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Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN model

Fengwei Jing (National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, China)
Mengyang Zhang (National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, China)
Jie Li (National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, China)
Guozheng Xu (National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, China)
Jing Wang (National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 17 October 2022

Issue publication date: 6 December 2022

56

Abstract

Purpose

Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm.

Design/methodology/approach

Aiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition.

Findings

The performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability.

Originality/value

This research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.

Keywords

Acknowledgements

This research was supported by the National Natural Science Foundation of China (52004029).

Citation

Jing, F., Zhang, M., Li, J., Xu, G. and Wang, J. (2022), "Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN model", Assembly Automation, Vol. 42 No. 6, pp. 773-781. https://doi.org/10.1108/AA-07-2022-0179

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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