Study on the sliding wear behaviour of hybrid aluminium matrix composites using Taguchi design and neural network

Abstract

This paper investigates the sliding wear behaviour of three different hybrid composites. Each hybrid composite consisted of two fillers. In the present study, combination of Cenosphere along with SiC and Al2O3 has been undertaken. Other factors such as applied normal load and sliding speed are also considered. Taguchi design of experimental technique is employed for the investigation of the effects of factors and their interactions on wear rate and coefficient of friction. It is observed that combination of SiC and Cenosphere hybrid composite showed better wear resistance than other hybrid composites. Regression and artificial neural network are used to develop a model to predict the wear rate and coefficient of friction. It is also observed that artificial neural network is more efficient in predicting the wear rate than regression.