Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing

Yuhao Zhang; Chen Wei; Xiaoqing Tian; Shouxu Song

doi:doi:10.11648/j.ijimse.20230802.12

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Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing

Yuhao Zhang, Chen Wei, Xiaoqing Tian^*, Shouxu Song

Published in International Journal of Industrial and Manufacturing Systems Engineering (Volume 8, Issue 2)

Received: 25 October 2023 Accepted: 9 November 2023 Published: 17 November 2023

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Abstract

The remanufacturing of rolling mill rolls offers significant economic, environmental, and societal benefits. However, the uncertainty surrounding the performance degradation of retired rolls and its associated timeline poses challenges to the efficiency and cost-effectiveness of roll remanufacturing operations. Therefore, the real-time monitoring of the degradation status of rolling mill rolls is of paramount importance. This study presents an approach that combines multi-sensor data fusion with a multilayer perceptron (MLP) model, which takes into account economic considerations to predict the degradation status of hot-rolling mill work rolls and make online decisions for active remanufacturing. The degradation process of rolling mill rolls is analyzed, and degradation performance indicators are established. Eddy current signals and torque signals from the rolling mill surface are collected during the roll degradation experiments. The friction coefficient and energy of the Hilbert spectrum of the eddy current signal are used as online input features for the MLP model, which is trained using the degradation experiment data. The superiority of the proposed MLP model is validated through rolling mill roll degradation experiments. Based on the predictions of the MLP model, the optimal timing for remanufacturing rolling mill rolls in the time domain is evaluated using Wiener and update-reward theories. This approach enables the online monitoring and quantitative characterization of the comprehensive degradation of high-speed steel work rolls and facilitates online decision-making regarding the optimal timing for active remanufacturing.

Published in	International Journal of Industrial and Manufacturing Systems Engineering (Volume 8, Issue 2)
DOI	10.11648/j.ijimse.20230802.12
Page(s)	30-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Hot Rolling Mill Rolls, Active Remanufacturing, Online Monitoring, Machine Learning

References

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APA Style

Zhang, Y., Wei, C., Tian, X., Song, S. (2023). Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing. International Journal of Industrial and Manufacturing Systems Engineering, 8(2), 30-41. https://doi.org/10.11648/j.ijimse.20230802.12

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ACS Style

Zhang, Y.; Wei, C.; Tian, X.; Song, S. Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing. Int. J. Ind. Manuf. Syst. Eng. 2023, 8(2), 30-41. doi: 10.11648/j.ijimse.20230802.12

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AMA Style

Zhang Y, Wei C, Tian X, Song S. Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing. Int J Ind Manuf Syst Eng. 2023;8(2):30-41. doi: 10.11648/j.ijimse.20230802.12

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@article{10.11648/j.ijimse.20230802.12,
  author = {Yuhao Zhang and Chen Wei and Xiaoqing Tian and Shouxu Song},
  title = {Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing},
  journal = {International Journal of Industrial and Manufacturing Systems Engineering},
  volume = {8},
  number = {2},
  pages = {30-41},
  doi = {10.11648/j.ijimse.20230802.12},
  url = {https://doi.org/10.11648/j.ijimse.20230802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijimse.20230802.12},
  abstract = {The remanufacturing of rolling mill rolls offers significant economic, environmental, and societal benefits. However, the uncertainty surrounding the performance degradation of retired rolls and its associated timeline poses challenges to the efficiency and cost-effectiveness of roll remanufacturing operations. Therefore, the real-time monitoring of the degradation status of rolling mill rolls is of paramount importance. This study presents an approach that combines multi-sensor data fusion with a multilayer perceptron (MLP) model, which takes into account economic considerations to predict the degradation status of hot-rolling mill work rolls and make online decisions for active remanufacturing. The degradation process of rolling mill rolls is analyzed, and degradation performance indicators are established. Eddy current signals and torque signals from the rolling mill surface are collected during the roll degradation experiments. The friction coefficient and energy of the Hilbert spectrum of the eddy current signal are used as online input features for the MLP model, which is trained using the degradation experiment data. The superiority of the proposed MLP model is validated through rolling mill roll degradation experiments. Based on the predictions of the MLP model, the optimal timing for remanufacturing rolling mill rolls in the time domain is evaluated using Wiener and update-reward theories. This approach enables the online monitoring and quantitative characterization of the comprehensive degradation of high-speed steel work rolls and facilitates online decision-making regarding the optimal timing for active remanufacturing.
},
 year = {2023}
}

