Journal article Open Access

Genetic Algorithm-Based Optimization of Friction Stir Welding Process Parameters on Aa7108

Maulikkumar B. Patel; Komal G. Dave

Sponsor(s)
Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)

This research paper deals with the characterization of friction stir welding aluminium 7108 with twin stir technology. The coupons of the above metal were friction stir welded using a cylindrical pin with counter-rotating twin stir technology using at constant speed 900, 1200, 1500,1800 with four different feed rates of 30,50,70,90 mm/min. Microstructure examination showed the variation of each zone and their influence on the mechanical properties. Also, tensile strength and hardness measurements were done as a part of the mechanical characterization and correlation between mechanical and metallurgical properties and deduced at the speed of 1500 rpm. Friction stir welding process parameters such as tool rotational speed (rpm), tool feed (mm/min) were considered to find their influence on the tensile strength (MPa) and hardness (HRB). A genetic algorithm (GA) was employed by taking the fitness function as a combined objective function to optimize the friction welding process parameters to predict the maximum value of the tensile strength and hardness. The confirmation test also revealed good closeness to the genetic algorithm predicted results and the optimized value of process parameters for different weights of the tensile and hardness have been predicted in the model. 

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