Comparative analysis of predictive modeling techniques for mechanical properties in dissimilar friction stir welding of AA6061 and AZ31B - Sambath, Yaknesh ,; Natarajan, Rajamurugu; Babu, Prakash K; Raju, K. Ramachandra; Alahmadi, Ahmad Aziz; Alwetaishi, Mamdooh; Khan, Sher Afghan (Springer, 2024)
Friction stir welding (FSW) is a solid-state joining method extensively utilized for dissimilar materials because it can reduce imperfections and uphold material characteristics. Within this investigation, AA6061-T6 aluminum alloy and AZ31B magnesium alloy were subjected to friction stir welding under various process parameters, with the primary objective being the examination of the mechanical attributes of the welds and the juxtaposition of the forecasting precision of artificial neural networks (ANN) and response surface methodology (RSM) models. Key performance indicators such as tensile strength, microhardness, and impact energy were assessed. The obtained experimental data unveiled a spectrum of mechanical characteristics, encompassing tensile strength ranging from 69 to 144 MPa, microhardness from 79 to 112 HV, and impact energy from 7.9 to 8.55 J, with the validation test yielding a satisfactory outcome within an acceptable margin of error. The comparative scrutiny of ANN and RSM prognostications indicated that ANN achieved marginally lower average error percentages across all parameters, highlighting its enhanced predictive capacity. These observations emphasize the efficacy of both modeling methodologies in capturing the intricate interrelations inherent in dissimilar FSW procedures, with ANN demonstrating a slight edge in predictive precision.