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@PHDTHESIS{Delbrgger:862994,
author = {Delbrügger, Tim},
othercontributors = {Roßmann, Heinz-Jürgen and Rehof, Jakob},
title = {{D}igital twin based decision support for factory
adaptation planning},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2023-00656},
pages = {1 Online-Ressource : Illustrationen, Diagramme},
year = {2022},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2023; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2022},
abstract = {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.},
cin = {615210},
ddc = {621.3},
cid = {$I:(DE-82)615210_20140620$},
pnm = {GRK 2193: Anpassungsintelligenz von Fabriken im dynamischen
und komplexen Umfeld (276879186)},
pid = {G:(GEPRIS)276879186},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2023-00656},
url = {https://publications.rwth-aachen.de/record/862994},
}
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