A simulation-based study on the effect of underwater friction stir welding process parameters using different evolutionary optimization algorithms

Abstract

This paper investigates the effect of underwater friction stir welding process parameters on the mechanical properties of the aluminum alloy 6082-T6 joint and further simulates this process using various evolutionary optimization algorithms. Three independent underwater friction stir welding process parameters, i.e. shoulder diameter (in mm) at two levels, rotational speed (in r/min) at three levels, and traverse speed (in mm/min) also at three levels, were varied according to the Taguchi’s L18 standard orthogonal array. The effect of variations in these parameters, on the ultimate tensile strength (in MPa), percentage elongation (in %), and impact strength (in J) of the welded joint was experimentally measured and recorded. In order to simulate this underwater friction stir welding process, three evolutionary optimization algorithms, i.e. particle swarm optimization, firefly optimization, and non-dominated sorting established on the genetic algorithm (NSGA-II), were employed. In these simulations, an artificial neural network with two layers, resembling a non-linear function, was employed as the cost function to predict the values of the response variables, i.e. ultimate tensile strength, elongation, and impact strength, which were experimentally measured earlier. In these simulations, several experiments were conducted using different randomly selected data set and subsequently, the accuracy of each individual simulation was compared. Results revealed that the firefly optimization-based simulation performed the best with least mean squared error while predicting the response variable values, as compared to the particle swarm optimization and the NSGA-II. The minimum value of the mean squared error for the firefly optimization-based simulation was observed to be as low as 0.009%, 0.004%, and 0.017% for ultimate tensile strength, elongation and impact strength, respectively. Furthermore, it was also observed that the computational time for the firefly optimization-based simulation was significantly lower than that of both particle swarm optimization and NSGA-II-based simulations.

Multicharacteristics optimization of electrical discharge micro hole drilling in Mg alloy using hybrid approach of GRA–regression–PSO

“…This empirical model has been solved for maximizing the grade value using PSO within the defined limits of the EDD input parameters. This PSO algorithm (Marini and Walczak, 2015;Wahid et al, 2020) is solved by calculations of the movement of swarms, fish or flocks. The number of generations, population of swarms and upper and lower limits play a pivotal role in the solutions suggested by PSO algorithm.…”

Optimization of Process Parameters in Under Water Friction Stir Welded AA2014 and AA6082 joints.

“…The mechanical properties of the aluminum alloy 6082-T6 joint are influenced by the process parameters of underwater friction stir welding. Various evolutionary optimization approaches are used to describe this process [8]. Electron backscatter diffraction technique was utilized to analyze microstructural changes and texture evolution in different 7003/6060 aluminum alloys during underwater friction stir welding [9] Microstructural analysis showed that most fractures are located in the thermomechanically damaged zone [10].…”

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

“…In the estimation results obtained, they reached data close to the results obtained from the experimental studies with very low error rates. Wahid et al 24 have benefited from hybrid artificial intelligence techniques in underwater friction stir welding process. In the created prediction model, shoulder diameter, rotation speed, rotation speed were selected as input parameters, while some mechanical properties of 6082-T6 aluminum alloy were selected as output values.…”