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Real-time monitoring of molten zinc splatter using machine learning-based computer vision

CALLUM O'DONOVAN, Cinzia Giannetti Orcid Logo, Cameron Pleydell-Pearce

Journal of Intelligent Manufacturing

Swansea University Authors: CALLUM O'DONOVAN, Cinzia Giannetti Orcid Logo, Cameron Pleydell-Pearce

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Abstract

During steel galvanisation, immersing steel strip into molten zinc forms a protective coating. Uniform coating thickness is crucial for quality and is achieved using air knives which wipe off excess zinc. At high strip speeds, zinc splatters onto equipment, causing defects and downtime. Parameters s...

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Published in: Journal of Intelligent Manufacturing
ISSN: 0956-5515 1572-8145
Published: Springer Science and Business Media LLC 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa66472
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Abstract: During steel galvanisation, immersing steel strip into molten zinc forms a protective coating. Uniform coating thickness is crucial for quality and is achieved using air knives which wipe off excess zinc. At high strip speeds, zinc splatters onto equipment, causing defects and downtime. Parameters such as knife positioning and air pressure influence splatter severity and can be optimised to reduce it. Therefore, this paper proposes a system that converges computer vision and manufacturing whilst addressing some challenges of real-time monitoring in harsh industrial environments, such as the extreme heat, metallic dust, dynamic machinery and high-speed processing at the galvanising site. The approach is primarily comprised of the Counting (CNT) background subtraction algorithm and YOLOv5, which together ensure robustness to noise produced by heat distortion and dust, as well as adaptability to the highly dynamic environment. The YOLOv5 element achieved precision, recall and mean average precision (mAP) values of 1. When validated against operator judgement using mean average error (MAE), interquartile range, median and scatter plot analysis, it was found that there was more discrepancy between the two operators than the operators and the model.This research also strategises the deployment process for integration into the galvanising line. The model proposed allows real-time monitoring and quantification of splatter severity which provides valuable insights into root-cause analysis, process optimisation and maintenance strategies. This research contributes to the digital transformation of manufacturing and whilst solving a current problem, also plants the seed for many other novel applications.
Keywords: Galvanisation; Steel manufacturing; Computer vision; Deep learning
College: Faculty of Science and Engineering
Funders: Cinzia Giannetti acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) projects EP/S001387/1, EP/V061798/1. The authors would also like to acknowledge the M2A funding from the European Social Fund via the Welsh Government (c80816). We further acknowledge the support of the Supercomputing Wales (c80898 and c80900) and Ser Cymru AccelerateAI projects, which are partly funded by the European Regional Development Fund (ERDF) via the Welsh Government. This research would not have been possible without the air knife splatter video footage from the ZODIAC hot-dip galvanising process line at Llanwern Works, which is part of Tata Steel Europe Ltd. who are also acknowledged.