Bayesian optimized-GBDT based prediction model for slag inclusion defects in continuous casting billets

To address the sub-optimal intelligence level in current defect prediction methods for continuous casting billets, this study proposes a gradient boosting decision tree (GBDT)-based model for predicting slag inclusion defects. The synthetic minority over-sampling technique (SMOTE) was employed to resolve data imbalance issues, while Bayesian optimization was applied to determine the model′s globally optimal hyper-parameters. Furthermore, the GBDT algorithm enabled the extraction of coupled process parameters governing slag inclusion, ranked by variable importance metrics. This research accomplishes slag inclusion prediction using continuous casting process parameters and quantifies the influence of individual parameters through Shapley additive explanations (SHAP). The results provide actionable insights for parameter adjustment in billet production. The proposed framework has been successfully implemented in steel plant, it not only enhances the scientific rigor of process control in continuous casting but also lays a foundation for subsequent process optimization and new technology development.