Prediction on friction characteristics of industrial brakes using artificial neural networks

Abstract

Safety and reliability are the main requirements for brake devices in the mining winding installations. Therefore, selection of the right materials for the friction brake elements (pads and discs) is the most challenging task for brake system designers. The friction coefficient for such friction couples should be relatively high but, above all it should be stable. In order to achieve the desired brake friction couple performance, a new approach to the prediction of the tribological processes versus friction materials formulation is needed. The paper shows that the application of the artificial neural network can be productive in modelling complex, multi-dimensional functional relationships directly from experimental data. The artificial neural network can learn to produce the model of friction brake behaviour.