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.
