APPLYING VARIOUS TRAINING ALGORITHMS IN DATA ANALYSIS OF NANO COMPOSITES

Abstract

In this study, SiC nano-particles were incorporated into the A356 aluminum alloy to fabricate metal matrix nano composites (MMNCs) with uniform reinforcement distribution. The tribological and mechanical properties of A356 nano composites were experimentally investigated. It was revealed that the presence of nano-SiC reinforcement led to significant improvement in hardness, 0.2% yield strength and UTS. The highest yield strength and UTS was obtained by 3.5 vol. % of SiC nano-particles. The wear sliding test disclosed that the wear resistance of the nano SiC reinforced composites is higher than that of the unreinforced alloy. The system accuracy of each artificial neural network training algorithm in finite element technique modeling of nano composites behaviors was then investigated.