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


Part series dataset of milling processes for time series prediction
Ströbel, R.; Kader, H.; Mau, M.; Deucker, S.; Bless, D.; Oexle, F.
2025, April 9. doi:10.35097/ctvuj6dgzepzmk0g
Intelligente Prozessüberwachung für die flexible Produktion – Integration von Physics-Informed Machine Learning und Active Learning
Ströbel, R.; Kader, H.; Hutt, L.; Zhou, H.; Mau, M.; Puchta, A.; Noack, B.; Fleischer, J.
2025. Zeitschrift für wirtschaftlichen Fabrikbetrieb, 120 (s1), 224–231. doi:10.1515/zwf-2024-0154
Finding Predictive Features for Energy Consumption of CNC Machines
Kader, H.; Ströbel, R.; Puchta, A.; Fleischer, J.; Noack, B.; Spiliopoulou, M.
2024. GFaI Tagungsband 2024. Hrsg.: A. Iwainsky, 89–95, Gesellschaft zur Förderung der Abwassertechnik (GFA)
Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes
Kader, H.; Ströbel, R.; Puchta, A.; Fleischer, J.; Noack, B.; Spiliopoulou, M.
2024. 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Pilsen, Czech Republic, 04-06 September 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MFI62651.2024.10705783
Training and validation dataset 3 of milling processes for time series prediction
Ströbel, R.; Mau, M.; Kader, H.; Erd, D.; Bless, D.; Deucker, S.; Puchta, A.; Fleischer, J.; Noack, B.
2024, Juni 18. doi:10.35097/feFwILjideOropmh
Enhancing efficiency and environmental performance of laser-cutting machine tools: An explainable machine learning approach
Krause, A.; Dannerbauer, T.; Wagenmann, S.; Tjaden, G.; Ströbel, R.; Fleischer, J.; Albers, A.; Bursac, N.
2024. Procedia CIRP, 130, 1674–1679. doi:10.1016/j.procir.2024.10.299
Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling
Ströbel, R.; Mau, M.; Puchta, A.; Fleischer, J.
2024. Machine Learning and Knowledge Extraction, 6 (2), 1072–1086. doi:10.3390/make6020049
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
Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability
Bott, A.; Anderlik, S.; Ströbel, R.; Fleischer, J.; Worthmann, A.
2024. Machines, 12 (3), Art.-Nr.: 153. doi:10.3390/machines12030153
Towards a Testing Framework for Machine Learning Model Deployment in Manufacturing Systems
Heider, I.; Baumgärtner, J.; Bott, A.; Ströbel, R.; Puchta, A.; Fleischer, J.
2024. 10th CIRP Conference on Assembly Technology and Systems (CIRP CATS 2024) Hrsg.: Fleischer , Jürgen; Jörg, Krüger, 127, 122–128. doi:10.1016/j.procir.2024.07.022
Analytical approach for parameter identification in machine tools based on identifiable CNC reference runs
Gönnheimer, P.; Ströbel, R.; Fleischer, J.
2023. Production at the Leading Edge of Technology – Proceedings of the 12th Congress of the German Academic Association for Production Technology (WGP), University of Stuttgart, October 2022. Ed.: M. Liewald, 494–503, Springer International Publishing. doi:10.1007/978-3-031-18318-8_50
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