The ANN analysis and Taguchi method optimisation of the brake pad composition

Abstract

In this study, the Taguchi Optimisation technique was utilized with the aim of establishing the composition of an asbestos-free brake pad. As a result of optimization outputs, 18 various compositions were obtained. Produced specimens according to outputs were subjected to Friction Assessment and Screening Tests (FAST) to evaluate their average friction coefficient and friction surface temperature. Assessing the results, the composition of brake pads, which can be used in vehicle brakes, was exhibited. Experimental results also examined by variance analysis to demonstrate effect factors. Furthermore, when the test results are evaluated in terms of friction coefficient, wear rate and temperature of the brake disc, a high coefficient of friction, a moderate wear rate and an acceptable temperature of the brake disc were obtained in Sample 13. Composition of Sample 13 with the highest friction coefficient is: 3 g. brass powder, 9 g. cashew, 9 g. carbon fiber, 9 g. copper dust and 9 g. graphite. In addition, average friction coefficients of brake pads were estimated through Artificial Neural Network (ANN) analysis. In respect to the results, it was discovered that, the generated ANN model promises high ratio estimation capacity.