accurate cost estimation. Historical production outcomes, material consumption, manufacturing times and sales data can be used to improve early-stage quotations. What is Lantek iQuoting? Lantek iQuoting is a cloud-based quoting solution for sheet metal manufa... ### Get ahead of the 95 percent: customize AI solutions for success Canonical URL: https://machinetoolnews.ai/siemens-customize-ai-solutions-manufacturing/ Published: 2026-08-20T07:03:50+00:00 Modified: 2026-08-20T07:06:36+00:00 Author: Jelena Radojcic Categories: AI in Machining, Germany, Software Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/siemens-ai-manufacturing-1600x900-1.webp Featured image alt: Siemens industrial AI solutions helping engineers analyse manufacturing data and optimise production workflows By Rahul Garg, VP for Industrial Machinery Vertical Software Strategy, Siemens Digital Industries Software AI can augment existing knowledge, unlocking productivity and efficiency gains when deployed correctly (Image credit: Siemens) Manufacturers, especially machine builders, can no longer ignore AI as what was once thought to be visionary is here and rapidly evolving. AI has the potential to reward machine builders and manufacturers alike with gains in productivity, efficiency and even automation. Despite the tangible rewards of AI integration, however, it can be easy to encounter stumbling blocks that prevent companies from reaching their full potential. This is why it is critical that manufacturers strategically implement AI instead of applying it indiscriminately to all functions and workflows. Businesses will best see the advantages of AI when they take traditional, generative and agentic into their own hands and work to tailor it to their workflows and goals. Factories that develop a roadmap for how they will deploy AI ensure that their projects align with their needs, enabling them to improve their chances of successfully implementing AI and positioning them for a future lead by AI. From crawling to flying Expediting AI integration starts with understanding where an organization is at in its digital transformation journey, so that the organization can deploy AI to scale. This process of becoming digitally mature is distinct for each business. Approaching AI transformation while acknowledging specific problem areas sets the foundation for businesses to tackle problems that will become more and more complex in the future. Not to mention, jumping immediately into integrating more advanced solutions like LLMs and agentic AI prematurely may lead to unforeseen disruptions. For this reason, companies should start simple and gradually introduce more complex AI functionalities. Straightforward tools such as command prediction during the product or part design process, for example, can reduce repetitive tasks for engineers and designers. While companies get a feel for their distinct AI needs and determine what is and is not working. Then companies can start forging their unique path toward digital transformation. AI unshackles data for the user, improving design and engineering off the machine (Image credit: Siemens) Once the organization is comfortable with less demanding solutions and has a good grasp of what its specific AI future looks like, it can begin introducing more advanced features like AI-enhanced topology optimization for part and component design or other tools more catered to its needs. This gradual ramp up enables businesses to identify best practices and utilize historical data to move full steam ahead with their AI implementation. At this point, a company can begin training its own AI models to enhance and optimize its processes. With these customized solutions designed to meet the company’s own specific challenges, machine builders can fully integrate generative AI and agents into their processes to create new engineering and manufacturing content and even automate complex workflows. Empower everyone with AI Digital transformation will not look the same for each company or even across individual departments and even individual users in one company. And they shouldn’t, which is why it is essential that businesses consider who will be using each solution. To empower everyone regardless of AI training and knowledge within the organization to use AI to its full advantage, organizations must deploy solutions that work for every position. If all AI skill levels can reap the benefits of data-driven manufacturing, businesses can proficiently use their data to solve issues; especially those unique to its operations. AI unlocks data for engineers and designers, helping them build better machines quicker (Image credit: Gorodenkoff/stock.adobe.com) Solutions out right now such as condition monitoring and predictive maintenance are already available in the market make machine data more easily digestible without the need for formal training in app development or programming. Designed to be easy and intuitive, these tools can help provide transparency into equipment status and key performance indicators for frontline employees. Meanwhile, design engineers and production engineers with a deeper understanding of machine programming and production processes can utilize AI to configure tailor-made tools for quality prediction and process optimization. They can also interact with copilots and other LLMs using natural language to complete tasks such as command searching or asking design-related questions. Due to the versatility of AI-infused tools, both large businesses and SMBs can leverage them very effectively. SMBs benefit from the low barrier to entry due to its software as service model, eliminating the need for heavy on-site infrastructure. Meanwhile, large enterprises can leverage the scalability and easily integrate the capabilities into their existing systems. In all, it is crucial to leverage AI solutions that can support a wide variety of workers and businesses at any point in their digital maturity journey. Tailor-made success The future is clear: companies will be using intelligent tools to drive efficiency, innovation and transformation across every aspect of engineering. More and more companies are ramping up efforts to integrate AI into their everyday processes to keep pace with competitors and partners. And with AI solutions becoming more advanced by the day, companies who have started their digital transformation journeys are already reaping the benefits. Get a leg up on the competition and avoid common pitfalls by putting AI-powered tools to use in the right places with the right people. AI is not one size fits all, but integrating AI is now easier than ever and it all starts with understanding the organization’s needs. About the author: Rahul Garg is Vice President for Industrial Machinery Vertical Software Strategy at Siemens Digital Industries Software. As a customer-centric leader, one of his great joys is helping simplify complex problems for customers and enabling success by delivering powerful, effective solutions that support small and mid-sized businesses. Throughout his career having worked at 3 start-ups and now a large enterprise, Rahul has worked closely with SMBs & in technology-led industries to overcome key challenges and drive revenue growth with strategic solutions, smarter services and better business practices. Connect with Rahul FAQ Why does Siemens believe manufacturers need to customize AI solutions? Siemens argues that businesses will see the greatest benefits when AI is tailored to their own workflows, goals and operational requirements. A clear deployment roadmap can help manufacturers align AI projects with specific business needs. How should machine builders begin implementing AI? The article recommends understanding the... ### MVTec MERLIC AI Machine Vision Moves From Endress+Hauser Pilot to Global Assembly Rollout Canonical URL: https://machinetoolnews.ai/mvtec-merlic-ai-machine-vision-endress-hauser/ Published: 2026-08-19T07:33:39+00:00 Modified: 2026-08-19T07:33:41+00:00 Author: Jelena Radojcic Categories: Germany, News, Software Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/mvtec-merlic-endress-hauser-ai-machine-vision-1600x900-1.webp Featured image alt: MVTec MERLIC AI machine vision system supporting Endress+Hauser assembly and in-process quality inspection For manufacturers looking for evidence of where AI machine vision is delivering practical value on the production floor, MVTec MERLIC at Endress+Hauser provides a compelling example. At the Swiss flow measurement specialist, MERLIC is working alongside production employees to guide complex manual assembly, verify critical process steps and document quality. The application combines conventional machine vision with AI-based semantic segmentation and object detection, while integrating directly into Endress+Hauser’s existing Manufacturing Execution System. The project has already progressed beyond its initial deployment. Endress+Hauser began using MERLIC in March 2025 and, after establishing the stability and reliability of the system, started rolling the process out to additional global manufacturing sites later that year. For MVTec, it is another example of its machine vision technology moving directly into real production workflows. It also provides a practical counterpart to the wider AI developments MachineToolNews.ai recently explored in our MVTec VISION 2026 preview, where AI is set to play a central role across the company’s machine vision portfolio. AI Machine Vision Works Hand in Hand With Production Employees Machine vision already performs inspection, identification, code reading and robot guidance across modern manufacturing. The Endress+Hauser application tackles a particularly interesting manufacturing challenge: very high component variation combined with extremely low production volumes. In these conditions, complete automation can become difficult to justify or implement. Endress+Hauser therefore developed a digital assistance system in which machine vision supports the production employee throughout the assembly process. Endress+Hauser Flow, headquartered in Reinach, Switzerland, specialises in flow measurement technology and fluid management solutions. “At Endress+Hauser, our goal is to be technologically leading, to leverage innovations, and to actively drive them forward. This applies not only to our products, but also to our assembly processes,” explains Julius Krause from the Industrial Engineering department at Endress+Hauser. Krause supports the company’s global production sites with the design, procurement and commissioning of camera-based inspection systems and digital assistance systems. He was also closely involved in developing the new machine-vision-assisted assembly process. At its centre is MVTec MERLIC. MERLIC Integrates Directly Into the Endress+Hauser MES MERLIC has been integrated into Endress+Hauser’s proprietary web-based Manufacturing Execution System. Before assembly begins, the MES checks whether a device has reached the correct process stage, whether the workstation is suitable for the operation and whether the employee is authorised to carry it out. Once approval is granted, the MES presents the relevant assembly instructions using text and images. MERLIC is then launched in the background with the recipe data and parameters associated with that particular manufacturing step. The machine vision system verifies whether the process has been performed correctly and provides an additional layer of quality assurance. Once the operation has been completed, the result is returned to the MES. Process results, production data and image information can then be recorded. This integration allows machine vision to become part of the wider manufacturing workflow rather than operating as an isolated inspection system. Moving Quality Inspection Directly Into the Assembly Process Before the machine vision system was introduced, quality assurance typically involved double checks by two employees together with additional testing. Endress+Hauser wanted to strengthen this process while providing production workers with more support during assembly. “The goals of further development were to relieve production staff, provide optimal support, and ensure the highest level of product quality. In addition, in-process inspection allows errors to be corrected easily and prevents failures in subsequent process steps or end-of-line tests,” explains Julius Krause. This is an important aspect of the application. Quality inspection takes place while the product is being assembled, giving employees the opportunity to identify and correct a potential problem immediately. Endress+Hauser initially investigated commercially available assembly assistance systems. However, the company found that available systems did not fulfil all of its requirements. Cloud-based systems raised data protection and availability considerations, while integrating alternative systems into the existing MES would have required additional development and potentially more third-party hardware. Integrating standard machine vision software provided a more flexible route. “Machine vision enables fast, contactless testing in real time. At the same time, our production control system already has numerous interfaces that we could use to integrate image processing software, allowing it to run on hardware managed by us,” Krause explains. The company selected MVTec MERLIC. Why Endress+Hauser Selected MVTec MERLIC MERLIC is MVTec’s no-code machine vision software, designed to allow complete industrial vision applications to be created without conventional programming knowledge. That flexibility was important to Endress+Hauser because the same software platform can be used across different machine vision tasks. “We chose MERLIC for a number of reasons. The most important is that, as a standard software, it serves as a tool we can use for various tasks. As a result, we do not have to train our employees on different types of software. Finally, process integration is straightforward. MERLIC’s numerous open interfaces – such as GenICam – give users great flexibility regarding compatible hardware and make software integration simple,” explains Julius Krause. The workstation itself uses a streamlined machine vision configuration consisting of an industrial PC, laser distance sensor and a camera equipped with a liquid lens for variable focus. A camera positioned above the assembly workstation accompanies the employee as the component moves through each production step. Batch Size One Manufacturing Makes the Application Particularly Interesting The components being assembled are typically flowmeters approaching the final stages of production. A code associated with each component contains information specifying how that particular product must be assembled and what the production worker needs to consider. This becomes particularly important because products can effectively be produced in a batch size of one. Once the component has been identified, the workstation screen presents messages and images showing the employee how the assembly step must be completed. MERLIC then operates in the background to verify that critical operations have been performed correctly. One example is checking whether required s... ### Festo AI Strategy: From GripperAI to Predictive Maintenance and Self-Optimising Automation Canonical URL: https://machinetoolnews.ai/festo-ai-strategy-gripperai-predictive-maintenance/ Published: 2026-08-18T07:17:08+00:00 Modified: 2026-08-18T07:17:09+00:00 Author: Jelena Radojcic Categories: Germany, Robotics, Software / CAM / IIoT Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/festo-ai-strategy-1600x900-1.jpg Featured image alt: Festo AI automation system for intralogistics and intelligent material handling in industrial manufacturing Following our recent coverage of Festo GripperAI, which examined how artificial intelligence can help robots identify unfamiliar objects, select suitable gripping points and handle highly varied product mixes, MachineToolNews.ai wanted to explore the wider strategy behind the technology. GripperAI is one part of a much broader shift within Festo. The automation specialist is also applying AI to predictive maintenance, energy management, quality monitoring, engineering tools and increasingly intelligent automation components. In this follow-on Q&A, Andreas Schoch, Product Management GSS, Global System Solutions, and Eberhard Klotz, Global Sales Director Industry 4.0/Digitalisation at Festo, explain how the company is moving AI from individual applications into its wider automation portfolio. They also discuss how GripperAI understands unknown objects, the commercial scaling of the Würth logistics project, the role of Festo AX Motion Insights and Smartenance, and why intelligence will increasingly be embedded directly into valves, drives, grippers and other automation products. Festo has moved beyond talking about AI as a research topic and is now applying it to gripping, predictive maintenance, quality and energy management. How central will AI become to Festo’s overall automation strategy over the next few years? Andreas Schoch and Eberhard Klotz: AI will become a very central part of Festo’s automation strategy. We are moving from AI being regarded as an exotic or unknown topic to it becoming a normal part of our lives, businesses, engineering processes and automation products. For Festo, this path began much earlier than the current AI discussion. When we introduced valve terminals with internal communication and decentralised intelligence around 30 years ago, known at the time as intelligent pneumatics, we began creating the foundations for using data in automation. The decisive step now is combining connected data with domain knowledge. Data alone does not improve a production machine. You need to understand the application, its physical behaviour and the automation technology behind it. That combination will be central to predictive maintenance, quality, energy management and engineering. AI will influence our products, the services surrounding them and engineering tools such as digital twins, simulation and virtual assistants. These tools are already beginning to support engineers with machine design, sizing, commissioning and optimisation. Within the next three to five years, we expect most parts of the Festo portfolio to be supported by AI in some form, whether directly within a product, through software, as part of an engineering workflow or through a service surrounding the machine. GripperAI can identify different objects, select the appropriate gripping tool and handle an extremely varied product mix without conventional teaching. What was the biggest technical breakthrough that made this possible? Andreas Schoch and Eberhard Klotz: As patent applications are still pending, we cannot discuss every technical detail. The main breakthrough, however, is the way GripperAI understands unknown objects. Instead of teaching the system every individual product, the AI is trained using generic basic geometries. The software analyses an object, breaks it down into these basic shapes and calculates one or several possible grasping points. It can then evaluate which gripping point has the highest probability of success. Another important element is its camera-independent approach. GripperAI can work with high-end 3D camera systems and analyse a depth image of the bin. It transfers the selected gripping point to the robot, allowing it to pick parts that have not previously been taught using conventional methods. The AI software is only one part of the solution. Festo also contributes a large gripper portfolio and extensive application knowledge. This helps answer the practical question of how an object should be handled, whether with a two-finger gripper, one or several suction cups, or a more universal gripping concept. The combination of AI, 3D image processing, robotics and gripper expertise is what makes GripperAI effective. Multi-gripper solutions such as the Festo HPSX universal adaptive gripper add further flexibility by handling different contours and surfaces with a single tool. The Würth application demonstrated GripperAI handling products weighing up to 20 kilograms and working with different robot and vision systems. Is Festo now turning that customised project into a scalable commercial solution that other manufacturers can deploy? Andreas Schoch and Eberhard Klotz: Yes, that is the overall objective. Festo’s AI-based products often begin as pilot projects before developing into scalable solutions for wider use. With GripperAI, we see strong potential in logistics, warehouse automation and packing stations where large numbers of different products must be picked reliably. The Würth application demonstrates this direction. We are also using GripperAI in the Festo Customer Solution Center, where a robotic application demonstrates traceability and provides a basis for further internal and global scaling. The go-to-market approach must also be clearly defined. Festo offers handling systems, particularly Cartesian robot systems, as complete solutions that can include control cabinets and automation technology. These systems can be combined with third-party vision systems and GripperAI. For applications involving other robot or cobot brands, scaling will often take place through integrators and technology partners. Festo will not act as the system integrator for every robot available on the market. Our goal is to make GripperAI usable across different robot, camera and gripper environments while scaling the solution through the appropriate partner ecosystem. GripperAI has initially been demonstrated in intralogistics and bin picking. Where else do you see the strongest opportunities, particularly in machine tending, assembly, packaging and flexible manufacturing? Andreas Schoch and Eberhard Klotz: The strongest opportunities exist wherever a robot must handle unknown or highly variable objects. That is the main strength of GripperAI. This makes logistics and intralogistics natural starting points, particularly packing stations, order fulfilment and applications involving a broad product mix. There may also be opportunities in semi-automated retail environments such as drugstores and supermarkets, where many different objects need to be handled. Flexible manufacturing may offer further applications, although these must be assessed carefully. Assembly and machine tending are often different. In many of these applications, the parts are already known, CAD data is available and the process requires precise positioning or a strict operating sequence. CAD-based matching or a dedicated automation solution may therefore be more suitable. When a robot grips an unknown o... ### CloudNC Launches CAM Assist for GibbsCAM With AI-Generated CNC Strategies Canonical URL: https://machinetoolnews.ai/cam-assist-for-gibbscam-ai-cnc-programming/ Published: 2026-08-04T08:16:40+00:00 Modified: 2026-08-04T08:16:42+00:00 Author: Publisher Categories: AI in Machining, General, News, Software, Software / CAM / IIoT Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/cloudnc-cam-assist-gibbscam-1600x900-1.webp Featured image alt: CloudNC CAM Assist AI software generating 3-axis and 3+2-axis CNC machining strategies inside GibbsCAM CloudNC has officially launched CAM Assist for GibbsCAM, bringing AI-generated machining strategies and toolpaths directly into the established CAM environment used by machine shops around the world. The new AI-enabled add-on supports 3-axis and 3+2-axis CNC machining, helping GibbsCAM programmers reduce the repetitive work involved in creating machining strategies for complex components. Rather than requiring manufacturers to move into a separate software environment, CAM Assist for GibbsCAM operates inside the programmer’s existing workflow. CloudNC says this allows users to accelerate part programming while retaining control over the final machining strategy. AI Programming Inside GibbsCAM CAM Assist uses AI and advanced computer science techniques to analyse a component and generate appropriate machining strategies and toolpaths. According to CloudNC, strategies that could take an experienced CNC programmer hours or even days to produce manually can be generated in minutes or seconds, depending on the complexity of the part. This addresses one of the biggest bottlenecks facing machine shops: the time and specialist knowledge required to move from a component model to a machine-ready CNC program. Theo Saville, chief executive and co-founder at CloudNC, said: “GibbsCAM is a powerful fit for machine shops that want a smart, flexible CAM solution that can scale from day-to-day production work to produce complex components. Bringing CAM Assist to GibbsCAM helps those users go even faster, meaning programmers can spend less time on repetitive toolpath creation, stay firmly in control of the process, and focus more of their expertise on getting high-quality parts out of the door.” The release also strengthens the AI automation capabilities available within GibbsCAM 2026, which can generate complete 3-axis and 3+2-axis toolpaths using CloudNC’s cloud-based technology. CAM Assist 2.0 Gives Programmers Greater Control GibbsCAM users will also gain access to CAM Assist 2.0 and its Strategy Editor. The latest version combines automated strategy generation with a more structured review process. Programmers can configure machines, materials and tool assemblies, assess components before machining, review AI recommendations and adjust strategies before toolpaths are returned to GibbsCAM. Thorsten Strauss, President of GibbsCAM, said: “At GibbsCAM, our focus has always been to provide manufacturers with powerful, practical solutions that help them produce parts more efficiently. By integrating CAM Assist, we are bringing cutting-edge AI technology directly into the workflows our customers already know and trust. This collaboration with CloudNC enables programmers to automate repetitive programming tasks, accelerate part programming, and focus their expertise where it delivers the greatest value, all while maintaining full control of the machining process.” The Strategy Editor is particularly important because it gives programmers visibility into how the AI has approached the component. Users can inspect, adjust and approve the proposed machining strategy rather than immediately accepting an automatically generated result. MachineToolNews.ai previously examined how CAM Assist 2.0 gives CNC programmers greater clarity and control over AI-generated CAM decisions. MTN Analysis The GibbsCAM integration represents another significant expansion of CloudNC’s AI CAM ecosystem. Its commercial importance comes from placing AI inside software that programmers already use. Machine shops can introduce AI-assisted programming without completely redesigning their existing model-to-machine workflow. That approach could make adoption easier for manufacturers facing shortages of experienced CNC programmers, increasing quoting pressure and growing demands for shorter lead times. The programmer remains responsible for reviewing and approving the strategy, while the AI handles a larger share of the repetitive analysis and initial toolpath creation. This is also another example of the emerging manufacturing copilot model, where AI assists skilled employees directly inside established engineering and production software. CAM Assist is available for GibbsCAM through CloudNC and GibbsCAM’s global reseller network. CloudNC’s official product information currently lists compatibility with GibbsCAM 2025 and 2026. Frequently Asked Questions What is CAM Assist for GibbsCAM? CAM Assist for GibbsCAM is an AI-enabled add-on that generates machining strategies and toolpaths inside the GibbsCAM programming environment. Which CNC machining processes does CAM Assist support? The current GibbsCAM integration supports 3-axis and 3+2-axis CNC milling. Does CAM Assist replace the CNC programmer? No. CAM Assist generates and recommends machining strategies, while the programmer can review, modify and approve the strategy before the toolpaths are returned to GibbsCAM. What does the CAM Assist 2.0 Strategy Editor do? The Strategy Editor allows programmers to inspect AI recommendations, configure manufacturing resources, adjust machining decisions and approve the final strategy before toolpath generation is completed. Further Reading CloudNC: CAM Assist for GibbsCAM GibbsCAM: CloudNC CAM Assist and GibbsCAM 2026 MachineToolNews.ai: CAM Assist 2.0 Interview MachineToolNews.ai: Is 2026 the Rise of the Manufacturing Copilot? ### Lantek Expands KAI AI with v46 Ahead of EuroBLECH 2026 Canonical URL: https://machinetoolnews.ai/lantek-kai-ai-v46-euroblech-2026/ Published: 2026-08-04T07:19:56+00:00 Modified: 2026-08-04T07:19:58+00:00 Author: Jelena Radojcic Categories: General, Hero - Home, Software, Software / CAM / IIoT Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/08/lantek-kai-ai-v46-euroblech-2026-1600x900-1.jpg Featured image alt: Lantek team showcasing KAI AI and Lantek v46 at the EuroBLECH exhibition ahead of the software's EuroBLECH 2026 launch for intelligent sheet metal manufacturing. Lantek will use EuroBLECH 2026 to unveil Lantek v46, the latest version of its manufacturing software platform, introducing new AI-assisted quotation capabilities, expanded automation and broader machine connectivity designed to help sheet metal manufacturers work more efficiently from quotation through to production. While the release includes enhancements across CAD/CAM, MES, bending and machine integration, one of the most significant developments for manufacturers is the continued expansion of KAI, Lantek’s artificial intelligence technology. Rather than focusing on programming alone, KAI now supports manufacturers much earlier in the production process by helping engineering teams prepare faster, more accurate quotations for increasingly complex jobs. For manufacturers facing growing pressure to respond quickly to customer enquiries while maintaining profitability, the ability to automate parts of the quotation process could become a significant competitive advantage. This fits a broader trend across industrial software, CAM and IIoT, where manufacturers are using AI to improve decisions before production begins. AI moves upstream into manufacturing quotations One of the headline developments within Lantek v46 is a major evolution of the company’s iQuoting solution. The redesigned platform introduces an enhanced part import assistant capable of processing both individual 3D components and complete assemblies. It provides tools to detect and manage geometries, organise layers and prepare imported models before costing begins. Once imported, integrated feasibility rules automatically evaluate whether parts can be manufactured using the available production technologies and workshop capabilities. These checks are combined with configurable costing models, predefined work plans and production logic that allow manufacturers to reflect their own manufacturing routes, resources and pricing strategies. Supporting many of these processes is KAI, Lantek’s AI technology. According to Lantek, KAI analyses manufacturing information, interprets engineering design data and assists with complex decision making throughout the quotation workflow. Rather than replacing engineering expertise, the AI is designed to reduce repetitive manual tasks while improving quotation speed, consistency and accuracy. The company has also strengthened the connection between iQuoting and the wider Lantek software portfolio, allowing accepted quotations to move more smoothly into downstream manufacturing processes. This reflects the continuing shift towards connected manufacturing, where commercial, engineering and production information flows through a single digital thread. Programming enhancements across punching and nesting Lantek has also expanded the capabilities of Lantek Expert v46, particularly for punching and combined punch-cut applications. New programming functions automate the preparation of both individual parts and complete nests while supporting increasingly sophisticated machining strategies. A notable addition is the extension of NOS, or Nesting Optimisation Service, to punching machines. Previously available only for cutting technologies, the optimisation engine now brings advanced material utilisation to punching environments, extending optimisation across a broader range of sheet metal manufacturing processes. Although Lantek has not described NOS itself as an AI technology, the development represents another important step towards more intelligent and automated programming. It also strengthens the relevance of v46 to manufacturers following developments in AI in sheet metal, where software is increasingly being used to reduce programming time, improve material utilisation and automate repetitive engineering tasks. Automation extends beyond the cutting machine Automation receives considerable attention throughout the new release. Lantek has redesigned Stackmaster, delivering a more intuitive palletising programming environment that is fully integrated within Lantek Expert. At the same time, the company is expanding support for automated storage systems capable of coordinating cutting machines, warehouses, production workflows and material inventory within a single automation framework. For tube and structural steel manufacturers, Lantek Flex3D v46 introduces a new module for creating tube-to-tube connections directly inside 3D assemblies using parametric joint libraries. The software also supports automatic unbending of bent tubes during 3D file import, generating developed geometries ready for manufacturing preparation. Lantek has also broadened its integration ecosystem by introducing connectivity with Tekla PowerFab, complementing its existing StruMIS integration for structural steel production management. These integrations give manufacturers greater flexibility to connect Lantek software with their preferred production management systems. Greater flexibility for bending operations Another important enhancement focuses on bending. Lantek v46 enables manufacturers to calculate multiple validated bending solutions for the same component across different press brakes. Production teams can evaluate technical feasibility, tooling requirements and setup information before selecting the most appropriate machine based on current availability, workload or tooling conditions. By delaying the final machine selection until closer to production, manufacturers gain additional flexibility while retaining validated process information for each available option. This type of workflow flexibility is becoming increasingly important as manufacturers use connected software to manage changing production priorities across multiple machines and work centres. More connected workshops Machine connectivity also continues to expand. Lantek is extending connectivity beyond cutting machines to include press brakes and additional CNC equipment. The updated Control Panel introduces a redesigned interface that improves visibility of live machine information while supporting smoother real-time monitoring of production events. By collecting data from a wider range of production assets, manufacturers can improve production visibility, strengthen OEE reporting and gain more accurate operational insights through Lantek MES and Lantek Analytics. This continues the industry’s move towards connected manufacturing environments where software provides real-time visibility across the entire workshop. Speaking ahead of EuroBLECH 2026, Adam Ball, Commercial Director of Lantek UK, said: “We are looking forward to revealing our latest smart manufacturing software at EuroBLECH. We will be offering demonstrations of v46 to showcase its unique and innovative features. EuroBLECH is the largest show in our industry in Europe and we will be reinforcing our position as an international supplier with a strong local presence, supported by a worldwide network of offices and specialised teams.” Lantek will be demonstrating v46 th... ### Festo GripperAI Lets Robots Pick the Unknown Without Reprogramming Every SKU Canonical URL: https://machinetoolnews.ai/festo-gripperai-robot-picking/ Published: 2026-07-30T08:11:54+00:00 Modified: 2026-07-30T08:11:56+00:00 Author: Jelena Radojcic Categories: Germany, Robotics, Robotics & Cobots Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/ChatGPT-Image-Jul-30-2026-09_09_27-AM.png Featured image alt: Festo GripperAI robotic gripper using AI vision to identify and handle mixed objects while an engineer monitors the system. Festo is attacking one of the most stubborn problems in robotic automation: product variation. At Würth’s logistics centre in Künzelsau, Germany, products arriving at final packaging can range from small items such as USB sticks and spray cans to boxes weighing as much as 20 kilograms. Instead of following a fixed gripping routine, a robot using Festo GripperAI examines each item, calculates where it should be gripped and selects the most appropriate tool available. The result is a robot cell designed to cope with the unknown, rather than a predictable sequence of identical components. That distinction matters. Manufacturers have become extremely effective at automating repetitive tasks where every component arrives in the same position. Mixed products, changing batches, unfamiliar geometries and inconsistent surface conditions remain far harder and more expensive to automate. Festo GripperAI is designed to reduce that complexity by turning the gripping decision into an AI-driven software process. The Real Challenge Is Product Variation Traditional robot cells work best when engineers can define exactly what the robot will encounter. The gripping point, tool, component orientation and robot movement can then be programmed and repeatedly executed. That model becomes increasingly difficult when hundreds or thousands of different products pass through the same operation. Würth has approximately one million products within its wider portfolio. Its final packaging stations therefore have to deal with enormous differences in product size, shape, weight, material and surface finish. Heavy products also place considerable physical strain on employees responsible for removing items from sorter trays and packing them into shipping cartons. Festo’s answer was to develop a robot cell capable of making several decisions autonomously: Where should the product be gripped? Which available gripper or suction cup is most suitable? How should the robot approach the product? What should happen when the first gripping attempt fails? This is where GripperAI moves beyond conventional pick-and-place programming. A Robot That Identifies Its Own Gripping Tool The Würth robot cell includes a tool station containing different mechanical grippers and suction cups. An integrated camera captures information about the objects arriving in the trays. GripperAI then assesses characteristics including the object’s type, shape and surface before deciding which gripping tool should be used. The selected gripping position is sent to the robot’s path-control system, which performs the movement. When a gripping attempt is unsuccessful, the software can recalculate the gripping point and try again. This allows the cell to continue operating without immediately requiring an engineer to stop the system and create a new programme. The software operates locally on a standard industrial PC connected to a 3D camera. Processing at the cell supports real-time performance while allowing operational data to remain within the factory or logistics facility, according to a technical overview of Festo’s AI-based picking system. Festo Recreated Würth’s Conveyor System in Its Laboratory The project involved more than installing a camera and connecting an AI model. Festo’s Advanced Development Analytics and Control team built a replica of Würth’s sorter conveyors inside its own laboratory. Over a development period of more than two years, the team worked on object-dependent gripper selection, removing different objects from trays, packing shipping cartons and handling the trays and cartons themselves. This approach gave Festo a controlled environment in which it could repeatedly test the application before transferring the technology into Würth’s live logistics operation. The completed robot cell has been operating at Würth since spring 2023 and can serve the designated sorter lines. Festo and Würth have also identified further optimisation and development steps for the system. That live deployment provides an important proof point. GripperAI is already being tested against the variation, interruptions and physical demands found in a genuine industrial logistics operation. Robot-Agnostic Software Could Be the Bigger Breakthrough Festo says GripperAI is compatible with most industrial robots, cobots and Cartesian handling systems equipped with path control. Its software architecture can also operate across different camera types, allowing manufacturers and system integrators to select suitable vision hardware without becoming tied to one proprietary camera platform. This could become one of the system’s most commercially important qualities. Flexible automation projects frequently become dependent on a tightly integrated combination of robot, vision system, gripper and specialist software. Changing one part of that combination can require additional integration, testing and programming. A robot-agnostic AI layer gives manufacturers greater freedom to use existing equipment, expand cells with different robot models and select vision hardware according to the application and budget. Standard integration work is still required. The camera must be mounted and aligned, lighting conditions must be checked, the robot and camera coordinate systems must be calibrated, and the relevant picking parameters must be configured. Once that work has been completed, the system can handle product changes without loading individual templates between SKUs. MTN Analysis The breakthrough within GripperAI lies in reducing the amount of engineering required every time the product mix changes. Robot hardware has long been capable of moving quickly, accurately and repeatedly. The commercial difficulty appears when manufacturers need the same robot cell to handle unpredictable objects, short production runs, changing packaging and components that cannot be presented in a perfectly controlled orientation. Each variation can create additional programming, tooling, fixturing and vision-integration work. That engineering burden makes many potential automation projects too expensive or inflexible to justify. GripperAI shifts more of those decisions into software. The system sees the object, calculates a gripping point, selects the available tool and automatically attempts a revised strategy when necessary. Peter Potters, Product Manager for End-of-Arm Tooling at Festo, described the technology as “a strong example of AI being applied practically to address operational issues” in Festo’s announcement about the GripperAI system. For manufacturers, the practical test will be whether this approach can deliver dependable cycle times and gripping success rates across their own product mix. The Würth pilot shows where the opportunity is strongest today: operations with high SKU variation, difficult manual handling, changing product geometries and limited appetite for constant robot reprogramming. Festo GripperAI is therefore more than an intelligent gripper appli... ### Siemens’ AI-Powered Simcenter Release Could Change How Manufacturers Design Before They Build Canonical URL: https://machinetoolnews.ai/ai-powered-simcenter-release/ Published: 2026-07-29T08:03:41+00:00 Modified: 2026-07-29T08:03:43+00:00 Author: Publisher Categories: General, News, Software / CAM / IIoT Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/ChatGPT-Image-Jul-29-2026-09_00_34-AM.png Featured image alt: Engineer using Siemens AI-Powered Simcenter simulation software across three monitors to analyse vehicle chassis, motor and fluid dynamics designs before manufacturing Siemens has launched a major new release of its Simcenter software portfolio, bringing Siemens and Altair simulation technologies together inside a unified engineering simulation offer. The release adds expanded AI-driven simulation, GPU acceleration and new multiphysics workflows designed to help engineers explore more design options, reduce reliance on slow simulation cycles and make better decisions earlier in product development. For manufacturers, this is a significant industrial AI development because simulation is moving closer to the front end of design, validation and production planning. Instead of using simulation only as a late-stage check, Siemens is positioning Simcenter as a platform that can help engineering teams evaluate more possibilities before tooling, machining, forming, casting or assembly decisions are locked in. The update also marks an important step in Siemens’ integration of Altair technologies following its acquisition of the simulation software specialist. Siemens says this is the first Simcenter release to bring Siemens and Altair technologies together in a unified simulation portfolio. For readers following AI in machining, Software / CAM / IIoT and the wider rise of industrial AI in factories, the question is simple: can AI-powered simulation help manufacturers test more ideas before anything reaches the shop floor? AI Moves Deeper Into Engineering Simulation The most important part of the release is the expansion of Simcenter PhysicsAI, Siemens’ geometric deep learning technology for simulation. Siemens says PhysicsAI can turn simulation data into predictive models that allow engineers to evaluate more concepts and make faster design decisions. According to Siemens, the technology can help engineers make design decisions up to 1,000 times faster than traditional solver simulations. That matters because detailed simulation has often been powerful but slow. Engineering teams may know simulation can improve a product, but they do not always have the time to run every scenario, test every variant or analyse every design direction before project deadlines arrive. By expanding PhysicsAI across the Simcenter portfolio, Siemens is trying to make simulation less of a bottleneck and more of a real-time design tool. The technology is now being integrated across more Simcenter products, including Simcenter Inspire, where it can support structural simulation, casting, moulding, die stamping and extrusion processes. That makes this launch highly relevant to metal manufacturers. Casting, forming, extrusion, structural performance and thermal behaviour all sit close to the production challenges faced by machine builders, automotive suppliers, industrial equipment manufacturers and component producers. Why This Matters For Metal Manufacturers Metal manufacturers are under constant pressure to produce lighter, stronger, more efficient and more cost-effective components. That usually requires more design exploration, more simulation, more validation and tighter links between engineering and manufacturing. The problem is that conventional simulation workflows can limit how many options engineers can realistically test. A team may only have time to validate a few promising designs, rather than explore a much wider design space. Siemens is trying to change that equation. The latest Simcenter release is designed to help engineering teams identify promising concepts earlier, reduce time spent on repetitive simulation runs and increase confidence before a design moves toward production. For manufacturers, that could mean: Faster design explorationMore design variants before productionEarlier simulation during product developmentReduced physical prototypingBetter decisions before tooling or machining beginsImproved confidence in thermal, structural and fluid behaviourMore connected engineering workflows across teams This is important because AI in manufacturing is no longer only about robots, cameras or CNC controls. It is also moving into the engineering layer that shapes what gets manufactured in the first place. That links directly with broader MachineToolNews.ai coverage of industrial AI in CNC machining and the rise of the manufacturing copilot, where AI tools are beginning to support decisions before the first cut is made. GPU Acceleration Could Push CFD Workflows Forward The update also expands GPU acceleration across the Simcenter portfolio. Siemens says multi-GPU support in Simcenter STAR-CCM+ allows engineers to train AI models from computational fluid dynamics simulations significantly faster, with speed improvements of 10 times and beyond. For engineering teams working on cooling, airflow, fluid systems, electric powertrains or thermal management, this could be an important improvement. CFD is highly relevant to manufacturing because many products now need to be optimised for performance, energy use, heat transfer and airflow before they are released into production. Faster CFD-based AI model training could allow companies to explore more design alternatives without waiting for every case to be solved through traditional simulation. For automotive, aerospace, energy, industrial machinery and electronics manufacturers, this is where simulation becomes a competitive tool. The ability to test more designs faster can affect product performance, bid response, development cost and time-to-market. PhysicsAI Generate Points To Generative Engineering Siemens has also introduced Simcenter PhysicsAI Generate, which allows teams to create new design concepts using existing product data, target dimensions and performance requirements. This is important because it moves AI beyond prediction and into concept generation. Siemens says PhysicsAI Generate uses a physics-aware generative AI engine to create design concepts in seconds. The strongest implication is that AI could help engineers explore a wider design space before narrowing the options for detailed simulation and production review. For metal manufacturing, that opens an important question. Could future AI simulation workflows help engineers generate components that are lighter, stronger, easier to cool, easier to cast, easier to form or easier to machine? That is where AI-powered simulation starts to overlap with design for manufacturing. Simulation Is Moving Earlier In The Design Process Another important part of the release is the integration of Simcenter Simsolid with Siemens Designcenter. Simcenter Simsolid allows simulation to run directly on geometry without creating a mesh, helping designers assess performance in minutes rather than hours. This could be highly relevant to manufacturers because simulation often becomes a bottleneck when only specialist analysts can run complex studies. If more engineers can evaluate performance earlier in design, problems may be found before they become expensive manufacturing issues. The release also adds stronger multiphysics connections. Siemens says engineers... ### Okuma IMTS 2026 Line-Up Puts AI Robot Cell at Centre of Four CNC Debuts Canonical URL: https://machinetoolnews.ai/okuma-imts-2026-ai-robot-cell-cnc-launches/ Published: 2026-07-28T09:46:53+00:00 Modified: 2026-07-28T09:46:54+00:00 Author: Publisher Categories: AI in CNC, Events, General, USA Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/ChatGPT-Image-Jul-28-2026-10_43_45-AM.png Featured image alt: Okuma and Intrinsic logos side by side representing their AI-powered robotics and manufacturing partnership Okuma will use IMTS 2026 to show how CNC machines, robotics, automated work handling and artificial intelligence are beginning to operate as one connected production system. The machine-tool builder’s newly announced IMTS 2026 line-up includes four CNC machines making their Americas debuts, ten automation systems and an AI automation robot cell developed through a partnership with Intrinsic. Nine of the 11 Okuma machines displayed in Chicago will be paired with automation, making this one of the company’s most automation-heavy IMTS exhibitions to date. The announcement also strengthens the wider trend examined in our recent analysis of industrial AI at IMTS 2026, where AI is moving beyond standalone software and into CNC programming, machine tending, robotics, inspection and production control. Four New Okuma CNC Machines Will Make Their Americas Debuts The Okuma IMTS 2026 exhibition will include four machines being shown officially in the Americas for the first time: Okuma MB-100V vertical machining centre Okuma GENOS M4000H-e horizontal machining centre Okuma MS-320H horizontal machining centre Okuma MULTUS U1000 multitasking lathe Together, the four machines cover large-component machining, compact automated production, horizontal machining and complete multitasking. Okuma MB-100V Targets Large-Part Machining The MB-100V is a high-precision bridge-style vertical machining centre developed for larger components, including parts used in semiconductor manufacturing equipment and automotive die and mould production. It provides a maximum machining volume of 3,200 x 1,250 x 750 mm and a table measuring 3,000 x 1,000 mm. Okuma is positioning the machine around high-speed machining, thermal stability, efficient chip management and the ability to produce large components within a relatively compact footprint. GENOS M4000H-e Combines Speed With a Smaller Footprint The GENOS M4000H-e is a thermally stable horizontal machining centre designed to reduce non-cutting time while supporting both heavy-duty cutting and higher-speed production. The machine includes a 400 x 400 mm pallet, a 560 x 560 x 625 mm machining envelope, rapid traverse speeds of up to 60 m/min and a standard 64-tool magazine. At IMTS, the machine will be paired with an internal two-station automatic pallet changer, giving manufacturers an example of compact horizontal machining combined with automated workpiece movement. MS-320H Is Designed for Automation-Ready Production The MS-320H is a compact four-axis horizontal machining centre developed with automated production in mind. It comes with an eight-port hydraulic through-table system to simplify the integration of automated clamping. Okuma says the machine can support applications ranging from high-mix manufacturing to mass production and long periods of unattended machining. The machine offers rapid feed rates of up to 60 m/min, 1G axis acceleration and a 15,000 rpm spindle. At IMTS, it will be connected to the updated Okuma Robot Loader Drawer II system. MULTUS U1000 Brings Multitasking Into a Compact Platform The MULTUS U1000 combines turning and milling with a B-axis machining head, automatic tool changer and optional second spindle and lower turret. Its 240-degree B-axis range, 12,000 rpm milling spindle and 80-tool automatic tool changer are intended to help manufacturers complete complex parts in fewer setups. Okuma says the machine supports full-contouring C-axis machining and includes its Collision Avoidance System as standard with the OSP-P500 configuration. Okuma and Intrinsic Will Demonstrate an AI Automation Robot Cell The most important AI announcement in the Okuma IMTS 2026 programme is the planned robot cell being developed with Intrinsic. Okuma has confirmed that the cell will form part of its automation display, although it has not yet disclosed the precise robot hardware, CNC machine pairing or production task involved. That makes the live demonstration particularly significant. Intrinsic is developing software and modular workcell technology intended to make industrial robots more adaptable and easier to reconfigure. Its approach uses skill-based automation, compatible hardware components and AI capabilities that can be combined inside a production cell. At Automate 2026, the company unveiled an early version of its Intrinsic Intelligence Cell, a modular robot workcell running on IntrinsicOS. Intrinsic says the reference design can support rapid process and tool reconfiguration, different hardware and software combinations, and AI-enabled robotic behaviour. A demonstration version used a FANUC robot to perform electronics assembly tasks. Okuma has not said whether its IMTS cell will use this exact architecture. However, the partnership signals a move towards robotics that can be reconfigured through software rather than being engineered around one fixed task. This follows a wider industry shift towards Physical AI and more adaptable industrial robots, with machine builders and robotics companies looking for ways to reduce the programming and integration effort required for automated production. Nine of 11 Okuma Machines Will Be Automated Okuma will display ten different automation systems in its 17,000-square-foot booth. Nine of the 11 CNC machines will be connected to automation, including robotic loaders, pallet changers, bar feeders, gantry loaders and automated unloading systems. Three Okuma Factory Automation developments will make their Americas debuts: Okuma Robot Loader series enhancements and additions A vertical two-level Tower Pallet Changer with capacity for 13 pallets A new Modular Automatic Tool Changer The updated Robot Loader range will include the Robot Loader Drawer II, Robot Loader for Machining Centers II and Robot Loader Plus with Pallet. These systems are designed to handle loading and unloading tasks while reducing the separate integration work normally associated with adding a robot to a CNC machine. This is similar to the wider movement towards simpler AI machine-tool automation, where robot operation, diagnostics and production control are increasingly being connected directly to the machine environment. OSP-P500 Will Connect Machines and Automation Okuma’s OSP-P500 CNC control will be installed on nine of the 11 machines and will also be available through standalone interactive simulators. The control is becoming an important part of Okuma’s strategy because it links machining, machine monitoring, applications and automation through a common CNC platform. Okuma will also display the OHP-C8 high-pressure coolant system, which the company released earlier in 2026. MTN Analysis: AI Is Moving Into the Complete Machining Cell The importance of Okuma’s announcement is not limited to the four machine launches. The stronger story is that most of the machines will arrive at IMTS already connected to automation, with an AI-enabled robot cell sitting alongside the wider CNC line-up. For manufacturers, this points towar... ### SimScale Survey Says 80% of Engineering Organisations Are Stuck in AI Pilot Purgatory Canonical URL: https://machinetoolnews.ai/simscale-survey-ai-pilot-purgatory/ Published: 2026-07-24T08:25:45+00:00 Modified: 2026-07-24T08:25:48+00:00 Author: Publisher Categories: AI in Machining, General, News, Software / CAM / IIoT Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/simscale-survey-ai-pilot-purgatory.webp Featured image alt: SimScale engineering simulation model showing AI-driven design analysis, digital twin visualisation and cloud-based CAE workflows A new SimScale survey has revealed a widening divide between engineering organisations experimenting with artificial intelligence and the small group that has successfully deployed it at scale. According to SimScale’s State of Engineering AI 2026 report, 80% of engineering organisations are running AI pilots, while only 9% have established mature, scaled AI programmes. The findings suggest that interest in engineering AI is growing quickly, yet many companies remain unable to move beyond isolated experiments and integrate AI into everyday design, simulation and product-development workflows. For machine builders, component manufacturers and engineering businesses, the report highlights the commercial difference between discussing AI and using it to accelerate real engineering decisions. SimScale Survey Covers 350 Senior Engineering Leaders The full SimScale Engineering AI report is based on responses from 350 senior engineering leaders working for organisations with more than 1,000 employees. Participants included chief technology officers, vice presidents of engineering, heads of engineering and senior simulation leaders across the United States, United Kingdom and Germany. Industries represented included: Machinery and industrial equipment Automotive and transportation Electronics and high technology Energy Architecture, engineering and construction Life sciences and healthcare The research was independently administered by Global Surveyz Research during February 2026. 