AI at EuroBLECH 2026 was one of the biggest themes as the sheet metal industry returned to Hanover from 20–23 October, with around 1,000 exhibitors covering everything from laser cutting and bending to robotics, inspection, software and connected production.
We were there, spending several days across the halls looking at where AI and automation are actually making a difference.
What became clear fairly quickly is that AI in sheet metal is becoming much more practical.
The conversation is moving beyond what AI could potentially do. We saw technology being applied to real production problems: identifying parts, programming bending jobs, inspecting welds, preparing quotations, sorting components and making factory data easier to use.
Automation was just as important. Faster machines are creating greater pressure on everything around them, from material handling and sorting to software and production planning.
Here are the technologies that caught our attention, along with the bigger trends we took away from AI at EuroBLECH 2026.
AI at EuroBLECH 2026: What Caught Our Attention
InspecVision: Seeing GAV in Action
One technology we particularly wanted to see at EuroBLECH was GAV from InspecVision.
GAV, Guidance & Verification, is designed to learn a manual task, turn that task into reusable instructions and then check that the next operator completes the sequence correctly.
The starting point is an experienced operator demonstrating the process. GAV observes the sequence and creates work instructions that can then be saved and reused.
That is particularly interesting for manual manufacturing tasks where years of knowledge can sit with individual operators.
When an experienced employee leaves, some of that knowledge can leave with them.
Technology capable of capturing the process offers a different way of looking at AI. Rather than replacing the operator, it can help preserve knowledge and make work easier to repeat consistently.
At EuroBLECH, Dr. Jan Antonis showed us GAV in action and explained where InspecVision sees the technology fitting into manufacturing.
Seeing the technology on the show floor reinforced something we noticed throughout EuroBLECH.
The most convincing AI demonstrations were usually attached to a very specific problem.
They were not trying to reinvent the entire factory. They were making one difficult task easier, more repeatable or less dependent on manual intervention.
AMADA: Using Previous Experience to Make Bending Smarter
At AMADA, intelligent bending was one of the areas that caught our attention.
AMADA’s AiBEND functionality within VPSS 4ie Bend uses experience from previously programmed, similar jobs when preparing bending work.
It addresses a very real manufacturing challenge.
Bending knowledge is often built through years of experience. When manufacturers are dealing with high-mix production and frequent job changes, repeatedly programming similar parts from scratch takes time.
Using information from previous jobs gives manufacturers a way to reuse some of that knowledge.
The wider point is bigger than one bending system. As experienced operators become harder to find, software that can retain and reuse manufacturing knowledge is likely to become increasingly valuable.
ASTES4 and Mitsubishi Electric: From Intelligent Laser Cutting to Sorted Parts
ASTES4 put the spotlight on a problem that becomes more pressing as sheet metal lasers get faster: what happens to the components after cutting?
At EuroBLECH, the company presented the combination of Mitsubishi Electric laser-cutting technology and ASTES4 automated part sorting. Mitsubishi Electric’s latest laser solutions include AI-assisted cutting functions, while the ASTES4 handling system addresses the loading, unloading and sorting stages around the machine.
That distinction matters. The artificial intelligence in the cutting process and the mechanical automation of finished-part handling have different jobs, but both are needed to make the production line more productive. ASTES4’s objective is to take the material flow from sheet loading through to finished-part sorting with less manual intervention.
A faster laser only creates value if the components can move efficiently to the next operation. If operators spend their time separating and collecting parts, the bottleneck has moved downstream.
For manufacturers evaluating automated laser systems, ASTES4 was a reminder to look beyond cutting speed and consider the entire path from sheet to sorted component.
DAVI: AI-Vision Measures Plate Curvature as the Rollers Work
DAVI brought a different use of AI into the EuroBLECH conversation: taking some of the guesswork out of plate rolling.
Its AI-Vision laser measurement system captures thousands of points across the plate surface during rolling to determine the actual curvature. Those measurements feed into DAVI’s iRoll eXtreme control system, allowing the rolling sequence to be corrected as the material is formed.
That feedback loop is important because plate rolling can depend heavily on operator experience, repeated measurements and adjustments for variations in material behaviour. DAVI’s wider Autorolling approach combines AI-Vision, iRoll eXtreme and its electrically driven MCE e-POWER machine to support more autonomous rolling.
