Stamping process parameter optimization method based on GA-BP model and NSGA-Ⅱ algorithm

In order to solve the problems of serious wrinkling, insufficient forming and high energy consumption in the stamping process of special-shaped and thin-walled parts, a GA-BP neural network optimization model with blank holder force and friction coefficient as design variables and maximum thickness, thickness reduction rate and blank holder energy consumption as optimization objectives was innovatively established, and it was solved by non-dominated sorting genetic (NSGA-Ⅱ) algorithm, and the optimal combination of process parameters in the stamping process was obtained. The results show that the maximum thickness, thickness reduction rate and blank holder energy consumption under the optimal combination of process parameters are 0.269 mm, 15.8% and 8720 J, respectively, which are improved by 0.74%, 3.06% and 11.32%, respectively, effectively solving the problems of serious wrinkling, insufficient forming and high energy consumption of special-shaped thin-walled parts. © 2024 Central South University of Technology. All rights reserved.