Cited by 557Machine Learning in Materials ScienceAdditive Manufacturing and 3D Printing TechnologiesCellular and Composite Structures

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Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

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Gu, Grace X., Chen, Chun-Teh, Richmond, Deon J., Buehler, Markus J.·2018·Materials Horizons

DOI 10.1039/c8mh00653a

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Abstract

A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.

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Authors: Gu, G. X., Chen, C.-T., Richmond, D. J., & Buehler, M. J.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Buehler, Markus J.
ORCID: Gu, Grace X.
Chen, Chun-Teh
Buehler, Markus J.
Venue: Materials Horizons
Publisher: Royal Society of Chemistry (RSC)
Published: 2018
Volume: 5
Issue: 5
Pages: 939-945
ISSN: 2051-6347 (ISSN-L)
2051-6355
DOI: 10.1039/c8mh00653a
OpenAlex: W2883482411
Cited by: 556
References: 35
Citations by year: 2026: 53 · 2025: 72 · 2024: 85 · 2023: 85 · 2022: 87 · 2021: 71 · 2020: 71 · 2019: 30 · 2018: 2
FWCI: 16.23
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/3.0/ (vor, 207 d)
Locations: Materials Horizons (publishedVersion, cc-by)
The Royal Society of Chemistry
Funders: Office of Naval Research (N000141612333)
Air Force Office of Scientific Research (FATE MURI FA9550-15-1-0514)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Additive Manufacturing and 3D Printing Technologies
Physical Sciences › Engineering › Mechanical Engineering › Cellular and Composite Structures
Keywords: biomimetic design, convolutional neural networks, machine learning for materials design, additive manufacturing, mechanical testing of composites

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