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Bi-directional evolutionary 3D topology optimization with a deep neural network

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Abstract

The FEM-based topology optimization repeats usually finite element analyses many times to converge to the stopping criteria. If the near-optimal topology data are available in advance at the beginning of an optimization process, the iterative computation could be greatly reduced. In an effort to obtain swiftly optimum topology solutions, the deep learning and neural networks with a special segmentation scheme of digital images are combined with the BESO (bi-directional evolutionary structural optimization) topology method in this study. The pre-trained digital images of 3200 optimum topologies construct the design domain for the main topology optimization. Additionally, a new post-processor is developed in order to reconstruct the relative locations among finite elements in the raw outputs generated by the neural network. The proposed method has been demonstrated to be efficient in lowering the iterations with several 2D and 3D optimization examples. The iteration counts can be reduced 63% for a 2D example and by 72.5% for a 3D one, compared to BESO results alone.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2C1102742).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Kyungpook National University, Daegu, 41566, Korea

    Junseok Shin & Cheol Kim

Authors
  1. Junseok Shin
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  2. Cheol Kim
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Corresponding author

Correspondence to Cheol Kim.

Additional information

Cheol Kim’s research interests include design optimization, mechanics of composite materials and Li-ion battery materials. He received Ph.D. from the University of Illinois at Urbana-Champaign.

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Shin, J., Kim, C. Bi-directional evolutionary 3D topology optimization with a deep neural network. J Mech Sci Technol 36, 3509–3519 (2022). https://doi.org/10.1007/s12206-022-0628-2

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  • DOI: https://doi.org/10.1007/s12206-022-0628-2

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