Springback prediction for sheet metal cold stamping using convolutional neural networks

Lei Zhu; Nan Li

doi:10.1117/12.2675249

28 June 2023 Springback prediction for sheet metal cold stamping using convolutional neural networks

Lei Zhu, Nan Li

Proceedings Volume 12720, 2022 Workshop on Electronics Communication Engineering; 1272012 (2023) https://doi.org/10.1117/12.2675249
Event: 2022 Workshop on Electronics Communication Engineering (WECE 2022), 2022, Xi'an, China

Abstract

Springback is a crucial factor in cold stamping that causes geometric inaccuracy of the stamped component after removal of tools. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the thinning and springback behaviours for cold stamping. Datasets were created based on two cold stamping case studies, i.e., a U-bending case and an outer car door panel stamping case. The datasets were then applied to train the CNN-based surrogate models. The results show that the surrogate models can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Citation Download Citation

Lei Zhu and Nan Li "Springback prediction for sheet metal cold stamping using convolutional neural networks", Proc. SPIE 12720, 2022 Workshop on Electronics Communication Engineering, 1272012 (28 June 2023); https://doi.org/10.1117/12.2675249

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