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- E. Santecchia
- Mechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
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- A. M. S. Hamouda
- Mechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
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- F. Musharavati
- Mechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
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- E. Zalnezhad
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea
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- M. Cabibbo
- Dipartimento di Ingegneria Industriale e Scienze Matematiche (DIISM), Università Politecnica delle Marche, 60131 Ancona, Italy
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- M. El Mehtedi
- Dipartimento di Ingegneria Industriale e Scienze Matematiche (DIISM), Università Politecnica delle Marche, 60131 Ancona, Italy
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- S. Spigarelli
- Dipartimento di Ingegneria Industriale e Scienze Matematiche (DIISM), Università Politecnica delle Marche, 60131 Ancona, Italy
書誌事項
- 公開日
- 2016
- 権利情報
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- http://creativecommons.org/licenses/by/4.0/
- DOI
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- 10.1155/2016/9573524
- 公開者
- Wiley
この論文をさがす
説明
<jats:p>Metallic materials are extensively used in engineering structures and fatigue failure is one of the most common failure modes of metal structures. Fatigue phenomena occur when a material is subjected to fluctuating stresses and strains, which lead to failure due to damage accumulation. Different methods, including the Palmgren-Miner linear damage rule- (LDR-) based, multiaxial and variable amplitude loading, stochastic-based, energy-based, and continuum damage mechanics methods, forecast fatigue life. This paper reviews fatigue life prediction techniques for metallic materials. An ideal fatigue life prediction model should include the main features of those already established methods, and its implementation in simulation systems could help engineers and scientists in different applications. In conclusion, LDR-based, multiaxial and variable amplitude loading, stochastic-based, continuum damage mechanics, and energy-based methods are easy, realistic, microstructure dependent, well timed, and damage connected, respectively, for the ideal prediction model.</jats:p>
収録刊行物
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- Advances in Materials Science and Engineering
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Advances in Materials Science and Engineering 2016 1-26, 2016
Wiley