The dry friction structure is a commonly used vibration-damping method for railway vehicles. Insufficient vibration damping performance will cause excessive vibration of the vehicle, which is not conducive to the safety of the vehicle. However, the mechanism of vibration damping and the cause of clamping stagnation have not been well resolved. This paper uses the analytical method, numerical method, and finite element method to analyze the vertical dynamic characteristics of the simple suspension system with dry friction and demonstrates that the numerical method is an effective method to study the dry model. The conditions for the system to produce sticking events were analyzed by the numerical method. The analysis shows that the system's excitation is too small, which causes clamping stagnation to the system. The reduction of the wedge angle and the friction coefficient are conducive to eliminating sticking. A negative side frame angle is conducive to reducing the high-frequency energy of the excitation. Decreasing spring stiffness or increasing system mass to reduce system frequency can reduce sticking events. The mutual verification of different methods confirms the correctness of the analysis method and analyzes the cause of sticking or clamping stagnation from the mechanism, which provides a new idea for the design and improvement of the dry friction damping system of railway vehicle bogies.

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.