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

: Kübler, K.; Schopper, D.; Riedel, O.; Rudolph, S.

Volltext ()

Procedia manufacturing 52 (2020), S.258-265
ISSN: 2351-9789
International Conference on System-Integrated Intelligence (SysInt) <5, 2020, Online>
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAO ()

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.