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Data-driven simulation for general-purpose multibody dynamics using Deep Neural Networks

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Abstract

In this paper, we introduce a machine learning-based simulation framework of general-purpose multibody dynamics (MBD). The aim of the framework is to construct a well-trained meta-model of MBD systems, based on a deep neural network (DNN). Since the main advantage of the meta-model is the enhancement of computational efficiency in returning solutions, the modeling would be beneficial for solving highly complex MBD problems in a short time. Furthermore, for dynamics problems, not only the accuracy but also the smoothness in time of motion solutions, such as displacement, velocity, and acceleration, are essential aspects to consider. We analyze and discuss the influence of training data structures on both aspects of solutions. As a result of the introduced approach, the meta-model provides motion estimation of system dynamics without solving an analytical equation of motion or a numerical solver. Numerical tests demonstrate the performance of the proposed meta-modeling for representing several MBD systems.

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Acknowledgements

This research is supported by 2019 KyungHee University research program and Functionbay Inc., and the authors would like to acknowledge the support for Grzegorz Orzechowski from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project No. 845600 (RealFlex).

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Authors and Affiliations

  1. Department of Mechanical Engineering (Integrated Engineering), Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea

    Hee-Sun Choi, Seongji Han, Jin-Gyun Kim & Jin Hwan Choi

  2. Department of Industrial and Management Systems Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea

    Junmo An & Jae-Yoon Jung

  3. R&D Center, FunctionBay, Inc., 5F, Pangyo Seven Venture Valley 1 danji 2 dong, 15, Pangyo-ro 228 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea

    Juhwan Choi

  4. Department of Mechanical Engineering, LUT University, Yliopistonkatu 34, 53850, Lappeenranta, Finland

    Grzegorz Orzechowski & Aki Mikkola

Authors
  1. Hee-Sun Choi
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  2. Junmo An
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  3. Seongji Han
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  4. Jin-Gyun Kim
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  5. Jae-Yoon Jung
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  6. Juhwan Choi
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  7. Grzegorz Orzechowski
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  9. Jin Hwan Choi
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Correspondence to Jin Hwan Choi.

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Choi, HS., An, J., Han, S. et al. Data-driven simulation for general-purpose multibody dynamics using Deep Neural Networks. Multibody Syst Dyn 51, 419–454 (2021). https://doi.org/10.1007/s11044-020-09772-8

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  • DOI: https://doi.org/10.1007/s11044-020-09772-8

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