[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125577-en":3,"doc-seo-125577-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125577,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques","Within aerospace and automotive manufacturing, quality assurance relies heavily on inspection or tests at different production steps, often without leveraging process data for in-process inspection and certification at the point of manufacture. Detecting defects during manufacturing can improve consistency and reduce scrap rates. This work applies infrared thermal imaging and machine learning to inspect enamel removal on Litz wire. Temperature profiles are recorded for bundles with and without enamel, then classifier models are evaluated for automated enamel detection. Results compare classification accuracy and evaluation time, with the Gaussian Mixture Model (EM) achieving the best performance.","[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk)  \nUniversities of Leeds, Sheffield and York  \nDeposited via The University of Sheffield.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/198256/](https://eprints.whiterose.ac.uk/id/eprint/198256/)  \nVersion: Published Version  \nArticle:  \nTiwari, D. , Miller, D. , Farnsworth, M. et al. (2023) Inspection of enamel removal using infrared thermal imaging and machine learning techniques. Sensors, 23 (8) . 3977.  \n[https://doi.org/10.3390/s23083977](https://doi.org/10.3390/s23083977)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \nsensors   \nArticle  \nInspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques  \nDivya Tiwari 1,*, David Miller 1, Michael Farnsworth 1, Alexis Lambourne 2, Geraint W. Jewell 3 and Ashutosh Tiwari 1  \nCitation: Tiwari, D.; Miller, D.; Farnsworth, M.; Lambourne, A.; Jewell, G.W.; Tiwari, A. Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques. Sensors 2023, 23, 3977. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s23083977  \nAcademic Editor: Yuan Yao  \nReceived: 27 February 2023  \nRevised: 20 March 2023  \nAccepted: 7 April 2023  \nPublished: 14 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK  \n2 Rolls-Royce, Derby DE24 9HY, UK  \n3 Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK  \n* Correspondence: d.tiwari@sheffi[eld.ac.uk](eld.ac.uk)  \nAbstract: Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classificatio","cbCaitkOzmnmI6sl","https://ap.wps.com/l/cbCaitkOzmnmI6sl","pdf",1989976,1,17,"English","en",105,"# Introduction\n## Methodology\n## Results and Model Comparison\n## Conclusion","[{\"question\":\"What problem does the paper address in aerospace and automotive manufacturing?\",\"answer\":\"It addresses the lack of research and practical methods for performing in-process inspection and certification using process data during manufacturing, specifically for enamel removal at termination steps.\"},{\"question\":\"How is enamel removal inspected in this study?\",\"answer\":\"Bundles of Litz wire are inspected using infrared thermal imaging to record temperature profiles for wires with and without enamel, enabling automated analysis.\"},{\"question\":\"Which machine learning model performed best for enamel classification and what were its key metrics?\",\"answer\":\"The Gaussian Mixture Model with expectation maximisation produced the best results, with 85% training accuracy and 100% enamel classification accuracy, while also having the fastest evaluation time of 1.05 s.\"}]","Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques | 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problem does the paper address in aerospace and automotive manufacturing?","Question",{"text":75,"@type":76},"It addresses the lack of research and practical methods for performing in-process inspection and certification using process data during manufacturing, specifically for enamel removal at termination steps.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is enamel removal inspected in this study?",{"text":80,"@type":76},"Bundles of Litz wire are inspected using infrared thermal imaging to record temperature profiles for wires with and without enamel, enabling automated analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best for enamel classification and what were its key metrics?",{"text":84,"@type":76},"The Gaussian Mixture Model with expectation maximisation produced the best results, with 85% training accuracy and 100% enamel classification accuracy, while also having the fastest evaluation time of 1.05 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