80% Are Experimenting, While Only 9% Have Scaled AI The headline finding from the SimScale survey is the scale of the gap between experimentation and operational deployment. The proportion of organisations experimenting with AI pilots has risen from 42% in 2025 to 80% in 2026. However, the percentage reporting mature, scaled AI programmes has increased only slightly, from 7% to 9%. This means engineering teams are adopting AI at the pilot level far more quickly than they are building the infrastructure, processes and governance required to use it consistently across the organisation. SimScale describes this position as “pilot purgatory”, where companies can demonstrate that an AI application works but struggle to connect it with established engineering systems, data, teams and decision-making processes. The pattern reflects a wider challenge across industrial AI in manufacturing. Manufacturers increasingly have access to capable AI tools, while practical deployment depends on integration, ownership, workforce confidence and measurable business outcomes. AI Is Already Being Used in 36% of Engineering Projects Despite the difficulty of scaling AI across entire organisations, the technology is already influencing a significant proportion of engineering work. The report found that an average of 36% of design and simulation projects used AI or agentic engineering during the previous 12 months. SimScale describes this as a potential tipping point where AI begins moving from a specialist experiment into a normal part of the engineering process. Most respondents reported using AI in between 26% and 50% of their projects, suggesting that many engineering departments are expanding deployment gradually while maintaining conventional workflows alongside the new systems. This development connects with the rise of the manufacturing copilot, where AI assistants and agents are being integrated into CAM, simulation, robotics, maintenance and production-management software. AI Workflows Can Triple Simulation Speed One of the strongest findings concerns the speed of engineering simulation. Conventional simulation requests took an average of 17 hours, compared with approximately six hours using AI-enabled workflows. SimScale says this represents close to a threefold improvement in turnaround time. The most advanced teams reported completing some simulation requests in less than one hour. Faster simulation can allow engineering teams to test design decisions earlier, reduce waiting between departments and keep analysis aligned with the speed of product development. It can also expand the number of ideas that engineers are able to investigate. Teams using AI-enabled workflows evaluated an average of 56 design variants per programme, compared with 17 variants through conventional processes. That gives engineering teams the opportunity to explore more possible designs, identify better-performing configurations and reduce the risk of reaching physical prototyping with an unsuitable concept. Simulation is also becoming increasingly connected with digital twin technology in manufacturing, where virtual models are used to test machines, products and processes before changes are introduced in production. Engineering AI Could Accelerate RFQ Responses The commercial implications extend beyond product development. According to the report, engineering teams using AI-supported workflows achieved average RFQ and technical-bid turnaround times of two days, compared with six days using conventional workflows. In 11% of cases, AI-enabled teams completed the engineering work required for an RFQ response in less than one day. For machine builders, system integrators and specialist component manufacturers, that could create a direct sales advantage. Faster engineering analysis provides more time to: Explore alternative technical solutions Improve cost and performance assumptions Produce stronger proposals Respond to more opportunities Validate whether a proposed design is achievable This is particularly relevant for companies producing complex, customised machinery or components where every quotation requires engineering input. Similar pressures are influencing the development of AI CAM software, automated estimating platforms and intelligent production-planning systems. Data Remains the Biggest Barrier The most commonly reported barrier to scaling engineering AI was data preparation and availability, cited by 74% of respondents. Governance and compliance concerns were identified by 48%, while 42% highlighted software interoperability. Other barriers included: Lack of AI skills and knowledge Intellectual-property concerns Cultural resistance Computing constraints Legacy desktop CAE systems However, SimScale argues that companies may be overestimating the amount of perfectly structured data required before an AI project can produce value. Physics AI applications such as surrogate modelling can require large volumes of organised simulation data. Agentic assistants, workflow automation and engineering-support tools may be able to operate with less preparation by using existing specifications, procedures and contextual engineering information. Cloud-based engineering platforms may help address some of these barriers by connecting simulation, computing resources, APIs and engineering data within a shared environment. SimScale found that 75% of organisations with mature AI programmes identified cloud-native platforms as an important enabler of... ### Ultima Forma Launches ElectroformIT AI Engineering Agent for Advanced Manufacturing Canonical URL: https://machinetoolnews.ai/ultima-forma-electroformit-ai-engineering-agent/ Published: 2026-07-23T10:09:12+00:00 Modified: 2026-07-23T10:09:14+00:00 Author: Publisher Categories: General, News, Software / CAM / IIoT, United Kingdom Tags: top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/ChatGPT-Image-Jul-23-2026-11_06_53-AM.png Featured image alt: Ultima Forma ElectroformIT AI engineering agent launch with Alan Fisher and Steve Newbury at Farnborough Airshow UK engineering company Ultima Forma has launched ElectroformIT, an AI-powered engineering agent designed to help manufacturers identify new applications for electroforming technology. Unveiled at the Farnborough International Airshow, ElectroformIT captures an engineer’s requirements before assessing potential materials, manufacturing processes and component design approaches. The system is intended to help engineering teams explore solutions that may not be identified through conventional design and manufacturing methods. For aerospace, defence, space, transport and energy manufacturers, the practical opportunity is significant. ElectroformIT could help engineers identify lighter components, alternative material combinations, more resilient structures and manufacturing routes that would otherwise require extensive specialist research. What Is ElectroformIT? ElectroformIT combines Ultima Forma’s specialist knowledge of electroforming with AI-assisted engineering analysis. The system begins by capturing the technical requirements of an engineering challenge. It then evaluates suitable electroformed materials, hybrid manufacturing processes and possible design solutions. According to Ultima Forma, the AI can recommend approaches that engineers may not discover using traditional design processes alone. This makes ElectroformIT more than a general-purpose engineering chatbot. It is being positioned as a specialist AI agent built around a defined manufacturing process and Ultima Forma’s accumulated materials and engineering knowledge. Steve Newbury, Managing Director of Ultima Forma, said: “Our aim is to help engineers develop breakthrough engineering solutions, making it easier to access the benefits of electroforming technology. “ElectroformIT combines years of manufacturing expertise with AI-driven analysis to generate innovative concepts, recommend materials and manufacturing processes, and connect industry challenges with tailored design solutions.” How Does Electroforming Work? Electroforming is an advanced manufacturing process in which metal is deposited onto a form, mould or substrate through controlled electrodeposition. Rather than removing material from a solid block, as happens during conventional machining, electroforming builds a metallic structure layer by layer. Once the required thickness and material characteristics have been achieved, the underlying form can either be removed or retained as part of a hybrid component. The process can produce: Lightweight metallic components Seamless structures without welds or joins Complex geometries Thin-walled components High-precision surfaces Components with tailored wall thicknesses Multi-material and hybrid structures Corrosion-resistant and high-performance surfaces Ultima Forma specialises in applying the process to challenging engineering applications where conventional casting, forming or machining may be difficult, expensive or unable to deliver the required performance. AI Could Make Specialist Manufacturing Knowledge More Accessible One of the challenges facing specialist manufacturing processes is awareness. An engineer cannot consider a manufacturing method they do not know exists or do not fully understand. Even where electroforming could provide a strong technical solution, engineering teams may initially default to machining, casting, additive manufacturing, fabrication or composite construction. Ultima Forma developed ElectroformIT after recognising that many engineering teams had limited awareness of what electroforming could achieve. The AI agent is intended to reduce that knowledge barrier by connecting an engineering requirement with potential materials, geometries and production methods. Newbury said: “We work across aerospace, defence, space, transport and energy sectors, where organisations are under increasing pressure to improve performance, reduce weight, strengthen supply chain resilience and accelerate innovation. “While these are our core markets, the technology has the potential to support a much wider range of industries.” ElectroformIT Launches at Farnborough Airshow ElectroformIT was launched on the Farnborough Aerospace Consortium stand at the 2026 Farnborough International Airshow. Ultima Forma showcased a selection of example components that it says have benefited from its agentic AI design methodology. Newbury said the launch generated significant interest from engineers attending the show. “Engineers are excited by the opportunity to use AI not simply to automate tasks, but to discover new design solutions that may otherwise remain unexplored. “We showcased a range of example products at the airshow, all of which have benefitted from our agentic AI design methodology and demonstrate how advanced manufacturing and intelligent design tools can work together to deliver innovative outcomes.” The company’s Farnborough display also included lightweight electroformed structures, composite erosion protection, hydrogen containment technology, thermal-management solutions and net-shaped hot isostatic pressing canisters. Potential Applications Across Aerospace and Defence Weight reduction is a major priority across aerospace, space and defence manufacturing. Reducing the mass of a component can improve aircraft efficiency, increase payload capacity, lower fuel consumption and support the transition toward electric and hydrogen-powered aviation. Electroforming can also enable seamless metallic structures with controlled wall thicknesses, allowing material to be concentrated where strength or protection is required. Potential applications include: Aircraft leading-edge protection Lightweight heat exchangers Hydrogen storage and transfer systems Radio-frequency waveguides Thermal-management components Defence structures Space hardware Composite protection systems Net-shape manufacturing tooling The combination of AI-assisted concept generation and an advanced manufacturing process could help engineers evaluate these applications earlier in a development programme. Alan Fisher, CEO of the Farnborough Aerospace Consortium, said: “ElectroformIT is an exciting example of how advanced manufacturing knowledge and artificial intelligence can be combined to help solve real-world engineering challenges. We are delighted to support the company as it introduces this technology to the industry.” MTN Analysis ElectroformIT represents an important direction for industrial AI. Many of the most valuable manufacturing AI systems will be highly specialised rather than general purpose. They will combine AI models with proprietary engineering knowledge, materials data, process limitations and real production experience. This is particularly important in advanced manufacturing, where an apparently promising design concept must also be manufacturable, certifiable, commercially viable and capable of meeting strict performance requirements. ElectroformIT also addresses a genuine industrial problem. Engineers are... ### MVTec at VISION 2026: The AI Vision Solver Could Be the Show’s Must-See Machine Vision Demonstration Canonical URL: https://machinetoolnews.ai/mvtec-at-vision-2026-ai-vision-solver/ Published: 2026-07-17T12:45:23+00:00 Modified: 2026-07-17T12:45:26+00:00 Author: Publisher Categories: Events, General, Germany, Metrology & Vision Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/MVTec-at-VISION-2026-AI-Vision-Solver.png Featured image alt: MVTec at VISION 2026 exhibition stand showcasing machine vision software, AI Vision Solver technology and industrial AI applications Anyone attending VISION 2026 with an interest in industrial AI, quality inspection, intelligent robotics or faster machine vision development should put MVTec at VISION 2026 near the top of their schedule. From October 6 to 8, MVTec will take over Hall 8, Booth 8C56 at Messe Stuttgart with an exhibition built around artificial intelligence and the future direction of industrial machine vision. The headline attraction will be the AI Vision Solver, a forward-looking prototype that demonstrates how AI agents could help users create machine vision applications through prompts. This is far more than another deep learning inspection demonstration. MVTec is preparing to show how AI could begin supporting the entire machine vision workflow, including application development, deployment, image processing and the operation of vision systems. For manufacturers, automation specialists, machine builders and vision developers, this could be one of the most important demonstrations at the entire show. Visit the official VISION 2026 website AI Takes Centre Stage at MVTec’s VISION 2026 Booth MVTec has organised its VISION 2026 presence around the theme “Pushing machine vision to the edge.” Artificial intelligence will run through every major part of the exhibition, including image processing, development environments, speed, cybersecurity, industrial partnerships and new approaches to creating vision applications. The company will demonstrate new capabilities across its established software portfolio: MVTec HALCON MVTec MERLIC MVTec Deep Learning Tool HDevelopEVO The new AI Vision Solver prototype New releases for all three principal MVTec software products are scheduled for autumn 2026, and visitors to the stand will be among the first to see the forthcoming features. “VISION in Stuttgart has always been of central strategic importance to MVTec. As the industry’s premier gathering, our trade show presence focuses on engaging with prospective customers, existing customers, and partners while showcasing new machine vision technologies and innovations,” said Dr. Olaf Munkelt, Managing Director of MVTec. The AI Vision Solver Is the Demonstration You Should Not Miss The most exciting announcement is the MVTec AI Vision Solver. MVTec will present the system within the “Disruption” area of its stand as an early demonstration of how AI agents could transform the way machine vision applications are developed. Visitors will be able to choose different application scenarios through a touchscreen. The selected task will then be executed in HDevelopEVO through a prompt, with the system drawing on HALCON’s extensive machine vision library as its knowledge base. This provides a glimpse of a future in which users can describe the vision task they need to perform and receive AI-assisted support with building the application. MVTec is presenting the AI Vision Solver as a forward-looking prototype, giving visitors an early opportunity to see where its development technology may be heading. The potential implications are substantial. AI agents could help reduce development time, make sophisticated machine vision tools more accessible and allow engineers to concentrate on solving production problems rather than manually assembling every stage of an application. The demonstration also signals that the next stage of industrial AI will extend beyond training models to classify parts or identify defects. AI could become an active development partner within the machine vision engineering process. Why AI Agents Could Change Machine Vision Development Industrial machine vision applications frequently require specialist knowledge, extensive testing and careful integration with cameras, lighting, automation equipment and production systems. The introduction of AI agents could make parts of that process faster and more accessible. For experienced developers, an AI assistant could accelerate repetitive development work and help identify suitable HALCON methods. For manufacturers with limited internal vision expertise, it could provide a more approachable route into advanced image processing. This will be one of the critical questions surrounding the AI Vision Solver at VISION 2026: how far can AI-assisted development reduce the time and specialist knowledge needed to move from a production challenge to a functioning machine vision application? MVTec has already been moving in this direction through visual prompting and the development of HDevelopEVO. MachineToolNews.ai previously examined this shift in our coverage of MVTec HALCON 26.05 and our detailed interview with Dr. Maximilian Lückenhaus. The AI Vision Solver now brings those developments together in a demonstration that visitors will be able to experience directly. MVTec and NEURA Robotics Bring Physical AI to the Stand MVTec will also demonstrate the role machine vision plays within Physical AI through a collaboration with NEURA Robotics. Physical AI systems need to perceive their surroundings, interpret what they see and respond safely and accurately. Machine vision provides the visual intelligence required for robots and autonomous automation systems to understand objects, people and changing production environments. The demonstration will show how MVTec machine vision technology can provide a foundation for intelligent robotics and advanced automation applications. This makes the stand particularly relevant for companies exploring robot guidance, automated handling, bin picking, flexible production, inspection and adaptive manufacturing. MachineToolNews.ai has explored the growing connection between vision, robotics and intelligent automation in our guide to Physical AI in robotics and automation. Maximum AI Performance Across Different Hardware Platforms MVTec will also address one of the biggest practical challenges facing industrial AI: computing performance. Deep learning applications can require substantial processing power, particularly when inspection and recognition tasks need to be completed within demanding production cycle times. At VISION 2026, MVTec will demonstrate how deep learning inference can be executed across different target platforms using modern AI hardware accelerators. The company has developed interfaces and integrations supporting technologies from: NVIDIA Qualcomm Intel AMD MVTec’s hardware-independent approach allows manufacturers and system integrators to select the combination of software, processors, cameras and automation hardware that best suits their production environment. This flexibility matters because industrial AI applications vary significantly. A high-speed inspection line, an embedded vision device and a robot guidance application may all require different levels of performance, power consumption and integration. HALCON, MERLIC and the Deep Learning Tool Visitors will also be able to explore the latest developments within MVTec’s core software platforms. HALCON is designed for demanding industrial machine vi... ### IMTS 2026 Industrial AI: Why Chicago Could Host Manufacturing’s Most Important Technology Show of the Year Canonical URL: https://machinetoolnews.ai/imts-2026-industrial-ai/ Published: 2026-07-16T13:54:24+00:00 Modified: 2026-07-16T15:24:59+00:00 Author: Publisher Categories: Events, General, News, USA Tags: Editors Pick, Featured, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/IMTS-2026-1600x900-1.webp Featured image alt: IMTS 2026 industrial AI exhibition at McCormick Place with manufacturing professionals, CNC machine tool stands and automation technology on the show floor At IMTS 2026, Industrial AI will take centre stage in Chicago this September as manufacturers get their clearest opportunity yet to see how artificial intelligence, automation, digital twins, additive manufacturing and connected production systems are changing the factory floor. Taking place at McCormick Place from September 14 to 19, 2026, the International Manufacturing Technology Show will bring together more than 1,800 exhibitors across 1.2 million square feet of exhibition space. However, the biggest reason manufacturers should pay attention this year is the scale at which AI will appear across the entire manufacturing process. Visitors will see industrial AI integrated into CNC machining, CAM programming, robotics, machine vision, inspection, additive manufacturing, predictive maintenance, production planning and digital factory platforms. For manufacturers asking where AI can deliver genuine operational value, IMTS 2026 could be the most important manufacturing technology event of the year. Read the official IMTS 2026 announcement IMTS 2026 Industrial AI Arrives at a Critical Moment The latest figures published by AMT, the organisation that owns and produces IMTS, suggest that investment in American manufacturing technology is accelerating. Manufacturing technology orders reached $2.19 billion during the first four months of 2026, representing a 28.9% increase compared with the same period in 2025. The ISM Manufacturing PMI reached 53.3% in June, while manufacturing labour productivity increased by 3.2% during the first quarter of 2026. Manufacturing output rose by 3.3% without an increase in hours worked. These figures point towards manufacturers gaining more output from their existing workforces through improved machinery, automation, software and digital production technology. “Production demands and workforce constraints are pushing manufacturers to take a closer look at technologies like AI, digital twins, additive, and advanced automation, all of which are woven through the manufacturing ecosystem at IMTS,” said Mike Cicco, president and CEO of FANUC America and chairman of the AMT board. The commercial question facing manufacturers is increasingly straightforward: how can existing people, machines and production data be used more effectively? AI will form a major part of the answer presented at IMTS 2026. Industrial AI Moves Across the Full Manufacturing Stack Previous manufacturing AI events have often concentrated on individual technologies such as predictive maintenance, automated inspection or generative design. IMTS 2026 will show how these capabilities connect across the manufacturing technology stack. Visitors will be able to explore AI within: CNC machines and controls CAD and CAM software Industrial robots and cobots Machine vision and quality inspection Additive manufacturing Digital twins and simulation Quoting and production planning Predictive maintenance Tooling and process optimisation Cloud and edge computing Connected factory platforms This connected approach is important because manufacturers rarely operate technology in isolation. A machining company may need to connect quoting software with CAM, tooling data, CNC controls, robot loading, inspection results and its wider production management systems. AI becomes far more valuable when it supports decisions across this complete workflow. MachineToolNews.ai previously examined this shift in our guide to industrial AI in factories and our earlier preview of AI for job shops at IMTS 2026. The New IMTS Industrial AI Arena One of the biggest additions to the show will be the new IMTS Industrial AI Arena. The dedicated area will bring together 32 industrial AI exhibitors, alongside Sandia National Laboratories, to demonstrate production-focused AI applications. The arena will cover practical manufacturing challenges including: Quality and inspection Process optimisation Downtime reduction Cybersecurity Operator ergonomics Workplace safety Demand forecasting Production planning The exhibitor list includes a mixture of established industrial technology companies and AI-native manufacturing businesses. Atomic Industries is applying software and AI across the injection moulding process, covering mould design, tooling and finished-part production. Ignizia is developing an industrial AI platform intended to help manufacturers identify applications with measurable return on investment and move projects from pilot stage into production. C-Infinity’s AutoAssembler AI uses CAD and PLM information to generate production-ready assembly instructions. Purchaser.ai applies artificial intelligence to manufacturing RFQs, extracting information from PDFs, spreadsheets and email submissions before converting it into a consistent format for assessment. These companies represent a significant development within industrial AI. Manufacturing-specific platforms are emerging to solve problems that general-purpose AI systems were never designed to understand. The First IMTS Industrial AI Conference IMTS will also hold its first dedicated Industrial AI Conference on Wednesday, September 16. The full-day conference will run from 10 a.m. to 4 p.m. at McCormick Place. Sessions will focus on areas including: Predictive maintenance AI quality applications Edge AI compared with cloud deployment Manufacturing data readiness AI implementation planning Moving AI projects into production Identifying commercially valuable applications This practical focus is essential. Many manufacturers understand that AI could improve their operations but remain uncertain about where to begin, what data is required and how to assess likely returns. The conference is intended to help manufacturers build an implementation roadmap rather than leaving Chicago with another collection of disconnected AI demonstrations. Google, Microsoft and AWS Move Deeper Into Manufacturing The presence of Amazon Web Services, Google Cloud and Microsoft demonstrates how rapidly major technology companies are moving into industrial production. These companies provide the cloud infrastructure, AI models, data platforms and development tools that can connect engineering, operational technology and enterprise IT systems. Google Cloud will demonstrate how Gemini Enterprise can support specialised AI agents that interpret information from manufacturing systems. “The real magic happens at the intersection of data from engineering technology, operational technology, and IT software,” said Praveen Rao, global director and manufacturing industry lead at Google Cloud. According to Rao, specialised agents could interpret live visual and sensor information, help prevent downtime and address manufacturing productivity gaps. MachineToolNews.ai has previously examined the increasing influence of technology companies in Big Tech Has Found the Factory Floor. For machine shops, the important development is the connection between these cloud platforms and the equipment already opera... ### KUKA AMP Goes Live in North American Automotive Factory as Physical AI Moves Into Production Canonical URL: https://machinetoolnews.ai/kuka-amp-physical-ai-automotive-production/ Published: 2026-07-14T08:33:04+00:00 Modified: 2026-07-14T08:33:07+00:00 Author: Publisher Categories: General, Robotics, Robotics & Cobots, Software, Software / CAM / IIoT, USA Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/ChatGPT-Image-Jul-14-2026-09_27_24-AM.png Featured image alt: KUKA AMP Physical AI platform using industrial robots to weld an automotive body inside a North American car factory KUKA AMP is now live and delivering production parts inside a major automotive factory in North America, according to a new update from KUKA Group Chief Software & AI Officer Marc Fleischmann. Fleischmann revealed the production deployment in his July 13 article, The Next Frontier in AI, marking an important step for a platform that KUKA publicly unveiled at NVIDIA GTC earlier this year. The automotive manufacturer has not been identified. Fleischmann indicated that it is a large vehicle producer whose cars are widely used, suggesting this is a genuine industrial deployment rather than a controlled research project or trade show demonstration. For manufacturers watching the development of Physical AI, the significance lies in KUKA AMP moving from a platform announcement into an operational factory environment where it is involved in producing real components. KUKA AMP Moves From the AI Lab to the Factory Floor KUKA introduced the KUKA Automation Management Platform in March 2026 as a software layer positioned between AI agents and physical automation equipment. The platform is designed to orchestrate industrial robots, autonomous mobile robots, production cells, digital twins, software services and factory data within a common environment. Traditional industrial robots perform predefined movements with high levels of repeatability. Changes in products, parts, workflows or factory conditions normally require additional programming, configuration and engineering. KUKA AMP is intended to give automation systems more contextual understanding, allowing them to interpret an intended production outcome and coordinate the physical actions required to achieve it. Fleischmann summarised the principle by writing: “Scaling models makes AI smarter; building the context layer makes AI useful.” The context layer is designed to give AI systems shared information about the user’s objective, the available equipment, the actions each machine can perform, the condition of the production environment and the outcome of previous tasks. How KUKA AMP Connects AI With Industrial Automation KUKA describes KUKA AMP as an open and composable platform built around three main capabilities: semantics, actions and data. Semantics allow the system to understand the intended outcome of a task. Instead of concentrating entirely on individual robot movements or device instructions, the platform interprets what the production process is expected to achieve. Actions provide standardised functions that AI agents can use across robots, mobile platforms, work cells and other automation equipment. These actions could include picking a component, moving material, loading a machine or delivering a pallet to a workstation. Data provides structured information about equipment status, process conditions and previous operations. This allows the platform to evaluate results and use production experience to improve future tasks. KUKA says the platform operates through a continuous cycle of observing, acting, measuring and predicting. It can connect the physical factory with an embedded digital twin, enabling production workflows to be simulated, monitored and adjusted within the same environment. The company refers to this development as part of its move from conventional Automation 1.0 towards AI-supported Automation 2.0. AI Agents Could Orchestrate Complete Factory Workflows One of the most important elements of KUKA AMP is its emphasis on AI agents as users of industrial automation interfaces. A conventional application programming interface is normally designed for a software developer. Under the KUKA AMP model, an AI agent could discover available production capabilities and combine them to complete a wider objective. For example, a production instruction could involve identifying a particular component, collecting it from a storage area, transporting it to a machine, loading the machine, removing the finished component and delivering it to inspection. Several robots, machines, sensors and material handling systems may be involved. KUKA AMP is intended to provide the shared context and standardised actions needed to coordinate these systems. This reflects the wider rise of the manufacturing copilot and industrial AI agent, where natural-language instructions and contextual AI are beginning to support programming, simulation, diagnostics and factory operations. Digital Twins Provide a Testing and Learning Environment Digital twin technology is central to the KUKA AMP strategy. A digital twin creates a virtual representation of the production environment, including robots, equipment, physical layouts and production processes. Proposed changes can be tested virtually before they are applied to live equipment. KUKA says the digital twin within KUKA AMP could also allow manufacturers to replay previous factory operations, investigate production problems and simulate how future changes might affect output. This could help manufacturers evaluate altered production sequences, new component variants, equipment changes and capacity requirements without immediately disrupting live production. It also creates an environment where AI-generated actions can be evaluated before they are sent to physical equipment, providing an additional layer of control for autonomous factory systems. Governance and Safety Are Built Into the Platform Fleischmann also highlighted the need to place governance and operational guardrails within the automation platform itself. Physical AI systems can directly influence machines, materials and people. Their decisions therefore need to operate within defined safety, security and compliance requirements. KUKA’s approach is to codify these requirements at the platform level. AI agents should only be able to discover and execute actions that comply with the approved operational boundaries of the production system. This could become increasingly important as AI agents gain access to more production equipment and begin coordinating workflows across several machines or factory areas. Why KUKA AMP Matters to Metal Manufacturers For metalworking and machine tool companies, the most immediate opportunities could appear in machine tending, robotic welding, material handling, inspection, deburring and internal logistics. A machine shop could eventually define an intended production outcome while an AI orchestration platform coordinates the machines, robots, fixtures, inspection systems and material movements required to complete it. KUKA is already involved in efforts to make robotic machine tending more accessible. A recent integration involving Siemens, KUKA and SYIL allows robot functions to be controlled through the CNC interface, as covered in our report on AI machine tool automation for job shops. KUKA AMP takes the idea further by providing a potential orchestration layer above individual robots and machines. For high-mix manufacturers, this could reduce the engineering required when switching between... ### ABB Autonomous Forklift Launch Completes AI-Powered Visual SLAM AMR Portfolio Canonical URL: https://machinetoolnews.ai/abb-autonomous-forklift-visual-slam-amr/ Published: 2026-07-09T07:32:04+00:00 Modified: 2026-07-09T07:32:06+00:00 Author: Jelena Radojcic Categories: News, Robotics, Robotics & Cobots Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/F712-VSLAM-AMR-Studio-2-SML.jpg ABB autonomous forklift technology is moving deeper into factory intralogistics with the launch of the new Flexley® Stack F712, an AI-powered autonomous mobile robot designed for pallet transport, warehouse storage and high-density material handling. ABB Robotics has expanded its Autonomous Mobile Robotics portfolio with the launch of the Flexley® Stack F712, completing what the company describes as a full interoperable ecosystem across all major Visual SLAM AMR types. The new Flexley Stack F712 brings ABB Robotics’ AI-powered Visual SLAM technology to autonomous forklifts, allowing customers to combine forklifts, tugs and movers on a common navigation, fleet management and software platform. For manufacturers, this matters because intralogistics is becoming one of the clearest areas where AI, automation and mobile robotics can deliver measurable operational gains. From line supply and end-of-line handling to warehouse storage and retrieval, factories are under pressure to move materials faster, more safely and with fewer manual bottlenecks. The launch of the ABB autonomous forklift means customers can now deploy mixed fleets of Visual SLAM-powered tugs, movers and forklifts through one unified system. That creates a more scalable route for manufacturers looking to automate material flow without having to manage separate mobile robot platforms. ABB Flexley Stack F712 Designed For Heavy Industrial Material Handling The Flexley® Stack F712 has been developed for demanding material handling, end-of-line storage and warehouse operations across sectors including automotive manufacturing. According to ABB Robotics, the F712 can handle multiple load types and sizes, including open and closed pallets, containers and racks. It can carry loads of up to 2,000 kg and reach lifting heights of up to 8.5 metres. That gives the ABB autonomous forklift a clear role in factories and warehouses where palletised goods need to be moved between storage areas, production lines, press shops, body shops and light buffer zones. The F712 joins the existing Flexley® Tug and Flexley® Mover within ABB Robotics’ growing Visual SLAM AMR portfolio. The wider portfolio supports intralogistics tasks such as warehouse storage and retrieval, line supply, end-of-line handling, body-shop and press-shop logistics, drive-in storage and light buffer applications. “Across intralogistics operations, businesses are being asked to process greater volumes in less time, while working with increasingly limited resources,” said Marc Segura, President, ABB Robotics. “They are under pressure to move goods faster and with greater flexibility, while labour availability is becoming a critical constraint. As part of our journey to more autonomous and versatile robotics (AVR™), we have combined advanced vision, mobility and intelligence in the Flexley® Stack F712 forklift AMR, completing our scalable, AI-powered AMR portfolio.” AI-Powered Visual SLAM Removes The Need For Fixed Infrastructure Unlike conventional autonomous forklift systems that may depend on markers, reflectors or fixed navigation infrastructure, the Flexley® Stack F712 uses Visual SLAM to map and navigate its environment. ABB says the AI-enabled Visual SLAM system supports the autonomous decision-making required for complex and dynamic warehouse operations. The F712 offers market-leading positional accuracy of ±10 mm and can safely operate at speeds of up to 1.7 m/s while loaded. This is important for manufacturers because factory and warehouse layouts often change. Production cells move, storage areas are adjusted, and logistics routes need to adapt as demand changes. By using Visual SLAM, ABB Robotics says the F712 can adapt when a warehouse or production floor layout changes, helping reduce the commissioning and reconfiguration burden for customers. For readers following wider developments in Physical AI in robotics and automation, this is another example of AI moving from software-only environments into machines that make decisions in the physical world. AMR Studio Supports Faster Commissioning And Mixed Fleet Management The ABB autonomous forklift is fully integrated with AMR Studio®, ABB Robotics’ no-code software suite for setup, fleet coordination, traffic management and real-time visualisation. Powered by AMR Studio®, ABB says the portfolio can enable up to 20 percent faster commissioning while supporting interoperability and safe, reliable operation. The system is also VDA5050 compatible, helping the F712 integrate with ABB Robotics’ Visual SLAM AMRs and existing systems within a unified project. This allows customers to manage more complex mobile robot deployments where different AMR types need to operate together in the same layout. For example, a manufacturer could use tugs for line supply, movers for component handling and the Flexley® Stack F712 for pallet storage and retrieval, all coordinated through one software environment. That interoperability is becoming increasingly important as manufacturers move beyond single automation cells and start connecting entire material flow processes. MachineToolNews.ai has previously covered how ABB is using AI and simulation in industrial robotics through its ABB RobotStudio NVIDIA Omniverse integration. The Flexley® Stack F712 builds on the same broader direction: using software, AI and real-world robotics together to reduce deployment friction and increase manufacturing flexibility. MTN Analysis The launch of the ABB autonomous forklift is significant because it shows how AI-powered robotics is moving into the practical logistics layer of manufacturing. Many discussions around AI in manufacturing focus on machining, inspection, robot programming or digital twins. Those areas are important, but material movement is one of the biggest day-to-day constraints inside factories. If pallets, parts, containers or racks are not in the right place at the right time, production efficiency suffers. That makes autonomous mobile robots highly relevant to manufacturers that want to reduce waiting time, improve traceability and make labour go further. ABB’s key move here is not simply launching another forklift AMR. The bigger message is that the F712 completes a broader Visual SLAM-powered AMR portfolio covering tugs, movers and forklifts. That gives customers a more joined-up route into factory intralogistics automation. For the metal manufacturing and machine tool sector, the relevance is clear. As factories adopt more AI-enabled CNC systems, robotic cells, machine vision and connected software, the next pressure point becomes how materials flow between those assets. A smarter production cell is only as effective as the material flow around it. The Flexley® Stack F712 is aimed directly at that problem. Key Takeaways The ABB autonomous forklift launch extends ABB Robotics’ AI-powered Visual SLAM technology into forklift AMRs. The Flexley® Stack F712 can handle loads up to 2,000 kg and lift to heights of up to 8.5... ### Techman Robot AI Cobots Bring 360-Degree Inspection To Quanta’s German Automotive Line Canonical URL: https://machinetoolnews.ai/techman-robot-ai-cobots-quanta-germany/ Published: 2026-07-06T09:06:59+00:00 Modified: 2026-07-06T09:09:30+00:00 Author: Publisher Categories: General, Germany, Robotics & Cobots Tags: Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/techman-quanta-ai-cobots-1600x900-1.jpg Featured image alt: Techman Robot AI cobots inspect automotive electronics at Quanta Computer’s German facility using AI vision, 360-degree inspection, AOI quality control and production traceability for advanced automotive manufacturing. Techman Robot AI cobots have been deployed at Quanta Computer’s automotive electronics manufacturing facility in Germany, giving manufacturers another clear example of how AI-powered robotics is moving from exhibition halls into real production lines. The project brings together Techman Robot’s collaborative robot arms, built-in AI vision and automated optical inspection capability to support the production of high-density automotive electronics for electric vehicles, advanced driver assistance systems and autonomous driving applications. According to Techman Robot, the AI cobots are now being used at Quanta’s German automotive computer and electronics manufacturing facility to improve inspection efficiency, production traceability and quality control across complex automotive electronic control unit production. For manufacturers, the important point is that this is not a lab demo. It is a real production deployment where AI vision, robotic handling and automated inspection are being combined in a factory application where quality, reliability and repeatability matter at automotive scale. Why Techman Robot AI Cobots Matter For Automotive Manufacturing Automotive electronics are becoming more complex as electric vehicles, software-defined vehicles, ADAS and autonomous driving platforms demand more computing power inside the car. Techman Robot said the automotive electronic control units required for ADAS can contain nearly 5,000 components on a single motherboard. That makes manual inspection increasingly difficult and puts more pressure on manufacturers to catch defects before products leave the factory. This is exactly where AI cobots are starting to become more valuable. A collaborative robot with integrated vision can move around a component, inspect from different positions, read production data and check key assembly features without the same fatigue or inconsistency issues that can affect manual inspection. In Quanta’s German facility, Techman Robot says its AI cobots are being used for 360-degree inspection, combining precision motion control with AI vision to inspect high-performance automotive circuit boards. The system is being applied to tasks including QR code scanning, OCR reading of PCBA labels, assembly verification, connector checks, screw-fastening confirmation, inspection of water-cooling pipe rubber sleeves, foreign object detection and missing component detection. That is a strong manufacturing use case because it places AI directly into the quality gate rather than treating it as a separate analytics tool. AI Inspection Is Becoming A Factory Floor Requirement We see this as another signal that AI inspection is becoming a practical requirement for manufacturers making high-value, high-density products. For metal manufacturers, machine builders and automation suppliers, the lesson is clear. The same direction of travel that is happening in automotive electronics is also relevant to machining, fabrication, welding, assembly, metrology and machine tending. As parts become more complex and customers demand more documentation, manufacturers need inspection systems that can check quality, capture data and link that data back to production records. Techman’s AI Vision platform is positioned around functions such as part positioning, barcode reading, dimension measurement and visual inspection. Its TM AI Cobot Series combines a collaborative robot arm with machine vision for applications including automated optical inspection, quality assurance, testing, assembly, packaging and machine tending. That combination is important because it reduces the gap between robot movement and inspection intelligence. Instead of using a robot simply to move a part from one place to another, the robot becomes part of the quality control process. The Quanta Link Gives This Deployment Extra Weight This deployment is also commercially significant because Techman Robot is backed by Quanta Group. Techman describes Quanta Computer as one of the world’s largest notebook manufacturers and a top-tier electronics manufacturing services provider. That matters because this is not a demonstration with a small test cell. It is an internal manufacturing deployment inside a major electronics manufacturing group, focused on automotive-grade products where reliability and consistency are essential. Terrisa Chung, VP & GM of Automotive Business at Quanta Computer, said: “Quanta continues to drive the intelligentization and automation upgrade of our global manufacturing sites, employing rigorous quality management to meet the stringent reliability and consistency requirements of automotive electronics.” “The introduction of Techman Robot’s AI vision and cobot solutions enhances the efficiency and stability of our inspection processes, reflecting Quanta Group’s ongoing commitment to excellence in smart manufacturing and automotive electronics.” Scott Huang, Chief Operating Officer of Techman Robot, added: “We are deeply honored to collaborate closely with Quanta, a global leader in electronics manufacturing.” “The successful application at the Germany plant not only proves that our solutions can satisfy the most demanding automotive-grade standards, but also demonstrates the collective capability of both companies to drive the future of automotive production automation.” MTN Analysis This story matters because it shows the shift from cobots as flexible labour support to cobots as intelligent inspection assets. For years, collaborative robots have been sold around ease of use, safety and flexibility. Those points still matter, but AI vision is changing the value proposition. Manufacturers are increasingly looking for robots that can see, check, verify and record, not only pick and place. In Quanta’s case, the challenge is high-density automotive electronics. In a metalworking environment, the same principle could apply to checking machined features, confirming part presence, reading serial numbers, verifying welds, supporting in-process inspection or loading inspection data into a wider production traceability system. That is why this story is relevant beyond electronics. It shows how AI robotics is becoming tied to quality assurance, production documentation and factory-gate confidence. As automotive, aerospace, energy and precision engineering customers demand more proof of quality, the pressure on manufacturers to automate inspection will only grow. Why This Matters For Machine Tool Buyers For machine tool buyers, the message is that AI automation is increasingly becoming part of the wider production ecosystem. A CNC machine, press brake, laser cutter or machining cell may still be the centre of production, but the value around that machine is now being shaped by AI inspection, robot loading, process monitoring, traceability and automated quality control. The Quanta and Techman Robot deployment shows how manufacturers are combining robotics and AI vision to reduce inspection bottlenecks an... ### Nidec NC Twin Launch Brings Digital Twin Machining Into Real CNC Production Canonical URL: https://machinetoolnews.ai/nidec-nc-twin-digital-twin-cnc-machining/ Published: 2026-07-03T09:56:26+00:00 Modified: 2026-07-03T09:56:30+00:00 Author: Publisher Categories: AI in CNC, AI in Machining, General, Software, Software / CAM / IIoT Tags: Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/ChatGPT-Image-Jul-3-2026-10_51_28-AM.png Featured image alt: Nidec NC Twin digital twin platform for CNC machining, showing Nidec Machine Tool MVR Series double-column machining centre with virtual NC programme simulation for machine operation, cycle time verification, interference checking and su... Nidec NC Twin has been launched by Nidec Machine Tool as a new digital twin platform for its MVR Series double-column machining centres, giving manufacturers a way to validate NC programmes, simulate machine motion and predict machining outcomes before production begins. The launch matters because large CNC machining still has one of the most expensive bottlenecks in metal manufacturing: proving out programmes on the actual machine. When a large double-column machining centre is being used for dry runs, interference checks, trial cuts or programme verification, it is often unavailable for revenue-generating production. Nidec Machine Tool says its new Nidec NC Twin digital twin platform has been created to move more of that verification work into a PC-based virtual environment. For manufacturers working on large components, high-value parts and complex five-face machining, that could make digital twin technology a more practical part of daily production planning. For us, this is a strong example of where AI in machine tools is heading. The announcement is framed by Nidec as a digital twin launch, while its relevance sits directly inside the wider movement toward smarter CNC preparation, virtual prove-out, automated simulation and more consistent machining decisions. Why Nidec NC Twin matters for machine tool users Nidec NC Twin is designed for the MVR Series, with Nidec listing the applicable models as the MVR-Ax double-column five-face milling machine and the MVR-Hx double-column five-face milling machine. That makes this a CNC machining story, rather than a sheet metal story. The target user is likely to be a manufacturer running large machining centres for heavy components, complex workpieces, five-face machining, long cycle times and high-value production. According to Nidec, Nidec NC Twin recreates machine movements and processes in a virtual environment so manufacturers can validate programmes before production starts. The aim is to replace part of the traditional prove-out process with high-precision simulation, allowing operators to complete setup and verification remotely on a PC while keeping the real machine available for production. That point is important. Many digital manufacturing tools promise better visibility, while the real value comes when they reduce non-cutting time, shorten setup, improve first-time-right production or free up machine capacity. Nidec NC Twin is aimed directly at those pressure points. For more background on the wider technology, read our guide to digital twin in manufacturing 2026. The three key functions inside Nidec NC Twin Nidec lists three core simulation functions inside Nidec NC Twin: machine operation simulation, machining time simulation and surface quality simulation. The first is machine operation simulation. This allows the same NC programme used on the real machine to be executed in a PC-based virtual environment. Nidec says the platform can reproduce complex machine movements and workpiece conditions, including various attachments, and can visualise potential interference between the spindle, workpiece and machine structure. The second is machining time simulation. This is highly relevant for shops quoting large machining work, planning production schedules or trying to understand whether a process will meet the expected cost model. Nidec says the system uses the same machine configuration as the actual machine and can keep cycle-time error within 1%, excluding motion errors linked to auxiliary equipment such as automatic tool changers and automatic attachment changers. The third is surface quality simulation. This is where the launch becomes especially interesting for high-value machining. Nidec says operators can predict machined surface quality by evaluating machining parameters before cutting. The platform can assess surface condition, tool-path deviation, acceleration and surface roughness values such as Sa and Sz. For machine shops, that links directly to one of the most important questions in AI-driven machining: can manufacturers make better decisions before the first cut? We explored the same issue in our feature on AI in machining and CAM reliability, where the value of AI and automation was tied to more reliable programmes, shorter prove-out and greater confidence at the machine. A digital twin built around real CNC behaviour One of the strongest parts of the Nidec NC Twin announcement is its use of a genuine FANUC NC control environment. Nidec says this allows the system to verify complex machine operations, including macros, conditional branching and measurement cycles. That matters because many CAM simulations can give a useful toolpath preview, while the real machine may still behave differently once controller logic, machine configuration, attachments, probing cycles and macro behaviour enter the process. For large CNC machines, the difference between a clean simulation and a real-world collision risk can be expensive. By standardising NC programme verification around the actual control environment, Nidec is trying to close that gap. The benefit is especially clear for manufacturers using multiple CAM systems, because the NC Twin platform is designed to verify programmes independently of the source CAM software. That could make Nidec NC Twin relevant to large manufacturers with several production sites, different programming teams and different levels of operator experience. Nidec points to this challenge directly, saying that machining quality and efficiency can vary when production is spread across multiple facilities. For a wider look at how CAM automation is developing, read our comparison of AI CAM software in 2026. Why this launch is relevant to industrial AI Nidec NC Twin should be treated carefully from an editorial point of view. This is a digital twin platform, and Nidec’s release does not present it as a pure AI launch. Its importance for MachineToolNews.ai is that it sits inside the same industrial intelligence stack that AI-enabled manufacturing depends on. AI in machine tools needs high-quality process data, realistic simulation, machine-level context and repeatable virtual testing environments. Digital twins provide much of that foundation. A platform that can reproduce machine motion, verify NC programmes, estimate cycle time and predict surface quality gives manufacturers more structured information before cutting starts. That is why digital twin machining is becoming a major area for AI in CNC. It connects programming, simulation, process planning, quality prediction and production utilisation. These are exactly the areas where AI is already moving from general factory analytics into real machine tool workflows. Nidec NC Twin also reflects a bigger market shift. Manufacturers are under pressure from labour shortages, rising costs and the need to transfer skilled machining knowledge to the next generation. When a platform captures more of that knowledge inside repeatable simulation... ### Schneider Electric’s $3.1bn Cognite Deal Could Change How Metal Manufacturers Use Industrial AI Canonical URL: https://machinetoolnews.ai/schneider-electric-cognite-industrial-ai-metal-manufacturers/ Published: 2026-07-01T13:09:59+00:00 Modified: 2026-07-01T13:10:01+00:00 Author: Publisher Categories: General, News, Software, Software / CAM / IIoT Tags: top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/07/schneider-electric-cognite-industrial-ai-1600x900-small.jpg Featured image alt: Schneider Electric Cognite industrial AI deal could help metal manufacturers connect machine data, energy use, quality control and factory operations Schneider Electric has made one of the biggest industrial AI moves of the year, agreeing to acquire Cognite in a $3.1 billion all-cash deal that could reshape how manufacturers use artificial intelligence on the factory floor. For metal manufacturers, this is more than a software acquisition. It is a sign that industrial AI is moving deeper into production, maintenance, energy management, asset performance, machine data and operational decision-making. The companies making CNC parts, cutting sheet metal, welding assemblies, running robotic cells or managing high-value production assets should be watching this closely. Schneider Electric announced that it has entered into a definitive agreement to acquire 100% of Cognite Holding B.V., a provider of industrial data and AI software. Cognite will be integrated with AVEVA, Schneider Electric’s wholly owned industrial software business, once the transaction is completed. The deal is expected to close in the coming quarters, subject to customary closing conditions and regulatory approvals. Why This Deal Matters The headline number is huge: $3.1 billion in cash. But the bigger story is what Schneider Electric is buying. Cognite is known for industrial data platforms that help companies connect, structure and understand complex operational data. Its technology is built around bringing together information from machines, assets, sensors, engineering systems and enterprise platforms, then making that data usable for analytics, AI and automated workflows. That is exactly the problem many metal manufacturers face. A modern metalworking business might have CNC machines from different suppliers, CAM software, ERP, inspection systems, robots, laser cutting machines, press brakes, compressors, energy systems, maintenance logs and quality records. The data exists, but it is often trapped in separate systems. That makes it hard to use AI properly. Schneider Electric’s move for Cognite points directly at this issue. Industrial AI needs trusted industrial data before it can make useful decisions. Why Metal Manufacturers Should Pay Attention For CNC machine shops, sheet metal fabricators and automated metalworking plants, the real impact could come from the combination of Schneider Electric, AVEVA and Cognite. AVEVA already has a major industrial software footprint, including operations, data management, production optimisation, SCADA, HMI, predictive analytics and manufacturing execution tools. Cognite adds a stronger industrial data and AI layer, including AI agents and data contextualisation. In plain English, that means manufacturers could see more powerful tools for joining up what is happening across the factory. For metal manufacturers, that could include: Machine utilisation data from CNC machines and laser cutters. Energy data from compressors, drives, cooling systems and factory infrastructure. Maintenance data from critical assets. Inspection and quality data from metrology and machine vision systems. Production data from ERP, MES and scheduling tools. Robot and automation data from machine tending, welding, handling and palletising cells. The opportunity is to move from isolated dashboards to connected industrial intelligence. That means AI could help manufacturers understand why a machine is underperforming, why a part is drifting out of tolerance, why energy use is rising, or why a bottleneck keeps appearing in the same production cell. From Dashboards To Factory Decisions Schneider Electric’s announcement says industrial AI is shifting from supporting analytics to executing operations. That is the line manufacturers should pay attention to. For years, the promise of industrial digitalisation has been about visibility. Connect the machines. Collect the data. Build the dashboard. The next phase is different. The next phase is about AI systems that can understand factory context and help people make better decisions faster. For a metal manufacturer, that could mean an AI assistant that understands a machine alarm in relation to the job being run, the material being cut, the tool life history, the inspection result and the maintenance record. It could mean an AI system that spots when an energy spike is linked to a specific machine cycle or process route. It could mean faster troubleshooting across production, maintenance and quality teams. This is where Cognite’s industrial AI positioning becomes relevant. Cognite Atlas AI is built around industrial agents, AI-ready industrial data and tools that can automate workflows across assets and sites. That matters because metal manufacturers are under pressure to do more with fewer people. Skilled labour is tight. Energy costs are volatile. Delivery times are squeezed. Quality expectations are rising. AI that can connect the operational picture across the factory becomes more valuable in that environment. Why Schneider Electric Wants Cognite Schneider Electric is already a major player in energy management, automation and industrial software. The acquisition of Cognite gives it a stronger industrial AI data foundation to sit alongside AVEVA CONNECT. According to the announcement, Cognite’s Data Fusion platform and knowledge