Optimization of tribological behaviour on squeeze cast Al6061/Al2O3/SiC/Gr HMMCs based on Taguchi methodandArtificial Neural Network

L.Natrayan, M.Senthil Kumar

This article investigates the wear rate of AA6061 reinforced Al2O3/SiC/Gr fabricates by squeeze casting technique. Taguchi method used to optimize the process parameters such as load (10, 20, 30), sliding velocity (1, 1.5, 2 m/s) and sliding velocity (800, 1200, 1600 m). L27orthogonal array was selected for the experimental design. Analysis of variance carried out to understand the influence of specific factors and relations on the wear rate. Wear mechanism, surface morphologies, and composition of the composites have been studied by using scanning electron microscopy (SEM) with Energy-dispersive X-ray spectroscopy (EDS). Optimized results were predicted with the artificial neural network (ANN). The results indicate that load and sliding velocity has a significant contribution to controlling the wear behaviour of hybrid composites. Worn surface exposed adhesive wear was predomination in the samples. EDS revealed the presence of elements in the composite. The ANN and regression model predicts the wear rate with up to 95 % accuracy.

Volume 11 | Issue 7

Pages: 493-500