信自成 编委

学术贡献

• 1、[1] Xin Z, Zhang J, L a n M , et al. TabPFN-SHAP-based slag viscosity prediction model with high accuracy, efficiency, and interpretability[J]. Metallurgical and Materials Transactions B, 2025, Online. (SCI, Q1, IF3.1, 冶金学科 TOP 期刊)
• 2、[2] Xin Z, Zhang J, Peng K, et al. Explainable machine learning model for predicting molten steel temperature in LF refining process[J]. International Journal of Minerals, Metallurgy and Materials, 2024, 31(12): 2657-2669. (SCI, Q1, IF7.2, 冶金学科 TOP 期刊)
• 3、[3] Xin Z, Zhang J, Z h a n g J , et al. Predicting the alloying element yield in ladle furnace using stacking ensemble learning and SHapley Additive exPlanations analysis[J]. Steel Research International, 2025, 2500040. (SCI, Q2, IF2.5, 封面文章)
• 4、[4] Xin Z, Zhang J, Peng K, et al. Modeling of LF refining process: a review[J]. Journal of Iron and Steel Research International, 2024, 31(2): 289-317. (SCI, Q1, IF3.6, 冶金学科 TOP 期刊,年度优秀论文)
• 5、[5] Xin Z, Zhang J, Zhang J, et al. Predicting temperature of molten steel in LF refining process using IF-ZCA-DNN model[J]. Metallurgical and Materials Transactions B, 2023, 54(3): 1181-1194. (SCI, Q1, IF2.4, 冶金学科 TOP 期刊)
• 6、[6] Xin Z, Zhang J, Zheng J, et al. A hybrid modeling method based on expert control and deep neural network for temperature prediction of molten steel in LF[J]. ISIJ International, 2022, 62(3): 532-541. (SCI, Q2, IF1.8, 冶金学科 TOP 期刊)
• 7、[7] Xin Z, Liu Q. 《 Analysis and modeling of ladle furnace refining process 》[M] . 北京： 冶金工业出版社, 2025. (学术专著，第一作者)