Prediction of wear properties of graphene-Si3N4reinforced titanium hybrid composites by artificial neural network

Abstract

In this study, we have employed artificial neural network (ANN) method to predict wear properties of titanium hybrid composites produced by powder metallurgy (PM) method. Titanium (Ti) was used as a matrix materials and graphene nano-platelets (GNPs)-Si3N4 were used as reinforcement materials in hybrid composites. A back-propagation neural network with 3-6-1 architecture was developed to predict wear rates by considering weight fraction reinforcements, load and density as model variables. The well trained ANN system predicted the experimental results in a good agreement with the experimental data. This refers that ANN can be used to evaluate wear rate of samples in a cost effective way.