Investigation on Wear Behaviour of Al6061-Al2O3-Graphite Hybrid Metal Matrix Composites using Artificial Neural Network

Abstract

Aluminum metal matrix composites are widely used in engineering applications, specially automobile, aerospace, marine and mineral processing industries owing to their improved wear properties compared to conventional monolithic aluminum alloys. Nowadays hybrid composites plays vital role in engineering application. In this present work Alumina and Graphite are added as reinforcement particle into Aluminum 6061 alloy for preparing hybrid composites. The hybrid composite is produced by liquid metallurgy route. This method is less expensive and very effective. The objective of this work is to predict the wear behavior of Al2O3 Graphite reinforced with Al6061 hybrid metal matrix composites by using feed forward back propagation algorithm. The design of experiment is planned based on taguchi (L0) orthogonal array and it is performed by various control factors such as sliding speed, sliding distance, applied load and percentage of reinforcement. Artificial neural network is very accurate compare to other prediction techniques genetic algorithm, taguchi method.