Incremental Update of a Digital Twin of a Production System by Using Scan and Object Recognition

Autor: Markus Sommer, Sebastian Stobrawa, Josip Stjepandić
Rok vydání: 2021
Předmět:
DOI: 10.3233/atde210086
Popis: While production plants are subjects of frequent change, for instance due to changed processes, new products or new machine tools, the process plans must be subsequently updated. This generally affects all planning processes for production management and thus in particular also modern planning methods such as the Digital Twin of a production system. 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. Supposed that the process of generation of the Digital Twin was completed once using scans and deep learning applied to a point cloud of a production system, this paper describes a conceptual approach to provide an incremental update of this Digital Twin, as often as necessary. The solution alternatives are presented and discussed, in particular the use of flexible devices (360-camera) for the object acquisition. A particular attention is given to the integration of the entire change process for updating an existing Digital Twin.
Databáze: OpenAIRE