Autoencoder based Wear Assessment in Sheet Metal Forming

IOP Conference Series: Materials Science and Engineering - Tập 1157 Số 1 - Trang 012082 - 2021
Philipp Niemietz1, Martin Unterberg1, Daniel Trauth1, Thomas Bergs2,1
1Laboratory for Machine Tools and Production Engineering (WZL) of the RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany
2Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany

Tóm tắt

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.

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Thông tin xuất bản

IOP Conference Series: Materials Science and Engineering

Tập 1157 Số 1

012082

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