上海交通大学学报

上海交通大学学报（自然版） ›› 2014, Vol. 48 ›› Issue (03): 377-381.

• 自动化技术、计算机技术 • 上一篇    下一篇

带筋车门密封系统的神经网络优化方法

朱文峰1，王杰1，夏国勇2，林佩剑1
  

  1. （1.同济大学 机械与能源工程学院， 上海 201804； 2.申雅密封件有限公司， 上海 201712）
     
  • 收稿日期:2013-07-01 出版日期:2014-03-28 发布日期:2014-03-28
  • 基金资助:

    国家自然科学基金面上项目资助（51275359），上海市复杂薄板结构数字化制造重点实验室开放课题资助（2012005），青浦同济科研合作平台项目资助

Optimization Method of Automotive Door Sealing System with Bar Structure Based on Neural Networks

ZHU Wenfeng1,WANG Jie1,XIA Guoyong2,LIN Peijian1
  

  1. (1.College of Mechanical Engineering, Tongji University, Shanghai 201804， China; 2.HuayuCooper Standard Sealing Systems Co, Ltd., Shanghai 201712， China)
  • Received:2013-07-01 Online:2014-03-28 Published:2014-03-28

摘要：

车门密封系统截面设计是确保防水防尘、减振隔音的重要因素.针对典型带筋结构的车门头道密封非规则截面，将形成密封压缩负荷的海绵泡管区细分为5个子区域.以带筋区域厚度和角度为优化变量，基于企业工程实际，建立以设计压缩负荷为指标的优化目标函数.通过神经网络确立截面结构参数与压缩负荷的非线性映射，实现车门密封系统参数的并行优化.工程应用表明，该设计开发周期可缩短15%.
 
 

关键词: 车门密封, 压缩负荷, 截面优化, 神经网络

Abstract:

 In this paper, the first-seal cross-section with bar structure of a typical automotive door  was analyzed. The sponge tube of the weatherstrip, which produces most of the compressive load, was divided into five sub-areas whose thicknesses and angles were selected as optimization variables, considering the compression space of the door sheet metal and nonlinear deformation process of the sealing system. Based on  the engineering practice, an optimization objective function using the demanded compression load deflection (CLD) criterion was established. The nonlinear mapping between cross-section parameters and compression load was built by BP neural network and the parallel intelligent optimization was realized for ideal cross-section structure parameter. The engineering application proved that 15% of cycle time can be reduced using this computer-aided design method.

Key words: auto door sealing, compression load, cross-section optimization, neural networks

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