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

Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised... 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/approachAiming 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.FindingsThe 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/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

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

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References (31)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0144-5154
eISSN
0144-5154
DOI
10.1108/aa-07-2022-0179
Publisher site
See Article on Publisher Site

Abstract

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/approachAiming 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.FindingsThe 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/valueThis 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.

Journal

Assembly AutomationEmerald Publishing

Published: Dec 6, 2022

Keywords: Hot strip rolling; Coil shape defects; Siamese network; DAE

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