Issue 5, 2018

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

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

Supplementary files

Article information

Article type
Communication
Submitted
05 Jūn. 2018
Accepted
24 Jūl. 2018
First published
27 Jūl. 2018
This article is Open Access
Creative Commons BY license

Mater. Horiz., 2018,5, 939-945

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. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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