Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
Autor: | Grace X. Gu, Markus J. Buehler, Deon J. Richmond, Chun-Teh Chen |
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Přispěvatelé: | Massachusetts Institute of Technology. Department of Mechanical Engineering, Gu, Grace Xiang |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Alternative methods
business.industry Computer science Process Chemistry and Technology Composite number 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Finite element method 0104 chemical sciences Mechanics of Materials General Materials Science Artificial intelligence Electrical and Electronic Engineering Biomimetics 0210 nano-technology business Design space computer |
Zdroj: | Royal Society of Chemistry |
Popis: | 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. United States. Office of Naval Research (Grant ONR N000141612333) |
Databáze: | OpenAIRE |
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