Machine learning in continuous casting of steel: a state-of-the-art survey

Cemernek, David; Cemernek, Sandra; Gursch, Heimo; Pandeshwar, Ashwini; Leitner, Thomas; Berger, Matthias; Klösch, Gerald; Kern, Roman

doi:10.1007/s10845-021-01754-7

Cited by 52 publications

(24 citation statements)

References 42 publications

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“…In the above formulas, Abn-Recall is the probability that abnormal slabs are correctly predicted, N-Recall is the recall rate of normal slabs, G_mean is the comprehensive tradeoff between Abn-Recall and N-Recall, MCC was first used in biomedical filed [33], and also applicable to the assessment of class imbalance [7], [34]. In addition, the range of MCC is [-1,1], -1 means that the predicted result is completely inconsistent with reality, 0 indicates that the predicted result is not as good as the random prediction, 1 denotes that the prediction is absolutely consistent with the actual, and the closer the value is to 1, the better the model performance will be.…”

Section: B Experimental Setupmentioning

confidence: 99%

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Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

Liu

Zhang

et al. 2022

IEEE Access

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A slab quality prediction model based on machine learning plays an important role in improving final slab quality. However, the class imbalance of continuous casting datasets has a negative impact on the training of basic machine-learning models. In this study, weighted extreme learning machine (WELM) models are constructed to predict the slab quality of under different operation patterns by feeding millions of data. The results show that WELM models can achieve better prediction performance on the two types of continuous casting datasets than the basic algorithms. The superiority of WELM is demonstrated by the relatively high-precision identification of every kind of slab. The performance of WELM models with different weighting schemes is studied and the model with the golden section ratio weighting method is recommended for application as a quality prediction model. Meanwhile, WELM can still maintain a good predictive performance and generalization ability when training a large amount of data. This model can satisfy the demands for slab quality prediction and optimize the continuous casting process.INDEX TERMS Quality prediction, weighted extreme learning machine, continuous casting, class imbalance.

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Section: B Experimental Setupmentioning

confidence: 99%

“…In contrast to the aforementioned methods, new machine-learning-based approaches can consider the effect of more process parameters on slab quality, and build the mapping model between the parameters and the corresponding slab quality by adopting the supervised learning methods [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

Liu

Zhang

et al. 2022

IEEE Access

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“…In recent years, this complexity, the increasing global competition, and the drive for more efficient lower waste production created a high demand for new methods to optimize the steel production processes and the mechanical properties of final products. [ 1 ] In such a technology‐intensive industry, even the smallest variation during the production process causes costly and time‐consuming post‐processing or an increase in scrappage. [ 2 ] Therefore, smart, agile data‐driven prediction models are necessary and urgently needed.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these components of data‐driven modeling comes with a challenge, especially when considered in the context of steelmaking optimization. The “class imbalance,” [ 1 ] i.e., the extensive variation in data classes such as steel grades, subgrades, target composition, and process variables each with limited data per combinatorial group, still remains one of the main challenges in the data‐driven modeling for the steel industry. [ 11,12 ] Further, the correlations between variables and targets are hardly reflected in the data which brings significant obstacles to data‐driven modeling and prediction of material properties.…”

Section: Introductionmentioning

confidence: 99%

Metallurgical Data Science for Steel Industry: A Case Study on Basic Oxygen Furnace

Nenchev

Panwisawas

Yang

et al. 2022

steel research int.

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Steel production is one of the biggest and most important industries in the world outputting hundreds of tons of steel daily. A steelmaking plant pushes the conventional methods of process monitoring and control to their limits due to the complexity and multidimensionality involved in the physical, mechanical, and chemical metallurgy. The manufacturing process of steel plates involves multiple steps such as blast furnace smelting, converter smelting, and ladle furnace refining, followed by continuous casting, heating, rolling, and cooling. Each physical process generates numerous "key process variables" such as steel composition, additives, environmental control, cooling, and other process parameters, all influencing each other, the subsequent processing steps, and hence the final product. Therefore, modeling and digitally twinning of such processes and predicting the quality of steel through comprehensive finite element approach (FEA) calculations or experimental trials is time-consuming, costly, and impractical. In recent years, this complexity, the increasing global competition, and the drive for more efficient lower waste production created a high demand for new methods to optimize the steel production processes and the mechanical properties of final products. [1] In such a technology-intensive industry, even the smallest variation during the production process causes costly and time-consuming postprocessing or an increase in scrappage. [2] Therefore, smart, agile data-driven prediction models are necessary and urgently needed. To satisfy the demanding requirements for "Industry

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“…Neural networks have been wildly used in continuous casting production, including breakout prediction, steel defect prediction, nozzle clogging detection, steel temperature prediction and mold level fluctuation detection, etc. [22]. Normanton et al [23] introduced the work conducted by the European Coal and Steel Community (ECSC) for the surface and internal quality prediction of continuous casting products.…”

Section: Introductionmentioning

confidence: 99%

Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

Zou

Zhang

Han

et al. 2021

Metals

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The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability.

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Machine learning in continuous casting of steel: a state-of-the-art survey

Cited by 52 publications

References 42 publications

Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

Metallurgical Data Science for Steel Industry: A Case Study on Basic Oxygen Furnace

Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

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