• seit Mai 2021 Wissenschaftlicher Mitarbeiter am FBK
• Forschungsschwerpunkt: Digitale Technologien für Produktionssysteme
   

P. Ruediger-Flore, M. Glatt, M. Hussong, J.C. Aurich: CAD-based data augmentation and transfer learning empowers part classification in manufacturing. The International Journal of Advanced Manufacturing Technology 125 (2023): S. 5605–5618. 10.1007/s00170-023-10973-6 

M. Hussong, M. Glatt, J. C. Aurich: Deep Transfer Learning in der Arbeitsplanung - Konzept zur Anwendung von Deep Transfer Learning am Beispiel der Fertigungsvorgangsermittlung. WT Werkstatttechnik online 113/6 (2023): S.224-228. 10.37544/1436-4980-2023-06-16

P. Ruediger-Flore, M. Klar, M. Hussong, J. Mertes, L. Yi, M. Glatt, P. Kölsch, J. C. Aurich: Neural Radiance Fields in der Fabrikplanung - Untersuchung von Neural Radiance Fields zur Modellrekonstruktion in der Fabrikplanung. WT Werkstattstechnik  113/6 (2023) S.219-223 10.37544/1436-4980-2023-06-11

L. Yi, P. Langlotz, M. Hussong, M. Glatt, F. J. P. Sousa, J. C. Aurich: An integrated energy management system using double deep Q-learning and energy storage equipment to reduce energy cost in manufacturing under real-time pricing condition: A case study of scale-model factory. CIRP Journal of Manufacturing Science and Technology 38 (2022): S. 844-860.

M. Hussong, M. Glatt, P. Rüdiger-Flore, S. Varshneya, P. Liznerski, M. Kloft, J. C. Aurich: Deep Learning zur Unterstützung der Arbeitsplanung: Ein Konzept zur Ermittlung von Vorgangsfolgen durch künstliche neuronale Netze. ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 116/10 (2021): S. 648-651.

P. Ruediger-Flore, M. Klar, M. Hussong, A. Mukherjee, M. Glatt, J.C. Aurich:Comparing Binary Classification and Autoencoders for Vision-Based Anomaly Detection in Material Flow. Procedia CIRP 121 - Proceedings of the 11th CIRP Global Web Conference (2024): S. 138-143. 10.1016/j.procir.2023.09.241

M. Klar, P. Rüdiger, M. Scheidt, M. Hussong, M. Glatt , B. Ravani, Jan C. Aurich: Development of a Machine Learning Model that represents the characteristics of a Manufacturing Systems. Procedia CIRP 122 - Proceedings of the 31st CIRP Conference on Life Cycle Engineering (2024): S. 175-180. 10.1016/j.procir.2024.01.026

M. Hussong, S. Varshneya, P. Rüdiger-Flore, M. Glatt, M. Kloft, J.C. Aurich: A process planning system using deep artificial neural networks for the prediction of operation sequences. Procedia CIRP 120 - Proceedings of the 56th CIRP International Conference on Manufacturing Systems (2023): S. 135-140. 10.1016/j.procir.2023.08.025

P. Langlotz, M. Klar, L. Yi, M. Hussong, F. Sousa, J.C. Aurich: Concept of hybrid modeled digital twins and its application for an energy management of manufacturing systems. Procedia CIRP 112 - Proceedings of the 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering (2022): S. 549-554 DOI: 10.1016/j.procir.2022.09.098

M. Klar, M. Hussong, P. Ruediger, L. Yi, M. Glatt, J.C. Aurich: Scalability investigation of Double Deep Q Learning for factory layout planning. Procedia CIRP 107 - Proceedings of the 55th CIRP Conference on Manufacturing Systems (2022): S.161-166 DOI: 10.1016/j.procir.2022.04.027