Automated generation of a digital twin of a manufacturing system by using scan and convolutional neural networks

Abstract

The simulation of production processes using a Digital Twin is a promising means for prospective planning, analysis of existing systems or processparallel monitoring. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a Digital Twin is cost-, time- and resource-intensive and IT expertise is required. This obstacle can be removed by a novel approach to generate a Digital Twin using fast scans of the shop floor and subsequent object recognition in the point cloud. We describe how parameters and data should be acquired in order to generate a Digital Twin automatically. An overview of the entire process chain is given. A particular attention is given to the automatic object recognition and its integration into Digital Twin.