• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (1): 203-212.doi: 10.3901/JME.2015.01.203

• 数字化设计与制造 • 上一篇    

扫码分享

基于神经网络与遗传算法的薄壁件多重装夹布局优化

秦国华, 赵旭亮, 吴竹溪   

  1. 南昌航空大学航空制造工程学院 南昌 330063
  • 出版日期:2015-01-05 发布日期:2015-01-05
  • 作者简介:秦国华,男,1970年出生,博士后,教授。主要研究方向为数控加工过程建模与仿真、工件装夹分析与综合、刀具磨损检测方法、全制造周期残余应力分析与预测、制造业信息化技术。
  • 基金资助:
    国家自然科学基金(51165039,51465045)和江西省科技支撑计划重点(2010BGB00300)资助项目

Optimization of Multi-fixturing Layout for Thin-walled Workpiece Based on Neural Network and Genetic Algorithm

QIN Guohua, ZHAO Xuliang, WU Zhuxi   

  1. School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063
  • Online:2015-01-05 Published:2015-01-05

摘要: 在多重装夹元件装夹过程中,由于装夹顺序、夹紧力、定位元件位置等装夹布局参数的不同,薄壁件的装夹变形程度也不一样。单个装夹布局参数引起的工件装夹变形规律能够通过有限元方法获得。但是,若同时考虑多个装夹布局参数的影响,仅仅利用有限元方法难以揭示装夹布局参数与装夹变形之间的关系。为此,针对薄壁件的装夹布局方案建立三维有限元模型,以便利用有限元法获取神经网络的训练样本。借助神经网络的非线性映射能力,通过有限的训练样本构建装夹变形的预测模型。以减小工件的最大装夹变形为目标,并根据每一代装夹布局中工件的最大装夹变形定义个体的适应度,建立装夹布局方案的优化模型及其遗传算法求解技术。试验结果表明,网络预测值与相应的有限元仿真值、试验数据之间的相对误差均不超过3%。提出的基于神经网络与遗传算法的装夹变形“分析-预测-控制”方法,不仅能够提高装夹变形的计算效率,而且为薄壁件装夹布局方案的合理设计提供基础理论。

关键词: 薄壁件, 神经网络, 遗传算法, 装夹变形

Abstract: In the fixturing processing with the multiple clamps, the different fixturing parameters, including the fixturing sequence, the magnitude of clamping force, the locator position, and so forth, can cause the different fixturing deformation of the thin-walled workpiece. Deformation law of workpiece which is caused by a single fixturing parameter can be obtained by finite element method. However, if multiple fixturing parameters are synchronously considered, finite element method is difficult in revealing the relationship between the fixturing parameters and fixturing deformation of workpiece. Therefore, the finite element model of the multi-fixturing layout is above all established for the thin-walled workpiece. Fixturing deformations can be analyzed to be the training samples of neural network. And then, with the nonlinear mapping of neural network, the prediction model of fixturing deformation is suggested according to the training samples. Finally, the optimal model of multi-fixturing layout with the objective of minimizing the minimum fixturing deformation is presented. According to the maximum fixturing deformation of each generation, the fitness of the individual is defined to develop the genetic algorithm so that the optimal model can be solved to obtain the fixturing sequence and the locator position. The prediction model is able to?predict experimental results?within a 3%?error margin as well as predict simulated results within a 3%?error margin. The presented “analysis-prediction-control” method of fixturing deformation can not only improve the calculation efficiency of fixturing deformation, but also provide a basic theory of multi-fixturing layout design for the thin-walled workpiece.

Key words: fixturing deformation, genetic algorithm, neural network, thin-walled workpiece

中图分类号: