网站首页期刊简介编委会过刊目录投稿指南广告合作征订与发行联系我们English
基于熵权TOPSIS决策的汽车吸能盒冲压成形质量多目标优化
英文标题:Multiobjective optimization on stamping quality for automotive energy absorption box based on entropy weight TOPSIS decision-making
作者:赵洪林1 姜金花1 赵永顺2 李冬芳1 
单位:1.天津翔铄车身科技有限公司 2.天津丰通晟源科技有限公司 
关键词:吸能盒 压料力 脱料力 模具间隙 Kriging近似模型 
分类号:TG386
出版年,卷(期):页码:2023,48(10):67-74
摘要:

 针对汽车吸能盒顶部圆角区域在冲压时易产生材料过度减薄进而引起开裂的成形缺陷问题,以压料力、脱料力和模具间隙为试验因素,以吸能盒最大减薄率最小化和成形极限图安全域占比最大化为质量优化目标,应用拉丁超立方试验设计方法结合有限元分析构建试验因素同质量优化目标之间的多种近似模型,并对近似模型的预测精度进行分析。基于多目标粒子群算法(MOPSO),在优选出的克里金(Kriging)近似模型内进行多目标寻优计算并得到帕累托(Pareto)解集,提出基于熵权逼近理想解排序法(TOPSIS),从Pareto解集中决策出1组最优工艺参数组合,并进行模拟和实际冲压生产验证。试验结果证明所提方法的可靠性及有效性,可为具有类似结构的汽车吸能盒的冲压生产提供有益借鉴。

  For the problem of forming defects in the rounded corner area at the top of automobile energy absorbing box that was prone to excessive material thinning and cracking during the stamping, taking pressing force, stripping force and die clearance as the test factors and the minimization of  maximum thinning rate of energy-absorbing box and the maximization of safe domain proportion of forming limit diagram (FLD) as the quality optimization objective, a variety of approximate models between experimental factors and quality optimization objective were constructed by the Latin Hypercube experiment design method combined with finite element analysis, and the prediction accuracy of the approximate models was analyzed. Furthermore, based on the multi-objective particle swarm algorithm (MOPSO), the multi-objective optimization calculation was carried out within the optimized Kriging approximate model to obtain the Pareto solution set, and based on the entropy weight approximation ideal solution sorting method (TOPSIS), a set of optimal process parameter, combination was determined from Pareto solution set. Finally, the process was simulated and verified by actual stamping production. The experimental results show that the proposed method is effective and can provide useful reference for the stamping production of automobile energy-absorbing box with similar structure.

基金项目:
天津市科技型中小企业技术创新资金项目(13ZXCXGX67600)
作者简介:
赵洪林(1970-),男,硕士,工程师 E-mail:772180168@qq.com
参考文献:

 
[1]廉冰娴,樊文渊.基于RSM的汽车不锈钢板件冲压模具磨损CAE分析
[J].锻压技术,2022,47(6):113-117.


Lian B X, Fan W Y. CAE analysis on stamping mold wear for automobile stainless steel plate based on RSM
[J].Forging & Stamping Technology,2022,47(6): 113-117.


[2]Dambarudhar D, Debasish M, Asish T, et al. Optimisation of drawbead design in sheet metal forming of an part using RSM and LSDYNA
[J]. International Journal of Engineering and Technology, 2018, 11(5):1747-1754.


[3]Kleiber M, Knabel J, Rojek J. Response surface method for probabilistic assessment of metal forming failures
[J].International Journal for Numerical Methods in Engineering, 2004, 60 (1):51-67.


[4]Kitayama S, Tamada K, Takano M, et al. Numerical optimization of process parameters in stamping forming process for and clamping force using conformal cooling channel
[J]. Journal of Manufacturing Processes, 2018, 32: 782-790.


[5]刘强,俞国燕,梅端. 基于Dynaform与RBFNSGAII算法的冲压成形工艺参数多目标优化
[J].塑性工程学报,2020,27(3):16-25.

