Numerical Simulation And Process Optimization Of Stamping Of The Complex Bending Part

As an important part of adjusting the window lift plate of the car window,the assembly performance and the performance of the window lift plate directly determine the stability and smoothness of the window.The window lifter has the characteristics of complex shape,large variation of curvature and high requirement of the assembly dimension.In order to get good quality parts in the actual production process,it is necessary to reduce the springback of the parts as much as possible.Due to the nonlinear relationship of strong interaction,many influence factors and complex constraints between the springback of the window lifter plate forming and the stamping process parameters,so using a single mathematical analysis method can not solve the functional equation between the problem,and can not accurately predict the amount of deformation.Aiming at the problem,based on the integrated application of numerical simulation technology,experimental design method,approximate model and optimization algorithm,this paper realizes the springback prediction and forming process parameter optimization of window lift plate.The research content and innovation of this paper are mainly reflected in the following aspects:(1)Based on the 100 sets of sample data obtained by the Latin hypercube sampling method,the approximate model between the springback and the forming process parameters is constructed based on the support vector machine(SVM),and for radial basisfunction(RBF)kernel function.(2)The K fold cross validation(CV)and the particle swarm optimization(PSO)algorithm are used to optimize the penalty factor and the kernel parameter of the support vector machine(SVM)in the constrained interval respectively.It improves the prediction accuracy of the support vector machine to the rebound of the car window lift plate.(3)The algorithm is optimized by introducing the inertia weight,the learning factor and,the limit maximum velocity and the maximum position,the population number and the iteration number,which follow decreasing strategy.(4)The mean square error and mean error are used to evaluate the CV-SVM and PSO-SVM approximate models,respectively.Combined with particle swarm optimization algorithm for global optimization and springback prediction of forming process parameters.Under the DYNAFORM software,the numerical simulation of the forming parameters is carried out,and the optimal solution is determined.(5)The optimal forming parameters are applied to the production practice,and the reliability and accuracy of the forming process parameters predicted by the support vector machine model are tested.The algorithm and the optimization process are applied to the trial production of the enterprise.After the example verification,the springback of the window lift plate has been effectively controlled,and it has not affected the accuracy and performance of the assembly.