Submerged friction stir welding (SFSW) under water is a relatively new solid state joining process, which combines heating and mechanical work for deformation to achieve high quality, defect-free joints. In the present research aluminum-6061 alloy has been welded by using SFSW. Tool rotational speed, feed and water temperature has been taken as important control variables to estimate the joint performance in terms of hardness and tensile strength. It was observed that the average grain size obtained in stir zone is around 3.5 μm, the maximum hardness obtained is 87 HV and maximum tensile strength is 175 MPa. Predictive models using artificial neural networks (ANNs) were developed for both the hardness and tensile strength, followed by process optimization utilizing four distinct evolutionary optimization techniques: arithmetic optimization algorithm (AOA), Jaya algorithm and Rao-3 algorithm. Among these, the AOA demonstrated superior performance within the present manufacturing environment. As compared to the maximum experimental values of hardness and tensile strength, the optimization by using AOA, show the improvement of 6.33% and 0.35% in hardness and tensile strength, respectively.
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