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Franz Listl

M.Sc.

Doktorand
Institut für Automatisierungstechnik und Softwaresysteme

Kontakt

E-Mail

Siemens AG
Otto-Hahn-Ring 6
81739 München

Publikationen

Zeitschriften und Konferenzen:

2021
1. F. G. Listl, J. Fischer, R. Rosen, A. Sohr, J. C. Wehrstedt, und M. Weyrich, „Decision Support on the Shop Floor Using Digital Twins: Architecture and Functional Components for Simulation-Based Assistance“, in International Conference on Advances in Production Management Systems, (APMS), Nantes, Frances, 5-9 Sept. 2021, published in: Dolgui A., Bernard A., Lemoine D., von Cieminski G., Romero D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilent Production Systems IFIPAICT, vol. 630, pp. 284-292, Springer, Cham, 2021.
  Zusammenfassung
  Increased flexibility and improved resilience in production and manufacturing processes are goals that are becoming more and more important in the context of Industry 4.0 and from the experience of the Covid-19 pandemic. At the same time, efficient operation of production must be guaranteed to achieve economic as well as ecological objectives. Intelligent assistance systems follow the idea to support stakeholders of production systems in their decisions and can thus be useful applications for helping to master the various challenges and to meet these goals. In this paper, we describe functional components of these decision support systems: data provision and data extraction, knowledge base, simulation models, model execution and analytics, and application and user interaction. We show the underlying technologies and illustrate why these assistant systems are valuable for several stakeholders.
  BibTeX
  @conference{listl2021decision, abstract = {Increased flexibility and improved resilience in production and manufacturing processes are goals that are becoming more and more important in the context of Industry 4.0 and from the experience of the Covid-19 pandemic. At the same time, efficient operation of production must be guaranteed to achieve economic as well as ecological objectives. Intelligent assistance systems follow the idea to support stakeholders of production systems in their decisions and can thus be useful applications for helping to master the various challenges and to meet these goals. In this paper, we describe functional components of these decision support systems: data provision and data extraction, knowledge base, simulation models, model execution and analytics, and application and user interaction. We show the underlying technologies and illustrate why these assistant systems are valuable for several stakeholders.}, author = {Listl, Franz Georg and Fischer, Jan and Rosen, Roland and Sohr, Annelie and Wehrstedt, Jan Christoph and Weyrich, Michael}, booktitle = {International Conference on Advances in Production Management Systems, (APMS), Nantes, Frances, 5-9 Sept. 2021, published in: Dolgui A., Bernard A., Lemoine D., von Cieminski G., Romero D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilent Production Systems IFIPAICT, vol. 630, pp. 284-292, Springer, Cham}, doi = {10.1007/978-3-030-85874-2_30}, title = {Decision Support on the Shop Floor Using Digital Twins: Architecture and Functional Components for Simulation-Based Assistance}, url = {https://link.springer.com/chapter/10.1007%2F978-3-030-85874-2_30}, year = 2021 }
  Link
  https://link.springer.com/chapter/10.1007%2F978-3-030-85874-2_30
2020
1. F. G. Listl, J. Fischer, D. Beyer, und M. Weyrich, „Knowledge Representation in Modeling and Simulation: A survey for the production and logistic domain“, 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 8-11 Sept. 2020, Vienna, Austria, pp. 1051-1056, 2020.
  Zusammenfassung
  Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.
  BibTeX
  @article{listl2020knowledge, abstract = {Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.}, author = {Listl, F. G. and Fischer, J. and Beyer, D. and Weyrich, M.}, doi = {10.1109/ETFA46521.2020.9211994}, journal = {25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 8-11 Sept. 2020, Vienna, Austria, pp. 1051-1056}, title = {Knowledge Representation in Modeling and Simulation: A survey for the production and logistic domain}, url = {https://ieeexplore.ieee.org/abstract/document/9211994}, year = 2020 }
  Link
  https://ieeexplore.ieee.org/abstract/document/9211994
2019
1. T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, und K. Janschek, „Analytical toolset for model-based stochastic error propagation analysis: extension and optimization towards industrial requirements“, Системная инженерия и информационные технологии, 1(1), pp.41-46, 2019.
