Please use this identifier to cite or link to this item: 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, H.Ö.
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
Issue Date: 2023
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

Show full item record



CORE Recommender

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.