graph help integrate, model and contextualise engineering, operational and enterprise data at scale. Cognite Atlas AI adds generative and agentic AI capabilities for industrial workflows. Schneider Electric CEO Olivier Blum described Cognite as “a truly industrial grade AI platform.” That phrase matters. Industrial AI is very different from consumer AI. A factory cannot afford vague answers or disconnected recommendations. Any useful AI tool in a production environment needs reliable data, context, traceability and trust. For metal manufacturers, this is where the acquisition becomes interesting. Many companies already have the machine tools, automation and software. The missing layer is often the trusted data foundation that lets AI understand the whole operation. Why This Could Accelerate AI Adoption In Metalworking The metalworking sector has plenty of AI activity already. We are seeing AI in CNC optimisation, AI-powered machine vision, predictive maintenance, robotic welding, CAM automation, digital twins and scheduling tools. The challenge is scale. Many AI projects stay limited to one machine, one production line or one software system. Schneider Electric’s acquisition of Cognite suggests the market is moving toward wider industrial AI platforms that connect multiple systems across the factory. That could be important for larger metal manufacturers, aerospace suppliers, automotive component producers, fabrication groups and advanced engineering businesses. These companies need AI that can work across sites, machines, assets and departments. For smaller machine shops, the impact may arrive more slowly through software partners, OEMs, automation providers and platform integrations. If Schneider Electric and AVEVA can turn this into usable products, the benefi... ### Siemens and IFS Announce Groundbreaking Industrial AI Partnership: What Does It Mean for Metal Manufacturers? Canonical URL: https://machinetoolnews.ai/siemens-and-ifs-metal-manufacturers/ Published: 2026-06-29T13:33:23+00:00 Modified: 2026-06-29T13:33:25+00:00 Author: Publisher Categories: General, News, Software / CAM / IIoT Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/siemens-and-ifs-industrial-ai-partnership-metal-manufacturers-small.jpg Featured image alt: Siemens and IFS Industrial AI partnership for metal manufacturers showing AI software, robotics, factory automation, digital twin data and predictive maintenance Siemens and IFS have announced a major new Industrial AI partnership that could have significant upsides for metal manufacturers, machine shops, fabrication companies and industrial production sites. According to the official Siemens and IFS announcement, the partnership is designed to connect design, production and asset performance in one continuous loop. In plain English, Siemens and IFS want to help manufacturers close the gap between how a factory is planned and how it actually performs on the shop floor. For metal manufacturers, this matters because factory performance is rarely held back by one single problem. Downtime, maintenance delays, disconnected production data, machine performance, supply chain pressure and engineering changes all affect output. The Siemens and IFS partnership is aimed at joining those areas together using Industrial AI. Siemens brings industrial software, automation, manufacturing execution, engineering data and digital twin technology. IFS brings enterprise asset management, field service, asset lifecycle data and Industrial AI for complex operations. The result could be a more practical version of the smart factory, where production planning, maintenance, service history and engineering data are no longer trapped in separate systems. Why Siemens and IFS Matter to the Metals Industry Siemens and IFS matter to the metals industry because metal manufacturing is an asset-heavy business. A machining company depends on expensive CNC equipment. A sheet metal company depends on laser cutting machines, press brakes, automation systems and material handling. A welding operation depends on robots, fixtures, inspection systems and skilled operators. A metal forming business depends on presses, tooling, process control and reliable maintenance. When those assets stop, production stops. The challenge is that many metal manufacturers still manage production, maintenance and engineering data through separate systems. A maintenance team may know a machine is showing problems. A production planner may know the same machine is needed for an urgent job. An engineering team may understand how the process was designed. But if those teams are not working from connected data, decisions are slower and less accurate. This is where the Siemens and IFS partnership becomes relevant. The aim is to connect engineering intent with real operational performance. That means comparing what should happen in production with what is actually happening on the factory floor. For metal manufacturers, that could help answer practical questions such as: Which machines are most likely to cause downtime? Which maintenance jobs should be prioritised because they protect production? Which assets are not performing as designed? Which production problems are linked to maintenance history? Which design or process changes are causing issues later in manufacturing? That is the real value of Industrial AI. It is not about adding another dashboard. It is about making better operational decisions from connected factory data. Industrial AI Needs Real Factory Data One of the strongest points in the Siemens and IFS announcement is that Industrial AI needs industrial context. Tony Hemmelgarn, president and chief executive officer at Siemens Digital Industries Software, said: “Industrial AI only delivers value when it is grounded in both engineering intent and real-world performance.” That is exactly the issue for metal manufacturers. A general AI system can produce answers, but a factory needs answers that understand machines, processes, production schedules, maintenance history, quality requirements, safety and compliance. A wrong recommendation in a metalworking environment can create scrap, downtime, delivery delays or safety risks. This is why Industrial AI needs trusted data, clear governance and a strong connection to real production systems. Siemens and IFS are positioning this partnership around that problem. Siemens provides the engineering, simulation and manufacturing context. IFS provides the service history, asset behaviour and operational lifecycle data. For metal manufacturers, that combination is important because the best decisions often sit between departments. A maintenance decision is also a production decision. A production delay may also be an asset performance issue. A quality problem may also be linked to machine behaviour. A design change may also affect manufacturability. Industrial AI becomes much more useful when it can see those connections. The Digital Twin Becomes More Useful for Metal Manufacturers Digital twins have been discussed in manufacturing for years, but the Siemens and IFS partnership points to a more practical use case for the metals industry. A digital twin in manufacturing is a virtual model of a machine, production line, process or factory. It can help manufacturers test changes, simulate performance and understand what should happen before action is taken in the real world. The missing link has often been live operational feedback. That is where the Siemens and IFS partnership becomes interesting. Siemens’ digital twin technology can show how a machine, process or factory should work. IFS’ asset management data can show how it actually behaves over time. For a metal manufacturer, that could mean comparing the expected performance of a CNC machining cell, robotic welding line, laser cutting system or press shop with actual production data, maintenance records and service history. That matters because the factory floor always reveals things that engineering models alone cannot show. Tools wear. Machines drift. Operators change methods. Jobs vary. Materials behave differently. Maintenance windows get missed. Urgent production jobs disrupt planned schedules. A closed-loop digital twin could help manufacturers understand those differences faster and act with more confidence. Why Asset Performance Is Now Part of the AI Conversation IFS’ role in the Siemens and IFS partnership is especially important because asset performance is a major issue for metal manufacturers. In machining, fabrication, welding, forming and automated production, the equipment is often expensive, specialised and central to delivery performance. Losing one key machine can affect an entire production schedule. IFS says its EAM software uses Industrial AI to turn condition data and work history into insights that help reduce unplanned downtime and optimise maintenance windows. That is the kind of outcome metal manufacturers need. The opportunity is not only predictive maintenance. It is better decision-making around the whole production system. For example, AI could help a manufacturer understand whether a maintenance job should be brought forward because a machine is critical to upcoming work. It could help identify whether a recurring stoppage is linked to a specific asset, part, process or work order history. It could also help connect maintenance risk with customer delivery ri... ### June 2026 AI Manufacturing Releases: Kawasaki, ABB, Siemens, Mitsubishi, Zimmerman, Festo, Osterwalder and CloudNC Canonical URL: https://machinetoolnews.ai/june-2026-ai-manufacturing-releases/ Published: 2026-06-26T09:32:07+00:00 Modified: 2026-06-26T10:09:15+00:00 Author: Publisher Categories: AI in CNC, AI in Machining, AI in Sheet Metal, General, News Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/june-2026-ai-manufacturing-releases-cnc-robots-machine-tools.png Featured image alt: June 2026 AI manufacturing releases displayed as CNC machine tools, Physical AI robots and machined parts in a sunny beach launch scene As the majority of Europe and the UK are currently seeing record breaking temperatures in the sun. The month of June has also been pretty hot when it comes to news releases in the market. June 2026 has been one of the clearest months yet for AI moving closer to real manufacturing equipment. The strongest launches were not only about software dashboards or future factory concepts. They were tied to robots, CNC control, CAM programming, production machinery, digital twins, weld inspection and the automation systems that manufacturers are already using on the shop floor. For this round-up, we have focused on releases that matter to machine shops, machine tool users, production engineers, automation teams and manufacturers looking at practical AI. That means the article includes machine tool technology, CNC control and CAM, alongside robotics and Physical AI where the connection to manufacturing is strong. The main June stories come from Kawasaki Robotics, ABB Robotics, Siemens, Mitsubishi Electric, Osterwalder and CloudNC. Together, they show a market moving toward smarter machines, AI-assisted programming, more adaptive robots and better-connected factory systems. Kawasaki Robotics RL030N: Physical AI Moves Into Industrial Robots One of the strongest June launches was Kawasaki Robotics’ RL030N Physical AI robot, unveiled for Automate 2026. Kawasaki describes the RL030N as an 8 Degree of Freedom robot platform designed for Physical AI applications. The important point is flexibility. Traditional industrial robots are usually programmed for repeatable paths and fixed tasks. The RL030N is designed for applications that need more adaptive motion, obstacle avoidance, confined-space manipulation and complex motion planning. That makes this a major release for manufacturers watching the next phase of automation. A robot with added dexterity, real-time external orchestration and support for AI-driven control points to a future where robots are less limited by fixed paths and more capable of handling changing production environments. The launch also included Kawasaki’s Pulseboard weld inspection technology. Pulseboard synchronises image acquisition with robot tool-tip displacement in real time, helping robotic inspection systems capture high-resolution images during motion. Kawasaki says this can deliver up to 10 times faster weld inspection while supporting precise defect localisation. For machine shops, fabricators and manufacturers, Kawasaki’s June launch matters because it links robotics, AI, vision, weld inspection and motion control in one clear industrial automation story. It shows how Physical AI is starting to move into factory-ready robot platforms rather than staying as a research concept. MachineToolNews.ai has already covered this release in detail here: Kawasaki Physical AI Robot 2026 ABB Robotics Physical AI Toolchain: Training Robots Before They Reach the Factory ABB Robotics also had one of June’s most important Physical AI stories with the debut of its Physical AI Toolchain at Automate 2026. ABB’s Physical AI Toolchain is a software stack covering data generation, training and validation, deployment and optimisation. The goal is to help industrial robot AI models reach factory-level accuracy by using simulated, synthetic and real-world data. This matters because one of the biggest challenges in industrial robotics is the gap between simulation and real production. A robot may perform well in a controlled digital model, but real factories bring vibration, part variation, human movement, lighting changes, tooling differences and unpredictable handling conditions. ABB’s approach is important because it treats robot AI as an industrial engineering problem. Physical AI needs training, validation and deployment discipline. Manufacturers cannot afford robots that work well in a demo and fail in production. They need systems that can be tested, trained and improved before they are trusted on live factory tasks. ABB is also connecting this to its wider Autonomous Versatile Robotics direction, including sensing, perception, learning, reasoning, motion control, navigation and dexterity. For manufacturers, the key point is that robotics suppliers are now building the software infrastructure needed to make AI-controlled robots more reliable in real factories. This belongs in the June round-up because it gives the article a stronger Physical AI backbone. Kawasaki shows the robot hardware direction. ABB shows the training, simulation and deployment layer behind industrial robot AI. Festo GripperAI: AI-Based Gripping for Mixed Product Handling Festo also deserves a place in this June 2026 AI manufacturing release round-up because GripperAI shows how artificial intelligence is moving into one of the most difficult areas of factory automation: mixed product handling. Festo’s Smart bin picking information describes GripperAI as an AI-supported solution for flexible and reliable bin picking. The system is designed to help robots and handling systems grip randomly placed objects of different shapes in a fully automated process. That matters because mixed product handling is one of the areas where traditional automation can become difficult. If every part is identical, in the same position and moving through a predictable process, robotic handling is easier to automate. The challenge comes when products vary in shape, surface, orientation and position. Festo’s GripperAI pilot customer project explains how the system uses an integrated camera to detect different objects and select the most suitable tool from a tool station. Depending on the type, shape and surface finish of the object, GripperAI determines which suction cup or gripper is most suitable. For manufacturers, this is a practical AI story. It is not about AI sitting separately from production. It is about using AI to make robots more flexible when handling unfamiliar, mixed or randomly positioned items. That makes GripperAI relevant for intralogistics, machine tending, component handling, kitting, goods handling and flexible automation cells. In the wider June release picture, Festo fits neatly alongside Kawasaki and ABB. Kawasaki shows Physical AI moving into robot hardware. ABB shows the training and validation layer for robot AI. Festo shows how AI can help robots make better gripping decisions in real handling applications. Zimmermann: AI, Digital Twins and Cyber Resilience Move Into Portal Milling One of our own June stories was Zimmermann’s work around cyber resilience, digital twins and AI in precision portal milling. This is important because modern machine tools are no longer only mechanical platforms. High-value gantry and portal milling machines now include CNC controls, industrial PCs, internal networks, service connectivity, digital models and production data. That means machine tool builders increasingly need to think about security, simulation and intelligent assistance as part of the machine itself. Zimm... ### Kawasaki Physical AI Robot 2026: New 8-Axis RL030N Points to More Flexible Factory Automation Canonical URL: https://machinetoolnews.ai/kawasaki-physical-ai-robot-2026/ Published: 2026-06-24T08:04:51+00:00 Modified: 2026-06-24T08:04:53+00:00 Author: Publisher Categories: General, News, Robotics, Robotics & Cobots, USA Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/kawasaki-robot-1600x900-1.jpg Featured image alt: Kawasaki Physical AI Robot 2026 RL030N industrial robot arm demonstrating adaptive factory automation, robotic inspection and AI-controlled production handling at Automate 2026. Kawasaki Physical AI Robot 2026 is one of the clearest signs yet that industrial robotics is moving into a more adaptive phase, where robot motion, machine vision, real-time control and AI software are increasingly being designed to work together on the factory floor. Kawasaki Robotics has unveiled a new 8 Degree of Freedom robot platform, the RL030N, for Physical AI applications at Automate 2026 in Chicago. The company is also presenting its patented Pulseboard inspection technology, the new BA013L arc welding robot and the MXP360L heavy-duty material handling robot. For manufacturers, the most important point is this: Kawasaki is positioning the RL030N around more flexible, AI-orchestrated robot behaviour. That matters because many factories are now trying to automate tasks that do not fit neatly into old-style fixed robot paths. We have already seen Physical AI in robotics and automation become one of the strongest themes in industrial automation this year. Kawasaki’s launch gives that trend another important shop-floor signal. Why Kawasaki’s RL030N Matters for Physical AI Kawasaki says the RL030N is an 8 Degree of Freedom robot platform designed for Physical AI applications. The additional axis gives the robot more dexterity than a conventional 6-axis industrial robot, helping it move in tighter spaces and handle more complex motion tasks. That extra movement could be important for applications where a robot needs to adapt to its environment, avoid obstacles, work inside confined areas or follow motion plans generated by external AI software. The RL030N is powered by Kawasaki Robotics’ open KRNX real-time control API. According to the company, this allows external AI software, ROS environments, machine learning systems, vision platforms and third-party orchestration systems to control the robot in real time. That is the key shift. The robot is being presented as part of a wider intelligent automation stack, where software outside the robot controller can help shape motion and decision-making. Kawasaki Robotics President Seiji Amazawa said future automation will involve systems that “seamlessly integrate perception, motion, and decision-making.” For manufacturers watching the rise of AI in robotics, that phrase is important. The competitive edge is no longer only about repeatability, payload and reach. It is increasingly about how well a robot can connect to perception, AI planning and real-time process feedback. Pulseboard Brings AI-Linked Inspection Closer to Production Speed Kawasaki is also demonstrating its patented Tool Tip Displacement Output Function technology, known as Pulseboard. The company is showing the technology in a robotic weld inspection system developed with Fives DyAG. The demonstration combines a Kawasaki RS013N robot, a laser 3D profile camera and Kawasaki’s high-speed motion synchronization technology. The aim is to inspect complex weld geometries and curved surfaces at higher speed. The key claim is that Pulseboard can synchronize image acquisition with the robot’s tool-tip displacement in real time. That means the robot does not need to repeatedly stop for image capture in the same way as conventional inspection systems. Kawasaki says the result can be up to 10 times faster weld inspection, with reduced setup requirements, precise defect localization and high-speed inspection without sacrificing accuracy. For metal manufacturers, this is highly relevant. Weld inspection is one of the areas where AI vision and machine inspection can quickly connect to measurable production value. Faster inspection can reduce bottlenecks, improve traceability and help manufacturers catch defects earlier. Fives DyAG CEO Wade Rickard said the Kawasaki partnership helps “accelerate inspections” and identify defects while maintaining quality. Closed-Loop Dispensing Shows the Same Factory Trend Kawasaki is also showing a BU015X 7-axis robot in an adhesive dispensing and inspection demonstration developed with Coherix. The system simulates an automotive production environment by applying sealant to a Ford F-150 door skin while measuring and adjusting adhesive bead placement in real time. According to Kawasaki, the system can make real-time adjustments at speeds of up to 400 times per second. It combines Kawasaki’s hollow-arm robot design with Coherix’s 3D laser-based Adaptive Process Control technology. This is another example of the same wider trend: robots are moving from fixed motion machines into systems that can sense, measure, adjust and optimize during production. Scott Childs, Senior Director, Automotive Group at Kawasaki Robotics, described the demonstration as the “next evolution of intelligent manufacturing.” That is where this launch becomes relevant beyond automotive. Closed-loop process control, robot-mounted inspection and real-time measurement are becoming important across welding, fabrication, machining, dispensing, assembly and quality control. New BA013L and MXP360L Expand Kawasaki’s Automation Portfolio Alongside the RL030N and Pulseboard demonstrations, Kawasaki is also introducing the BA013L and MXP360L robots. The BA013L is a 13 kg arc welding robot being shown inside a compact welding cell. The system includes a Kawasaki PST500-L2 dual-axis positioner, Rollon Glider RTU, Strong Hand welding table and Miller welding power source. Kawasaki says the BA013L features a redesigned architecture with a 50 mm hollow wrist for internal cable routing. The robot also supports high-current welding torches, reduced cable wear and improved accessibility. Its extended 2,093 mm reach and high-speed axis performance are designed to help improve cycle times while maintaining weld quality. The MXP360L is aimed at heavy-duty material handling. It has a 360 kg payload capacity, extended reach and advanced vibration-control technology. At Automate 2026, Kawasaki is displaying the robot handling a Kawasaki Z125S motorcycle on a Rollon RTU linear axis. These launches show that Kawasaki is addressing both ends of the market: highly adaptive Physical AI applications and more traditional industrial automation tasks where payload, reach, speed and reliability still matter. MTN Analysis: Physical AI Is Becoming a Real Industrial Robot Category The Kawasaki Physical AI Robot 2026 launch is important because it gives manufacturers a clearer view of where robot development is heading. Physical AI is not only about humanoid robots or futuristic demonstrations. In factories, it is more likely to appear first through industrial robot arms connected to vision, sensors, real-time control APIs, machine learning systems and adaptive process software. That is exactly the direction Kawasaki is highlighting with the RL030N, Pulseboard weld inspection and closed-loop dispensing. For machine shops, fabricators and industrial automation buyers, the practical question is simple: can these systems reduce programming time, improve inspection speed, deal with variation and make automati... ### IMTS 2026 Industrial AI: How AI Is Moving Into Job Shops, CAM and Quality Control Canonical URL: https://machinetoolnews.ai/imts-2026-industrial-ai-job-shops-cam-quality-control/ Published: 2026-06-23T07:53:31+00:00 Modified: 2026-06-23T07:53:33+00:00 Author: Publisher Categories: Events, News, Software, Software / CAM / IIoT, USA Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/ChatGPT-Image-Jun-23-2026-08_41_45-AM.png Featured image alt: IMTS 2026 industrial AI trade show floor with manufacturers exploring AI CNC technology, CAM software, machine tool automation, metrology systems and smart manufacturing solutions IMTS 2026 industrial AI is shaping up to be one of the strongest themes at this year’s International Manufacturing Technology Show, with AI now appearing across quoting, CAM programming, cobot control, CNC optimisation, tool management and quality assurance. This is also one of the reasons as to why we as Machine Tool News.ai are an official media partner. The latest IMTS 2026 press release makes the direction clear. The show is being positioned around productivity, automation and digitally connected manufacturing, but the most important signal for job shops is the growing role of AI-enhanced software and shop-floor decision support. For manufacturers, this matters because AI is now moving into the practical areas where time is lost every day: estimating jobs, checking whether a part can be made, generating toolpaths, adjusting feeds and speeds, supporting unattended production and reducing inspection workload. IMTS 2026 takes place from Sept. 14 to 19, 2026, at McCormick Place in Chicago. According to IMTS, the event will bring together more than 86,000 registrants, 1,800 exhibitors and 10 Technology Sectors across 1.2 million square feet of exhibit space. For readers following AI in CNC, AI CAM software and industrial automation, the message is straightforward: IMTS 2026 is becoming a major shop-floor AI showcase. Why AI Is Becoming A Job Shop Productivity Tool The strongest part of the IMTS announcement is not the broad promise of digital transformation. It is the practical use of AI to help smaller and mid-sized manufacturers do more with the people, machines and skills they already have. IMTS highlights persistent labour constraints, rising demand and the need for manufacturers to increase output without adding unnecessary complexity. That is exactly where industrial AI is beginning to prove its value. We see three areas standing out. First, AI is helping manufacturers reduce the time between receiving an RFQ and making a confident production decision. Second, AI-enhanced CAM is beginning to compress programming time. Third, AI and automation are helping shops move closer to longer unattended operation, especially where operators are under pressure. This is the important point for job shops: industrial AI is becoming less about experimental projects and more about removing everyday bottlenecks. AI-Enhanced CAM And Quoting Will Be A Key IMTS 2026 Theme One of the clearest examples in the IMTS release is Toolpath, which offers AI-powered estimating and CAM software. The system is described as being able to determine whether a shop has the tooling and capability to machine a part, assess cost and profitability, and generate the toolpath in seconds or minutes. That is exactly the type of AI application that makes sense for contract manufacturers. Many job shops lose time before a machine starts cutting. They need to assess drawings, estimate costs, check tooling, understand fixturing, decide whether the work fits their capability and produce a quote that makes commercial sense. AI tools that speed up that front-end decision process can affect both productivity and profitability. IMTS also points to Mastercam Copilot, which brings AI automation into programming tasks. According to the release, machinists will be able to adjust feed rates and spindle speeds across multiple operations using voice or text commands, with confirmation prompts for safety. That is significant because it places AI inside the normal CAM and machining workflow. It is not replacing the machinist. It is acting as a productivity layer for skilled people who already understand the process. This fits the wider pattern we have been tracking in AI CAM software and digital twin manufacturing: the biggest early gains are coming from AI that supports programming, simulation, quoting, inspection and process optimisation. Cobots And AI Are Making Automation Easier To Adopt The IMTS release also highlights easier automation, especially for job shops that have historically avoided robots because of cost, complexity or lack of internal programming expertise. Universal Robots, Formic Technologies, Standard Bots, Hirebotics and Gimbel Automation are among the automation names referenced in the announcement. Standard Bots is especially relevant to the industrial AI story because its approach is based on robots being taught rather than traditionally programmed. IMTS says visitors will be able to demonstrate a task to the robot, which then performs it autonomously in a learning mode and self-adapts to variations in the environment. That points toward a future where robot adoption becomes more accessible for job shops that do not have dedicated automation engineers. Gimbel Automation is also relevant because it focuses on in-machine CNC automation, including spindle grippers, part-flipping modules and pallet automation systems. Its pitch is that automation can be integrated into the CNC environment without demanding deep robotics expertise. For MTN readers following Robotics & Cobots, this is the shift to watch. AI and simpler automation interfaces are making robotics less intimidating for smaller manufacturers. Industrial AI Is Also Moving Into CNC Controls AI at IMTS 2026 will not only appear in software and robots. It is also increasingly connected to CNC controls and machine visibility. The IMTS release references new CNC controller capabilities such as Mitsubishi Electric Automation’s M8V Series, which is positioned around shorter cycle times, better surface finish, faster setup and clearer visibility into the machining process. That matters because the CNC control is becoming a richer decision point. The next stage of productivity improvement will depend on how well machine data, operator input, CAM software, tool data and inspection results can work together. This is why MTN continues to treat edge AI in manufacturing as one of the most important themes for the next phase of factory intelligence. AI becomes far more useful when it is close to the process, connected to live production data and able to support decisions at machine level. Tool Management And Quality Assurance Are Becoming AI-Ready The IMTS release also highlights tooling, workholding and quality assurance as major productivity areas. Tool management exhibitors including Zoller, WinTool USA, TDM Systems and Big Daishowa, through its Speroni brand, are expected to showcase centralised platforms that connect tool data across CAD/CAM, ERP and shop-floor systems. This matters because AI cannot deliver strong manufacturing outcomes without reliable data. Tool data, machine data, process data and inspection data all need to be organised before manufacturers can gain the full benefit of AI-enhanced decision-making. Quality assurance is another major area. IMTS says exhibitors including Hexagon, Lumafield, Nikon Metrology and Zeiss will feature systems that unify metrology data, images, analytics and data management into connected platforms. That is... ### The EU AI Act: What Manufacturing Leaders Need to Know Canonical URL: https://machinetoolnews.ai/eu-ai-act-manufacturing/ Published: 2026-06-18T07:58:51+00:00 Modified: 2026-06-18T07:58:53+00:00 Author: Publisher Categories: France, Germany, Italy, Netherlands, Opinion, Poland, Spain Tags: Editors Pick, Featured Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/ChatGPT-Image-Jun-18-2026-08_45_46-AM.png Featured image alt: EU AI Act manufacturing expert Johann Diaz explains AI governance, data quality, accountability and compliance readiness for manufacturers using artificial intelligence in factory operations Artificial Intelligence is rapidly moving from experimentation to operational reality. Across manufacturing, AI is already helping organisations optimise production schedules, improve quality control, predict equipment failures, streamline supply chains, and support engineering and customer service teams. The question is no longer whether AI will impact manufacturing. The question is how organisations can use AI safely, responsibly and effectively. That is where the EU AI Act comes in. What Is the EU AI Act? The EU AI Act is perhaps the most significant AI regulatory framework introduced to date and is expected to influence AI governance well beyond Europe. Its purpose is not to stop organisations using AI. Quite the opposite. The intention is to create a framework that encourages innovation whilst ensuring AI is deployed responsibly, transparently and safely. The Act takes a risk-based approach. The higher the potential risk to people, organisations or society, the greater the level of oversight and governance required. For most manufacturers and other organisations, this means understanding where AI is being used, how it is being used, and whether appropriate controls are in place. The EU AI Act is essentially asking five questions: Do you know where AI is being used? Can you trust the data feeding it? Can you explain what it is doing? Does somebody own the outcome? Is the level of control proportionate to the level of risk? Interestingly, these are not actually AI questions at all. They are the same leadership, governance, accountability and operational management questions that organisations should already be asking of every critical business capability. The good news is that most manufacturers are not developing highly complex AI models themselves. They are typically deploying AI capabilities embedded within software platforms, machinery, industrial systems or operational workflows. Why Has the Compliance Deadline Been Delayed? One of the more interesting developments has been the decision to delay certain aspects of the compliance timetable, from August 2026 to later next year. At first glance, some organisations may assume this means regulators are backing away from AI governance. That is not what has happened. The technology has moved faster than the supporting operating model. In many respects, this is exactly the challenge organisations themselves are facing. AI capability is advancing rapidly, but governance, accountability, data quality, skills and operational processes often struggle to keep pace. Regulators recognised that businesses cannot realistically comply with requirements when some of the supporting standards, guidance documents, testing procedures and certification mechanisms are still being developed. In other words, the delay is less about relaxing the rules and more about ensuring organisations have a realistic opportunity to comply with them. In my view, this should be viewed as a positive step rather than a retreat. So, What Should Manufacturers Be Doing Now? Whilst some compliance dates may have moved, manufacturers should not view this as a reason to pause. Instead, it provides valuable time to prepare. There are four practical areas worth focusing on. 1. Understand Where AI Is Being Used Many organisations are already using AI without fully realising it. AI capabilities may exist within: • Production planning systems• Scheduling tools• Predictive maintenance platforms• Quality assurance systems• Customer support applications• Engineering and design software The first step is simply visibility. After all, you cannot govern what you cannot see. 2. Review Data Quality AI relies heavily on data. Poor-quality data often produces poor-quality outcomes. Manufacturers should ensure that data used within AI-enabled processes is accurate, consistent and appropriately governed. The old principle of “poor data in, poor data out” (or words to that effect) still applies. 3. Establish Clear Accountability One of the most common challenges I encounter across organisations is that nobody truly owns an outcome, end-to-end. Responsibility is often fragmented across departments, suppliers, technologies and processes. If nobody owns the customer outcome end-to-end, then nobody really owns the outcome at all. AI does not solve this problem. In fact, it usually exposes it. Clear ownership and accountability become even more important as AI adoption increases. 4. Strengthen Governance Organisations should begin developing governance structures that answer simple questions, such as: • Who approves AI use cases?• How are decisions monitored?• How are risks assessed?• How are outputs reviewed?• How do we demonstrate compliance if required? The organisations that establish these disciplines early will find future compliance significantly easier. The Bigger Challenge Isn’t AI In my experience, the biggest obstacle facing most organisations is not the technology itself. After all, AI didn’t create disconnected departments, fragmented workflows, poor data quality or unclear accountability. Those issues already existed. AI simply shines a brighter light on them. So, the biggest obstacle is the operational environment into which that technology is being introduced. Many manufacturers still operate across: • Organisational silos• Disconnected systems• Fragmented workflows• Multiple data sources• Inconsistent processes AI did not create these issues. But it makes them much more visible. This is why discussions about AI readiness should really begin with service and operational readiness. Before organisations can fully exploit AI, they need confidence that their people, processes, technology, data and knowledge are working together as one connected service system. Without those foundations, AI risks scaling inefficiency. With them, AI can become a powerful accelerator of productivity, innovation and customer value. Herein lies the significant opportunity for use of AI in manufacturing. Manufacturing Is Becoming More Service-Led There is another important trend running alongside AI. Many manufacturers are evolving beyond simply selling products. Increasingly, they are selling ‘outcomes’. This is one of the driving forces behind the continued shift towards Equipment-as-a-Service, Outcome-as-a-Service and wider Everything-as-a-Service (XaaS) business models. We see this through: • Predictive maintenance services• Remote monitoring• Performance guarantees• Subscription-based offerings• Equipment-as-a-Service models• Outcome-based commercial agreements Customers are becoming less interested in the asset (product/equipment/machine) itself and more interested in what that asset enables them to achieve. That shift places even greater importance on data, service delivery, operational visibility and customer outcomes. AI has the potential to accelerate this transformation significantly. Five Questions Every Manufacturing Leader Should Therefore Be Asking As AI adoption continues t... ### Big Tech Has Found the Factory Floor: Why Google, Microsoft, Amazon and NVIDIA Are Moving Into Metal Manufacturing Canonical URL: https://machinetoolnews.ai/big-tech-factory-floor-metal-manufacturing/ Published: 2026-06-11T08:34:26+00:00 Modified: 2026-06-11T09:01:25+00:00 Author: Publisher Categories: General Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/big-tech-factory-floor-1600x900-1.jpg Featured image alt: Big Tech in metal manufacturing 2026 showing Google, Microsoft, AWS, NVIDIA and Siemens logos above a modern CNC factory floor with robotics, precision-machined metal parts, connected industrial AI systems and smart factory automation Big Tech in metal manufacturing 2026 is no longer a future trend. It is already changing how factories use robotics, machine data, predictive maintenance, digital twins, quality control and production intelligence. For decades, metal manufacturing was often seen from the outside as an old-economy market. CNC machining, sheet metal, welding, grinding, inspection and fabrication were viewed as heavy industrial sectors dominated by machine builders, automation suppliers and specialist engineering companies. That view now looks outdated. Google, Microsoft, Amazon and NVIDIA are moving closer to the factory floor because metal manufacturing has something every AI company needs: real-world operational data. Machine tools, robots, sensors, cameras, quality systems, maintenance platforms and factory software all generate valuable information. The companies that can connect that data, understand it and turn it into useful factory decisions will have a powerful role in the next phase of industrial AI. At MachineToolNews.ai, we see this as one of the most important shifts in manufacturing. Big Tech is not replacing traditional machine tool companies. It is becoming the intelligence layer around them. Why Big Tech Now Cares About Metal Manufacturing Metal manufacturing is becoming attractive to Big Tech because the factory floor is one of the hardest environments for AI to master. Unlike office software, manufacturing involves physical machines, live production constraints, safety risks, material variation, downtime, scrap, tool wear, part tolerances and skilled human decision-making. That makes it difficult, but also valuable. The prize is huge. If AI can help a manufacturer reduce downtime, improve machine utilisation, detect defects earlier, train robots faster or shorten production planning time, the commercial impact is immediate. That is why Big Tech is now targeting areas such as robotics and physical AI, factory data platforms, machine monitoring, predictive maintenance, digital twins, visual inspection, AI copilots for operators and engineers, production planning and troubleshooting. This is where the evidence becomes important. The clearest proof is not in vague AI claims. It is in named partnerships, live industrial use cases and factory-floor systems that are already being tested or deployed. Google: Physical AI Moves Into Industrial Robotics One of the strongest recent proof points is the collaboration between FANUC and Google. In May 2026, FANUC announced a collaboration with Google to accelerate physical AI for industrial robots. Intrinsic, Google’s robotics software company, also said it was working with FANUC to bring high-performance support for FANUC robots, including the CRX collaborative robot range, to the Intrinsic platform. That matters for metal manufacturing because FANUC robots are already widely used across automated production, machine tending, handling, assembly and industrial robot cells. Google is not entering the market by building a CNC machine or a press brake. It is entering through the software and intelligence layer that could help robots become easier to program, more flexible and more useful in real production environments. This is a major shift. Traditional industrial robots are extremely powerful, but they are often limited by programming time, fixture setup, repeatability requirements and the need for specialist integration. Physical AI aims to make robots better at understanding tasks, objects, environments and instructions. For metal manufacturers, the potential effect is clear: easier robot deployment, faster automation projects and more flexible use of robots around CNC machines, inspection stations, welding cells and material handling systems. The credible evidence here is not that every robot is suddenly autonomous. The evidence is that one of the world’s largest robot manufacturers is working with Google’s AI and robotics ecosystem to bring physical AI closer to industrial use. Microsoft: Factory Data Becomes a Troubleshooting Tool Microsoft’s role in manufacturing is already visible through factory data and AI agents. A strong example is Schaeffler, the global motion technology and precision components manufacturer. In a Microsoft customer story on Schaeffler, Microsoft says Schaeffler is using Microsoft Fabric and Azure AI to modernise factory data insights and connect information across manufacturing operations. In the same case study, Schaeffler says workers can use the AI agent to search for the reason behind downtime and the best way to solve it. That is one of the most important effects Big Tech is having on the factory floor. The impact is not only about automation hardware. It is about helping engineers, maintenance teams and production managers understand what is happening across complex factories. A factory problem can sit across several systems at once. A quality issue might involve machine parameters, material batches, shift data, inspection results, tool condition and maintenance history. In a traditional setup, investigating that problem can mean manually searching through different systems. Microsoft’s manufacturing AI push is aimed at connecting that information and making it easier to query. Wired also reported on Microsoft’s Factory Operations Agent being used in a Schaeffler factory environment to help analyse defects, downtime and production data across multiple systems. For metal manufacturers, this is highly relevant. If an AI system can help identify why a grinding process is drifting, why a bearing line is producing defects, why a CNC cell is losing uptime or why energy use is rising, it moves AI from theory into daily operations. That is a real factory-floor effect. Amazon AWS: Predictive Maintenance and Industrial Sensor Data Amazon’s role is different. AWS is moving into manufacturing through cloud infrastructure, machine learning, industrial data services and predictive maintenance. One of the clearest examples is Amazon Lookout for Equipment, an AWS machine learning service designed to monitor industrial equipment. AWS says the service analyses sensor data to detect abnormal machine behaviour, diagnose issues and help manufacturers act before unplanned downtime occurs. That is directly relevant to metals because downtime is one of the most expensive problems on any shop floor. A failed spindle, pump, motor, compressor, press, laser system, robot cell or extraction system can stop production, delay jobs and damage margins. Predictive maintenance is not new, but Big Tech changes the scale. AWS brings cloud computing, machine learning models, data infrastructure and deployment tools that can help industrial companies build maintenance intelligence around existing equipment. For smaller and mid-sized metal manufacturers, the most useful development may come from technology partners building applications on top of AWS. The factory may never think of itself as an AWS customer, but the software it uses fo... ### AI Adoption in Manufacturing: A Free Practical Toolkit for UK SMEs. Free download Canonical URL: https://machinetoolnews.ai/ai-adoption-in-manufacturing-made-smarter-toolkit/ Published: 2026-05-05T07:46:16+00:00 Modified: 2026-06-16T07:57:08+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, AI in Sheet Metal, Case Studies, General, United Kingdom Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/AI-toolkit-Landscape.png Featured image alt: AI adoption in manufacturing for UK SMEs showing a factory worker using a digital tablet to monitor smart manufacturing data and Made Smarter toolkit guidance AI adoption in manufacturing is no longer something SME manufacturers can afford to leave on the horizon. Across the sector, businesses are under pressure to improve productivity, reduce waste, make better use of data, and stay competitive. AI can help, but for many manufacturers the same questions remain: Where do we start?How do we manage risk?How do we make sure AI delivers real value? That is exactly what Made Smarter’s new white paper is designed to answer. AI Adoption in Manufacturing: A Practical Toolkit from Made Smarter is a free-to-download guide created to help SME manufacturers move from AI interest to practical action. Download the Made Smarter white paper here A practical route into AI The toolkit has been developed by Made Smarter North West and co-authored by Professor Chris Dungey, AI Champion for Advanced Manufacturing at the Department for Business and Trade. Rather than focusing on complex technology, the guide helps manufacturers identify where AI can solve real operational problems. That could mean reducing low-value admin, improving decision-making, supporting quality control, making better use of production data, or helping teams work more efficiently. For SMEs, this matters. AI adoption does not need to start with a major investment or a full factory transformation. It can start with one clear business challenge and one controlled pilot. Scan, Pilot, Scale At the centre of the guide is a simple framework: Scan, Pilot, Scale. Scan helps manufacturers identify where AI could create genuine value. Pilot helps them test AI safely, with clear success measures and human oversight. Scale helps businesses move from a proven pilot to wider adoption across everyday operations. This step-by-step approach is important because manufacturing is different from many other sectors. AI in a factory environment can affect quality, productivity, compliance and safety. That means adoption needs to be practical, measured and controlled. Why SMEs should download it Made Smarter’s message is clear: AI is now within reach for SME manufacturers. The opportunity is not about chasing hype. It is about using AI to solve practical business problems and build stronger, more productive operations. The toolkit is designed to help manufacturers: Understand where AI fits in their business Identify realistic use cases Avoid common adoption mistakes Test AI safely before scaling Focus on measurable business value For manufacturers unsure where to begin, this guide offers a clear starting point. MTN Analysis For MachineToolNews.ai, the value of this white paper is its practicality. Many manufacturers know AI is important, but they need a route into adoption that feels realistic. Made Smarter’s toolkit gives SMEs that route. It does not promise instant transformation. It shows how manufacturers can start small, test safely and scale what works. That makes it a useful guide for any manufacturer looking to move from AI curiosity to real factory impact. Download the white paper If your business is exploring AI, this Made Smarter toolkit is a strong place to start. It gives SME manufacturers a practical framework for understanding AI, testing it responsibly and applying it where it can deliver measurable results. Download AI Adoption in Manufacturing: A Practical Toolkit from Made Smarter FAQ: AI Adoption in Manufacturing for UK SMEs What is the Made Smarter AI Adoption in Manufacturing toolkit? The Made Smarter toolkit is a free practical guide created to help UK SMEs understand, test and scale AI in a structured way. It explains how smaller manufacturers can move from AI curiosity to real operational impact. Who is the Made Smarter AI toolkit for? The toolkit is aimed at UK SME manufacturers that want to explore AI but need a clear starting point. It is especially useful for businesses that know AI could help but are unsure where to begin. How can AI help UK SME manufacturers? AI can help UK SME manufacturers improve productivity, reduce repetitive work, support better decision-making, make better use of data, improve quality control and identify efficiency opportunities across operations. Does AI adoption require major investment for UK SMEs? No. One of the key messages from Made Smarter is that SME manufacturers can start small. UK businesses can begin with one practical use case, test it safely, measure the results and then scale what works. What does Scan, Pilot, Scale mean? Scan, Pilot, Scale is Made Smarter’s practical framework for AI adoption. It helps UK SMEs identify where AI could create value, test solutions safely, and then expand successful projects across the business. Why is AI adoption different in manufacturing? AI in manufacturing can affect production quality, machine performance, compliance and safety. That means UK SME manufacturers need a structured approach, clear governance, human oversight and careful testing before wider deployment. Where can UK SMEs download the white paper? UK SMEs can download AI Adoption in Manufacturing: A Practical Toolkit from Made Smarter from the Made Smarter website here:www.madesmarter.uk Read More: AI in CNC machining Smart Factory / Factory AI in Sheet Metal This article is an advertorial courtesy of Made Smarter ### IDS Vision AI Label Reader: How AI Is Making Goods-In Inspection Faster and More Reliable Canonical URL: https://machinetoolnews.ai/ids-vision-ai-label-reader-goods-in-inspection/ Published: 2026-06-10T07:19:48+00:00 Modified: 2026-06-10T07:19:51+00:00 Author: Publisher Categories: General, Metrology & Vision, News Tags: top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/ids-case-study-comi-label-reader-header-3000x2000-1.jpg Featured image alt: IDS Vision AI Label Reader using an IDS uEye CP industrial camera to capture label data for AI-based goods-in inspection, logistics automation, traceability and machine vision quality control IDS Vision AI Label Reader: How AI Is Making Goods-In Inspection Faster and More Reliable IDS Vision AI Label Reader is showing how industrial AI and machine vision can remove one of the most frustrating bottlenecks in goods-in, logistics and electronics manufacturing: manually reading and checking labels that arrive in different layouts, languages and code formats. For manufacturers handling thousands of components, labels are no small issue. Part numbers, batch details, manufacturer information, 1D codes, 2D codes, multilingual markings and damaged barcodes all have to be captured correctly. When this is done manually, the process can slow down production, introduce errors and weaken traceability. That is why the latest application involving IDS Imaging Development Systems and collective mind GmbH is important. The Vision AI Label Reader from collective mind GmbH, also known as COMI, uses AI-based image processing to automatically capture and interpret label information regardless of layout, language or code type. A uEye CP industrial camera from IDS provides the image data needed for the analysis. For us, this is a strong example of practical industrial AI. The system is not being presented as an abstract AI concept. It is already being used in a real goods-in environment at Rutronik Elektronische Bauelemente GmbH, a broad-line distributor of electronic components. Why IDS Vision AI Label Reader matters for manufacturing IDS Vision AI Label Reader matters because goods-in is often where poor data quality starts. If label information is captured incorrectly at the beginning of a process, that mistake can travel through the factory, warehouse or ERP system. In sectors such as electronics, medical technology and regulated manufacturing, this can affect traceability, quality documentation and customer confidence. The Vision AI Label Reader is designed to recognise all labels on an object, read printed text, process 1D and 2D codes and interpret the content using artificial intelligence. According to IDS, handwritten entries can also be processed where required. The key point is that recognition does not rely on predefined label standards. New layouts, languages and code formats can be handled without retraining. That is important because logistics teams rarely control the exact label design used by every supplier. For manufacturers, this means AI can support a more flexible goods-in process without requiring every supplier label to follow the same structure. Camera and AI working together The camera is central to the system. COMI uses an IDS uEye CP industrial camera to capture labels and packaging surfaces at high resolution. This image data is then used by the AI system to identify and interpret label content. Goods-in environments can be difficult for machine vision. Reflective packaging, dry packs, damaged codes and changing lighting conditions can all reduce recognition reliability. IDS says the system addresses this through the combination of high-resolution image capture and a coordinated lighting concept. The camera used by COMI is equipped with Sony’s IMX183 rolling shutter CMOS sensor from the STARVIS series. The uEye CP camera has a compact magnesium housing measuring 29 × 29 × 29 mm and weighs around 50 g. IDS says the model provides 20.44 megapixel resolution and a frame rate of almost 20 frames per second. Tobias Husemann, Senior Consultant at COMI, said: “With a resolution of 20.44 megapixels and a frame rate of almost 20 frames per second, the camera provides exactly the level of detail we need to reliably capture even very small label information.” That level of detail is especially relevant when a label includes small batch codes, supplier markings, compact product IDs or dense 2D codes. From label reading to structured ERP data The real value of IDS Vision AI Label Reader is not limited to reading a label. After image acquisition, the AI analyses the information in several stages. Labels are localised, content is extracted and the system then semantically interprets the data. This allows the system to assign part numbers, batch details and manufacturer information more clearly. The results can then be transferred directly to connected ERP systems such as SAP or proALPHA, including real-time comparison and validation. This is where the technology becomes more than a camera application. It becomes part of the data layer of the factory or warehouse. For companies with complex inventories, this can reduce manual inspection steps, improve data quality and support complete documentation of item movements. IDS says the system can support 100 per cent traceability, which is increasingly important for downstream industries with stricter regulatory requirements. Efficiency gains in goods receipt IDS says practical use shows an efficiency gain of around 30 per cent in item capture compared with conventional multi-label readers. That is a useful figure because it connects AI vision directly to operational performance. In goods-in departments, a 30 per cent improvement in item capture can reduce bottlenecks, free up staff time and improve process reliability. The system also supports automated plausibility checks. This means label content can be checked earlier in the process, helping teams identify possible errors before they move further into production, stock control or customer delivery workflows. For manufacturers dealing with high component volumes, that can have a meaningful impact on throughput and traceability. MTN analysis: why this is a useful industrial AI example We see IDS Vision AI Label Reader as part of a wider shift in machine vision. AI inspection and AI recognition systems are now moving beyond final quality control. They are increasingly being applied at the point where production, logistics and data quality meet. Goods-in is a good example because the work is repetitive, data-heavy and error-sensitive. This also connects with a wider trend we have covered on MachineToolNews.ai: industrial AI becomes most valuable when it is linked to a specific production problem. In this case, the problem is not vague digital transformation. It is the daily challenge of reading inconsistent, multilingual, damaged or reflective labels quickly and accurately. That makes the application relevant to electronics manufacturing, precision component supply, warehouse automation, medical technology supply chains and any manufacturer where traceability starts at goods receipt. It also shows why camera hardware still matters in AI systems. The AI model can only interpret what the image acquisition system captures. Resolution, lighting, sensor performance, depth of field and reliable integration all affect the outcome. From tabletop scanner to automated warehouse system IDS says the Vision AI Label Reader is expected to move beyond tabletop scanner use and become more fully integrated into automated warehouse and material flow systems. That direction is important. I... ### UK Manufacturing AI Adoption: Make UK Warns Only 2% Have Embedded AI Canonical URL: https://machinetoolnews.ai/uk-manufacturing-ai-adoption-make-uk-report/ Published: 2026-06-08T11:06:47+00:00 Modified: 2026-06-08T11:06:49+00:00 Author: Publisher Categories: Case Studies, General, News, United Kingdom Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/ChatGPT-Image-Jun-8-2026-12_02_25-PM.png Featured image alt: UK manufacturing AI adoption in a modern CNC factory with precision machining, automated robotics and a stylish Union Jack design showing advanced British manufacturing technology UK manufacturing AI adoption remains at an early stage, with only 2% of manufacturers saying artificial intelligence is widely embedded across their operations, according to a new report from Make UK. The report, AI, Skills and the Future of the UK Manufacturing Sector, has been released as London Tech Week gets underway, placing a timely spotlight on the gap between the UK’s national AI ambition and the practical reality inside many manufacturing businesses. Make UK warns that AI could unlock major productivity gains for UK manufacturing, but skills shortages, limited training capacity and low levels of adoption risk preventing manufacturers from realising the full benefit. According to the report, only 2% of manufacturers say AI is widely embedded across their business. A further 37% are using AI moderately in some departments or pilot projects, while 43% remain limited to small-scale experiments. Almost one in five manufacturers, 18%, have not adopted AI at all. For the UK manufacturing sector, the finding is significant. Make UK estimates that