Comparative analysis of sliding wear behaviour of A356 nanocomposites using response surface methodology and artificial neural network approaches

Abstract

Abstract The present study is aimed at analysing the predictive capacity of response surface methodology and artificial neural network of wear behaviour of A356/Al2O3 nanocomposites. In order to develop nanocomposites with different Al2O3 content the mechanical milling and powder metallurgy routes were adopted. The wear testing experiments were conducted using pin on disc tribometer to study the influence of parameters such as Al2O3 content, load, sliding speed and distance on wear loss. The testing was conducted based on the experimental design generated through Taguchi’s L27 technique. The response surface methodology and artificial neural network were used to predict the wear loss of A356 nanocomposites and comparative analysis was performed to analyse the predictive capability of these two techniques. Analysis of variance results showed significant influence of sliding speed on the wear loss while impact of sliding distance was minimal. The average relative error between the artificial neural network predicted and experimental value was 4.861% while for response surface methodology it was 9.307%. This comparative analysis indicated better predicting capacity for artificial neural network model. Worn surface analysis showed dominant abrasion and mild delamination as wear mechanisms for both unreinforced and nanocomposite samples.