Surrogate Model for Shape Prediction in Press Forming Simulation

Abstract

In the design process of press forming dies, the finite element method (FEM) is widely used to obtain die shapes that satisfy formability and dimensional accuracy of pressed parts. However, high computation costs and increasingly complex part geometries have prolonged the cycle of modifying die designs based on simulation results and re-analysis, hindering overall efficiency. To address these challenges, we propose a method to construct highly accurate surrogate models based on FEM outcomes. Specifically, we apply PointNeXt, a deep learning model originally developed for 3D point cloud shape classification and semantic segmentation, to learn the relationship between press die and the resulting pressed part shapes. The surrogate model can predict in minutes with an average error of approximately 0.3 mm, meeting practical accuracy requirements. This approach is expected to streamline the design process, shorten development cycles, and reduce lead times in die design.