基于遗传算法优化神经网络的拼焊板压边力预测

通过数值模拟与神经网络技术对拼焊板盒形件拉深成形过程中的压边力预测问题进行研究.运用数值模拟分析压边力加载形式对拼焊板盒形件成形性能的影响，找到一种较优的变压边力加载方式.建立适用于拼焊板盒形件拉深成形压边力预测的BP神经网络模型.采用遗传算法对神经网络模型进行优化，在基因选择过程中加入精英保留策略，最终通过基于遗传算法优化的神经网络模型获取了理想的压边力曲线，用以预测随拉深行程变化的压边力数值，为实现智能化冲压奠定了技术基础.

Blank Holder Force Prediction of Tailor Welded Blank Based on Neural Network Optimized by Genetic Algorithm

Numerical simulation and neural network technology were used for the blank holder force(BHF) prediction of tailor welded blank(TWB) box part in deep drawing. The influence of BHF loading type on the formability of TWB box part was analyzed by numerical simulation， and a preferable BHF loading type was obtained. Then BP neural network model suitable for BHF prediction was developed. At last， the neural network model was optimized by the genetic algorithm. The elitism strategy was introduced into the course of gene selection， and finally an ideal BHF curve was obtained by the neural network model optimized by the genetic algorithm， which can predict BHF value varying in deep drawing process and can lay the technical foundation for intelligent stamping.