Material parameter identification for clinching process simulation using neural network metamodels
Duc Vinh Nguyen, Pai-Chen Lin, Minh Chien Nguyen, Yang-Jiu Wu, Hoang Son Tran, Xuan Van Tran
DOI: http://dx.doi.org/10.15439/2022R31
Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 183–188 (2022)
Abstract. Clinching is a mechanical joining method in which two sheet workpieces are clamped and locked together using a punch and a die. Process parameters for such joint elements are often designed based on numeric simulation. Before this step, the identification of good material parameters is crucial to get validated computational results. In this paper, neural network metamodels are used for this specific task as a means to deal with large computation time. The identified material parameters reduce significantly the error between computational results and experimental results.
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