Taguchi and Neural Network Analysis for Predicting Abrasive Wear Behavior of Carbon Epoxy Composites

In this study, an approach for predicting the three-body abrasive wear behavior of unfilled and graphite filled carbon fabric reinforced epoxy composite using two modeling techniques - Taguchi analysis and artificial neural network are presented. A set of experiments were conducted using an orthogonal array based on Taguchi techniques to acquire data in a controlled manner. The results showed that the addition of graphite particulate into carbon epoxy composite led to a decrease in its abrasive wear resistance, and the wear loss increased with an increase in abrading distance and loads. To investigate the effect of control parameters on the wear behavior of the composites, an analysis of variance was performed, and the S/N ratio was calculated. The results found that the normal load had the highest physical as well as statistical influence on the abrasive wear of the composites followed by abrading distance and filler content. To predict the wear properties of composites as a function of testing conditions, 3-[5]1-1 neural network architecture with Levenberg Marquardt (LM) training algorithm was used. By comparing the correlations obtained by Taguchi regression analysis and artificial neural network with the experimental results it was found that the artificial neural network predicts the wear rate better than regression analysis. Therefore, a well-trained artificial neural network system can be very helpful in estimating the weight loss in the complex three-body abrasive wear situation of polymer composites.