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
汽车发动机罩的神经网络-强繁殖NSGA-II算法冲压参数优化
英文标题:Parameter optimization on stamping of neutral network-strong reproduction NSGA-II algorithm for automobile engine hood
作者:王慧怡 王岫鑫 刘学 
单位:长春汽车工业高等专科学校 重庆邮电大学 长春汽车工业高等专科学校 
关键词:发动机罩内板 冲压 BP神经网络 强繁殖NSGA-II算法 最大减薄率 最大增厚率 
分类号:TP319
出版年,卷(期):页码:2022,47(7):100-106
摘要:

 为了提高车辆发动机罩内板的冲压质量,以减小冲压制件的最大减薄率和最大增厚率为目标,提出了基于神经网络-强繁殖NSGA-II算法的冲压参数优化方法。建立了减小最大减薄率和最大增厚率的多目标优化模型。使用最优拉丁抽样法在思维空间抽取了采样点,依据数值模拟获得了采样点的性能参数。使用BP神经网络拟合冲压参数与质量参数的关系,经验证,回归精度较高,BP神经网络可以用于质量参数的预测。定义了多点随机交叉和排交叉位随机变异法,将其应用于NSGA-II算法,给出了基于强繁殖NSGA-II算法的优化模型求解方法。经验证,强繁殖NSGA-II算法的Pareto解集可以支配NSGA-II算法解集,验证了改进策略的有效性。优化后最大减薄率均值和最大增厚率均值分别减小了15.14%和18.93%,验证了优化方法的有效性和优越性。

 In order to improve the stamping quality of automobile engine hood inner panel and reduce the maximum thinning rate and the maximum thickening rate of stamping parts, a stamping parameter optimization method based on neural network-strong propagation NSGA-II algorithm was proposed, and a multi-objective optimization model for reducing the maximum thinning rate and the maximum thickening rate was established. Then, the sampling points in the thinking space were extracted by using the optimal Latin sampling method, and the performance parameters of the sampling points were obtained according to the numerical simulation. Furthermore, through using BP neural network to fit the relationship between stamping parameters and quality parameters, it was verified that the regression accuracy was high, and the BP neural network could be used to predict quality parameters. Finally, the multi-point random crossover and row crossover random mutation methods were defined and applied to NSGA-II algorithm, and the solution method of optimized model based on strong reproduction NSGA-II algorithm was given. The verification results show that the Pareto solution set of strong reproduction NSGA-II algorithm can dominate the solution set of NSGA-II algorithm, which verifies the effectiveness of the improved strategy. After optimization, the average values of the maximum thinning rate and the maximum thickening rate are reduced by 15.14% and 18.93% respectively, which verifies the effectiveness and superiority of the optimized method.

基金项目:
吉林省职业教育与成人教育教学改革研究课题(2020ZCY205)
作者简介:
作者简介:王慧怡(1982-),女,硕士,副教授 E-mail:gg_hy2490@163.com
参考文献:

 [1]王艳艳, 高崇阳.车辆底板冲压的响应面拟合与改进蜂群算法优化[J].锻压技术,2021,46(3):89-95.


Wang Y Y, Gao C Y. Response surface fitting and improved bee colony algorithm optimization for vehicle bottom plate in stamping [J]. Forging & Stamping Technology, 2021, 46(3):89-95.

[2]么大锁. 汽车引擎盖外板拉延成形工艺参数优化研究[J].机电工程,2020,37(7):795-800.

Yao D S. Optimization of drawing process parameters for automobile engine hood outer plate [J]. Journal of Mechanical & Electrical Engineering, 2020, 37(7):795-800.

[3]韦韡, 姚佐平,李开文,等. 基于Autoform的汽车侧围回弹补偿分析[J].精密成形工程,2021,13(3):172-178.

Wei W, Yao Z P, Li K W, et al. Analysis on compensation for spring back of auto-bodyside based on Autoform [J]. Journal of Netshape Forming Engineering, 2021, 13(3):172-178.

[4]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.

[5]蒋磊, 龚剑,王龙,等. 侧围外板浅拉延成形工艺数值模拟[J].塑性工程学报,2020,27(9):73-81.

Jiang L, Gong J, Wang L, et al. Numerical simulation of shallow drawing for body side outer panel [J]. Journal of Plasticity Engineering, 2020, 27(9):73-81.

[6]胡锦达. 汽车后围内板冲压工艺的高斯扰动粒子群优化[J].锻压技术,2020,45(12):46-52.

Hu J D. Stamping process optimization of automobile rear inner panel based on Gaussian perturbation particle swarm [J]. Forging & Stamping Technology, 2020, 45(12):46-52.

[7]夏明勇. 汽车用6016铝合金板材预时效工艺研究及冲压成形数值模拟[D]. 重庆:重庆大学,2018.

Xia M Y. Study on Pre-aging Technology and Numerical Simulation of Stamping Forming of 6016 Aluminum Alloy Sheet for Automobile [D]. Chongqing: Chongqing University, 2018.

[8]刘文辉, 罗号, 谭永胜, 等. 横轧对6016铝合金组织及力学性能的影响[J].稀有金属,2020,44(3):242-248.

Liu W H, Luo H, Tan Y S, et al. Effects of cross-rolling on microstructure and mechanical properties of 6016 Aluminum alloy[J]. Chinese Journal of Rare Metals,2020,44(3):242-248.

[9]孙晓东, 刘健,陈雅琪,等.往复式压缩机轴系扭振参数优化设计[J].机械设计与制造,2019,(5):171-174.

Sun X D, Liu J, Chen Y Q, et al. Optimization design of reciprocating compressor shafting torsional vibration parameters [J]. Machinery Design & Manufacture, 2019, (5): 171-174.

[10]Hecht-Nielsen R. Neurocomputer Applications[M]. New York:Springer-Verlag New York Inc., 1989.

[11]Sun C. Creep deformation constitutive model of BSTMUF601 superalloy using the BP neural network method[J]. Rare Metal Materials and Engineering, 2020, 49(6):1885-1893.

[12]郑夏, 马良. 一种多目标非线性优化的NSGA-II改进算法[J].微电子学与计算机,2020,37(7):47-53.

Zheng X, Ma L. An improved NSGA-II algorithm for multi-objective nonlinear optimization [J]. Microelectronics & Computer,2020, 37(7):47-53.

[13]顾清华, 莫明慧,卢才武,等. 求解约束高维多目标问题的分解约束支配NSGA-Ⅱ优化算法[J].控制与决策,2020,35(10):2466-2474.

Gu Q H, Mo M H, Lu C W, et al. Decomposition-based constrained dominance principle NSGA-II for constrained many-objective optimization problems [J]. Control and Decision, 2020, 35(10):2466-2474.
服务与反馈:
本网站尚未开通全文下载服务】【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 北京机电研究所有限公司 中国机械工程学会塑性工程分会主办
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