Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles

Abstract

The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi L27 orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures.