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Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
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A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.
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“ML is applied to improve those processes, for example through failure detection [722,730] or material design [309].”
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Metadata
- Authors
- Gu, G. X., Chen, C.-T., Richmond, D. J., & Buehler, M. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- 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
- 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
- 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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