E-Thesis 100 views 61 downloads
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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Swansea University, Wales, UK
2024
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| Institution: | Swansea University |
| Degree level: | Doctoral |
| Degree name: | EngD |
| Supervisor: | Giannetti, C., and Pleydell-Pearce, C. |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa68815 |
| 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 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. |
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| Keywords: |
Computer Vision, Deep Learning, Machine Learning, Remote Monitoring, Steel Manufacturing |
| College: |
Faculty of Science and Engineering |
| Funders: |
WEFO |