Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques
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. I...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3977 |
| _version_ | 1797603492162961408 |
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| author | Divya Tiwari David Miller Michael Farnsworth Alexis Lambourne Geraint W. Jewell Ashutosh Tiwari |
| author_facet | Divya Tiwari David Miller Michael Farnsworth Alexis Lambourne Geraint W. Jewell Ashutosh Tiwari |
| author_sort | Divya Tiwari |
| collection | DOAJ |
| description | 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 classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s. |
| first_indexed | 2024-03-11T04:32:53Z |
| format | Article |
| id | doaj.art-4e3015f03e794cd5adf3cbf7ba83a17e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:53Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4e3015f03e794cd5adf3cbf7ba83a17e2023-11-17T21:17:19ZengMDPI AGSensors1424-82202023-04-01238397710.3390/s23083977Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning TechniquesDivya Tiwari0David Miller1Michael Farnsworth2Alexis Lambourne3Geraint W. Jewell4Ashutosh Tiwari5Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UKDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UKDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UKRolls-Royce, Derby DE24 9HY, UKDepartment of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UKDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UKWithin 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 classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.https://www.mdpi.com/1424-8220/23/8/3977process inspectionterminationsinfrared thermal imagingmachine learning |
| spellingShingle | Divya Tiwari David Miller Michael Farnsworth Alexis Lambourne Geraint W. Jewell Ashutosh Tiwari Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques Sensors process inspection terminations infrared thermal imaging machine learning |
| title | Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques |
| title_full | Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques |
| title_fullStr | Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques |
| title_full_unstemmed | Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques |
| title_short | Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques |
| title_sort | inspection of enamel removal using infrared thermal imaging and machine learning techniques |
| topic | process inspection terminations infrared thermal imaging machine learning |
| url | https://www.mdpi.com/1424-8220/23/8/3977 |
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