▎ 摘　　要

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.