Marcin Sadurski

Projekte

• LLM-basierte Datenextraktion für den digitalen Produktpass / Projektleiter:in / laufend
• Dr. Chiller – Immersives AR-Framework für den First-Level-Wartungssupport / Projektleiter:in / laufend
• PRIME – Process Refinement for Improved Manufacturing Efficiency / Projektleiter:in / laufend
• SmartAssets - Digital Transformation in Industry 4.0 / Projektleiter:in / laufend
• From Supply Chain Data to Digital Twins: A GS1–AAS Interoperability Bridge / Projektleiter:in / abgeschlossen
• Human-Robot Teaming Cobotic Base Cell for Industrial Applications / Co-Projektleiter:in / abgeschlossen
• MaintAIn - AI supported hybrid Predictive Maintenance / Projektleiter:in / abgeschlossen
• Predictive Process Tuning - PreTune / Projektleiter:in / abgeschlossen

Publikationen

Schriftliche Konferenzbeiträge, peer-reviewed

• Fathi, K. et al. (2026) 'Next-gen contextual anomaly detection for sustainable and efficient production in Industry 4.0', in Barata, J., Madani, K., and Panetto, H. (eds). Cham: Springer, pp. 191–203. doi: 10.1007/978-3-032-15576-4_13.
• Fathi, K. et al. (2025) 'Towards Industry 5.0 : AAS/MLOps-driven model maintenance for data-centric production'. SciTePress, pp. 495–502. doi: 10.5220/0013716300003982.
• Braunisch, N. et al. (2025) 'Recognizing and integrating legacy assembly diagrams into Industry 4.0', in IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society. IEEE. doi: 10.1109/iecon58223.2025.11221583.
• Perez Olaya, S. S. et al. (2025) 'Digital twin in Industrie 4.0 implementation for embedded systems', in 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE). IEEE, pp. 1760–1765. doi: 10.1109/case58245.2025.11163805.
• Braunisch, N. et al. (2025) 'Leveraging software development of I4.0 digital twins for PLC programming', in 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE). IEEE. doi: 10.1109/isie62713.2025.11124616.
• Braunisch, N. et al. (2025) 'The link between programmable logic controllers and Industry 4.0 digital twins'. IEEE. doi: 10.1109/icecet63943.2025.11472600.
• Fathi, K. et al. (2024) 'Sustainability in semiconductor production via interpretable and reliable predictions', in IFAC-PapersOnLine. Elsevier, pp. 174–179. doi: 10.1016/j.ifacol.2024.07.213.
• Fathi, K. et al. (2024) 'Detection of novel asset failures in predictive maintenance using classifier certainty', in 2024 32nd Mediterranean Conference on Control and Automation (MED). IEEE, pp. 50–56. doi: 10.1109/med61351.2024.10566204.
• Fathi, K. et al. (2024) 'Domain adaptation via simulation parameter and data perturbation for predictive maintenance', in Do, P. and Ezhilarasu, C. (eds) Proceedings of the PHM Society European Conference 2024. PHM Society, pp. 389–399. doi: 10.36001/phme.2024.v8i1.3985.
• Grivet, K., Sadurski, M. and van de Venn, H. W. (2024) 'Smart factory production process using virtual cost optimization', in 2024 IEEE International Conference on Industrial Technology (ICIT). IEEE. doi: 10.1109/icit58233.2024.10540930.
• Braunisch, N. et al. (2024) 'Digital twin in Industrie 4.0 for embedded systems', in 2024 IEEE 20th International Conference on Factory Communication Systems (WFCS). IEEE. doi: 10.1109/wfcs60972.2024.10540968.
• Fathi, K. et al. (2023) 'Source component shift detection & classification for improved remaining useful life estimation in alarm-based predictive maintenance', in 2023 23rd International Conference on Control, Automation and Systems (ICCAS). IEEE, pp. 975–980. doi: 10.23919/iccas59377.2023.10316874.

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