Strip snap analytics in cold rolling process using machine learning

Zheyuan Chen; Ying Liu; Agustin Valera-Medina; Fiona Robinson

doi:10.1109/COASE.2019.8842967

Strip snap analytics in cold rolling process using machine learning

Zheyuan Chen, Ying Liu, Agustin Valera-Medina, Fiona Robinson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Strip snap, also known as strip breakage or belt tearing, is an undesirable quality incident which results in yield loss and reduced work speed in the cold rolling process of strip products. Therefore, it is necessary to reveal a functional relationship between certain selected variables and strip snap event for the aim of quality improvement. In this study, the probability of strip snap occurrence was quantified by a selected measured variable. Several machine learning algorithms were adopted to predict this target probability. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The excessively good performance indicates several variables which are strongly correlated with the target.

Original language	English
Title of host publication	2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
Publisher	IEEE Computer Society
Pages	368-373
Number of pages	6
ISBN (Electronic)	978-1-7281-0355-6, 978-1-7281-0356-3 , 978-1-7281-0357-0
DOIs	https://doi.org/10.1109/COASE.2019.8842967
Publication status	Published - Aug 2019
Externally published	Yes
Event	15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada Duration: 22 Aug 2019 → 26 Aug 2019

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2019-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Country/Territory	Canada
City	Vancouver
Period	22/08/19 → 26/08/19

Access to Document

10.1109/COASE.2019.8842967Licence: CC BY-NC-ND

Cite this

Chen, Z., Liu, Y., Valera-Medina, A., & Robinson, F. (2019). Strip snap analytics in cold rolling process using machine learning. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 368-373). [8842967] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8842967

@inproceedings{ba58abbc5b41456b9022cb697925c79d,

title = "Strip snap analytics in cold rolling process using machine learning",

abstract = "Strip snap, also known as strip breakage or belt tearing, is an undesirable quality incident which results in yield loss and reduced work speed in the cold rolling process of strip products. Therefore, it is necessary to reveal a functional relationship between certain selected variables and strip snap event for the aim of quality improvement. In this study, the probability of strip snap occurrence was quantified by a selected measured variable. Several machine learning algorithms were adopted to predict this target probability. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The excessively good performance indicates several variables which are strongly correlated with the target.",

author = "Zheyuan Chen and Ying Liu and Agustin Valera-Medina and Fiona Robinson",

year = "2019",

month = aug,

doi = "10.1109/COASE.2019.8842967",

language = "English",

series = "IEEE International Conference on Automation Science and Engineering",

publisher = "IEEE Computer Society",

pages = "368--373",

booktitle = "2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019",

note = "15th IEEE International Conference on Automation Science and Engineering, CASE 2019 ; Conference date: 22-08-2019 Through 26-08-2019",

}

Chen, Z, Liu, Y, Valera-Medina, A & Robinson, F 2019, Strip snap analytics in cold rolling process using machine learning. in 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019., 8842967, IEEE International Conference on Automation Science and Engineering, vol. 2019-August, IEEE Computer Society, pp. 368-373, 15th IEEE International Conference on Automation Science and Engineering, CASE 2019, Vancouver, Canada, 22/08/19. https://doi.org/10.1109/COASE.2019.8842967

Strip snap analytics in cold rolling process using machine learning. / Chen, Zheyuan; Liu, Ying; Valera-Medina, Agustin et al.

2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE Computer Society, 2019. p. 368-373 8842967 (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Strip snap analytics in cold rolling process using machine learning

AU - Chen, Zheyuan

AU - Liu, Ying

AU - Valera-Medina, Agustin

AU - Robinson, Fiona

PY - 2019/8

Y1 - 2019/8

N2 - Strip snap, also known as strip breakage or belt tearing, is an undesirable quality incident which results in yield loss and reduced work speed in the cold rolling process of strip products. Therefore, it is necessary to reveal a functional relationship between certain selected variables and strip snap event for the aim of quality improvement. In this study, the probability of strip snap occurrence was quantified by a selected measured variable. Several machine learning algorithms were adopted to predict this target probability. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The excessively good performance indicates several variables which are strongly correlated with the target.

AB - Strip snap, also known as strip breakage or belt tearing, is an undesirable quality incident which results in yield loss and reduced work speed in the cold rolling process of strip products. Therefore, it is necessary to reveal a functional relationship between certain selected variables and strip snap event for the aim of quality improvement. In this study, the probability of strip snap occurrence was quantified by a selected measured variable. Several machine learning algorithms were adopted to predict this target probability. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The excessively good performance indicates several variables which are strongly correlated with the target.

U2 - 10.1109/COASE.2019.8842967

DO - 10.1109/COASE.2019.8842967

M3 - Conference contribution

AN - SCOPUS:85072984880

T3 - IEEE International Conference on Automation Science and Engineering

SP - 368

EP - 373

BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019

PB - IEEE Computer Society

T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019

Y2 - 22 August 2019 through 26 August 2019

ER -

Strip snap analytics in cold rolling process using machine learning

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