Bilkent University
Mechanical Engineering Department
Dr. Nevzat Bircan Buğdaycı
Postdoctoral Researcher, Smart and Sustainable Automation Research Lab, University of Michigan, USAAn overview of physics-based data-driven process modeling in advanced manufacturing operations
ABSTRACT
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.
ZOOM DETAILS
https://zoom.us/j/3033518323?pwd=cTJSTUQ3YWJmV01LcC9reG5GV3J4QT09
Meeting ID: 303 351 8323.
Passcode: 04ujBE.
Date: November 24, 3023, Friday. Time: 13:30.
CONTACT
Dr. Yiğit Karpat, Bilkent University