Neural Network Modelling to Characterize Steel Continuous Casting Process Parameters and Prediction of Casting Defects

Hore, S; Das, Suchandan K; Humane, M M; Peethala, Anil Kumar

doi:10.1007/s12666-019-01767-0

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…Many studies have used machine learning methods such as decision trees (DT), random forest (RF), and support vector machines (SVM) [ 17 , 18 , 19 , 20 , 21 ]. Karunakar and Datta [ 17 ] predicted major casting defects using a backpropagation neural network collected from a steel producer.…”

Section: Related Workmentioning

confidence: 99%

“…They reported the effectiveness of using an artificial neural network (ANN) for defect prediction because factory workers are warned when a defective casting is about to be manufactured. Hore et al [ 18 ] outlined the application of a data-driven MLP-based ANN model to characterize the effects of melt composition, tundish temperature, tundish superheat, casting speed, and mold oscillation frequency on important processing parameters and to predict the occurrence of defects in the cast product. The chemical composition of the melt was used as the input of the model, and the average values of tundish temperature, tundish superheat, cast speed, and mold oscillation frequency were recorded at four time intervals.…”

Section: Related Workmentioning

confidence: 99%

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Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Bazarbaev¹,

Chuluunsaikhan

et al. 2021

Sensors

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Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Bazarbaev¹,

Chuluunsaikhan

et al. 2021

Sensors

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show abstract

“…More than 95% of the steel in the world is produced by continuous casting every year. 1) It has prominent advantages such as high efficiency, energy saving, and product quality improvement. However, the cast billet inevitably has various defects, including inclusions, segregations and cracks.…”

Section: Introductionmentioning

confidence: 99%

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

Zhou

et al. 2022

ISIJ Int.

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“…Zhao et al [24] established a quality prediction model based on a least-squares support vector machine (SVM) and optimized the model using an improved particle swarm optimization algorithm. Hore et al [25] established the prediction model based on a multi-layer sensor to predict the oscillation mark depth, mold powder consumption rate, metallurgical length and cracks in the cast products. Varfolomeev et al [26] and Ye et al [27] used the random forest algorithm to predict crack occurrence in the continuous casting billets.…”

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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Neural Network Modelling to Characterize Steel Continuous Casting Process Parameters and Prediction of Casting Defects

Cited by 17 publications

References 19 publications

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

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

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