Abstract
Cyber-Physical Production Systems (CPPS) should adapt to new products or product variants efficiently and without extensive manual engineering effort. In comparison to rewriting the automation software for each adaption, manual engineering effort can be reduced by reusable software components with free parameters, which must be adjusted to individual production scenarios. This paper introduces CyberOpt Online, a novel online parameter estimation approach for reusable automation software components. In contrast to classic mathematical modeling approaches, such as Mixed Integer Nonlinear Programming (MINLP), our approach requires no predefined models that represent the system. Models, e. g., of the energy consumption of CPPS, are learned automatically from data observed during the operation of the production system. Therefore, the manual engineering effort is minimized as postulated by the paradigm of CPPS. The presented approach combines MINLP, process mining and black-box optimization techniques for calculating optimal timing parameter configurations for automation software components with free parameters in the domain of discrete manufacturing.
Zusammenfassung
Cyber-physische Produktionssysteme (CPPS) sollten sich effizient und ohne große manuelle Engineering-Aufwände an neue Produkte oder Produktvarianten anpassen. Anstatt die Automatisierungssoftware bei jeder Anpassung umzuschreiben, können die manuellen Engineering-Aufwände durch wiederverwendbare Softwarekomponenten mit freien Parametern, welche an individuelle Produktionsszenarien angepasst werden müssen, reduziert werden. Im Gegensatz zu klassischen mathematischen Modellierungsansätzen, wie zum Beispiel gemischt-ganzzahlige nichtlineare Programmierung (MINLP), benötigt unser Ansatz keine vordefinierten Modelle, die das System beschreiben. Modelle, z.B. für den Energieverbrauch des CPPS, werden automatisch während des Betriebs des Produktionssystems aus beobachteten Daten gelernt. Dadurch werden manuelle Engineering-Aufwände, wie durch das CPPS-Paradigma postuliert, reduziert. Der vorgestellte Ansatz kombiniert MINLP-, Process-Mining- und Black-Box-Optimierungstechniken zur Berechnung optimaler Zeitparameter für Automatisierungssoftwarekomponenten mit freien Parametern im Bereich der diskreten Fertigung.
About the authors
Jens Otto received his B.Sc. and M.Sc. degrees in computer science from the University of Bielefeld, Germany, in 2008 and 2010 respectively. He is currently working towards a PhD degree at the Institute of Automation and Information Systems, Technical University of Munich, Garching, Germany, and is a group leader at Fraunhofer IOSB-INA in Lemgo, Germany.
Birgit Vogel-Heuser is a full professor and director of the Institute of Automation and Information Systems at the Technical University of Munich. Her main research interests are systems and software engineering, and modeling of distributed and reliable embedded systems. She is a coordinator of the Collaborative Research Centre SFB 768: Managing cycles in innovation processes – integrated development of product-service systems based on technical products, member of acatech, chair of VDI/VDE working group on industrial agents and vice chair of IFAC TC 3.1 computers in control.
Oliver Niggemann is a full professor of computer science at the Institute Industrial IT (inIT) of OWL University of Applied Sciences in Lemgo, Germany. Furthermore, he is the deputy head of Fraunhofer IOSB-INA, as well as one of the chairs of the International Graduate School of Intelligent Systems in Automation Technology (ISA) and the scientific head of the Graduate Center of OWL University of Applied Sciences.
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