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Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals

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Abstract

Additive manufacturing (AM) has emerged as an advanced technique for the fabrication of complex near-net shaped and light-weight metallic parts with acceptable mechanical performance. The strength of AM metals has been confirmed comparable or even superior to that of metals manufactured by conventional processes, but the fatigue performance is still a knotty issue that may hinder the substitution of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates. As essential complements to high-cost and time-consuming experimental fatigue tests of AM metals, models for fatigue performance prediction are highly desirable. In this review, different models for predicting the fatigue properties of AM metals are summarized in terms of fatigue life, fatigue limit and fatigue crack growth, with a focus on the incorporation of AM characteristics such as AM defect and processing parameters into the models. For predicting the fatigue life of AM metals, empirical models and theoretical models (including local characteristic model, continuum damage mechanics model and probabilistic method) are presented. In terms of fatigue limit, the introduced models involve the Kitagawa–Takahashi model, the Murakami model, the El-Haddad model, etc. For modeling the fatigue crack growth of AM metals, the summarized methodologies include the Paris equation, the Hartman-Schijve equation, the NASGRO equation, the small-crack growth model, and numerical methods. Most of these models for AM metals are similar to those for conventionally processed materials, but are modified and pay more attention to the AM characteristics. Finally, an outlook for possible directions of the modeling and prediction of fatigue properties of AM metals is provided.

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Acknowledgements

The authors acknowledge the support from National Science and Technology Major Project (J2019-IV-0014-0082), National Key Research and Development Program of China (2022YFB4600700), 15th Thousand Youth Talents Program of China, the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures (MCMS-I-0419G01), the Fundamental Research Funds for the Central Universities (1001-XAC21021), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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  1. Wei Tang and Ziming Tang have contributed equally to this work.

Authors and Affiliations

  1. State Key Lab of Mechanics and Control of Mechanical Structures & Institute for Frontier Science & Key Lab for Intelligent Nano Materials and Devices of Ministry of Education & College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, 210016, China

    Wei Tang, Ziming Tang & Min Yi

  2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Wenjun Lu & Shuai Wang

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Contributions

WT: Conceptualization, Investigation, Data curation, Writing–original draft. ZT: Conceptualization, Investigation, Data curation, Writing–original draft. WL: Investigation, Writing–review & editing. SW: Investigation, Writing–review & editing. MY: Conceptualization, Resources, Supervision, Project administration, Funding acquisition, Writing–original draft & review & editing.

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Correspondence to Min Yi.

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Tang, W., Tang, Z., Lu, W. et al. Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals. Acta Mech. Solida Sin. 36, 181–213 (2023). https://doi.org/10.1007/s10338-023-00380-5

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