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A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill

Sammy` Latham, Cinzia Giannetti Orcid Logo

SN Computer Science, Volume: 4, Issue: 5

Swansea University Authors: Sammy` Latham, Cinzia Giannetti Orcid Logo

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DOI (Published version): 10.1007/s42979-023-02104-5

Abstract

Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feed...

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Published in: SN Computer Science
ISSN: 2661-8907
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64007
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The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feedback, and knock-on effects in successive steel strip products. Automated tools which utilize Artificial Intelligence and Machine Learning for defect detection and Root Cause Analysis in hot rolling have not yet been adopted outside of surface defect detection and roller force optimization. In this paper, we propose an automated tool for the detection and Root Cause Analysis of width-related defects in the hot rolling process which utilizes a combination of expert knowledge and several Machine Learning models. Through this, we aim to increase the scope, and encourage further development, of Machine Learning applications within the Hot Strip Mill process. Both classical algorithms and Computer Vision methods were used for the Machine Learning component of the tool, namely, classification trees and pre-trained convolutional neural networks. The tool is trained and validated using data from an existing hot rolling mill and thus the challenges of collecting and processing real-world legacy data are highlighted and discussed. The Machine Learning models used are shown to perform optimally by validation performance metrics. The tool is found to be suitable for the specified purpose and would be further improved with more training data.</abstract><type>Journal Article</type><journal>SN Computer Science</journal><volume>4</volume><journalNumber>5</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2661-8907</issnElectronic><keywords>Steel Industry, Hot Strip Mill, Automation, Data Analytics, Machine Learning, Classification, Defect Detection, Root Cause Analysis, Knowledge Integration, Legacy Data</keywords><publishedDay>22</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-08-22</publishedDate><doi>10.1007/s42979-023-02104-5</doi><url>http://dx.doi.org/10.1007/s42979-023-02104-5</url><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>The authors would like to acknowledge the Materials and Manufacturing Academy (M2A) funding from the European Social Fund via the Welsh Government (c80816) and Tata Steel Europe that has made this research possible. Prof. Giannetti would like to acknowledge the support of the UK Engineering and Physical Sciences Research Council (EP/V061798/1). 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spelling v2 64007 2023-08-01 A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill 4b67f2411a98832eab6881a50263c4ab Sammy` Latham Sammy` Latham true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2023-08-01 FGSEN Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feedback, and knock-on effects in successive steel strip products. Automated tools which utilize Artificial Intelligence and Machine Learning for defect detection and Root Cause Analysis in hot rolling have not yet been adopted outside of surface defect detection and roller force optimization. In this paper, we propose an automated tool for the detection and Root Cause Analysis of width-related defects in the hot rolling process which utilizes a combination of expert knowledge and several Machine Learning models. Through this, we aim to increase the scope, and encourage further development, of Machine Learning applications within the Hot Strip Mill process. Both classical algorithms and Computer Vision methods were used for the Machine Learning component of the tool, namely, classification trees and pre-trained convolutional neural networks. The tool is trained and validated using data from an existing hot rolling mill and thus the challenges of collecting and processing real-world legacy data are highlighted and discussed. The Machine Learning models used are shown to perform optimally by validation performance metrics. The tool is found to be suitable for the specified purpose and would be further improved with more training data. Journal Article SN Computer Science 4 5 Springer Science and Business Media LLC 2661-8907 Steel Industry, Hot Strip Mill, Automation, Data Analytics, Machine Learning, Classification, Defect Detection, Root Cause Analysis, Knowledge Integration, Legacy Data 22 8 2023 2023-08-22 10.1007/s42979-023-02104-5 http://dx.doi.org/10.1007/s42979-023-02104-5 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors would like to acknowledge the Materials and Manufacturing Academy (M2A) funding from the European Social Fund via the Welsh Government (c80816) and Tata Steel Europe that has made this research possible. Prof. Giannetti would like to acknowledge the support of the UK Engineering and Physical Sciences Research Council (EP/V061798/1). All authors would like to acknowledge the support of the IMPACT and AccelerateAI projects, part-funded by the European Regional Development Fund (ERDF) via the Welsh Government. 2023-10-02T13:37:21.8233063 2023-08-01T08:39:35.0757317 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Sammy` Latham 1 Cinzia Giannetti 0000-0003-0339-5872 2 64007__28543__a41f4997c8ca4bc09c6948cb9a3dc1c3.pdf 64007.VOR.pdf 2023-09-15T10:21:36.9091108 Output 2719336 application/pdf Version of Record true © The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
spellingShingle A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
Sammy` Latham
Cinzia Giannetti
title_short A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
title_full A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
title_fullStr A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
title_full_unstemmed A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
title_sort A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
author_id_str_mv 4b67f2411a98832eab6881a50263c4ab
a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv 4b67f2411a98832eab6881a50263c4ab_***_Sammy` Latham
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti
author Sammy` Latham
Cinzia Giannetti
author2 Sammy` Latham
Cinzia Giannetti
format Journal article
container_title SN Computer Science
container_volume 4
container_issue 5
publishDate 2023
institution Swansea University
issn 2661-8907
doi_str_mv 10.1007/s42979-023-02104-5
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
url http://dx.doi.org/10.1007/s42979-023-02104-5
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description Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feedback, and knock-on effects in successive steel strip products. Automated tools which utilize Artificial Intelligence and Machine Learning for defect detection and Root Cause Analysis in hot rolling have not yet been adopted outside of surface defect detection and roller force optimization. In this paper, we propose an automated tool for the detection and Root Cause Analysis of width-related defects in the hot rolling process which utilizes a combination of expert knowledge and several Machine Learning models. Through this, we aim to increase the scope, and encourage further development, of Machine Learning applications within the Hot Strip Mill process. Both classical algorithms and Computer Vision methods were used for the Machine Learning component of the tool, namely, classification trees and pre-trained convolutional neural networks. The tool is trained and validated using data from an existing hot rolling mill and thus the challenges of collecting and processing real-world legacy data are highlighted and discussed. The Machine Learning models used are shown to perform optimally by validation performance metrics. The tool is found to be suitable for the specified purpose and would be further improved with more training data.
published_date 2023-08-22T13:37:25Z
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score 11.013148

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