Deep Learning Based Tool Wear Estimation Considering Cutting Conditions

Procedia CIRP, vol. 130, pp. 133–138

Abstract

Tool wear conditions impact the final quality of the workpiece. In this study, we propose a deep learning approach based on a convolutional neural network that incorporates cutting conditions as extra model inputs, aiming to improve tool wear estimation accuracy and fulfill industrial demands for zero-shot transferability. Through a series of milling experiments under various cutting parameters, we evaluate the model’s performance in terms of tool wear estimation accuracy and its transferability to new fixed or variable cutting parameters. The results consistently highlight our approach’s advantage over conventional models that omit cutting conditions, maintaining superior performance irrespective of the stability of the wear development or the limitation of the training dataset. This finding underscores its potential applicability in industrial scenarios.

Authors 3

1. Zongshuo Li corresponding Aachen Manufacturing Technology Institute (MTI)

   RWTH Aachen University

   Affiliation as printed

   Manufacturing Technology Institute (MTI) of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

2. Markus Meurer Aachen Manufacturing Technology Institute (MTI)

   RWTH Aachen University

   Affiliation as printed

   Manufacturing Technology Institute (MTI) of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

3. Thomas Bergs Aachen Fraunhofer Institute for Production Technology IPT Manufacturing Technology Institute (MTI)

   RWTH Aachen University · Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany

   Manufacturing Technology Institute (MTI) of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany