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

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.