Prof. Dr.-Ing. Jörg Franke

Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)

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 Development and Validation of a Material Application Tool for the Covering Process of a Wire Harness (2020) Heisler P, Hausecker E, Regler J, Süß-Wolf R, Franke J Conference contribution Prediction of the Joint Cross-Section of Laser-Welded Hairpin Windings Based on 2D Image Data Using Convolutional Neural Networks (2020) Mayr A, Hauck L, Meiners M, Franke J Conference contribution, Conference Contribution Towards an Inline Quality Monitoring for Crimping Processes Utilizing Machine Learning Techniques (2020) Meiners M, Mayr A, Kuhn M, Raab B, Franke J Conference contribution