Cited by 187Machine Learning in Materials ScienceMicrostructure and mechanical propertiesMicrostructure and Mechanical Properties of Steels

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Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods

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Herriott, Carl, Spear, Ashley D.·2020·Computational Materials Science

DOI 10.1016/j.commatsci.2020.109599

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Authors: Herriott, C., & Spear, A. D.
Affiliations: University of Utah (US)
Corresponding author: Spear, Ashley D.
ORCID: Herriott, Carl
Spear, Ashley D.
Venue: Computational Materials Science
Publisher: Elsevier BV
Published: 2020-04
Volume: 175
Pages: 109599
ISSN: 0927-0256 (ISSN-L)
DOI: 10.1016/j.commatsci.2020.109599
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 334 d)
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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Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods

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