Modelling wear behaviour of Al–SiC metal matrix composites: Soft computing technique

Abstract

The focus of this paper is to design and develop an adaptive neuro fuzzy inference system (ANFIS) model for predicting the wear rate of Al–SiC metal matrix composites (MMCs). The data have been generated from a mathematical model developed by using the response surface method on Al–SiC MMCs. The input parameters of the model are reinforcement volume fraction, reinforcement particle size, sliding load, sliding velocity and sliding distance, and the output parameter is the wear rate of Al–SiC MMCs. The performance of the ANFIS model is evaluated by comparing the experimental data with ANFIS models, and the proposed model can be used for predicting the wear rate of Al–SiC MMCs.