Compensation of Twist Springback in High-strength Steel Based on Gradient Die Radius

doi:10.3901/JME.2019.02.091

Abstract

Abstract: In order to reduce the twist springback appearing after the stamping of high-strength steel, a compensation method with gradient die radius is proposed. The double C rail of TRIP780 high-strength steel is taken as the research object. The sheet metal stamping simulation software DYNAFORM is used to numerically simulate the stamping and twist springback processes of the double C rail. An index to evaluate the twist springback of the double C rail is proposed. The experiment of twist springback for the double C rail is carried out. The twist springback angle is measured by means of a three-coordinate measuring instrument, and the finite element model is validated. The twist springback appearing after stamping is taken as the optimization target, and the related process parameters are taken into account. BP neural network is used to establish the network model between the gradient variation of die radius, process parameters and the twist springback angle based on orthogonal test. Finally, the model is iteratively optimized using genetic algorithm to obtain the optimum gradient variation of die radius and process parameters. The twist springback angle is compared before and after the optimization, which proves the optimization flow efficient to reduce the twist springback of the double C rail. This method provides a new way for the control of the twist springback.

Key words: BP neural network, genetic algorithm, gradient die radius, high-strength steel, twist springback

CLC Number:

TG156

XIE Yanmin, ZHANG Fei, WANG Zihao, HUANG Renyong, YANG Junfeng, HU Yunchuan. Compensation of Twist Springback in High-strength Steel Based on Gradient Die Radius[J]. Journal of Mechanical Engineering, 2019, 55(2): 91-97.

References

[1] ANDERSSON A. Numerical and experimental evaluation of springback in advanced high strength steel[J]. Journal of Materials Engineering and Performance,2007,16(3):301-307.
[2] 胡康康,彭雄奇,陈军,等. 基于Yoshida-Uemori材料模型的汽车结构件冲压回弹分析[J]. 材料科学与工艺,2011,19(6):43-47. HU Kangkang,PENG Xiongqi,CHEN Jun,et al. Springback prediction of automobile body panel based on Yoshida-Uemori material model[J]. Material Science & Technology,2011,19(6):43-47.
[3] XUE X,LIAO J,VINCZE G,et al. Twist springback characteristics of dual-phase steel sheet after non-axisymmetric deep drawing[J]. International Journal of Material Forming,2015,10(2):267-278.
[4] 郭超群,陈军,陈劼实,等. 超高强度薄板扭曲回弹特性的数值模拟与试验分析[J]. 上海交通大学学报,2010,44(4):468-472. GUO Chaoqun,CHEN Jun,CHEN Jieshi,et al. Numerical simulation and experimental validation of distortional springback of advanced high-strength steel sheet metal forming[J]. Journal of Shanghai Jiaotong University,2010,44(4):468-472.
[5] 黄仁勇,谢延敏,唐维,等. 基于混合硬化模型的TRIP780高强钢双C梁扭曲回弹仿真与试验[J]. 工程设计学报,2017,24(6):668-674. HUANG Renyong,XIE Yanmin,TANG Wei,et al. Experiment and simulation on twist springback for the double C rail of TRIP780 high strength steel based on mixed hardening model[J]. Chinese Journal of Engineering Design,2017,24(6):668-674.
[6] 吴磊,李光耀,曹昭展,等. 高强钢材料性能对汽车零件扭曲回弹的影响[J]. 塑性工程学报,2009,16(3):13-17. WU Lei,LI Guangyao,CAO Zhaozhan,et al. The effect of material properties influenced on torsion springback of high strength steel parts[J]. Journal of Plasticity Engineering,2009,16(3):13-17.
[7] 谢延敏,孙新强,田银,等. 基于改进粒子群算法和小波神经网络的高强钢扭曲回弹工艺参数优化[J]. 机械工程学报,2016,52(19):162-167. XIE Yanmin,SUN Xinqiang,TIAN Yin,et al. Optimization of parameters in twist springback process for high-strength sheets based on improved partical swarm optimization algorithm and neural network[J]. Journal of Mechanical Engineering,2016,52(19):162-167.
[8] 徐正兴,路平,江开勇. 高强度钢冲压成形中扭曲回弹仿真与工艺参数优化[J]. 机械设计与制造,2012(10):262-264. XU Zhengxing,LU Ping,JIANG Kaiyong. Torsion springback simulation and optimization of process parameters of high strength steel in the stamping forming[J]. Machinery Design & Manufacture,2012(10):262-264.
[9] 刘强,阮锋,薛新,等. 三维非规则冲压件的回弹扭转补偿控制[J]. 华南理工大学学报,2009,37(9):93-97. LIU Qiang,RUAN Feng,XUE Xin,et al. Compensation control of springback torsion for 3D irregular stamping parts[J]. Journal of South China University of Technology,2009,37(9):93-97.
[10] 李洪周. 先进高强度钢扭曲回弹研究[D]. 长沙:湖南大学,2009. LI Hongzhou. A study on twist springback of advance high strength steel[D]. Changsha:Hunan University,2009.
[11] BARLAT F,LEGE D J,BREM J C. A six-component yield function for anisotropic materials[J]. International Journal of Plasticity,1991,7(7):693-712.
[12] 张卿卿,李大永,彭颖红,等. TRIP780高强度钢板动态力学特性的研究[J]. 塑性工程学报,2009,16(6):6-10. ZHANG Qingqing,LI Dayong,PENG Yinghong,et al. Research on the dynamic mechanical characteristics of TRIP780 high strength steel sheets[J]. Journal of Plasticity Engineering,2009,16(6):6-10.
[13] 林忠钦,刘罡,李淑慧,等. 应用正交试验设计提高U形件的成形精度[J]. 机械工程学报,2002,38(3):83-89. LIN Zhongqin,LIU Gang,LI Shuhui,et al. Application orthogonal experiment design in increasing dimensional accuracy of U-shaped parts[J]. Chinese Journal of Mechanical Engineering,2002,38(3):83-89.
[14] 姜奎华. 冲压工艺与模具设计[M]. 北京:机械工业出版社,2005. JIANG Kuihua. Pressing technology and die design[M]. Beijing:China Machine Press,2005.
[15] INAMDAR M,DATE P P,NARASIMHAN K,et al. Development of an artificial neural network to predict springback in air vee bending[J]. International Jounal of Advanced Manufacturing Technology,2000,16(5):376-381.
[16] 谢延敏,唐维,黄仁勇,等. 基于SA-RBF神经网络的冲压成形拉延筋优化[J]. 西南交通大学学报,2017,52(5):970-976,993. XIE Yanmin,TANG Wei,HUANG Renyong,et al. Drawbead optimisation in stamping using SA-RBF neural networks[J]. Journal of Southwest Jiaotong University,2017,52(5):970-976,993.
[17] 秦国华,谢文斌,王华敏. 基于神经网络与遗传算法的刀具磨损检测与控制[J]. 光学精密工程,2015,23(5):1314-1321. QIN Guohua,XIE Wenbin,WANG Huamin. Detection and control for tool wear based on neural network and genetic algorithm[J]. Optics and Precision Engineering,2015,23(5):1314-1321.