manufacturers lose around £6 billion in output each year because of unfilled vacancies and digital capability gaps. It also points to previous research showing that wider digitalisation could add £150 billion to UK GDP by 2035. For MachineToolNews.ai, the report highlights one of the most important issues facing UK industry. AI is already active across manufacturing, including machining, inspection, maintenance, scheduling, design, production planning and factory intelligence. The challenge for UK manufacturers is scaling those applications from isolated pilots into daily operational performance. UK Manufacturing AI Adoption Remains Stuck in the Pilot Phase The Make UK report shows that many manufacturers understand the potential of AI, although relatively few have moved into wide deployment. Only 2% of manufacturers report widespread AI use across production or business operations. Around 37% describe their adoption as moderate, with a few departments using AI tools or piloting projects. A further 43% are carrying out limited small-scale experiments, while 18% have not adopted AI at all. This matters because AI adoption in manufacturing is most valuable when it moves beyond isolated use cases. Manufacturers can gain from AI in areas such as predictive maintenance, machine monitoring, production scheduling, quality inspection, energy optimisation, supply chain planning and AI in CNC machining. The report suggests that many firms are still at the stage of testing AI tools rather than embedding them into systems, workflows and production decision-making. That creates a pilot-to-production gap, especially for firms that need to integrate AI with existing machines, ERP systems, MES platforms, quality data, maintenance records and legacy factory infrastructure. For larger manufacturers, the challenge is often complexity. They may have more resources, but they also have more systems, more sites, more governance requirements and more integration barriers. For smaller manufacturers, the issue is often capacity. SMEs may be interested in AI, but lack the time, internal skills and spare resource to identify use cases, test tools and scale successful projects. Most AI Use Is Still in Back-Office Functions One of the clearest findings in the Make UK report is where AI is currently being used. The report says 83% of manufacturers using AI are applying it in business support functions such as HR, marketing, finance and administration. These areas are often easier to adopt because many AI tools are already mature, affordable and familiar to staff. Adoption in core manufacturing operations remains much lower. Make UK says 24% of firms are using AI in design and R&D, 11% in production and operations, 7% in supply chain and logistics, and 6% in quality control. This is a key point for UK manufacturing. Back-office AI can improve efficiency, reduce repetitive work and support business processes. The largest productivity gains are likely to come when AI is connected to the operational heart of manufacturing. That means AI being used to improve machine uptime, reduce scrap, detect process variation, optimise production schedules, support operators, improve inspection and help engineers make faster decisions. It also means connecting AI to industrial software, CAM and IIoT, where data can be used to improve performance across machines, lines and factories. From our perspective, the report shows that UK manufacturing is not short of AI interest. The bigger issue is turning AI into measurable factory outcomes. Skills Shortages Are the Main Barrier to AI Adoption Make UK identifies skills shortages as the biggest barrier preventing manufacturers from adopting AI more effectively. More than half of manufacturers say skills and capability gaps are their main constraint on using AI. The report says shortages are especially acute at technician and operator level, where firms need people who can work confidently with AI-enabled systems, production data and connected factory technologies. This is an important point because the AI skills challenge in manufacturing is not limited to software developers or advanced data scientists. Manufacturers also need people with practical production knowledge who can understand data, interpret AI outputs, improve processes and operate digital systems safely. The Make UK report says manufacturers are prioritising broad, adaptable capabilities. Data literacy and analytics are among the most important skills, followed by process improvement, problem solving, leadership and change management. Ethical and responsible AI understanding, along with AI system operation and maintenance, are also highlighted. This reflects what we see across the machine tool and metal manufacturing sector. The most successful AI deployments are likely to come when AI tools are placed in the hands of people who understand machining, inspection, maintenance, planning, production flow and customer requirements. In a factory, AI needs operators, technicians, engineers and managers who know what good output looks like. AI can provide insight, recommendations and alerts, while real industrial value comes when those outputs are connected to practical manufacturing action. AI Is Beginning to Change Manufacturing Jobs The Make UK report also examines how AI is affecting jobs and skills. So far, AI’s impact on jobs in manufacturing has been limited and focused mainly on task automation. The report says AI is being used to automate repetitive activities, especially routine administration and data entry, rather than redesigning entire roles at scale. However, Make UK says 46% of manufacturers expect AI to reshape jobs and ways of working within the next two years. Examples already emerging include maintenance engineers using predictive analytics, planners using AI-supported scheduling tools and quality inspectors moving from manual checks to exception mana... ### Zimmermann Builds Cyber Resilience, Digital Twins and AI Into Precision Portal Milling Strategy Canonical URL: https://machinetoolnews.ai/zimmermann-cyber-resilience-digital-twins-ai/ Published: 2026-06-04T08:14:50+00:00 Modified: 2026-06-04T08:14:52+00:00 Author: Publisher Categories: AI in CNC, AI in Machining, General, Germany, USA Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/06/ChatGPT-Image-Jun-4-2026-09_08_26-AM.png Featured image alt: Zimmermann engineer using laptop beside CNC control system for cyber resilience digital twin AI and high precision portal milling machine development Zimmermann cyber resilience AI developments are placing digital security, simulation and intelligent assistance at the centre of high-precision portal milling machine construction, as the machine tool manufacturer prepares for new European cyber security requirements and increasingly connected manufacturing environments. The company says modern high-precision portal milling machines are no longer purely mechanical systems. They are complex digital platforms built around CNC control systems, industrial PCs, sensor technology, software, drives and internal networks. As these machines become more connected, cyber resilience is becoming an integral part of machine tool design. For Zimmermann, the issue is especially important because its machines are used in demanding sectors including aerospace, automotive, mouldmaking, transportation, off-highway and mechanical engineering. In applications where machined components may later be used in safety-relevant areas such as commercial or military aviation, the integrity of data, production parameters and machine performance becomes a critical part of overall precision. Christian Gaarz, Head of Software Development & Commissioning at Zimmermann, explains that machine safety and cyber security address different risks. “What distinguishes machine security from cyber security? The former protects the operator from hazards posed by the system. Safety features prevent mechanical or electrical risks and ensure that people are not harmed. Cyber security, on the other hand, affects the entire digital network of a machine,” explains Gaarz. “All networked components with IP addresses must be secured in such a way that no unauthorized access from the outside is possible. Such an intervention could change processes, cause production to fail and have serious consequences go unnoticed, such as data loss or falsified production parameters. This is particularly critical for components that will later be used in safety-relevant applications such as commercial and military aviation.” Gaarz and his team are responsible for the digital systems that bring Zimmermann’s portal milling machines to life, from drives and sensor technology through to software solutions. The goal is to turn mechanical components into process-reliable complete systems where digital security is considered from the start. Zimmermann cyber resilience AI strategy becomes a regulatory requirement Zimmermann is preparing for a changing European regulatory environment. The company points to the Cyber Resilience Act, the Machinery Regulation (EU) 2023/1230 and the NIS 2 Directive as part of a wider legal framework affecting digital products, machine systems and connected industrial equipment. “For us as a mechanical engineering company, this means that cyber resilience is no longer a voluntary additional service, but a regulatory obligation,” reports Gaarz. “We must meet this binding framework for compliance as early as 2027.” This changes how machines are developed. Security requirements influence machine design, software architecture, documentation and internal processes at an early stage. Zimmermann describes the key principle as “security by design,” meaning security is built into machine development from the beginning. The company says it is working in industry specialist groups and collaborating with control manufacturers including Heidenhain and Siemens, as well as external specialists. “Especially for a medium-sized mechanical engineering company such as Zimmermann, it is crucial to bundle regulatory know-how to develop practical solutions. At the same time, we are adapting internal processes and training our staff in a very targeted manner,” says Gaarz. How Zimmermann is applying security by design Zimmermann’s approach begins with analysing which requirements apply to each machine configuration. Network architectures are then reviewed, segmented and supplemented with suitable security mechanisms. Industrial PCs and the main CNC on the machine are secured, while software vulnerabilities are assessed through defined processes for updates and documentation. The aim is to reduce digital risk without affecting machine performance. “We want to minimize digital attack surfaces without impairing the dynamics or precision of the machines,” explains Gaarz. That balance matters because Zimmermann operates in special machine construction, where each system can differ in detail. Although machines are based on existing Zimmermann series, individual configurations may include different milling heads, peripherals, automation systems, axes, drives, safety functions and material handling equipment. Each machine therefore requires its own electrical plan, topology and parameterisation. Mechanical differences can directly affect control behaviour, which means cyber security must be integrated into existing machine structures without limiting the machine’s precision, dynamics or productivity. Digital twins support commissioning, service and training Alongside cyber resilience, Zimmermann is also looking to digital twins as a way to improve efficiency and process reliability. The company says a virtual machine environment can allow collision checks, parameterisation and software adjustments to be carried out earlier in the development and commissioning process. By testing some elements digitally before work takes place on the factory floor, commissioning time can be reduced, risks can be lowered and processes can be stabilised. Digital twins can also support operator training before the physical machine is fully deployed. In service situations, a fault status can be reproduced in the digital model so causes can be identified more quickly. For customers, this has the potential to support both machine productivity and process reliability. AI as an engineering assistant Artificial intelligence is also being explored as part of Zimmermann’s wider digital strategy. The company is building an internal knowledge database that can be evaluated with AI support. “In pilot projects with control manufacturers, we are investigating how AI can support us in programming or analyzing error messages more quickly,” says Gaarz. “In the future, recurring tasks can be accelerated and proposed solutions generated.” Zimmermann is positioning AI as an assistance system rather than as a replacement for engineering responsibility. The company says results must be checked and validated by engineers, with transparency and traceability remaining essential. AI could also support the faster creation of digital twins, helping make the structural development process of a machine more efficient. For MachineToolNews.ai, this is where the story becomes especially relevant to the future of AI in machine tools. Zimmermann is showing how AI can sit inside a wider industrial digital framework that includes cyber resilience, control systems, simulation, engineering knowledge and machine performance. In this model, AI is not treated... ### FANUC Accelerates Physical AI Through Collaboration with Google Canonical URL: https://machinetoolnews.ai/fanuc-physical-ai-google-robots/ Published: 2026-05-22T10:33:09+00:00 Modified: 2026-05-22T10:33:11+00:00 Author: Publisher Categories: AI in CNC, AI in Sheet Metal, General, Metrology & Vision, News, Robotics, Robotics & Cobots Tags: top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/FANUC_Google_Physical_AI_1600x900.jpg Featured image alt: Physical AI FANUC and Google collaboration showing industry representatives at the FANUC Google announcement for AI agents and industrial robot automation AI Agent Powered by Google Operates Robots FANUC is advancing open platforms and Physical AI capabilities to accelerate automation by applying the latest developments in artificial intelligence to Robotics. As part of this initiative, FANUC announced the enhancement of its Physical AI Robot System through a strategic collaboration with Google, leveraging Google’s state of the art technologies. “Physical AI” – the integration of cognitive intelligence with physical action – has drawn global attention with recent advancements in AI, particularly large language models (LLMs). This includes robots that perceive their environment through sensors, and can make autonomous decisions, and execute tasks accordingly. This represents a leading application of Physical AI in this emerging field. FANUC robots support ROS, the industry standard open platform for robot control, through FANUC’s open source ROS drivers. They also offer broad compatibility with widely used technologies, including the Python programming language for AI development, high speed communication interfaces for external robot control, and interfaces that enable easy operation from PLCs. Google is a prominent contributor and maintainer of ROS through their Intrinsic robotics AI group. These open platform capabilities allow manufacturers to seamlessly deploy Physical AI robot systems, combining FANUC’s highly reliable lineup from small robots with 3kg payload to large robots with 2.3-ton payload including the CRX collaborative (cobot) series, with the latest Physical AI technologies for actual factories. Since the release of FANUC’s Physical AI system at the International Robot Exhibition last December, customer interest has continued to grow rapidly. Following the exhibition, FANUC has already shipped more than 1,000 robots for Physical AI related applications, and demand continues to accelerate. Through the new collaboration with Google, FANUC has developed a Physical AI system for industrial robots that incorporates latest innovations from Google Cloud, including Gemini Enterprise, an enterprise grade generative AI with robust security. At its New Product Open House Show in May, FANUC demonstrates a next generation Physical AI Robot System featuring generative AI Gemini. The demonstration showcases an AI agent built with Gemini Enterprise, understanding human instructions, recognising objects, and autonomously operating FANUC robots to perform assigned tasks. This marks a revolutionary “AI-Powered Agent System for Industrial Robots” enabling collaborative robots and non-collaborative robots to work together as a single cell based on simple natural language instructions. In addition, all the FANUC robots, including CRX, will be fully supported on the enterprise software platform from Intrinsic, which enables users to build, test and implement AI robot solutions. Based on that platform, FANUC provides full support for robot control compatible with Intrinsic’s development environment, “Flowstate”. Flowstate software is interoperable with ROS and allows for the quick and easy development of highly adaptable AI solutions by leveraging FANUC’s open platform technologies. FANUC is also participating in Google DeepMind’s “Gemini Robotics Trusted Tester Program” to advance AI research on foundational robotics models for AI. Visitors to FANUC’s New Product Open House Show in May have the opportunity to interact directly with systems that integrate FANUC robots and Google’s generative AI Gemini. Visitors can issue commands in natural language, and experience firsthand how the latest Physical AI technologies can be applied in real world manufacturing environments. FANUC invites all attendees to witness the latest demonstrations of Physical AI and AI agents powered by FANUC robots. About FANUC The FANUC Corporation is a global leader in industrial automation, supplying CNC control systems, robots, cobots, and production machinery (ROBODRILL, ROBOCUT and ROBOSHOT) to factories around the world. Since 1955, FANUC has made a significant contribution to the efficiency and productivity of manufacturing companies worldwide. Operating in more than 280 locations globally and serving more than 100 countries with over 10,000 employees, FANUC offers a dense network in sales, technical support, research & development, logistics and customer service. For more information, please contact: Fanuc Further Reading On MachineToolNews.ai What Is Physical AI in Robotics and Automation? Comau AI Robotics 2026: How AI Is Changing Robot Monitoring, Vision And Flexible Automation AI Predictive Monitoring for Metrology: Hexagon APOLLO Transforms Asset Performance in 2026 MVTec HALCON 26.05 Launches May 20 with Faster AI and Machine Vision Performance FAQ What is FANUC’s Physical AI collaboration with Google? FANUC’s Physical AI collaboration with Google enhances FANUC’s Physical AI Robot System by using Google technologies, including Gemini Enterprise, to support AI agents for industrial robots. What does Physical AI mean? Physical AI refers to the integration of cognitive intelligence with physical action, allowing robots to perceive their environment, make autonomous decisions and execute tasks. What is Gemini Enterprise used for in FANUC’s robot system? FANUC says Gemini Enterprise is used in an AI agent that understands human instructions, recognises objects and autonomously operates FANUC robots to perform assigned tasks. What is Intrinsic Flowstate? Intrinsic Flowstate is a development environment that enables users to build, test and implement AI robot solutions. FANUC says its robots, including CRX, will be fully supported on the platform. Why does this matter for manufacturing? It matters because FANUC says the technology can be applied in real world manufacturing environments, allowing AI agents and robots to work together in industrial production settings. ### IDS Camera Technology Brings AI-Supported Inspection Closer to the Shop Floor Canonical URL: https://machinetoolnews.ai/ai-visual-inspection-ids-camera-technology/ Published: 2026-05-20T14:02:13+00:00 Modified: 2026-05-20T14:02:15+00:00 Author: Jelena Radojcic Categories: General, Metrology & Vision, News, United Kingdom, USA Tags: Featured, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/ids-camera-ai-visual-inspection-shop-floor-quality-control.png Featured image alt: ids-camera-ai-visual-inspection-shop-floor-quality-control.jpg IDS AI visual inspection 2026 is becoming a strong example of how AI-supported quality control is moving closer to everyday production environments. I have been looking at a new case study from IDS Imaging Development Systems, which explains how its uEye XC autofocus camera is being used within preML’s modular inspection solution. The system combines AI software, edge computing, lighting and IDS camera technology to help manufacturers carry out visual quality inspection without needing specialist image processing knowledge. That matters because quality control is under growing pressure across sectors such as automotive, mechanical engineering and electronics manufacturing. Manufacturers are having to inspect a wider range of components, often under changing conditions, while keeping integration time and operating complexity under control. The preML system has been developed to address that challenge. According to IDS, the solution is supplied as a complete modular kit, including camera, lighting, edge computing and pre-installed software. This allows it to be used as a plug-and-play inspection system for industrial quality control. At the centre of the system is the IDS uEye XC industrial camera. The camera uses a 13-megapixel sensor and autofocus to capture detailed images, with consistent sharpness even when component heights, working distances or surfaces change. That makes it relevant for inspection tasks where the parts being checked are not always identical or positioned in exactly the same way. For wider context on how this fits into the market, I would also link this story internally to our Metrology & Vision coverage and our recent article on MVTec and ZEISS advancing AI inspection. IDS AI visual inspection 2026 and no-code inspection models One of the clearest aspects of the case study is the focus on usability. The preML Vision Lab software allows users to create, manage and evaluate AI-supported inspection models directly on the device. In one example, the system captures a plastic dowel and automatically checks it for deviations. Test models can then be managed and evaluated in real time. The system is also being used in electronics manufacturing. IDS highlights TOP. Thomas Preuhs GmbH as a practical example, where the inspection system is used to check manually assembled printed circuit boards. That is a useful application area because PCB inspection can involve different variants, changing surfaces and varying lighting conditions. In that environment, the ability to adapt inspection models without complex programming can be important. David Fehrenbach, founder of preML GmbH, says the solution is intended to fill a gap in the market by allowing skilled personnel without knowledge of image processing or AI to independently teach, manage and perform visual quality inspections on the device itself. For me, that is the strongest part of the story. The value is not simply that AI is being added to inspection. The value is that AI-supported inspection is being packaged in a way that production staff can use more directly. This also connects well with the wider shift toward factory-level AI, which we have covered in our explainer on what industrial AI is and how it is used in factories. Why the IDS uEye XC camera matters The IDS uEye XC is positioned as a camera that combines the ease of use of a webcam with the reliability of an industrial camera. A single cable connection is used for installation and commissioning, which is useful in production environments where systems may be converted, expanded or reconfigured. Inside the camera is a 13 MP AR1335 sensor from onsemi, which provides high-resolution images for fault detection. The autofocus function is also central to the application. It automatically adjusts focus and helps maintain image quality when object distances change. IDS says the sensor delivers 20 frames per second at full resolution and, thanks to BSI technology, can cope with changing lighting conditions. Jürgen Hejna, Product Manager 2D Machine Vision at IDS, describes the uEye XC as being designed for users who need reliable image quality without significant integration effort. The case study also notes that a quick-change macro attachment lens extends use at close range. This allows small structures to be detected more precisely, including conductor tracks, material defects and geometric deviations. Additional camera functions such as digital zoom, auto white balance and colour correction support detailed inspection work. The exact model used in the case study is the IDS U3-36L0XC, which IDS lists as part of the uEye XC camera family. Depending on the inspection requirement, preML says up to four uEye XC cameras can be used per system. That allows the inspection setup to cover multi-sided processes, complex geometries or combined surface and detail checks. For readers following wider developments in AI inspection, this also sits alongside other factory quality applications such as our InspecVision GAV AI system review and MVTec HALCON 26.05 machine vision coverage. Edge computing keeps the system self-sufficient Another important detail is that the inspection system runs on a compact edge computing unit. The system operates independently and does not require an internet connection. That makes it suitable for isolated production environments where cloud connectivity may not be desired or available. The preML Vision Lab software runs as a web application directly on the device. It is described as a no-code solution, allowing inspection models to be trained and executed without programming knowledge. IDS says models can be adapted to new products in a few minutes. That is particularly relevant for production environments with many variants, where inspection systems need to be reconfigured quickly rather than rebuilt around every product change. This is also why the story fits into the wider move toward edge AI in manufacturing, where more intelligence is being processed directly on or near production equipment. Use at TOP: manual assembly and 100 per cent inspection The case study gives a practical example from TOP GmbH & Co. KG, which uses the system to inspect manually assembled printed circuit boards. Emil Kurowski, Managing Director of TOP, explains that the company has been assembling components and end devices for more than two decades, from prototypes through to small and large series. The company works according to lean production principles, with many assemblies produced by hand and followed by inductive soldering. Because TOP requires 100 per cent inspection of manufactured parts, the company uses the preML system as part of its quality control process. This is where the application becomes especially relevant for manufacturers. Manual assembly remains important in many production environments, particularly where product variation is high. A flexible inspection system that can be adapted without programming knowledge can support qual... ### BLM GROUP AI Tube Bending 2026: 7 Key Technologies From Tube Canonical URL: https://machinetoolnews.ai/blm-group-ai-tube-bending-2026/ Published: 2026-05-19T07:49:49+00:00 Modified: 2026-05-19T07:49:51+00:00 Author: Jelena Radojcic Categories: AI in Sheet Metal, General, Italy, News Tags: top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/BLM_GROUP_AI_tube_bending_2026_1600x900.jpg Featured image alt: BLM GROUP AI tube bending 2026 showing precision tube bending technology for connected tube processing, automation and smart manufacturing software BLM GROUP AI tube bending 2026 was one of the more relevant technology stories for manufacturers following Tube 2026, with the company using the Düsseldorf event to present a broader message around connected tube processing, automation, software and lifecycle support. At Tube 2026, which took place in Düsseldorf from April 13 to 17, BLM GROUP presented its theme of “Many technologies, ONE partner,” positioning itself as a single technology partner for tube, sheet metal and three-dimensional metal profile processing. The company said the message reflected more than 60 years of industrial development and a growing market demand for integrated, reliable production solutions. For MachineToolNews.ai readers, the most important part of the launch was the way BLM GROUP connected tube bending, laser tube cutting, 5-axis laser cutting, robotic welding, CAD/CAM, MES software, AI strategies, monitoring and service support into one wider production ecosystem. BLM GROUP AI Tube Bending 2026 Focused On Easier Programming One of the headline messages from the release was BLM GROUP’s new approach to tube bending, described by the company as a “visionary new solution” where AI is already part of the process. The system combines advanced software, artificial intelligence, digital services and mechanical innovation to make tube bending more accessible to operators. The aim is to reduce dependence on specialist know-how by supporting guided programming through VGPNext, bending tool management, tool generation, tool purchasing and automatic calculation of technological parameters using artificial intelligence. That matters because programming and setup remain major bottlenecks in many tube manufacturing environments. When experienced staff are unavailable, production speed and quality can suffer. BLM GROUP’s approach suggests a move toward systems that guide the operator through complex decisions, rather than relying entirely on manual expertise. LT14 FIBER Targets Large-Diameter Tube Processing BLM GROUP also highlighted the LT14 FIBER, a fiber Lasertube system designed for tubes and profiles up to 355 mm in diameter and weighing up to 100 kg per metre. According to the company, the system uses a multi-fiber laser source to reduce cutting times on thin materials while improving quality on thicker materials. The machine also includes three mandrels to support clamping based on thickness, diameter and material type. BLM GROUP said this enables automatic setup, improved precision and time savings. The company also pointed to a “zero scrap” mode, where the tail carriage detaches to cut the end of the bar and make better use of available material. For fabricators working with expensive tube and profile material, that type of feature could become highly relevant to margin control. Scalable Robotic Cell Concept Another important theme at Tube 2026 was integration. BLM GROUP showed a robotic cell concept built around Lasertube processing, tube bending, 5-axis laser cutting and welding. The system displayed at the exhibition included LT6, E-TURN63 and LT-FREE technologies, producing components for the automotive and motorcycle sectors. The robotic welding cell LW-S completed the processing of one of the parts, with some of its main components also shown. The significance here is scalability. BLM GROUP said these technologies can operate as stand-alone systems while also being integrated with one another after purchase. That gives manufacturers a way to invest gradually rather than committing to a fully automated line from day one. PRO-RUNNER28 Combines Coil Processing And Mandrel Bending BLM GROUP also presented PRO-RUNNER28, a machine designed to combine coil processing, mandrel bending and in-line cutting. The system uses orbital cutting technology to cut during the bending phase, helping reduce cycle time and material waste. Programming is handled through VGPNext, which guides operators from part import through to simulation. For manufacturers producing high-volume tube components where quality and repeatability matter, this type of machine could appeal because it combines multiple operations into one production flow. MyBLM Turns Service Into A Productivity Tool Beyond the hardware, BLM GROUP introduced MyBLM as a tailored consulting service designed to help customers improve productivity and return on investment. The service includes customised training and on-site assistance during start-up, with the aim of helping operators become autonomous quickly and helping machines reach stable production conditions from the beginning. Once the system is operational, MyBLM supports performance monitoring, process analysis and the identification of improvement opportunities over time. This is a useful direction for the market. Manufacturers increasingly need machine suppliers to support performance after installation, especially where software, automation and data are part of the production system. Software Suite Links Machines, Programming And Production BLM GROUP also highlighted its Elements software suite, covering process programming, simulation, production management and monitoring. The suite includes VGPNext for tube and wire bending, ArTube for Lasertube CAD/CAM programming, ArGo for offline programming of 3D laser cutting cells, PartViewer for simulation and cost estimation, and Prometheus as the company’s MES software. Prometheus is especially relevant for smart manufacturing because it manages connected BLM GROUP systems from a single interface. The company said it can prepare work orders, plan production, send orders to machines, track operating modes and automatically collect workshop data. It also said Prometheus applies AI implements strategies to optimize production and connects with customer ERP systems to support Industry 4.0 smart applications. BLMportal And BLManalytics Support Data-Driven Operation The service layer also included BLMportal and BLManalytics. BLMportal gives customers access to machine information, documentation, production statistics, service tickets, technical support, consumables and updates. BLManalytics enables real-time monitoring of machine production data, helping identify plant utilisation, production performance, critical issues and improvement opportunities. For tube and sheet metal manufacturers, these tools matter because machine uptime and production visibility are becoming as important as raw machine speed. MTN Analysis: Why This Matters For Tube Manufacturers The BLM GROUP AI tube bending 2026 launch shows how the tube processing market is moving beyond isolated machine launches. The story is now about connected production systems. For manufacturers, the strongest message is the shift toward software-led automation. BLM GROUP is not simply presenting AI as a marketing label. The company is tying it to bending parameters, operator guidance, MES data, production monitoring and lifecycle support. That is where AI and automation become practical. A tube... ### Comau AI Robotics 2026: How AI Is Changing Robot Monitoring, Vision And Flexible Automation Canonical URL: https://machinetoolnews.ai/comau-ai-robotics-2026/ Published: 2026-05-13T08:05:42+00:00 Modified: 2026-05-13T08:17:40+00:00 Author: Publisher Categories: Italy, Robotics, Robotics & Cobots Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/Horiz_Di-Stefano_Logo.png Featured image alt: Comau AI robotics 2026 interview with Giovanni Di Stefano on robot monitoring, MI.RA vision systems, predictive maintenance and flexible automation Comau AI robotics 2026 is about far more than making robot arms smarter. Across industrial automation, artificial intelligence is now being used to simplify programming, improve robot monitoring, support predictive maintenance, enhance machine vision and make automation more flexible for real production environments. In this exclusive MachineToolNews.ai interview, Giovanni Di Stefano, Head of Engineering Advanced Robotics at Comau, explains how Comau is applying AI across its robotics and automation portfolio, including MI.RA, in.Grid Robot Monitoring, AI-driven vision systems, condition monitoring and future engineering workflows. For manufacturers, the key question is no longer whether AI belongs in industrial robotics. It is where AI can deliver measurable value on the shop floor. Comau AI Robotics 2026: The Interview MTN: Comau is widely known for industrial robotics, but your recent work shows a strong push into AI. How would you define Comau’s approach to AI in manufacturing today? Giovanni Di Stefano: Since 2018, Comau has recognized software and AI as core strategic pillars of industrial automation, leading the company to internalize and further develop its digital and engineering know-how. Today, our engineering-driven approach is structured across three main areas. First, we integrate AI directly into standard, scalable solutions such as MI.RA and in.Grid, thus embedding intelligence into robotics, vision systems, and digital platforms. Second, we leverage AI as a key enabler within automation projects, allowing systems to adapt to variability, simplify programming, and support more flexible and dynamic manufacturing environments. Third, we apply AI internally to optimize engineering processes, reduce complexity, and accelerate deployment timelines. MTN: When manufacturers hear “AI robotics,” many still think of a smarter robot arm. Where are you actually seeing the biggest real-world gains from AI across your portfolio? Giovanni Di Stefano: The biggest gains, in addition to robot motion, deal with how AI-guidance enhances the overall robustness, adaptability, and effectiveness of automated processes. We are seeing tangible impact in reducing the complexity of programming robotic cells, enabling faster deployment and easier reconfiguration, particularly in high-mix production environments. At the same time, AI-powered vision systems are improving output quality through real-time inspection, defect detection, and adaptive process control. By collecting and analyzing large volumes of data, solutions such as our in.Grid intelligent platform provide actionable feedback and insights that improve production KPIs, optimize throughput, and support predictive maintenance strategies. This allows operational teams to anticipate failures, minimize downtime, and transition from reactive to predictive operations. MTN: Comau’s in.Grid Robot Monitoring platform focuses on condition monitoring and performance. In practical shop-floor terms, what does it track, and what decisions does it enable? Giovanni Di Stefano: in.Grid Robot Monitoring tracks and optimizes process-level performance and overall equipment efficiency within applications such as spot welding, arc welding, and material handling. It also aggregates critical robot KPIs, operational parameters, and performance trends into a unified environment. By leveraging AI-driven advanced analytics and high-frequency data collection, the platform enables faster, more informed decision-making through a lean, cloud-based architecture that connects machines, processes, and operators. From a practical standpoint, this allows manufacturers to monitor equipment health, identify deviations, optimize cycle times, and improve product quality. At the same time, it supports after-sales teams with remote diagnostics and rapid intervention capabilities, while automatically generating reports for maintenance and production teams. MTN: At IVECO’s Valladolid plant, this technology is already in use. What has that deployment shown about the real value of AI-driven condition monitoring in production? Giovanni Di Stefano: The deployment at IVECO’s Valladolid plant has demonstrated that in high-density robotic environments, even marginal efficiency gains translate into significant operational impact. By consolidating data into intuitive, real-time dashboards, the in.Grid platform has let IVECO quickly identify inefficiencies and has been able to significantly reduce machine downtime and streamline monitoring as a result. Furthermore, the process-level analysis allows for the optimization of consumables based on actual usage patterns rather than fixed maintenance schedules, improving both cost efficiency and sustainability. Continuous parameter monitoring also enables fine-tuning of production processes, ensuring maximum efficiency and performance. MTN: Predictive maintenance is often discussed, but rarely quantified. From your perspective, what separates meaningful AI condition monitoring from systems that simply generate more data? Giovanni Di Stefano: The big difference lies in engineering depth and domain-specific expertise. Comau’s more than 50 years of experience in industrial automation allow us to focus immediately on the variables that truly impact performance and reliability. Instead of adopting a generic, data-first approach, where large volumes of information are collected without clear prioritization, or using third-party platforms, Comau applies an engineering-driven methodology that targets the specific precursors of critical failure modes. This ensures that the system delivers actionable insights rather than data overload, enabling maintenance teams to intervene proactively and prevent downtime. In other words, we focus on extracting the right data, contextualizing it, and translating it into decisions that improve operational continuity and asset performance. MTN: With MI.RA and vision-based systems, Comau is enabling robots to handle randomly placed parts without CAD input. How important is that capability for flexible manufacturing environments? Giovanni Di Stefano: In highly variable environments such as logistics, e-commerce, and advanced manufacturing, traditional programming approaches no longer work due to the unpredictability and diversity of objects and scenarios. For these applications, solutions like MI.RA, which can detect, identify, and autonomously handle randomly placed objects without prior CAD input, are essential. Without the need for CAD input or fixed positioning, MI.RA allows robots to handle random parts autonomously, providing the essential flexibility required for modern, dynamic manufacturing. This capability enables true flexible and adaptive automation, whereby robots can now operate effectively in unstructured environments where variability is the norm. It also significantly reduces setup time, programming effort, and dependency on specialized skills, making automation more accessible and scalable... ### Is 2026 the Rise of the Manufacturing Copilot? Canonical URL: https://machinetoolnews.ai/is-2026-the-rise-of-the-manufacturing-copilot/ Published: 2026-05-12T08:57:49+00:00 Modified: 2026-05-12T08:57:51+00:00 Author: Publisher Categories: Featured, Software, Software / CAM / IIoT Tags: Featured, Popular, Strategy Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/copilot-article-image.png Featured image alt: Manufacturing copilot AI assistant supporting CNC machining with a smart CAM interface, robotic guidance and industrial automation in a bright futuristic factory Manufacturing copilot technology is becoming one of the clearest industrial AI trends of 2026. Across Hannover Messe, MACH, Tube, BIEMH and Global Industrie, manufacturers and industrial software companies are moving beyond broad promises about artificial intelligence and starting to place AI directly inside the tools used by CNC programmers, automation engineers, robot users, maintenance teams and production managers. That raises a serious question for the sector: is 2026 the rise of the manufacturing copilot? The answer is increasingly yes. CloudNC, Siemens, Bosch, Schneider Electric, Beckhoff, KUKA, ABB, Mastercam, Hexagon, OPEN MIND, BLM Group and other industrial technology companies are all pushing AI into practical manufacturing workflows. Some call these systems copilots. Others call them AI agents, industrial assistants, co-intelligence platforms or agentic manufacturing systems. The names differ, but the direction is the same: AI is becoming a working interface between people, software and machines. What is a manufacturing copilot? A manufacturing copilot is an AI assistant or AI agent built into an industrial workflow. Instead of sitting outside the factory as a generic chatbot, it appears inside CAM software, automation engineering platforms, robotics tools, maintenance systems, production dashboards or industrial data platforms. That matters because manufacturers do not need AI that gives vague answers. They need AI that understands machines, production data, engineering logic, toolpaths, robot simulations, maintenance issues, cycle times and process constraints. The strongest manufacturing copilot examples in 2026 are not standalone chat tools. They are embedded into the systems engineers, programmers and operators already use. CloudNC CAM Assist puts the copilot idea directly into CNC programming One of the strongest CAM examples in the manufacturing copilot trend is CloudNC CAM Assist. CloudNC does not brand the product as “CloudNC Copilot,” but the company now clearly places CAM Assist inside the wider CAM copilot category. In its 2026 guide to AI-assisted CNC programming, CloudNC defines a CAM copilot as AI CAM software that helps CNC programmers inside or alongside their CAM system, including guidance, setup support, strategy suggestions and toolpath generation. CloudNC also says CAM Assist fits the CAM copilot category because it integrates with CAM packages to generate machining strategies and toolpaths. CAM Assist is designed to reduce the manual effort between CAD model and machine-ready CAM program. CloudNC says the software integrates with CAM software to generate machining strategies and toolpaths using AI, helping users complete up to 80% of a CAM program in minutes. The company’s dedicated pages for CAM Assist for Mastercam and CAM Assist for Siemens NX show how the product is being positioned across major CAM ecosystems. Both pages describe CAM Assist as a way to generate precise and efficient machining strategies inside established CAM workflows. For machine shops, this is one of the most practical versions of the copilot idea. AI does not sit outside the programming process. It helps generate machining strategies and toolpaths inside the workflow CNC programmers already use. For MTN readers, CloudNC is important because it brings the manufacturing copilot discussion directly into one of the biggest bottlenecks in metalworking: CAM programming productivity. CNC shops are under pressure to quote faster, program faster and reduce reliance on scarce specialist programming time. CAM Assist is one of the clearest examples of AI being used to attack that problem. Mastercam brings AI assistance into everyday CNC programming Mastercam is another major CAM example. Mastercam Copilot is an AI-powered assistant built directly into the Mastercam CAM environment. Users can ask questions in plain language using voice or text, receive answers without leaving the workflow, adjust feeds and speeds, and create machine groups from simple instructions. Mastercam 2026.R2 also includes AI-powered assistance that allows users to control feed rates, spindle speeds and machine parameters across Mill, Lathe and Router operations using voice or text commands. The release also includes hands-free operation using the “Copilot” keyword. This matters because many CNC manufacturers already rely heavily on CAM software, but the systems can be complex. A copilot inside the CAM environment could help experienced programmers move faster and help newer users understand software functions, toolpath options and programming steps more easily. Hexagon adds Copilot to EDGECAM Hexagon is also moving directly into the CAM copilot space. In April 2026, Hexagon introduced Copilot for EDGECAM as part of EDGECAM 2026.1. Hexagon says the Copilot brings natural-language assistance into the CAM environment, allowing programmers to ask questions, locate functions and access guidance more easily inside the workflow. The aim is to reduce the time programmers spend searching menus or documentation inside increasingly capable CAM systems. This is a strong example of where manufacturing copilot technology is likely to gain traction first. CNC programmers do not always need AI to take over. They often need help finding the right function, understanding an option, speeding up repetitive steps or reducing time spent searching documentation. Siemens NX brings AI suggestions into CAM programming Siemens is also pushing AI into CAM. In 2026, Siemens highlighted AI Make Machining Suggestion inside NX X Manufacturing. Siemens describes the new capability as a copilot that gives CNC programmers multiple options for machining specific areas of a part. The programmer then reviews the options and selects the most suitable approach, reducing manual effort in toolpath planning while maintaining human control over the final decision. That is a strong fit for the manufacturing copilot trend. The value is not only in automation. It is in shortening the decision process, reducing repetitive programming work and helping programmers compare machining strategies more quickly. OPEN MIND shows the automation-first version of CAM intelligence OPEN MIND also belongs in the wider CAM copilot conversation. At MACH 2026, the company promoted hyperMILL Intelligence, a technology platform combining rule-based automation, tool-based automation, machine-based optimisation and AI-supported programming. OPEN MIND’s MACH 2026 material says hyperMILL Intelligence includes an AI assistant designed to personalise real-time guidance for programming workflows. This is important because not every manufacturing copilot will look like a chatbot. In CAM, the strongest systems may combine rules, automation, digital twins, machine knowledge and targeted AI support. OPEN MIND’s approach shows how AI can sit alongside proven manufacturing logic rather than replacing it. Bosch brings Manufacturing Co-Intelligence to industrial AI Bosch is... ### AI in machining: the real value starts before the first cut Canonical URL: https://machinetoolnews.ai/ai-in-machining-hexagon-cam-reliability/ Published: 2026-05-07T07:57:35+00:00 Modified: 2026-05-07T07:57:40+00:00 Author: Jelena Radojcic Categories: AI in Machining, Software, Software / CAM / IIoT Tags: Editors Pick, Featured, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/ChatGPT-Image-May-6-2026-02_50_29-PM.png Featured image alt: AI in machining expert Stephen Graham from Hexagon discusses CAM software, EDGECAM Copilot, ProPlanAI and more reliable CNC programming Stephen Graham, Vice President Product & Technology, Production Software Division, Hexagon, explains why the most meaningful applications of AI in CAM will be measured by more reliable programmes, shorter prove-out, and greater confidence at the machine MTN: Has the industry misunderstood where AI actually creates value in machining? I think the industry is seeing the most visible part of AI first. A lot of the current conversation is focused on the interface: copilots, prompts, assistants, and natural-language interaction. Those things are useful, and they will make software easier to use. But they are only one part of the picture. In machining, the deeper value comes when AI starts to support the decisions that determine whether a programme will run correctly on a real machine tool, with real posts, tooling, setups, and production constraints. The hard part is rarely creating a toolpath in isolation. It is building a process that reflects the part, the material, the machine, the workholding, the tooling, and the realities of production. That is where AI becomes more meaningful. It moves from helping someone use software faster to helping manufacturers make better decisions before the first cut. MTN: Where does that challenge show up most clearly? It shows up wherever programming complexity is high and the cost of getting it wrong is significant. Mill-turn and Swiss-type machining are good examples. In those environments, the programmer is managing far more than geometry. They are thinking about synchronisation, tool access, machine limits, channel sequencing, collision risk, and how each operation affects the next. Small decisions at that stage can have a large effect later. A tool choice, an approach strategy, or the way operations are sequenced can determine whether the programme runs cleanly or needs intervention at the machine. That is where uncertainty enters the process. It often starts before production, but it shows up during production, when the options are more limited and the cost of delay is higher. MTN: Is that where AI starts to make a difference? Yes, but the important point is that AI is not replacing the programmer. The value is in making proven decisions easier to repeat, and in helping engineers manage the trade-offs they already make every day. In machining, there is rarely a single perfect answer. Programmers are constantly balancing speed, tool life, surface finish, machine availability, part quality, set-up constraints, and risk. Experienced programmers carry a huge amount of knowledge about those trade-offs: what strategies work, where problems tend to occur, how a specific machine behaves, and which approaches are safest for certain families of parts. The challenge is that this knowledge is not always available at the right moment, to every person, on every job. It may sit with one expert, one site, or one shift. Under pressure, even experienced teams can make decisions differently from one job to the next. AI becomes useful when it can help capture those proven approaches and apply them in context. That means supporting the programmer with knowledge that reflects real manufacturing practice, rather than leaving every new part to start from a blank screen. MTN: What does that look like in practice? Automated process planning is one of the clearest examples. Instead of asking the programmer to manually define every step from the beginning, the system can analyse the part, recognise features, and suggest machining strategies based on approaches that have worked before. The key is that this should be grounded in real machining context, not generic automation. With solutions such as ProPlanAI, the goal is to bring proven manufacturing knowledge into the programming workflow. That can reduce programming time significantly, but the bigger benefit is predictability. Manufacturers can create programmes that are more consistent from the start, with less dependence on individual interpretation. In practical terms, it helps turn expert knowledge into a shared capability. That is especially important as manufacturers face skills pressure, shorter lead times, and more complex parts. MTN: Where do tools like EDGECAM Copilot fit into this? Copilots are part of the story, but they are most valuable when they are connected to real manufacturing context. A copilot that simply helps someone navigate software faster can be useful. But in machining, the bigger opportunity is to help users make decisions with more confidence. That means connecting assistance to the machines, tooling, strategies, and workflows that manufacturers actually use. With EDGECAM Copilot, the aim is to reduce friction in the programming process. It helps users find the right commands, understand options more quickly, and move through the workflow with greater confidence. For less experienced users, that creates a stronger starting point. For experienced programmers, it can remove some of the repetitive effort that slows them down. The important point is that this is a practical route into AI. It does not ask manufacturers to change everything at once. It builds on the way programming teams already work and helps them improve incrementally. MTN: Where does CAM fit into this shift more broadly? CAM is where manufacturing intent has to become machine reality. It is one thing to create a toolpath. It is another to know that the programme will behave correctly on a specific machine, with a specific setup, using the right post-processor, under real production conditions. That is why machine-aware programming, simulation, and post-processing are so important. In complex machining, confidence comes from knowing that what has been programmed and validated in CAM will match what happens on the machine. Platforms such as ESPRIT EDGE are designed around that principle. The goal is to close the gap between programming and execution, so manufacturers can reduce prove-out risk and move into production with more confidence. Q: A lot of vendors are positioning AI as a “copilot”. Is that enough? It depends what problem the copilot is solving. A copilot can make software easier to use. This matters, especially as manufacturers bring new people into programming roles and ask experienced teams to handle more complex work. But in machining, ease of use is only one part of the challenge. There is a wider debate around whether AI puts CAM programming at risk. I see it differently. CAM programming is built on experience, judgement, and engineering trade-offs that are difficult to separate from the realities of production. A programmer is rarely choosing between a right answer and a wrong answer. They are balancing tool life, cycle time, surface finish, machine availability, part quality, setup constraints, and risk, while making trade-offs against the cost, performance, and production requirements of the part itself. That is where the real distinction lies. AI can be extremely helpful when it supports those de... ### MVTec and ZEISS Team Up to Advance AI Inspection Canonical URL: https://machinetoolnews.ai/mvtec-zeiss-ai-inspection/ Published: 2026-05-08T09:07:12+00:00 Modified: 2026-05-08T09:07:14+00:00 Author: Publisher Categories: General, Metrology & Vision, Software, Software / CAM / IIoT Tags: Featured, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/05/zeiss_blockwise_monitor_keyboard.jpg Featured image alt: AI inspection software showing ZEISS Blockwise automated microscopy workflow powered by MVTec HALCON machine vision MVTec ZEISS AI inspection is moving into a new phase after MVTec Software and ZEISS announced a strategic collaboration around automated microscopy and machine vision. The partnership sees MVTec HALCON machine vision software integrated as the image processing foundation for ZEISS Blockwise automated microscopy platform, the new ZEISS software platform for automated microscopy. ZEISS Blockwise was released on April 30, 2026, and is designed to simplify complex measurement, inspection and analysis tasks in electron microscopy. For manufacturers working in high precision sectors such as semiconductors, microelectronics, battery production, electronics and advanced quality control, the announcement matters because it brings together ZEISS microscopy workflows with MVTec’s established machine vision software. Why MVTec ZEISS AI Inspection Matters The MVTec ZEISS AI inspection collaboration is important because industrial inspection is becoming more automated, more software-led and more dependent on intelligent image processing. In semiconductor and microelectronics production, manufacturers need stable processes, high precision and the ability to process large volumes of data reliably. The release highlights these as key requirements for Blockwise users, especially in semiconductor environments. By using HALCON as the image processing foundation, ZEISS is building Blockwise around a machine vision platform that supports both rule-based image processing and deep learning methods. HALCON Becomes the Image Processing Foundation for ZEISS Blockwise The central point of the collaboration is that MVTec HALCON will form the foundation for image processing within ZEISS Blockwise. That means Blockwise users can benefit from HALCON’s machine vision capabilities when building automated microscopy workflows. These workflows can support measurement, analysis and inspection tasks that would otherwise require more manual intervention. MVTec said HALCON is particularly suited to these environments because it combines a broad methodological range with industrial maturity. The software can support both rule-based approaches and deep learning methods within a single environment. ZEISS Blockwise Targets Complex Measurement and Analysis Tasks ZEISS Blockwise has been designed as a toolbox for automated microscopy. Its role is to simplify complex measurement and analysis tasks in electron microscopy. A key strength of Blockwise is that image acquisition can be integrated directly into the microscope workflow. According to the release, this allows Blockwise to define the next steps based on captured data. That is important for industrial users because inspection becomes part of the workflow itself. The system can move from image capture to image processing and follow-up action in a more connected way. For production environments where repeatability and speed matter, this kind of workflow automation can support more consistent inspection routines. Rule-Based Vision and Deep Learning in One Environment One of the reasons the collaboration is relevant to AI inspection is HALCON’s ability to combine traditional machine vision with deep learning. Not every inspection task needs AI. Some applications are better handled by rule-based machine vision, especially where the inspection criteria are stable and clearly defined. Other tasks, such as defect detection, classification or visual variation analysis, may benefit from deep learning. HALCON includes more than 2,100 operators and combines rule-based methods with deep learning-based approaches. This means users can build inspection workflows around the method that best fits the task, rather than being forced into a single approach. The wider development of HALCON 26.05 machine vision features also shows how MVTec continues to develop its platform across classical and deep-learning-based machine vision workflows. Why This Matters for Semiconductor and Microelectronics Inspection The collaboration has a clear relevance for semiconductor and microelectronics manufacturers. These sectors rely on highly precise, repeatable inspection workflows. They also generate large amounts of image and measurement data. As inspection demands grow, manufacturers need software platforms that can support complex analysis without adding unnecessary friction. MVTec said HALCON has already