Genetic algorithm based optimization of friction welding process parameters on AA7075-SiC composite

Abstract

Friction welded joints of Aluminium metal matrix composite is witnessing tremendous developments in aeronautical and marine applications. The aim of this work is to optimize the process parameters of the rotary friction welding of Al-SiC composite. The aluminium based metal matrix composites containing 10% vol of SiC particles of 20 µm average size was manufactured by stir casting technique. By investigating the mechanical properties such as the micro hardness and tensile strength, the friction welded joints integrity was examined. The friction welding process parameters such as Spindle Speed (SS), Friction Pressure (FP), Upset Pressure (UP) and Burn-Off-Length (BOL) were considered to find their influence on Ultimate Tensile Strength (UTS) and Hardness (HD). Genetic Algorithm (GA) was employed by taking the fitness function as Combined Objective Function (COF) to optimize the friction welding process parameters to predict the maximum value of UTS and HD. Scanning Electron Microscope (SEM) images of the fractured specimen clearly indicated the difference in the failure mode based on the preferred weights combination. The confirmation test also revealed good closeness to the Genetic Algorithm predicted results. The optimised values of process parameters for different weights of UTS and HD had been predicted. Keywords: Friction welding, Aluminium metal matrix composite, Design of experiment, Optimization, Ultimate tensile strength, Hardness, Genetic algorithm