The DOI matches its Crossref record; 35 of the 39 references it cites were found in a registry or an index.
Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods
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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 [438]
Citations
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Research works printed but not linked: 3
- A. Mangal, Applied Machine Learning to Predict Stress Hotspots in Materials (Ph.D. thesis), Carnegie Mellon University, 2018. Copyright – Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; Last updated – 2018-08-24.
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Cited by
Sorted by citations
Artificial intelligence and machine learning in design of mechanical materials
Guo, K., Yang, Z., Yu, C., & Buehler, M. J. (2020) · Materials Horizons · doi:10.1039/d0mh01451f · Cited by 732
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
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
Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing
Ng, W. L., Goh, G. L., Goh, G. D., Ten, J. S., & Yeong, W. Y. (2024) · Advanced Materials · doi:10.1002/adma.202310006 · Cited by 348
Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm
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Metadata
- Authors
- Herriott, C., & Spear, A. D.
- Affiliations
- University of Utah (US)
- Corresponding author
- Spear, Ashley D.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 175
- Pages
- 109599
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W3007681970
- Cited by
- 187
- References
- 39
- Citations by year
- 2026: 39 · 2025: 26 · 2024: 34 · 2023: 39 · 2022: 28 · 2021: 15 · 2020: 6
- FWCI
- 8.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computational Materials Science (acceptedVersion)
- Funders
- U.S. Department of Defense
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Metals and Alloys › Microstructure and mechanical properties
- Physical Sciences › Engineering › Mechanical Engineering › Microstructure and Mechanical Properties of Steels
- Keywords
- microstructure-property relationship, additive manufacturing, 316L stainless steel, mechanical property prediction, 3D convolutional neural networks, crystal orientation, XGBoost, ridge regression, feature importance, grain structure, data-driven modeling
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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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