The DOI matches its Crossref record; 61 of the 64 references it cites were found in a registry or an index.
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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“Machine learning models can be developed to predict mechanical properties of materials fabricated through additive manufacturing as they can learn and model highly complex relationships [438-445].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [442]
Citations
Cites
Additive manufacturing of metallic components – Process, structure and properties
DebRoy, T., Wei, H. L., Zuback, J. S., Mukherjee, T., Elmer, J. W., Milewski, J. O., Beese, A. M., Wilson-Heid, A., De, A., & Zhang, W. (2018) · Progress in Materials Science · doi:10.1016/j.pmatsci.2017.10.001 · Cited by 8,805
Additive manufacturing (3D printing): A review of materials, methods, applications and challenges
Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T. Q., & Hui, D. (2018) · Composites Part B: Engineering · doi:10.1016/j.compositesb.2018.02.012 · Cited by 8,431
Random forest in remote sensing: A review of applications and future directions
Belgiu, M., & Drăguţ, L. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.011 · Cited by 7,744
Additive manufacturing of metals
Herzog, D., Seyda, V., Wycisk, E., & Emmelmann, C. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.07.019 · Cited by 4,951
Samaniego, E. P., Anitescu, C., Goswami, S., Nguyen‐Thanh, V. M., Guo, H., Hamdia, K. M., Zhuang, X., & Rabczuk, T. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.112790 · Cited by 2,139
Brandl, E., Heckenberger, U., Holzinger, V., & Buchbinder, D. (2011) · Materials & Design (1980-2015) · doi:10.1016/j.matdes.2011.07.067 · Cited by 1,154
Materials for additive manufacturing
Bourell, D. L., Kruth, J. P., Leu, M. C., Levy, G. N., Rosen, D. W., Beese, A. M., & Clare, A. T. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.05.009 · Cited by 1,097
Ahmad, M. W., Mourshed, M. M., & Rezgui, Y. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.04.038 · Cited by 1,030
Mechanical behavior of additive manufactured, powder-bed laser-fused materials
Mower, T. M., & Long, M. (2015) · Materials Science and Engineering A · doi:10.1016/j.msea.2015.10.068 · Cited by 1,013
On some modifications of kinematic hardening to improve the description of ratchetting effects
Chaboche, J. L. (1991) · International Journal of Plasticity · doi:10.1016/0749-6419(91)90050-9 · Cited by 1,001
Cited by
Sorted by citations
Metal additive manufacturing in aerospace: A review
Blakey-Milner, B., Gradl, P., Snedden, G., Brooks, M., Pitot, J., Lopez, E., Leary, M., Berto, F., & du Plessis, A. (2021) · Materials & Design · doi:10.1016/j.matdes.2021.110008 · Cited by 2,263
Wu, Y., Fang, J., Wu, C., Li, C., Sun, G., & Li, Q. (2023) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2023.108102 · Cited by 455
Wang, H., Li, B., Gong, J., & Xuan, F. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109242 · Cited by 289
Fatigue modeling using neural networks: A comprehensive review
Chen, J., & Liu, Y. (2022) · Fatigue & Fracture of Engineering Materials & Structures · doi:10.1111/ffe.13640 · Cited by 270
A novel method of multiaxial fatigue life prediction based on deep learning
Yang, J., Kang, G., Liu, Y., & Kan, Q. (2021) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2021.106356 · Cited by 226
Metadata
- Authors
- Zhan, Z., & Li, H.
- Affiliations
- Beihang University (CN)
- Nanyang Technological University (SG)
- Corresponding author
- Li, Hua
- ORCID
- Venue
- International Journal of Fatigue
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 145
- Pages
- 106089
- ISSN
- 0142-1123 (ISSN-L)
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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