Machine Learning-Based Prediction of Stacking Fault Energy in High-Manganese Steels: A Comparative Study of Ensemble and Kernel Methods

Accurate prediction of the stacking fault energy (SFE) is critical for controlling deformation mechanisms, specifically transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP), in high-manganese (high-Mn) austenitic steels, which are of growing importance in automotive and structural applications that demand exceptional strength–ductility combinations. This study presents a systematic comparative evaluation of six supervised machine learning (ML) models—Multiple Linear Regression (MLR), Random Forest (RF), Extra Trees (ETs), Gradient Boosting (GB), Support Vector Regression (SVR), and a stacking ensemble—trained on a curated, outlier-cleaned experimental database of Fe-Mn-C-Si-Al-Cr-Ni-N spanning SFE values from 5.0 to 63.0 mJ/m2 (mean 23.7 ± 11.2 mJ/m2). After Z-score outlier removal (|Z| > 3) and 80/20 train–test splitting with nested 5-fold cross-validation hyperparameter optimization using GridSearchCV, ET and GB achieved training R2 values of 0.988 and 0.990, respectively, confirming that SFE is highly predictable from alloy composition alone. The stacking ensemble delivered the best generalization on the independent held-out test set (test R2 = 0.603, RMSE = 5.60 mJ/m2, MAE = 4.86 mJ/m2), outperforming all the individual learners. Random Forest feature importance analysis identified Al (22.3%), Fe (20.5%), and Mn (17.7%) as the three most influential compositional variables, collectively explaining 60.6% of the predicted variance. Pearson correlation analysis confirmed that Al was the strongest individual linear predictor (r = +0.421, p < 0.001), whereas Fe showed a significant negative correlation (r = −0.327, p < 0.001). Mn, C, and the remaining elements showed no statistically significant linear correlations with SFE, underscoring the dominance of nonlinear compositional interactions. Composition–SFE design maps derived from the GB model delineate the TRIP/TWIP regime boundaries in the Mn–C and Mn–Al composition spaces, providing a validated computational tool for targeted high-Mn steel alloy design.