###### 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. b +
Footnote †: journal: Applied  
definition  
optimal brake pad design to reduce squeal noise through various design factors using the response surface methodology. A finite element model at the disc brake was used to investigate brake squeal. [PERSON] [8] employed an artificial neural network (ANN) to predict the wear rate and friction coefficient of brake pads. The predicted wear rate and friction coefficient obtained through the ANN models were compared to the measured values using various statistical indicators. The results showed that the ANN accurately predicted the wear rate and friction coefficient of the developed automotive brake pads. [PERSON] [9] proposed the shape optimization of the brake pad, which is performed to reduce uneven wear. The nonuniform contact pressure distribution on the pad surface that occurs during single braking was confirmed using a coupled thermomechanical analysis. The wear amount in the brake pad was measured using a brake dynamometer test to confirm the correlation between the nonuniform contact pressure distribution and uneven wear. [PERSON] [10] conducted a comparative analysis of multiple linear regression and ANNs to predict the wear rate and coefficient of friction of brake pads. The results demonstrated that the ANN model was equally effective in predicting both the wear rate and coefficient of friction for the brake pads. [PERSON] [11] presented a relationship between the stopping distance of a vehicle and its brake pad thickness using simple machine learning algorithms. [PERSON] [12] investigated the load-dependent wear behavior of copper-free semimetallic brake material. Through [PERSON]'s method and ANOVA, the impact of the normal load on the wear process was found to be maximum. The optimum condition for the minimum wear rate was proposed. [PERSON] [13] presented a new method for the handling of data-intensive vibration tests to gain better insights into friction brake system vibrations and noise generation mechanisms. A recurrent neural network was employed to learn the parametric patterns that determined the dynamic stability of an operating brake system. This model can predict the occurrence and onset of brake squeal with high accuracy. The current literature on braking performance and brake disc and pad optimization mainly emphasizes reducing brake squeal and predicting the wear rate. However, there is a noticeable research gap in optimizing the brake pads to mitigate thermal stress and limit the peak temperature experienced by the brake disc.