Multiscale Convolutional and Recurrent Neural Network for Quality Prediction of Continuous Casting Slabs

Wu, Xing; Jin, Hanlu; Ye, Xueming; Wang, Jianjia; Lei, Zuosheng; Liu, Ying; Wang, Jie; Guo, Yike

doi:10.3390/pr9010033

Cited by 9 publications

(9 citation statements)

References 35 publications

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“…Continuous casting process has signific effect of the overall quality of the steel products [15]. In [16], the quality of continuous casting slabs was predicted using a time series classification. Mold level fluctuations were measured at 0.5-s intervals, and the data were combined with inspection machine data.…”

Section: Related Workmentioning

confidence: 99%

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

et al. 2022

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The steelmaking industry is one of the most energy-intensive industries and is responsible for 4% of the world's total greenhouse gas emissions. Solutions to improve operational efficiency can therefore bring major improvements to the overall environmental performance of the entire industry. This article proposes a novel steel quality prediction system based on gradient boosting trees that can be used to predict the quality of steel products during manufacturing. The prediction system enables the detection of possible surface defects in the early phase of the manufacturing process, thus avoiding costly and time-consuming manufacturing efforts to address defective products. In this study, we trained a prediction model with data collected from an SSAB Europe steelmaking plant in Raahe, Finland. From the 296 process parameters measured in the liquid steel stage of steelmaking, we selected 89 input features to train and test the prediction model. The model was then integrated into a quality monitoring tool (QMT) to utilize real-time manufacturing data in its predictions. The validation process showed that the prediction model can find more than 50% of defective steel products by marking only about 10% of the steel products as potentially at risk of surface defects in plate rolling. This can potentially save time in the quality control phase and improve process efficiency. To gain more insights into the model predictions, we used SHAP (SHapley Additive exPlanations) to find a potential connection between the process input parameters and surface defects.

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Section: Related Workmentioning

confidence: 99%

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

et al. 2022

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“…Compared with the classical decision tree, and SVM, the results showed that the improved random forest model could deal with the negative effects of the class imbalance problem in accordance with the recall index. Wu et al [24] created a framework with multiscale convolutional and recurrent neural networks for reliable CC slab quality prediction. Moreover, they generated different category distributions based on the random undersampling method to alleviate the impact of skewed data distribution in the face of natural imbalances in industrial data.…”

Section: Related Workmentioning

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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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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“…When this hybrid PSO-SVM-based model with RBF kernel function is tested on an experimental dataset, the coefficient of determination and average width are equal to 0.98 and 0.97, respectively. Wu et al [71] suggested a novel multiscale convolutional and recurrent neural network MCRNN architecture for which the input is converted at various scales and frequencies, recording both long-term patterns and short-term shifts in time series. The suggested system outperforms traditional time series classification approaches with improved feature representation.…”

Section: Steelmentioning

confidence: 99%

Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review

Kim

Kong

Lee

et al. 2021

Int. J. Precis. Eng. Manuf.

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The recent advances in artificial intelligence have already begun to penetrate our daily lives. Even though the development is still in its infancy, it has been shown that it can outperform human beings even in terms of intelligence (e.g., AlphaGo by DeepMind), implying a massive potential for its broader application in various industrial sectors. In particular, the growing public interest in industry 4.0, which focuses on revolutionizing the traditional manufacturing scene, has stimulated a deeper investigation of its possible applications in the related industries. Since it has several limitations that hinder its direct usage, research on the convergence of artificial intelligence with other engineering fields, including precision engineering and manufacturing, is ongoing. This overview looks to summarize some of the important achievements made using artificial intelligence in some of the most influential and lucrative manufacturing industries in hopes of transforming the manufacturing sites.

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Multiscale Convolutional and Recurrent Neural Network for Quality Prediction of Continuous Casting Slabs

Cited by 9 publications

References 35 publications

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

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

Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review

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