AI-Powered Tool Condition Monitoring in Automated Steel Sample Preparation

Reliable surface preparation is essential for achieving accurate and reproducible OES analysis of steel samples. This Application Note compares a conventional algorithm-based image evaluation method with a deep learning-based segmentation approach for automated monitoring of milling insert wear in the HS-F 1000 milling machine equipped with PrepMaster Vision. The deep learning model provides significantly improved robustness and accuracy under challenging industrial imaging conditions such as reflections, varying illumination, and complex insert geometries, while achieving substantially lower deviations from light-microscopy reference measurements and simplifying adaptation to different insert types and operating conditions.