  Zusammenfassung
  Model-Based System Engineering (MBSE) is a popular mathematical and visual approach to the design of complex control, signal processing, and communication systems. It is used in safety critical industrial domains including aerospace, automotive, transportation, medical and robotics applications. Our group develops methods and tools for model-based system reliability and safety analysis with the main focuses on stochastic modelling of error propagation processes. This article is devoted to the optimisation and extensions to our analytical toolset. We have investigated the key modeling paradigms, requirements and industrial needs and have formulated the list of particular extensions.
  BibTeX
  @article{fabarisov2019, abstract = {Model-Based System Engineering (MBSE) is a popular mathematical and visual approach to the design of complex control, signal processing, and communication systems. It is used in safety critical industrial domains including aerospace, automotive, transportation, medical and robotics applications. Our group develops methods and tools for model-based system reliability and safety analysis with the main focuses on stochastic modelling of error propagation processes. This article is devoted to the optimisation and extensions to our analytical toolset. We have investigated the key modeling paradigms, requirements and industrial needs and have formulated the list of particular extensions.}, author = {Fabarisov, T. and Yusupova, N. and Ding, K. and Morozov, A. and Janschek, K.}, journal = {Системная инженерия и информационные технологии, 1(1), pp.41-46}, title = {Analytical toolset for model-based stochastic error propagation analysis: extension and optimization towards industrial requirements}, url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2019_ANALYTICAL_TOOLSET_FOR_MODEL-BASED_STOCHASTIC_ERROR_PROPAGATION_ANALYSIS-EXTENSION_AND_OPTIMIZATION_TOWARDS_INDUSTRIAL_REQUIREMENTS.pdf}, year = 2019 }
  Link
  https://www.ias.uni-stuttgart.de/dokumente/publikationen/2019_ANALYTICAL_TOOLSET_FOR_MODEL-BASED_STOCHASTIC_ERROR_PROPAGATION_ANALYSIS-EXTENSION_AND_OPTIMIZATION_TOWARDS_INDUSTRIAL_REQUIREMENTS.pdf
2. F. Biesinger und M. Weyrich, „The Facets of Digital Twins in Production and the Automotive Industry“, in 23rd International Conference on Mechatronics Technology – ICMT 2019, 2019, 23rd-26th. October, Salerno, Italy, 2019.
  Zusammenfassung
  The digital twin is a great term that is frequently used in the context of digitalization and Industry 4.0. In discussions with other researchers and colleagues about the digital twin, it oftentimes becomes clear that everyone understands something different about the digital twin. This paper therefore compares the different facets of digital twins in the industrial environment of production. Different applications are shown, and a list of added values of a digital twin is derived based on these applications. The paper also covers the different facets of digital twins in the automotive industry and evaluates their practical benefits. In addition, the different technologies for realizing a digital twin are examined, and a concept for modeling the lifecycle of a production line via the digital twin is presented.
  BibTeX
  @conference{biesinger2019facets, abstract = {The digital twin is a great term that is frequently used in the context of digitalization and Industry 4.0. In discussions with other researchers and colleagues about the digital twin, it oftentimes becomes clear that everyone understands something different about the digital twin. This paper therefore compares the different facets of digital twins in the industrial environment of production. Different applications are shown, and a list of added values of a digital twin is derived based on these applications. The paper also covers the different facets of digital twins in the automotive industry and evaluates their practical benefits. In addition, the different technologies for realizing a digital twin are examined, and a concept for modeling the lifecycle of a production line via the digital twin is presented.}, author = {Biesinger, F. and Weyrich, M.}, booktitle = {23rd International Conference on Mechatronics Technology – ICMT 2019, 2019, 23rd-26th. October, Salerno, Italy}, title = {The Facets of Digital Twins in Production and the Automotive Industry}, url = {https://ieeexplore.ieee.org/document/8932101}, year = 2019 }
  Link
  https://ieeexplore.ieee.org/document/8932101