The simulation of production processes using a Digital Twin is a promising means for prospective planning, analysis of existing systems or process-parallel 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.

The simulation of processes in production systems is a powerful tool for factory planning. The application of simulation methods within the Digital Factory is becoming increasingly relevant as developments in the field of digitalization lead to more comprehensive, efficient, embedded and cost-effective simulation methods. Especially the integration within a Digital Twin, allows these advantages to be achieved for simulations. Here, the Digital Twin can be utilized for prospective planning, analysis of existing systems or process-oriented monitoring. In all cases, the Digital Twin offers manufacturing companies room for improvement in production and logistics processes leading to cost savings. However, many companies do not apply the technology, because the generation of a Digital Twin is cost-, time- and resource-intensive and IT expertise is required. This paper presents an approach for generating a Digital Twin in the built environment automatically and for utilization in factory planning. The obstacles will be overcome by using a scan of the shop floor, subsequent object recognition, and predefined frameworks for factory planning within the Digital Twin. Here, the effort for scanning the production hall is additional, while the subsequent object recognition, the generation of the CAD model and the simulation model, as well as the factory planning can be to a great extent automated and therefore carried out with a minimum of effort. Therefore, considerable cost savings can be expected here, which more than offset the additional effort for scanning.

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Digital Twin has been recognized as a strategic approach in the modern manufacturing industry to improve both the flexibility and the efficiency. To efficiently generate the Digital Twin of an existing real object in the factory, powerful methods are necessary. Hereby, a fast data acquisition including object recognition and model reconstruction methodology has been combined to resolve these issues. Such a data set often has to replace the missing original digital model. Subsequently, a model reconstruction plan has to be derived so that an editable CAD model, which fulfils process requirements, can be generated using standard geometry creation tools. Such a reverse-engineered CAD model preferably contains form-feature based design intent and can be easily modified due to new design and manufacturing constraints. The presented paper describes an industrial approach for a commercial service being in the implementation to generate the Digital Twin based on fast scanning on a factory.