带筋车门密封系统的神经网络优化方法

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%.

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

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.