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Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes / CALLUM O'DONOVAN

Swansea University Author: CALLUM O'DONOVAN

DOI (Published version): 10.23889/SUThesis.68815

Abstract

Computer vision (CV) is a branch of artificial intelligence (AI) that enables machines to understand visual input. The recent rise of deep learning (DL) has empowered CV significantly, leading to well-established applications such as autonomous vehicles, medical diagnosis and facial recognition. These...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: EngD
Supervisor: Giannetti, C., and Pleydell-Pearce, C.
URI: https://cronfa.swan.ac.uk/Record/cronfa68815
first_indexed 2025-02-06T13:07:53Z
last_indexed 2025-02-07T05:56:37Z
id cronfa68815
recordtype RisThesis
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spelling 2025-02-06T13:18:29.6519316 v2 68815 2025-02-06 Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes 424be877f02ec76255f2917d6c54c665 CALLUM O'DONOVAN CALLUM O'DONOVAN true false 2025-02-06 Computer vision (CV) is a branch of artificial intelligence (AI) that enables machines to understand visual input. The recent rise of deep learning (DL) has empowered CV significantly, leading to well-established applications such as autonomous vehicles, medical diagnosis and facial recognition. These new capabilities extend to the manufacturing sector, however they have not been widely adopted to monitor processes in the steel industry due to challenges related to harsh environmental conditions such as poor lighting, heat distortion, dust particles and vibrations. As a result, existing datasets are limited and advances have predominantly been evaluated within research settings but not real-world settings. Therefore, this project investigates the application of CV for monitoring steel production processes and how integration impacts state-of-the-art technology. This work aims to produce CV systems capable of monitoring different processes and utilise them to draw valuable real-world insights for industry. Also, it aims to investigate how these systems, and CV as a whole, can enhance the efficiency, quality and sustainability of steel manufacturing. This research involves the development of CV models tailored to three processes: ladle pouring, galvanising and gas stirring. In each case study, DL and traditional methods are used to monitor real or simulated production environments and extract useful information. Primary outcomes of this research include a foundation for monitoring ladle pouring to reduce emissions, a deployed system for quantifying zinc splatter occurring during galvanisation in real-time, and a tool for comparing the wear rate and stirring efficiency of different gas stirring approaches. Outcomes of this work highlight the revolutionary benefits of applying CV in production environments for process monitoring and control. By developing CV models for monitoring processes, overcoming harsh conditions typical in production environments, and drawing valuable insights from CV application, this work establishes a strong foundation for real-world implementation of CV in manufacturing. E-Thesis Swansea University, Wales, UK Computer Vision, Deep Learning, Machine Learning, Remote Monitoring, Steel Manufacturing 13 12 2024 2024-12-13 10.23889/SUThesis.68815 COLLEGE NANME COLLEGE CODE Swansea University Giannetti, C., and Pleydell-Pearce, C. Doctoral EngD WEFO WEFO 2025-02-06T13:18:29.6519316 2025-02-06T12:55:35.5341285 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering CALLUM O'DONOVAN 1 68815__33520__92ef776fd5cf427f88d9e4b6b4176923.pdf 2024_O'Donovan_C.final.68815.pdf 2025-02-06T13:06:22.5523852 Output 59999750 application/pdf E-Thesis – open access true Copyright: The Author, Callum O'Donovan, 2024 true eng
title Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
spellingShingle Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
CALLUM O'DONOVAN
title_short Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
title_full Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
title_fullStr Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
title_full_unstemmed Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
title_sort Application of Computer Vision Techniques for Monitoring Steel Manufacturing Processes
author_id_str_mv 424be877f02ec76255f2917d6c54c665
author_id_fullname_str_mv 424be877f02ec76255f2917d6c54c665_***_CALLUM O'DONOVAN
author CALLUM O'DONOVAN
author2 CALLUM O'DONOVAN
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.68815
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 Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
active_str 0
description Computer vision (CV) is a branch of artificial intelligence (AI) that enables machines to understand visual input. The recent rise of deep learning (DL) has empowered CV significantly, leading to well-established applications such as autonomous vehicles, medical diagnosis and facial recognition. These new capabilities extend to the manufacturing sector, however they have not been widely adopted to monitor processes in the steel industry due to challenges related to harsh environmental conditions such as poor lighting, heat distortion, dust particles and vibrations. As a result, existing datasets are limited and advances have predominantly been evaluated within research settings but not real-world settings. Therefore, this project investigates the application of CV for monitoring steel production processes and how integration impacts state-of-the-art technology. This work aims to produce CV systems capable of monitoring different processes and utilise them to draw valuable real-world insights for industry. Also, it aims to investigate how these systems, and CV as a whole, can enhance the efficiency, quality and sustainability of steel manufacturing. This research involves the development of CV models tailored to three processes: ladle pouring, galvanising and gas stirring. In each case study, DL and traditional methods are used to monitor real or simulated production environments and extract useful information. Primary outcomes of this research include a foundation for monitoring ladle pouring to reduce emissions, a deployed system for quantifying zinc splatter occurring during galvanisation in real-time, and a tool for comparing the wear rate and stirring efficiency of different gas stirring approaches. Outcomes of this work highlight the revolutionary benefits of applying CV in production environments for process monitoring and control. By developing CV models for monitoring processes, overcoming harsh conditions typical in production environments, and drawing valuable insights from CV application, this work establishes a strong foundation for real-world implementation of CV in manufacturing.
published_date 2024-12-13T05:21:50Z
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score 11.089572

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