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https://hdl.handle.net/20.500.11851/10742
Title: | A review from physics based models to artificial intelligence aided models in fatigue prediction for industry applications | Authors: | Gürgen, M. Bakır, M. Bahceci, E. Ünver, Hakkı Özgür |
Keywords: | aerospace alloys artificial intelligence fatigue prediction machine learning metal fatigue reliability Fiber reinforced plastics Functional materials Machine learning Aerospace alloys Aviation industry Critical properties Fatigue prediction Fibre-reinforced composite Industry applications Machine-learning Mechanical parts Physics-based models Service requirements Fatigue of materials |
Publisher: | Inderscience Publishers | 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. | URI: | https://doi.org/10.1504/IJMMS.2023.133400 https://hdl.handle.net/20.500.11851/10742 |
ISSN: | 1753-1039 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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