Prediction of impact damage to Fe-based amorphous coatings via interpretable machine learning

Predicting the impact damage of Fe-based amorphous coatings remains a significant challenge due to the complex, nonlinear effects of key coating properties. This study develops an interpretable machine learning (ML) framework to accurately predict impact performance and elucidate the governing physical mechanisms. Twelve regression models were systematically evaluated. The Multilayer Perceptron (MLP) demonstrating superior performance with the highest determination coefficient (R2) of 0.968 and the lowest error metrics (MSE: 0.066 mm2, RMSE: 0.257 mm, MAE: 0.150 mm) on the testing set. Interpretability analysis via Shapley additive explanations (SHAP) quantified the global importance of input features, revealing the following hierarchy: shear stress depth ratio (γ) > residual stress (σ) > through-porosity rate (p) > maximum shear stress (τ). This ranking provides a critical, data-driven design insight: optimising the through-thickness distribution of shear stress (γ) is more crucial for mitigating interfacial delamination than merely increasing compressive residual stress. Further, an explicit, quantitative formula was derived from the MLP model, enabling rapid prediction of impact damage. This work establishes a robust ML-SHAP paradigm that not only deliver a high-accuracy predictive tool but also translates complex model outputs into actionable physical understanding and design priorities for advanced coating engineering.