This study investigates the surface properties of the dissimilar AA7075/Mg-WE43 joints wherein the effect of positioning the AA7075 on the advancing or retreating sides during friction stir welding (FSW) was scrutinized. The EBSD analysis revealed that the average grain size reduced from 4.11 ± 0.7 µm to 2.04 ± 0.9 µm when AA7075 was shifted from advancing to retreating side. The results showed that positioning AA7075 on the retreating side significantly reduced the wear rate from 0.41 mm³/Nm to 0.27 mm³/Nm and mitigates the problem of brittle intermetallic compound (IMC) formation, consequently reducing the average microhardness. Corrosion resistance improved from 77.59 mpy to 56.299 mpy. The higher grain refinement improved wear resistance due to higher grain boundary density and greater chances of formation of protective oxide films. The current work thus enhances the practical applicability of AA7075/Mg-WE43 welds for lightweighting of automotive structures.

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications. After undergoing friction stir processing (FSP), the refined grain structure leads to a notable decrease in corrosion rate. However, a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking. The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters (rotational speed, traverse speed, and shoulder diameter) for WE43 alloy. The Taguchi L₂₇ design of experiments was used for the experimental analysis. In addition, synthetic data was generated using particle swarm optimization for virtual sample generation (VSG). The application of VSG has led to an increase in the prediction accuracy of machine learning models. A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate. The shoulder diameter had a significant impact in comparison to the traverse speed. A graphical user interface (GUI) has been created to predict the corrosion rate using the identified factors. This study focuses on the WE43 alloy, but its findings can also be used to predict the corrosion rate of other magnesium alloys.