Model-based systems engineering has gained increasing application in the industrial development of a large number of technical systems. The use of various models is decisive for interdisciplinary innovations. However, it also comprises many challenges. The first challenge is the heterogeneous model landscape, which is characterized in particular by overlapping, partially redundantly modelled information. Second, the development, production and service processes are constantly subject to internal and external development cycles. To overcome these challenges, various methods and techniques can be used. In this paper, different approaches are investigated regarding their advantages and limitations: inconsistency management of coupled models in engineering, cross-disciplinary management of the engineering workflow, and the importance of smart data approaches.
Challenges of interdisciplinary development of Cyber-Physical Production Systems / Vogel-Heuser, B.; Fantuzzi, C.; Wimmer, M.; Bohm, M.; Fay, A.. - In: AUTOMATISIERUNGSTECHNIK. - ISSN 0178-2312. - 67:6(2019), pp. 445-454. [10.1515/auto-2018-0144]
Challenges of interdisciplinary development of Cyber-Physical Production Systems
Fantuzzi C.;
2019
Abstract
Model-based systems engineering has gained increasing application in the industrial development of a large number of technical systems. The use of various models is decisive for interdisciplinary innovations. However, it also comprises many challenges. The first challenge is the heterogeneous model landscape, which is characterized in particular by overlapping, partially redundantly modelled information. Second, the development, production and service processes are constantly subject to internal and external development cycles. To overcome these challenges, various methods and techniques can be used. In this paper, different approaches are investigated regarding their advantages and limitations: inconsistency management of coupled models in engineering, cross-disciplinary management of the engineering workflow, and the importance of smart data approaches.
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