proven itself in many applications in the semiconductor and microelectronics industries. Klaus Schrenker, Business Development Manager at MVTec, said the two software products are “an excellent match” because both are designed as comprehensive toolboxes. What It Means for Wider Manufacturing Although the direct application is automated microscopy, the wider message is relevant for the machine tool and metal manufacturing sectors. Across AI in CNC machining, sheet metal production, welding, robotics and metrology, inspection is becoming more software-driven. Manufacturers are looking for ways to reduce manual variation, improve data quality and connect inspection more closely to production decisions. The MVTec ZEISS AI inspection announcement reflects that wider movement. It shows how major technology suppliers are combining machine vision, workflow automation and AI-enabled analysis to support more autonomous inspection. This also connects with the wider role of industrial AI software in manufacturing, where data, vision systems and automation platforms are becoming more closely connected. MTN Analysis: Inspection Software Is Becoming Strategic For MachineToolNews.ai readers, this story is about more than one software integration. The bigger trend is the shift from standalone inspection tools toward integrated software platforms that connect image capture, image processing, analysis and workflow automation. That is where the industrial value lies. Manufacturers do not only need better cameras or microscopes. They need systems that can turn captured data into repeatable inspection actions. The MVTec and ZEISS collaboration suggests that machine vision is becoming a deeper part of the production intelligence stack. For high precision manufacturers, that could mean faster analysis, more consistent quality control and a stronger foundation for scalable AI inspection. It also matters for companies exploring machine vision and robotics automation, where inspection, guidance and automated decision-making are increasingly linked. Key Takeaways MVTec and ZEISS have announced a strategic collaboration. ZEISS is integrating MVTec HALCON into ZEISS Blockwise. HALCON will act as the image processing foundation for Blockwise. ZEISS Blockwise is designed for automated microscopy workflows. The platform supports complex measurement, inspection and analysis tasks. HALCON combines rule-based machine vision with deep learning methods. The collaboration is especially relevant to semiconductor and microelectronics inspection. The wider trend points toward more integrated AI inspection platforms across manufa... ### That’s Sorted: Inside TCI Cutting’s AI Smart Sorting Revolution Canonical URL: https://machinetoolnews.ai/tci-cutting-smart-sorting-system/ Published: 2026-04-27T07:49:32+00:00 Modified: 2026-04-27T07:49:35+00:00 Author: Jelena Radojcic Categories: AI in Sheet Metal, Featured, General, Spain Tags: Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/tci-ai-smart-sorting-robot-1600x900-1.png Featured image alt: TCI Cutting AI Smart Sorting robotic automation system handling sheet metal parts for automated laser cutting and material sorting Well, that’s “sorted” then, as we say here in the UK, meaning something has been properly dealt with, efficiently and without unnecessary complication. And in many ways, TCI Cutting have well and truly sorted one of the most persistent challenges in modern sheet metal manufacturing. Because while cutting technology has advanced rapidly, what happens after the cut has often remained slow, manual and inefficient. With the launch of AI Smart Sorting®, TCI Cutting is directly targeting that bottleneck, bringing intelligent automation to a stage of production that has long limited overall factory productivity. To understand exactly how this works in practice, we sat down with TCI Cutting to find out more. Q&A: AI Smart Sorting® and the Future of Sheet Metal Manufacturing TCI Cutting unveiled AI Smart Sorting® at BIEMH. Can you explain, in practical shop-floor terms, what the system does? In practical terms, AI Smart Sorting® takes care of a critical stage that begins immediately after cutting: unloading, identifying, sorting and palletising the parts. In other words, it automates a process that in many factories still depends heavily on manual handling, downtime and significant operator intervention.What sets it apart is that the system does not simply move parts. Using production data, it can automatically calculate the optimum extraction formula from the cutting table, so that each part is removed in the most efficient way possible and directed correctly to the pallet, container or production flow to which it belongs. This logic is executed by the robot in real time, with self-learning capability and continuous improvement.In addition, extraction and palletising can be carried out simultaneously, reducing unproductive time and clearly accelerating the manufacturing flow. Ultimately, what matters is that parts do not just leave the machine cut, but already prepared and linked to downstream processes such as bending, welding or assembly.All of this fits fully within our MAS Concept®, which integrates machinery, automation and software into a connected digital ecosystem. For us, Smart Sorting® is not a standalone solution, but a very clear example of where industrial manufacturing is heading: smarter, more autonomous, digital environments increasingly supported by AI to optimise real plant productivity. What specific sheet metal processing challenges were you aiming to solve with AI Smart Sorting®? The challenge was to solve one of the sector’s major bottlenecks: what happens after cutting. In many plants, the process of unloading, separating, sorting and organising parts is still slow, labour-intensive, unsafe and prone to error. That is often where a significant part of the productivity gained at the cutting machine is lost.With AI Smart Sorting®, we address precisely that critical point. The solution automates post-cutting operations in order to reduce manual handling, minimise errors and ensure that parts leave the system already organised for the next production step.Ultimately, it is not just about automating a task, but about preventing a factory that is highly advanced in cutting from still relying on inefficient downstream processes. That is where there is a major opportunity to raise overall plant productivity and bring greater continuity to the entire value chain. How does the system actually “understand” and identify the parts? What role does AI play compared with traditional automation or rules-based sorting? The main difference is that we are not talking about rigid automation. Conventional systems usually operate with fixed programming or predefined rules. In our case, AI Smart Sorting® works with production data and nesting information to recognise the parts, calculate the best extraction logic and manage sorting in the most efficient way.That is where AI comes in: not as a label, but as a genuine capability to optimise decisions in real time and improve performance as the system accumulates experience and data. In other words, it is not just automation; it is intelligent automation.This approach is aligned with how many of our customers see us: as a company at the forefront of industrial AI adoption, always applied to real factory needs. We are not interested in using AI for the sake of trend; we apply it where it delivers speed, consistency, autonomy and measurable operational value. From the customer’s perspective, where are you seeing the biggest measurable gains? Is it labour reduction, speed, fewer errors, or something else? In most cases, the improvements do not come from a single factor, but from the combination of several. There is a clear reduction in manual handling, but customers also see faster post-cutting flow, fewer sorting errors, greater traceability and a much more structured connection between cutting and subsequent operations.What the customer gains is overall productivity, greater process stability and a stronger ability to sustain demanding production rates with less friction between stages.And that is highly important, because competitiveness today is not only about cutting quickly, but about ensuring that the entire production cell operates as a coordinated system. That is precisely the logic behind MAS Concept®: the integration of cutting, automation and software to maximise productivity and autonomy. How easily can AI Smart Sorting® be integrated into an existing cutting line or factory? Our approach is based on real industrial flexibility. AI Smart Sorting® is designed to adapt to the customer’s production environment, not the other way round. This means it can be integrated into existing cutting lines and can also form part of a broader intelligent manufacturing ecosystem, depending on each company’s level of automation and growth strategy.One of the keys to this ease of implementation is TCI Sorter 3.0®, our software for full sorting and palletising programming, which makes it possible to configure, adjust and prioritise operations very quickly, in just a few clicks. This greatly simplifies commissioning and system adaptation for different part types, stacking criteria and production requirements, making automation far more accessible and practical for the customer.One of our strengths is precisely this end-to-end vision. We do not approach automation as an isolated module, but as part of a scalable architecture that can grow with the customer. Under MAS Concept®, a company can integrate highly autonomous cutting machinery, loading and unloading automation, intelligent sorting, robotics and production management software within a single connected environment.This allows each customer to move at their own pace towards a more autonomous, data-driven factory, without having to undertake a closed or rigid transformation from day one. Many manufacturers remain cautious about AI because of complexity and cost. How are you addressing those concerns with this solution? That caution is entirely understandable, and we fully recognise i... ### MVTec HALCON 26.05 Launches May 20 with Faster AI and Machine Vision Performance Canonical URL: https://machinetoolnews.ai/mvtec-halcon-26-05/ Published: 2026-04-14T13:18:27+00:00 Modified: 2026-04-14T13:18:29+00:00 Author: Publisher Categories: Germany, Metrology & Vision, Software, Software / CAM / IIoT Tags: Editors Pick, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/mvtech-pills-.png Featured image alt: MVTec HALCON 26.05 AI object detection software identifying capsules on a high-speed pharmaceutical production line using machine vision technology MVTec HALCON 26.05, launching on May 20, 2026, focuses on improving speed across both AI-based and rule-based machine vision, including faster object detection, more robust data augmentation, and new tools for reading codes on curved surfaces. A clear focus on speed across industrial vision MVTec Software GmbH is set to release the latest version of its machine vision platform, MVTec HALCON 26.05, continuing its regular update cycle with a strong focus on performance. This release is centred on a simple but important goal. Make machine vision systems faster without compromising reliability. Jan Gärtner, Product Manager for HALCON, explains that the aim is to ensure applications across industrial environments not only remain precise and robust, but also operate at significantly higher speed, especially in demanding automation scenarios. Faster AI object detection without losing accuracy One of the standout updates in HALCON 26.05 is the new generation of deep learning-based object detection. According to MVTec, this delivers: Up to 5x faster inference High detection accuracy Reliable performance across small objects and varying object sizes The system also includes built-in data augmentation, helping models stay stable when conditions change on the shop floor, whether that is lighting variation, rotation, or partial occlusion. Reading Data Matrix codes on curved surfaces A practical addition in this release is the new rectification capability for Data Matrix codes. In many real-world applications, codes are placed on curved or flexible materials, which can distort their geometry and make them harder to read. HALCON 26.05 addresses this by correcting those distortions before decoding. This is particularly relevant for: Cylindrical components Curved packaging Flexible materials The feature can also be integrated into existing workflows, making it easier to adopt without reworking entire systems. Shape matching becomes more stable with less manual work HALCON 26.05 also introduces automatic contour optimisation for shape matching. In practice, reflections, shadows, and textures often create unstable contours that reduce reliability and require manual cleanup. The new approach removes those problematic contours automatically using sample images. The result is: Faster matching Improved stability Higher accuracy This is especially useful in environments with reflective or textured parts where consistency is difficult to maintain. Smarter data augmentation built into the workflow Another update comes in the way data augmentation is handled. HALCON 26.05 moves from a procedure-based approach to an operator-based system, allowing developers to define augmentation pipelines directly within their deep learning workflows. This gives more flexibility and helps: Improve model robustness Support better generalisation Reduce the need for large training datasets HDevelopEVO preview expands development capabilities Alongside the main release, MVTec is also introducing a new preview version of its development environment, HDevelopEVO. With this update: Scripts can now be integrated into applications via the .NET interface Multimodal LLM support has been expanded Developers can use visual prompting, incorporating image data directly into prompts for the AI Assistant This points toward a more integrated way of developing machine vision applications, combining traditional workflows with AI-driven tools. Why this matters for manufacturers What stands out in HALCON 26.05 is not a single headline feature, but a consistent focus on improving how systems perform in real production environments. Speed improvements in object detection directly impact cycle times.More robust augmentation reduces the need for constant retraining.Better contour handling cuts down on manual tuning. Taken together, these changes are about making machine vision systems easier to deploy and more reliable once they are running. MTN Analysis This release reflects where machine vision is heading right now. There is a clear shift toward making AI systems more practical on the shop floor. Faster inference matters when inspection is part of a high-speed process. Stability matters when conditions are not controlled. The continued focus on both AI and rule-based methods also reflects how most manufacturers operate today. These systems are rarely standalone. They work together. The addition of visual prompting in HDevelopEVO is another signal. Development environments are starting to blend traditional engineering with AI-assisted workflows, and that trend is only going to accelerate. Key Takeaways MVTec HALCON 26.05 launches May 20, 2026 Up to 5x faster AI object detection inference New Data Matrix rectification for curved and deformed surfaces Automatic contour optimisation improves matching stability Enhanced data augmentation integrated into workflows HDevelopEVO preview introduces visual prompting and .NET integration FAQ: MVTec HALCON 26.05 What is MVTec HALCON 26.05? The latest version of MVTec’s machine vision software, focused on improving speed and performance across AI and rule-based methods. When will it be released? May 20, 2026. What is the biggest improvement? Faster performance, including up to 5x faster inference in object detection, alongside broader workflow enhancements. Where is HALCON used? Across industries such as electronics, semiconductors, battery production, food, agriculture, and logistics. Find more information here: https://www.mvtec.com Read More about MVTec ### AI at Hannover Messe 2026 – The exhibitors you can not afford to miss Canonical URL: https://machinetoolnews.ai/ai-at-hannover-messe-2026-exhibitors/ Published: 2026-04-01T12:47:26+00:00 Modified: 2026-04-01T13:02:07+00:00 Author: Jelena Radojcic Categories: Events, General, Germany Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/hannover_1600x900.jpg Featured image alt: Hannover Messe 2026 AI in manufacturing smart factory automation industrial AI robotics digital factory technologies exhibition preview Think Tech Forward AI at Hannover Messe 2026 is no longer a concept. It is being deployed across CNC machining, robotics, industrial software, and factory systems, with hundreds of exhibitors showcasing how artificial intelligence is transforming real manufacturing environments. Across more than 4,000 exhibitors at Hannover Messe, a significant proportion now integrate AI either directly into products or as part of their broader digital strategy. This is a complete breakdown of all exhibitor groups connected to AI. ENGINEERING, CAD/CAM AND DIGITAL TWIN AI These exhibitors define how products and factories are designed before production begins. Key exhibitors: Siemens Dassault Systèmes Autodesk PTC SAP Altair AI role: AI copilots for engineering Generative design Simulation with predictive intelligence Digital twins with real-time optimisation CNC, MACHINE TOOLS AND PROCESS AI AI is now embedded directly into machining, cutting, and forming. This is where AI is already delivering measurable results in production, particularly in areas such as AI-driven machining optimisation. Key exhibitors: DMG MORI Mazak TRUMPF Okuma Makino DN Solutions GF Machining Solutions Brother Industries AI role: Adaptive machining Toolpath optimisation Autonomous process control AI-assisted CNC programming ROBOTICS, COBOTS AND PHYSICAL AI AI is turning robots into adaptive production systems. Key exhibitors: FANUC KUKA ABB Yaskawa Universal Robots Stäubli AI role: Vision-guided robotics Autonomous handling Adaptive motion control Real-time decision making VISION, METROLOGY AND INSPECTION AI Quality control is now one of the fastest-growing AI applications. Key exhibitors: Hexagon Zeiss MVTec Cognex Keyence AI role: Deep learning inspection Automated defect detection AI-driven metrology Inline quality control AUTOMATION, PLC AND EDGE AI SYSTEMS AI is now embedded inside control systems and factory infrastructure. Key exhibitors: Beckhoff Schneider Electric Rockwell Automation Bosch Connected Industry Festo Omron AI role: Edge AI processing Smart PLC systems Autonomous control Real-time optimisation INDUSTRIAL DATA, CLOUD AND AI PLATFORMS This is the layer connecting everything together. Key exhibitors: German Edge Cloud Bosch Microsoft Amazon Web Services Google Cloud IBM AI role: Industrial AI platforms Data ecosystems Factory-wide intelligence AI deployment infrastructure STARTUPS, RESEARCH AND EMERGING AI PLAYERS This is where the next wave is coming from. Includes: AI startups in industrial automation University research groups Innovation hubs RIICO – a very exciting exhibitor who are shaping the future of shop floor design AI role: Autonomous scheduling AI copilots for SMEs Edge AI applications Experimental production systems HOW MANY EXHIBITORS ARE ACTUALLY USING AI? At Hannover Messe 2026: Total exhibitors: 4,000+ Exhibitors with AI relevance: hundreds to over 1,000 depending on definition Core AI-focused exhibitors: 200+ AI is now embedded across nearly every category. Why AI adoption is accelerating at Hannover Messe 2026 AI at Hannover Messe 2026 is not growing by chance. Several clear drivers are pushing adoption across the exhibitors on the show floor. First, manufacturers are under pressure to improve productivity without increasing headcount. AI is now being used to automate decision-making, reduce programming time, and optimise processes that previously relied on manual input. Second, the availability of industrial data has reached a tipping point. Machines, sensors, and software platforms are now generating vast amounts of data, and AI is the only practical way to extract value from it in real time. Third, competition across global manufacturing markets is forcing companies to adopt smarter systems. AI is increasingly becoming a differentiator, particularly in areas such as machining efficiency, quality control, and predictive maintenance. Finally, the technology itself has matured. What was experimental a few years ago is now stable, deployable, and delivering measurable results across real production environments. This is why AI at Hannover Messe 2026 is not presented as innovation for the future, but as a core capability for modern manufacturing. AI at Hannover Messe 2026 is now a central theme across machining, robotics, software, and factory-wide intelligence systems. MTN ANALYSIS: THIS IS THE FULL INDUSTRIAL AI STACK What Hannover Messe 2026 shows clearly is that AI is no longer a feature. It is an infrastructure layer across the entire factory. Design → Siemens, Autodesk Production → DMG MORI, TRUMPF Robotics → FANUC, KUKA Quality → Hexagon, Zeiss Control → Beckhoff, Schneider Data → German Edge Cloud, Bosch Special mention → RICOH, Zebra Technologies , Limitless CNC This is the first time the entire system is visible in one place. Key Takeaways AI at Hannover Messe 2026 spans every exhibitor category Hundreds of companies are actively deploying AI The strongest innovation is happening between systems, not within them Data and infrastructure players are becoming as important as machine builders The show represents a complete industrial AI ecosystem ### Brazil AI Manufacturing Hannover Messe 2026: From Data to Decision Canonical URL: https://machinetoolnews.ai/brazil-ai-manufacturing-hannover-messe-2026/ Published: 2026-04-17T07:30:11+00:00 Modified: 2026-04-17T07:30:12+00:00 Author: Publisher Categories: Events, General Tags: popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/Brazil-Hannover-messe-.jpg Featured image alt: brazil-ai-manufacturing-hannover-messe-2026.jpg How Brazil is deploying industrial AI at scale at Hannover Messe 2026 As the official Partner Country at Hannover Messe 2026, Brazil is stepping forward with a clear message: industrial competitiveness is now driven by data, artificial intelligence, and engineering working together in real production environments. Coordinated by ApexBrasil, the country’s presence highlights how AI is moving beyond pilot projects and into scalable industrial deployment, supported by infrastructure, policy, and a rapidly growing ecosystem of technology providers. For manufacturers across Europe and the U.S, this is a signal of how global competition in AI-led production is evolving. Why Brazil’s industrial AI strategy is gaining momentum Brazil’s position is built on three structural advantages that directly impact manufacturing performance. Energy advantage for AI workloads Brazil operates with nearly 90% renewable electricity, significantly reducing both operational costs and carbon footprint for data-heavy industrial processes. Rapid data centre expansion By 2025, Brazil had become the fourth-largest global destination for data centre investment, with over €9 billion committed. Global connectivity The EllaLink submarine cable connects Brazil directly to Europe, reducing latency by up to 50 percent. This is critical for real-time industrial applications such as remote monitoring and AI-driven optimisation. Where AI is being applied inside Brazilian industry The real value sits in execution. Brazilian companies are focusing on practical use cases that directly improve production: Real-time process monitoring AI-driven quality control Predictive optimisation Automated decision-making in complex operations This aligns closely with what we are seeing across AI in CNC and industrial AI software platforms on MachineToolNews.ai, where the shift is moving from automation to intelligence. Brazil’s AI ecosystem on the Hannover Messe floor Industrial AI players shaping the conversation StefaniniApplying AI and advanced analytics across industrial systems, focusing on automation and integration. DatawakeUsing AI and computer vision for real-time monitoring and production optimisation. MA.IADelivering AI-powered quality control and production intelligence on the shop floor. Wonder DataLabsAdvancing industrial data integration and automated decision-making. These companies represent a broader shift toward AI embedded directly into manufacturing workflows. Brazil’s AI manufacturing landscape at Hannover Messe Brazil’s delegation is not presenting concepts. It is showcasing companies already deploying AI in live industrial environments. Key AI-driven capabilities on display AI-driven industrial automation and integration Computer vision for production monitoring AI-based quality control systems Industrial data platforms and decision automation Policy acceleration: Nova Indústria Brasil The Nova Indústria Brasil programme is a major driver behind this growth. It is directing investment into artificial intelligence, cloud infrastructure, industrial IoT, and data engineering, creating alignment between innovation and industrial deployment. Industrial backing and global integration Brazil’s presence is supported by a wide network of industrial and institutional partners, including: Embraer Volkswagen Brazilian Development Bank National Confederation of Industry ABIMAQ ABINEE MTN Analysis: Brazil is building a new competitive model What makes Brazil stand out is the combination of infrastructure, policy, and execution. Renewable energy reduces AI operating costs Data centre investment supports scale Low-latency connectivity enables real-time systems Companies are already deploying AI in production Government policy is accelerating adoption Brazil is positioning itself as a data-driven industrial ecosystem capable of delivering efficiency through AI at scale. Key Takeaways Brazil is emerging as a global hub for industrial AI deployment Infrastructure and policy are aligned AI is being applied directly to production environments Hannover Messe 2026 showcases real industrial use cases FAQs What is Brazil showcasing at Hannover Messe 2026? Brazil is presenting real-world applications of industrial AI across manufacturing. Which Brazilian companies are using AI in manufacturing? Companies such as Stefanini, Datawake, MA.IA, and Wonder DataLabs are applying AI directly to industrial processes. Why is Brazil becoming important for industrial AI? Its combination of renewable energy, infrastructure, and policy creates an ideal environment for scaling AI. How does connectivity impact industrial AI? Infrastructure like the EllaLink cable reduces latency, enabling real-time optimisation. What is Nova Indústria Brasil? It is a national programme driving investment into AI and digital industrial technologies. For more information and tickets to Hannover Messe Click Here: Tickets Further Reading – AI in CNC , AI IN Sheet Metal ### NCMT launches new website to reflect expanded manufacturing capabilities Canonical URL: https://machinetoolnews.ai/ncmt-launches-new-website-to-reflect-expanded-manufacturing-capabilities/ Published: 2026-04-29T07:24:01+00:00 Modified: 2026-04-29T07:24:02+00:00 Author: Publisher Categories: General, News, United Kingdom Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/ncmt-new-website-april-2026.png NCMT has launched a new website designed to improve how manufacturers access its machine tool portfolio and engineering support, bringing together products, applications and services in a more structured format. NCMT, the UK’s exclusive supplier of machine tool brands including Okuma, Makino and Blue Photon, has launched a new website designed to better reflect the scale of its manufacturing capabilities and support offering. This launch forms part of a wider brand refresh, formally unveiled at MACH 2026. The company supplies a range of advanced technologies, including CNC machine tools, 5-axis machining centres, grinding systems and integrated production solutions, supporting customers across sectors such as aerospace, automotive, medical and energy. In addition to machine supply, NCMT provides application engineering, process development, installation, training and ongoing lifecycle support, positioning the business as a long term manufacturing partner. The new website – www.ncmt.co.uk – brings these capabilities together in a more accessible format. Users can explore the company’s offering by product type, industry and service area, making it easier to identify relevant technologies and support for specific manufacturing requirements. The website also links machines, applications and services more clearly, allowing users to move between related areas and build a better understanding of complete production solutions. The project was delivered in partnership with Birmingham-based ALT Agency, which led the redesign and development of the website. Craig Murphy, Managing Director at ALT Agency, said: “NCMT has a broad and technically detailed portfolio, so the focus was on structuring the website to make it easier for users to access relevant information while still reflecting the depth of the offering.” Maja Foster, Head of Marketing at NCMT, said: “Our new website is a true reflection of the refreshed NCMT brand. It gives a much clearer picture of who we are, how we work with our customers and the level of support we provide. We have also placed a strong emphasis on improving the overall website performance and user experience, so visitors can more easily explore our technologies and find relevant information.” “Working with ALT has been a very positive experience. We developed a simple and effective way of communicating and sharing data throughout the project, which is particularly important given the technical complexity of the content. Through close collaboration, attention to detail and a strong focus on meeting deadlines, we successfully delivered the website on time.” The website has also been rebuilt to improve performance and usability, with faster load times and a clearer structure for both new and existing customers. The new website is now live at www.ncmt.co.uk. Submission metadata Mode: Agency Submitted: 2026-04-28 09:26:58 ### MACH 2026 AI Exhibitors Not to Miss: CNC, Robotics and Software Leaders Canonical URL: https://machinetoolnews.ai/mach-2026-ai-exhibitors-not-to-miss/ Published: 2026-04-09T09:55:00+00:00 Modified: 2026-04-09T13:14:31+00:00 Author: Publisher Categories: Events, United Kingdom Tags: Editors Pick, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/MACH_1600x900.jpg Featured image alt: MACH 2026 is set to become the most important event in the UK for manufacturers looking to understand how artificial intelligence is being applied in real production environments. MACH 2026 AI exhibitors: The UK’s most important AI manufacturing showcase MACH 2026 is set to become the most important event in the UK for manufacturers looking to understand how artificial intelligence is being applied in real production environments. As an official media partner, MachineToolNews.ai will be covering the show live from the NEC Birmingham, bringing direct insight into the technologies, companies, and breakthroughs that are moving AI from discussion into deployment. With hundreds of exhibitors spanning CNC machining, robotics, software, and inspection, MACH offers something few events can: a clear, practical view of how AI is being used to increase productivity, reduce costs, and address the growing skills gap across manufacturing. For visitors, the challenge is not finding AI. It is knowing where to look. AI in CNC machining: Exhibitors not to miss Artificial intelligence is increasingly being embedded directly into machine tools, transforming how parts are programmed, cut, and monitored. Exhibitors not to miss: DMG MORI Mazak DN Solutions (via Mills CNC) Okuma These manufacturers are expected to demonstrate: Adaptive machining strategies Real-time process optimisation Predictive maintenance systems Digital twin integration Why it matters:AI inside the machine is delivering measurable gains in cycle time, consistency, and uptime, making it one of the most immediate opportunities for manufacturers to improve productivity. AI in CAM and manufacturing software: Where the biggest gains are happening While machine tools draw attention, the most rapid AI innovation is happening in the software layer. Exhibitors not to miss: Siemens Hexagon Autodesk OPEN MIND Technologies Mastercam These platforms are advancing: AI-assisted programming Automated toolpath generation Feature recognition and machining strategies Integrated digital manufacturing workflows MTN Analysis:The shift toward AI-assisted CAM is redefining programming. Tasks that once required deep expertise and significant time are increasingly being automated, reducing barriers to entry and accelerating production readiness. AI-native CAM automation: Exhibitor not to miss Exhibitor not to miss: CloudNC CloudNC represents one of the most advanced examples of AI-native manufacturing software, focused on accelerated CNC programming through its CAM Assist technology. Unlike traditional CAM systems that layer automation onto existing workflows, CloudNC is built around AI from the ground up, enabling: Accelerated toolpath generation Feature recognition and machining strategy selection Reduced programming time from hours to minutes Consistent output regardless of operator experience MTN Analysis:CloudNC highlights a major shift in the market. The focus is no longer on assisting experienced programmers, but on removing complexity from CNC programming entirely. For manufacturers facing skills shortages, this approach is about accelerating skilled programmers, enhancing productivity while keeping human expertise firmly at the centre of the process AI in robotics and automation: Exhibitors not to miss The Automation and Robotics zone at MACH will highlight how AI is enabling more flexible and responsive production systems. Exhibitors not to miss: ABB Fanuc KUKA Yaskawa Visitors can expect to see: AI-powered vision systems Autonomous robot programming Adaptive material handling Improved human-robot collaboration Why it matters:AI is enabling automation to move beyond fixed processes, allowing systems to respond dynamically to variation in parts, batches, and production demands. AI in inspection and quality control: Exhibitors not to miss Inspection remains one of the most commercially mature areas for AI deployment. Exhibitors not to miss: MVTec Software Cognex Keyence These companies will demonstrate: AI-driven defect detection Automated visual inspection systems Real-time quality feedback MTN Analysis:For many manufacturers, AI in inspection offers a clear and immediate return on investment through reduced scrap, improved quality, and faster throughput. AI in research and applied innovation Exhibitor not to miss: University of Sheffield AMRC The AMRC continues to play a critical role in: AI-driven machining optimisation Digital twin development Smart factory research Why it matters:This is where emerging technologies are tested and validated before being adopted across industry. AI in additive manufacturing and advanced processes Exhibitors not to miss: Markforged Formlabs Additive-X AI applications include: Build optimisation Process monitoring Automated parameter adjustment Key Takeaways: Where to focus at MACH 2026 AI is embedded across MACH, not presented as a standalone category Software and CAM platforms are driving the fastest innovation Machine tool builders are integrating AI directly into production workflows Robotics is becoming adaptive and more flexible Inspection offers one of the fastest and most accessible entry points for AI Why MACH 2026 matters for manufacturers MACH 2026 is not about future concepts. It is about technologies that are already delivering results in production environments. For manufacturers, the value of attending lies in: Seeing AI applied in real-world scenarios Understanding how solutions integrate with existing operations Identifying technologies that deliver measurable gains As AI adoption accelerates, events like MACH provide a critical opportunity to move from awareness to implementation. FAQs: MACH 2026 AI exhibitors Which companies are leading AI at MACH 2026? Siemens, Hexagon, Autodesk, ABB, Fanuc, DMG MORI, Mazak, and MVTec Software are among the key exhibitors demonstrating AI-driven technologies. Is AI a dedicated category at MACH? AI is not presented as a standalone category. It is embedded across CNC machines, robotics, software, and inspection systems. Where should visitors focus for AI at MACH? Visitors should focus on software platforms, robotics zones, and machine tool manufacturers integrating AI into machining processes. We will be there live streaming the show to bring you all of the best bits live from the show floor. For tickets to the show head here: Tickets ### hyperMILL Intelligence interview 2026: OPEN MIND’s Ken Baldwin on AI, automation and real-world machining Canonical URL: https://machinetoolnews.ai/hypermill-intelligence-interview-2026/ Published: 2026-04-07T08:25:57+00:00 Modified: 2026-04-07T08:25:58+00:00 Author: Publisher Categories: AI in CNC, General, Software, Software / CAM / IIoT, United Kingdom Tags: Editors Pick, Featured, Popular, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/04/ken_baldwin_fixed_1600x900.jpg Featured image alt: hyperMILL Intelligence interview 2026 with Ken Baldwin Managing Director OPEN MIND Technologies UK CAM software hyperMILL CNC machining automation As manufacturers push for greater efficiency, consistency, and control in CNC programming, the conversation around AI has become louder and often more confusing. With claims coming from every direction, the real question is where AI actually delivers value in manufacturing. As part of that shift, we sat down with Ken Baldwin of OPEN MIND Technologies to break down what hyperMILL INTELLIGENCE really means in practice, and how manufacturers should be thinking about automation, data, and AI as we move toward MACH 2026. MTN: hyperMILL INTELLIGENCE is positioned as a major step forward in CAM automation. How would you describe its role in helping manufacturers move towards more intelligent and data-driven machining processes? hyperMILL INTELLIGENCE is not a product or a single feature. It is our approach to combining smart automation with AI. It brings these two worlds together on one consistent technology platform. We are talking about making CAD/CAM programming more reliable and consistent, not just more “automated”. We’ve spent over 30 years building real manufacturing knowledge into the software – feature-based machining, tool selection, proven strategies. That’s the foundation. What we’re doing now is building on that with a mix of rule-based automation and AI, but only where it actually adds value. The focus isn’t AI as a buzzword. It’s about reducing programming time, improving consistency, and making sure what you program actually works on the machine. MTN: There is a lot of discussion around AI in manufacturing right now. Where does hyperMILL INTELLIGENCE sit within that landscape, and how should manufacturers think about rule-based intelligence versus full AI systems? hyperMILL INTELLIGENCE uses proven automation as a stable foundation and complements it with AI where it provides real, measurable benefits. AI is not a replacement for engineering expertise. It only delivers value when it builds on a strong technological base. If the fundamentals aren’t right, AI just makes bad decisions faster. AI is getting a lot of attention, but in CAM it’s often misunderstood. Core processes like toolpaths, collision avoidance, and machine control need to be predictable and exact. That’s not where AI fits – that’s where rule-based engineering is critical. Where AI does help is in handling data, recognising patterns, and supporting decisions. Giving an example, a rules-based process gives consistent results. Real AI should learn and you can get variable results in quality which is not what you want in this context. Our approach is to combine both. Use proven automation as the backbone, and apply AI where it genuinely improves the process. MTN: Does the rule-based approach of hyperMILL INTELLIGENCE directly improve machine tool productivity, or is its impact elsewhere in the process? Our hyperMILL automation technology doesn’t make the machine physically faster on its own, but it does improve how effectively the machine is used. Through the Virtual Machine and digital twin, toolpaths can be optimized for the specific machine and control. That means better use of the machine’s capabilities, smoother motion, and more efficient programs. At the same time, programming is faster and more consistent, and programs are far more likely to run correctly first time. The overall result is improved machine productivity – not by pushing the machine harder, but by removing inefficiencies, reducing rework, and making better use of the machine. MTN: One of the biggest challenges in CNC programming is consistency between programmers. How does hyperMILL help standardise processes and reduce variability across teams? Consistency is one of the biggest challenges within manufacturing processes. hyperMILL helps by letting you define proven processes once and reuse them, so everyone is programming in the same way rather than doing their own thing. You define how the process should be done, based on your machines, tools, and experience, not how the software thinks it should be done. Feature-based machining and automation remove a lot of the variation, so results don’t depend on who is sat at the CAM system. The result is more consistent output, less rework and no unexpected behaviour on the machine. MTN: At MACH 2026, what specific capabilities or live demonstrations will visitors be able to see on your stand related to hyperMILL INTELLIGENCE? At MACH 2026, visitors will see how our automation works in the real world, not just in theory. We’ll demonstrate how feature-based machining, tool and machine optimisation, and process automation combine to produce consistent and reliable results. The focus is on practical use – defining processes once, reusing them, and reducing programming time while avoiding issues on the machine. We’ll also be showing hyperMILL DATA Center, which captures and reuses customer data to improve consistency and efficiency. We’ll also highlight where data-driven technologies and AI add value, but always in a controlled and predictable way. MTN: Is AI in manufacturing the biggest revolution since digital programming? AI will shape the future of manufacturing, but it’s not a silver bullet. It’s still just a tool, and if it’s used in the wrong way, it can create more problems than it solves. In areas like CNC programming, you still need predictable, proven processes. That doesn’t change. Where AI helps is in supporting decisions and working with data, not replacing engineering. So yes, it’s important, but its value depends entirely on how it’s applied. MTN Analysis This interview lands exactly where the industry is heading, away from hype and toward controlled, usable intelligence. The key takeaway is that hyperMILL INTELLIGENCE is not trying to replace engineering judgement. It is reinforcing it. That matters because most CAM failures do not come from lack of automation, they come from inconsistency, poor decisions, and lack of process control. OPEN MIND’s position is clear: Rule based automation remains the backbone of CNC programming AI sits on top as a support layer, not the decision maker Consistency and reliability are more valuable than experimental optimisation This aligns with what high performing shops are actually prioritising in 2026: First time right machining Reduced rework and scrap Standardised programming across teams Better utilisation of existing machines rather than chasing marginal speed gains The mention of digital twin and Virtual Machine is also critical. This is where real gains are happening. Not in theoretical AI optimisation, but in ensuring that what is programmed runs exactly as expected on the shop floor. Looking ahead to MACH 2026, the emphasis on live, practical demonstrations is important. Buyers are becoming far more sceptical of AI claims. They want to see: Real parts Real programming workflows Real time savings No slides, no theory. The most important line in this entire discussion is simple. If the fundamentals are not right, AI makes bad decis... ### Digital Twin in Manufacturing 2026: The Plain-English Guide for Factories Canonical URL: https://machinetoolnews.ai/digital-twin-in-manufacturing-2026/ Published: 2026-03-30T09:38:27+00:00 Modified: 2026-06-08T14:25:16+00:00 Author: Jelena Radojcic Categories: AI in CNC, Robotics & Cobots, Software, What is ? Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/Gemini_Generated_Image_6dc3bq6dc3bq6dc3.png Featured image alt: Digital Twin in Manufacturing showing real CNC machine with real-time virtual twin and live data connection Digital Twin in Manufacturing 2026: A Plain-English Explanation for Manufacturers Digital Twin in Manufacturing 2026 is one of the clearest examples of how AI, live machine data and simulation are changing factory decision-making. In simple terms, a digital twin is a real-time virtual copy of a machine, robotic cell, production line or factory. It helps manufacturers test changes, monitor performance and spot problems before they affect production. Strong industrial authority and directly aligned with manufacturing use cases. Siemens explains how digital twins “analyze the past, reflect the present and predict the future,” which reinforces your article’s core message. This virtual model is continuously updated with live data from the physical environment, enabling simulation, monitoring, and optimization without interrupting production. What Is Digital Twin in Manufacturing? Digital Twin in Manufacturing is a dynamic digital representation of a physical asset, process, or system. Unlike a static 3D model, a digital twin is connected to real-world data through sensors and industrial software. This means it evolves in real time, reflecting exactly what is happening on the shop floor. In practice, a digital twin can represent: A single CNC machine A robotic cell A full production line An entire smart factory How Digital Twins Work At the core of Digital Twin in Manufacturing is continuous data exchange. Physical systems send data such as: Temperature Vibration Tool wear Cycle times Energy consumption This data feeds into a virtual model, which uses AI and simulation to: Predict future performance Identify inefficiencies Test process changes virtually The result is a feedback loop between the physical and digital worlds. Clear explanation of real-world value like optimization, real-time data use, and process improvement. It strengthens your practical angle. Why Digital Twins Matter in 2026 Manufacturers are adopting Digital Twin in Manufacturing because it directly impacts performance, cost, and risk. 1. Simulation Without Risk Production changes can be tested virtually before being applied in reality. 2. Faster Process Optimization Engineers can identify bottlenecks and improve workflows using real-time insights. 3. Predictive Maintenance Digital twins simulate machine behavior to detect failures before they occur. 4. Reduced Downtime Problems can be diagnosed and solved in the virtual environment before affecting production. Real-World Applications Digital Twin in Manufacturing is already being used across multiple industries: CNC Machining Simulating toolpaths, cutting forces, and machine dynamics before running actual parts. Robotics Testing robotic movements and workflows to optimize efficiency and avoid collisions. Factory Layout Planning Designing and validating production lines before physical installation. Energy Optimization Monitoring and reducing energy consumption across entire facilities. Digital Twin vs Simulation While often confused, they are not the same. Simulation is typically static and scenario-based Digital Twin is live, continuously updated with real data A digital twin evolves alongside the physical system, making it far more powerful for ongoing optimization. Technologies Behind Digital Twins The rise of Digital Twin in Manufacturing is driven by: Industrial IoT sensors Cloud and edge computing AI and machine learning models Advanced simulation software High-performance data processing These technologies enable accurate, real-time digital representations of physical systems. Challenges to Adoption Despite strong interest, adoption is not without challenges: High implementation cost Integration with legacy systems Data accuracy and consistency issues Complexity of building accurate models Manufacturers need clear ROI cases to justify investment. MTN Analysis Digital Twin in Manufacturing is moving from concept to operational tool. The biggest shift is how it changes decision-making. Instead of reacting to problems, manufacturers can now simulate outcomes before they happen. This has major implications for machine tool builders and software providers. The value is no longer only in the machine itself, but in the digital layer that sits alongside it. The companies that win in this space will be those that tightly integrate simulation, real-time data, and AI into a single environment. Digital twins are becoming a foundation for intelligent manufacturing rather than an optional add-on. Explore more real-world applications in our AI in CNC coverage, where digital twin technology is already improving machining performance. Key Takeaways Digital twins are real-time virtual replicas of physical systems They enable simulation, monitoring, and optimization They reduce risk by testing changes virtually They are already used in CNC, robotics, and factory planning They are becoming central to smart factory strategies FAQ: Digital Twin in Manufacturing What is Digital Twin in Manufacturing? A real-time digital replica of machines or production systems connected to live data. How is it different from simulation? A digital twin is continuously updated with real-world data, while simulation is static. What are the benefits? Improved efficiency, reduced downtime, predictive maintenance, and better decision-making. Where is it used? CNC machining, robotics, factory design, and energy optimization. Is it expensive to implement? Initial costs can be high, but ROI comes from efficiency gains and reduced downtime. ### AI in Manufacturing March 2026: Key Launches, Strategy Shifts and What They Actually Mean Canonical URL: https://machinetoolnews.ai/ai-in-manufacturing-march-2026/ Published: 2026-03-27T14:11:26+00:00 Modified: 2026-03-27T14:11:28+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, Metrology & Vision, Robotics, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/Gemini_Generated_Image_pqzg79pqzg79pqzg.png Featured image alt: AI in manufacturing March 2026 showing metrology inspection system, robotic arm and CNC machine in a modern factory environment AI in manufacturing March 2026 shows a shift away from experimentation and toward real deployment across metrology, robotics, and production software. The announcements this month are fewer in number, but they are far more targeted, focusing on measurable impact inside real factory environments. Rather than broad AI claims, this month’s developments highlight specific use cases where manufacturers are already seeing value. For a broader understanding of how AI is being applied across factories, see our breakdown of what industrial AI means for manufacturing. Here are the key announcements and what they actually signal. Hexagon Introduces Apollo AI for Predictive Metrology Monitoring In March 2026, Hexagon AB launched Apollo, an AI-powered predictive condition monitoring system designed specifically for metrology equipment. This is not a generic monitoring tool. It is built around the realities of inspection environments where: Equipment is high value Downtime directly impacts production validation Measurement accuracy must remain consistent Apollo uses machine data to identify patterns that indicate wear, drift, or failure before they occur. The goal is to move metrology from a reactive maintenance model to a predictive one. This makes it one of the strongest examples of AI in manufacturing March 2026 being applied to critical production systems. Why this matters Most AI maintenance systems have focused on production machines such as CNCs or robots. By targeting metrology, Hexagon is addressing a critical bottleneck. If inspection fails, production stops. That makes this one of the highest ROI areas for AI deployment. Lantek Positions AI as Core to Sheet Metal Software Strategy Also in March 2026, Lantek confirmed its global 2026 trade show programme, with a clear emphasis on AI across its software stack. While this is not a single product launch, it is strategically important. Software-driven optimisation is becoming a defining theme of AI in manufacturing March 2026. The company is doubling down on: AI-driven nesting to reduce material waste Automated production planning Real-time decision support across connected factories What this actually signals Software providers are no longer treating AI as a feature. It is becoming the decision-making layer of the factory. In sheet metal specifically, where margins are tight and material costs are high, even small optimisation gains translate directly into profit. Lantek’s positioning shows that AI in this segment is now: Commercial Competitive Expected by customers ABB Advances AI Simulation Through NVIDIA Integration ABB Ltd continued development of its RobotStudio platform in March 2026, building on integration with NVIDIA technologies. The focus is on enhancing: AI-driven simulation environments Digital twin accuracy Robot programming workflows This allows manufacturers to simulate entire production processes before deployment, reducing risk and commissioning time. Simulation is now a key pillar of AI in manufacturing March 2026, especially in robotics and automation. The deeper shift Simulation is becoming the first step in manufacturing, not an optional extra. AI adds another layer by enabling: Scenario testing Automated optimisation More accurate prediction of real-world performance This is where digital twins move from visual tools to decision engines. MTN Analysis March 2026 may look quiet on the surface, but the direction is clear. Across all announcements, three patterns stand out: AI is targeting high-cost failure points Not generic use cases. Not experimental pilots.The focus is on: Machine downtime Programming inefficiency Material waste These are areas where ROI is immediate and measurable. AI is being embedded, not added None of the March announcements position AI as a standalone product. Instead, it is: Built into metrology systems Integrated into software platforms Embedded in simulation tools This is a critical shift. Adoption increases when AI becomes invisible and part of the workflow. Software is becoming the control layer From Lantek to ABB, software platforms are evolving into systems that: Analyse Decide Optimise The physical machine is no longer the only source of value.The intelligence layer is where competitive advantage is being created. AI in manufacturing March 2026 is clearly focused on practical implementation rather than experimentation. Key Takeaways Hexagon AB introduced AI predictive monitoring for metrology, targeting a critical failure point in production Lantek confirmed AI as central to software-driven manufacturing workflows ABB Ltd continued advancing AI simulation through NVIDIA integration AI in manufacturing is now focused on measurable outcomes, not experimentation The shift toward embedded intelligence is accelerating across the industry FAQ Why were there fewer AI announcements in March 2026? March sits just before major industrial events such as Hannover Messe, where many companies time their largest product launches. As a result, March tends to focus on strategic positioning and targeted releases. Which area showed the strongest AI progress this month? Metrology and simulation stood out, with clear applications of AI in predictive maintenance and digital twin environments. Is AI now essential in manufacturing software? Yes. The March announcements reinforce that AI is no longer optional in competitive software platforms, particularly in CAM, nesting, and production planning. What should manufacturers be paying attention to next? The next wave of announcements is expected around major trade shows, where AI capabilities will expand further into machining, robotics, and factory-wide optimisation. ### AI Predictive Monitoring for Metrology: Hexagon APOLLO Transforms Asset Performance in 2026 Canonical URL: https://machinetoolnews.ai/ai-predictive-monitoring-metrology/ Published: 2026-03-26T11:27:58+00:00 Modified: 2026-03-26T11:28:00+00:00 Author: Jelena Radojcic Categories: AI in Machining, Metrology & Vision, News, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/mtn_1600x900.jpg Featured image alt: AI predictive monitoring for metrology with Hexagon APOLLO platform analysing machine performance and condition data in a modern manufacturing environment AI predictive monitoring for metrology is becoming a critical capability in modern manufacturing, and Hexagon’s launch of APOLLO marks a major step forward in how manufacturers manage measurement systems, uptime, and quality control. Hexagon has introduced APOLLO, an AI-powered predictive condition monitoring platform designed for metrology assets such as coordinate measuring machines (CMMs) and machine tools. The platform delivers real-time insights into machine performance while enabling a shift from reactive maintenance to predictive, data-driven operations. AI Predictive Monitoring for Metrology Systems Explained AI predictive monitoring for metrology uses machine learning and sensor data to continuously analyse how measurement systems behave in real production environments. APOLLO captures and processes data including: Machine performance metrics Environmental conditions such as temperature and vibration Operational status and usage patterns By analysing this data, the platform can detect anomalies and identify early warning signs of failure. Hexagon states that APOLLO can predict potential issues up to 90 days in advance, giving manufacturers time to act before breakdowns occur. For more details, see the official announcement from Hexagon AB Moving from Reactive Maintenance to Predictive Control AI predictive monitoring for metrology replaces traditional maintenance models that rely on scheduled servicing or operator experience. With APOLLO, manufacturers can: Reduce unplanned downtime Maintain consistent measurement accuracy Improve overall equipment effectiveness (OEE) Stabilise production output Instead of reacting to machine failures, teams can plan maintenance based on real-time insights and predictive alerts. This shift is particularly important in metrology, where even small deviations can affect product quality and compliance. Real-Time Visibility Across Metrology Assets A key advantage of AI predictive monitoring for metrology is full visibility across machine fleets. APOLLO provides a centralised dashboard that allows manufacturers to: Monitor uptime, runtime, and downtime Track environmental conditions in real time Receive alerts for warnings and abnormal behaviour Analyse OEE across multiple machines This gives operations teams a clear overview of asset health and helps identify performance issues early. Learn more about the platform here. Flexible Deployment for Modern Manufacturing APOLLO has been designed to integrate into complex manufacturing environments without requiring major infrastructure changes. It supports: Hexagon and third-party equipment Cloud deployment for scalability On-premises deployment for secure environments This flexibility allows manufacturers to adopt AI predictive monitoring for metrology while maintaining control over data and existing systems. Why AI Predictive Monitoring for Metrology Matters Now Manufacturers are dealing with increasing production complexity, tighter tolerances, and ongoing skills shortages. Traditional maintenance approaches often rely on manual tracking or undocumented operator knowledge, which can lead to inconsistent results. AI predictive monitoring for metrology addresses this by: Standardising monitoring across assets Reducing reliance on individual expertise Enabling faster, data-driven decisions This is part of a wider shift toward connected, intelligent factories where quality control is fully integrated into production systems. For a broader view, see our explainer on AI in manufacturing systems: MTN Analysis AI predictive monitoring for metrology is moving the industry beyond inspection and into intelligence. Most AI developments in manufacturing have focused on machining, robotics, or CAM. Metrology has often remained in the background, despite its critical role in ensuring quality. APOLLO changes that position. The standout feature is the 90-day prediction window, which shifts metrology from a reactive checkpoint into a forward-looking system that influences production planning. This opens the door to tighter integration between: Quality control Maintenance scheduling Production optimisation Vendors in metrology have traditionally competed on accuracy. The next phase will be defined by data, prediction, and system intelligence. Hexagon is positioning itself early in that shift. Key Takeaways AI predictive monitoring for metrology enables early detection of machine issues Hexagon APOLLO can predict failures up to 90 days in advance The platform supports both Hexagon and third-party machines Real-time dashboards provide full visibility across assets Predictive maintenance improves uptime, accuracy, and OEE FAQ What is AI predictive monitoring for metrology? It is the use of AI and sensor data to monitor measurement systems and predict failures before they happen. What is Hexagon APOLLO? APOLLO is an AI-powered platform that monitors metrology assets and provides predictive insights into machine performance. How far in advance can APOLLO detect issues? Hexagon states the platform can identify potential failures up to 90 days before they occur. Which machines are supported? Both Hexagon equipment and third-party metrology systems can be monitored. Why is this important for manufacturers? Because it reduces downtime, improves measurement accuracy, and enables more efficient production planning. ### What Is Edge AI in Manufacturing and Why It Matters in 2026 Canonical URL: https://machinetoolnews.ai/edge-ai-in-manufacturing-2026/ Published: 2026-03-24T12:26:44+00:00 Modified: 2026-03-24T12:26:46+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, Software / CAM / IIoT, What is ? Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/automated_robotic_arms.jpg Featured image alt: Edge AI in Manufacturing 2026 with industrial robots performing automated welding and real-time decision making in a smart factory environment Edge AI in Manufacturing 2026 is becoming one of the most important developments in industrial technology, as factories shift toward real-time, machine-level intelligence. Many manufacturers are already using artificial intelligence, yet fewer understand where that intelligence is actually processed and why it matters. Edge AI refers to running AI models directly on machines, devices, or local systems on the factory floor rather than relying on cloud-based processing. This shift is changing how quickly factories can respond, how securely they can operate, and how scalable AI deployments can become. As manufacturers push for faster decisions, lower latency, and greater control over data, Edge AI is moving from an emerging concept to a practical requirement. Edge AI in Manufacturing 2026 is enabling real-time decision making directly on machines. What Is Edge AI in Manufacturing? Edge AI in manufacturing is the deployment of artificial intelligence algorithms directly on physical equipment such as CNC machines, robots, vision systems, or industrial PCs. Instead of sending data to the cloud for analysis, the data is processed locally at the “edge” of the network, right where the machine is operating. This means: Decisions happen in real time Data does not need to leave the factory Systems can operate even without internet connectivity Typical edge AI systems combine sensors, embedded processors, and machine learning models that are trained either locally or in the cloud and then deployed on-site. The adoption of Edge AI in Manufacturing 2026 is being driven by speed and data volume challenges. Edge AI is already being deployed at scale, with platforms like NVIDIA’s industrial edge computing solutions enabling real-time AI processing directly on factory equipment. IBM also explains how edge computing supports real-time decision making in industrial environments How Edge AI Is Used in Factories Edge AI is already being applied across multiple areas of manufacturing. The most common use cases are focused on speed, accuracy, and operational efficiency. Many factories are combining cloud systems with Edge AI in Manufacturing 2026 for hybrid deployments. Real-Time Quality Inspection Machine vision systems powered by Edge AI can detect defects instantly during production. Instead of waiting for batch inspection, manufacturers can identify issues as they happen. This reduces scrap, improves consistency, and enables immediate corrective action. Predictive Maintenance on Machines Edge AI models can analyse vibration, temperature, and spindle data directly on a machine to detect early signs of failure. Because the processing happens locally, alerts are generated instantly without needing to send large datasets to external servers. Adaptive Machining and Process Control Advanced systems can adjust feeds, speeds, or toolpaths in real time based on live data. This allows machines to respond to material variation, tool wear, or unexpected conditions without operator intervention. Autonomous Robotics Robots equipped with Edge AI can make decisions based on what they see and sense in their environment. This is especially important for applications such as bin picking, assembly, and flexible automation where conditions change constantly. Edge AI vs Cloud AI in Manufacturing Both approaches are used in modern factories, but they serve different purposes. Edge AI: Ultra-low latency Real-time decision making Greater data privacy Works without internet Cloud AI: More computational power Centralised data analysis Easier model training and updates Better for long-term optimisation In practice, most manufacturers are moving toward a hybrid model where training happens in the cloud and execution happens at the edge. These challenges are driving demand for more user-friendly platforms and pre-built industrial AI solutions. Edge AI in Manufacturing 2026 is rapidly becoming a standard approach for factories looking to improve real-time performance and reduce reliance on cloud-based systems. Why Edge AI Matters Now Several factors are accelerating the adoption of Edge AI in manufacturing: Speed Requirements Modern production lines cannot afford delays. Even milliseconds can impact throughput and quality. Data Volume Machines generate huge amounts of data. Sending all of it to the cloud is expensive and inefficient. Cybersecurity Concerns Keeping sensitive production data on-site reduces exposure to external risks. Scalability Edge deployments allow manufacturers to roll out AI across multiple machines without overloading central systems. Challenges of Edge AI Adoption Despite the benefits, there are still barriers to widespread adoption: Limited processing power on edge devices compared to cloud systems Complexity in deploying and maintaining AI models across many machines Integration with legacy equipment Skills gap in AI and industrial data science MTN Analysis Edge AI is not replacing cloud AI. It is redefining where value is created. The shift toward edge-based intelligence reflects a deeper change in manufacturing. AI is moving closer to the machine, closer to the process, and closer to real-time decision making. Security is another reason why Edge AI in Manufacturing 2026 is gaining attention. Vendors that can combine strong cloud training environments with seamless edge deployment are likely to lead the market. This includes machine tool builders, software providers, and industrial automation companies that understand both data and production. For manufacturers, the opportunity is clear. The real gains will come from applying AI directly at the point of production where decisions actually impact performance. The future of Edge AI in Manufacturing 2026 will depend on easier deployment and integration. For a broader understanding of how AI is transforming factory environments, see our guide on what industrial AI means for manufacturing. Key Takeaways Edge AI processes data directly on machines rather than in the cloud It enables real-time decision making with minimal latency Key applications include inspection, maintenance, machining, and robotics Most factories will adopt a hybrid edge and cloud AI approach Adoption is growing due to speed, security, and scalability demands FAQ What is Edge AI in manufacturing in simple terms? Edge AI means running artificial intelligence directly on factory machines or devices so they can make decisions instantly without relying on the cloud. Is Edge AI better than cloud AI? Neither is better on its own. Edge AI is ideal for real-time decisions, while cloud AI is better for large-scale analysis and training. Where is Edge AI used in factories? It is commonly used in quality inspection, predictive maintenance, robotics, and adaptive machining. Do manufacturers need new machines for Edge AI? Not always. Many solutions can be added to existing machines using sensors, industrial PCs, or retrofitted systems. Edge AI in Manufacturing 2026 is expected to play a ce... ### German Edge Cloud Digital Industrial Engineer: Exclusive Interview with CEO Dieter Meuser Canonical URL: https://machinetoolnews.ai/digital-industrial-engineer-2026-gec/ Published: 2026-03-23T09:46:31+00:00 Modified: 2026-03-23T09:46:32+00:00 Author: Jelena Radojcic Categories: AI in Machining, General, Germany, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/gec_resized_1600x900.jpg Featured image alt: German Edge Cloud Digital Industrial Engineer 2026 visual showing AI connected factory network with robotics, data flows and industrial engineering intelligence system European manufacturing is entering a critical phase where knowledge loss, workforce shortages, and rising production complexity are colliding. At Hannover Messe, German Edge Cloud is presenting a solution designed to address this challenge head on. We spoke with Dieter Meuser, CEO of German Edge Cloud, about the company’s Digital Industrial Engineer (DIE) and how it fits into the next phase of industrial AI adoption. MTN: At Hannover Messe, you are presenting the “Digital Industrial Engineer.” In simple terms, what problem in modern manufacturing does this AI system solve that traditional digitalisation initiatives have struggled to address? Dieter Meuser:Industrial engineers are crucial to the stability of modern manufacturing. They develop work and test concepts, evaluate product changes, analyze disruptions, and ensure stable processes. Their experience helps avoid errors, spot deviations early, and maintain high standards even in highly variant production. But this knowledge-driven role is under pressure. Many experienced engineers will retire soon, while young talent isn’t replacing them fast enough. Much of this expertise built on years of undocumented decision-making is at risk of being lost. In high-variant environments, this directly threatens process stability, quality, and responsiveness. At the same time, we’re seeing more highly qualified foreign engineers enter the European market, but they face language and documentation barriers. The Digital Industrial Engineer gives them an AI assistant that makes technical terms, processes, and historical documents instantly accessible. They can contribute productively from day one. DIE systematically captures this knowledge where it originates in real production and planning situations. Through expert interviews, disruption analyses, ramp-ups, product changes, and existing documents like work plans, FMEAs, or test concepts, we capture decision logic, failure patterns, cause chains, and proven fixes. This creates standardized, scalable knowledge modules linked to actual contexts such as product variants, technologies, and revision levels. The result is a digital production memory with transparent relationships and reproducible decisions. The Digital Industrial Engineer is optionally available in our ONCITE Digital Production System. But let’s now turn to Germany’s digitalisation challenge: over 90 per cent of industrial companies are SMEs. In the manufacturing sector, a good two-thirds do not have a comprehensive digitalisation strategy. That is why we have developed the ONCITE Digital Production System (DPS). ONCITE DPS can complement traditional MES systems, take over a large part of typical MES functions, and integrate seamlessly into existing MES landscapes. We can create a 3D digital representation of production and offer secure data exchange with other companies or data ecosystems such as Catena-X or Manufacturing X. The DPS also facilitates access to industrial AI clouds. This ensures that the data owner always retains full data sovereignty. The DIE is an optional feature of ONCITE DPS. MTN: European manufacturing is facing both a demographic shift and increasing production complexity. How does the Digital Industrial Engineer practically capture and structure experiential knowledge before it disappears from the factory floor? Dieter Meuser: Knowledge capture and structuring begin with preparation. The great2know team works closely with stakeholders to analyze targeted knowledge domains and identify employees with crucial expertise. Using an AI-powered app, they create technical questions to guide structured interviews with these knowledge holders. Responses can be written, spoken, recorded as video, or extracted from existing documentation. AI then systematizes, structures, enriches, and contextualizes these inputs. Knowledge experts remain involved throughout the process, monitoring workflows and performing quality checks. This human-in-the-loop approach ensures reliable and company-specific results while the AI continues learning. With DIE support, industrial engineers become more productive because they can monitor more processes during their shift. MTN: You describe the solution as operating at maturity level 3. What does that mean in real industrial terms, and how do you build trust in AI-supported decision-making among experienced engineers? Dieter Meuser: A maturity level of 3 means AI is fully embedded in day-to-day engineering and production operations. The system captures experiential knowledge from real planning and production situations, links it with technical documentation and historical data, and delivers context-specific recommendations. Transparency is essential. Employees can see exactly which documents, historical cases, or boundary conditions underpin the AI’s suggestion. They receive clear explanations rather than black-box outputs. MTN: Manufacturers are under pressure to justify AI investments. Where do you see the most immediate measurable ROI from the Digital Industrial Engineer? Dieter Meuser:Companies see measurable ROI quickly once they begin digitalization because inefficiencies become visible. For example, ONCITE DPS Track and Trace reveals the conditions under which defective products were made. This allows parameter adjustments that reduce material loss. Energy consumption can also be optimized once data is properly understood. The Digital Industrial Engineer shows ROI when expertise is unavailable or when manual analysis would take too long. In situations where machines are idle and external specialists are not available, the AI solution pays for itself almost instantly. Engineers are a significant cost factor. DIE enables them to work more effectively, reducing personnel costs while addressing the skilled worker shortage. MTN: Many factories already collect large volumes of data. What differentiates the Digital Industrial Engineer from a conventional analytics platform? Dieter Meuser: Traditional analytics platforms visualize data but do not explain why something is happening or how to fix it. The Digital Industrial Engineer combines real-time production data with digitized human expertise. It captures implicit knowledge such as fault patterns and proven solutions and links it to specific product variants and process conditions. It delivers actionable recommendations and uses agent-based AI that can reason, not only report. It also helps overcome language barriers for international teams. MTN: Looking ahead five years, how will AI systems like the Digital Industrial Engineer reshape the role of the industrial engineer? Dieter Meuser:The role will evolve but not disappear. Creativity, technical judgment, and solution-oriented thinking remain essential. Industrial engineers will increasingly use AI tools like DIE, which will change how they approach problems. The human in the loop remains critical because industrial AI cannot fully validate its own outputs. I do not expect in... ### AI-Driven Sheet Metal Software: Lantek Expands Global Trade Show Presence in 2026 Canonical URL: https://machinetoolnews.ai/ai-sheet-metal-software-2026-lantek-trade-shows/ Published: 2026-03-19T09:56:53+00:00 Modified: 2026-03-19T10:04:05+00:00 Author: Jelena Radojcic Categories: AI in Sheet Metal, News, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/lantek_1600x900_full-1.jpg AI sheet metal software 2026 is becoming an increasingly important topic for manufacturers as factories adopt digital production tools that optimise cutting operations, production planning and shop floor data analysis. Against this backdrop, sheet metal software developer Lantek has confirmed it will strengthen its international presence through a major global trade show programme in 2026. The company plans to attend 11 industrial exhibitions across Europe, North America and Asia as part of its strategy to build closer relationships with manufacturers, machine builders and industrial technology partners. The exhibition schedule places Lantek at some of the most important manufacturing events of the year, where visitors will be able to explore software platforms that support modern sheet metal production. Global Industrie 2026 to open the trade show programme The first major appearance will take place at Global Industrie 2026 in Paris, France, from 30 March to 2 April. Lantek will be located at Stand 6G149, where the company aims to reinforce its position within the French manufacturing market and strengthen connections with industrial manufacturers and system integrators. The company’s participation reflects growing demand across Europe for software platforms capable of connecting design, production planning and machine operations within digital manufacturing environments. Expansion into Asia and the UK manufacturing sector Following its appearance in Paris, Lantek will travel to Asia for SIMTOS 2026 in Seoul, South Korea, taking place from 13 to 17 April. The company will exhibit with a four booth space at the event, highlighting its presence in one of the most active industrial regions in the global manufacturing sector. Later in April, Lantek will return to Europe for MACH 2026 in Birmingham, United Kingdom, which will take place from 20 to 24 April. The exhibition is considered one of the most important events for the British manufacturing industry and attracts machine tool builders, automation suppliers and advanced manufacturing software developers. Presence at major international machine tool exhibitions The company’s global schedule will continue with CIMES 2026 in Beijing, China, from 25 to 29 May. CIMES is one of Asia’s largest international exhibitions for machine tools and advanced manufacturing technologies and serves as an important platform for software developers supporting digital manufacturing environments. Events such as CIMES increasingly focus on intelligent factory software, production optimisation tools and connected manufacturing platforms that allow factories to integrate machines, software and operational data. First appearance at FABTECH Canada One of the most notable milestones in the company’s 2026 programme will be Lantek’s first appearance at FABTECH Canada, which will take place from 9 to 11 June in Toronto. The debut follows the establishment of Lantek’s first Canadian office earlier this year in Toronto and marks an important step in the company’s expansion strategy in North America. FABTECH Canada is widely regarded as one of the most important events for the metal fabrication sector in the region and brings together manufacturers, technology providers and industry experts focused on the future of production technologies. By participating in the event, Lantek aims to strengthen relationships with local manufacturers, technology partners and industrial professionals within a market that continues to invest in advanced production solutions and digital manufacturing software. Autumn exhibitions include EuroBLECH and FABTECH USA Trade show activity will resume in October with two major international exhibitions. From 20 to 23 October, Lantek will exhibit at EuroBLECH 2026 in Hannover, Germany, one of the world’s leading sheet metal working exhibitions. The company will present its software portfolio at a large format stand located in Hall 11, Booth C08. During the same week, from 21 to 23 October, the company will also participate in FABTECH USA 2026 in Las Vegas, one of the largest global events dedicated to the metal fabrication industry. The year’s trade show programme will conclude with Lantek’s presence at MetalMadrid 2026, which will be held on 4 and 5 November in Madrid, Spain. Lantek will exhibit at Booth 4E14 as part of the Advanced Manufacturing Madrid event. Building relationships with manufacturers worldwide Alberto López de Biñaspre, CEO of Lantek, explains: “By taking part in these international trade shows, we will continue to build close relationships with customers, partners and machine manufacturers, while showcasing our software solutions for sheet metal, tube and profile processing. We expect this will reinforce our position as an international supplier with a strong local presence, supported by a worldwide network of offices and specialised teams.” MTN Analysis: Why software vendors are increasing their trade show presence Manufacturing software companies are increasingly using international trade shows to demonstrate how digital tools are reshaping factory operations. Software platforms that connect CAD, CAM, MES and analytics systems allow manufacturers to manage production workflows more efficiently while gaining greater visibility across the shop floor. These systems can help optimise material usage, automate production planning and analyse factory performance data. As manufacturers continue to invest in connected factory environments, software providers are positioning their technologies alongside machine tools, robotics and automation systems at major industry events. Trade shows therefore remain an important environment for manufacturers to evaluate how digital manufacturing platforms and intelligent software solutions can support modern sheet metal production. For a practical breakdown of how these systems work in real factories, see our coverage of AI-powered nesting and production planning in manufacturing. About Lantek Lantek is a multinational company focused on the digital transformation of companies in the sheet metal and metal industry. Through its patented smart manufacturing software, Lantek enables factories to connect production systems and move toward Smart Factory environments. The company provides CAD, CAM, MES and ERP solutions for manufacturers producing metal parts from sheet metal, tubes and profiles using technologies such as laser, plasma, oxycut, waterjet, shearing and punching. Founded in 1986 in the Basque Country in Spain, one of Europe’s most important machine tool development regions, Lantek has built a global customer base of more than 37,000 companies across over 100 countries. The company operates through 23 offices in 17 countries and maintains an extensive international distributor network serving manufacturers worldwide. ### Bosch Agentic AI Manufacturing: Inside the Future of Intelligent Factories – An Interview with Norbert Jung Canonical URL: https://machinetoolnews.ai/agentic-ai-in-manufacturing-bosch/ Published: 2026-03-16T12:39:14+00:00 Modified: 2026-03-16T12:39:16+00:00 Author: Jelena Radojcic Categories: AI in Machining, Germany, Robotics, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/Norbert_Jung_2025.png Featured image alt: Agentic AI in manufacturing system used in a Bosch smart factory environment analysing production data with Manufacturing Co-Intelligence® technology. Source: Bosch Industrial AI is entering a new phase. While manufacturers have spent the past decade deploying machine learning systems to analyse production data and automate specific tasks, a new concept is beginning to reshape how factories operate: Agentic AI. Bosch Connected Industry is among the companies exploring how intelligent software agents can work alongside human experts to coordinate complex manufacturing processes. Rather than focusing on isolated AI use cases, this approach aims to orchestrate entire production environments using interconnected digital agents. In this exclusive interview with Norbert Jung, CEO of Bosch Connected Industry, he explains how Agentic AI differs from traditional industrial AI systems, where it is already delivering measurable results inside Bosch plants, and why semantic data structures and multi-agent systems could play a key role in the future of intelligent manufacturing. Bosch Connected Industry is exploring how Agentic AI in manufacturing can coordinate machines, software platforms, and production systems through intelligent software agents. MTN: Bosch has begun speaking about “Agentic AI” in manufacturing. In your view, what fundamentally differentiates Agentic AI from the industrial AI systems factories have been deploying over the past five years? Norbert Jung:The shift is fundamental. For the last five years, we’ve been using AI as a set of specific tools to automate narrow, repetitive tasks. Agentic AI is entirely different. We are no longer just automating tasks; we are orchestrating entire processes. These agents act as digital collaborators that can observe, reason, and proactively recommend actions to optimize the whole value chain. It’s the difference between a tool that does one job and an expert partner that helps you run the entire operation more intelligently. MTN: Traditional AI systems generate insights. Agentic AI suggests autonomous decision-making and coordinated action. How far are we from factories where AI agents actively manage production processes rather than simply supporting human operators? Norbert Jung:Let me be very clear: a “lights-out” factory, run entirely by AI, is not our goal. Our strategy is built on what we call Manufacturing Co-Intelligence®, which is a deliberate choice to keep our human experts at the heart of our operations. The role of AI is to augment and amplify the capabilities of our people, not to replace them. The agents provide world-class analysis and powerful recommendations, but the final decision and ultimate responsibility will always rest with our skilled workforce. We see the future as a powerful synergy between human experience and machine intelligence. MTN: Where are you already seeing measurable business impact from Agentic AI within Bosch’s own production network, whether in productivity, downtime reduction, quality improvement, or planning stability? Norbert Jung:The impact isn’t a future promise; it’s delivering real value today in our plants across Germany, Hungary, and India. For example, our Shopfloor AI Agent is slashing production downtime by helping our teams resolve disruptions three to five times faster. This translates to annual savings of approximately €850,000 per plant, from reduced machine down time. MTN: You emphasize the importance of semantic data structures for Agentic AI. Why is a semantic layer essential, and what happens when companies attempt to deploy advanced AI agents without that foundation? Norbert Jung:A semantic layer is the non-negotiable foundation for any serious industrial AI strategy. It solves what we call the “Data Growth Paradox,” where having more data doesn’t create more value because it’s locked in silos. The semantic layer provides a common language, a single source of truth, that allows our agents to understand the context and relationships between data from hundreds of different systems. Without it, companies are building on sand. They get trapped in a “permanent construction site,” trying to connect data for every single new use case. It’s inefficient, it doesn’t scale, and the AI simply cannot deliver reliable, high-quality results. MTN: Bosch has discussed multi-agent systems working together in manufacturing. Can you describe how these agents interact in a real production scenario, and what level of autonomy they currently operate with? Norbert Jung:Imagine a machine suddenly stops. In the past, this would trigger a lengthy, manual investigation. Today, our Shopfloor AI Agent is immediately notified. It autonomously analyzes historical data from a dozen systems to diagnose the root cause in seconds and provides the operator with clear, step-by-step instructions to fix it. Once resolved, it can automatically update the shift log and even trigger another agent to schedule a follow-up maintenance task. They operate with semi-autonomy within a strict “human-in-the-loop” framework. The agents can propose and prepare actions, but a human expert always makes the final call. MTN: There is growing debate about AI autonomy in industrial settings. How do you define the right balance between agent-driven decisions and human oversight in safety-critical production environments? Norbert Jung:The balance is crystal clear: the human is, and will remain, the ultimate authority. In our model, the AI agent is an expert assistant that provides data-driven recommendations of the highest quality. But the human operator, with their years of experience, validates that advice and makes the final decision. Responsibility is never delegated to the machine. For safety-critical tasks where there is zero margin for error, we rely on deterministic systems, not probabilistic AI. This combination of intelligent guidance and non-negotiable human oversight is what makes our approach truly “industrial grade.” MTN: Do you see Agentic AI becoming a competitive differentiator between European manufacturers, particularly as Manufacturing-X and sovereign data ecosystems evolve? Norbert Jung:Absolutely. Agentic AI is not just another technology; it is a strategic capability that will define competitiveness in manufacturing for the next decade. Sovereign data ecosystems like Manufacturing-X are critical because they allow us to train our agents on deep, proprietary production knowledge that isn’t publicly available. This creates a powerful competitive moat. By embedding our unique expertise into these agents, we can achieve levels of efficiency, agility, and resilience that our competitors simply cannot match. MTN: Many AI initiatives remain contained within pilots. What needs to change organizationally and technically for Agentic AI to scale across multiple plants and international production networks? Norbert Jung:Scaling Agentic AI requires a dual transformation. Technically, you must commit to building a scalable semantic data foundation. This is the central nervous system that allows you to reuse data models and digital twins across your entire global network. Y... ### What Is Industrial AI in CNC Machining and Why It Matters Now Canonical URL: https://machinetoolnews.ai/industrial-ai-in-cnc-machining-2026/ Published: 2026-03-17T13:29:58+00:00 Modified: 2026-03-17T13:29:59+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, Software / CAM / IIoT, What is ? Tags: Editors Pick, Featured, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/68e391d8c03753c1c743bed0_robotics-in-cnc-machining.webp Featured image alt: Industrial AI in CNC Machining 2026 robotic arm performing precision component handling in advanced automated manufacturing environment Industrial AI in CNC Machining 2026: What It Is and Why It Matters Now Industrial AI in CNC Machining 2026 is rapidly becoming one of the most important developments in modern manufacturing, enabling machines to move beyond automation into intelligent, data-driven decision making. In simple terms, Industrial AI in CNC machining refers to the use of machine learning, data analytics, and real-time feedback systems to optimise machining processes, reduce waste, and improve precision. As manufacturers face increasing pressure to deliver higher quality parts at lower cost, Industrial AI is emerging as a critical competitive advantage. For a broader understanding of how AI is being applied across factories, see our guide toIndustrial AI in manufacturing. What Is Industrial AI in CNC Machining? Industrial AI in CNC machining is the integration of artificial intelligence into CNC machines, CAM software, and connected factory systems to enhance performance and decision making. It allows machines and software to: Predict tool wear before it leads to failure Automatically adjust cutting parameters Detect anomalies during machining Improve consistency across production batches Reduce reliance on manual intervention Unlike traditional CNC automation, which follows fixed programs, Industrial AI in CNC Machining 2026 enables systems to learn from historical and real-time data, improving outcomes over time. How Industrial AI Is Used in CNC Environments Industrial AI is already being deployed across multiple areas of machining operations, delivering measurable gains in productivity and efficiency. 1. Predictive Tool Monitoring AI models analyse spindle load, vibration, and temperature data to predict when tools will fail. This reduces unexpected downtime and improves tool utilisation. Solutions such as Siemens industrial AI platforms are already enabling this type of capability in real production environments. 2. Adaptive Machining AI systems dynamically adjust feeds and speeds based on real-time conditions such as material variation or tool condition. This results in more stable machining processes and improved part quality. 3. Process Optimisation AI can evaluate thousands of machining scenarios to identify the most efficient strategies. This includes optimising toolpaths, cycle times, and energy consumption. 4. Integrated Quality Control Machine vision and AI systems can detect defects during machining, reducing scrap and ensuring consistent output without requiring separate inspection stages. Key Technologies Behind Industrial AI in CNC Machining Several core technologies enable Industrial AI in CNC environments: Machine Learning algorithms trained on machining data Edge computing integrated directly into CNC controllers Digital twins that simulate machining processes Industrial IoT platforms connecting machines and systems AI-enabled CAM software that improves programming efficiency These technologies work together to create a connected, intelligent machining ecosystem where decisions are informed by real-time data. Why Industrial AI in CNC Machining Matters in 2026 Manufacturers are facing a combination of challenges that are accelerating the adoption of Industrial AI. These include: Skilled labour shortages in machining roles Increasing demand for high precision components Pressure to reduce costs and improve margins The need for consistent quality across global production Industrial AI addresses these challenges by improving efficiency without requiring a complete overhaul of existing machinery. Many solutions can be retrofitted to current CNC equipment, making adoption more accessible. Real-World Impact and ROI Early adopters of Industrial AI in CNC Machining 2026 are reporting measurable improvements: Reduced tool costs through predictive monitoring Shorter cycle times through optimisation Lower scrap rates due to real-time quality control Increased machine uptime and utilisation In many cases, manufacturers are seeing return on investment within months, particularly in high-volume or high-value production environments. MTN Analysis Industrial AI in CNC Machining 2026 is moving from pilot projects into mainstream production. The most successful implementations focus on augmenting existing workflows rather than replacing them. The key shift is toward AI as a decision-support layer. Operators, programmers, and engineers remain central to the process, with AI providing insights that improve speed and accuracy. This approach reduces resistance to adoption and delivers faster results. It also aligns with the broader industry trend toward human and machine collaboration. Key Takeaways Industrial AI in CNC Machining 2026 enables data-driven optimisation Predictive and adaptive systems reduce downtime and improve quality Adoption is accelerating due to labour and cost pressures The focus is on augmenting human expertise rather than replacing it FAQ What is Industrial AI in CNC machining? Industrial AI in CNC machining is the use of artificial intelligence to improve machining processes through data-driven insights and automation. How is AI used in CNC machines? AI is used for predictive tool monitoring, adaptive machining, process optimisation, and quality control. Is Industrial AI expensive to implement? Costs vary, but many solutions can be integrated into existing machines, making adoption more accessible than full system replacement. What industries benefit from Industrial AI in CNC machining? Aerospace, automotive, medical manufacturing, and precision engineering all benefit from AI-driven machining improvements. ### Revolutionizing Cheese Production with AI and Machine Vision: A Success Story from Eberle Automatische Systeme Canonical URL: https://machinetoolnews.ai/ai-cheese-production-inspection/ Published: 2026-03-13T09:37:52+00:00 Modified: 2026-03-13T09:37:53+00:00 Author: Jelena Radojcic Categories: General, Metrology & Vision, News, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/Success_story_MVTec_Eberle_5_©Eberle.jpg Featured image alt: AI cheese production inspection system using machine vision camera and robotics monitoring cheese wheels during automated ripening process The food industry is experiencing a transformative shift in quality control, due in part to advances in artificial intelligence (AI). When combined with rule-based machine vision, AI is enabling automation of processes that were previously impossible, unlocking new levels of productivity and quality assurance. One such breakthrough has been developed by Eberle Automatische Systeme, a leader in automation solutions, with a focus on the cheese-ripening process. The Challenge: Rising Demand, Labor Shortages, and Sustainability: Cheese consumption is booming globally, and producers are facing increasing challenges as they scale production. Labor shortages, particularly in Europe, are pushing dairies to adopt automation to increase efficiency. Meanwhile, sustainability is becoming a central concern, with an increased focus on reducing waste and conserving resources. Additionally, consumers are demanding higher-quality products with more variety, further intensifying pressure on producers. As Eberle’s Machine Vision Engineer, Dorian Köpfle, explains: “The cheese-ripening process, which can last up to 14 months, requires constant monitoring to avoid mold and ensure quality. Manually inspecting thousands of cheese wheels is virtually impossible, which is why Gebr. Baldauf GmbH & Co. KG, a traditional dairy, turned to us for an automated solution.” The Solution: Automation with Machine Vision and AI Gebr. Baldauf, located in the Allgäu region, commissioned Eberle to solve these challenges. The result is a fully automated monitoring system, that combines a mobile care robot, cameras, and onboard image processing. The process begins with the inspection of cheese wheels for defects, such as mold spots or blemishes. A 4K camera captures high-resolution images, which are analyzed using advanced machine-vision algorithms from MVTec HALCON. The software uses deep-learning methods to detect anomalies earlier, minimizing process deviations and waste. The data is stored and made available via a web interface, enabling remote monitoring and control. Simultaneously, the mobile care robot performs its task of treating the cheese wheels, ensuring proper rind formation and removal of unwanted smear layers. This system not only increases efficiency by reducing manual inspection but also improves the consistency and quality of the final product. Key Outcomes and Business Impact: The deployment of this automated system has provided several key benefits for Gebr. Baldauf, including: Increased Efficiency: The mobile care robot operates autonomously, reducing manual labor while ensuring that each cheese wheel is inspected and treated thoroughly. Waste Reduction: Early detection of mold or defects allows for timely intervention, preventing rejected cheese and minimizing waste. Improved Quality Control: The system ensures more consistent and less subjective inspection results by replacing manual methods with AI. As a result, the process achieves a 100% inspection rate, applying the same inspection criteria throughout. Full Traceability: The integration of industrial image processing ensures complete product traceability. All inspection results are stored digitally for easy access, enabling better decision-making and long-term process optimization. Overcoming Technical Challenges with AI: A significant challenge in developing this system was the natural variability of cheese. Every wheel looks different and undergoes significant changes during the ripening process, which makes rule-based machine vision methods less effective. To overcome this, Eberle utilized AI and deep learning to create a system that could adapt to the unique characteristics of each cheese wheel. The MVTec HALCON software was instrumental in this process. By training a deep-learning network with a large dataset of cheese images, the system is able to reliably detect defects such as cracks, mold, and discoloration, while ignoring the natural variations inherent to the process. This technology ensures that even subtle anomalies are spotted, allowing for earlier intervention and better quality control. Enabling Full Automation The Path Forward Eberle’s goal was not only to automate the inspection process, but to fully integrate AI into the cheese-ripening workflow. Currently, the system is capable of performing real-time inspections and autonomous care, with minimal human involvement. However, the company is working on refining the system further to handle all types of cheese and stages of ripening, with the long-term goal of creating a fully automated, AI-driven system that requires no human input. The system also provides a solid foundation for future digitalization efforts, with the potential for integration into larger digital platforms, such as ERP systems and the cloud, to further optimize the production process. Looking Ahead: Scaling and Further Digitalization Building on the success of this project, Eberle is now focused on scaling the solution to meet the needs of the entire cheese industry. The company plans to standardize the system and integrate it into both mobile and stationary care robots for cheese production worldwide. Furthermore, the system’s AI capabilities are continually evolving. Eberle aims to refine the deep-learning models to handle different cheese types and ripening stages, enabling fully automated classification and inspection. This will allow producers to further reduce human involvement while maintaining the highest standards of quality. As Christoph Muxel of Eberle summarizes, “Our machine vision-based solution demonstrates how automation can sustainably improve quality, efficiency, and competitiveness in the food industry. This project is just the beginning, and we’re excited to take these innovations to a global scale.” About MVTec Software GmbH MVTec is a leading manufacturer of standard software for machine vision. MVTec products are used in a wide range of industries, such as semiconductor and electronics manufacturing, battery production, agriculture and food, as well as logistics. They enable applications like surface inspection, optical quality control, robot guidance, identification, measurement, classification, and more. By providing modern technologies such as 3D vision, deep learning, and embedded vision, software by MVTec also enables new automation solutions for the Industrial Internet of Things aka Industry 4.0. With locations in Germany, the USA, France, Benelux, Spain, China, Taiwan and South Korea as well as an established network of international distributors, MVTec is represented in more than 35 countries worldwide. MTN Analysis This project matters because it shows a realistic, commercially relevant use of AI in food manufacturing. Rather than presenting AI as a future concept, Eberle and Gebr. Baldauf are applying it to a specific production bottleneck where manual inspection is difficult, labor-intensive, and inconsistent. The combination of robotics, machine vision, and deep... ### ABB RobotStudio NVIDIA Omniverse Integration Brings Physical AI to Industrial Robotics Canonical URL: https://machinetoolnews.ai/abb-robotstudio-nvidia-omniverse-integration/ Published: 2026-03-12T11:50:30+00:00 Modified: 2026-03-12T11:50:33+00:00 Author: Jelena Radojcic Categories: General, Robotics, Robotics & Cobots, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/ABBROPR008-Image-2.jpg Featured image alt: ABB RobotStudio NVIDIA Omniverse Integration simulation showing AI industrial robots trained in digital twin environment ABB Robotics has announced a new collaboration with NVIDIA that aims to accelerate the deployment of physical AI in industrial robotics. The company is integrating NVIDIA Omniverse libraries into ABB’s RobotStudio® simulation platform, allowing manufacturers to train robotic systems in digital environments and deploy them into real-world production with unprecedented accuracy. The development is designed to close the long-standing gap between virtual robot training and real-world deployment. According to ABB Robotics, the technology can achieve up to 99 percent accuracy between simulation and real-world performance, allowing companies to design, train, and validate robotic automation systems before they reach the factory floor. The ABB RobotStudio NVIDIA Omniverse Integration represents a major step forward in how manufacturers design, simulate, and deploy AI-powered robotics systems. ABB and NVIDIA Bring Physical AI to Industrial Robotics The collaboration combines ABB Robotics’ software programming, design and simulation suite RobotStudio with the physically accurate simulation capabilities of NVIDIA Omniverse libraries. This allows developers to build digital twins of robotic systems, generate synthetic data, and train AI models in simulated production environments. Once validated, the trained models can be transferred directly to physical robots operating in real industrial workflows. Marc Segura, President of ABB Robotics, said the collaboration removes major barriers to deploying industrial AI at scale. “Today, using NVIDIA accelerated computing and simulation technologies, we have removed the last barriers to making industrial and physical AI a reality at a global scale by closing the sim-to-real gap,” said Marc Segura. “For more than 50 years, ABB Robotics has led the evolution of intelligent industrial automation, from pioneering the first generation of fully electric industrial robots to advancing digital twin simulation through RobotStudio and shaping a new area of autonomous and versatile mobile robots. Today’s announcement with NVIDIA brings physical AI to industry at scale.” RobotStudio HyperReality to Transform Production Scaling A key outcome of the collaboration is a new capability called RobotStudio HyperReality, which ABB plans to release in the second half of 2026. The technology will allow manufacturers to create highly realistic simulations of robotic production systems and continuously improve them using real-world operational data. These models can then be used to train large fleets of ABB robots anywhere in the world while maintaining the reliability and accuracy required for industrial applications. Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA, said physically accurate simulation is essential for scaling AI-powered robotics. “The industrial sector needs physically accurate simulation to bridge the gap between virtual training and the real-world deployment of AI-driven robotics at scale,” said Deepu Talla. “Integrating NVIDIA Omniverse libraries into RobotStudio brings advanced simulation and accelerated computing to ABB Robotics’ unique virtual controller technology, accelerating how manufacturers of all sizes bring complex products to market.” With the ABB RobotStudio NVIDIA Omniverse Integration, manufacturers will be able to train robotic systems in highly realistic digital twin environments before deploying them into production. Closing the Sim-to-Real Gap One of the biggest challenges in industrial robotics has been the sim-to-real gap, where simulation environments fail to accurately represent real-world factory conditions such as lighting, materials, and physical environments. By integrating NVIDIA Omniverse libraries into RobotStudio, ABB Robotics will deliver advanced robotics simulation and synthetic data generation capabilities designed to bridge this gap with up to 99 percent accuracy. ABB is also the only robot manufacturer with a virtual controller running the same firmware as the physical hardware, ensuring near-perfect correlation between simulation and real-world robot behaviour. Combined with ABB’s Absolute Accuracy technology, which reduces positioning errors from 8–15 millimetres to around 0.5 millimetres, the platform delivers the level of precision required for high-precision industrial applications. For manufacturers, this innovation allows entire production lines to be designed, tested, and optimized in a virtual environment before deployment. ABB estimates that companies could reduce commissioning times by up to 80 percent, reduce costs by up to 40 percent, and accelerate time to market for complex products by up to 50 percent. Foxconn Pilots the First Real-World Use Case A real-world pilot project is already underway with Foxconn, the world’s largest electronics contract manufacturer. The pilot focuses on automating the assembly of small components used in consumer electronics devices. These processes require extremely precise robotic control because multiple device variants demand different assembly methods and delicate metal structures must be handled carefully. Using RobotStudio HyperReality, Foxconn trains assembly robots in a virtual environment using synthetic data before transferring them to the production line with up to 99 percent accuracy. Dr. Zhe Shi, Chief Digital Officer of Foxconn, said the technology opens new possibilities for electronics manufacturing. “Precision is everything in consumer electronics manufacturing and until now, this level of accuracy and fidelity just wasn’t possible in simulation and digital twins,” said Dr. Zhe Shi. “We’re incredibly excited by the potential of ABB Robotics and NVIDIA’s collaboration, which enables parallel engineering for better designs, faster production ramp-up and greater product evolution through advanced AI inference and understanding.” The ABB RobotStudio NVIDIA Omniverse Integration allows companies like Foxconn to test complex assembly processes virtually before running them on real production lines. WORKR Demonstrates the Technology for Smaller Manufacturers The technology will also be demonstrated at NVIDIA GTC in San Jose. Robotic workforce company WORKR will showcase how the platform can deploy AI-powered robotic systems for small and mid-sized manufacturers across the United States. WORKR combines ABB’s industrial robotics with its proprietary WorkrCore™ AI platform to create robotic workforce systems capable of learning new tasks quickly and operating without requiring advanced programming skills. Ken Macken, CEO and Founder of WORKR, said the collaboration shows how industrial AI can now be deployed in real manufacturing environments. “This collaboration is about making industrial AI deployable today,” said Ken Macken. “Together with ABB and NVIDIA, we’re proving that advanced automation can work for manufacturers of any size.” MTN Analysis The ABB RobotStudio NVIDIA Omniverse Integration highlights a major shift in industrial r... ### What Is Physical AI in Robotics and Automation? The Technology Powering the Next Generation of Smart Machines Canonical URL: https://machinetoolnews.ai/physical-ai-robotics-automation/ Published: 2026-03-11T12:41:30+00:00 Modified: 2026-03-11T12:41:33+00:00 Author: Jelena Radojcic Categories: Robotics, What is ? Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/6c511361-23c6-4664-8164-a7c7001d35d4.png Featured image alt: Physical AI in robotics and automation guiding a machine vision robotic system performing intelligent inspection of industrial components inside a smart factory Physical AI in Robotics and Automation refers to artificial intelligence systems that interact directly with the physical world through machines such as robots, sensors, and industrial equipment. Unlike traditional AI that works mainly with digital data, Physical AI enables machines to perceive environments, make decisions, and perform real-world actions. Factories, warehouses, and logistics operations increasingly rely on Physical AI to allow robots and automated systems to adapt to complex environments, handle unpredictable tasks, and collaborate with human workers. The result is a new generation of intelligent machines capable of learning from real-world conditions rather than following rigid pre-programmed instructions. In modern manufacturing, Physical AI is becoming a critical foundation for robotics, autonomous production systems, and smart factories. Physical AI in robotics and automation is quickly becoming one of the most important technologies shaping the future of smart factories and industrial automation. What Exactly Is Physical AI? Physical AI in Robotics and Automation combines several technologies that allow machines to understand and interact with the physical environment. These technologies include: • Machine learning• Computer vision• Sensor fusion• Robotics control systems• Reinforcement learning Together these systems allow robots and automated equipment to interpret physical surroundings, understand objects, and perform actions with precision. For example, a robotic arm equipped with cameras and AI software can identify different components on a production line, determine how to pick them up, and adjust its movement in real time. This capability moves robotics beyond traditional automation, where machines repeat the same programmed motion without understanding the environment. How Physical AI Works in Industrial Robotics Physical AI operates through a loop of perception, reasoning, and action. 1. Perception Sensors gather information about the environment. Examples include: • Industrial cameras• LiDAR scanners• Force sensors• Temperature sensors• Motion detectors Computer vision systems analyse this data so machines can recognise objects, surfaces, or human workers. 2. Decision Making AI models process the collected data and determine the best action. For example, a robotic system may decide how to grip an irregular metal component or determine whether a part is defective. 3. Physical Action The robot then performs the task through motors, robotic arms, autonomous vehicles, or production machinery. The system continues learning from each action, improving performance over time. In modern factories, physical AI in robotics and automation allows machines to interpret real-world data from sensors and cameras before making decisions about movement, manipulation, or inspection tasks. Examples of Physical AI in Manufacturing Physical AI is already transforming several areas of industrial automation. Intelligent robotic assembly Robots can identify parts and assemble products even when components arrive in random orientations. Autonomous mobile robots Warehouse robots use AI to navigate complex environments and avoid obstacles while transporting materials. AI powered quality inspection Vision systems equipped with machine learning models detect defects in components during production. Adaptive CNC machining AI systems monitor cutting conditions and adjust feed rates or tool paths to maintain precision. Human robot collaboration Collaborative robots use AI to detect human movements and safely operate alongside workers. Why Physical AI Matters for the Future of Automation Traditional industrial automation relies on fixed programming and predictable environments. Physical AI changes this model by giving machines the ability to adapt. Key advantages include: • Greater production flexibility• Reduced need for manual programming• Faster deployment of robotic systems• Improved quality control• Increased automation in complex tasks These capabilities are particularly valuable in industries such as automotive manufacturing, aerospace machining, electronics production, and logistics. As factories move toward Industry 4.0 and smart manufacturing, Physical AI is expected to become a core technology behind intelligent production systems. Research groups such as the MIT Robotics Laboratory are developing AI systems that allow robots to learn how to manipulate objects and navigate environments using machine learning and sensor data. MTN Analysis: Why Physical AI Is Becoming a Strategic Technology The rapid development of Physical AI is driven by advances in computing power, sensors, and large-scale machine learning models. Major robotics and industrial technology companies are investing heavily in systems that combine AI with real-world robotics. Physical AI also supports predictive maintenance by analysing machine data and detecting anomalies before equipment failures occur. This shift is creating a new category of intelligent machines capable of learning directly from physical environments rather than relying only on digital simulations. For manufacturers, the strategic value of Physical AI lies in its ability to automate tasks that were previously considered too complex for robots. These include handling irregular objects, adapting to production changes, and working safely with humans. Over the next decade, Physical AI is expected to play a central role in the evolution of autonomous factories. As investment grows, physical AI in robotics and automation is expected to redefine how intelligent machines operate across modern manufacturing facilities. FAQ What is Physical AI? Physical AI is artificial intelligence designed to control machines that interact with the real world, such as robots, industrial equipment, and autonomous systems. How is Physical AI different from traditional AI? Traditional AI mainly processes digital data, while Physical AI connects AI models with sensors, robotics, and machines that perform physical actions. Where is Physical AI used in manufacturing? Physical AI is used in robotic assembly, machine vision inspection, autonomous logistics robots, collaborative robots, and adaptive manufacturing systems. Why is Physical AI important for factories? It allows machines to adapt to changing environments, making automation more flexible and capable of handling complex tasks. Key Takeaways • Physical AI enables robots and machines to interact intelligently with the physical world.• It combines sensors, computer vision, machine learning, and robotics.• Manufacturing, logistics, and industrial automation are major adoption areas.• The technology allows machines to adapt to unpredictable environments.• Physical AI is expected to become a key driver of next-generation smart factories. For manufacturers investing in smart factories, understanding physical AI in robotics and automation is becoming essential for future competitiveness. ### What Is Industrial AI and How Is It Used in Factories? Canonical URL: https://machinetoolnews.ai/industrial-ai-in-factories-2026/ Published: 2026-03-10T12:15:13+00:00 Modified: 2026-03-10T12:15:17+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, AI in Sheet Metal, What is ? Tags: Editors Pick, Featured, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/mtn_industrial_ai_factory_1600x900.jpg Featured image alt: Industrial AI in factories analysing CNC machine production data in a smart manufacturing environment Industrial AI in factories is transforming modern manufacturing by using artificial intelligence to analyse machine data, automate quality inspection, and optimise production processes across connected factory environments. As manufacturers deploy smarter machines and connected systems, Industrial AI in factories is becoming a critical technology that helps engineers monitor performance, reduce downtime, and improve production efficiency. Unlike traditional industrial automation that follows fixed rules, Industrial AI allows machines and software to learn from data. This enables factories to detect problems earlier, optimize production parameters automatically, and adapt to changing production conditions. For manufacturers operating CNC machines, robotic cells, and sheet metal production lines, Industrial AI in factories is becoming a key tool for improving productivity and maintaining competitive advantage. As a result, Industrial AI in factories is becoming essential for manufacturers seeking to increase productivity and maintain consistent production quality. Why Industrial AI Is Transforming Manufacturing Manufacturing environments generate massive amounts of data from sensors, machine controllers, quality systems, and production planning software. Historically, much of this data was underused. Industrial AI changes this by analysing real-time production data to uncover patterns that humans may not detect. Factories use Industrial AI to: • predict machine failures before they happen• optimize machining parameters and cutting conditions• detect quality defects automatically• improve production scheduling• reduce scrap and energy consumption As manufacturing becomes more complex, AI systems provide a way to manage large amounts of operational data and translate it into actionable insights for engineers and production teams. Key Technologies Behind Industrial AI Industrial AI combines several advanced technologies working together inside factory environments. Machine Learning Machine learning algorithms analyse historical and real-time machine data to identify patterns. These models can predict equipment failures, detect process variations, or recommend optimal production settings. Computer Vision AI-based vision systems use cameras and deep learning software to inspect components during production. These systems are commonly used for defect detection, dimensional inspection, and robotic guidance. Predictive Analytics Predictive analytics tools analyse machine sensor data to forecast equipment issues. For example, vibration, temperature, and spindle load data can reveal early signs of tool wear or machine failure. Edge Computing Many Industrial AI applications run directly on factory equipment through edge computing systems. This allows AI models to analyse machine data in real time without relying on external cloud infrastructure. How Industrial AI Is Used in Factories Industrial AI is already deployed across a wide range of manufacturing processes. Predictive Maintenance AI models analyse machine tool data to detect patterns that indicate mechanical issues. This allows maintenance teams to schedule repairs before unexpected breakdowns occur. Quality Inspection Machine vision systems powered by AI inspect parts during production to identify surface defects, dimensional deviations, or assembly errors. Process Optimization Industrial AI systems analyse machining parameters, tool wear, and production data to recommend optimal cutting conditions that improve productivity while maintaining part quality. Robotics and Automation AI allows industrial robots to adapt to changing environments. Vision-guided robots can identify parts, adjust positioning, and perform tasks that previously required manual intervention. Real-World Industrial AI Examples Several major industrial technology companies are developing Industrial AI solutions for manufacturing environments. Siemens is integrating Industrial AI into its Industrial Edge platform, enabling manufacturers to run AI applications directly on factory machines. FANUC uses AI technology in its FIELD system to analyse machine data and improve production efficiency. Hexagon develops AI-driven inspection and quality control systems that combine metrology with machine learning. MVTec Software GmbH provides machine vision tools such as HALCON that allow manufacturers to deploy AI-based inspection systems. These technologies are becoming increasingly important as factories move toward more connected and data-driven production environments. MTN Analysis Industrial AI in factories is shifting manufacturing from reactive decision-making toward predictive and autonomous operations. Historically, engineers relied on experience and manual monitoring to manage production processes. AI systems now analyse millions of data points from machines, sensors, and inspection systems to identify inefficiencies and improve performance. Over the next decade, Industrial AI is expected to play a central role in autonomous manufacturing environments where machines adjust parameters, schedule maintenance, and optimize production with minimal human intervention. For CNC machining and metal manufacturing, this transition could significantly increase productivity while reducing production waste and machine downtime. Manufacturers are increasingly deploying AI across machining processes, similar to the recent developments highlighted in our coverage of AI adoption in European manufacturing. Key Takeaways • Industrial AI combines machine learning, computer vision, and predictive analytics in factory environments.