Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10742
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dc.contributor.authorGürgen, M.-
dc.contributor.authorBakır, M.-
dc.contributor.authorBahceci, E.-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2023-10-24T07:01:52Z-
dc.date.available2023-10-24T07:01:52Z-
dc.date.issued2023-
dc.identifier.issn1753-1039-
dc.identifier.urihttps://doi.org/10.1504/IJMMS.2023.133400-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10742-
dc.description.abstractFor a mechanical part to be certified, it should be assessed whether its mechanical, optical or thermal properties satisfy service requirements. Fatigue is one of the critical properties of functional materials, particularly in the aviation industry, where new materials, such as alloys, fibre-reinforced composites and additively manufactured alloys, dominate increasingly. This trend puts a heavy burden on fatigue characterisation, which is expensive and time-consuming. However, recent developments in artificial intelligence offer novel methods to decrease the test load cost-effectively. Hence, this literature survey first summarises predominant fatigue models both theoretical and numerical, and then covers and classifies recent studies (2000–2023) using recent machine learning techniques. Copyright © 2023 Inderscience Enterprises Ltd.en_US
dc.language.isoenen_US
dc.publisherInderscience Publishersen_US
dc.relation.ispartofInternational Journal of Mechatronics and Manufacturing Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectaerospace alloysen_US
dc.subjectartificial intelligenceen_US
dc.subjectfatigue predictionen_US
dc.subjectmachine learningen_US
dc.subjectmetal fatigueen_US
dc.subjectreliabilityen_US
dc.subjectFiber reinforced plasticsen_US
dc.subjectFunctional materialsen_US
dc.subjectMachine learningen_US
dc.subjectAerospace alloysen_US
dc.subjectAviation industryen_US
dc.subjectCritical propertiesen_US
dc.subjectFatigue predictionen_US
dc.subjectFibre-reinforced compositeen_US
dc.subjectIndustry applicationsen_US
dc.subjectMachine-learningen_US
dc.subjectMechanical partsen_US
dc.subjectPhysics-based modelsen_US
dc.subjectService requirementsen_US
dc.subjectFatigue of materialsen_US
dc.titleA Review From Physics Based Models To Artificial Intelligence Aided Models in Fatigue Prediction for Industry Applicationsen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume16en_US
dc.identifier.issue2-3en_US
dc.identifier.startpage171en_US
dc.identifier.endpage200en_US
dc.identifier.scopus2-s2.0-85172868243en_US
dc.institutionauthor-
dc.identifier.doi10.1504/IJMMS.2023.133400-
dc.authorscopusid58627669100-
dc.authorscopusid57191162836-
dc.authorscopusid23468523900-
dc.authorscopusid6603873269-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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