Optimizing Friction Block Location on Brake Pads for High-Speed Railway Vehicles Using Artificial Neural Networks

Abstract

Brake discs play a crucial role in braking railway vehicles, but the frictional heat generated during the braking process can lead to high temperatures on the disc. Changes in the friction block location on the brake pad result in variations in the temperature distribution across the brake disc. This study aims to optimize the positioning of friction blocks on the brake pad using artificial neural networks (ANN) and the Design of Experiments (DOE) approach based on the Taguchi methodology. The primary objective of this study is to mitigate temperature discrepancies in the frictional heating rate among distinct sectors along a radius from the center of the brake disc. To analyze the temperature variations caused by frictional heat, finite element analysis (FEA) is executed to account for the thermomechanical characteristics of the brake disc. The optimized brake pad, obtained through the ANN, is evaluated based on the temperature and thermal stress applied to the brake disc. The optimized model displays a larger hot band on the brake disc compared to the original model, leading to a more even distribution of thermal stress across the brake disc. In conclusion, the use of optimized pads offers significant performance benefits, resulting in a reduced maximum temperature and thermal stress, thus improving the overall braking performance of railway vehicles.