Automated Flaw Detection in Multi-channel Phased Array Ultrasonic Data Using Machine Learning

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSiljama, Oskar
dc.contributor.authorKoskinen, Tuomas
dc.contributor.authorJessen-Juhler, Oskari
dc.contributor.authorVirkkunen, Iikka
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentVTT Technical Research Centre of Finland
dc.contributor.departmentAdvanced Manufacturing and Materials
dc.date.accessioned2021-08-25T06:52:11Z
dc.date.available2021-08-25T06:52:11Z
dc.date.issued2021-09
dc.description.abstractModern ultrasonic inspections utilize ever-richer data-sets made possible by phased array equipment. A typical inspection may include tens of channels with different refraction angle, that are acquired at high speed. These rich data sets allow highly reliable and efficient inspection in complex cases, such as dissimilar metal or austenitic stainless steel welds. The rich data sets allow human inspectors to detect cracks with low signal-to-noise ratio from the wider signal patterns. There's a clear trend in the industry to even richer data sets with full matrix capture (FMC) and related techniques. Convolutional neural networks have recently shown capability to detect flaws with human level accuracy in ultrasonic signals at the B-scan level. To enable automated flaw detection at human-level accuracy for critical applications, these neural networks need be developed to take advantage of today's rich phased array data-sets. In the present paper, we extend previous work and develop convolutional neural networks that perform highly reliable flaw detection on typical multi-channel phased array data on austenitic welds. The results show, that the modern neural networks can accommodate the rich ultrasonic data and display high flaw detection performance.en
dc.description.versionPeer revieweden
dc.format.extent13
dc.format.mimetypeapplication/pdf
dc.identifier.citationSiljama , O , Koskinen , T , Jessen-Juhler , O & Virkkunen , I 2021 , ' Automated Flaw Detection in Multi-channel Phased Array Ultrasonic Data Using Machine Learning ' , Journal of Nondestructive Evaluation , vol. 40 , no. 3 , 67 . https://doi.org/10.1007/s10921-021-00796-4en
dc.identifier.doi10.1007/s10921-021-00796-4
dc.identifier.issn0195-9298
dc.identifier.otherPURE UUID: 6e32fd88-40c5-4962-bd07-ef4b0b3031c8
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/6e32fd88-40c5-4962-bd07-ef4b0b3031c8
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85112009498&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/66493565/ENG_Siljama_et_al_Automated_Flaw_Detection_Journal_of_Nondestructive_Evaluation.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/109140
dc.identifier.urnURN:NBN:fi:aalto-202108258377
dc.language.isoenen
dc.publisherSPRINGER/PLENUM PUBLISHERS
dc.relation.ispartofseriesJournal of Nondestructive Evaluationen
dc.relation.ispartofseriesVolume 40, issue 3en
dc.rightsopenAccessen
dc.subject.keywordMachine learning
dc.subject.keywordNDT
dc.subject.keywordPhased array
dc.subject.keywordImage classification
dc.subject.keywordNEURAL-NETWORK
dc.subject.keywordSAMPLING STRATEGY
dc.subject.keywordFULL-MATRIX
dc.subject.keywordCLASSIFICATION
dc.subject.keywordRELIABILITY
dc.subject.keywordALGORITHM
dc.titleAutomated Flaw Detection in Multi-channel Phased Array Ultrasonic Data Using Machine Learningen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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