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

Q3 Engineering
Müge Gürgen, Mete Bakır, Ersin Bahceci, Hakkı Özgür Ünver
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引用次数: 0

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.
从基于物理的模型到人工智能辅助模型在工业应用中的疲劳预测综述
对于要认证的机械部件,应评估其机械、光学或热性能是否满足使用要求。疲劳是功能材料的关键性能之一,特别是在航空工业中,合金、纤维增强复合材料和增材制造合金等新材料日益占据主导地位。这种趋势给疲劳表征带来了沉重的负担,这既昂贵又耗时。然而,人工智能的最新发展提供了新的方法来经济有效地减少测试负载。因此,本文献综述首先总结了主要的理论和数值疲劳模型,然后涵盖并分类了使用最新机器学习技术的最新研究(2000-2023)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Mechatronics and Manufacturing Systems
International Journal of Mechatronics and Manufacturing Systems Engineering-Industrial and Manufacturing Engineering
CiteScore
1.90
自引率
0.00%
发文量
10
期刊介绍: IJMMS publishes refereed quality papers in the broad field of mechatronics and manufacturing systems with a special emphasis on research and development in the modern engineering of advanced manufacturing processes and systems. IJMMS fosters information exchange and discussion on all aspects of mechatronics (computers, electrical and mechanical engineering) with applications in manufacturing processes and systems.
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