E-Thesis 281 views 290 downloads
Combining Expert Knowledge and Machine Learning for Analysing Width Defects in a Hot Strip Mill / SAMUEL LATHAM
Swansea University Author: SAMUEL LATHAM
DOI (Published version): 10.23889/SUThesis.69634
Abstract
Thousands of steel strips are processed through the Port Talbot Steelworks’ Hot Strip Mill every year. Hundreds of these, however, are either scrapped, sold at a concession, or require further processing due to a variety of defects, a significant percentage of which are width-related. Tata Steel has...
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Swansea University, Wales, UK
2023
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| Institution: | Swansea University |
| Degree level: | Doctoral |
| Degree name: | EngD |
| Supervisor: | Giannetti, C., and Hart, R. |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa69634 |
| Abstract: |
Thousands of steel strips are processed through the Port Talbot Steelworks’ Hot Strip Mill every year. Hundreds of these, however, are either scrapped, sold at a concession, or require further processing due to a variety of defects, a significant percentage of which are width-related. Tata Steel has identified this process as being abundant with underutilised data and is currently investing in innovative research and development which aims to address this issue as well as other industry-wide challenges including decarbonisation and digitisation of legacy processes. Currently, Artificial Intelligence and Machine Learning has become a popular technology for identifying and analysing defects in manufacturing processes. However, the scope of applications which utilise these technologies within the steel industry are currently limited and are often contained to specific subprocesses. Simultaneously, gathering expert knowledge from employees with first-hand experience is crucial to understanding defects and how they can be analysed. This thesis aims to expand the scope of Machine Learning tools for defect analysis in steel-making processes and showcase their potential when combined with expert knowledge on a process-wide scale. This is achieved by understanding the challenges of collecting and utilising industrial data, exploring the technologies available for various types of analyses, and reviewing the history and development of these technologies in manufacturing and in the steel industry. From this, technologies and analyses specific to the Port Talbot Hot Strip Mill process are selected, providing a platform on which tools for the analysis of width-related defects are developed. An in-depth understanding of these defects and their root causes is gained through continual communication with employees at the Port Talbot Steelworks and, using this, several Machine Learning models are created for the purpose of identifying them in several Hot Strip Mill subprocesses. Further to this, a new, data-driven decision-making process is developed for the purpose of identifying width-related defects and determining their root causes across the entire Hot Strip Mill process as they occur. |
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| Item Description: |
A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. |
| Keywords: |
Steel Industry, Hot Strip Mill, Automation, Data Analytics, Machine Learning, Classification, Defect Detection, Root Cause Analysis, Knowledge Integration, Legacy Data |
| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC, European Social Fund, Tata Steel |