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TY  - JOUR
T1  - Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing
AU  - Yuhao Zhang
AU  - Chen Wei
AU  - Xiaoqing Tian
AU  - Shouxu Song
Y1  - 2023/11/17
PY  - 2023
N1  - https://doi.org/10.11648/j.ijimse.20230802.12
DO  - 10.11648/j.ijimse.20230802.12
T2  - International Journal of Industrial and Manufacturing Systems Engineering
JF  - International Journal of Industrial and Manufacturing Systems Engineering
JO  - International Journal of Industrial and Manufacturing Systems Engineering
SP  - 30
EP  - 41
PB  - Science Publishing Group
SN  - 2575-3142
UR  - https://doi.org/10.11648/j.ijimse.20230802.12
AB  - The remanufacturing of rolling mill rolls offers significant economic, environmental, and societal benefits. However, the uncertainty surrounding the performance degradation of retired rolls and its associated timeline poses challenges to the efficiency and cost-effectiveness of roll remanufacturing operations. Therefore, the real-time monitoring of the degradation status of rolling mill rolls is of paramount importance. This study presents an approach that combines multi-sensor data fusion with a multilayer perceptron (MLP) model, which takes into account economic considerations to predict the degradation status of hot-rolling mill work rolls and make online decisions for active remanufacturing. The degradation process of rolling mill rolls is analyzed, and degradation performance indicators are established. Eddy current signals and torque signals from the rolling mill surface are collected during the roll degradation experiments. The friction coefficient and energy of the Hilbert spectrum of the eddy current signal are used as online input features for the MLP model, which is trained using the degradation experiment data. The superiority of the proposed MLP model is validated through rolling mill roll degradation experiments. Based on the predictions of the MLP model, the optimal timing for remanufacturing rolling mill rolls in the time domain is evaluated using Wiener and update-reward theories. This approach enables the online monitoring and quantitative characterization of the comprehensive degradation of high-speed steel work rolls and facilitates online decision-making regarding the optimal timing for active remanufacturing.

VL  - 8
IS  - 2
ER  -

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Author Information

Yuhao Zhang

School of Mechanical Engineering, Hefei University of Technology, Hefei, China

Contact Email
Chen Wei

School of Mechanical Engineering, Hefei University of Technology, Hefei, China

Contact Email
Xiaoqing Tian

School of Mechanical Engineering, Hefei University of Technology, Hefei, China

Contact Email
Shouxu Song

School of Mechanical Engineering, Hefei University of Technology, Hefei, China

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Plain Text BibTeX RIS

APA Style

Zhang, Y., Wei, C., Tian, X., Song, S. (2023). Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing. International Journal of Industrial and Manufacturing Systems Engineering, 8(2), 30-41. https://doi.org/10.11648/j.ijimse.20230802.12

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ACS Style

Zhang, Y.; Wei, C.; Tian, X.; Song, S. Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing. Int. J. Ind. Manuf. Syst. Eng. 2023, 8(2), 30-41. doi: 10.11648/j.ijimse.20230802.12

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AMA Style

Zhang Y, Wei C, Tian X, Song S. Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing. Int J Ind Manuf Syst Eng. 2023;8(2):30-41. doi: 10.11648/j.ijimse.20230802.12

