Information Content Analysis of Direct and Indirect Force Measurements for Machine Learning-Based Process State Classification in Multi-Stage Sheet Metal Forming

The predictive power of machine learning models for process monitoring in sheet metal forming depends strongly on the information content of the sensor signals. This study investigates how force signal characteristics represent process conditions in a multi-stage forming process consisting of deep drawing and ironing, in which surface roughness evolves with a downstream tendency. Indirect and direct force measurement concepts are compared: While indirect sensors are prone to noise, direct sensors often show more clarity. Neural networks are trained on datasets covering multiple roughness and process configurations. Model performance is analysed using classification metrics and explainable AI methods. The results reveal a counterintuitive finding: Visually smooth force signals with high signal-to-noise ratio can provide limited or misleading information for convolutional neural networks due to temporal misalignment, whereas noisier signals with distributed dynamics show more robust predictions. The study shows the influence of signal clarity for data-driven process monitoring in Industry 4.0-enabled forming.