A novel method based on deep learning algorithms for material deformation rate detection

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dc.authoridOZDEM, Selim/0000-0002-5633-9543
dc.contributor.authorOzdem, Selim
dc.contributor.authorOrak, Ilhami Muharrem
dc.date.accessioned2024-09-29T15:51:14Z
dc.date.available2024-09-29T15:51:14Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractGiven the significant influence of microstructural characteristics on a material's mechanical, physical, and chemical properties, this study posits that the deformation rate of structural steel S235-JR can be precisely determined by analyzing changes in its microstructure. Utilizing advanced artificial intelligence techniques, microstructure images of S235-JR were systematically analyzed to establish a correlation with the material's lifespan. The steel was categorized into five classes and subjected to varying deformation rates through laboratory tensile tests. Post-deformation, the specimens underwent metallographic procedures to obtain microstructure images via an light optical microscope (LOM). A dataset comprising 10000 images was introduced and validated using K-Fold cross-validation. This research utilized deep learning (DL) architectures ResNet50, ResNet101, ResNet152, VGG16, and VGG19 through transfer learning to train and classify images containing deformation information. The effectiveness of these models was meticulously compared using a suite of metrics including Accuracy, F1-score, Recall, and Precision to determine their classification success. The classification accuracy was compared across the test data, with ResNet50 achieving the highest accuracy of 98.45%. This study contributes a five-class dataset of labeled images to the literature, offering a new resource for future research in material science and engineering.en_US
dc.description.sponsorshipHitit Universityen_US
dc.description.sponsorshipThe numerical calculations reported in this paper were fully/partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).en_US
dc.identifier.doi10.1007/s10845-024-02409-z
dc.identifier.issn0956-5515
dc.identifier.issn1572-8145
dc.identifier.scopus2-s2.0-85192990491en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s10845-024-02409-z
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3965
dc.identifier.wosWOS:001222379300003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Intelligent Manufacturingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectTransfer learningen_US
dc.subjectS235-JR structural steelen_US
dc.subjectDeformation rateen_US
dc.subjectMaterial life analysisen_US
dc.titleA novel method based on deep learning algorithms for material deformation rate detectionen_US
dc.typeArticleen_US

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