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 L-18 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.