Optimization of Wear Parameters in AISI 4340 Steel

Abstract

This study investigated the optimization of wear behavior of AISI 4340 steel based on the Taguchi method under various testing conditions. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the wear rate in 4340 steel. A back-propagation neural network (BPNN) was developed to predict the wear rate. In the development of a predictive model, wear parameters like sliding speed, applying load and sliding distance were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the wear rate. Finally, the Taguchi approach was applied to determine the optimum levels of wear parameters. The results show that using the optimal parameter setting (load3, sliding speed1, and sliding distance2) a lower wear rate is achieved. The error between the predicted and experimental values is only 3.19%, so good agreement between the actual and predicted results is observed.