Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement
Tool wear causes costs and quality problems in metal cutting manufacturing processes. This paper contains an approach of digitalization and big data analytical methods to quantify the wear of metal cutting tools. The method developed consists of a pipeline of deep learning operations for processing tool wear images collected with a digital microscope and is complemented by a rule-based approach to measuring wear along the cutting edge of machining tools. The end-to-end approach allows fully automated tool wear detection and measurement that can be used for inline measurements within CNC machine tools for machining applications.
Yavuz, Taha Berk
Werkzeugmaschinenlabor WZL der RWTH Aachen