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

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Abstract

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.

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Acknowledgments

The authors acknowledge support from the Regents of the University of California, Berkeley and the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges system at the Pittsburgh Supercomputing Center (PSC) through allocation TG-DMR180085, which is supported by National Science Foundation grant number ACI-1548562. The authors also acknowledge support from the Savio computational cluster resource provided by the Berkeley Research Computing program. Additionally, the authors acknowledge support by the Basic Science Research Program (2016R1C1B2011979) and Creative Materials Discovery Program (2016M3D1A1900038) through the National Research Foundation of Korea (NRF), as well as the support by the KAIST-funded Global Singularity Research Program for 2019 (N11190118).

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA

    Charles Yang & Grace X. Gu

  2. Department of Mechanical Engineering & KI for the NanoCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Korea

    Youngsoo Kim & Seunghwa Ryu

Authors
  1. Charles Yang
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  2. Youngsoo Kim
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  3. Seunghwa Ryu
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  4. Grace X. Gu
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Corresponding authors

Correspondence to Seunghwa Ryu or Grace X. Gu.

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The supplementary material for this article can be found at https://doi.org/10.1557/mrc.2019.49

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Yang, C., Kim, Y., Ryu, S. et al. Using convolutional neural networks to predict composite properties beyond the elastic limit. MRS Communications 9, 609–617 (2019). https://doi.org/10.1557/mrc.2019.49

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