Abstract
In the present study, wear and friction behaviour of Magnesium(Mg) Metal Matrix Composite(MMC) reinforced with Silicon carbide(SiC) doped reduced graphene oxide (r-GO) nanosheets is carried over. In addition, a mathematical model is developed to predict the influence of various control factors of the Mg composites fabricated through Solvent-based powder metallurgy process. Herein SiC is doped with varying wt. % (10, 20, 30) into r-GO nanosheets and its effect over dry sliding wear is studied at constant control parameters like that of load (10N), sliding distance (1000m) and sliding velocity (1 m/s). Optimal parameter for specific wear rate (SWR) is attained by Taguchi method and the mathematical model was developed using Artificial Neural Network in order to understand the wear behaviour of developed MMC under varying parametric condition. Analysis of variance result reveals that wt.% of r-GO have major influence on SWR and sliding velocity have least effect. Occurrence of delamination wear could also be notified over the worn out surface.
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