基于改进遗传算法的超高强钢硬化模型参数确定及折弯回弹预测

doi:10.3901/JME.2024.12.240

摘要/Abstract

摘要： 针对目前特殊用途车辆用超高强度钢板在折弯成形工艺中存在的回弹剧烈问题，提出一种基于改进遗传算法的超高强钢硬化模型参数确定方法，实现超高强度钢板折弯回弹的准确预测。以超高强钢板6252为研究对象，首先构建一个考虑超高强钢材料应变硬化行为和Bauschinger效应的Yoshida-Uemori硬化模型框架；然后结合有限元仿真软件LS-DYNA和改进遗传算法准确地确定硬化模型框架中的材料特性参数；最后基于DYNAFORM平台对6252超高强钢工件进行了折弯回弹的预测。实际的折弯试验表明，回弹模拟结果与实际回弹角度之间的误差在2%以内，验证所标定的超高强钢硬化模型参数的可靠性，说明在该参数集支持下的Yoshida-Uemori硬化模型能够较好地描述超高强钢板的动态特性。所提出的方法为超高强度钢零部件的高精度成形工艺设计提供有效指导。

关键词: 改进遗传算法, 超高强钢, 硬化模型, 折弯成形, 回弹预测

Abstract: For the severe spring back problem of ultra-high strength steel (UHSS) used in special purpose vehicles during the bending process, a method for determining the parameters of the hardening model of the UHSS based on an improved genetic algorithm is proposed and accurate prediction of the bending spring-back of the UHSS is achieved. Taking the UHSS 6252 as the example, a Yoshida-Uemori hardening model framework considering the strain-hardening behavior and Bauschinger effect of the UHSS is constructed. Then, the material properties of the hardening model framework are accurately determined by combining the modified genetic algorithm and the finite element simulation software LS-DYNA. Finally, the spring back in bending process is predicted for the UHSS 6252 based on the DYNAFORM platform. The actual bending test shows that the error of the spring-back angle between the simulation and experiment is within 2%, which verifies the reliability of the calibrated hardening model parameters for the UHSS and demonstrates that the Yoshida-Uemori hardening model supported by this parameter set can well describe the dynamic characteristics of the UHSS. The proposed method provides effective guidance for the design of high-precision forming processes for ultra-high-strength steel components.

Key words: modified genetic algorithm, ultra-high strength steel, hardening model, bending, spring back prediction

中图分类号:

TG318

刘承尚, 陈锐, 李芳, 王萌, 徐戊矫. 基于改进遗传算法的超高强钢硬化模型参数确定及折弯回弹预测[J]. 机械工程学报, 2024, 60(12): 240-249.

LIU Chengshang, CHEN Rui, LI Fang, WANG Meng, XU Wujiao. Parameters Determination of Hardening Model for Ultra-high Strength Steel Based on Modified Genetic Algorithm and Bending Spring Back Prediction[J]. Journal of Mechanical Engineering, 2024, 60(12): 240-249.

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http://www.cjmenet.com.cn/CN/Y2024/V60/I12/240

