Study on the explainability of deep learning models for time series analysis in sheet metal forming
Marco Becker, Philipp Niemietz, Thomas Bergs · Procedia CIRP · 2024
In recent research, force signals have been utilized to estimate the wear of tool components in sheet metal processing using, e.g., artificial neural networks (ANNs). ANNs learn predictive models from raw time series signals without requiring prior knowledge and manual feature engineering. However, ANNs are black-box models. Existing research on explainable AI provides methods to increase the transparency of ANNs, but mainly focusses on computer vision problems. This publication proposes an approach to learn explainable ANNs for time series data. The presented approach is applied to a tool wear prediction task using force signals from a fine blanking machine.