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A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
SN Computer Science, Volume: 4, Issue: 5
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Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feed...
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Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feedback, and knock-on effects in successive steel strip products. Automated tools which utilize Artificial Intelligence and Machine Learning for defect detection and Root Cause Analysis in hot rolling have not yet been adopted outside of surface defect detection and roller force optimization. In this paper, we propose an automated tool for the detection and Root Cause Analysis of width-related defects in the hot rolling process which utilizes a combination of expert knowledge and several Machine Learning models. Through this, we aim to increase the scope, and encourage further development, of Machine Learning applications within the Hot Strip Mill process. Both classical algorithms and Computer Vision methods were used for the Machine Learning component of the tool, namely, classification trees and pre-trained convolutional neural networks. The tool is trained and validated using data from an existing hot rolling mill and thus the challenges of collecting and processing real-world legacy data are highlighted and discussed. The Machine Learning models used are shown to perform optimally by validation performance metrics. The tool is found to be suitable for the specified purpose and would be further improved with more training data.
Steel Industry, Hot Strip Mill, Automation, Data Analytics, Machine Learning, Classification, Defect Detection, Root Cause Analysis, Knowledge Integration, Legacy Data
Faculty of Science and Engineering
The authors would like to acknowledge the Materials and Manufacturing Academy (M2A) funding from the European Social Fund via the Welsh Government (c80816) and Tata Steel Europe that has made this research possible. Prof. Giannetti would like to acknowledge the support of the UK Engineering and Physical Sciences Research Council (EP/V061798/1). All authors would like to acknowledge the support of the IMPACT and AccelerateAI projects, part-funded by the European Regional Development Fund (ERDF) via the Welsh Government.