Composite materials are important engineering materials due to their outstanding mechanical properties. Composite materials offer superior properties to conventional alloys for various applications as they have high stiffness, strength and wear resistance. The high cost and difficulty of processing these composites restricted their application and led to the development of reinforced composites. In the last two decades, wear studies on Particulate Metal Matrix Composites (PMMCs) reinforced with various reinforcements ranging from very soft materials like graphite, talc etc., to high hardened ceramic particulates like SiCp, Al2O3 etc., have been reported to be superior to their respective unreinforced alloys. Therefore, present work focused on the study of machinability of Al based binary composites reinforced with 8.5% SiC and Al based Hybrid composite reinforced with 8.5% SiC, 2% and 4% Graphite powder (Solid lubricant) have been studied by considering the effect of process parameters such as speed, feed, depth of cut and composition of material. Binary and hybrid composite materials have been casted by stir casting methodology. Experiments have been conducted using Design of Experiments approach to reduce the number of experiments and time. The cutting force and surface roughness in turning of both the binary and hybrid materials have been measured using cutting force dynamometer (4 component kistler dynamometer) and the roughness has been measured using surface roughness tester (Marsurf M400) simultaneously. The multi objective optimization has been carried out using Grey relational based Taguchi method. It was observed that feed was the most influencing factor compared to others factors and also results shown that the performance characteristics cutting force and the surface roughness are greatly enhanced by using Grey relational Analysis.

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