Robert Maack, M.Sc.
Scientific Researcher
Area of Research:
- Digital Image Processing
- Machine Learning
- Artificial Intelligence in Smart Manufacturing and Process Scheduling
Biography
Robert Maack joined the Institute for Technologies and Management of Digital Transformation at the University of Wuppertal as a research assistant and doctoral student in September 2019.
Mr. Maack studied electrical engineering and information technology at the Ruhr University Bochum. As a major, he moved in the area of embedded systems with a focus on algorithm optimization for multicore architectures and FPGAs. In his master thesis he dealt with hardware reverse engineering of IC circuits using digital image processing and machine learning.
Publikationen
- 2025
- Maack, R. F., Thun, L., Liang, T., Tercan, H., & Meisen, T. (2025). "PCAD: A Real-World Dataset for 6D Pose Industrial Anomaly Detection" in Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops . 1132—1141.
- 2024
- Hahn, Y., Maack, R. F., Buchholz, G., Purrio, M., Angerhausen, M., Tercan, H., & Meisen, T. (2024). "Quality Prediction in Arc Welding: Leveraging Transformer Models and Discrete Representations from Vector Quantised—VAE" in CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , Serra, Edoardo and Spezzano, Francesca, Eds. New York, United States : Association for Computing Machinery
ISBN: 979-8-4007-0436-9
- 2023
- Hahn, Y., Maack, R. F., Buchholz, G., Purrio, M., Angerhausen, M., Tercan, H., & Meisen, T. (2023). "Towards a Deep Learning-based Online Quality Prediction System for Welding Processes" , Procedia CIRP , 120 , 1047—1052.
- 2022
- Maack, R. F., Tercan, H., & Meisen, T. (2022). "Deep Learning based Visual Quality Inspection for Industrial Assembly Line Production using Normalizing Flows" in 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) , IEEE 329—334.
ISBN: 978-1-7281-7568-3
- Maack, R. F., Puiseau, C., Sokolova, A., Atsbha, H., Tercan, H., & Meisen, T. (2022). "Reducing the Sim2Real-Gap in Extrusion Blow Molding using Random Forest Regressors" , Manufacturing Letters , 33 , 843—849.
