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Prediction and Feature Analysis of Entrapped Slag Defect on Casting Slab Based on Optimized XGBoost Model

  • Topical Collection: 2023 Metallurgical Processes Workshop for Young Scholars
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Abstract

Machine learning (ML) algorithms have been proven to be effective methods in steel design and production processes. In this study, the feature analysis and prediction of slag entrapment in continuous casting process were carried out based on XGBoost model. Results show that the area under the curve (AUC) and accuracy of BO-XGBoost are 0.811 and 0.756, respectively, which are higher than PSO-XGBoost and GA-XGBoost. Feature analysis shows that mold flux, oscillation frequency, serial number, inner arc, casting speed (CS) and slab width are the key features which affect the entrapped slag defect, based on Gini coefficient and SHAP values. In addition, partial dependence plots of SHAP value suggest that it is conducive to product the qualified slabs with less entrapped slag defect, when the oscillation frequency is 60–140 cpm, inner arc is 7.5–9.5 m, CS is 0.35–1.2 m/min, serial number is less than 5, slab width is less than 1300 mm and 2# mold flux.

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Acknowledgments

This work was supported by the Natural Science Foundation of Hunan Province [Grant numbers 2022JJ10081], National Natural Science Foundation of China [Grant number 52174327, 52374348, U1760202], and the Fundamental Research Funds for the Central Universities of Central South University.

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Authors and Affiliations

  1. School of Metallurgy and Environment, Central South University, Changsha, 410083, P.R. China

    Yi Ji, Wanlin Wang, Lejun Zhou, Xiaocan Zhong & Xianzheng Si

  2. National Center for International Research of Clean Metallurgy, Central South University, Changsha, 410083, P.R. China

    Yi Ji, Wanlin Wang, Lejun Zhou, Xiaocan Zhong & Xianzheng Si

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  1. Yi Ji
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  2. Wanlin Wang
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Correspondence to Lejun Zhou.

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Ji, Y., Wang, W., Zhou, L. et al. Prediction and Feature Analysis of Entrapped Slag Defect on Casting Slab Based on Optimized XGBoost Model. Metall Mater Trans B (2024). https://doi.org/10.1007/s11663-024-03092-4

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