In order to analyze the welding thermal characteristics problem,the multiscale finite element(FE)model of T-shape thin-wall assembly structure for different thicknesses and the heat source model are established to emp...In order to analyze the welding thermal characteristics problem,the multiscale finite element(FE)model of T-shape thin-wall assembly structure for different thicknesses and the heat source model are established to emphatically study their welding temperature distributions under different conditions.Simultaneously,different welding technology parameters and welding directions are taken into account,and the fillet weld for different welding parameters is employed on the thin-wall parts.Through comparison analysis,the results show that different welding directions,welding thicknesses and welding heat source parameters have a certain impact on the temperature distribution.Meanwhile,for the thin-wall assembly structure of the same thickness,when the heat source is moving,the greater the moving speed,the smaller the heating area,and the highest temperature will decrease.Therefore,the welding temperature field distribution can be altered by adjusting welding parameters,heat source parameters,welding thickness and welding direction,which is conducive to reducing welding deformation and choosing an appropriate and optimal welding thickness of thin-wall parts and relative welding process parameters,thus improving thin-wall welding structure assembly precision in the actual large-size welding structure assembly process in future.展开更多
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
基金The National Natural Science Foundation of China(No.51675100)the National Numerical Control Equipment Major Project of China(o.2016ZX04004008)
文摘In order to analyze the welding thermal characteristics problem,the multiscale finite element(FE)model of T-shape thin-wall assembly structure for different thicknesses and the heat source model are established to emphatically study their welding temperature distributions under different conditions.Simultaneously,different welding technology parameters and welding directions are taken into account,and the fillet weld for different welding parameters is employed on the thin-wall parts.Through comparison analysis,the results show that different welding directions,welding thicknesses and welding heat source parameters have a certain impact on the temperature distribution.Meanwhile,for the thin-wall assembly structure of the same thickness,when the heat source is moving,the greater the moving speed,the smaller the heating area,and the highest temperature will decrease.Therefore,the welding temperature field distribution can be altered by adjusting welding parameters,heat source parameters,welding thickness and welding direction,which is conducive to reducing welding deformation and choosing an appropriate and optimal welding thickness of thin-wall parts and relative welding process parameters,thus improving thin-wall welding structure assembly precision in the actual large-size welding structure assembly process in future.
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.
