Interpretable Predictive Model for Inclusions in Electroslag Remelting Based on XGBoost and SHAP Analysis

Liu, Yuxiao; Dong, Yanwu; Jiang, Zhouhua; Chen, Xi

doi:10.1007/s11663-024-03037-x

Metall Mater Trans B

2024

DOI: 10.1007/s11663-024-03037-x

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Interpretable Predictive Model for Inclusions in Electroslag Remelting Based on XGBoost and SHAP Analysis

Yuxiao Liu,

Yanwu Dong,

Zhouhua Jiang

et al.

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2024

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Cited by 3 publications

References 26 publications

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Hybrid Model Integrating Locally Linear Embedding and LightGBM for Predicting Sulfur Content in Electroslag Remelting

Liu,

Dong,

Jiang

et al. 2024

steel research int.

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This study proposes a model for predicting the sulfur content in the electroslag remelting (ESR) process, by integrating locally linear embedding (LLE) and the LightGBM algorithm. The LLE dimensionality reduction method is employed to preprocess the factors influencing the sulfur content, preserving the local relationships between samples while reducing the data dimensionality. Subsequently, five models are established, including LLE‐LightGBM, LLE‐RR, LLE‐RF, LLE‐XGBoost, and LLE‐CatBoost. Bayesian optimization is used to tune the hyperparameters of these models, which are then trained and validated using production data. The results demonstrate that the integration of LLE and Bayesian optimization improved the performance of all five models. The incorporation of LLE enhanced the models’ understanding and predictive capabilities of the data, thereby improving their performance and robustness. Among the five models, the LLE‐LightGBM model exhibits the best performance, with the following metrics: MSE = 0, = 0.9894, MAE = 0, MEDAE = 0, EVS = 0.9895, and ME = 1.8 × 10−5. Therefore, the LLE‐LightGBM model can accurately predict the sulfur content in the ESR process, providing valuable guidance for end‐point control and decision‐making, and offering a new approach toward the intelligent automation of the ESR process.

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Hybrid Model Integrating Locally Linear Embedding and LightGBM for Predicting Sulfur Content in Electroslag Remelting

Liu,

Dong,

Jiang

et al. 2024

steel research int.

View full text Add to dashboard Cite

show abstract

Oxygen Content Control in the Electroslag Remelting Process: An Incremental Learning Strategy Based on Optimized Wasserstein Generative Adversarial Network with Gradient Penalty Data Augmentation

Chen,

Dong,

Jiang

et al. 2024

steel research int.

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Electroslag remelting (ESR) is essential for producing high‐end special steel, but its complex process and numerous influencing factors make quality control challenging. This study addresses oxygen content control during ESR using a big data machine learning approach. An incremental learning strategy is proposed based on an optimized Wasserstein generative adversarial network with gradient penalty (WGAN‐GP) for data enhancement, focusing on G20Cr2Ni4A bearing steel. The WGAN‐GP model enhances time‐series data and metadata, utilizing long short‐term memory networks, fully connected networks, and attention mechanisms. The effectiveness of data enhancement is verified using a deep neural network classifier and statistical methods. Data is divided into historical and data streams, with an incremental learning strategy based on histogram gradient boosting regression trees to prevent catastrophic forgetting and improve efficiency through knowledge distillation and real‐time hyperparameter adjustment. Results show that the data augmentation method significantly improves model generalization and accuracy in small sample metallurgy. The incremental learning strategy enhances prediction accuracy for oxygen content, contributing to better cleanliness quality of electroslag steel. This study offers a novel approach for addressing small sample challenges in metallurgical processes.

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Multi-task Learning Model of Continuous Casting Slab Temperature Based on DNNs and SHAP Analysis

He,

Zhou,

et al. 2024

Metall Mater Trans B

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Interpretable Predictive Model for Inclusions in Electroslag Remelting Based on XGBoost and SHAP Analysis

Cited by 3 publications

References 26 publications

Hybrid Model Integrating Locally Linear Embedding and LightGBM for Predicting Sulfur Content in Electroslag Remelting

Hybrid Model Integrating Locally Linear Embedding and LightGBM for Predicting Sulfur Content in Electroslag Remelting

Oxygen Content Control in the Electroslag Remelting Process: An Incremental Learning Strategy Based on Optimized Wasserstein Generative Adversarial Network with Gradient Penalty Data Augmentation

Multi-task Learning Model of Continuous Casting Slab Temperature Based on DNNs and SHAP Analysis

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