Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

• FSP was performed on WE 43 Mg alloy to assess the corrosion behavior using ML. • Synthetic data was generated using PSO for virtual sample generation. • Five different machine-learning models were compared using the original dataset. • A sensitivity analysis was performed using Shapley Additive Explanations. • A graphical user interface (GUI) has been created to predict the corrosion rate. 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 L 27 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.