东南大学学报（英文版）2022，Vol.38Issue(2)：126-136,11.DOI:10.3969/j.issn.1003-7985.2022.02.004
基于有限元和机器学习算法的薄壁件焊接装配特性映射关系分析
Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm
摘要
Abstract
The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8％,1.4％,and 4.4％,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.关键词
平行T形薄壁件/焊接装配特性/有限元分析/映射关系/机器学习算法Key words
parallel T-shaped thin-walled parts/welding assembly property/finite element analysis/mapping relationship/machine learning algorithm分类
矿业与冶金引用本文复制引用
潘明辉,廖文和,幸研,汤文成..基于有限元和机器学习算法的薄壁件焊接装配特性映射关系分析[J].东南大学学报（英文版）,2022,38(2):126-136,11.基金项目
The Natural Science Foundation of Jiangsu Province,China(No.BK20200470),China Postdoctoral Science Foundation(No.2021M691595),Innovation and Entrepreneurship Plan Talent Program of Jiangsu Province(No.AD99002). （No.BK20200470）
