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Procedia Manufacturing
Article . 2020 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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Procedia Manufacturing
Article
License: CC BY NC ND
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Towards an Automated Product-Production System Design - Combining Simulation-based Engineering and Graph-based Design Languages

Authors: Karl Kübler; Dominik Schopper; Oliver Riedel; Stephan Rudolph;

Towards an Automated Product-Production System Design - Combining Simulation-based Engineering and Graph-based Design Languages

Abstract

Abstract In this paper, the authors elaborate a combination of graph-based design and simulation-based engineering into a new concept called Executable Integrative Product-Production Model (EIPPM). Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. Therefore, Digital Twins (DT) are modeled and run in a simulation. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of DTs in a simulation-based engineering for the development of production systems. Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain "production systems technology" together with the knowledge of the corresponding "product" using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle, results of an automatic translation step in a compiler. Since the execution of the GBDL can be repeated as often as desired with modified boundary conditions (e.g. through feedback), a design of experiment is made possible, whereby also unconventional solutions are considered. The novel concept aims at the following advantages: Consistent linking of all mechatronic domains through a data model (graph) from the project start, automatic design cycles exploring multiple variants for optimized product-production system combinations, automatic generation of simulation models starting with the planning phase, feedback from simulation-based optimization back into the data model.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
gold