The DOI matches its Crossref record; 69 of the 72 references it cites were found in a registry or an index.
A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys
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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 [444]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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The status, challenges, and future of additive manufacturing in engineering
Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C. B., Wang, C. C. L., Shin, Y. C., Zhang, S., & Zavattieri, P. D. (2015) · Computer-Aided Design · doi:10.1016/j.cad.2015.04.001 · Cited by 2,657
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Research works printed but not linked: 1
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Cited by
Sorted by citations
Making sustainable aluminum by recycling scrap: The science of “dirty” alloys
Raabe, D., Ponge, D., Uggowitzer, P. J., Roscher, M., Paolantonio, M., Liu, C., Antrekowitsch, H., Kozeschnik, E., Seidman, D. N., Gault, B., et al. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.100947 · Cited by 685
Research and application of machine learning for additive manufacturing
Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., Simpson, T. W., Lu, Y., & Tang, Q. (2022) · Additive Manufacturing · doi:10.1016/j.addma.2022.102691 · Cited by 391
Zhao, L., Song, L., Macías, J. G. S., Zhu, Y., Huang, M., Simar, A., & Li, Z. (2022) · Additive manufacturing · doi:10.1016/j.addma.2022.102914 · Cited by 295
Zhang, X., Gong, J., & Xuan, F. (2021) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2021.106236 · Cited by 253
Silva, M. I., Malitckii, E., Santos, T. G., & Vilaça, P. (2023) · Progress in Materials Science · doi:10.1016/j.pmatsci.2023.101155 · Cited by 272
Metadata
- Authors
- Muhammad, W., Brahme, A. P., Ibragimova, O., Kang, J., & Inal, K.
- Affiliations
- University of Waterloo (CA)
- Corresponding author
- Inal, Kaan
- Venue
- International Journal of Plasticity
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 136
- Pages
- 102867
- ISSN
- 0749-6419 (ISSN-L)
- OpenAlex
- W3089021334
- Cited by
- 149
- References
- 72
- Citations by year
- 2026: 20 · 2025: 28 · 2024: 26 · 2023: 28 · 2022: 34 · 2021: 13
- FWCI
- 9.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Plasticity (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Metals and Alloys › Microstructure and mechanical properties
- Keywords
- local strain distribution, plastic anisotropy, fracture prediction, AlSi10Mg, laser powder bed fusion, additive manufacturing, porosity, computed tomography, digital image correlation, artificial neural networks, tensile deformation, shear bands, crack initiation, microstructure-property relationship
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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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