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

考虑到虚拟调试（VC）的要求，目前还没有一种整体的物料流模拟方法来确保大量件货的物料流动力学的时间确定性计算。随着运动精度的提高，大量单件货物的计算时间也随之增加。在预测复杂现象方面，学习模拟器的计算效率要比经典模拟器高得多。与求解微分方程的模拟器的经典方法相比，学习模拟器的方法是基于一个可参数化的函数，该函数使用数据和机器学习训练为模型行为。在自己的初步工作中表明，学习模拟器也可以适用于VC环境下的工业物料流，并且可以用图神经网络（GN）模拟器表示物料流行为的映射。这可以显著减少计算时间。为了对其适用性和通用性进行概括，必须研究一种合适的方法来自动生成合适的数据，并将GN模拟器集成到实时仿真环境中。这个项目的目的是使一个高分辨率的材料流模拟基于实时计算的GN内可用的VC。这将创造在VC模拟中实时模拟高分辨率行为模型的可能性。在此之前，GN将使用非实时的物理物质流模拟和来自真实系统的数据在真实模型行为上进行训练。

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.