Photoacoustic signal to image based convolutional neural network for defect detection

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dc.authorid0000-0002-1389-1784
dc.authorid0000-0003-4236-3646
dc.contributor.authorBalci, Zekeriya
dc.contributor.authorMert, Ahmet
dc.date.accessioned2026-02-08T15:15:30Z
dc.date.available2026-02-08T15:15:30Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this paper, we propose a novel photoacoustic (PA) signal to image conversion based convolutional neural network (CNN) model for defect detection in materials. A low-cost computer aided PA triggering and acquisition device has been developed, and then, PA signals are stored for four types of defected and intact materials. Variational mode decomposition is applied to the dataset to extract intrinsic mode functions to convert PA signals to images as the first step of the feature extraction, and then, a lightweight CNN architecture is trained and tested using converted grayscale PA images to detect as defected or intact material. The proposed model is performed on the PA signals of aluminum, iron, wood, and plastic depending on the within-class and all-class evaluation strategies. The mean accuracy levels of 0.977 (up to 1.0) for within-class (material dependent) and 0.942 (up to 0.955) for all-class (material independent) are yielded.
dc.description.sponsorshipBursa Technical University [210D003]; Research Fund of Bursa Technical University
dc.description.sponsorshipThis work was supported by the Research Fund of Bursa Technical University under Grant No. 210D003.
dc.identifier.doi10.1063/5.0275680
dc.identifier.issn0034-6748
dc.identifier.issn1089-7623
dc.identifier.issue8
dc.identifier.pmid40778833
dc.identifier.scopus2-s2.0-105012716764
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1063/5.0275680
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5818
dc.identifier.volume96
dc.identifier.wosWOS:001546975000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAip Publishing
dc.relation.ispartofReview of Scientific Instruments
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectModel
dc.titlePhotoacoustic signal to image based convolutional neural network for defect detection
dc.typeArticle

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