Jump to main content
Jump to site search

Issue 5, 2018
Previous Article Next Article

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

Author affiliations

Abstract

Biomimicry, adapting and implementing nature's designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.

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

Back to tab navigation

Supplementary files

Publication details

The article was received on 05 Jun 2018, accepted on 24 Jul 2018 and first published on 27 Jul 2018


Article type: Communication
DOI: 10.1039/C8MH00653A
Mater. Horiz., 2018,5, 939-945
  • Open access: Creative Commons BY license
  •   Request permissions

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

    G. X. Gu, C. Chen, D. J. Richmond and M. J. Buehler, Mater. Horiz., 2018, 5, 939
    DOI: 10.1039/C8MH00653A

    This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.

    Reproduced material should be attributed as follows:

    • For reproduction of material from NJC:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
    • For reproduction of material from PCCP:
      [Original citation] - Published by the PCCP Owner Societies.
    • For reproduction of material from PPS:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
    • For reproduction of material from all other RSC journals:
      [Original citation] - Published by The Royal Society of Chemistry.

    Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.

Search articles by author

Spotlight

Advertisements