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 (Cadev), 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 (AlₘulO, CadevₚlusTi, and CadevₘulTi) 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.
Wu et al. (Wed,) studied this question.
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