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DC Field | Value | Language |
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dc.contributor.author | Subramaniam, S | - |
dc.contributor.author | Kanfoud, J | - |
dc.contributor.author | Gan, TH | - |
dc.date.accessioned | 2023-08-07T07:37:52Z | - |
dc.date.available | 2023-08-07T07:37:52Z | - |
dc.date.issued | 2022-09-21 | - |
dc.identifier | ORCID iD: Tat-Hean Gan https://orcid.org/0000-0002-5598-8453. | - |
dc.identifier | 839 | - |
dc.identifier.citation | Subramaniam, S., Kanfoud, J. and Gan, T.H. (2022) 'Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images', Machines, 10 (10), 839, pp. 1 - 16. doi: 10.3390/machines10100839. | en_US |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/26905 | - |
dc.description | Data Availability Statement: Data available on request due to restrictions eg privacy or ethical. | en_US |
dc.description.abstract | Copyright © 2022 by the authors.Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety. | en_US |
dc.description.sponsorship | The research leading to these results has received funding from the UK’s innovation agency, Innovate UK, under grant agreement No. 103991. The research has been undertaken as a part of the project Amphibious robot for inspection and predictive maintenance of offshore wind assets (iFROG). The iFROG project is a collaboration between the following organizations: Innovative Technology and Science Ltd., Brunel University London, TWI Ltd., and ORE Catapult Development Services Ltd. | en_US |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2022 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://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | signal processing | en_US |
dc.subject | image processing | en_US |
dc.subject | automated defect detection | en_US |
dc.subject | smart manufacturing | en_US |
dc.subject | time-of-flight diffraction scanning (TOFD) | en_US |
dc.subject | wavelet transform | en_US |
dc.subject | segmentation | en_US |
dc.title | Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.3390/machines10100839 | - |
dc.relation.isPartOf | Machines | - |
pubs.issue | 10 | - |
pubs.publication-status | Published | - |
pubs.volume | 10 | - |
dc.identifier.eissn | 2075-1702 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Brunel Innovation Centre |
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FullText.pdf | Copyright © 2022 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://creativecommons.org/licenses/by/4.0/). | 6.27 MB | Adobe PDF | View/Open |
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