A review from physics based models to artificial intelligence aided models in fatigue prediction for industry applications

被引:0
|
作者
Gürgen M. [1 ,2 ]
Bakır M. [2 ,3 ]
Bahceci E. [4 ]
Ünver H.Ö. [1 ]
机构
[1] Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara
[2] Turkish Aerospace, Ankara
[3] Ankara Yildirim Beyazit University, Ankara
[4] Iskenderun Technical University, Hatay
关键词
aerospace alloys; artificial intelligence; fatigue prediction; machine learning; metal fatigue; reliability;
D O I
10.1504/IJMMS.2023.133400
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:171 / 200
页数:29
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