Dr.-Ing. Daniel Schoepflin
Contact
E-Mail Dr.-Ing. Daniel Schoepflin
Career
| 2019 - 2023 | Scientific employee at the Institute for Aircraft Production Technology |
| 2016 - 2019 | Master's degree in "Mechatronics" at the TUHH |
| 2018 | Semester abroad at National Taiwan University of Science and Technology |
| 2016 | Internship SKF Marine GmbH |
| 2013 - 2016 | Bachelor's degree in "Mechanical Engineering" at the TUHH |
Research
Synthetic generation of AI training data for industrial processes ( ILIdenT)
Visual object identification should increasingly be used for the comprehensive digitalization of production processes. In line with the current trend, neural networks are used as the basic architecture for AI object identification. Such neural networks must be trained with targeted data in order to map the diverse and specific process variants in the industry. These large amounts of data are usually not available in companies and manual generation of labeled data is uneconomical. The IFPT is therefore developing methods for generating AI training data for industrial processes. As part of the DEPOT and ILIdenT projects, a toolbox for generating synthetic training data is being developed developed with which individual data sets can be generated for visual applications in intralogistics.
| smart load carrier unit for production automation (DEPOT) As part of the project “Digitale Edevelopment, Production, Logistics and Transport" (DEPOT), the research consortium pursues a holistic digitalization of development, production and logistics. Processes in the aviation industry. This is intended to ensure transparency, plannability and quality of the processes at the interface between logistics and production. For this purpose, the IFPT is developing a modular and intelligent load carrier (a smart MDU) |
Teaching
Winter 19/20 team project mechanical engineering
SoSe 20 - WiSe 21/22 MSR Laboratory
Winter 20/21 - WiSe 22/23 Robotics
D. Schoepflin, M. Brand, M. Gomse, T. Schüppstuhl: Towards Visual Referencing for Location Based Services in Industrial Settings; Proceedings of the 52nd International Symposium on Robotics, 2020 VDE
M. Stender, M. Tiedemann, D. Spieler, D. Schoepflin, N. Hofmann, S. Oberst: Deep learning for brake squeal: vibration detection, characterization and prediction; Mechanical Systems and Signal Processing, 2021 (Link)
D. Schoepflin, A. Wendt, T. Schüppstuhl: Daten zur richtigen Zeit am richtigen Ort, Industrial Production. 2020. Jg, 1, Nr. 12, S. 46-47 (Link)
J. Gierecker, D. Schoepflin, M. Gomse, T. Schüppstuhl: Configuration and Enablement of Vision Sensor Solutions through a Combined Simulation Based Process Chain; Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021, Springer
D. Schoepflin, K. Iyer, M. Gomse, T. Schüppstuhl: Towards Synthetic AI Training Data for Object Identifiers in Intralogistic Settings; Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021, Springer
[accepted] D. Schoepflin. J. Koch, M. Gomse, T. Schüppstuhl: Smart Material Delivery Unit for the Production Supplying Logistics of Aircrafts; Procedia Manufacturing, 2021, Elsevier
[accepted] D. Schoepflin. D. Holst, M. Gomse, T. Schüppstuhl: Synthetic Training Data Generation for Visual Object Identification on Load Carriers; Procedia CIRP, 2021, Elsevier
Vorträge
M. Gomse, D. Schoepflin: Automatisierte Bilddatengenerierung als Trainingsdatensatz für eine KI-Objektidentifikation in der Intralogistik; 29. Hamburger Logistik-Kolloquium, 2020, Hamburg
D. Schoepflin: Massive Bilddatengenerierung als Trainingsdatensatz für eine KI-Objektidentifizierung in der Intralogistik; Machine Learning in Engineering, Train your Engineering Network, 2020, Hamburg (Video)
D. Schoepflin: Towards Visual Referencing for Location Based Services in Industrial Settings; 52nd International Symposium on Robotics, 2020, Munich
D. Schoepflin: Towards Synthetic AI Training Data for Object Identifiers in Intralogistic Settings; 5. Fachkolloquium der Wissenschaftlichen Gesellschaft für Montage, Handhabung und Industrierobotik; 2021 Hannover