基于数值模拟的B柱加强板成形工艺优化  

Numerical-simulation-based process optimization on B-pillar reinforcement panel forming

作　　者：王圣波[1] 詹艳然[2] 

机构地区：[1]一汽海马汽车有限公司,海南海口570216 [2]福州大学机械工程及自动化学院,福建福州350108

出　　处：《中国工程机械学报》2016年第1期62-68,共7页Chinese Journal of Construction Machinery

基　　金：福建省自然科学基金资助项目(2008J0153)

摘　　要：在对使用拼焊板拉深成形的汽车B柱加强板进行数值模拟的基础上,采用BP神经网络建立工艺参数与成形质量之间的非线性映射关系,通过多目标遗传算法NSGA-II获得最优成形工艺参数.研究结果表明:神经网络结合多目标遗传算法可以获得最优成形工艺参数,可较好地解决B柱加强板的成形问题.Based on the numerical simulation on car B-pillar reinforcement panel forming from the tailored welded blanks,a nonlinear mapping relation between process parameters and forming qualities is first established via BP neural network. By applying the multi-objective genetic algorithm, i. e. NSOA-II, for optimal parameters, it is found from results that the integration of neural network and multi-objective genetic algorithm can obtain the optimal parameters to resolve the B-pillar reinforcement panel forming problems.

关 键 词：B柱加强板 拼焊板 工艺优化 神经网络 遗传算法 

分 类 号：TG306[金属学及工艺—金属压力加工]