Digital twin based decision support for factory adaptation planning
| Autor: | Delbrügger, Tim |
|---|---|
| Přispěvatelé: | Roßmann, Heinz-Jürgen, Rehof, Jakob |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2022). doi:10.18154/RWTH-2023-00656 = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022 |
| DOI: | 10.18154/RWTH-2023-00656 |
| Popis: | Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2023). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022 Factory adaptation planning is a highly complex, interdisciplinary process which must be managed successfully under high time pressure and with competing goals. Decision support systems are one approach in making this process sustainable and successful. For decision support, the different disciplines often use specialized simulations to compare planning alternatives. In general, planning alternatives can differ by means of parametric variability or structural variability. This thesis identifies three gaps in the current state of research on 3D simulation-based decision support systems that result in optimization over structural variants being a largely manual process to date. This research fills the gaps by using Digital Twins in order to lay a foundation for novel 3D simulation-based decision support systems that are also suitable for complex structural factory adaptations. The new type of decision support is characterized by the fact that Digital Twins are implemented for relevant factory objects on different hierarchical levels of the factory, each of which also includes its own variability model. For example, a robot can have different collision avoidance systems to choose from, the robot workcell can have different types of robots, and the factory floor can have diverse layout and material transport variants. The combinations of these variants result in a large number of possible configurations of the factory, which are automatically searched by the decision support system for candidates with desired metrics. As a result, factory planners can specify desired metrics in a Virtual Testbed and receive suggestions for suitable configurations. The suggested configurations can be analyzed directly in a 3D simulation to support interdisciplinary communication effectively and efficiently. The distinctive feature of this approach is that it reflects the actual need of the factory planning team, which is to find Pareto optimal candidates for the desired factory system among all possible configurations, based on case-specific target metrics. The suitability of the developed system architecture and the underlying concepts is demonstrated by prototypical application to three examples. Published by RWTH Aachen University, Aachen |
| Databáze: | OpenAIRE |
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