A numerical method combining finite element and neural network model to study dynamic system prediction of dry friction

Abstract

Friction wedge is an important damping component of three-piece freight bogies, and the better damping performance is beneficial to improving the stability of the vehicle operation. This paper introduces an effective method for numerical simulation of dry friction system and the related experiment was conducted to verify the correctness of the method. On the basis of conducting the experimental of dry friction model to test the lateral and tangential forces of the dry friction, the dynamic friction coefficient under different speeds and pressures was calculated. The most suitable dry friction model was obtained by comparing the fitting accuracy of different models. The fitting accuracy of the neural network model is above 0.9, which is much higher than other models. Pressure is an important parameter of the friction coefficient and should be taken into account in the model. The dynamic implicit procedure was adopted in the simulation process with Abaqus/Standard solver, the user-subroutine FRIC integrated in the commercial package ABAQUS was coded to study the rate and pressure dependent dynamic friction during the movement of dry friction system. The calculation result is basically consistent with the experiment when the neural network model is combined with the user-subroutine FRIC. The feasibility of the co-simulation analysis method is verified. The neural network model is more accurate and convenient to establish the dynamic friction model, avoiding the difficulty of choosing the the dry friction model. It is verified that the neural network model can be used in finite element analysis, which provides a new idea for the combination of neural network and traditional calculation methods.