Using convolutional neural networks to predict composite properties beyond the elastic limit

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Composites are ubiquitous throughout nature and often display both high strength and toughness, despite the use of simple base constituents. In the hopes of recreating the high-performance of natural composites, numerical methods such as finite element method (FEM) are often used to calculate the mechanical properties of composites. However, the vast design space of composites and computational cost of numerical methods limit the application of high-throughput computing for optimizing composite design, especially when considering the entire failure path. In this work, the authors leverage deep learning (DL) to predict material properties (stiffness, strength, and toughness) calculated by FEM, motivated by DL's significantly faster inference speed. Results of this study demonstrate potential for DL to accelerate composite design optimization.
Publisher
CAMBRIDGE UNIV PRESS
Issue Date
2019-06
Language
English
Article Type
Article
Citation

MRS COMMUNICATIONS, v.9, no.02, pp.609 - 617

ISSN
2159-6859
DOI
10.1557/mrc.2019.49
URI
http://hdl.handle.net/10203/264277
Appears in Collection
ME-Journal Papers(저널논문)
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