A Study on the Prediction of Mechanical Properties of Sheet Metal based on Deep Learning for Stamping Process

In the manufacturing industry, efforts are being made to improve product quality by flexibly controlling processes based on material properties. Although several studies have developed methods considering material properties based on mechanics, these methods show insufficient accuracy due to assumptions involved in mechanics theory. Research utilizing artificial intelligence is needed to address these limitations. This work presents the feasibility of applying deep neural networks to predict the mechanical properties of metal sheets with limited data for real-time control of stamping processes. In a stamping process, the mechanical properties of a blank change due to plastic deformation, which can cause difficulties in quality control of products. Using a real-time control system with non-destructive material tests can improve product quality. This paper focused on improving the accuracy of real-time prediction for mechanical properties of blanks using a deep neural network algorithm when the data size is much smaller than that of general cases. Yield stress and plastic strain of metal sheets were predicted using a deep neural network-based approach from 27 features collected with an eddy-current material tester. By designing the model architecture with regularization, the deep learningbased solution provided results comparable to other machine learning approaches under limited sample conditions.