Influence of sliding velocity, load and sliding distance on AA7050 and TiC composites through casting technique with grey analysis and neural network

Abstract

The Titanium Carbide (TiC) is an estimated wide consideration for improving the potency in the aluminium metal matrix composites (AMCs). The intention of this study is to analyze the wear behavior like wear rate and coefficient of friction in the processed metal matrix composites (MMCs). These MMCs were composed between the AA7050 and TiC with various weight percentages (0, 3 and 6 %), respectively. The stir casting technique utilized to produce the MMCs of AA7050 and TiC composites with maintained process parameters. The experimental parameters are framed with Taguchi L9 array for understanding the layout of input parameters. The four input factors are weight percentage of TiC (0 to 6 %), applied load(10 to14 N), sliding velocity (1.5 to 2.5 m/s) & sliding distance (1000 to 1400 mm). and an outcomes are wear rate and coefficient of friction (COF). These all factors and responses are created empirical relationship with grey relational analysis (GRA). The grey method successfully optimized the input parameters to improving the wear rate and COF. Finally, the interaction of various input parameters and output responses are completely analyzed with contour plots. The optimal parameters are 6 % wt. TiC, 10 N of applied load, 2.5 m/s of sliding velocity and 1200 mm of sliding distance and its improves the wear rate. The neural network was employed with experimental values to forecast the predicted values utilizing back propagation algorithm.