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

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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.

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Acknowledgements

This work was supported by Project of the Key Program of National Natural Science Foundation of China (Grant numbers 11972155). This work is also partially supported by the China Scholarship Council and Hunan Provincial Innovation Foundation For Postgraduate.

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Authors and Affiliations

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, People’s Republic of China

    Zhenxing Cheng & Hu Wang

  2. Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH, 45221, USA

    Zhenxing Cheng & Gui-Rong Liu

Authors
  1. Zhenxing Cheng
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  2. Hu Wang
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  3. Gui-Rong Liu
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Correspondence to Hu Wang.

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Cheng, Z., Wang, H. & Liu, GR. Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics. J Intell Manuf 32, 1009–1023 (2021). https://doi.org/10.1007/s10845-020-01599-6

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