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Steel surface roughness parameter prediction from laser reflection data using machine learning models

ALEXANDER MILNE, Xianghua Xie Orcid Logo

The International Journal of Advanced Manufacturing Technology, Volume: 132, Issue: 9-10, Pages: 4645 - 4662

Swansea University Authors: ALEXANDER MILNE, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1007/s00170-024-13543-6

Abstract

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, e...

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Published in: The International Journal of Advanced Manufacturing Technology
ISSN: 0268-3768 1433-3015
Published: Springer Science and Business Media LLC 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66048
first_indexed 2024-05-22T12:04:20Z
last_indexed 2024-11-25T14:17:24Z
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spelling 2024-06-07T14:02:58.7145725 v2 66048 2024-04-12 Steel surface roughness parameter prediction from laser reflection data using machine learning models c6da9da5c99754b6850e882895b86ca5 ALEXANDER MILNE ALEXANDER MILNE true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2024-04-12 Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a Time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing. Journal Article The International Journal of Advanced Manufacturing Technology 132 9-10 4645 4662 Springer Science and Business Media LLC 0268-3768 1433-3015 Machine learning; On-line measurement; Surface roughness; Temper rolling; Time Series Extrinsic Regression (TSER) 1 6 2024 2024-06-01 10.1007/s00170-024-13543-6 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) EPSRC, EP/V51960/1 2024-06-07T14:02:58.7145725 2024-04-12T12:59:52.5139413 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science ALEXANDER MILNE 1 Xianghua Xie 0000-0002-2701-8660 2 66048__30433__b7e70dc0e4df473588b11876763d9432.pdf 66048.VoR.pdf 2024-05-22T13:04:48.8316264 Output 1759568 application/pdf Version of Record true © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title Steel surface roughness parameter prediction from laser reflection data using machine learning models
spellingShingle Steel surface roughness parameter prediction from laser reflection data using machine learning models
ALEXANDER MILNE
Xianghua Xie
title_short Steel surface roughness parameter prediction from laser reflection data using machine learning models
title_full Steel surface roughness parameter prediction from laser reflection data using machine learning models
title_fullStr Steel surface roughness parameter prediction from laser reflection data using machine learning models
title_full_unstemmed Steel surface roughness parameter prediction from laser reflection data using machine learning models
title_sort Steel surface roughness parameter prediction from laser reflection data using machine learning models
author_id_str_mv c6da9da5c99754b6850e882895b86ca5
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv c6da9da5c99754b6850e882895b86ca5_***_ALEXANDER MILNE
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author ALEXANDER MILNE
Xianghua Xie
author2 ALEXANDER MILNE
Xianghua Xie
format Journal article
container_title The International Journal of Advanced Manufacturing Technology
container_volume 132
container_issue 9-10
container_start_page 4645
publishDate 2024
institution Swansea University
issn 0268-3768
1433-3015
doi_str_mv 10.1007/s00170-024-13543-6
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
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 Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a Time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing.
published_date 2024-06-01T14:32:19Z
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score 11.048042

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