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

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

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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.

Iaith wreiddiol	Saesneg
Teitl	2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
Cyhoeddwr	IEEE Computer Society
Tudalennau	368-373
Nifer y tudalennau	6
ISBN (Electronig)	978-1-7281-0355-6, 978-1-7281-0356-3 , 978-1-7281-0357-0
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/COASE.2019.8842967
Statws	Cyhoeddwyd - Awst 2019
Cyhoeddwyd yn allanol	Ie
Digwyddiad	15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada Hyd: 22 Awst 2019 → 26 Awst 2019

Cyfres gyhoeddiadau

Enw	IEEE International Conference on Automation Science and Engineering
Cyfrol	2019-August
ISSN (Argraffiad)	2161-8070
ISSN (Electronig)	2161-8089

Cynhadledd

Cynhadledd	15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Gwlad/Tiriogaeth	Canada
Dinas	Vancouver
Cyfnod	22/08/19 → 26/08/19

Mynediad at Ddogfen

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

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Chen, Z., Liu, Y., Valera-Medina, A., & Robinson, F. (2019). Strip snap analytics in cold rolling process using machine learning. Yn 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (tt. 368-373). erthygl 8842967 (IEEE International Conference on Automation Science and Engineering; Cyfrol 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. yn 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019., 8842967, IEEE International Conference on Automation Science and Engineering, cyfrol. 2019-August, IEEE Computer Society, tt. 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. t. 368-373 8842967 (IEEE International Conference on Automation Science and Engineering; Cyfrol 2019-August).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

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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