Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach

Abstract

In the present work, the surface of the ZK60 Mg alloy was alloyed with tin (Sn), and the FSP process parameters have been optimized for the better mechanical properties by employing response surface methodology (RSM). Furthermore, RSM was combined with artificial neural networks (ANNs) to evaluate and compare the predictive capacity of both the models. FSP process parameters, namely, tool rotational speed (S), feed rate (F), number of passes (N), and weight percentage of Sn (W) were selected as influential parameters for optimization. The optimum conditions that were predicted by the RSM model to maximize the ultimate tensile strength (UTS) and % elongation (%EL) were a tool rotational speed of 2000 rpm, a feed rate of 0.39 mm/sec, 3 number of passes, and 8 wt.% of Sn which yields the maximum tensile strength of 217 MPa and the maximum %El of 26%.

Keywords

• ZK60 Mg alloy
• friction stir processing
• response surface methodology
• artificial neural networks
• ultimate tensile strength