DAVI says its approach is designed to reduce trial runs, improve first-part accuracy and limit scrap. The practical value for manufacturers lies in measuring what is happening to the workpiece and feeding the result back into the process rather than relying solely on an initial programme.
In a show full of AI-assisted programming and vision-guided robots, DAVI demonstrated why real-time measurement and correction may become equally important to the future of metal forming.
EMG: AI Moves Into Strip Processing and Galvanising
AI at EuroBLECH also extended into the continuous processing of steel and aluminium strip. At Hall 17, EMG presented EMG iCASS, its Intelligent Control and Analytical Software Solutions platform.
Developed through EMG’s work with AI specialist NeurologIQ, iCASS uses machine learning and predictive analysis to monitor, model and optimise flat-metal production processes. It can bring together sensor data, production equipment and higher-level systems such as MES or ERP, with applications extending to real-time process control.
One of the clearest applications is hot-dip galvanising, where coating thickness, process stability and zinc consumption have direct cost and quality implications. EMG describes functions for predicting coating behaviour, adjusting safety margins, optimising nozzle settings and supporting closed-loop coating control.
The company also showcased iCAM for strip-width measurement and hole detection, as well as SOLID LAB for coating and insulation-layer measurement. These measurement technologies provide the kind of reliable production information on which more advanced analysis can be built.
For steelmakers and metal service centres, the significance is straightforward: AI can improve an established, continuous industrial process by turning live measurements and historical operating data into better control decisions. It is a less visible use of industrial AI than a robot on a show stand, but potentially an important one.
Ermaksan: ERAI Brings an AI Assistant to Fibre Laser Cutting
Ermaksan’s EuroBLECH 2026 line-up included the 30 kW FIBERMAK Momentum Gen-5, a high-power fibre laser cutting system with AI-assisted features designed to help operators manage increasingly complex machines. The Turkish manufacturer showed the machine alongside its latest bending and automation technologies in Hall 13.
A key development is ERAI, Ermaksan’s optional AI assistant. According to the manufacturer, ERAI provides step-by-step operating guidance, helps users discover machine functions and suggests possible solutions when errors occur. Ermaksan also describes the assistant as helping support decisions during cutting.
The Gen-5 platform offers more than 30 kW of laser power. Its listed capabilities include camera-supported sheet detection, real-time NC graphics, optional automatic nozzle handling and smart beam-centering features. These should be assessed as distinct machine functions rather than assuming every automated operation is powered by AI.
What interests us is the role of an AI assistant directly at the machine. Many fabricators already have powerful cutting systems packed with functions that operators rarely use to their full potential. Easier guidance and troubleshooting could help them get more value from their equipment and reduce reliance on specialist support for routine questions.
The important test will be what ERAI can solve independently, how it handles unfamiliar faults and how consistently its recommendations stand up in real production. For now, Ermaksan has put a clear example of AI-assisted machine operation into its high-power laser portfolio.
EAGLE: Faster Cutting Changes Everything Around the Machine
EAGLE brought us back to cutting speed, but also to what happens when cutting performance continues to increase.
EAGLE’s laser technology does not currently use AI, but the company is working on developments in this area that we may be able to bring you more on soon.
The company has become closely associated with high-power fibre laser cutting, demonstrating just how far cutting technology has moved.
But the faster a laser processes material, the more important loading, unloading, sorting, storage and downstream operations become.
It changes the question manufacturers need to ask.
It is no longer simply, “How quickly can this machine cut?”
The better question is, “How quickly can the factory turn that sheet into a usable component ready for its next operation?”
That distinction came up repeatedly as we moved around the halls in Hanover.
Epifor: Bringing AI Across the Factory
Epifor took the AI conversation beyond individual machines and into factory-level intelligence.
Its approach is particularly interesting because the technology is designed to work across an entire manufacturing environment rather than being tied to one machine or one brand.
Factories often contain equipment from different manufacturers, of different ages and running very different software. Bringing information from those systems together has historically been one of the biggest barriers to truly connected production.
This is where AI agents become interesting.
Instead of expecting users to search through multiple systems, screens and reports, AI can provide a more natural way of interacting with manufacturing information.
The goal is not necessarily to give operators another AI application to manage. The better outcome is for AI to work behind the manufacturing process and make existing information easier to access and use.
For manufacturers with mixed machine environments, that factory-wide approach could be particularly significant.
