Artificial neural networks based prediction of wear and frictional behaviour of aluminium (A380)–fly ash composites

In the present study, a feed forward back propagation artificial neural networks (ANNs) model was developed for predicting the wear rate and coefficient of friction of aluminium–fly ash composites. Aluminium–fly ash composites reinforced with different weight percentages and particles sizes of fly ash were fabricated by stir casting technique. Wear tests were conducted using pin-on-disc wear tester at different loads and sliding speed. The data obtained from experiments were used for training and testing the ANNs model. It was found that the developed ANNs model can predict the wear rate and coefficient of friction up to 95% accuracy.