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
基于随机聚焦搜索算法的汽车后围内板冲压工艺优化设计
英文标题:Optimization of stamping process design for automobile body lower back panel based on stochastic focusing search algorithm
作者:龙玲1  张健2 董洁1 刘巧燕1 
单位:(1.成都航空职业技术学院 汽车工程学院 四川 成都 610100 2.中国汽车工程研究院 重庆 401122) 
关键词:汽车后围内板 冲压工艺 随机聚焦搜索算法 数值模拟 多目标优化 
分类号:TG386.1
出版年,卷(期):页码:2018,43(5):0-0
摘要:

 汽车覆盖件成形质量的影响因素及控制具有一定的复杂性和不确定性,仅凭经验和试错法,较难确定合理的工艺参数组合。以汽车后围内板为例,采用基于随机聚焦搜索算法的优化设计方法进行冲压工艺参数的优化设计。通过数值模拟与正交实验相结合获取优化模型的训练样本,利用BP神经网络构建随机聚焦搜索算法的优化目标函数模型,在此基础上应用随机聚焦搜索算法对工艺参数进行多目标优化,将优化后的工艺参数分别通过数值模拟和现场试模验证,均能得到较好且一致的成形质量,验证了该优化方法的正确性。

  Due to the complexity and uncertainty of influencing factors and controls on the automobile body forming quality, it is hard to determine reasonable combination of stamping process parameters only by experience and trialanderror method. The lower back panel of an automobile was taken as an example to optimize stamping process parameters by using an optimization design method based on stochastic focusing search algorithm. The training samples for the optimization model were obtained by combining numerical simulation with orthogonal experiments, and the BP neural network was used to construct the optimized objective function model for the stochastic focusing search algorithm. Based on the function model, the multiobjective optimization of process parameters was conducted by using the stochastic focusing search algorithm, which were verified and validated by numerical simulation and site die trial respectively. The result shows that the better and consistent forming quality is obtained by using the optimized process parameters, which verifies the validty of the optimization method.

基金项目:
四川省教育厅理工科重点项目(14ZA0308)
作者简介:
作者简介:龙玲(1979- ),女,博士,副教授 Email:sclongling@163.com
参考文献:

 
[1]Liu W, Yang Y Y. Multiobjective optimization of sheet metal forming process using Paretobased genetic algorithm
[J]. Journal of Materials Processing Technology, 2008, 208: 499-506.



[2]孙光永,李光耀,陈涛,等. 多目标粒子群优化算法在薄板冲压成形中的应用
[J]. 机械工程学报, 2009,45(5):153-159.

Sun G Y, Li G Y, Chen T, et al. Application of multiobjective particle swarm optimization in sheet metal forming
[J]. Journal of Mechanical Engineering, 2009, 45(5):153-159.


[3]胡亚男,王雷刚,黄瑶,等. 基于响应面模型和遗传算法的汽车发罩外板冲压工艺参数多目标优化
[J] . 锻压技术,2017,42(5):171-175.

Hu Y N,Wang L G,Huang Y,et al. Multiobjective optimization on stamping process for automobile hood panel based on response surface model and genetic algorithm
[J]. Forging & Stamping Technology, 2017,42(5):171-175.


[4]龙玲,殷国富,邹云,等. 基于随机聚焦搜索算法的冲压成形工艺优化
[J]. 计算机集成制造系统,2012,18(2):314-319.

Long L, Yin G F, Zou Y, et al. Optimization of sheet metal forming process based on stochastic focusing search algorithm
[J]. Computer Integrated Manufacturing System, 2012,18(2):314-319.


[5]龙玲,殷国富,宋超,等. 基于支持向量机的随机聚焦搜索算法优化冲压成形工艺
[J]. 四川大学学报:工程科学版,2012,44(5):220-225.

Long L, Yin G F, Song C, et al. Application of stochastic focusing search algorithm based on SVM in optimization of sheet metal forming process
[J]. Journal of Sichuan University:Natural Science, 2012,44(5):220-225.


[6]Zheng Y K, Chen W R, Dai C H, et al. Stochastic focusing search: A novel optimization algorithm for realparameter optimization
[J]. Journal of Systems Engineering and Electronics, 2009, 20(4): 869-876. 


[7]林忠钦,李淑慧. 车身覆盖件冲压成形仿真
[M] .北京: 机械工业出版社,2005.

Lin Z Q, Li S H. Sheet Metal Forming Simulation of Automotive Body Panels
[M]. Beijing: China Machine Press, 2005.


[8]涂小文.AutoForm原理技巧与战例实用手册
[M] . 武汉:湖北科学技术出版社,2013.

Tu X W.AutoFormHandbook of Practical Techniques and Examples
[M]. Wuhan: Hubei Science and Technology Press, 2013.


[9]上海科学技术交流站.正交实验设计法
[M].上海:上海人民出版社,1975.

Shanghai Science and Technology Exchange Center. Orthogonal Experiment Method
[M]. Shanghai: Shanghai People′s Press,1975. 


[10]张德丰. MATLAB神经网络应用设计
[M]. 北京: 机械工业出版社,2009.

Zhang D F. MATLAB Neural Network Application Design
[M]. Beijing: China Machine Press, 2009.

 
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  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