Feature Engineering-Driven Interpretable Machine Learning Study on the Corrosion Resistance of Zn-Al-Mg Coatings

Zn-Al-Mg (ZAM) coatings have attracted significant attention in the field of corrosion protection owing to their combination of excellent corrosion resistance and cost-effectiveness. However, the corrosion behavior of ZAM coatings was governed by the synergistic coupling effects of multiple factors, including alloy composition, coating thickness, corrosive medium, and multiphase microstructure, making it challenging for traditional empirical analysis to systematically reveal the underlying mechanisms. To address this challenge, we constructed a multidimensional corrosion dataset comprising alloy composition, corrosive medium, coating thickness, and phase composition, based on literature data from the past three decades combined with self-measured potentiodynamic polarization experimental results. After data normalization and correlation analysis, we introduced phase structure features—including the Al-rich phase, MgZn2 phase, Mg2Si phase, and eutectic microstructures—to enhance the model’s capability in representing microstructural factors. On this basis, we established random forest (RF), support vector regression (SVR), and artificial neural network (ANN) models to predict the corrosion current density, and subsequently conducted an interpretability analysis using the SHapley Additive exPlanations (SHAP) method. The results demonstrated that the expanded feature set significantly improved the prediction performance of the models. Among them, the RF model exhibited the best performance, achieving a determination coefficient (R2) of 0.7363 on the test set, which represented a substantial improvement over the baseline dataset. Feature importance analysis revealed that coating thickness, Mg content, NaCl concentration, and Zn content were the primary factors influencing the corrosion current density. Further SHAP analysis showed that the marginal contribution of the eutectic phase was more prominent in local samples. Meanwhile, the Mg element exhibited distinct non-linear regulation characteristics, exerting varying impacts on the corrosion behavior across different concentration ranges. This study demonstrated that the interpretable machine learning models constructed via feature engineering not only improved the prediction accuracy of the corrosion performance of ZAM coatings, but also provided a novel data-driven approach to revealing the intrinsic correlations among alloy composition, phase structure, and corrosion response.