摘要
以某车型前门外板为例,根据Auto Form初步数值模拟结果,将成形最大减薄率和修边后最大回弹量作为优化目标,以拉延R角半径、拉延筋阻力、摩擦系数、压边力、冲压速度为自变量,设计5因素4水平的正交试验。采用灰色关联分析法,对正交试验数据进行处理,计算各工艺参数对目标函数的关联系数和关联度,得到多目标优化的最优工艺参数组合：拉延R角半径为27 mm、拉延筋阻力为175 N·mm-1、摩擦系数为0.13、压边力为1450 k N、冲压速度为2500 m·s-1。使用优化过后的成形工艺参数在Auto Form中进行再次模拟,结果显示成形最大减薄率和修边后最大回弹量都得到合理控制。将优化后的工艺参数用于指导工艺设计和模面回弹补偿,然后进行模具结构设计、制造和试模,实际结果表明,前门外板冲压成形质量合格。
For automotive front door outer panel,according to preliminary numerical simulation results of Auto Form,taking the maximum thinning and the maximum springback after trimming as the optimization objectives,and taking the drawing circle radius R,drawbead resistance,friction coefficient,blank holder force and stamping speed as the independent variables,the orthogonal test of five factors and four levels was designed. Then,the data of orthogonal test were processed by grey relational analysis method,and the correlation coefficient and correlation degree of each process parameter to the objective function were calculated. Therefore,the optimal combination of process parameters for multi-objective optimization was obtained with the drawing circle radius of 27 mm,drawbead resistance of 175 N·mm-,friction coefficient of 0. 13,blank holder force of 1450 k N and stamping speed of 2500 m·s-1. Furthermore,the forming process was simulated again by Auto Form with optimized process parameters. The results show that the maximum thinning and the maximum springback after trimming are reasonably controlled,and the die is designed,manufactured and tested by the optimized process parameters to guide the process design and springback compensation. Thus,the actual results show that the stamping quality of the front door outer panel is qualified.
作者
朱超
王雷刚
黄瑶
Zhu Chao;Wang Leigang;Huang Yao(School of Materials Science and Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《锻压技术》
CAS
CSCD
北大核心
2018年第8期39-43,70,共6页
Forging & Stamping Technology
基金
国家自然科学基金资助项目(51275216,51775249)
关键词
前门外板
冲压
正交试验
灰色关联
多目标优化
front door outer panel
stamping
orthogonal test
gray relation
multi-objective optimization
