Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
Name
c8mh00653a.pdf
Size
2.84 MB
Format
Adobe PDF
Checksum (MD5)
1041f1d5bfe508f3c786ff6ab8e06491
Author(s)
Chen, Chun-Teh
Richmond, Deon J.
Buehler, Markus J.
Gu, Grace Xiang
Date Issued
July 2018
Journal
Materials Horizons
Citation
Gu, Grace X. et al. “Bioinspired Hierarchical Composite Design Using Machine Learning: Simulation, Additive Manufacturing, and Experiment.” Materials Horizons (2018)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 3.0 Unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/C8MH00653A
