Cited by 176Fatigue and fracture mechanicsAdditive Manufacturing Materials and ProcessesHigh Temperature Alloys and Creep

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A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing

Zhan, Zhixin, Li, Hua·2021·International Journal of Fatigue

DOI 10.1016/j.ijfatigue.2020.106089

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Authors: Zhan, Z., & Li, H.
Affiliations: Beihang University (CN)
Nanyang Technological University (SG)
Corresponding author: Li, Hua
ORCID: Zhan, Zhixin
Li, Hua
Venue: International Journal of Fatigue
Publisher: Elsevier BV
Published: 2021-04
Volume: 145
Pages: 106089
ISSN: 0142-1123 (ISSN-L)
DOI: 10.1016/j.ijfatigue.2020.106089
OpenAlex: W3113060123
Cited by: 176
References: 64
Citations by year: 2026: 26 · 2025: 31 · 2024: 40 · 2023: 43 · 2022: 22 · 2021: 14
FWCI: 10.09
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: International Journal of Fatigue (publishedVersion)
DR-NTU (Nanyang Technological University) (submittedVersion)
Funders: National Natural Science Foundation of China
Topics: Physical Sciences › Engineering › Mechanics of Materials › Fatigue and fracture mechanics
Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
Physical Sciences › Engineering › Mechanical Engineering › High Temperature Alloys and Creep
Keywords: fatigue life prediction, additive manufacturing, aerospace alloys, continuum damage mechanics, machine learning, artificial neural networks, random forest, cyclic loading

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A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing

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