Mechanism-Driven Feature Engineering and KNN-Guided Gaussian Perturbation Data Synthesis for Prediction of Al2O3 Inclusion Modification Efficiency in Medium-Carbon Steel

Effective modification of alumina (Al 2 O 3 ) non-metallic inclusion (NMI) is essential for improving medium-carbon steel quality. However, accurately predicting Al 2 O 3 modification efficiency under small-sample industrial conditions remains challenging due to limited labeled data and low information density. This study proposes a hybrid framework that integrates mechanism-driven feature engineering with a KNN-guided Gaussian perturbation (KNN-GNP) data synthesis method to predict the liquid NMI fraction in medium-carbon steel. Based on 25 industrial heats, 6 engineered features were constructed according to metallurgical mechanisms of Al 2 O 3 formation, Ca-S/Ti competition, and calcium deviation (Ca_dev), enhancing feature representation for inclusion modification prediction. The KNN-GNP approach synthetically expanded the dataset while preserving statistical consistency and metallurgical validity. Machine learning models were trained and compared using R 2 and RMSE metrics, and interpretability was analyzed through feature importance and partial dependence plots. The proposed method achieved an R 2 of 0.99 and an RMSE of 0.007, outperforming conventional regression and generative models. Key variables (Al_mul_O, Ca_dev_plus_Ti, and Ca_dev_mul_Ti) were identified as dominant factors influencing inclusion modification efficiency. This framework provides a reliable and interpretable data-driven approach for optimizing metallurgical processes under limited data conditions, contributing to intelligent and sustainable steel manufacturing.