Application of Variational Autoencoder Network to Real-Time Prediction of Steel Crown in the Hot Strip Rolling Mill Process

In the hot strip rolling mill (HSRM) process, accurate prediction and control of the strip crown are critical for quality assurance. In order to cope with this challenge, this study designed a real-time prediction and update system of strip crown based on the cloud-edgeend collaboration framework. F...

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Vydané v:IEEE sensors journal Ročník 25; číslo 22; s. 42389 - 42399
Hlavní autori: Zhang, Kai, Liu, Yundan, Wang, Yali, Zhang, Xiaowen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 15.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Adaptation models
Data models
Distributed model
Feature extraction
Industrial applications
Prediction algorithms
Predictive models
Production
Production lines
Quality assurance
Quality control
Real time
real-time prediction
Real-time systems
Rolling mills
Steel
steel crown
Strip
Strips
variational autoencoder (VAE)
ISSN:1530-437X, 1558-1748
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Shrnutí:In the hot strip rolling mill (HSRM) process, accurate prediction and control of the strip crown are critical for quality assurance. In order to cope with this challenge, this study designed a real-time prediction and update system of strip crown based on the cloud-edgeend collaboration framework. First, this work optimizes the traditional variational autoencoder (VAE) network by refining the loss function structure to improve feature extraction and prediction, tailoring the VAE to the unique requirements of crown prediction. Second, according to the characteristics of multistand distribution in the HSRM process, a distributed framework is constructed to enable distributed extraction and fusion of crown-related features, generating predictions based on the fused features. In addition, to adapt to different strip specifications, a global and local update method is proposed to dynamically optimize model parameters, marking a notable advancement in adaptability for real-time industrial applications. The application results from two actual HSRM production lines (2150 and 1580 mm) demonstrate that the proposed method can decrease the prediction error to 2.650 <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>m on average. Finally, by using a cloud-edge-end prototype system with a 50-ms sampling interval, the system enables real-time prediction and supports online local model updates, significantly improving traditional methods while enhancing both operational efficiency and quality control.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3617319