Multi-Objective Optimization for Enhanced Surface Roughness and Microhardness of AA 6063/TiO2 Cast Aluminium Metal Matrix Nanocomposites Fabricated by Friction Stir Processing

Abstract

In this chapter, friction stir processing (FSP) is used as an effective technique to improve the strength and hardness of various AA6063 aluminium alloys so as to alter the nanocomposite by using TiO2. This FSP process has also been used to impart superplasticity for eliminating casting defects. The FSP process parameters are tools with different rotation speeds of 1050, 1200, 1450 rpm; tool transverse feed of 30, 45, 60 mm/min; tool pin profile shapes of conical, rectangular, and threaded forms of the nano-reinforced particles that are strategically varied during the FSP process. The FSP experiments are conducted by using L9 Taguchi-based design of experiment to fabricate cast nanocomposite. Subsequently, the nanocomposites fabricated through FSP undergo few mechanical tests to evaluate mechanical metallurgical properties such as microhardness, tensile strength, and surface roughness. Multi-objective model using Grey relational analysis is proposed to solve the problem. Analysis of variance is used to determine the percentage of contribution of the given process parameters, and the tool rotation speed is high influenced parameter, 32threaded tool profile is second influenced parameter, and finally, tool feed rate is the low influenced parameter.