© 2025 Hexagon AB and/or its subsidiaries
© 2025 Hexagon AB and/or its subsidiaries
Identify surface defects on materials such as glass, metal, plastics, ceramics and textiles.
HxGN Visual Detection uses AI to learn from a small set of training images, so it can be quickly reconfigured to support a change of product.
Locate and recognise defects with an excellent level of accuracy. Reduce rework, limit quality-related downtime and minimise project costs.
Quickly train HxGN Visual Detection to identify errors by drawing rectangles around scratches or dents in your sample image set.
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.
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.
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.
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.
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