• Manufacturers use AI to improve maintenance, inspection, automation, and production optimization.• AI systems allow factories to analyse machine data and detect patterns that improve efficiency.• Major industrial technology companies are investing heavily in Industrial AI platforms. FAQ What is Industrial AI in manufacturing? Industrial AI refers to the use of artificial intelligence technologies to analyse machine data, optimize production processes, and automate decision-making within manufacturing environments. How does Industrial AI improve factory efficiency? Industrial AI analyses machine and production data to detect inefficiencies, predict equipment failures, and recommend optimized production settings. Which industries use Industrial AI the most? Industrial AI is widely used in automotive manufacturing, aerospace production, electronics assembly, and precision machining. Is Industrial AI replacing human workers? Industrial AI is primarily designed to assist engineers and operators by analysing complex production data and supporting decision-making rather than replacing skilled workers. ### Agile Robots Interview: How Physical AI Is Transforming Industrial Robotics Canonical URL: https://machinetoolnews.ai/agile-robots-physical-ai-robotics-interview/ Published: 2026-03-09T13:12:59+00:00 Modified: 2026-03-09T13:13:01+00:00 Author: Jelena Radojcic Categories: AI in Machining, General, Robotics, Robotics & Cobots Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/Portrait_Sven_1600x900px-1.jpg Featured image alt: Sven Parusel Head of Research Partnerships at Agile Robots discussing Physical AI robotics and intelligent automation in manufacturing Artificial intelligence is rapidly redefining the capabilities of industrial robotics. While robots have long been used for repetitive automation tasks, the integration of AI is allowing machines to perceive their environment, adapt to change, and make intelligent decisions in real manufacturing environments. Agile Robots, a robotics company originally spun out of the German Aerospace Center (DLR), is developing systems that combine advanced robotics with artificial intelligence to create adaptive automation capable of operating in complex industrial environments. In this interview, Sven Parusel, Head of Research Partnerships at Agile Robots, explains how Physical AI is reshaping robotics automation, why industrial data is critical for scaling intelligent robots, and how AI-driven robotics could transform the role of engineers and technicians across modern factories. Agile Robots Physical AI robotics represents a new generation of intelligent automation systems designed to operate in dynamic manufacturing environments. Agile Robots describes itself as combining robotics with advanced AI. What distinguishes your approach from traditional industrial robotics automation? Sven Parusel, Head of Research Partnerships at Agile Robots, explains: At Agile Robots, we leverage the power of Physical AI. Instead of relying on rigid instructions, robots gain the ability to perceive, interpret, and respond to their environment in real time. They no longer just execute predefined paths – they adapt, optimize, and make decisions based on what is actually happening around them. Physical AI transforms robots from automated tools into adaptive systems – capable of handling variability, learning from experience, and operating effectively in dynamic, unpredictable environments. While many competitors rely solely on synthetic or generic data, Agile Robots also trains its AI models with real data from our own manufacturing. Combining real industrial data with simulated and human-generated data enables robots to rapidly adapt to new tasks, and to execute them with high precision. Since our founding as a spin-off from the German Aerospace Center (DLR), we have developed, designed and manufactured nearly all our products in-house. As a result, we have developed a valuable repository of data that has proved to be a strategic advantage. Industrial robots have existed for decades. Where does AI genuinely change the capability of a robot rather than simply improving programming efficiency? Sven Parusel: Robots without AI are merely machines capable of movement but not intelligence. Traditional programming always works the same way. You define a position and a trajectory, and the robot simply follows it. When conditions change, the process must be manually adjusted. AI elevates a robot’s capabilities beyond the limitations of traditional robotics, moving it from fixed, preprogrammed actions to adaptive intelligence. Traditional computer vision has long allowed robots to react to changes in their environment, but each solution typically required expert knowledge to design a custom system for the specific task. With Physical AI, robots can perceive, interpret, and respond to their environment in real time, handling variability and uncertainty that conventional robots cannot. In this way, AI does more than make programming more efficient. It transforms robots into adaptive systems that learn from experience, optimize their actions, and operate effectively in dynamic, real-world conditions that traditional industrial robots cannot handle. Where are customers seeing the most immediate measurable returns from AI-enabled robotic systems, whether in cycle time, defect reduction, labour efficiency, or throughput? Sven Parusel: AI-enabled systems offer measurable results in all of the above. By adapting in real time, AI-driven robots reduce delays caused by part misalignment, inconsistent materials, or workflow changes, accelerating cycle times without sacrificing quality. At the same time, these robots handle repetitive or complex tasks autonomously, allowing human workers to focus on higher-value activities. Adaptive AI systems also maintain consistent performance in dynamic environments, enabling faster changeovers and higher throughput for small-batch or customized production. In short, AI transforms production from rigid, step-by-step operations into intelligent, self-correcting workflows that deliver immediate, measurable gains in speed, quality, and efficiency. Traditional robots require precise programming. To what extent can your systems learn tasks, adapt to variation, or improve performance over time? Sven Parusel: Our AI-driven automation solutions leverage our own Robotics Foundation Models. They are specifically designed to integrate multimodal inputs such as camera images, tactile measurements, and human commands to break down complex tasks into actionable motion sequences and interactions in the physical world. Overall, generative AI models form the cognitive-control core of our Physical AI systems. They allow our robots not only to process data but to understand meaning and context and translate this understanding into adaptive physical behaviour. Through methods like reinforcement learning and imitation learning, where the AI gradually learns optimal strategies by interacting with its environment, these systems continuously improve. Combined with sensor fusion, multimodal representation, and generative learning, this creates a new form of embodied intelligence that increasingly blurs the line between the digital and physical worlds. How important is real-time, on-device AI processing in robotics compared to cloud-based intelligence, especially in high-speed manufacturing environments? Sven Parusel: In high-speed manufacturing, real-time movements are critical because even the slightest delays can accumulate and affect overall cycle time. Cloud-based intelligence introduces latency, bandwidth dependency, and potential connectivity risks that are unacceptable in such time-sensitive processes. That’s why we rely on onboard AI, which ensures ultra-low-latency decision making, deterministic response times, and uninterrupted operational continuity. While cloud-based intelligence is ideal for large-scale model training, it complements rather than replaces on-device processing. By combining onboard AI with cloud-based insights, we deliver an architecture that provides immediate, precise control on the shop floor while continuously improving performance over time. There is concern that intelligent robots may replace skilled labour. How will AI-driven robotics reshape the role of technicians and production engineers over the next five years? Sven Parusel: This concern is as old as technology itself. Yet history shows that new innovations tend to create opportunities rather than eliminate them. AI-driven robotics will transform the role of technicians and production engineers, shifting their focus f... ### AI Toolpath Optimisation 2026: How CAM Software Is Learning From Real Cutting Data Canonical URL: https://machinetoolnews.ai/ai-toolpath-optimisation-2026/ Published: 2026-03-05T10:54:06+00:00 Modified: 2026-03-05T10:54:09+00:00 Author: Jelena Radojcic Categories: AI in CNC, Germany, Software / CAM / IIoT, USA Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/ai-toolpath-optimisation-2026-hero-1600x900-1.jpg Featured image alt: AI Toolpath Optimisation 2026 illustrated by a 5 axis CNC machining centre cutting a complex aluminium aerospace component AI Toolpath Optimisation 2026 is no longer about shaving seconds from roughing cycles. For experienced CAM programmers the real shift is how modern CAM systems are modelling cutting behaviour with far greater precision and using that intelligence to improve programming decisions. Traditional optimisation approaches relied on deterministic strategies. Tool engagement was estimated, feed rates were calculated from static parameters and post processing assumed ideal machine behaviour. In 2026 that model is evolving. Modern CAM systems are beginning to incorporate deeper engagement modelling, predictive load management and in early cases feedback from real production data. These changes are increasingly visible across modern CAM platforms, particularly when comparing the architectures discussed in our analysis of AI CAM Software 2026. This transition is redefining how programmers approach toolpath strategy. Modern CAM platforms are increasingly structured around AI Toolpath Optimisation 2026, where machining strategies adapt to predicted cutting conditions rather than relying only on static programming rules. Tool Engagement Modelling Is the Core of Modern CAM Optimisation The fundamental constraint in toolpath optimisation remains chip load stability. Experienced programmers understand the problem well. Inconsistent engagement leads to unstable loads, premature tool wear and unpredictable surface quality. Modern CAM engines increasingly address this through advanced engagement modelling. Rather than calculating feed rates only from geometry, newer toolpath algorithms consider: instantaneous cutter engagement angle radial chip thinning effects axial depth stability tool deflection potential stock boundary interaction This allows CAM software to maintain more consistent tool loading throughout complex toolpaths. High speed roughing strategies such as adaptive clearing already rely heavily on this modelling. The difference in AI Toolpath Optimisation 2026 is that the modelling depth continues to improve. Toolpaths are no longer optimised purely for path efficiency. They are increasingly optimised for cutting stability. Predictive Material Removal Modelling Another major development in AI toolpath optimisation is the improvement of material removal prediction. Earlier CAM generations relied heavily on geometric simulation. While visually accurate these simulations often failed to capture the true mechanical behaviour of cutting processes. Today several CAM platforms are integrating more sophisticated modelling approaches including: volumetric material removal prediction engagement based load calculation dynamic feed rate adjustment during path generation The result is toolpaths that maintain more stable cutting forces across varying geometries. This is particularly relevant in: aerospace components with variable wall thickness mould tools with complex surface transitions multi axis machining where tool orientation constantly changes Accurate modelling of cutting engagement is becoming the backbone of reliable toolpath optimisation. Feed Rate Stability and Adaptive Motion Another area evolving quickly is feed rate management. In conventional programming workflows feed rates are often conservative to protect tools and machines. This results in under utilised machine capacity. This is where AI Toolpath Optimisation 2026 becomes particularly valuable, allowing CAM systems to stabilise chip load and cutting forces across complex toolpaths. AI assisted optimisation approaches attempt to address this by adjusting feed rates dynamically according to predicted cutting conditions. Typical strategies now include: automatic feed rate smoothing across toolpath segments engagement based feed adjustments acceleration aware path smoothing machine specific kinematic constraints The goal is not maximum feed rate. The goal is stable cutting conditions across the entire toolpath. Maintaining stable cutting forces improves both tool life and surface quality. How CAM Vendors Are Implementing Toolpath Intelligence Different CAM vendors are approaching AI toolpath optimisation from different architectural directions. Some systems prioritise deterministic control and stable engagement modelling. Others emphasise ecosystem integration with machine controllers and digital twins. For example: Some platforms optimise toolpaths primarily through advanced geometric modelling. Others attempt to integrate simulation with machine behaviour models. This difference is visible when analysing the architectures behind modern CAM systems such as hyperMILL, NX CAM, Mastercam and Fusion. Advanced engagement modelling strategies can be seen in systems such as hyperMILL, where toolpath algorithms are designed to maintain stable cutter load during complex 5 axis machining operations. What is clear across vendors is that optimisation is shifting from purely geometric logic toward deeper cutting physics modelling. The Emerging Role of Real Machine Data Perhaps the most important development in AI Toolpath Optimisation 2026 is the early integration of real machine data. Historically CAM systems operated in isolation from production feedback. Programmers created toolpaths. Machines executed them. CAM rarely learned from the outcome. That gap is beginning to close. Emerging approaches attempt to incorporate production data such as: spindle load variation tool wear patterns surface deviation measurements machine vibration signals While still limited in commercial deployment these feedback loops represent the next stage of CAM intelligence. When CAM systems begin adjusting future toolpaths based on real machining outcomes optimisation will move beyond simulation toward adaptive manufacturing. MTN Analysis AI toolpath optimisation is approaching a structural transition. For decades optimisation improvements focused on geometry and path efficiency. Modern CAM systems are now incorporating deeper models of cutting physics and machine behaviour. The next phase will depend on closing the feedback loop between CAM programming and production telemetry. Several conditions must be met for this to become widespread: reliable machine data collection standardised telemetry interfaces integration between CAM and machine control systems data models capable of learning across multiple machining environments Some ecosystem driven platforms may be structurally positioned to implement these feedback loops earlier. However across the industry the shift toward adaptive feedback informed toolpath optimisation is still emerging. When it matures it will fundamentally change how machining strategies are generated. For further technical coverage and industry developments explore our AI in CNC section. The long term impact of AI Toolpath Optimisation 2026 will depend on how effectively CAM software can combine simulation models with real machine data. Key Takeaways AI Toolpath Optimisation 2026 is moving beyond geometry based strategies Engagement... ### AI CAM Software 2026: hyperMILL vs NX vs Mastercam vs Fusion – Who Truly Leads Intelligent Machining? Canonical URL: https://machinetoolnews.ai/ai-cam-software-2026-comparison/ Published: 2026-03-03T13:33:38+00:00 Modified: 2026-03-03T13:33:41+00:00 Author: Jelena Radojcic Categories: AI in CNC, Germany, Software / CAM / IIoT, USA Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/03/ai-cam-software-2026-hero-1600x900-1.jpg Featured image alt: High-precision CNC end mill positioned above a machined aluminium component inside a modern industrial environment AI CAM Software 2026 is no longer differentiated by feature recognition or dynamic motion strategies. Those are baseline capabilities. The separation in 2026 is architectural: How deeply tool engagement modelling influences path generation How tightly machine kinematics are embedded in simulation Whether CAM connects to production telemetry Whether intelligence scales across installations This comparison evaluates hyperMILL, NX CAM, Mastercam and Fusion strictly through those lenses. For a broader architectural breakdown of intelligent machining systems, see our articles here. The Only Comparison Model That Matters in 2026 For experienced CAM engineers, meaningful comparison requires five layers: Deterministic Toolpath Control Engagement & Material Removal Modelling Kinematic Fidelity Adaptive Optimisation Logic Production Feedback Integration Most platforms are strong in layers 1–2.Very few operate meaningfully in layer 5. hyperMILL: Deterministic 5-Axis Stability hyperMILL remains strongest in high-complexity 5-axis environments. Technical differentiation: Axis acceleration smoothing in simultaneous motion Stable recalculation for long tool overhang scenarios Residual stock detection improving rest machining Reliable collision avoidance when machine models are accurate For aerospace and mould sectors, deterministic stability and surface consistency matter more than experimental adaptivity. However, hyperMILL remains largely rule-driven. Production telemetry does not yet drive adaptive cross-installation recalculation at scale. Position in AI CAM Software 2026:Very strong deterministic optimisation. Limited closed-loop depth. NX CAM: Structural Ecosystem Advantage NX CAM’s differentiation is architectural. When deployed inside Siemens ecosystems: CAM aligns with controller behaviour Digital twin continuity reduces commissioning variability Machine kinematics modelling improves simulation fidelity Enterprise data continuity preserves machining intelligence NX is less about faster programming clicks and more about system-level production consistency. The trade-off remains implementation scale and complexity. Position in AI CAM Software 2026:Strongest ecosystem-level architecture. Emerging closed-loop capability. Mastercam: Production-Focused Efficiency Mastercam prioritises throughput and workflow speed. Technical strengths: Efficient dynamic motion roughing Reliable general-purpose stock modelling Strong template reuse across part families Mature post ecosystem It does not attempt deep digital twin orchestration. Instead, it reduces friction in day-to-day programming. Position in AI CAM Software 2026:High accessibility. Moderate adaptive depth. Limited telemetry integration. Fusion: Cloud-Driven Iteration Fusion’s advantage lies in rapid iteration and CAD–CAM continuity. Architectural strengths: Scalable cloud simulation Tight design-to-manufacturing flow API extensibility Continuous update velocity Closed-loop production feedback and controller-aligned digital twin depth remain developing. Position in AI CAM Software 2026:Agile innovation platform. Ecosystem depth still maturing. Comparative Structural Positioning PlatformDeterministic ControlKinematic FidelityAdaptive LogicClosed-Loop PotentialEcosystem IntegrationhyperMILLVery StrongStrongModerateLimitedModerateNX CAMStrongVery StrongStrongEmergingVery StrongMastercamStrongModerateModerateLimitedModerateFusionModerateModerateEmergingLimitedCloud-Centric MTN Analysis The competitive battleground in AI CAM Software 2026 is shifting from automation to adaptive architecture. Programming automation is mature.Simulation fidelity is advanced.Digital twin integration is expanding. What remains largely unrealised across the industry is true production feedback integration. The next phase of competitive separation will be defined by: Spindle load data influencing future toolpath recalculation Tool wear modelling feeding geometry offsets Surface deviation correction through adaptive refinement Cross-machine learning across distributed fleets No vendor has fully operationalised large-scale adaptive closed-loop CAM. NX CAM is structurally closest due to ecosystem integration.hyperMILL leads in deterministic precision.Mastercam leads in accessibility.Fusion leads in iteration velocity. The first vendor to bridge deterministic toolpath control with real production telemetry at scale will define the next generation of intelligent machining. Key Takeaways AI CAM Software 2026 differentiation is architectural, not feature-based NX CAM leads in ecosystem integration depth hyperMILL dominates deterministic 5-axis stability Mastercam excels in production workflow efficiency Fusion accelerates cloud-based innovation Closed-loop adaptive machining remains emerging FAQ – AI CAM Software 2026 What distinguishes AI CAM Software 2026 from previous CAM automation? The difference lies in architectural integration. AI CAM Software 2026 increasingly connects simulation, machine kinematics and enterprise data layers rather than operating as isolated toolpath automation. Which platform currently offers the deepest ecosystem integration? NX CAM demonstrates the strongest structural integration when deployed within Siemens digital manufacturing environments. Is fully adaptive closed-loop CAM commercially mature in 2026? Not at scale. While simulation and predictive modelling are advanced, real production telemetry integration into autonomous recalculation remains limited. Does deterministic optimisation still matter? Yes. In high-precision aerospace and mould applications, deterministic 5-axis stability often outweighs experimental adaptive logic. ### AI Predictive Maintenance France 2026: Inside the Data Systems Protecting High-Value CNC Assets Canonical URL: https://machinetoolnews.ai/ai-predictive-maintenance-france-2026/ Published: 2026-02-26T11:10:18+00:00 Modified: 2026-02-26T11:10:21+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, France, News Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/ChatGPT-Image-Feb-26-2026-12_07_33-PM.png Featured image alt: AI Predictive Maintenance France 2026 monitoring a high-value CNC machining centre with real-time vibration and thermal data analytics in a bright industrial environment AI Predictive Maintenance France 2026: Inside the Data Systems Protecting High-Value CNC Assets AI Predictive Maintenance France 2026 is rapidly becoming core infrastructure inside French CNC manufacturing environments. In January and February 2026, industry reporting confirmed predictive maintenance as one of the most commercially scalable AI applications in industrial production, particularly across aerospace, automotive, and precision machining sectors. France’s AI in manufacturing and predictive maintenance market is valued at approximately USD 1.5 billion, according to analysis released in early 2026 by Ken Research. That valuation reflects accelerating deployment of intelligent monitoring systems designed to reduce unplanned downtime and protect high-value equipment. For manufacturers operating advanced 5-axis machining centres, predictive intelligence is no longer a future concept. It is becoming an operational safeguard. Why High-Value CNC Assets Demand Predictive Intelligence A single unexpected spindle failure can halt production for days. For aerospace suppliers working under strict contractual deadlines, that disruption directly affects revenue and customer confidence. Traditional preventive maintenance relies on fixed servicing intervals. This approach ignores real operating conditions. Two identical machines cutting different materials experience different stress loads, yet scheduled maintenance treats them the same. Modern predictive systems replace time-based servicing with condition-based analysis using: Continuous vibration tracking Thermal pattern monitoring Motor current behaviour modelling Acoustic anomaly detection Lubrication flow analytics By identifying deviation patterns early, intelligent monitoring platforms allow intervention before catastrophic failure occurs. The Data Architecture Behind AI Predictive Maintenance France 2026 AI Predictive Maintenance France 2026 is powered by layered industrial data systems rather than isolated sensors. Multi-Sensor Monitoring High-frequency vibration signatures are captured from spindle bearings, axis assemblies, and rotating components. These signals establish behavioural baselines unique to each machine tool. Edge Analytics and Local Processing Many French manufacturers favour edge-based analytics to maintain control over industrial data. Technology reviews published in January 2026 by IIoT World highlight predictive maintenance platforms capable of running machine learning models locally before synchronising results with enterprise dashboards. This architecture reduces latency and strengthens cybersecurity resilience. Remaining Useful Life Forecasting Instead of reacting to alarm thresholds, machine learning models estimate probability curves for degradation. Spindle wear, ball screw misalignment, and motor overheating trends are forecast based on pattern drift rather than static limits. This predictive approach transforms maintenance planning from reactive repair to strategic scheduling. Connecting Predictive Stability to Automation Strategy Predictive reliability supports broader automation initiatives. When CNC uptime improves, manufacturers gain confidence to expand: AI-guided CAM automation Semi-autonomous machining cells Closed-loop process control Reliable machines are the foundation of advanced automation. Without predictive stability, next-generation machining strategies cannot scale effectively. What French Manufacturers Are Monitoring in Early 2026 Across January and February 2026 industrial reporting, CNC manufacturers are prioritising monitoring of: Spindle harmonic distortion Axis torque irregularities Tool holder imbalance Coolant system performance Motor temperature anomalies These factors directly influence machining precision, surface quality, and repeatability. For comparison with wider European developments, see our coverage of AI manufacturing developments in the Netherlands earlier this month, where similar predictive strategies are being deployed in high-value production environments. MTN Analysis: A Structural Shift in CNC Operations The expansion of AI Predictive Maintenance France 2026 represents a structural change in operational philosophy. Rather than treating maintenance as a cost centre, French manufacturers are integrating predictive analytics into their production strategy. Stability becomes a competitive advantage. Autonomous machining, adaptive toolpath optimisation, and AI-driven inspection all depend on reliable mechanical infrastructure. Predictive systems create that reliability layer. Implementation Roadmap for CNC Workshops Manufacturers evaluating predictive monitoring should follow a structured rollout: Identify the machine with the highest downtime cost Install vibration and thermal sensors Establish 60 to 90 day behavioural baselines Measure downtime reduction and maintenance savings Expand to additional critical assets Incremental deployment reduces risk while demonstrating measurable return. Why AI Predictive Maintenance France 2026 Is Becoming Standard Practice AI Predictive Maintenance France 2026 is converting raw machine data into operational foresight. By forecasting degradation rather than reacting to breakdown, French manufacturers are strengthening production resilience. Benefits include: Reduced unplanned downtime Extended spindle lifespan Improved scheduling accuracy Lower emergency repair costs Greater operational confidence As margin pressure increases across European manufacturing, predictive intelligence is shifting from innovation initiative to essential infrastructure. Key Takeaways Predictive maintenance remains one of the fastest-scaling AI applications in French manufacturing Layered sensor and edge analytics systems protect high-value CNC assets Early 2026 reporting confirms strong adoption momentum Reliable machines form the foundation of broader automation strategy FAQ What makes AI Predictive Maintenance France 2026 different from preventive maintenance? AI Predictive Maintenance France 2026 uses machine learning models to analyse real-time machine condition and forecast failure probability instead of relying on fixed servicing schedules. Is predictive monitoring suitable for mid-sized workshops? Yes. Scalable sensor packages and local analytics platforms allow gradual implementation without full smart factory transformation. How quickly can measurable improvements be seen? Industry reporting in early 2026 suggests downtime reduction can be observed within several months when systems are properly calibrated and integrated. ### AI Manufacturing in the Netherlands: 7 Advances Transforming Factories in 2026 Canonical URL: https://machinetoolnews.ai/ai-manufacturing-netherlands-february-2026/ Published: 2026-02-23T10:56:14+00:00 Modified: 2026-06-09T07:54:58+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, Netherlands, News, Robotics, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/ChatGPT-Image-Feb-23-2026-11_52_18-AM.png Featured image alt: AI Manufacturing Netherlands February 2026 advanced CNC machining cell with robotic machine tending in a bright smart factory environment AI Manufacturing in the Netherlands Is Becoming a Serious Competitive Advantage AI manufacturing in the Netherlands is becoming one of Europe’s most important industrial trends in 2026. Dutch manufacturers are using automation, data, robotics and industrial AI to improve productivity, reduce pressure on skilled labour and build more resilient factories. For machine shops, sheet metal companies and advanced manufacturers, the Netherlands is becoming a useful signal of where European factory technology is heading next. Below are seven concrete February 2026 developments shaping CNC machining, robotic automation, predictive systems, and materials innovation. Key Takeaways AI manufacturing in the Netherlands is accelerating as Dutch factories invest in automation, robotics and industrial AI. The Netherlands is becoming a useful European benchmark for data-driven production and smart factory adoption. Manufacturers should watch Dutch AI adoption because it shows how smaller advanced economies can compete through technology. Brainport’s Industrial Data Infrastructure to Unlock AI Scaling On February 2, Brainport Eindhoven published detailed updates on regional grid congestion and its impact on manufacturing expansion. More than a thousand companies remain on waiting lists for electricity connections. Rather than delay innovation, Brainport is coordinating structured data exchange between manufacturers and grid operator Enexis using the Data Safe House framework. Over 200 companies across 21 municipalities are participating in structured annual data updates to improve capacity forecasting and sustainability planning. AI Manufacturing Netherlands February 2026 depends on this foundation. Industrial AI systems scale only when operational data flows securely across stakeholders. The Netherlands is building that backbone deliberately. Energy Hubs Supporting AI-Ready Production Planning Energy Hubs within Brainport allow companies to stagger high-consumption processes such as cleanroom startups. For advanced CNC operations, predictable capacity planning supports AI-driven scheduling, predictive maintenance modelling, and intelligent production balancing. AI Manufacturing Netherlands February 2026 therefore includes ecosystem-level optimisation, not only machine-level intelligence. TNO’s Remanufacturing Lab as a Test-and-Experimentation Facility TNO’s Remanufacturing Lab at Brainport Industries Campus continues operating as a European AI Test and Experimentation Facility under the AI-MATTERS programme. The facility supports: AI-based decision support systems Human-robot interaction validation Autonomous parts handling AI-driven quality inspection Manufacturers can test robotics and AI systems in industrial conditions before committing to full deployment. For SMEs, this reduces adoption risk and shortens integration timelines. AI Manufacturing Netherlands February 2026 shows structured validation pathways rather than experimental automation. Vision-Language-Action Robotics for Machine Tending Mid-February updates from the Operator of the Future programme detailed Vision-Language-Action AI applied to robotic machine tending. The validation involved: Five part geometries Five matching machine cavities Simulation training prior to physical deployment This architecture allows robots to interpret part orientation and contextual instructions rather than follow fixed motion routines. For CNC environments with frequent changeovers, AI Manufacturing Netherlands February 2026 now includes adaptive robotic tending that reduces manual programming overhead. SEAMIIC Autonomous Handling and Quality Control Within TNO’s experimentation ecosystem, the SEAMIIC module focuses on autonomous parts handling combined with quality verification. This supports: Reduced manual inspection workload Integrated robotic quality control Digital traceability across production steps AI Manufacturing Netherlands February 2026 demonstrates convergence between robotics, inspection, and compliance. TU Eindhoven’s €1.5 Million AI Materials Initiative Eindhoven University of Technology secured €1.5 million through Horizon Europe’s SimuLingua project to develop multimodal AI models for materials discovery. The objective is a scientific foundation model capable of linking natural language queries with physics simulations, image data, and experimental results. Although upstream from machining, this research influences material performance characteristics, cutting behaviour, and process optimisation strategies. AI Manufacturing Netherlands February 2026 therefore extends into long-term capability building. SME Digitalisation Through Smart Industry The Smart Industry initiative continues coordinated digital transformation across Dutch SMEs. The programme promotes: Digital twins Predictive maintenance frameworks Robotics integration AI-enabled production monitoring This ensures AI Manufacturing Netherlands February 2026 is not limited to flagship technology leaders but diffused across the broader supply chain. Why AI Manufacturing Netherlands February 2026 Matters for European Machine Tool Buyers The Dutch model in February highlights three strategic realities: Infrastructure readiness is treated as part of AI strategy Robotics validation environments reduce deployment risk Adaptive automation is moving from lab concept to industrial validation For UK and German manufacturers, the Netherlands provides a near-term reference model for integrated intelligent production cells. MTN Analysis AI Manufacturing Netherlands February 2026 reflects coordinated ecosystem development rather than isolated machine upgrades. The Netherlands is aligning: Regional data governance Physical robotics testbeds Adaptive machine tending Advanced materials research This layered strategy reduces friction between pilot and production. Manufacturers evaluating AI investments in 2026 should note that competitive advantage may increasingly depend on ecosystem alignment rather than standalone machine capability. FAQ What is the most practical AI trend for CNC environments in the Netherlands this month? Vision-Language-Action robotic machine tending validated through simulation and industrial deployment. Where can manufacturers test AI robotics in the Netherlands? At TNO’s Remanufacturing Lab within Brainport Industries Campus under the AI-MATTERS programme. Is AI adoption limited to large Dutch firms? No. Smart Industry programmes are structured to support SME integration across supply chains. How does this influence European machine tool purchasing decisions? It raises expectations around adaptive automation, predictive capability, and secure data integration. ### BIEMH 2026 AI Show Preview: The Exhibitors You Cannot Afford to Miss Canonical URL: https://machinetoolnews.ai/biemh-2026-ai-show-preview/ Published: 2026-02-20T11:01:33+00:00 Modified: 2026-02-20T17:58:25+00:00 Author: Publisher Categories: Events, Hero - Home, News, Spain Tags: Editors Pick, Featured, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/BIEMH-2026_page-0001.jpg Featured image alt: BIEMH 2026 AI Show Preview Bilbao, Spain BIEMH 2026 AI Show Preview BIEMH 2026 AI Show Preview starts with a clear reality: this year’s exhibition in Bilbao is not only about machines. Artificial intelligence, automation, digitalisation and smart manufacturing systems are becoming one of the strongest themes across the entire show floor, during the BIEMH show. Taking place from 2–6 March 2026 at Bilbao Exhibition Centre, BIEMH remains one of Europe’s most important advanced manufacturing events, bringing together machine builders, software developers, automation specialists and technology providers shaping the future of industrial production. For visitors focused on AI in machining, the challenge is simple. With hundreds of exhibitors across multiple halls, identifying where AI is being demonstrated in a meaningful, practical way can be difficult. This guide highlights the exhibitors showcasing AI-driven software, robotics, machine vision, digital intelligence and smart automation so you can plan your visit with purpose. Why BIEMH 2026 Is Different This year, automation, robotics and digitalisation occupy a significant part of the exhibition footprint, signalling a broader industry shift toward intelligent manufacturing environments. AI at BIEMH is showing up in several clear layers: AI-powered automation and robotics AI-driven inspection and machine vision Intelligent manufacturing software Data-driven optimisation and digital machining workflows Rather than isolated demos, many exhibitors are presenting AI as part of connected production environments. AI Robotics & Intelligent Automation Exhibitors ABB ABB continues to expand its AI-powered robotics offerings, including smarter programming environments and optimisation tools designed to reduce setup time and improve production efficiency. Expect strong demonstrations around connected robot workflows and intelligent automation. Siemens Siemens is expected to highlight industrial AI integration across automation, control systems and digital manufacturing platforms. Their presence reflects the growing importance of data-driven production environments. Beckhoff Automation Beckhoff focuses on intelligent control architecture and data-centric automation. Visitors can expect solutions designed to support adaptive and highly connected manufacturing cells. KUKA KUKA brings advanced robotics integration, with increasing emphasis on flexible, intelligent production systems that allow faster adaptation to changing manufacturing requirements. Yaskawa Yaskawa continues to push smart robotics and motion control systems aimed at improving efficiency in manufacturing environments where precision and repeatability remain critical. Zimmer Group Known for automation components used in smart production environments, Zimmer Group supports the broader move toward AI-enabled manufacturing cells. AI Machine Vision & Inspection Technology Machine vision remains one of the fastest-growing AI segments in manufacturing, and BIEMH 2026 shows a strong presence in this area. MVTec Software MVTec is expected to showcase AI-powered machine vision solutions using deep learning for inspection, quality control and automation workflows. This area is especially relevant for manufacturers exploring real-time process intelligence. Sariki Sariki will demonstrate advanced metrology, vision and inspection technologies aligned with automated quality workflows and digital inspection environments. Baumer Baumer’s sensor and vision solutions support intelligent inspection environments where data is increasingly used to drive automation decisions. AI Software & Industrial Intelligence Platforms AI at BIEMH is not only visible through robotics or vision systems. Software-driven intelligence is becoming a key growth area. Open Mind Technologies Known for hyperMILL developments, Open Mind continues to push intelligent CAM workflows, automation and connected manufacturing strategies that align closely with AI-driven programming trends. Lantek Lantek’s digital manufacturing software focuses on intelligent planning and optimisation for production environments, where data and automation play a growing role. Sisteplant Smart manufacturing optimisation and intelligent production systems aimed at improving efficiency and reducing operational waste. Semantic Systems Industrial digital intelligence solutions supporting connected manufacturing environments. Applied AI & Advanced Manufacturing Demonstrators IDEKO IDEKO is expected to showcase advanced manufacturing technologies including robotics, vision-based systems and digitally driven production strategies, particularly within aerospace manufacturing environments. IBARMIA Live automated machining demonstrations will give visitors insight into how automation and intelligent workflows are being applied directly on the shop floor. Other Exhibitors Worth Visiting for AI Conversations Several major exhibitors at BIEMH are active in AI, automation or digital manufacturing strategy even if AI is not the sole focus of their stand: FANUC Fagor Automation Renishaw TRUMPF Schunk Carl Zeiss Dassault Systèmes Danobat Soraluce These companies remain important stops for visitors exploring how AI is being integrated into machining, measurement and process optimisation. What This Means for Visitors The biggest takeaway from BIEMH 2026 is clear. AI is no longer appearing as a future concept. Instead, exhibitors are presenting: AI assisting operators intelligent optimisation tools smarter robotics integration adaptive inspection workflows digital manufacturing intelligence layers Visitors should plan their route around these categories rather than specific brands alone. MachineToolNews.ai Analysis The evolution happening on the BIEMH show floor reflects a wider market shift. We are now seeing three major AI layers emerging in manufacturing: AI generating or assisting programming AI verifying and optimising processes AI supporting decisions through data and intelligence systems BIEMH 2026 demonstrates how these layers are moving from theory into real production environments. For manufacturers, the focus has clearly moved toward practical outcomes: improved efficiency better quality control faster decision making reduced risk on the shop floor Key Takeaways BIEMH 2026 places strong emphasis on AI, automation and digitalisation Robotics, vision and intelligent software dominate the AI conversation Hall areas dedicated to automation highlight the industry’s shift toward smart manufacturing Visitors should plan around AI categories rather than only machine builders FAQ: BIEMH 2026 AI Show Preview What is BIEMH 2026 and why is it important for AI in manufacturing? BIEMH 2026 is one of Europe’s leading advanced manufacturing exhibitions, held at the Bilbao Exhibition Centre. The event brings together machine builders, automation specialists, software developers and industrial technology providers. In 2026, AI is expected to be a major theme, with exhibitors demonstrating intelligent aut... ### When metal stops being the bottleneck Canonical URL: https://machinetoolnews.ai/ai-cnc-lead-time-reduction-2026/ Published: 2026-02-19T10:54:59+00:00 Modified: 2026-02-19T10:55:04+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, News, Software, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/697ce4f9eeb4769065831f7f_CloudNC-Tom-Joy-IMG_2367-p-2000.jpg Featured image alt: AI CNC Lead Time Reduction 2026 concept showing automated machining and faster manufacturing workflows inspired by CloudNC insights AI CNC Lead Time Reduction 2026 is becoming one of the biggest conversations in modern manufacturing as automation begins collapsing traditional machining delays. The article below, originally published by CloudNC, looks at how lead times, tooling delays and programming bottlenecks influence the entire manufacturing ecosystem, from design decisions to delivery schedules. We are sharing it because it offers a thought-provoking perspective for machinists, CAM programmers and engineering teams looking to understand how faster workflows could reshape metalworking in the years ahead. The modern world doesn’t run on ideas; it runs on lead times. Lead time is the quiet tax on every physical thing – paid in inventory, in risk, in “we’ll ship next quarter,” in the million small compromises that turn great hardware into something mediocre. Today, even “simple” metal parts are slow in all the ways that matter. Not because a CNC spindle can’t cut quickly, but because the system around it is full of waits: quoting, CAM programming, scheduling, tool availability, inspection, rework loops, and the relay race of outside processes (heat treatments, anodizing, plating, grinding). In mainstream precision machining, typical lead times are often measured in weeks – a commonly cited expectation for reputable CNC suppliers is 4-6 weeks. And the “secondary ops” are a huge hidden anchor: heat treatment alone can add 5-10 days, and stacked finishing steps can easily turn into multiple weeks of calendar time. Zoom out and it gets harsher. In aerospace-grade metals, the bottleneck can start upstream at raw material: titanium lead times around nine months and certain high-demand alloys quoted at 70-80 weeks are real-world examples of how “metal time” can dominate a program plan. Tooling itself contributes: traditional tooling lead times cited at ~20 weeks (and sometimes far longer to first part) are part of why entire categories of production move slowly. So what happens to the world if CloudNC’s software achieves what we want it to and ultimately removes the friction in the global machining sector – even, perhaps, one day making it ‘single click’? Well: you collapse the time constant of the physical economy. And once you do that, behaviors change – non-linearly. ‍Where metal sits in the lead-time stack (and why it shapes what gets made) Metal components are the skeleton of most “real” products: housings, brackets, shafts, frames, mounts, gears, tooling, fixtures, thermal paths, structural members. Even when a product is “electronics,” its manufacturability is often gated by metalwork: heat sinks, chassis, connector retention, EMI shielding, precision alignment. And metal has three nasty properties as a bottleneck: It sits on the critical path. If a key bracket or casting is late, the assembly can’t proceed. No bracket, no build. It’s variance-heavy. Quality escapes and rejects don’t just cost money; they blow schedules. Rework sends parts back through the maze and turns a plan into roulette. It’s coordination-heavy. The moment you need outside processes (treat/coat/inspect/certify), calendar time explodes. This is why long metal lead times don’t merely delay shipments – they shape the design space. They force teams into: Over-forecasting and bulk buys (tying up capital, creating obsolete inventory). Design freeze culture (“we can’t change it now; the parts are already on order”). BOM choices based on availability, not performance (“use the standard extrusion; it’s in stock”). Offshoring as default (because the pain of coordination is already so large that adding oceans feels “worth it,” especially if unit price is lower). Lead time becomes a filter on reality: it decides which startups survive, which products get attempted, which features get cut, which repairs get done versus thrown away. The Institute for Supply Management even treats supplier delivery time as a core benchmark because delivery variability ripples into inventory decisions and customer satisfaction. That’s how foundational this is. Going 10x Now run the thought experiment: What if we could go 10× faster, with near-zero rejects (everything made perfectly), and be local by default? Note – “everything made perfectly” is not physically realistic – manufacturing lives in tolerances, entropy, tool wear. But if we mean that the quality becomes predictable enough that rework stops dominating schedules, then the effect is essentially the same: variance in the system collapses. And when that happens, buffers collapse: less safety stock, less expediting, less managerial thrash, less “just in case.” So, if lead time compresses by ~10×, three big things happen immediately: Inventory stops being the world’s insurance policy. Companies hold inventory because they fear time. Shrink time, and you can run closer to true demand. That frees working capital, reduces obsolescence, and makes whole categories of “forecasting theater” less relevant. Hardware iteration stops being a quarterly event and becomes a weekly habit. A product team today might get 2–4 serious physical design turns a year if metal parts are gating. If turns become weekly, you don’t just go “10× faster.” You change the evolutionary math: more experiments, more learning, more survival of the best designs. That’s how software outpaces hardware: not because coders are smarter, but because feedback loops are short. Geography changes. If it’s cheap enough to make locally, the rationale for long, fragile supply chains weakens. You don’t eliminate global trade – but you shift it away from “I must offshore to survive” toward “I source globally when it’s strategically optimal.” “All manufacturing industries”: who gets hit, and how So what does this mean for manufacturing – and beyond? After all, even “non-metal” industries get pulled into this because every factory is made of machines, and machines are made of metal. Faster metal parts means faster maintenance, less downtime, quicker line changes, cheaper tooling, and more flexible production everywhere. That said, the disruption is most dramatic in metal-heavy and precision-critical sectors: Fabricated Metal + Machinery: becomes the “AWS layer” of the physical economy – capacity on demand, shorter queues, and a shift from artisanal programming to automated workflows. Margins compress for commodity work; value migrates to speed, reliability, and integrated finishing/inspection. Transportation: automotive, EVs, rail – faster fixture/tooling cycles and faster engineering change orders. More trims, more variants, more customization without penalty. Aftermarket and spares become a service game, not a warehousing game. Aerospace/Defense: where long lead times and certification dominate, compressing metal-part lead time changes readiness, MRO, and upgrade cadence – if traceability and documentation can be made just as “single-click” as the cutting. Raw materials are still a constraint here (titanium/alloys). Energy + Industrial Infrastructure... ### OPEN MIND to showcase hyperMILL® INTELLIGENCE AI at MACH 2026 Canonical URL: https://machinetoolnews.ai/hypermill-intelligence-ai-cam-2026/ Published: 2026-02-18T11:31:03+00:00 Modified: 2026-02-18T12:31:17+00:00 Author: Jelena Radojcic Categories: AI in CNC, Events, Software Tags: Editors Pick, Featured, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/MDC_Tablet_overview_1600x900.jpg Featured image alt: hyperMILL Intelligence AI CAM automation interface From the 20th to the 24th of April, OPEN MIND Technologies will once again be exhibiting at the MACH exhibition at the Birmingham NEC. The CAD/CAM specialist will present the newest advancements from the latest version of its hyperMILL® CAD/CAM suite on Stand 230 in Hall 17. The focus will be on solutions for digital process chains, connected manufacturing, automation, turning, and hyperMILL® VIRTUAL Machining. OPEN MIND will provide live demonstrations and insights into various practical applications of hyperMILL®. The collaboration between a CAM system and IT solutions, such as MES, PLM, or a tool management system, is becoming increasingly common in modern manufacturing. On the OPEN MIND stand at MACH, visitors will see how hyperMILL® integrates with Hummingbird MES to make production processes more transparent, efficient, and flexible. All production-related data is accessible anywhere in the company at any time. This supports consistent process organisation and distinguishes Hummingbird from many other MES solutions. Every change, such as the completion of a component, is immediately visible to all users without needing to click a refresh button. An integral component of Hummingbird MES is the Kanban board for planning and workshop oversight. While the planning board provides comprehensive information about processes without assigned tasks, the team board acts as a central communication tool for the entire team. This is where all information on pending orders is consolidated. Every team member has access and can view the process status. MES and CAM The Hummingbird MES supports all CAM systems and production machines and integrates seamlessly with hyperMILL®. The digitalisation through Hummingbird also includes a digital tool management suite in its portfolio. With the ‘Tool Logistics’ module in Hummingbird MES, users can achieve highly efficient digital tool management effortlessly. A lack of transparency in tool management leads to delays caused by searching for and allocating provisions, as well as costs from inefficient warehousing with excess tool stocks or premature tool changes. In the worst cases, manufacturers may experience significant downtime due to missing or incorrect data. This is where modernisation becomes crucial. OPEN MIND experts can implement digital processes based on CAM and MES to deliver highly efficient digital tool management through five simple steps. The first step is to analyse existing processes and systems. This involves reviewing available tools and storage data, or how tool storage and setup processes are organised. Potential improvements are identified, and a detailed roadmap for system implementation is developed. Standardising data in the hyperMILL® or other CAM system tool database is a necessary prerequisite for error-free, efficient processes. hyperMILL® INTELLIGENCE – Breaking boundaries with rules-based & AI Also making its MACH debut will be the new hyperMILL® INTELLIGENCE. The clear mission of hyperMILL® INTELLIGENCE is to solve real manufacturing challenges with five core areas that range from rule-based automation to tool and machine optimisation, and also the AI Assistant with hyperMILL® CHATBOT and hyperMILL® DATA Center. This philosophy reliably addresses key issues such as routine tasks, tool errors, processing risks, and knowledge loss. Combining rule-based strategies with AI, this technology combines automation, optimisation, and AI in a meaningful way to deliver measurable results on the shopfloor that gives the end user the right technology for every operation. For example, the rule-based automation can streamline repetitive CAD/CAM tasks by utilising pre-defined rules, whilst the tool-based automation enables customers to leverage the power of digital twins to ensure the right tools, strategies and parameters are integrated into both CAM and MES. This is enhanced by machine-based optimisation and an AI assistant to personalise real-time guidance for seamless programming workflows. With the latest 2026 version of hyperMILL® set for release ahead of MACH, show visitors will witness the European premiere of this new version. The new version 2026 promises exciting innovations, and the OPEN MIND UK team will be delighted to discuss these with visitors to MACH. About OPEN MIND Technologies AG OPEN MIND Technologies AG is one of the world’s leading developers of powerful CAD/CAM solutions for machine and controller-independent programming.OPEN MIND develops optimized CAD/CAM solutions that include innovative and unique features that can deliver significantly higher performance in both programming and machining. hyperMILL® is a completely modular CAD/CAM solution that provides state-of-the-art CAM technologies on its own CAD platform: from 2.5D, 3D and 5-axis machining as well as turning strategies and solutions for additive manufacturing, HSC and HPC machining. Whether automation, simulation or virtual machine – trendsetting technologies expand the product range and enable continuous digital process chains. Special applications, seamless interaction with all popular CAD solutions and exceptional customer service rounds out the company’s products and capabilities. According to the “NC Market Analysis Report 2025” compiled by CIMdata, hyperMILL® is a leading, worldwide CAD/CAM solution. Innovative CAD/CAM technologies fulfill the highest demands in aerospace, automotive, tool and mold manufacturing, production machining, medical, job shops, energy and semiconductor industries. OPEN MIND’s majority stake in manufacturing execution system (MES) developer Hummingbird expands the CAD/CAM manufacturer’s product portfolio and enhances the range of connected digitalized manufacturing technologies. OPEN MIND is a Mensch und Maschine company and has subsidiaries and qualified sales partners on all continents. You can find more information at www.openmind-tech.com. ### AI CNC Predictive Maintenance 2026: IPercept CEO on Machine Intelligence and Downtime Reduction Canonical URL: https://machinetoolnews.ai/ai-cnc-predictive-maintenance-2026/ Published: 2026-02-17T10:39:12+00:00 Modified: 2026-02-17T10:39:16+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, News, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/BxR_25_09_23_01912.jpg Featured image alt: Karoly Szipka, CEO of IPercept, discussing AI CNC Predictive Maintenance 2026 and machine intelligence for downtime reduction AI CNC Predictive Maintenance 2026 is becoming a critical investment area for manufacturers trying to reduce downtime, extend machine life, and protect part quality. For a broader view of how software is transforming machining, see our coverage of AI in CNC machining. We spoke with Karoly Szipka, CEO & Co-Founder of IPercept Technology AB, about the real problems behind CNC downtime, what measurable gains shops are seeing, and how AI-driven machine intelligence could redefine maintenance over the next five years. MTN: What problem in CNC manufacturing did you originally set out to solve with IPercept, and why is it so critical for machine shops today? IPercept didn’t start with a business idea. It started with a real industrial need, coming directly from some of Sweden’s largest manufacturing enterprises. This is one of the great strengths of the Swedish innovation ecosystem: large enterprises, in many cases competing directly with each other, choose to collaborate through Swedish research institutions to solve shared challenges. In our case, leading Swedish manufacturers had been benchmarking every available solution to lift the efficiency and reliability of their CNC machines to the next level. They couldn’t find anything that delivered the depth of insight they needed, so they turned to academia. That’s where I worked as a researcher at KTH Royal Institute of Technology, specialising in precision engineering and metrology. Every time we worked with manufacturers, regardless of industry, we saw the same pattern: incredibly expensive, high-precision CNC machines running without anyone truly knowing what was happening inside them. Maintenance teams relied on fixed schedules, gut feeling, or waited until something broke. And when it broke, the costs were staggering. The scale of this problem is hard to overstate. Manufacturing globally loses roughly one trillion euros every year to machine downtime, inefficiencies, and quality failures. And the tools available to address this? Most are based on principles dating back to the 1970s – simple vibration sensors that work for basic rotating components but completely miss the complex, interconnected degradation patterns inside a modern CNC machine. That means most manufacturers still perform just two thorough inspections per year per machine, and the rest of the time they’re essentially flying blind. I watched brilliant maintenance engineers waste days trying to diagnose problems that our early prototypes could pinpoint in minutes. Technicians spent more time debating what might be wrong than fixing things. That’s not a technology problem, that’s a knowledge and maybe most importantly a data gap. And that’s what we set out to close. Today, with skilled workers retiring faster than they can be replaced and supply chains still under pressure, this knowledge gap is more critical than ever. If you can’t see what’s happening inside your machines, you can’t plan maintenance intelligently, you can’t prevent quality deviations, and you certainly can’t compete at the level the market demands. MTN: For a typical machining company, what measurable improvements can they expect after implementing the IPercept system? I always prefer to let the numbers speak, because the improvements are significant enough that they don’t need exaggeration. We serve essentially all industrial verticals where CNC machines are used. From aerospace and automotive to defence, energy, mining, and general equipment manufacturing. Across our customer base, we consistently see a 30% improvement in overall equipment effectiveness, a 50% reduction in unplanned downtime, and a 40% reduction in scheduled maintenance that turns out to have been unnecessary in the first place. On top of that, our customers report roughly 10% savings on maintenance parts and tools, because they’re replacing components based on actual condition rather than calendar-based assumptions. But what really brings this to life are the individual stories. One customer had a large metal-working machine where we were introduced to monitor the linear axes. On the very first test cycles, our system identified a significant degradation on a ball screw nut. As the calibration continued, the degradation further developed, and we could predict that the risk of quality issues or failure was imminent. Knowing this, the customer could order specific replacement parts and schedule a maintenance action – saving significant costs in unplanned downtime. Another case from equipment manufacturing: a sudden localized defect emerged on the drive-side bearing of a linear axis after a collision event. The defect was severe and could have propagated to other components in the feed drive. Our system immediately alerted the maintenance team, who inspected and replaced the bearing before further damage occurred. Later part inspection confirmed that the machine had started producing scrap right after the collision. Estimated impact: 150,000 to 180,000 euros saved from preventing both scrap production and fault escalation. In the automotive industry, we helped a customer with several mill-turn machines and with three spindles each. They lacked objective data to prioritize which spindle required replacement first. IPercept benchmarking identified the one spindle showing early degradation, and maintenance was scheduled for it first, avoiding unnecessary replacements on the other five. The result: 315 hours of avoided downtime and supported warranty claims, with an estimated financial effect of approximately 94,500 euros. Customers typically see their first actionable insight within seven days of installation, and many report a 10x return on investment within weeks, not months. MTN: Many shops struggle with mixed fleets and older machines. How does IPercept work across different brands, controllers, and IT environments? This is actually where we believe IPercept changes the game most fundamentally, because the mixed-fleet reality is the norm, not the exception. Most machine shops I visit have a floor that looks like a United Nations of CNC machines: a Mazak from 2005 next to a DMG Mori from last year, an Okuma mill-turn that’s been running since the early 2000s, maybe a Burkhardt+Weber or Unisign for heavy operations. Different controllers, different ages, different levels of digital readiness. And every traditional monitoring solution I’ve seen requires access to the machine controller, IT integration, network configuration. It becomes a six-month project before you get your first data point. We designed IPercept from day one to be completely independent of all of that. Our Smart Device mounts directly onto the machine’s kinematic chain. It uses aerospace-grade motion sensors to capture the machine’s mechanical behaviour with extraordinary precision. It requires no connection to the machine controller, no IT integration, and no changes to your network. All you need is a power outlet after a simple 1–3 hours installation pr... ### IDEKO turns BIEMH into a showcase oftechnologies to drive advanced aerospace manufacturing Canonical URL: https://machinetoolnews.ai/ai-aerospace-machining-at-biemh-2026/ Published: 2026-02-16T10:39:33+00:00 Modified: 2026-02-16T10:39:35+00:00 Author: Jelena Radojcic Categories: AI in CNC, Events, Spain Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/ideko-biemh.jpg Featured image alt: AI aerospace machining at BIEMH 2026 demonstrated by IDEKO robotic inspection and vision systems The Basque technology centre IDEKO will use its presence at BIEMH 2026 as a platform to demonstrate how robotics, digitalisation, computer vision and artificial intelligence are converging to transform aerospace production. From 2–6 March at the Bilbao Exhibition Centre, the organisation will also mark its 40th anniversary, highlighting four decades of work developing advanced manufacturing technologies in close collaboration with industrial partners. The aerospace sector has become one of IDEKO’s core strategic areas, with long-term partnerships across international manufacturers. At Stand E20, Hall 1, the centre will present solutions designed for real industrial deployment, combining precision machining, robotics and manufacturing digitalisation. This focus on AI aerospace machining at BIEMH 2026 reflects a broader shift toward intelligent, data-driven production cells across the aerospace supply chain. According to Sales Director Harkaitz Urreta, the technologies on display have already been validated in production environments and are intended to solve concrete aerospace factory challenges, with potential applications in energy, automotive and rail sectors as well. Precision robotics and AI-driven digital factories IDEKO’s stand will centre on two aerospace component demonstrators: a structural part and an engine component. The structural component setup will feature a collaborative robot performing inspection tasks through 3D scanning. The system integrates vision technologies that track the robot end-effector across large workspaces and correct its position in real time, enabling more accurate and efficient machining operations. The engine component demonstrator focuses on the digitalisation of an aerospace production line. Here, quality control is embedded into the process through machine monitoring and condition analysis supported by artificial intelligence. The platform is designed for flexible, secure and scalable data management across complex production environments. IDEKO reports that this approach can improve machine behaviour, availability and overall productivity while significantly reducing scrap and rejected parts. Vision systems, vibration control and surface quality Another demonstrator will present MULTISense, a solution combining several advanced vision technologies to detect defects in casting and machining operations. The stand will also feature technologies for vibration mitigation during machining, including the DynAQ portable tool, which measures, diagnoses and resolves vibration issues to improve machine efficiency and reliability. In addition, IDEKO will present solutions for detecting and mitigating microscopic surface marks in high-precision grinding and machining, as well as surface texturing techniques aimed at improving friction, wear and lubrication performance. 