HxGN Visual Detection
Automated surface inspection application ideal for defect detection
HxGN Visual Detection can be deployed to support a wide range of use cases. It can manage high-speed throughput within a large manufacturing cell or when checking small numbers of high-value components during a proof of concept. HxGN Visual Detection uses CNN (convolutional neural network) deep learning models to train, identify and categorize surface defects.
Ready for use with Hexagon’s Optiv vision CMMs, HxGN Visual Detection can easily be incorporated into metrology workflows with PC-DMIS Vision to add surface inspection. The fully automated, one-shot measurement and defect detection approach will increase autonomy and accuracy and improve efficiency in any workflow.
Benefits And Features
HxGN Visual Detection in the metrology workflow
AI powered speed and flexibility
Hexagon Visual Detection uses AI to learn from a set of training images, so it can be quickly trained to recognise defects and reconfigured to support a range of products.
Improve quality
Locate defects across a wide range of materials such as glass, metal, plastics, ceramics and textiles with an excellent level of accuracy. Reduce rework, limit quality-related downtime and minimise project costs.
Simple to use
Intuitive and easy to use. Quickly train Hexagon Visual Detection to identify production defects by drawing rectangles around scratches or dents in your sample image set.
Training and Resources
Support
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