Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading-Reference-Cited by-同舟云学术

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Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading

Published:2024-09 Issue: Volume: Page:108608
ISSN:0142-1123
Container-title:International Journal of Fatigue
language:en
Short-container-title:International Journal of Fatigue

Author:

Bartošák Michal^ORCID,Halamka Jiří^ORCID,Beránek Libor,Koukolíková Martina^ORCID,Slaný Michal,Pagač Marek^ORCID,Džugan Jan

Publisher

Elsevier BV

Reference67 articles.

1. Effect of build geometry on the microstructural development of 316L parts produced by additive manufacturing;Leicht;Mater Charact,2018

2. Surface roughness effects on the fatigue strength of additively manufactured Ti-6Al-4V;Pegues;Int J Fatigue,2018

3. The effect of defect population on the anisotropic fatigue resistance of AlSi10Mg alloy fabricated by laser powder bed fusion;Wu;Int J Fatigue,2021

4. Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review;Sanaei;Prog Mater Sci,2021

5. Multiaxial fatigue of additively manufactured metallic components: A review of the failure mechanisms and fatigue life prediction methodologies;Foti;Prog Mater Sci,2023

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