November 14, 2018
Abstract: The rise of additive manufacturing (AM) is poised to enable mass customization in the next industrial revolution. By progressively adding small amounts of material to the part being fabricated, AM — also called 3D printing — builds three-dimensional objects of unprecedented complexity at low unit costs. Powder bed fusion (PBF), in which new material is added by applying and selectively melting a powdered feedstock, is a popular form of AM for broad applications ranging from advanced jet-engine components to custom-designed medical implants. This talk discusses system-theoretical approaches towards critically needed higher accuracy, greater reproducibility, and extended sustainability in AM processes. Drawing on subfields of mechatronics, sampled-data controls, and heat transfer, we will address (1) the creation of tractable online signal processing that understands multi-scale thermomechanical interactions and discards unnecessary information to make full use of data-intensive sensor sources like high-speed video, and (2) the realization of control strategies for quality assurance in the presence of limited-bandwidth sensor feedback. Broader impact of the research is augmented by extension to other complex systems, from semiconductor manufacturing and information storage to robot collaborations.
Bio: Dr. Xu Chen received his M.S. and Ph.D. degrees in Mechanical Engineering from the University of California, Berkeley in 2010 and 2013, respectively, and his Bachelor’s degree with honors from Tsinghua University, China in 2008. In 2014, he joined the faculty of the Department of Mechanical Engineering at the University of Connecticut, with joint appointments in the UTC Institute for Advanced Systems Engineering and the Institute of Materials Science. His research expertise include theory and practice of dynamic systems, information fusion, learning and adaptive controls, with applications to additive and advanced manufacturing, agile robots, vision-based autonomy, and precision mechatronics. Dr. Chen has 4 issued patents, over 50 papers in ISI-indexed high-impact journals and refereed conference proceedings since 2012. He is a recipient of the National Science Foundation CAREER Award, the Young Investigator Award and the Best Paper Award from ISCIE / ASME International Symposium on Flexible Automation, the 2017 Best Vibrations Paper Award from the ASME Dynamic Systems and Control Division, the first UTC Institute for Advanced Systems Engineering Breakthrough Award in 2016, and the 2017 UConn University Teaching Fellow Award Nominee.