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Steel surface roughness parameter prediction from laser reflection data using machine learning models
The International Journal of Advanced Manufacturing Technology, Volume: 132, Issue: 9-10, Pages: 4645 - 4662
Swansea University Authors:
ALEXANDER MILNE, Xianghua Xie
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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...
| Published in: | The International Journal of Advanced Manufacturing Technology |
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| ISSN: | 0268-3768 1433-3015 |
| Published: |
Springer Science and Business Media LLC
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66048 |
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| 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, 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. |
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| Keywords: |
Machine learning; On-line measurement; Surface roughness; Temper rolling; Time Series Extrinsic Regression (TSER) |
| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC, EP/V51960/1 |
| Issue: |
9-10 |
| Start Page: |
4645 |
| End Page: |
4662 |