An artificial neural network for prediction of the friction coefficient of multi-layer polymeric composites in three different orientations

Abstract

In the present work, an artificial neural network (ANN) model was developed to predict frictional performance of a polymeric composite. The experimental dataset at different applied loads (30–100 N), sliding speeds (300–700 r/min), and up to 10 min of sliding duration was used to train the model. The ANN model was trained with a large volume of experimental data (7389 sets). In addition to that, fibre mat orientation was considered in ANN development. Various configurations with different functions of training were used to find the optimal model. As a result of this work, single-layered models with large number of neurons showed high accuracy, up to 90 per cent in prediction, when trained with the Levenberg–Marqurdt function.