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 ...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.展开更多
Traditional variation analysis methods are not applicable to non-rigid assemblies due to possible part deformation during the assembly process. This paper presents the use of finite element methods to simulate assembl...Traditional variation analysis methods are not applicable to non-rigid assemblies due to possible part deformation during the assembly process. This paper presents the use of finite element methods to simulate assembly deformation. The relationship between the parts’ variation and the variation of the key points in final assembly for quality control is set up by calculating the spring back deformation after assembly. Moreover, the optimization method for non-rigid assembly variations based on finite element analysis is presented. The optimal objective is to reduce the manufacturing cost. The approach is implemented by using ANSYS and MATLAB. The test example shows that the proposed method is effective and applicable.展开更多
基金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).
文摘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.
基金Supported by the Natural Science Foundation of China (No. 50205028) and the Natural Science Foundation of Chongqing City (No. 2005BB2022 ).
文摘Traditional variation analysis methods are not applicable to non-rigid assemblies due to possible part deformation during the assembly process. This paper presents the use of finite element methods to simulate assembly deformation. The relationship between the parts’ variation and the variation of the key points in final assembly for quality control is set up by calculating the spring back deformation after assembly. Moreover, the optimization method for non-rigid assembly variations based on finite element analysis is presented. The optimal objective is to reduce the manufacturing cost. The approach is implemented by using ANSYS and MATLAB. The test example shows that the proposed method is effective and applicable.