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Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics

Author

Listed:
  • Zhenxing Cheng

    (Hunan University
    University of Cincinnati)

  • Hu Wang

    (Hunan University)

  • Gui-Rong Liu

    (University of Cincinnati)

Abstract

This study proposed a deep convolutional neural network (DCNN) aided optimization (DCNNAO) method to improve the quality of deposition during the cold spray process which was simulated by molecular dynamics (MD). The idea of the DCNNAO is to extract the value of the objective function from the MD simulation snapshots directly by DCNN aided image process technique. Considering the huge memory requirement for MD result files, the main superiority of DCNNAO is to reduce the memory requirement and improve the efficiency of the optimization process by using a contour image (several hundred kilobytes) as the input instead of an MD result file (several hundred gigabytes). To complete this strategy, a Python script is written to generate required snapshots from result files automatically. Moreover, three boosted decision trees based optimization methods including surrogate optimization and heuristic algorithms are also implemented for comparison study. A detailed optimization result demonstrates that all the above methods can obtain an acceptable solution. The comparison is also given for an informed selection of them based on the trade-off between efficiency and accuracy.

Suggested Citation

  • Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01599-6
    DOI: 10.1007/s10845-020-01599-6
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    References listed on IDEAS

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    1. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    2. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
    3. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
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    Cited by:

    1. Thinh Quy Duc Pham & Truong Vinh Hoang & Xuan Tran & Quoc Tuan Pham & Seifallah Fetni & Laurent Duchêne & Hoang Son Tran & Anne-Marie Habraken, 2023. "Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1701-1719, April.

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