参考文献

[1] 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 particle swarm optimization algorithm and wavelet neural network[J]. Journal of Mechanical Engineering，2016，52(19)：162-167.
[2] TOROS S，POLAT A，OZTURK F. Formability and springback characterization of TRIP800 advanced high strength steel[J]. Materials and Design，2012，41(1)：298-305.
[3] HASSAN H，TRAPHÖNER H，GÜNER A，et al. Accurate springback prediction in deep drawing using pre-strain based multiple cyclic stress-strain curves in finite element simulation[J]. International Journal of Mechanical Sciences，2016，110(1)：229-241.
[4] KOMGRIT L，HAMASAKI H，HINO R，et al. Elimination of springback of high-strength steel sheet by using additional bending with counter punch[J]. Journal of Materials Processing Technology，2016，229(1)：199-206.
[5] LIU X，CAO J，CHAI X，et al. Investigation of forming parameters on springback for ultra high strength steel considering Young’s modulus variation in cold roll forming[J]. Journal of Manufacturing Processes，2017，29(1)：289-297.
[6] JULSRI W，SURANUNTCHAI S，UTHAISANGSUK V. Study of springback effect of AHS steels using a microstructure based modeling[J]. International Journal of Mechanical Sciences，2018，135(1)：499-516.
[7] HILL R. The mathematical theory of plasticity[M]. Oxford：Clarendon Press，1950.
[8] PRAGER W. A new method of analyzing stress and strains in work hardening solids[J]. Journal of Applied Mechanics，1956，23(4)：493-496.
[9] ZIEGLER H. A modification of Prager’s hardening rule[J]. Mathematics：Quarterly of Applied Mathematics，1959，17(1)：55-65.
[10] FREDERICK C O，ARMSTRONG P J. A mathematical representation of the multiaxial bauscinger effect[J]. Materials at High Temperatures，2007，24(1)：1-26.
[11] CHUN B K，JINN J T，LEE J K. Modeling the Bauschinger effect for sheet metals，part I：theory[J]. International Journal of Plasticity，2002，18(5)：571-595.
[12] YOSHIDA F，UEMORI T. A model of large-strain cyclic plasticity describing the Bauschinger effect and workhardening stagnation[J]. International Journal of Plasticity，2002，18(5-6)：661-686.
[13] YOSHIDA F，UEMORI T，FUJIWARA K. Elastic-plastic behavior of steel sheets under in-plane cyclic tension-compression at large strain[J]. International Journal of Plasticity，2002，18(5-6)：633-659.
[14] YOSHIDA F，UEMORI T. A model of large-strain cyclic plasticity and its application to springback simulation[J]. International Journal of Mechanical Sciences，2003，45(10)：1687-1702.
[15] 袁秋菊. 基于Yoshida-Uemori模型的TRIP钢成形回弹分析[D]. 长春：吉林大学，2014. YUAN Qiuju. Analysis of springback of the TRIP steel based on the Yoshida-Uemori model[D]. Changchun：Jilin University，2014.
[16] PHONGSAI T，UTHAISANGSUK V，CHONGTHAIRUNGRUANG B，et al. Simplified identification of material parameters for Yoshida-Uemori kinematic hardening model[C]//Bangkok：International Conference on Experimental Mechanics，2014：9234.
[17] LEE J S，CHOI S Y，LEE S K，et al. Roll forming process of automotive seat rail with 980 DP steel using Yoshida-Uemori kinematic hardening model[J]. Procedia Manufacturing，2018，15：796-803.
[18] XIA X，GONG M，WANG T，et al. Parameter identification of the Yoshida-Uemori hardening model for remanufacturing[J]. Metals，2021，11(11)：1859.
[19] FINN M J，GALBRAITH P C，WU L，et al. Use of a coupled explicit-implicit solver for calculating spring-back in automotive body panels[J]. Journal of Materials Processing Technology，1995，50(1-4)：395-409.
[20] MAKINOUCHI A. Sheet metal forming simulation in industry[J]. Journal of Materials Processing Technology，1996，60(1-4)：19-26.
[21] BANU M，TAKAMURA M，HAMA T，et al. Simulation of springback and wrinkling in stamping of a dual phase steel rail-shaped part[J]. Journal of Materials Processing Technology，2006，173(2)：178-184.
[22] GIL T，MENDIGUREN J，GALDOS L，et al. Influence of the pressure dependent coefficient of friction on deep drawing springback predictions[J]. Tribology International，2016，103(1)：266-273.
[23] LEE S W，YANG D Y. An assessment of numerical parameters influencing springback in explicit finite element analysis of sheet metal forming process[J]. Journal of Materials Processing Technology，1998，80-81：60-67.
[24] WAGONER R H，LI M. Simulation of springback：through-thickness integration[J]. International Journal of Plasticity，2007，23(3)：345-360.
[25] 李贵，柳玉起，柯俊逸，等. 先进高强度钢板DP590回弹试验及数值模拟研究[J]. 锻压技术，2013，38(5)：50-55. LI Gui，LIU Yuqi，KE Junyi，et al. Experimental research and numerical simulation of springback for advanced high-strength steel-DP590[J]. Forging and Stamping Technology，2013，38(5)：50-55.
[26] 李新军，万敏，周贤宾. 数值模拟在汽车覆盖件成形中的应用[J]. 塑性工程学报，1998，5(4)：45-49. LI Xinjun，WAN Min，ZHOU Xianbin. Application of numerical simulation in car panel forming[J]. Journal of Plasticity Engineering，1998，5(4)：45-49.
[27] EGGERTSEN P A，MATTIASSON K. On the modelling of the bending-unbending behaviour for accurate springback predictions[J]. International Journal of Mechanical Sciences，2009，51(7)：547-563.
[28] YILAMU K，HION R，HAMASAKI H，et al. Air bending and springback of stainless steel clad aluminum sheet[J]. Journal of Materials Processing Technology，2010，210(2)：272-278.
[29] YAN J W，HU Q，WANG Z Z，et al. A comparison study of different hardening models in springback prediction for stamping of the third generation ultra high strength steel[J]. Journal of Shanghai Jiao Tong University，2017，51(1)：1334-1339.
[30] ZHU J，HUANG S Y，LIU W，et al. Semi-analytical or inverse identification of Yoshida-Uemori hardening model[J]. Key Engineering Materials，2018，775(1)：531-535.
[31] 徐杰，王田，张荣. 基于模拟退火算法的高强钢Yoshida-Uemori材料模型参数反演分析[J]. 锻压技术，2016，41(6)：133-137. XU Jie，WANG Tian，ZHANG Rong. Parameter inversion on Yoshida-Uemori material model of high strength steel based on simulated annealing algorithm[J]. Forging & Stamping Technology，2016，41(6)：133-137.
[32] DEJONG D A. An analysis of the behavior of a class of genetic adaptive systems[D]. Detroit：University of Micchigan，1975.
[33] SRINIVAS M，PATNAIK L M. Adaptive probabilities of crossover and mutation in genetic algorithms[C]// IEEE Transactions on Systems，Man and Cybernetics，1994，24(4)：656-667.
[34] 邹海珍. 基于改进自适应遗传算法的排课系统设计[D]. 广州：华南理工大学，2016. ZHOU Haizhen. Timetable design base on improvement adaptive genetic algorithm[D]. Guangzhou：South China University of Technology，2016.
[35] ZHANG L，ZHANG B，YU J. Improvement of adaptive genetic algorithm and its application in examination timetabling optimization problem[C]// International Conference on Computer Science and Service System (CSSS)，Naijing，2011：1326-1329.
[36] 杨从锐. 改进的自适应遗传算法在函数优化中的应用[D]. 昆明：昆明理工大学，2018. YANG Congrui. An improved adaptive genetic algorithm for function optimization[D]. Kunming：Kunming University of Science and Technology，2018.