曲轴锻模新型飞边结构的智能优化设计  

Intelligent optimization design of novel flash structure for crank-shaft forging dies

作　　者：张渝[1] 安治国[1] 周杰[2] 

机构地区：[1]重庆交通大学机电与汽车工程学院,重庆400074 [2]重庆大学材料科学与工程学院,重庆400044

出　　处：《重庆大学学报（自然科学版）》2010年第11期70-76,共7页Journal of Chongqing University

基　　金：重庆市教委科学技术研究项目(KJ100414)

摘　　要：针对锻模新型飞边结构——阻力墙,对其结构参数进行了优化研究。通过应用部分析因设计方法,对阻力墙结构参数的效应进行了分析,筛选出了关键因子。将得到的设计变量应用拉丁超立方抽样,对得到的样本点进行有限元模拟。以阻力墙结构参数为变量、有限元模拟结果为响应,建立代理模型。采用线性加权法将所得近似模型转化为单目标函数,利用粒子群算法进行全局寻优,最后应用遗传算法对该优化问题进行了比较和验证。结果表明,采用粒子群算法能够得到最优化的阻力墙结构参数,且收敛速度远高于传统遗传算法。The parameters of resistance wall,which is a novel flash structure for forging die,are studied.The effects of the resistance wall＇s parameters are analyzed and the important influence factors are screened by using the fractional factorial design.The Latin hypercube method is used to select sample points of the important design variables which are analyzed by finite elements simulation.The surrogate models are established by taking the simulation result as response and the parameters of the resistance wall structure as variables.The model is converted into single objective function by linear weighting method and is optimized by using particle swarm optimization algorithm for global optimization.Finally,the optimization results are compared and verified with those obtained by genetic algorithm.The results show that the PSO（particle swarm optimization） algorithm has better convergence than the traditional genetic algorithm and can realize optimization of the parameters of the resistance wall structure.

关 键 词：阻力墙 部分析因设计 粒子群算法 遗传算法 

分 类 号：TG315.2[金属学及工艺—金属压力加工]