BP神经网络和遗传算法用于曲轴填充性能的优化设计  

BP neural network and genetic algorithm for the filling properties optimization of crankshaft

作　　者：周杰[1] 卢先正[1] 舒锐志[1] 凌君 

机构地区：[1]重庆大学材料科学与工程学院,重庆400044 [2]重庆大江杰信锻造有限公司,重庆401321

出　　处：《重庆大学学报（自然科学版）》2012年第5期52-56,共5页Journal of Chongqing University

基　　金：重庆市自然科学基金重点资助项目(CSTC2009BA4065)

摘　　要：针对曲轴空间分模模具存在的深型腔难填充结构,提出了采用楔形飞边槽结构改善填充性能的方法。基于MATLAB平台,将BP人工神经网络与遗传算法应用于楔形飞边槽结构参数优化设计。首先利用正交试验设计安排试验样本,对所得的样本进行有限元模拟,获得各方案坯料的最小未填充距离,作为BP神经网络训练的导师信号。再结合遗传算法,以最小未填充距离为目标,得到楔形飞边槽结构的最优参数。最后通过数值模拟验证并比较遗传算法预测结果与数值模拟结果的误差。结果表明,误差在5%以内。将优化参数应用于实际生产,坯料能够完全充满模具型腔,材料利用率由75.7%提高到81.4%,验证了楔形飞边槽结构优化设计的正确性。The structure of wedge flash is proposed to improve the filling properties for deep-cavity structure of crankshaft die with dimensional splitting mold. BP genetic algorithm is applied to optimize the structure parameters of wedge flash based on Matlab. Samples which are selected by orthogonal test are analyzed via FEM, and the minimum unfilled distance obtained are employed to conduct the BP neural network training. Then the optimum parameters with minimum unfilled distance are gained from genetic algorithm. Error between the parameters predicted and the results get from simulations is less than 5%. The productive practice indicates that the cavity is fully filled and the material utilization ratio increases from 75.7% to 81.4%, which confirms the correctness of optimization of wedge flash structure.

关 键 词：曲轴 楔形飞边槽 神经网络 遗传算法 

分 类 号：TG316.3[金属学及工艺—金属压力加工]