Artificial neural network approach for the prediction of abrasive wear behavior of carbon fabric reinforced epoxy composite

Artificial neural networks have emerged as a good candidate to mathematical wear models, due to their capabilities of handling nonlinear behavior, learning from experimental data and generalization. In the present work the potential of using neural networks for the prediction of abrasive wear properties of unfilled and graphite filled carbon fabric reinforced epoxy composite under various testing conditions is investigated. Back propagation neural network with 3-5-1 architecture has been used to predict the weight loss in abrasive wear situation. The network performance of different training algorithms is evaluated using the coefficient of determination B, sum squared error, mean relative error, mean squared error and regression as a quality measure. The results show that the performance of Levenberg-Marquardt (LM) training algorithm is superior to all other algorithms. Finally, the well-optimized and trained neural network with LM training algorithm is used to predict the wear properties as a function of testing conditions, according to the input data sets. The results show that the predicted data are perfectly acceptable when compared to the actual experimental test results. Hence, a well-trained artificial neural networks system is expected to be very helpful for estimating the weight loss in the complex three-body abrasive wear situation of polymer composites.