Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26905
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dc.contributor.authorSubramaniam, S-
dc.contributor.authorKanfoud, J-
dc.contributor.authorGan, TH-
dc.date.accessioned2023-08-07T07:37:52Z-
dc.date.available2023-08-07T07:37:52Z-
dc.date.issued2022-09-21-
dc.identifierORCID iD: Tat-Hean Gan https://orcid.org/0000-0002-5598-8453.-
dc.identifier839-
dc.identifier.citationSubramaniam, 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.urihttp://bura.brunel.ac.uk/handle/2438/26905-
dc.descriptionData Availability Statement: Data available on request due to restrictions eg privacy or ethical.en_US
dc.description.abstractCopyright © 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.sponsorshipThe 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.extent1 - 16-
dc.format.mediumElectronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.rightsCopyright © 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.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectsignal processingen_US
dc.subjectimage processingen_US
dc.subjectautomated defect detectionen_US
dc.subjectsmart manufacturingen_US
dc.subjecttime-of-flight diffraction scanning (TOFD)en_US
dc.subjectwavelet transformen_US
dc.subjectsegmentationen_US
dc.titleZero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Imagesen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3390/machines10100839-
dc.relation.isPartOfMachines-
pubs.issue10-
pubs.publication-statusPublished-
pubs.volume10-
dc.identifier.eissn2075-1702-
dc.rights.holderThe authors-
Appears in Collections:Brunel Innovation Centre

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