关键词: 拉深成形, 动态遗传BP神经网络模型, 灰色关联度, 多目标优化

Abstract: Aiming at the low global precision and uncertain sample size for static surrogate model in sheet metal forming optimization,a dynamic Genetic Algorithm Back Propagation neural network (GABP) modeling method that used multi-stage supplementing samples to gradually improve the prediction accuracy of the surrogate model was proposed.The method increased the global precision by adding the sample according to maximum minimum distance criterion,and improved the local precision by supplementing the optimized solution as a sample,which could achieve a reduction in sample size and an improvement in computational efficiency.Based on the grey system theory,a new iterative formulation of grey relativity was derived.The multi-objective problem was transformed into single-objective optimization with maximum grey relativity,and the optimization framework of joint genetic algorithm and dynamic GABP model was established.The function example showed that the dynamic equivalent could cut down the sample size by about 20% and had better prediction accuracy by comparing with the static GABP model,and the new grey relativity formulation could promote the stable convergence of optimization iteration.The framework was used to conduct the forming process optimization of NUMISHEET 93 square box.Compared with the initial design,the thinning index and the wrinkle index of the optimized design was reduced by 16.62% and 8.26%respectively,which effectively improved the forming quality of the square box parts.

Key words: deep drawing, dynamic genetic algorithm back propagation neural network model, grey relativity, multi-objective optimization