Figure it: Bringing Intelligence to the Quotation Stage
Figure it takes the AI conversation much further upstream, before a job has even reached production.
Quoting can be one of the less visible bottlenecks in sheet metal manufacturing.
Its technology works with component geometry and manufacturing information to support the calculation of quotations for sheet metal parts and assemblies.
It is an interesting area for intelligent software because experienced estimators do far more than simply look at the dimensions of a component.
They understand processes, materials, complexity and what a particular part will realistically require to manufacture.
Turning more of that manufacturing information into structured data can help companies prepare quotations faster and more consistently.
Manufacturers spend enormous amounts of time making their machines faster, but a customer can still be lost before production begins if it takes too long to return an accurate quotation.
LISSMAC and fruitcore robotics: Giving Deburring Machines Intelligent Part Handling
LISSMAC’s approach to automated deburring provided a practical example of AI vision and robotics working together. Its LI-CELL automation unit incorporates technology from fruitcore robotics, including the HORST robot and HORST COSMOS platform.
The system uses 3D vision and AI-supported recognition to identify different sheet metal components and guide robotic loading into LISSMAC deburring and grinding machines. LISSMAC positions the solution for high-mix, low-volume environments, where repeatedly teaching a robot each new part can make automation uneconomic.
Rather than relying on every component arriving in a fixed position, the robot can identify suitable parts and handle a variety of shapes and dimensions. LISSMAC also describes intelligent sheet separation and double-blank detection as part of the handling process.
The collaboration with fruitcore robotics is particularly significant because it links the specialist knowledge of a metal-finishing machine builder with robot hardware, vision and accessible automation software. LISSMAC also offers retrofit possibilities for existing machines.
For smaller fabrication businesses, the question is whether robotics can take over repetitive loading tasks without turning each product change into a programming exercise. LISSMAC and fruitcore are tackling that issue directly.
It was another example of machine vision becoming the connection between AI software and physical action on the shop floor.
The AI sees. The robot acts.
LVD: Giving a Bending Robot Vision
LVD’s RBS 40 Vision provided one of the clearest examples of that connection between AI vision and physical automation.
The vision-guided robotic bending system can identify and locate parts and verify their orientation before bending.
That sounds like a small change until you consider what it means for automation.
If a robot needs every component to arrive in precisely the same location and orientation, somebody or something else has to make sure that happens.
Giving the system the ability to recognise the workpiece creates more flexibility, particularly for high-mix production.
LVD also had another reason to celebrate in Hanover.
CADMAN Suite 9 won the EuroBLECH Award in the Advanced Manufacturing Software & AI category.
Together, CADMAN Suite 9 and RBS 40 Vision showed two sides of the same shift: more intelligence is moving into both the software that prepares production and the physical systems carrying it out.
Valk Welding: AI-Powered Recognition Takes Robotic Welding Beyond Manual Programming
Robotic welding specialist Valk Welding brought a different perspective to EuroBLECH, focusing on how manufacturers can automate welding even when batch sizes are small and products keep changing. Its stand in Hall 16 highlighted Automatic Robot Programming (ARP), ARC-EYE seam tracking and a new integrated laser welding solution.
With ARP, welding programmes can be generated directly from 3D CAD files. Valk Welding says this can be up to ten times faster than manual programming. ARC-EYE uses a laser sensor to track weld seams and adjust for workpiece variation during the process.
The company’s wider 2026 AI development is particularly interesting. Its AI Driven Similarities function, announced for the ARP software environment, is designed to recognise similarities between component geometries and match them with previously validated welding parameters. Valk Welding says the objective is to reduce repetitive programming by reusing knowledge from earlier jobs.
The distinction matters: ARP programming and ARC-EYE seam tracking were specifically identified in the company’s EuroBLECH demonstrations, while AI Driven Similarities is part of its announced 2026 technology direction. We have not confirmed a live EuroBLECH demonstration of that particular AI function.
For subcontract welders, the potential benefit is substantial. A robot that can draw on proven settings for comparable components could become more economical for high-mix, low-volume production. As with other AI-assisted manufacturing systems, welding quality still depends on validated procedures, fit-up, material and inspection, so the next question is how reliably that knowledge transfers to a new job.
Precitec: Using AI to Look More Closely at Quality
At Precitec, the AI conversation moved into quality control.
