Prof. Dr.-Ing. Jörg Franke

Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)

Development and application of load profiles for thermal qualification testing of receptacle automotive connectors (2021) Friedlein M, Gräf D, Stegner J, Franke J Journal article A strategic approach to improve the development of use-oriented knowledge-based engineering configurators (KBEC) (2021) Schäffer E, Shafiee S, Mayr A, Franke J Journal article, Review article Process-Driven Approach within the Engineering Domain by Combining Business Process Model and Notation (BPMN) with Process Engines (2021) Schäffer E, Stiehl V, Schwab P, Mayr A, Lierhammer J, Franke J Journal article, Review article Seven Levels of Detail to structure use cases and interaction mechanism for the development of industrial Virtual Reality applications within the context of planning and configuration of robot-based automation solutions (2021) Schäffer E, Metzner M, Pawlowskij D, Franke J Conference contribution Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems (2021) Bakakeu J, Baer S, Klos HH, Peschke J, Brossog M, Franke J Book chapter / Article in edited volumes RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments (2021) Kedilioglu O, Lieret M, Schottenhamml J, Würfl T, Blank A, Maier A, Franke J Conference contribution Enhanced X-Ray Inspection of Solder Joints in SMT Electronics Production using Convolutional Neural Networks (2020) Schmidt K, Thielen N, Voigt C, Seidel R, Franke J, Milde Y, Bonig J, Beitinger G Conference contribution Data Mining System Architecture for Industrial Internet of Things in Electronics Production (2020) Seidel R, Hassan Amada M, Fuchs J, Thielen N, Schmidt K, Voigt C, Franke J Conference contribution Development and Test of a Data Framework for Prediction of Soldering Quality in Selective Wave Soldering Applying K-Nearest Neighbors (2020) Seidel R, Thielen N, Schmidt K, Voigt C, Franke J Conference contribution In-situ identification of material batches using machine learning for machining operations (2020) Lutz B, Kißkalt D, Mayr A, Regulin D, Pantano M, Franke J Journal article