Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/10742
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gürgen, M. | - |
dc.contributor.author | Bakır, M. | - |
dc.contributor.author | Bahceci, E. | - |
dc.contributor.author | Ünver, Hakkı Özgür | - |
dc.date.accessioned | 2023-10-24T07:01:52Z | - |
dc.date.available | 2023-10-24T07:01:52Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 1753-1039 | - |
dc.identifier.uri | https://doi.org/10.1504/IJMMS.2023.133400 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/10742 | - |
dc.description.abstract | For 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.iso | en | en_US |
dc.publisher | Inderscience Publishers | en_US |
dc.relation.ispartof | International Journal of Mechatronics and Manufacturing Systems | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | aerospace alloys | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | fatigue prediction | en_US |
dc.subject | machine learning | en_US |
dc.subject | metal fatigue | en_US |
dc.subject | reliability | en_US |
dc.subject | Fiber reinforced plastics | en_US |
dc.subject | Functional materials | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Aerospace alloys | en_US |
dc.subject | Aviation industry | en_US |
dc.subject | Critical properties | en_US |
dc.subject | Fatigue prediction | en_US |
dc.subject | Fibre-reinforced composite | en_US |
dc.subject | Industry applications | en_US |
dc.subject | Machine-learning | en_US |
dc.subject | Mechanical parts | en_US |
dc.subject | Physics-based models | en_US |
dc.subject | Service requirements | en_US |
dc.subject | Fatigue of materials | en_US |
dc.title | A Review From Physics Based Models To Artificial Intelligence Aided Models in Fatigue Prediction for Industry Applications | en_US |
dc.type | Article | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.volume | 16 | en_US |
dc.identifier.issue | 2-3 | en_US |
dc.identifier.startpage | 171 | en_US |
dc.identifier.endpage | 200 | en_US |
dc.identifier.scopus | 2-s2.0-85172868243 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1504/IJMMS.2023.133400 | - |
dc.authorscopusid | 58627669100 | - |
dc.authorscopusid | 57191162836 | - |
dc.authorscopusid | 23468523900 | - |
dc.authorscopusid | 6603873269 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q3 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.7. Department of Mechanical Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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