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

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

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.

License to Publish Agreement signed. © Nasir, T. and Yousif, B. F. and McWilliam, S. and Salih, Nbhan D. and Hui, L. T. (2010) The definitive, peer reviewed and edited version of this article is published in Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 224 (2). pp. 419-429. doi:10.1243/09544062JMES1677