wbk

Robin Ströbel, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Robin Ströbel, M.Sc.

Forschungs- und Arbeitsgebiete:

  • Software-defined Manufacturing und digitale Zwillinge
  • Themenbereiche der Industrie 4.0

Allgemeine Aufgaben:

Projekte:

  • SDM4FZI: Software-defined Manufacturing in der Fahrzeug- und Zulieferindustrie
  • SDMflex: Flexible SDM through Continuously Self-Learning Quality-Aware Digital Twins

Lebenslauf:

seit 04/2022

Wissenschaftlicher Mitarbeiter am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT) 

10/2019-03/2022 

Studium des Maschinenbaus (M.Sc.) am Karlsruher Institut für Technologie (KIT)

09/2017-01/2018 

Auslandsstudium an der Jiaotong-Universität Shanghai (SJTU)

10/2015-09/2019 

Studium des Maschinenbaus (B.Sc.) am Karlsruher Institut für Technologie (KIT)

Veröffentlichungen


Demands resulting from climate change and rising energy costs compel companies to handle resources more consciously along their value chains.; [Vom Verbrauchsmonitoring zur Verbrauchsprognose Untersuchung des Umgangs produzierender Unternehmen mit Energieverbrauchsmonitoring und vorhersage]
Ströbel, R.; Bott, A.; Hutt, L.; Groß, S.; Fleischer, J.
2024. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 119 (1-2), 80 – 84. doi:10.1515/zwf-2024-1009
Interoperable system for automated extraction and identification of machine control data in brownfield production
Gönnheimer, P.; Ströbel, R.; Dörflinger, R.; Mattes, M.; Fleischer, J.
2023. Manufacturing Letters, 35, 915 – 925. doi:10.1016/j.mfglet.2023.08.010
Training and validation dataset 2 of milling processes for time series prediction
Ströbel, R.; Mau, M.; Deucker, S.; Fleischer, J.
2023, September 15. doi:10.35097/1738
Software-Defined Manufacturing for the Entire Life Cycle at Different Levels of Production
Behrendt, S.; Martin, M.; Puchta, A.; Ströbel, R.; Fisel, J.; May, M.; Gönnheimer, P.; Fleischer, J.; Lanza, G.
2023. Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains : Stuttgart Conference on Automotive Production (SCAP2022). Ed.: N. Kiefl, 25–34, Springer International Publishing. doi:10.1007/978-3-031-27933-1_3
Training and validation dataset of milling processes for time series prediction
Ströbel, R.; Probst, Y.; Fleischer, J.
2023, April 14. doi:10.5445/IR/1000157789
Generalizability of an Identification Approach for Machine Control Signals in Brownfield Production Environments
Gönnheimer, P.; Ströbel, R.; Dörflinger, R.; Mattes, M.; Alexander, P.; Wuest, T.; Fleischer, J.
2023. Procedia CIRP, 120, 649 – 654. doi:10.1016/j.procir.2023.09.053
A Model-Driven Digital Twin for Manufacturing Process Adaptation
Spaney, P.; Becker, S.; Ströbel, R.; Fleischer, J.; Zenhari, S.; Möhring, H.-C.; Splettstößer, A.-K.; Wortmann, A.
2023. 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Västerås, 1st-6th October 2023, 465 – 469, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MODELS-C59198.2023.00081
Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis
Ströbel, R.; Bott, A.; Wortmann, A.; Fleischer, J.
2023. Machines, 11 (11), Art.Nr.: 1032. doi:10.3390/machines11111032
Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models
Ströbel, R.; Probst, Y.; Deucker, S.; Fleischer, J.
2023. Machines, 11 (11), Art.Nr.: 1015. doi:10.3390/machines11111015
Potential of systematically generated training datasets on the accuracy and generalization of AI-based approaches for the automated identification of machine control signals
Gönnheimer, P.; Ströbel, R.; Roßkopf, A.; Dörflinger, R.; Walter, I.; Becker, J.; Fleischer, J.
2023. 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering CIRP ICME ‘22, Italy. Hrsg.: R. Teti, D. D’Addona, 145 – 150, Elsevier. doi:10.1016/j.procir.2023.06.026
Software-Defined Workpiece Positioning for Resource-Optimized Machine Tool Utilization
Ströbel, R.; Probst, Y.; Hutt, L.; Fleischer, J.
2023. Journal of Machine Engineering, 23 (1), 71–84. doi:10.36897/jme/161660
Generation of identifiable CNC reference runs with high information content for machine learning and analytic approaches to parameter identification
Gönnheimer, P.; Ströbel, R.; Netzer, M.; Fleischer, J.
2022. Procedia CIRP, 107, 734–739. doi:10.1016/j.procir.2022.05.054