Institute of Forming Technology, University of Stuttgart Germany
Hans Dimitri Papdo Tchasse, born on July 14, 1996 in Cameroon 🇨🇲, is a dynamic scientific researcher in the field of forming technology, digitalization, and artificial intelligence. Currently based in Germany 🇩🇪, he works as a Research Associate at the Institute for Metal Forming Technology, University of Stuttgart. With a strong background in mechatronics and AI-driven manufacturing, he bridges academic research and industrial innovation to optimize forming processes and predictive control systems in manufacturing.
Profile
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🎓 Education
Hans pursued his Bachelor’s and Master’s degrees in Mechatronics from Friedrich-Alexander University Erlangen-Nuremberg (FAU) between 2016 and 2022. His Master’s thesis, graded 1.3, focused on predicting process quality using machine learning, while his Bachelor’s thesis (graded 1.7) involved autonomous collision avoidance systems for aerial robots. He also completed intensive German language training at Karlsruhe Institute of Technology (DSH 3) and the Goethe Institute Yaoundé (B1 certificate) to facilitate his academic career in Germany. 📘🔧
💼 Professional Experience
Since July 2022, Hans has been working at the University of Stuttgart, where he leads several AI-driven research projects in forming technology and supervises students. His work emphasizes process simulation, sensor technology, and digital automation. Previously, at Siemens AG – Digital Industries, he contributed to the development of HMI interfaces, PLC programming, and machine learning applications for quality optimization. His blend of industrial and academic experience uniquely positions him to innovate in the manufacturing sector. 🏭💡
🔬 Research Interests
Hans is passionate about advancing digital manufacturing, with research focused on:
- Metal forming and shear cutting
- Sensor-based process monitoring
- Artificial Intelligence in manufacturing
- Deep learning for quality prediction
- Human-centered smart factories His projects aim to make production more adaptive, efficient, and intelligent, promoting sustainability and digital transformation in the automotive and metal industries. 🤖📊
🏆 Awards & Nominations
Hans is a promising young innovator being nominated for this award due to his cutting-edge contributions in intelligent forming technologies and real-world application of AI in mechanical engineering. His interdisciplinary expertise, leadership in research, and publications in reputable conferences make him a strong candidate for distinction. 🌟👏
📚 Publications Top Notes:
Hans has authored several high-impact publications in international conferences and journals, reflecting his interdisciplinary expertise.
Here’s a list of selected publications:
Detection of Defective Deep Drawn Sheet Metal Parts by Using Machine Learning Methods for Image Classification – WGP 2023 📅 (Cited by: 4)
Temperature Prediction of Multi-Stage Cold Forging Processes Using Deep Learning – SENAFOR 2024 (upcoming) 📅
Development of an Intelligent Metal Forming Robot and Application to Multi-Stage Cold Forging – Submitted 📤
Supervised Learning Methods for the Monitoring and Prediction of the Part Quality of Multi-Stage Cold Forging Processes – ICFG 2024 (accepted) 📅
Material Characterization for Sheet Metal Forming Processes Using Deep Learning Methods for Time Series Processing – TMS 2025 (upcoming) 📅
Simulative Design of a Model-Driven Control Strategy for Deep Drawing Processes and Numerical Validation Using Deep Learning – WGP 2024 (accepted) 📅
Monitoring and Prediction of the Process Energy in Multi-Stage Cold Forging Using Recurrent and Self-Attention Based Neural Networks – Submitted 🧠