Copy | Download

@article{10.11648/j.ijimse.20230802.12,
  author = {Yuhao Zhang and Chen Wei and Xiaoqing Tian and Shouxu Song},
  title = {Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing},
  journal = {International Journal of Industrial and Manufacturing Systems Engineering},
  volume = {8},
  number = {2},
  pages = {30-41},
  doi = {10.11648/j.ijimse.20230802.12},
  url = {https://doi.org/10.11648/j.ijimse.20230802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijimse.20230802.12},
  abstract = {The remanufacturing of rolling mill rolls offers significant economic, environmental, and societal benefits. However, the uncertainty surrounding the performance degradation of retired rolls and its associated timeline poses challenges to the efficiency and cost-effectiveness of roll remanufacturing operations. Therefore, the real-time monitoring of the degradation status of rolling mill rolls is of paramount importance. This study presents an approach that combines multi-sensor data fusion with a multilayer perceptron (MLP) model, which takes into account economic considerations to predict the degradation status of hot-rolling mill work rolls and make online decisions for active remanufacturing. The degradation process of rolling mill rolls is analyzed, and degradation performance indicators are established. Eddy current signals and torque signals from the rolling mill surface are collected during the roll degradation experiments. The friction coefficient and energy of the Hilbert spectrum of the eddy current signal are used as online input features for the MLP model, which is trained using the degradation experiment data. The superiority of the proposed MLP model is validated through rolling mill roll degradation experiments. Based on the predictions of the MLP model, the optimal timing for remanufacturing rolling mill rolls in the time domain is evaluated using Wiener and update-reward theories. This approach enables the online monitoring and quantitative characterization of the comprehensive degradation of high-speed steel work rolls and facilitates online decision-making regarding the optimal timing for active remanufacturing.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Research on the Online Monitoring of the Service Status of Hot-Rolling Mill Work Rolls and Online Decision-Making Method for Active Remanufacturing
AU  - Yuhao Zhang
AU  - Chen Wei
AU  - Xiaoqing Tian
AU  - Shouxu Song
Y1  - 2023/11/17
PY  - 2023
N1  - https://doi.org/10.11648/j.ijimse.20230802.12
DO  - 10.11648/j.ijimse.20230802.12
T2  - International Journal of Industrial and Manufacturing Systems Engineering
JF  - International Journal of Industrial and Manufacturing Systems Engineering
JO  - International Journal of Industrial and Manufacturing Systems Engineering
SP  - 30
EP  - 41
PB  - Science Publishing Group
SN  - 2575-3142
UR  - https://doi.org/10.11648/j.ijimse.20230802.12
AB  - The remanufacturing of rolling mill rolls offers significant economic, environmental, and societal benefits. However, the uncertainty surrounding the performance degradation of retired rolls and its associated timeline poses challenges to the efficiency and cost-effectiveness of roll remanufacturing operations. Therefore, the real-time monitoring of the degradation status of rolling mill rolls is of paramount importance. This study presents an approach that combines multi-sensor data fusion with a multilayer perceptron (MLP) model, which takes into account economic considerations to predict the degradation status of hot-rolling mill work rolls and make online decisions for active remanufacturing. The degradation process of rolling mill rolls is analyzed, and degradation performance indicators are established. Eddy current signals and torque signals from the rolling mill surface are collected during the roll degradation experiments. The friction coefficient and energy of the Hilbert spectrum of the eddy current signal are used as online input features for the MLP model, which is trained using the degradation experiment data. The superiority of the proposed MLP model is validated through rolling mill roll degradation experiments. Based on the predictions of the MLP model, the optimal timing for remanufacturing rolling mill rolls in the time domain is evaluated using Wiener and update-reward theories. This approach enables the online monitoring and quantitative characterization of the comprehensive degradation of high-speed steel work rolls and facilitates online decision-making regarding the optimal timing for active remanufacturing.

VL  - 8
IS  - 2
ER  -

Copy | Download