Research On Fatigue Performance Of Self-Piercing Riveted Parts Of High-Rail Aluminum Alloy Body

China’s high-speed railway speed has reached 350km/h,which is the leading position in the world.The increase in speed of high-speed trains not only relies on bullet-shaped fronts and streamlined bodies,but also because of the replacement of steel materials by industrial aluminum alloys to achieve lightweight body.However,the commonly used welding connection methods for aluminum alloy bodies are prone to welding defects such as pores and cracks,which greatly reduces the service life of the high-speed rail.Therefore,a connection technology is urgently needed to solve the problem of difficult welding of high-speed aluminum alloy bodies.Self-piercing riveting technology emerged at the historic moment.However,self-piercing riveting technology is currently mostly used in the automotive field,and whether it meets the fatigue performance requirements of high-speed rail bodies remains to be further studied.Therefore,this paper uses ABAQUS to build a self-piercing riveting unit finite element analysis model,analyzes the influence of various structural parameters on fatigue performance,and provides multiple feasible solutions for self-piercing riveting components based on central composite experiments and multi-objective optimization technology.An optimization method aimed at improving the fatigue performance of self-piercing riveting parts is established,and its feasibility is verified through experiments.The main research contents and achievements of this paper are as follows:1.Theoretical derivation of fatigue properties of aluminum alloy self piercing riveting.Through a variety of fatigue research methods,the factors affecting the fatigue strength of self piercing riveted parts are analyzed in detail,the Weibull distribution parameters are used for calculation,and the S-N curve fitting formula of components is derived by double weighted least square method,so as to provide a theoretical basis for the study of improving the fatigue performance of self-piercing riveted parts.2.A finite element analysis model of self-piercing riveting parts is established.UG is used to establish a three-dimensional model of self-piercing riveting components,and ABAQUS is used to perform finite element analysis on the shear performance and fatigue performance of self-piercing riveting elements,and the validity of the finite element model is verified by experiments.The sensitivity analysis method is used to analyze the influence of various structural parameters on the fatigue performance of self-piercing riveted structural parts,and 3 main influencing factors are obtained,and the optimal structural parameter interval under a single variable is obtained by changing each parameter analysis to prepare for multi-objective optimization.3.Multi-objective optimization.The center composite experiment method is used to design the experimental plan,and the mapping relationship between the maximum shear force and fatigue life of self-piercing riveted components and the structural parameters is obtained through the Design export software,which is imported into the Isight software,and finally the NSGA-II algorithm is invoked to obtain a comparative analysis.Taking high shear force and obtaining high fatigue life as the goal,the Pareto optimal solution of each structural parameter of the self-piercing riveted member was calculated.And in a reasonable case,find the practical optimal solution as the final solution.4.Experimental verification.The self-piercing riveted component is manufactured with the optimized structural parameters,the fatigue performance of the self-piercing riveted component is verified through fatigue experiments,and the fatigue life curve is drawn based on the data obtained from the test.The results show that the fatigue life of components before optimization has increased significantly compared with that after optimization.Through multi-factor analysis of variance on the data,the maximum shear force after optimization has been increased from 7358 N to 7894 N compared with before optimization,an increase of 7.28%;fatigue life Increased from 103721 times to1,15064 times,an increase of 10.94%.