Sustainable friction stir spot welding of 6061-T6 aluminium alloy using improved non-dominated sorting teaching learning algorithm

Swept Friction Stir Spot Welding is provoked and fascinated for its captive performance in building space frame structures, ship buildings and panels of automotive vehicles. The aim of the current research is to avoid the wastage in welding of 6061-T6 aluminium alloy and to improve the sustainabilty of the weld process. Al2O3 nanoparticles were added into the guide hole for improving the weld characteristics. The percentage of reinforcement was varied by changing the guide hole diameter as 1.5, 2.0, 2.5 and 3.0 mm. An improved NSTLBO algorithm was proposed and applied. The proposed algorithm predicted the optimum welding parameters as D = 2.8 mm, N = 1387 rpm and F = 17 mm/minute with the predicted wear, lap shear load and microhardness of 197 microns, 6134 N and 94.6 HV, respectively. The validation experiments were further done and the error within 0.5% was found between the actual and predicted results. The results of proposed algorithm was evaluated with other popular meta-heuristic algorithms for the effectiveness. The microscopic examinations on weld zones and fracturerd samples were also presented. Industries can use these best process parameters predicted by the proposed algorithm for earning the best weld characteristics from eco-friendly welding.