Cardinality-based variability modeling with AutomationML

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00317893" target="_blank" >RIV/68407700:21230/17:00317893 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/8247711/" target="_blank" >http://ieeexplore.ieee.org/document/8247711/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ETFA.2017.8247711" target="_blank" >10.1109/ETFA.2017.8247711</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cardinality-based variability modeling with AutomationML

Popis výsledku v původním jazyce

Variability modeling is an emerging topic in the general field of systems engineering and, with current trends such as Industrie 4.0, it gains more and more interest in the domain of production systems. Therefore, it is not sufficient to describe systems in several specific cases, but instead families of systems have to be used. In this paper we introduce a role class library for AutomationML to explicitly represent variability. This allows to exchange not only system descriptions but also system family descriptions. We argue for a light-weight extension of AutomationML. The variability-based modeling approach is based on cardinalities, which is a well-known concept from conceptual modeling and feature modeling. Furthermore, we also show how instantiations of variability models can be validated by our EMF-based AutomationML workbench.

Název v anglickém jazyce

Cardinality-based variability modeling with AutomationML

Popis výsledku anglicky

Variability modeling is an emerging topic in the general field of systems engineering and, with current trends such as Industrie 4.0, it gains more and more interest in the domain of production systems. Therefore, it is not sufficient to describe systems in several specific cases, but instead families of systems have to be used. In this paper we introduce a role class library for AutomationML to explicitly represent variability. This allows to exchange not only system descriptions but also system family descriptions. We argue for a light-weight extension of AutomationML. The variability-based modeling approach is based on cardinalities, which is a well-known concept from conceptual modeling and feature modeling. Furthermore, we also show how instantiations of variability models can be validated by our EMF-based AutomationML workbench.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

ISBN

978-1-5090-6505-9

ISSN

—

e-ISSN

1946-0740

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Limassol

Datum konání akce

12. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000427812000146