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基于克里金模型和多目标遗传算法的转向节模具参数优化
英文标题:Optimization on steering knuckle mold parameters based on Kriging model and multi-objective genetic algorithm
作者:徐杰 
单位:重庆工商职业学院 
关键词:转向节 克里金模型 多目标遗传算法 数值模拟 成形载荷 
分类号:TG316
出版年,卷(期):页码:2022,47(7):213-219
摘要:

 为了解决某转向节成形后存在填充不满、折叠、成形载荷太大以及表面成形质量差等缺陷,提出了基于克里金模型、多目标遗传算法和数值模拟技术相结合的优化策略。选取模具冲头斜度、冲头头部长羊角侧圆角半径、杆部与法兰盘连接处圆角半径3个参数作为设计变量,杆部末端欠填充距离和终锻最大成形载荷作为优化目标。首先,通过正交实验设计方法得到实验方案;其次,使用Deform-3D对每组实验进行数值模拟,得到设计变量与优化目标之间的响应数据;再次,使用克里金模型近似设计变量与优化目标之间的映射关系,并用多目标遗传算法对该近似模型进行全局寻优,获得前沿最优解集;最后,将优化的最优参数进行数值模拟和生产验证,模拟结果和生产结果证明了优化策略的有效性。采用最优模具参数能够得到成形效果良好的产品,且可将材料利用率从原来的75%提高至85%。

 In order to solve the defects of insufficient filling, folding, too large forming load and poor surface forming quality of a steering knuckle after forming, an optimization strategy based on Kriging model, multi-objective genetic algorithm and numerical simulation technology was proposed. The punch draft angle, the fillet radius of long sheep horn side for punch head and the fillet radius of connection between rod and flange were selected as the design variables, and the unfilled distance at the end of rod and the maximum forming load of final forging were selected as the optimization objectives. Firstly, the experimental scheme was obtained by orthogonal experimental design method. Secondly, each group of experiments was simulated by Deform-3D, and the response data between design variables and optimization objectives was obtained. Thirdly, the mapping relationship between design variables and optimization objectives was approximated by Kriging model, and the approximate model was globally optimized by multi-objective genetic algorithm to obtain the frontier optimal solution set. Finally, the optimized parameters were simulated and verified in production. The results show that the simulation results and production results prove the effectiveness of the optimization strategy. Therefore, the products with good forming effect are obtained by the optimized mold parameters, and the material utilization rate is increased from 75% to 85%. 

基金项目:
重庆自然科学基金资助项目(cstc2020jcyj-msxmX0940)
作者简介:
作者简介:徐杰(1981-),男,硕士,副教授 E-mail:xujie6896@163.com
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