MULTI-RESPONSE OPTIMIZATION AND INVESTIGATION OF DRY SLIDING WEAR BEHAVIOR OF Al7075 SURFACE HYBRID NANOCOMPOSITE USING RESPONSE SURFACE METHODOLOGY

Abstract

Aluminum alloy-based (Al7075) surface hybrid nanocomposite (SHNC) was fabricated by incorporating a reinforcement mixture of nano-aluminum oxide, micro-boron carbide, and graphite by utilizing friction stir processing (FSP). The graphite particle ratio was varied in the reinforcement mixture and its influence on the tribological properties of Al7075 SHNC was studied. In the metal matrix surface composite, the scanning electron microscope (SEM) and field emission SEM (FESEM) depict a homogeneity in the distribution of reinforcements. The nanocomposite’s wear behavior under dry sliding environments was investigated by adopting a central composite design (CCD) at three levels by response surface methodology (RSM). The designed experiments were executed in pin-on-disc (POD) apparatus, with load, sliding distance, and graphite ratio as input variables. The influence of applied factors and their interaction with the response were determined using an analysis of variance. To predict the wear characteristics, a mathematical model is formulated. Load is discovered to be a significant factor influencing the wear rate and friction coefficient. In addition, an increase in graphite% results in a lower wear rate for the given quantity of load and sliding distance. SEM image shows a severe wear pattern for higher load and lower graphite content. The optimum combination of the parameter obtained from multi-response optimization was load 10[Formula: see text]N, sliding distance 503.86[Formula: see text]m, and 14.99% graphite for reducing the wear rate and friction coefficient by applying the desirability function approach.