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

  • Charles Yang (a1), Youngsoo Kim (a2), Seunghwa Ryu (a2) and Grace X. Gu (a1)

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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Corresponding author

Address all correspondence to Seunghwa Ryu at ryush@kaist.ac.kr and Grace X. Gu at ggu@berkeley.edu

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These authors contributed equally to this work.

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References

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MRS Communications
  • ISSN: 2159-6859
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