40 years of advanced manufacturing R&D IDEKO’s anniversary at BIEMH highlights a long track record in advanced manufacturing research. The centre currently participates in more than 200 technological projects each year, has coordinated European projects for over 35 years, and holds 42 patents as of the end of 2025. The organisation operates across four main research areas: Dynamics and Control, Manufacturing Processes, ICT and Automation, and Design and Precision Engineering. MTN analysis: AI moves deeper into aerospace production IDEKO’s BIEMH showcase reflects a broader trend across the aerospace supply chain, where AI is being embedded directly into production processes rather than treated as an external analytics layer. The focus on robotic inspection, real-time vision correction and AI-based machine condition monitoring points to a shift toward fully digitalised production cells. In aerospace, where tolerances are tight and scrap costs are high, these systems can deliver measurable gains in uptime, yield and traceability. Similar developments can be seen across the wider AI in CNC sector. What stands out is the integration of multiple technologies into a single environment. Robotics, machine monitoring, advanced vision and data platforms are being presented as one coordinated system rather than separate upgrades. That approach is becoming a defining pattern across high-value manufacturing sectors. For machine tool builders and aerospace suppliers, this signals where investment is heading over the next few years: intelligent robotic cells, AI-driven quality control and digital production platforms designed to run continuously with minimal manual intervention. ### MVTec Interview: Dr. Maximilian Lueckenhaus on HALCON, MERLIC, and the Next Phase of AI Machine Vision Canonical URL: https://machinetoolnews.ai/ai-machine-vision-halcon-merlic-2026/ Published: 2026-02-12T13:23:27+00:00 Modified: 2026-02-12T13:23:29+00:00 Author: Jelena Radojcic Categories: AI in CNC, Germany, News, Software Tags: Editors Pick, Featured, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/mvtec_dr.maximilian_lueckenhaus_1600x900px.png Featured image alt: AI Machine Vision HALCON MERLIC 2026 with Dr. Maximilian Lueckenhaus from MVTec AI Machine Vision HALCON MERLIC 2026 is entering a new phase. Artificial intelligence in machine vision is moving beyond inspection accuracy and into lifecycle management, edge deployment, hybrid AI approaches, and real production integration. We spoke with Dr. Maximilian Lückenhaus from MVTec to understand how HALCON and MERLIC are evolving, where customers are seeing real value, and what the next two to three years could bring for AI-driven vision systems. MTN: MVTec has been a major name in machine vision for decades. What was the original vision behind HALCON and later MERLIC? Dr. Lückenhaus:MVTec started in November 1996 in Munich as a spin-off from the Technical University of Munich and FORWISS. From the beginning, our vision was industrial-grade machine vision that is robust, fast, and ready for real production constraints. That shaped HALCON early on. Its first market version already included 3D camera calibration in June 1997, and later we expanded into embedded platforms. MERLIC followed in 2014 to remove the barrier that you must be a programmer. It lets users build complete vision applications through a graphical workflow. MTN: What are the most important AI capabilities that have been added recently to HALCON? Dr. Lückenhaus:A key step was Continual Learning for Classification in the latest HALCON release in November 2025. It lets users update models with few images, add classes, and avoid catastrophic forgetting. We also focused on real-world speed. HALCON 25.11 optimized deep learning models for classification and code reading, and introduced faster Deep OCR models for resource-constrained devices. In HALCON 25.05 we advanced hybrid AI for robotics with Deep 3D Matching for robust pose estimation and bin picking, including training with synthetic data generated from CAD models. MTN: As AI becomes more embedded in manufacturing, how is MERLIC’s role evolving? Dr. Lückenhaus:MERLIC’s role is to make AI-based vision operational for production teams. Thanks to MERLIC’s graphical user interface and powerful tools, users can create complete machine vision applications easily and intuitively – without any programming. With its “all-in-one” approach, the no-code software covers the entire process: from image acquisition, image processing, integrated communication interfaces, to visualization of the results. Recent releases focus on integration and process reliability. That includes improved communication plug-ins, a Linux frontend, container deployment examples, stronger error handling, and Siemens Industrial Edge connectivity. MTN: How do HALCON and MERLIC complement each other, and how should customers choose between them? Dr. Lückenhaus:They are complementary by design. Choose HALCON if you need maximum flexibility, custom logic, deep integration into your own software stack, or advanced engineering workflows such as multi-camera or complex robotics. Choose MERLIC if you want fast time-to-result with a graphical approach, straightforward PLC integration, and a packaged runtime for production. MTN: How do you balance rule-based machine vision with deep learning? Dr. Lückenhaus:We treat deep learning as another tool, not a replacement. Rule-based methods still win when you need determinism, strict explainability, or minimal data. Where variability is high, deep learning is often the right choice. The best results frequently come from hybrids, such as Deep 3D Matching or Deep OCR workflows that combine classic methods with AI. MTN: What have been the most surprising customer use cases so far? Dr. Lückenhaus:Two things stand out. First, how far customers push machine vision beyond classic inspection, even into extreme environments. HALCON supported NASA’s humanoid robot R2 on the International Space Station because of strong 3D vision capabilities. Second, how quickly teams turn AI experiments into production once workflows become maintainable in terms of data, retraining, deployment, and monitoring. MTN: What are the biggest technical challenges when integrating advanced AI into production? Dr. Lückenhaus:The hardest challenges are practical ones: data scarcity, labeling effort, domain shift over time, compute limits at the edge, and validation or compliance. We address these with our Deep Learning Tool that reduces labeling and training friction, lifecycle features like Continual Learning. Besides we offer compliance enablers such as SBOMs in HALCON 25.11 to support modern security and regulatory requirements. MTN: How is MVTec adapting to AI accelerators and edge computing? Dr. Lückenhaus:We optimize both software and pre-trained models for constrained hardware. HALCON 25.11 targets faster inference and efficient model support, while Continual Learning is designed to work in edge environments. We also collaborate with hardware partners, including Siemens industrial PCs with embedded AI acceleration and Qualcomm NPUs for smart cameras. Our AI² Accelerator Interface allows customers to use supported AI accelerator hardware such as Nvidia TensorRT or Intel OpenVINO. MTN: Looking ahead two or three years, what should manufacturers expect in deep learning and machine vision? Dr. Lückenhaus:The next leap will come from AI as a framework rather than a single technology. AI will automate many tasks in machine vision, including setting the right parameters for applications. This will also increase the importance of rule-based processes, because they can then be used more efficiently. We are working on interfaces to AI agents to simplify development and deployment of machine vision applications. MTN: Where do you see generative AI intersecting with industrial vision? Dr. Lückenhaus:I see two main intersections. First is synthetic data generation to reduce data bottlenecks. Generating labeled training data from CAD for Deep 3D Matching already points in that direction. Second is AI-assisted development and operations. Assistants can help users navigate tooling, generate boilerplates, and speed up engineering. MTN: How important are partnerships and ecosystem integration for scaling AI vision? Dr. Lückenhaus:Interoperability will decide whether AI vision scales. Partnerships that align software, computers, cameras, PLCs, and industrial platforms matter most. Our Technology Partner Program is key here, with collaborations such as Siemens and Qualcomm focused on integrated performance and predictable deployment. MTN: What is the biggest misconception about AI in industrial vision today? Dr. Lückenhaus:The biggest misconception is that AI will magically replace engineering. In industry, success depends on data strategy, validation, and maintainable deployment. AI tools can help with parameter selection and speed up development, but many vision tasks are very specific and still require experienced application engineers to create robust, 24/7 production solutions. MTN Analysis: AI Vision Moves from Accuracy to Lifecycle Control Three themes stand out from this conversation... ### Gefond Interview: Tiziana Tronci on Perpetuo and the Shift to Predictive Die Casting Canonical URL: https://machinetoolnews.ai/gefond-perpetuo-ai-predictive-maintenance-2026/ Published: 2026-02-10T11:23:06+00:00 Modified: 2026-02-10T11:23:08+00:00 Author: Jelena Radojcic Categories: General, Italy, News, Software, Software / CAM / IIoT Tags: Editors Pick, Featured, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/9A1A6028.jpg Featured image alt: Gefond Perpetuo AI predictive maintenance 2026 platform for die casting equipment monitoring Gefond Perpetuo AI predictive maintenance 2026 is entering real production environments as die casting companies begin using artificial intelligence to reduce downtime and stabilise operations. At Euroguss, Gefond officially introduced Perpetuo, its AI-based predictive maintenance platform designed to extend equipment life and stabilise production. We spoke with CEO Tiziana Tronci about how the system was developed, what results it is delivering, and how predictive intelligence is reshaping the foundry floor. MTN: What is the name of your new AI software and what specific problem in the die casting industry does it solve? Tiziana Tronci:Our artificial intelligence-based software is called Perpetuo. We chose this name because the basic concept is that the software works to extend the life cycle of equipment and maintain the continuity of the production process. It was created to address one of the most critical problems in die casting and the manufacturing sector: the difficulty of predicting failures, process anomalies, and performance drops before they result in machine downtime or production instability. Perpetuo transforms data from machines and equipment into predictive indicators, helping foundries move from reactive to predictive and data-driven maintenance. MTN: How did the concept for this AI solution originate inside Gefond, and what were the first steps in turning the idea into a product? Tiziana Tronci:The idea came about almost by chance. Some time ago, I heard about predictive maintenance for the first time. Driven by the curiosity that has always guided my professional career, I decided to explore the topic further. Through continuous research and discussions with Gefond technicians, a very clear picture emerged: most of our customers only intervened when a fault occurred, in emergency situations, often under considerable pressure due to machine downtime and production stoppages. Spare parts were not always available in stock and were subject to technical procurement times. I realised that operating in this way makes it extremely difficult to optimise work and be truly efficient. This led to the question: are there tools that can help customers reduce breakdown interventions? Looking at other industrial sectors, I realised that ours also needed to evolve, moving from a reactive approach to a preventive and predictive one. From our direct experience in foundries, we realised the data was already present in the equipment, but it was not being used in a structured way. The first step was to collect reliable real-time data from production machines, analyse it together with foundry technicians, and develop models capable of recognising abnormal patterns. Only after this field phase did the project become a structured software product. MTN: Can you describe the core technologies powering the software and how they compare to existing solutions on the market? Tiziana Tronci:Perpetuo uses machine learning algorithms and advanced data analysis to identify anomalies in equipment behaviour. Unlike many traditional systems, it does not simply display historical data, but is designed to interpret it predictively, progressively adapting to the specific behaviour of each piece of equipment. The system transforms data collected from sensors or machine PLCs into meaningful information for predictive maintenance of mechanical, electrical, hydraulic, and pneumatic parts subject to wear or failure. Our predictive maintenance software represents a unique approach: it was developed based on in-depth knowledge of production processes rather than theoretical models alone. This bottom-up approach allows us to speak the language of operators and maintenance technicians, offering concrete and technically effective solutions. MTN: At Euroguss you officially launched the software. What was the reaction from attendees and early adopters? Tiziana Tronci:At Euroguss, we saw a lot of interest, especially from foundries that are looking for concrete tools to improve reliability and production continuity. At a time when profit margins are falling, being able to monitor energy, compressed air, and release agent consumption in real time can drastically reduce hidden costs. Many visitors appreciated the practical approach: a solution born from real process experience. MTN: How does this AI software integrate with existing production systems used by die casting manufacturers? Tiziana Tronci:Perpetuo is designed to integrate with machines, PLCs, sensors, software, and ERP systems without requiring infrastructure replacement. It connects to existing production data and organises it in a structured way, making it usable for predictive purposes. MTN: What measurable results have pilot customers reported so far? Tiziana Tronci:To date, we have connected over 150 pieces of equipment to Perpetuo, with concrete results in terms of reduced unscheduled downtime, greater production stability, and improved maintenance planning. In pilot projects and early industrial installations, we have seen measurable benefits typical of predictive maintenance, including: 35% reduction in unscheduled downtime 15% extension of equipment life 16% increase in production 20% energy savingsInterview def In one case study, predictive monitoring of hydraulic parameters reduced pipe replacement interventions by 83%, oil leak interventions by 63%, and filter or heat exchanger replacements by 86%. The improvements resulted in a total saving of 248 hours of production time. MTN: What role does data play in the performance of the AI, and what support do customers need to get started? Tiziana Tronci:Data is the foundation of the system. The more complete and reliable it is, the more accurate the model becomes. Implementation follows a structured four-phase process: analysis of the customer’s systems and objectives, hardware integration when sensors are required, software configuration and model training, and finally the predictive phase where the system generates operational recommendations. Throughout the process, Gefond also supports customers with training activities, because technology only becomes effective when people know how to interpret the data and use it in their daily decisions. MTN: What product enhancements or new modules are on your roadmap? Tiziana Tronci:We are working on new features that increasingly integrate Perpetuo with other Gefond systems. This has led to the creation of the Perpetuo digital ecosystem, designed to bring foundries into a new era where every machine communicates and every decision is guided by predictive intelligence. In addition to Perpetuo, we have developed Die.Tective, which monitors die operating parameters, and Foundry.Focus, which supervises and optimises production processes. Together, these tools make the factory more efficient, sustainable, and connected. MTN: How does this new software fit into Gefond’s broader strategy for growth and innovation? Tiziana Tronci:Gefond combines t... ### This Week in AI for Machine Tools: Key Signals from 2–6 February 2026 Canonical URL: https://machinetoolnews.ai/machine-tool-ai-weekly-roundup-february-2026/ Published: 2026-02-06T13:14:48+00:00 Modified: 2026-02-06T13:54:34+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, AI in Sheet Metal, News, Robotics Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/b5c520c4-c257-440f-a6d4-2a40d33fc64a.png Featured image alt: machine tool AI weekly roundup February 2026 Machine Tool News weekly roundup February 2026: This week we have seen how the industry is moving from pilot projects to full production-scale intelligence. This is one of the clearest financial signals, showing investor confidence in intelligent production. This week, capital investment, AI robotics deployments, and strategic industry events all pointed in the same direction: artificial intelligence is moving deeper into the core of machine tools, not as an add-on, but as a control-level technology. From Monday 2 February to Friday 6 February 2026, the industry showed clear signs that AI is moving deeper into the core of manufacturing systems, from the control loop to the balance sheet. Our Weekly MachineToolNews Round-Up: What Moved in AI This Week One of the week’s most important developments came from China, where Shenzhen Han’s CNC Technology set the price for its Hong Kong listing at the top end of the range, with shares expected to begin trading on 6 February 2026. According to a Reuters report on Han’s CNC’s planned listing, strong investor demand is pushing the company’s IPO pricing to the top of its expected range. The company is offering around 50 million shares at HK$95.80 each, aiming to raise billions of Hong Kong dollars in fresh capital. Han’s CNC specialises in high-precision equipment used in advanced manufacturing sectors. Strong demand for the listing reflects growing investor confidence in companies positioned around automation, smart factories, and AI-enabled production. MTN analysis Capital markets are increasingly treating machine tool companies as technology plays rather than pure hardware suppliers. Large-scale funding rounds like this typically accelerate: AI-driven control development Robotics integration Digital twin platforms Smart factory software stacks In practical terms, this means more AI features will reach production machines faster. AI robotics facility demonstrates full-scale finishing automation This week also saw continued momentum in AI robotics, with major industry coverage focusing on the expansion of AI-driven production systems and the shift toward what analysts are calling “physical AI” inside factories. According to semiconductor supplier NXP, demand is growing rapidly for AI integrated directly into factory automation, robotics, and industrial systems, with its industrial chip business expanding at around 20% growth driven by these applications. Why this matters for metalworking Finishing, polishing, and complex handling processes have long resisted traditional automation. AI-enabled robotics, combining vision and adaptive force control, is now making these tasks viable for production cells. These developments reinforce the direction highlighted in the machine tool AI weekly roundup February 2026, where AI is moving from monitoring tools into real production processes. MTN analysis The significance is not only in robotics adoption.It shows that AI is moving: From monitoring systems Into real-time physical processes Inside machines and robots themselves This is the transition from digital analytics to physical machine intelligence. Global AI events confirm shift from pilots to production Several major AI-focused industrial events took place this week, including the AI & Big Data Expo Global in London (4–5 February 2026), one of Europe’s leading enterprise AI conferences. The event brought together thousands of attendees to focus on: Enterprise AI deployment Real-world industrial applications Scaling AI beyond pilot projects Coverage from the same event highlighted the emergence of agentic and enterprise-scale AI systems, with organisations preparing for autonomous digital co-workers and intelligent production environments. MTN analysis For years, AI discussions in manufacturing centred on proof-of-concept projects.This week’s event agenda shows a different conversation: How to scale AI across entire factories How to integrate AI into daily operations How to build AI-first production strategies That language signals a maturity shift across the industry. “Physical AI” becomes a core industrial trend Multiple announcements this week pointed to the same strategic direction:AI is moving out of dashboards and into machines. Industry executives described strong demand for “physical AI”, meaning artificial intelligence embedded directly in industrial systems, robotics, and automation platforms. At the same time, enterprise AI events highlighted the rise of agent-style AI systems designed to act autonomously within production environments. What “physical AI” means in machine tools In practical CNC and metalworking terms, this translates into: Real-time feed and speed adjustments AI-driven toolpath optimisation Autonomous inspection and correction Self-tuning machining processes MTN analysis The biggest structural shift in machine tool AI is not better dashboards or analytics.It is the integration of AI directly into the control loop. A similar trend can be seen in applications like this autonomous welding robot for maritime maintenance, where AI is already being used to handle complex metalworking tasks in real industrial environments. This creates three new capabilities: Real-time process adaptation Autonomous error correction Self-optimising production cells This is the technical foundation for autonomous machining. Southern Manufacturing show reinforces automation focus In the UK, the Southern Manufacturing show took place this week from 3–5 February 2026, bringing together machinery, automation, and supply-chain technology suppliers. While not exclusively an AI event, the show reflects how automation, digitalisation, and intelligent production technologies are becoming central to mainstream manufacturing exhibitions. The machine tool AI weekly roundup February 2026 also shows how investment, robotics, and strategic industry events are now moving in the same direction across global manufacturing. MTN analysis Trade shows often reveal buying intent earlier than product launches.When automation and AI dominate exhibition themes, it usually indicates: Strong buyer demand Budget allocation for digitalisation Competitive pressure to adopt AI The three strategic signals from this week Across all developments between 2 and 6 February, three consistent signals emerge. 1) Capital is flowing into AI-ready machine tool companies The Han’s CNC IPO shows that investors are backing smart manufacturing platforms. 2) AI robotics is entering physical production environments Industrial demand for “physical AI” indicates real deployment, not only pilot projects. 3) The industry is moving from pilots to autonomous systems Events and executive commentary confirm a shift toward enterprise-scale, agent-style AI. The industry is progressing through three stages: Monitoring → Prediction → Autonomous control This week shows the third phase is beginning to take hold. What this means for machine tool buyers For many manufacturers, the machine tool AI... ### Why AI in Sheet Metal Is Delivering Faster Payback Than Robotics Canonical URL: https://machinetoolnews.ai/ai-vs-robotics-roi-in-sheet-metal/ Published: 2026-02-05T11:57:15+00:00 Modified: 2026-02-05T11:57:18+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, AI in Sheet Metal, Robotics Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/e9a11abb-41f6-4dd6-9a78-096f63efc7dc.png Featured image alt: AI vs robotics ROI in sheet metal fabrication workflows AI vs robotics ROI in sheet metal is becoming a decisive factor for fabricators as AI-driven software delivers faster and lower-risk payback than robotic automation. Quoting, nesting, scheduling, quality control, and uptime all sit upstream of production. When these improve, the financial impact compounds across material, labour, and throughput. Across modern fabrication businesses, AI vs robotics ROI in sheet metal has become a central decision point as manufacturers compare software-led gains with capital-intensive automation investments. Robotics still plays a critical role in modern fabrication, especially in welding, handling, and repetitive loading. The difference is not effectiveness, it is timing and risk. AI tends to pay back earlier because it attaches to high-frequency decisions across the entire business rather than concentrating capital into a small number of automated cells. AI vs robotics ROI in sheet metal UK and European fabricators are operating in a high-mix, low-margin environment. Volatile demand, skilled labour shortages, and rising material costs mean that improvement projects must show results quickly. Faster payback reduces risk and allows companies to modernise incrementally rather than betting heavily on a single automation project. AI-led improvements also scale across mixed machine fleets, including older lasers, punches, press brakes, and inspection steps, which reflects the reality of many job shops. Where AI delivers faster payback in sheet metal Quoting speed and margin accuracy Quoting remains one of the most underestimated profit levers in fabrication. Slow quotes lose work. Inaccurate quotes win work at the wrong margin. As The Fabricator has noted in its coverage of digital quoting workflows: “Quoting is one of the most underestimated bottlenecks in a fabrication shop. If it takes too long or relies on guesswork, you either lose the job or lose money on it.” This front-end impact is a key reason why AI vs robotics ROI in sheet metal increasingly favours AI-first investments. AI-assisted quoting systems draw on CAD data, historical jobs, and actual production outcomes. This reduces reliance on tribal knowledge and dramatically cuts the time spent preparing estimates. The same publication highlights why the financial impact shows up quickly: “AI-assisted nesting and quoting tools focus on the two biggest cost drivers in fabrication: material and time. Even small percentage improvements here have a major effect on profitability.” The payback comes from higher quote throughput, faster response times, and fewer margin leaks caused by underestimating setup, cutting, or handling time. Nesting, material yield, and shop-floor flow Material is often the single largest cost in sheet metal fabrication. AI-assisted nesting focuses directly on scrap reduction, remnant reuse, and cut-time optimisation. According to Lantek, material efficiency is one of the fastest ways to see measurable return: “Material optimization remains one of the fastest routes to measurable ROI in sheet metal. Digital and AI-based planning tools allow fabricators to reduce scrap without changing machines.” Improvements in material efficiency and workflow stability are a major reason why AI vs robotics ROI in sheet metal continues to favour AI-led investments in high-mix fabrication environments. Because these gains apply to every sheet processed, even small improvements in yield translate into immediate cost savings. Better nesting logic also stabilises downstream processes by reducing urgent re-nesting, part confusion, and scheduling disruption. AI quality inspection and defect capture Quality inspection is another fast-payback area because it reduces scrap, rework, and customer complaints simultaneously. Oxmaint, which has analysed multiple Vision AI deployments in metal processing, explains the appeal: “Vision AI systems are being adopted because they catch defects earlier and more consistently than manual inspection, particularly in high-volume metal processing environments.” In applications where defect costs are high, the return can be rapid: “In applications where defect costs are high, AI-based visual inspection can reach return on investment in under twelve months by reducing scrap, rework, and customer complaints.” The key is early detection. Catching a defect after cutting or bending is far cheaper than discovering it after welding, finishing, or shipment. Bending and forming intelligence Press brake efficiency is highly sensitive to setup time, bend sequencing, and feasibility errors. AI-driven feasibility checks and automated bend validation reduce engineering iterations and shop-floor trial runs. Research published in the Computer-Aided Design Journal notes: “Automated feasibility checks and bend sequence validation reduce engineering iteration and shop-floor trial runs, which directly impacts lead time and labor efficiency.” The result is fewer aborted setups, higher first-pass success, and reduced dependence on individual operator experience. Predictive maintenance and uptime Downtime in sheet metal operations rarely affects a single job. It cascades across schedules, deliveries, and labour utilisation. Optimi AI highlights why predictive maintenance can pay back quickly when applied correctly: “Predictive maintenance delivers the most value when applied to bottleneck equipment, where even short periods of unplanned downtime have cascading effects on production schedules.” When AI models are focused on critical assets, many manufacturers report payback well within the first year. Why robotics payback often takes longer Robotics delivers clear benefits when processes are stable, repeatable, and highly utilised. In sheet metal fabrication, that is not always the case. According to analysis from AMD Machines on automation ROI: “Robotic automation delivers strong returns when utilization is high and process variation is low. In mixed or high-mix fabrication environments, integration time and changeover complexity can extend payback periods.” Robotic welding benchmarks reinforce this reality: “Most successful robotic welding installations achieve payback between one and three years, depending on part consistency, volume, and programming requirements.” These timelines are not a failure of robotics. They reflect the capital intensity, integration effort, and dependency on upstream process stability. A simple payback model fabricators can use A practical way to compare projects is to calculate weekly value rather than theoretical annual ROI. Payback in months equals total implementation cost divided by weekly benefit multiplied by 4.33. Weekly benefit can include: Material saved through scrap reduction Engineering and inspection labour hours avoided Increased shipped value due to higher throughput Quality-related costs avoided such as rework and claims AI often wins this model early because its benefits start in quoting and engineering and then cascade onto the... ### Top 10 Breakthrough Announcements in January Transforming Manufacturing Canonical URL: https://machinetoolnews.ai/ai-in-machine-tools-2026-top-10/ Published: 2026-02-04T12:56:30+00:00 Modified: 2026-02-04T12:59:01+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, AI in Sheet Metal, Robotics, Software, Software / CAM / IIoT Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/02/ChatGPT-Image-Feb-4-2026-01_47_58-PM.png Featured image alt: AI in machine tools 2026 visualised as a connected digital network above precision metal manufacturing surfaces AI in machine tools 2026 has entered a new phase as leading automation suppliers move artificial intelligence directly into CNC control, robotics, inspection, and motion systems. The announcements made in January 2026 show that AI in machine tools 2026 is no longer limited to analytics or dashboards. It is becoming part of how machines are designed, programmed, and optimised for real production in metal manufacturing. Below are the ten most important industrial announcements shaping this shift. Siemens and NVIDIA expand industrial AI partnership Siemens announced deeper integration of NVIDIA AI and simulation into Siemens Xcelerator. The goal is to create AI-driven digital twins of machine tools and robotic systems that behave like their real-world counterparts. Why it matters:This lays the foundation for AI-native CNC and robot control architectures. Instead of tuning processes only on physical machines, optimisation can be trained inside virtual factories and transferred to production with far lower risk. ABB unveils Autonomous Versatile Robotics ABB introduced adaptive robotic systems capable of changing workflows using AI rather than relying only on fixed programs. Why it matters:This represents a move toward self-adjusting production lines. For machine tending, welding, and handling, robots can respond to variation instead of stopping when conditions change. This is critical for high-mix metal manufacturing. KUKA and Algorized launch predictive robot safety KUKA and Algorized introduced an AI system that uses radar and machine learning to predict collisions between humans and robots before they happen. Why it matters:Safety has limited how closely robots and humans can work together. Predictive safety allows robots to anticipate human movement, enabling more compact cells and higher utilisation of collaborative automation. Qualcomm launches full robotics AI platform Qualcomm launched a suite of edge AI processors and a robotics software stack designed specifically for automation and robotics. Why it matters:Physical AI requires real-time processing close to the machine. This creates dedicated compute hardware for robots and automation cells rather than relying on repurposed consumer chips. Siemens expands SINUMERIK Run MyRobot Siemens expanded SINUMERIK Run MyRobot so that robots and CNC machines can be programmed as a single coordinated system. Why it matters:Machine tending and hybrid automation become easier to deploy. CNC and robot motion no longer need separate engineering workflows, lowering the barrier for job shops to automate machining cells. GrayMatter Robotics opens AI finishing facility GrayMatter Robotics opened a 100,000-square-foot facility dedicated to AI-driven robotic grinding, polishing, and deburring. Why it matters:Finishing has been one of the hardest metalworking operations to automate. This facility shows that AI can now replace manual finishing in real production environments. Schunk announces AI-driven gripping platform Schunk introduced grippers that adapt in real time to part geometry using AI control. Why it matters:Flexible gripping enables random-part handling and reduces dependence on dedicated fixtures. This supports bin picking, CNC automation, and mixed-part production. Hexagon expands AI metrology portfolio Hexagon launched AI-based surface defect detection and predictive measurement tools. Why it matters:Inspection moves from post-process checking toward in-process intelligence. AI detects problems during machining, reducing scrap and closing the loop between quality and production. Siemens launches Industrial AI OS concept Siemens revealed a software architecture designed to deploy AI consistently across machines and factories. Why it matters:This prepares factories for AI-native CNC platforms where learning and optimisation are built into the control layer rather than added as external software. Bosch Rexroth launches AI motion optimisation Bosch Rexroth introduced motion systems that tune themselves using AI based on load, vibration, and cycle conditions. Why it matters:Machines begin self-optimising for performance and energy use. This shifts motion control from fixed parameter tuning to continuous optimisation. MTN Analysis: What these announcements mean for metal manufacturing These developments confirm that AI in machine tools 2026 is moving into machine control, robotics coordination, and inspection workflows rather than remaining in external analytics platforms. The boundary between CNC, robot, and inspection system is starting to disappear. Digital twins are becoming training environments for AI models. Robots are shifting from programmed sequences to learning-based behaviour. Quality control is moving inside the machining process instead of sitting at the end of the line. Over the next three years, AI in machine tools 2026 will define competitiveness for CNC machining, robotics integration, and metal manufacturing automation. Key Takeaways • AI in machine tools 2026 is driving convergence between CNC and robots• AI is enabling flexible automation instead of rigid cells• Inspection is moving inside the process• Motion systems are becoming self-optimising• Digital twins are now part of production strategy FAQ: AI in machine tools 2026 What is AI in machine tools 2026?AI in machine tools 2026 refers to artificial intelligence embedded directly into CNC machines, robots, inspection systems, and motion control to optimise cutting, handling, and quality in metal manufacturing. How does AI in machine tools 2026 improve CNC machining?It improves CNC machining by monitoring cutting stability, tool wear, vibration, and part quality in real time, allowing machines to adjust parameters automatically. Is AI in machine tools 2026 only for large factories?No. AI in machine tools 2026 is increasingly available for mid-sized and small manufacturers through integrated robot cells, AI-driven inspection, and edge computing platforms. What is the biggest benefit of AI in machine tools 2026?The biggest benefit is flexible automation. Machines can adapt to variation instead of relying on rigid programs and fixtures. ### AI Machine Tool Automation Becomes Accessible for Job Shops Canonical URL: https://machinetoolnews.ai/ai-machine-tool-automation-siemens-kuka-syil/ Published: 2026-02-03T11:50:30+00:00 Modified: 2026-02-03T11:51:15+00:00 Author: Jelena Radojcic Categories: AI in CNC, AI in Machining, General, Robotics Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/01/Siemens_2.jpg Featured image alt: AI machine tool automation integrating Siemens SINUMERIK CNC with KUKA robot on a SYIL machine tool AI machine tool automation is becoming accessible to small and mid-sized job shops through a new integration between Siemens SINUMERIK CNC, KUKA robotics and SYIL machine tools. Siemens has expanded its SINUMERIK Run MyRobot platform, enabling KUKA robot part handling and articulation functions to be operated directly from the CNC control screen of SYIL machine tools. This allows robot motion, diagnostics and handling routines to be managed within the same interface used for machining operations. AI machine tool automation is increasingly being used to connect CNC control, robotics and part handling into a single operating environment. AI machine tool automation through partnership in the machine shop This development is based on a technology partnership between Siemens, KUKA and SYIL, aimed at machine shops that manage multiple part families and short production runs. The system combines a digital twin of the SINUMERIK 828 CNC with integrated robot control to simplify part handling programming for machine tool operators. The concept was demonstrated at a recent trade show, where KUKA Robotics Corporation showed a robot working alongside a SYIL vertical milling machine. The installation used the SYIL R1 machine-tending automation package, developed with KUKA system partner Waybo. The package integrates pick-and-place, part articulation and CNC communication directly within the control environment. According to Frans Buikema, Chief Marketing Officer at SYIL, the objective is to give small and mid-sized shops an entry point into automation without major capital investment. Ron Bergamin, Key Technology Manager for Machine Tool Automation at KUKA, adds that the system can support lights-out second or third shifts, potentially shortening return on investment. For many job shops, AI machine tool automation reduces the need for specialist robot programming and complex external integration. Simplifying robotics through the CNC Historically, robot integration in machine tools required specialist programming, separate robot pendants and external system integration. These requirements limited adoption, particularly among smaller shops. Industry data shows that fewer than a quarter of small job shops in the United States currently use robotics in daily production. Tiansu Jing, Product Manager for SINUMERIK CNC systems at Siemens, explains that this approach removes much of that complexity. Setup, programming, operator interface and diagnostics are all handled through the SINUMERIK Operate HMI. The SINUMERIK Run MyRobot/Handling application connects directly to the KUKA robot controller, allowing teach-in and operation to be carried out within the CNC. Robot control is displayed on the same screen as the machine tool, eliminating the need for a separate pendant. This reduces training requirements and allows the same operating logic to be used across multiple machines. This approach to AI machine tool automation keeps robot setup, teach-in and diagnostics inside the CNC interface. From complexity to clarity The SINUMERIK 828D CNC is designed for turning centres, milling machines and vertical machining centres commonly used in job shops. With Run MyRobot/Handling enabled, programmers can use the same G-code structure for both the robot and the machine tool. Robot programming, operation and diagnostics are executed from the CNC HMI, providing a unified interface for both machining and handling tasks. Designed for job shops and affordable lights-out manufacturing KUKA’s PLC mxAutomation interface allows robot functions to be programmed and controlled directly within the CNC environment using a PLCopen-certified interface. This enables machine builders to offer robotic automation with reduced engineering effort. Jing notes that the simplified integration allows robotic packages to be offered at lower cost than traditional solutions. Installation uses Ethernet and safety cabling, and Bergamin states that systems can often be operational within a single day. With the addition of lights-out shifts, machine utilisation can be increased without additional operators, improving spindle uptime and supporting faster payback. Philip Peloso, Business Development Manager for Machine Tool Automation at KUKA, explains that the system was developed to address skills shortages and price sensitivity in smaller machine shops. Working with Waybo, KUKA created an entry-level solution for the 75 percent of job shops that currently operate without robotic automation. A new benchmark for machine tool builders Siemens introduced the robotic integration concept to KUKA, which worked with Waybo to develop the system adopted by SYIL as the R1 automation package. From the machine builder perspective, SYIL describes the development as aligned with its focus on small and mid-sized manufacturers facing labour shortages and limited automation experience. Wesley Wang, Managing Director of SYIL North America, highlights the company’s use of SINUMERIK 828D CNC across its product range, citing motion control, monitoring and communications capability alongside built-in support for machine-tending robotics. Real results on the shopfloor SYIL states that automation packages priced at around $60,000 can achieve payback within approximately one year, depending on operating conditions. The system enables shops to extend production capacity without increasing headcount while maintaining consistent part quality. Expanding the vision SYIL builds CNC milling machines, turning centres and Swiss-type lathes and plans to extend robotic automation across its full product range using the SINUMERIK Run MyRobot/Handling platform. Buikema describes the integration as a major step toward making automation accessible to small and mid-sized shops. The same technology has been demonstrated jointly by Siemens and KUKA at industry trade shows and is now available as a standard SYIL offering. SYIL describes its relationship with Siemens as having evolved into a strategic engineering partnership, supporting both machine development and market expansion in North America. Siemens also provides dealer and software support across the United States, Canada and Mexico. Technical overview of the SYIL R1 automation package The SYIL R1 system is designed for part handling up to 3 kg, with a robot payload of 6.7 kg and a reach of 901 mm. It carries an IP65/67 rating for industrial environments and automates loading and unloading of metal parts from the cutting zone. The system supports high-mix, low-volume production and can be integrated with conveyors, bins and pallet systems. Standard equipment includes a pneumatic clamping force block, with an optional dual gripper for parts with different blank and finished geometries. The Siemens, KUKA and SYIL collaboration shows how AI machine tool automation can move from complex projects to standard machine options. Robot and machine programs are stored within the CNC and accessed through the SINUMERIK Operate... ### AI Takes Center Stage at GrindingHub 2026 as Smart Grinding Becomes the New Industry Standard Canonical URL: https://machinetoolnews.ai/ai-at-grindinghub-2026/ Published: 2026-01-29T12:10:53+00:00 Modified: 2026-01-29T12:10:55+00:00 Author: Jelena Radojcic Categories: AI in Machining, Events, Germany Tags: Editors Pick, Popular, popular topics, top stories Featured image: https://machinetoolnews.ai/wp-content/uploads/2026/01/Bild_5_HS_Furtwangen_Mensch_und_KI.jpg Featured image alt: AI at GrindingHub 2026 showing smart grinding machines with sensor-based process control AI at GrindingHub 2026 is putting smart grinding, automation, and sensor-based process control at the centre of precision manufacturing. GrindingHub 2026, taking place in Stuttgart from May 5–8, will place artificial intelligence, automation, and data-driven grinding firmly in the spotlight. Modern grinding machines are rapidly evolving into intelligent manufacturing systems, using AI models, sensor technology, and closed-loop process control to deliver higher productivity, greater flexibility, and stable precision. From unmanned closed-loop manufacturing and automated compensation to AI-supported process monitoring and grinding for humanoid robot components, GrindingHub 2026 will showcase how grinding technology is becoming a core pillar of intelligent production. AI at GrindingHub 2026: Smart Grinding Moves Into the Production Core “With intelligent automation 4.0, we have long since heralded the change,” says Marie-Sophie Maier, Managing Director of Adelbert Haas GmbH. “Intelligent, fully automated complete manufacturing has overtaken traditional complete machining. Today, extremely high productivity and flexibility are essential, as is zero compromise on precision.” At Adelbert Haas, fully automated closed-loop manufacturing is already standard practice. Grinding, measuring, and re-grinding processes run continuously in unmanned operation across 21 shifts per week, ensuring consistent quality and efficiency. Service strategies are also changing through AI. “Service is not a reaction, but prevention,” Maier explains. “AI agents analyze machine data in real time, detect anomalies, and initiate measures before a shutdown threatens: automatic ticket creation, prioritization of critical alarms, log files at the touch of a button. Rule-based programming is a thing of the past. Today, we learn from data and act proactively.” GrindingHub 2026 will provide the platform to discuss how automation and AI are transforming grinding technologies and how these systems will shape future production environments. For visitors, AI at GrindingHub 2026 provides a practical view of how predictive service, anomaly detection, and closed-loop manufacturing are already being applied in real production environments. Digital Production Control and Automated Compensation At GrindingHub, Anca Europe GmbH will focus on automation and production control alongside new products for stream finishing and microtool manufacturing. “We will be focusing on automation and production control,” says Martin Winterstein, Managing Director of Anca Europe GmbH. “The goal is to help users manufacture competitively under their given conditions through high quality, low manufacturing costs, and creative solutions.” Anca will present a server-based digital production control system that combines app-based worker guidance with automated measurement and compensation. The system organizes workflows across machines and pallets, issuing step-by-step digital work instructions that eliminate manual data entry, reduce errors, and improve traceability. Closed measurement loops and automatic compensation ensure parts remain within tolerance without operator intervention. All process data is stored centrally, allowing full traceability and enabling skilled workers to supervise more machines simultaneously. The result is faster training, reduced waste, and more predictable production shifts. Grinding for the Age of Humanoid Robotics Grinding precision is also becoming critical for emerging applications such as humanoid robotics. Transmission elements for joints and drives require extremely high dimensional accuracy and surface quality to ensure smooth movement and low noise levels. “Our grinding machines guarantee high dimensional and geometrical accuracy, process reliability, and flexibility with high output,” says Jan Schmid, Head of Engineering & Project Planning at Erwin Junker Maschinenfabrik GmbH. “Thanks to our many years of experience in thread grinding, we are able to offer customized grinding solutions for all types of threads.” Junker offers specialized grinding solutions using ceramic and electroplated CBN grinding wheels and grinding pins. Internal thread grinding allows threads to be machined directly from solid material, including cone threads, ball threads, and special profiles. Even internal thread diameters below 10 mm can be produced reliably. “When grinding threaded spindles, even threads with high pitch angles can be produced thanks to the flexible machine concept,” Schmid explains. “For maximum flexibility, we offer solutions for single-tooth machining and multi-tooth machining for particularly high output.” Sensors and AI for Stable Precision Processes According to Prof. Bahman Azarhoushang, Head of the KSF Institute at Furtwangen University, grinding is moving toward fully integrated, AI-supported control loops. “Modern machine tools combine classic NC and path controls with real-time sensor monitoring, integrated in-situ measurement technology, and increasingly also with additional measuring cells directly next to the machine,” he explains. Force sensors, vibration sensors, and spindle monitoring systems continuously generate process data. AI models use this data to predict grinding wheel wear, detect unstable process conditions through anomaly detection, and forecast component quality. Cutting parameters such as feed rate, cutting speed, and infeed can then be automatically optimized. This data also forms a long-term process knowledge database, helping manufacturers retain expertise despite the shortage of skilled workers. “Modern intelligent grinding machines integrate particularly dynamic drives, high-resolution measuring chains, and adaptive control algorithms that continuously analyze and autonomously optimize the grinding process,” Azarhoushang says. “The combination of sensor technology, integrated measurement technology, and AI-supported process control enables more sustainable, economical, and more consistent quality precision machining.” He adds that the major challenge lies in fully integrating these technologies into existing production environments. “The future competitiveness of industrial manufacturing locations depends largely on how consistently grinding processes are digitized, automated, and operated in closed control loops. Intelligent machine tools and artificial intelligence will play a key role in this.” The technologies presented under the banner of AI at GrindingHub 2026 highlight how sensor data and machine learning are being combined to stabilise precision processes. Grinding Reimagined Through AI GrindingHub 2026 will demonstrate how automation, end-to-end digitalization, and AI-supported control loops are transforming grinding from a traditional machining process into a self-optimizing manufacturing system. Fully automated closed-loop processes enable high productivity, flexibility, and stable quality, even under demanding conditions. Sensors, intelligent production cont... Generated automatically by MachineToolNews.ai AI Discovery plugin version 3.0.0.