1 research outputs found
Data-driven simulation for general purpose multibody dynamics using deep neural networks
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.Comment: 32 pages, 17 figures, 11 table