Access the full text.
Sign up today, get DeepDyve free for 14 days.
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.
Acta Mechanica Solida Sinica – Springer Journals
Published: Apr 1, 2023
Keywords: Additive manufacturing; Fatigue properties; Metals; Modeling and prediction; Microstructure
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
To subscribe to email alerts, please log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Reference ManagersExport to EndNote
To get new article updates from a journal on your personalized homepage, please log in first, or sign up for a DeepDyve account if you don’t already have one.