Liu Q, Yu G Y, Mei D. Multiobjective optimization of stamping forming process parameters based on Dynaform and RBFNSGAII algorithm
[J].Journal of Plasticity Engineering, 2020,27(3):16-25.


[6]GB/T 228.1—2010,金属材料拉伸试验第1部分:室温试验方法
[S].

GB/T 228.1—2010,Metallic material—Tensile testing—Part1: Method of test at room temperature
[S].


[7]季宁,张卫星,于洋洋,等. 基于最优拉丁超立方抽样方法和NSGAII算法的注射成型多目标优化
[J].工程塑料应用,2020,48(3):72-77.

Ji N, Zhang W X,Yu Y Y,et al.MultiObjective optimization of injection molding based on optimal Latin Hypercube sampling method and NSGAII algorithm
[J]. Engineering Plastics Application,2020,48(3):72-77.


[8]Zhang F M, Cui H B, Li Z K, et al. Interactive multiobjective optimization of microgrid based on improved NSGAII algorithm
[J]. Power System Protection and Control, 2018, 46(12): 24-31.


[9]Qian P Z G. Sliced Latin Hypercube designs
[J].Journal of the American Statistical Association,2012,107 (497):393-399.


[10]季宁,张卫星,于洋洋,等.基于Kriging代理模型和MOPSO算法的注塑成型质量多目标优化
[J].塑料工业,2020,48(5):67-71.

Ji N,Zhang W X,Yu Y Y,et al.Multiobjective optimization of injection molding quality based on Kriging agent model and MOPSO algorithm
[J]. China Plastics Industry,2020,48(5):67-71.


[11]张俊红,陈孔武,王健,等. 基于EBF神经网络和粒子群算法的注射成型优化设计
[J].中国塑料,2015,29(9):54-59.

Zhang J H,Chen K W,Wang J,et al.Optimization design of injection molding based on EBF neural networkand particle swarm algorithm
[J].China Plastics,2015,29(9):54-59.


[12]季宁,张卫星,于洋洋,等. 基于径向基函数神经网络和多岛遗传算法的注射成型质量控制与预测
[J].工程塑料应用,2020,48(4):62-68.

Ji N,Zhang W X,Yu Y Y,et al.Quality control and prediction of injection molding based on RBF Neural Network and MIGA
[J]. Engineering Plastics Application,2020,48(4):62-68.


[13]Borhanazad H, Mekhilef S. Optimization of microgrid system using MOPSO
[J].Renwable Energy,2014,(71):295-306.


[14]Ghorbani N, Kasaeian A, Toopshekan A,et al.Optimizing a hybrid windPVbattery system using GAPSO and MOPSO for reducing cost and increasing reliability
[J].Energy,2017,(154):581-591.


[15]张庆,葛东东,何也能. 基于NSGAII和熵权TOPSIS法的注塑工艺参数多目标优化
[J].塑料工业,2022,50(9):95-100,197.

Zhang Q,Ge D D,He Y N.Multiobjective optimization of injection molding process parameters based on NSGAII algorithm and entropy weight TOPSIS method
[J].China Plastics Industry,2022,50(9):95-100,197.


[16]董长青,陈辰,程旭,等.基于 MOPSO 算法与改进熵权 TOPSIS 法的混合动力汽车多目标优化决策
[J].制造业自动化,2018,40 (11): 155-156.

Dong C Q, Chen C, Cheng X, et al.Multiobjective optimization decision for hybrid vehicle based on MOPSO algorithm and improved entropy weight TOPSIS method
[J].Manufacturing Automation,2018,40 (11): 155-156.
服务与反馈:
文章下载】【加入收藏
《锻压技术》编辑部版权所有

中国机械工业联合会主管  中国机械总院集团北京机电研究所有限公司 中国机械工程学会主办
联系地址:北京市海淀区学清路18号 邮编:100083
电话:+86-010-82415085 传真:+86-010-62920652
E-mail: fst@263.net(稿件) dyjsjournal@163.com(广告)
京ICP备07007000号-9