Its technology combines laser processing expertise with intelligent monitoring and inspection, including AI-supported image processing for laser weld inspection.
This is a particularly logical industrial AI application because modern production generates huge amounts of visual information.
The challenge is turning those images into useful decisions.
Can the system recognise a defect?
Can it distinguish acceptable variation from a genuine quality problem?
Can it do that consistently at production speed?
Those questions ultimately determine whether AI inspection creates value.
What makes this area particularly interesting is that AI does not have to replace an entire process. It can focus on a narrowly defined quality task and perform it repeatedly.
That kind of specific, measurable application may ultimately be where manufacturers become most comfortable trusting AI.
SigmaTEK Systems: Smarter Machines Still Need Smarter Software
SigmaTEK Systems brought us back to something that can sometimes get lost among impressive machines on a trade-show floor: software still determines a huge part of how efficiently those machines are used.
Its SigmaNEST environment sits within that wider production workflow, where nesting, programming, material utilisation and production management all influence the real productivity of sheet metal equipment.
A laser can be exceptionally fast and still spend too much time waiting.
A press brake can be highly automated and still become part of a bottleneck.
A factory can own excellent equipment and still struggle with visibility across production.
This is why software remains central to the smart-factory conversation.
Making an individual machine more intelligent is useful. Making the entire production flow more intelligent is the bigger challenge.
Around the Halls: The Bigger EuroBLECH 2026 Picture
The technologies above gave us a close-up view of where AI and automation are heading, but EuroBLECH was much bigger than those individual conversations.
Around 1,000 exhibitors represented practically every part of the sheet metal production chain.
And there was plenty more to see.
Bystronic: Connecting the Production Chain
Bystronic’s presence reflected the growing focus on connecting cutting, bending, software and automation.
That wider production-chain approach was one of the clearest themes at EuroBLECH.
Individual machine performance still matters, but the gaps between machines are receiving far more attention.
Every time someone has to find a component, move material, enter the same information again or wait for another process, productivity is lost.
The next generation of automation is increasingly about removing those gaps.
Prima Power: AI Moves Into Predictive Maintenance
Prima Power demonstrated another very practical use for AI: finding potential machine problems before they turn into unexpected downtime.
Prima Link predictive maintenance analyses alarms from connected sheet metal machines to identify potential equipment issues.
Warnings can then be passed to service experts who determine whether preventive action is required.
It is a very different application from AI vision or robotic handling, but the goal is similar.
Use information already being generated by manufacturing equipment to intervene earlier and keep production moving.
Prima Power’s wider EuroBLECH presence also demonstrated highly automated production flows connecting different sheet metal processes.
Salvagnini: Designing Production Around Change
Salvagnini’s flexible manufacturing approach addressed another reality facing sheet metal manufacturers: production requirements rarely stay predictable.
Batch sizes change.
Product mixes change.
Delivery expectations change.
Automation therefore cannot only be productive when everything stays the same.
The challenge is creating manufacturing systems that remain productive as the work changes.
That focus on flexibility appeared again and again at EuroBLECH and is becoming increasingly important as manufacturers move towards smaller batches and greater product variation.
TRUMPF: AI Becomes Part of the Production System
TRUMPF provided some of the clearest examples of AI moving directly into sheet metal production.
The TruLaser Weld 3000 uses TeachLine AI to detect deviations between the programmed weld position and the actual component and correct the robot path.
The company also highlighted AI-supported autonomous sorting, where cameras identify parts so they can be separated without programming every individual geometry.
These are two different applications, but they address the same underlying challenge: reducing the amount of preparation needed before automation can deal with changing work.
More importantly, they were presented as part of a much wider production system.
Cutting, welding, bending, sorting, material flow, robotics and production software are increasingly being connected rather than treated as isolated technologies.
That may ultimately be more significant than any individual machine launch.
More AI Worth Watching From EuroBLECH
Beyond the major names, the show floor contained a much wider group of companies experimenting with AI in specific parts of the sheet metal workflow.
SafanDarley brought intelligence into bending through Adaptive Angle Control, aimed at interpreting changes in material behaviour during the bend.
Bodor continued to develop intelligent cutting assistance and fault diagnosis.
In robotics and handling, Schmalz combined intelligent recognition with 3D vision and vacuum gripping, while Teqram demonstrated how vision can make robotic finishing more flexible.
