| Research project: | "DEPOT" - Digitale Edevelopment, Production, lOgistics and Transport |  |
| Research area: | Digitization, sensors, data management | |
| Supported by: | Federal Ministry of Economics and Technology (LuFoIV-3) | |
| In collaboration with: | Airbus Operations GmbH, SAP, Liebherr, AOA, Fraunhofergesellschaft, TUHH Institute for Technical Logistics, Favendo, DAKO | |
| Start of project: | January 2018 | |
| End of project: | June 2021 |
Description:
As part of the project “Digital Edevelopment, Production, L ogistics and Transport” (DEPOT), the research consortium is pursuing 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), which can fulfill functions for a digitalized logistics process:
• Identification of the load
• Communication with a higher-level control system
• Human-machine interaction with workers
In order to enable sustainable retrofitting of existing load carrier fleets, the IFPT is developing smart sensor shelves (Smart Sensor Boards), which replace existing shelves. The Smart Sensor Boards can be equipped with various modules as required. Bluetooth low energy and RFID modules are available. In addition, an AI module enables marker-free identification of components.
The Smart Sensor Boards were manufactured in prototype form and different designs for demonstrators. A battery pack was also designed and implemented, which enables long-term autonomous operation of a smart grid trolley.
In addition, integration into the modular load carriers implemented by the partner institute ITL.
Veröffentlichungen
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
D. Schoepflin, A. Wendt, T. Schüppstuhl: Daten zur richtigen Zeit am richtigen Ort, Industrial Production. 2020. Jg, 1, Nr. 12, S. 46-47
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
Studierende mit Interesse an folgenden Themen können sich gerne zwecks einer Abschlussarbeit bei Daniel Schoepflin (daniel.schoepflin(at)tuhh(dot)de) melden:
• Entwicklung, Erprobung, Implementierung von IoT Hardware
• Sensorik zur Identifikation von Bauteilen
• Microcontroler (Arduino, ESP32) zur Anbindung der Sensorik an die Haupt CPU
• Digitalisierte Produktionsabläufe, Schnittstellen zwischen digitalisierten und manuellen Prozessen
• Künstliche Intelligenz zur Objektidentifizierung
• Design minimalistischer Neuronale Netzwerke für Kleinstanwendungen
• Generierung synthetischer Trainingsdaten für industrielle Kontexte
Ansprechpartner am Institut: M.Sc. Daniel Schoepflin