车身覆盖件回弹控制的成形工艺多目标优化

上海交通大学学报（自然版） ›› 2011, Vol. 45 ›› Issue (11): 1673-1677.

车身覆盖件回弹控制的成形工艺多目标优化

阳湘安1,2，阮锋2

(1. 广东技术师范学院机电学院，广州 510643； 2. 华南理工大学机械与汽车工程学院，广州 510640)

收稿日期:2010-11-19 出版日期:2011-11-30 发布日期:2011-11-30
基金资助:
国家自然科学基金资助项目（50805050）

Multi-Objective optimization of Forming Process for Auto-body Panel Based on Springback Control

(1. College of Electromechanial Engineering, Guangdong Polytechnic Normal University,Guangzhou 510643, China; 2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China）

Received:2010-11-19 Online:2011-11-30 Published:2011-11-30

摘要/Abstract

摘要： 分析了车身覆盖件成形工艺优化问题中回弹评价目标函数的确定准则，建立了合理的基于回弹控制的成形工艺多目标优化模型.采用实验设计 (DOE)、径向基函数神经网络(RBF)和非支配排序遗传算法(NSGAII)相结合的优化策略对问题进行了全局优化求解，在与相应的单目标优化结果对比后，分析了压边力和拉延筋力对回弹的影响机理.结果表明，压边力主要通过对工件的塑性变形产生影响，进而影响回弹，随着压边力的增加工件塑性变形程度加大而回弹急剧减少，但是当压边力增大到一定程度后工件的回弹反而增大；拉延筋力则主要对工件的内应力分布产生较大影响，并因此影响工件切边后的回弹.与相应的单目标优化模型相比，文中的多目标优化模型在控制回弹的同时能够更全面地考虑板料成形的其他缺陷.

关键词: 回弹, 优化, 板料冲压, 成形工艺

Abstract: Selection criteria of objective function in the optimization model aimed to reduce springback for auto-body panel were discussed. Taking the automobile roof panel as an example, a multi-objective optimization model was established to control springback. The design of experiment (DOE),radial basis function neural networks (RBF) and non-dominated sorting genetic algorithm (NSGA-II) were employed to solve the model. The solution was compared with that of the corresponding singleobjective optimization models to analyze the influence of forming process on springback. The results show that blankholder force influences the amount of plastic deformation significantly, and thus the amount of springback. large plastic deformation would occur and springback would decrease rapidly with increasing blankholder force, but when it increases to a certain extent springback would also increase slightly, while drawbead forces have great effect on the internal stress distribution of the sheet metal, thus eventually influence trimming springback. Compared with corresponding single-objective optimization models, the proposed multi-objective optimization model considers forming defects more comprehensively and is more reasonable for application.

Key words: springback, optimization, sheetmetal stamping, forming process

中图分类号:

TG386

阳湘安1,2，阮锋2. 车身覆盖件回弹控制的成形工艺多目标优化[J]. 上海交通大学学报（自然版）, 2011, 45(11): 1673-1677.

参考文献

［1］Hayashi H, Nakagawa T. Recent trends in sheet metals and their formability in manufacturing automotive panels ［J］. Materials Processing Technology, 1994, 46(34): 455487.

［2］Wang H, Li G Y, Zhong Z H. Optimization of sheet metal forming processes by adaptive response surface based on intelligent sampling method ［J］. Journal of Materials Processing Technology, 2008, 197(13): 7788.

［3］Ohata T, Nakamura Y, Katayama T, et al. Development of optimum process design system for sheet fabrication using response surface method ［J］. Journal of Materials Technology, 2003, 143144: 667–672.

［4］Kazan R, Firat M, Tiryakia A E. Prediction of springback in wipebending process of sheet metal using neural network ［J］. Materials and Design, 2009, 30(2): 418423.

［5］朱东波, 马雷, 李涤尘, 等. 复杂形状板料冲压件回弹评价指标研究［J］. 机械科学与技术, 2000, 19 (6): 953955.

ZHU Dongbo, MA Lei, LI Dichen, et al. On springback evaluation for 3D sheet metal stamping parts ［J］. Mechanical Science and Technology, 2000, 19(6): 953955.

［6］Liu W, Yang Y Y, Xing Z W, et al. Springback control of sheet metal forming based on the responsesurface method and multiobjective genetic algorithm ［J］. Materials Science and Engineering: A, 2009， 499(12):325–338.

［7］郑超, 刘全坤, 胡龙飞, 等. 基于回弹控制的汽车横梁拉延成形工艺多目标优化研究［J］. 合肥工业大学学报, 2008, 31(1): 8992.

ZHENG Chao, LIU Quankun, HU Longfei, et al. Study on multiobjective optimization of stamping of the cross member based on springback control ［J］. Journal of Hefei University of Technology, 2008, 31(1):8992.

［8］Song J H, Huh H, Kim S H. Stressbased springback reduction of a channel shaped autobody part with highstrength steel using response surface methodology ［J］. Journal of Engineering Materials and Technology, 2007, 129(3): 397406.

［9］Ingarao G, Lorenzo R D, Micari F. Analysis of stamping performances of dual phase steels: A multiobjective approach to reduce springback and thinning failure ［J］. Materials and Design, 2009, 30: 44214433.

［10］陈劼实, 周贤宾, 刘长丽. 数值模拟中应用最小厚度准则预测板料成形极限［J］. 中国机械工程, 2006, 17(S1): 6770.

CHEN Jieshi, ZHOU Xianbin, LIU Changli. Sheet metal forming limit prediction with minimum thickness criterion in numerical simulation ［J］. China Mechanical Engineering, 2006, 17(S1): 6770.

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