Dr. Nevzat Bircan BuğdaycıPostdoctoral Researcher, Smart and Sustainable Automation Research Lab, University of Michigan, USA
An overview of physics-based data-driven process modeling in advanced manufacturing operations
The integration of physics-based data-driven process modeling in advanced manufacturing operations represents a pivotal stride toward enhancing efficiency, precision, and adaptability in industrial processes. By combining the principles of physics with data-driven insights, these models provide a holistic understanding of complex manufacturing dynamics, enabling accurate predictions and optimizations. Moreover, the advent of Digital Twins, virtual replicas mirroring physical systems, further elevates the manufacturing landscape. Digital Twins serve as dynamic counterparts, allowing for continuous monitoring, analysis, and optimization, thereby fostering innovation, reducing downtime, and ultimately ensuring the longevity and competitiveness of advanced manufacturing operations in the modern industrial landscape. This talk provides an overview of current research areas and relevant publications in the field of manufacturing process modeling and monitoring, with a focus on three main directions: enhancing physics-based manufacturing process models, applying machine learning algorithms to manufacturing processes, and improving system dynamics identification.
ABOUT THE SPEAKER
N. Bircan Bu˘gdaycı received his B.Sc. degree in mechanical engineering from Middle East Technical University, Ankara, in 2011 and his M.Sc. degree in mechanical engineering from Koc University, Istanbul, in 2013. Then, he completed his Ph.D. degree on Improved process characterization in milling at ETH Zurich, Switzerland, in 2022. He is currently a postdoctoral researcher at the Smart and Sustainable Automation Research Lab at the University of Michigan. His research focuses on physics-based data-driven process modelling of advanced manufacturing operations, Machine Learning and Artificial Intelligence, and Digital Twins.
Meeting ID: 303 351 8323.
Date: November 24, 3023, Friday. Time: 13:30.
Dr. Yiğit Karpat, Bilkent University