Multi-response modeling for sliding wear behavior of AA5052/TiC composites by stir casting: A comparative analysis using response surface methodology and fuzzy logic system

The present research deals with the modeling of wear performance of AA5052 metal matrix composites (MMCs) reinforced by in-situ formed TiC particles using two methods, namely statistical-based non-linear regression and fuzzy logic system. A pin-on-disc apparatus is employed to obtain the wear data of the composites. Three different process parameters, namely weight percentage of TiC, sliding distance, and applied load are considered as the input variables, and the volumetric wear loss and coefficient of friction are taken as output variables of the wear process. Regression models are formulated to study the output responses based on the wear results. To check the significance and contribution of the various control parameters, analysis of variance method was performed. Further, confirmatory tests were also conducted to validate both the models. It has been observed that the sliding distance and applied load have shown a substantial effect on the volumetric wear loss. It is also observed that the coefficient of friction is found to be increased with the rise in the sliding distance. The novelty of the work lies in using fuzzy logic to establish the wear model for aluminum 5052 MMC. A comparative study is performed to analyze the prediction accuracy of the models for the wear characteristics of the aluminum composites. Consequently, this wear modeling investigation will give a better understanding the effect of parameters and the responses influence of wear phenomena of Al 5052/TiC composites, which further will help in developing wear components for industrial applications.