Study On Riv-bonding Process And Mechanical Properties Of Aluminum-Steel Sheet

With the rapid development of lightweight vehicles,aluminum alloys and high-strength steels have been more and more widely used in the automotive industry.Due to the large difference in physical properties between aluminum and steel,the resistance spot welding technology currently widely used in thin plate connection is difficult to apply to the connection of aluminum and steel.With the wide application of aluminum alloys in car bodies,adhesive bonding,self-piercing riveting,and riv-bonding processes have gradually gained attention.The simple self-piercing riveting structure has a large stress concentration,and the original structure is destroyed due to the need to pierce the sheet.Adhesive joints have lower peel strength.The combination of self-piercing riveting and bonding processes can improve the problems of difficult fixing and crack extension of the bonding technology.At the same time,it has also eased the problem of excessive concentrated stress caused by the riveting technology.Therefore,the riv-bonding connection process is a superior solution in the dissimilar metal connection technology.In this paper,DP780 steel plate and AA6111 aluminum alloy are used as research objects.The cross-section morphology and mechanical properties of riv-bonding joints,selfpiercing riveting joints and bonding joints are compared and studied;The effects of the rivbonding process parameters on the quality of the joints were studied through experiments and simulations;the traditional BP neural network was improved based on genetic algorithms,and the joint strength of the riv-bonding joint was predicted.The specific research content is as follows:(1)Proposing a joint quality evaluation method and establishing a process parameter optimization model:In this paper,the stacking order of the plates is determined through experiments,and the dimensional parameters of the rivet and the die to be used in this paper are calculated through related theories.Then the mechanical properties and cross-section morphology of the riv-bonding joint are compared and analyzed,and the influence of process parameters on the mechanical properties of the joint is studied.The results show that the static failure load and energy absorption of riv-bonding joints are higher than those of self-piercing riveting joints,and the use of adhesive does not affect the joint failure form.Based on the grey correlation theory,the impact of riveting pressure on shear strength is the largest,and the thickness of the aluminum plate is the smallest.The maximum tensile and shear forces and failure displacements of the three joints increase with the increase of the overlap width.When the overlap width is 30mm-35 mm,the mechanical properties and energy absorption of the riv-bonding joint reached a better level.(2)Establishing a simulation model for the forming process of riv-bonding joints based on Simufact.forming software:Based on Simufact.forming software,the finite element simulation of the forming process of aluminum-steel riv-bonding joints and self-piercing riveting joints was performed.The forming process of riv-bonding joints and self-piercing riveting joints was compared and analyzed.The parameters of the joints were studied by single factor analysis Appearance.The results show that the maximum riveting force of self-piercing riveting is higher than that of riv-bonding due to the cushioning and vibration absorbing effect of the adhesive;as the thickness of the adhesive layer increases,the undercut amount decreases,and the thickness of the adhesive layer is proportional to the height of the rivet head.When the thickness of the adhesive layer is 0.1mm,the undercut of the joint is the largest,and the molding effect is better at this time.(3)Establishing a prediction model of mechanical properties of riv-bonding joints based on the BP neural network improved by GA:The traditional BP neural network is improved by genetic algorithm,and then the Prediction model of mechanical properties of riv-bonding joints is established.The results show that although GA-BPNN has a longer convergence time than BPNN,GA-BP network has better correlation coefficients,better regression performance,and better generalization ability.Finally,the last ten sets of verification data were substituted into the trained neural network prediction model for verification,and it is found that the mean absolute error predicted by the GA-BP neural network is 40% of that of the BPNN,and the GA-BP neural network has better prediction performanceIn this paper,the introduction of adhesives in self-piercing riveting joints effectively improves the mechanical properties of the joints.In addition,the optimization of the rivbonding parameters of this article has a strong value in use.The improved BP neural network strength prediction model of the riv-bonding joints based on genetic algorithms has strong explore value.