The friction-wear performance of friction pair materials of the foundation brake gear is an important guarantee for the operation safety of trains. In this paper, a customized friction-wear test bench was applied to carry out friction braking tests under different speed conditions. Besides, a thermo-mechanical-wear coupled model was established to simulate tests. By comparing and analyzing test results and FE (finite element) simulation results, the thermo-mechanical performance and friction-wear performance of brake friction pair material were studied.
Due to the time-consuming shortcomings of the current brake disc temperature prediction method, this paper studies the rapid prediction method of the maximum temperature of the brake disc based on BP neural network. The finite element model of the brake disc is constructed by using ANSYS software, and the error between the simulation results of the finite element model and the test results is verified to be less than 5%, which verifies the accuracy of the simulation model. Based on the simulation data of the finite element model, a BP neural network temperature prediction model is constructed to realize rapid prediction of the maximum temperature of the brake disc. The results show that using the trained neural network model to predict multiple sets of test samples, the maximum relative error is about 1%, and the average relative error is only 0.6%. The model shows a good prediction effect.
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