Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement

IFAC-PapersOnLine, vol. 55, pp. 534–539

Abstract

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.

Authors 5

1. Carsten Holst corresponding Aachen Fraunhofer Institute for Production Technology

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology, Steinbachstraße 17, 52074 Aachen, Germany

2. Taha Berk Yavuz Aachen Fraunhofer Institute for Production Technology

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology, Steinbachstraße 17, 52074 Aachen, Germany

3. Pranjul Gupta corresponding

   Justus-Liebig-Universität Gießen

   Affiliation as printed

   Dept. of Experimental Psychology, Justus-Liebig-Universität, Otto-Behaghel-Strasse 10F, 35394 Giessen, Germany

4. Philipp Ganser Aachen Fraunhofer Institute for Production Technology

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology, Steinbachstraße 17, 52074 Aachen, Germany

5. Thomas Bergs corresponding Aachen Werkzeugmaschinenlabor WZL

   RWTH Aachen University

   Affiliation as printed

   Werkzeugmaschinenlabor WZL der RWTH Aachen, Campus-Boulevard 30, 52074 Aachen, Germany