Prediction of fretting wear behavior of surface mechanical attrition treated Ti–6Al–4V using artificial neural network

In the present work, multi layer perceptron feed forward artificial neural network (ANN) technique was employed to predict the fretting wear behavior of surface mechanical attrition treated and untreated Ti–6Al–4V fretted against alumina and steel counterbodies. A three-layer neural network with a gradient descent learning algorithm was used to train the network. Three input parameters normal load (L), surface hardness of the test material (H) and hardness of counterbody material (CB) were employed in construction of ANN. Tangential force coefficient (TFC), fretting wear volume and wear rate obtained from a series of fretting wear tests were used in the training and testing data sets of ANN. Ranking of the importance of input parameters on the output TFC was found to be in the order of L > CB > H. For wear volume and wear rate, it was found in the order of L > H > CB. The degrees of accuracy of predictions were 96.6%, 96.1% and 92.2% for TFC, wear volume and wear rate respectively. Owing to the good correlation between the predicted values and the experimental results, ANN can be used in the prediction of fretting wear behavior.