Summary of Most Relevant Topic Papers
Systems engineering is the interdisciplinary engineering and management that focuses on the design and management of complex CPS over their life cycles. Systems engineering can and should be applied to a great variety of fields from automotive, to avionics, robotics, medical devices, production systems, and so on.
We have a long tradition on contributing to model-based systems engineering in automotive [FND+98] and [GHK+08a]. We have been able to condense these into a new comprehensive model-driven development process for automotive software including function testing with the BMW Group [KMS+18], [DGH+19]. In this, we leverage SysML to enable the vertical flow starting with informal requirements through various kinds of modeled viewpoints to implementations, which was well-received by the software developers.
Moreover, we are conducting intensive research to intensivy model-driven systems engineering that leverages methods and concepts from software engineering to make the systematic engineering of CPS more efficient. To facilitate modeling products, resources, and processes in the context of Industry 4.0 we also conceived a multi-level framework for production engineering based on our modeling concepts [BKL+18].
In a holistic model-driven engineering approach for CPS, we bridge the gap between functions and the physical product architecture to enable agile development driven by automation by modeling mechanical functional architectures in SysML [DRW+20]. For that purpose we also did a detailed examination of the upcoming SysML 2.0 standard [JPR+22].
[BBR20] contains an examination of the current state-of-the-art of advanced systems engineering techniques, argues that the use of models in dedicated modeling languages serves as essential basis for advanced techniques in systems engineering, and finally discusses how the SPES/CrEST methodology can be advanced to fully assist a software as well as a physical system engineering approach.
A highly relevant part of physical systems will in the future be their Digital Twin. We use Digital Twins in a broader sense, not only for simulation, but also for collecting data and providing aggregation and information services about the physical twin. Our RWTH’ Excellence cluster Internet of Production considers fast decision making at production time with low latencies using contextual data traces of production systems, also known as Digital Shadows (DS) [SHH+20]. We have investigated how to derive Digital Twins and applied this for example for injection molding [BDH+20]. We generalized this approach in a technique allowing us to generate interfaces between a cyber-physical system and its Digital Twin [KMR+20] and have proposed model-driven architectures for efficient engineering of a Digital Twin Cockpit [DMR+20], which cumulates the connection to the physical twin, the collection and storage of the operation data, and finally the aggregation and visualization of information in order to understand, control and optimize the physical machine.
- Model-Driven Systems Engineering is interdisciplinary, including engineering and management of complex CPS largely based and driven by models.
- SysML is capable of modeling mechanical functional architectures together wit their controlling software.
- A Digital Twin should not only be for simulation, but also for collecting data and providing aggregation and information services about the physical twin.
- A Digital Twin Cockpit contains the user interface of such digital twin with its services, visualizations and potentially control and optimization options.
Selected Topic-Specific Publications
[BBR20]fortiss. Forschungsinstitut für softwareintensive Systeme, Munich, Jul. 2020.
[BDH+20]In: International Conference on Advanced Information Systems Engineering (CAiSE’20), S. Dustdar, E. Yu, C. Salinesi, D. Rieu, V. Pant (Eds.), Volume 12127, pp. 85-100, Lecture Notes in Computer Science, Springer International Publishing, Jun. 2020.
[BKL+18]Multi-Level Modeling Framework for Machine as a Service Applications Based on Product Process Resource Models.In: Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control (ISCSIC’18), ACM, Sep. 2018.
[DGH+19]In: Software: Practice and Experience, R. Buyya, J. Bishop, K. Cooper, R. Jonas, A. Poggi, S. Srirama (Eds.), Volume 49(2), pp. 301-328, Wiley Online Library, Feb. 2019.
[DMR+20]In: Conceptual Modeling, G. Dobbie, U. Frank, G. Kappel, S. W. Liddle, H. C. Mayr (Eds.), pp. 377-387, Springer International Publishing, Oct. 2020.
[DRW+20]In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pp. 79-89, ACM, Oct. 2020.
[FND+98]In: SAE’98, Cobo Center (Detroit, Michigan, USA), Society of Automotive Engineers, 1998.
[GHK+08a]In: Tagungsband des Dagstuhl-Workshop MBEES: Modellbasierte Entwicklung eingebetteter Systeme IV, Informatik Bericht 2008-02, TU Braunschweig, 2008.
[JPR+22]In: Journal of Object Technology, Volume 21, AITO - Association Internationale pour les Technologies Objets, Jul. 2022.
[KMR+20]Model-driven Digital Twin Construction: Synthesizing the Integration of Cyber-Physical Systems with Their Information Systems.In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pp. 90-101, ACM, Oct. 2020.
[KMS+18]In: International Conference on Software Engineering: Software Engineering in Practice (ICSE’18), pp. 172-180, ACM, Jun. 2018.
[SHH+20]In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, W. Bauer, W. Volk, M. Zäh (Eds.), Volume 115(special), pp. 105-107, Carl Hanser Verlag, Munich, Apr. 2020.
- Generative Software Engineering
- MontiCore - Language Workbench
- Robotics Architectures and Tasks
- Digital Twins and Digital Shadows in Engineering, Operation and Production
- Internet of Things (IoT)