Optimal design of aluminum alloy mortise and tenon for vehicle

Abstract

Aiming at the problems of large amount of calculation and low efficiency in the fine simulation of aluminum alloy mortise and tenon joints, a fast finite element method for calculating the mortise and tenon joints based on mixed spring beam element was proposed, and the method was applied to optimize the structure of aluminum alloy frame. Radial basis neural network was used to train the semi-rigidity of the mortise and tenon joints in different design variables. Under the constraint of the ultimate stress, the minimum output value of the response surface of the structure mass were taken as the optimization objective, and the linear weighting algorithm was used to obtain the optimal solution meeting the requirements of the working conditions. The results show that the numerical model with mixed spring beam element has higher computational efficiency than the solid element model and the error is only 9.52%. The maximum stress of the frame is reduced by 6.64%, the mass is reduced by 6.38%, and the energy absorption is increased by 8.07%. In addition to improving the safety performance of the car, it realizes the lightweight, which helps to improve the driving range of the car.