Abstract
In the present work an artificial neural network (ANN) model was developed to predict the wear rate and coefficient of friction of WC-12Co nanocomposite microwave clads. Various combinations of the transfer function and number of neurons in the hidden layer was used to optimise the neural network. The influence of nature of reinforcement, normal load and sliding distance on the wear rate of the conventional and nanostructured microwave clads was evaluated using the ANN model. The mean square error of 500 epochs was considered to evaluate the performance of the ANN model. The wear rate and coefficient of friction predicted through the ANN model was then compared with experimental results. The predictions of the ANN model were consistent with experimental results. It can be therefore concluded that ANN is an effective modeling technique to predict the wear rates of the WC-12Co microwave clads.
