Simulation of industrial material flows for virtual commissioning with graph neural networks

点击翻译按钮获取中文摘要

Considering the requirements of virtual commissioning (VC), there is no holistic approach to material flow simulation that ensures time-deterministic calculation of the dynamics of material flows for a large number of piece goods. As the accuracy of the movement increases, the computation time for a large number of piece goods increases. Learning simulators can be much more computationally efficient than classical simulators in predicting complex phenomena. In contrast to classical approaches for simulators, which solve differential equations, the approach of learning simulators is based on a parameterizable function that is trained to a model behavior using data and machine learning. In own preliminary work it is shown that a learning simulator can also be suitable for industrial material flow in the context of VC and that the mapping of material flow behavior can be represented with a Graph Neural Network (GN) simulator. This can significantly reduce the computation time. For a general statement of the applicability and generalizability, a suitable methodology for the automatic generation of suitable data as well as an integration of a GN simulator into a real-time simulation environment must be researched. The aim of this project is to make a high-resolution material flow simulation based on a real-time calculation within a GN usable in a VC. This should create the possibility to simulate high-resolution behavior models within a VC simulation in real-time. In advance, the GN will be trained on the real model behavior using non-real-time capable physical material flow simulations and data from a real system.