AI was also increasingly visible in welding and programming, with companies exploring ways to reduce the amount of manual work required to prepare robotic processes.
On the software side, the same shift was happening much earlier in the production journey.
AI is beginning to help interpret enquiries, drawings, manufacturing information and quotation data before a job reaches the shop floor.
This variety was important.
There was no single “EuroBLECH AI technology.”
Instead, AI appeared at many small points across the workflow, each aimed at removing a particular piece of repetitive work or making a production decision easier.
MTN Analysis: The Best AI at EuroBLECH Didn’t Feel Like AI
After several days looking at AI at EuroBLECH 2026, one thing stayed with us.
The most interesting AI applications were often the least dramatic.
Recognise this part.
Find this defect.
Use what we learned from the previous bending job.
Calculate this quotation.
Tell me what is happening in production.
Sort these components.
Predict a machine problem before it stops production.
None of those sound like the sweeping autonomous-factory predictions that have surrounded industrial AI.
But they solve actual manufacturing problems.
And that is probably a much better indication of where the technology is heading.
AI Is Moving Closer to the Machine
At IMTS 2026, we saw AI spreading rapidly across the wider manufacturing software landscape.
AI at EuroBLECH 2026 gave us a more concentrated look at what happens when that intelligence reaches physical production.
Computer vision was particularly noticeable.
LISSMAC uses recognition to make part handling more flexible. LVD uses vision to help a robot understand the workpiece in front of it. InspecVision uses AI-supported vision to capture and verify manual processes. Precitec applies intelligent image processing to inspection. TRUMPF uses cameras and AI for sorting.
Different applications, same underlying change.
Machines are getting better at understanding what is physically happening around them.
The Skills Shortage Is Shaping the Technology
Another theme was difficult to ignore.
Manufacturers need to do more with fewer experienced people.
That helps explain why so much of the technology at EuroBLECH focused on easier programming, knowledge reuse and autonomous operation.
AMADA can draw on experience from previous bending jobs. InspecVision is looking at ways to capture tasks that would traditionally depend on operator knowledge. AI-assisted robotic systems reduce some of the programming required to deal with changing parts.
This does not make manufacturing expertise less important.
It makes capturing that expertise more important.
Faster Machines Are Forcing Factories to Think Differently
There was also a lesson that had very little to do with AI itself.
Machines are getting extraordinarily fast.
But eventually, making the machine faster stops being the biggest opportunity.
If a laser finishes cutting and the parts sit waiting for someone to sort them, cutting faster does not solve the real problem.
That is why the automation we saw around laser cutting, sorting, material handling and connected production is so important.
The focus is moving from machine productivity to factory productivity.
AI Is Moving Upstream Too
Another interesting development was how early AI is beginning to enter the manufacturing process.
Not every application starts on the factory floor.
Figure it is looking at quotation and manufacturing information before a job enters production. Other software providers are using AI to interpret RFQs, drawings and customer information.
This matters because improving production speed has limited value if manufacturers still lose hours preparing the job before it reaches a machine.
The smart factory increasingly starts before the factory floor.
From IMTS to EuroBLECH: Industrial AI Is Growing Up
Coming to Hanover after our time at IMTS gave us an interesting comparison.
IMTS showed us the breadth of industrial AI.
EuroBLECH showed us what happens when that technology becomes deeply focused on one manufacturing sector.
Quoting.
Programming.
Cutting.
Bending.
Welding.
Inspection.
Sorting.
Material handling.
Production management.
Predictive maintenance.
AI is beginning to appear at almost every point in the sheet metal workflow.
But the strongest technologies were not necessarily the ones making the biggest claims.
They were the ones where you could immediately understand the problem being solved.
That may be the biggest takeaway from AI at EuroBLECH 2026.
The industry does not need AI for the sake of having AI.
It needs technology that makes production faster, more flexible, easier to manage and less dependent on repetitive manual work.
After what we saw from AI at EuroBLECH 2026, that is increasingly where industrial AI appears to be heading.
Further Reading
EuroBLECH Preview: Your Ultimate Guide to AI Exhibitors
LVD RBS 40 Vision: Powerful AI Bending as CADMAN Wins EuroBLECH 2026 Award
Prima Link Predictive Maintenance: Prima Power Uses AI to Flag Machine Problems Early
IMTS 2026 Post-Show Review: What the Biggest Manufacturing Show in the West Revealed About AI
