Modeling and prediction of wear rate of aluminum alloy (Al 7075) using power law and ANN

Abstract

The wear process significantly influences machine partsduring their useful life. The wear process is complex, and therefore, it is very difficult to develop a comprehensive model involving all the operating parameters. In the present study, wear rate is measured during the wear process at different operating parameters such as force (load), sliding distance, and velocity. Power lawand Artificial neural network (ANN) approaches are used to model the wear rate of Al7075alloy. Power law and neural network-based models are compared using statistical methods with a coefficient of determination (R2), mean absolute percentage error (MAPE), and means square error (MSE). It is seen that the proposed models are competent to predict the wear rate of Al7075 alloy. The ANN model estimates the wear rate with high accuracy compared to that of the power lawmodel. The models developed for wear rate were found to be consistent with the experimental data.ANOVA analysis revealed that the load hasa significant effect on the wear rate than the sliding speed and sliding distance.