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arXiv · 1909.02391

Data-driven simulation for general purpose multibody dynamics using deep neural networks

Hee-Sun Choi·Junmo An·Jin-Gyun Kim·Jae-Yoon Jung·Juhwan Choi·Grzegorz Orzechowski·Aki Mikkola·Jin Hwan Choi
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Abstract

In this paper, a machine learning-based simulation framework of general-purpose multibody dynamics is introduced. The aim of the framework is to generate a well-trained meta-model of multibody dynamics (MBD) systems. To this end, deep neural network (DNN) is employed to the framework so as to construct data-based meta-model representing multibody systems. Constructing well-defined training data set with time variable is essential to get accurate and reliable motion data such as displacement, velocity, acceleration, and forces. As a result of the introduced approach, the meta-model provides motion estimation of system dynamics without solving the analytical equations of motion. The performance of the proposed DNN meta-modeling was evaluated to represent several MBD systems.

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Hee-Sun Choi, Junmo An, Jin-Gyun Kim, Jae-Yoon Jung, Juhwan Choi, Grzegorz Orzechowski, Aki Mikkola, Jin Hwan Choi. 2019-09-02. Data-driven simulation for general purpose multibody dynamics using deep neural networks. https://arxiv.org/abs/1909.02391

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