Autoencoder based Wear Assessment in Sheet Metal Forming

IOP Conference Series Materials Science and Engineering, vol. 1157, pp. 012082

Abstract

Abstract The amount of information contained in process signals such as acoustic emission and force signals has proven vital for the detection of changes in physical conditions or quality feature prediction in sheet metal forming applications. Both signal types have also been researched in the context of wear detection, yet systems that reliably identify the wear state at a given time in sheet metal forming processes based on these signals do not exist. This paper proposes an architecture to assess the wear increase within a given time frame in an experiment based on an autoencoder. The ability of autoencoders to encode and decode signals has been widely studied and this approach leverages the fact that autoencoders are more likely to learn representative encodings on stable and homogeneous signals than on heterogeneous signals with high fluctuations. This approach utilizes the circumstance that high tool wear leads to changes in the signal and signal fluctuation. In consequence, autoencoders can be utilized to track tool wear progression without the need for labelled data. The findings show a strong similarity to physical models for the wear progression of tool components, indicating the validity of this approach. Additionally, an analysis of the signals yields characteristic effects of the considered force signals that could specifically represent wear resistance.

Authors 4

1. Philipp Niemietz corresponding Aachen Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering WZL of the RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

2. Martin Unterberg Aachen Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering WZL of the RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

3. Daniel Trauth Aachen Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering WZL of the RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

4. Thomas Bergs Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production Engineering WZL

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

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

   Laboratory for Machine Tools and Production Engineering WZL of the RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany