M.Sc.

Hans Aoyang Zhou

13B82560E584E5E68E93A2D486C089B6
Room
D 2.09
Phone
+49 241 80 91144
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Hans Zhou joined the Cybernetics Lab at the RWTH Aachen as a research associate in February 2020. In May 2019 Mr. Zhou successfully completed his studies in Mechanical and Process Engineering at the TU Darmstadt. His research interests lies in the application of machine learning for production processes and mechatronic systems. In his dissertation, Mr. Zhou investigates how to predict laser powder bed fusion processes with generative models using in situ monitoring data.

Recent Publications

A Conceptual Framework for Production Process Parameter Optimization with Modular Hybrid Simulations
Olbrych, Sylwia; Kemmerling, Marco; Zhou, Hans; Lütticke, Daniel; Schmitt, Robert
Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops, Masci, Paolo; Bernardeschi, Cinzia; Graziani, Pierluigi; Koddenbrock, Mario; Palmieri, Maurizio, Springer, Cham, pp. 17-25
2023
Controlled Synthesis of Fibre-reinforced Plastics Images from Segmentation Maps using Generative Adversarial Neural Networks
Schaaf, Nicolas; Zhou, Hans; Enslin, Chrismarie; Brillowski, Florian; Lütticke, Daniel
Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, SciTePress, Online, pp. 801-809
2022
Application of Transfer Learning for Detecting Fiber Orientations in Images of Fiber Reinforced Plastics
Zhou, Hans; Brillowski, Florian; Greb, Christoph; Lütticke, Daniel
Materials Science Forum, Trans Tech Publications Ltd, Singapur, pp. 79-85
2022
Evaluating the Communication Infrastructure of a Digital Process Platform for Future Construction Sites
Gannouni, Aymen; Otte, Thomas; Zhou, Hans; Hosan, Arif; Odenthal, Jonas; Abdelrazeq, Anas; Neumann, Stephan; Jacobs, Georg; Hees, Frank
2022 2nd International Conference on Computing and Machine Intelligence (ICMI)
2022
Explanations of Performance Differences in Segment Lining for Tunnel Boring Machines
Zhou, Hans; Gannouni, Aymen; Bazazo, Tala; Tröndle, Johannes; Abdelrazeq, Anas; Hees, Frank
Intelligent Data Engineering and Automated Learning - IDEAL 2022, Springer, Manchester, pp. 122-133
2022