UTC-IASE Faculty Spotlight: Dr. Xu Chen


This week’s faculty spotlight is on Professor Xu Chen, who is an assistant professor in the Department of Mechanical Engineering. Dr. 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 from Tsinghua University, China in 2008.  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 2017 UConn University Teaching Fellow Award Nominee, and the 2012 Chinese Government Award for Outstanding Students Abroad.


Professor Chen is the principle investigator for the Machine, Automation and Control Systems Laboratory (MACS) through the Department of Mechanical Engineering. The overall research goal of his lab is to seek better understanding and engineering of the systematic interplay between data, systems and controls in machines and automation processes. For instance, fast situational awareness and agile response is imperative to advancing system operation in this information age. To reach such capabilities, Prof. Chen’s team exploits approaches to reliably and quickly combine all data from heterogeneous sources in a feedback control system. An example of such is a project conducted from Dr. Chen’s smart manufacturing research called “Model-Based Sparse Information Recovery by Collaborative Sensor Management”. This project provides a novel approach to collect dense information from a group of collaborative sensors at a significantly reduced computation burden and in real time. The result is particularly impactful for applications such as imagining-based automation, where vision data take time to collect and complex elaborations must be performed to extract information from the raw data. More broadly, this work relates to the overarching challenge of making full use of data to infer and respond to fast evolving situations in decentralized environments, and provides a pathway to better integrate multiple data-intensive sources.


During the summer of 2018, Dr. Chen and his laboratory team traveled to three flagship conferences in the fields of controls, automation, and 3D printing: The American Control Conference at Milwaukee in June, the International Symposium on Flexible Automation at Kanazawa, Japan in July, and the Annual international Solid Freedom Fabrication at Austin, Texas in August. The three published papers from Prof. Chen’s team discussed smart controls approaches for critically needed quality assurance of additive manufacturing (AM), a nascent manufacturing technology that offers untapped potential in a wide range of products for the energy, aerospace, automotive, healthcare and biomedical industries. In particular, the focused powder bed fusion process is increasingly preferred in applications ranging from advanced jet-engine components to custom-designed medical implants. Prof. Chen’s research looks into the convoluted thermomechanical interactions in the multi-physics multi-scale manufacturing process, and has been generating award-winning, internationally recognized results to enable substantially higher accuracy and greater reproducibility in AM. For instance, the recent paper titled “Synthesis and Analysis of Multirate Repetitive Control for Fractional-Order Periodic Disturbance Rejection in Powder Bed Fusion” was featured in the proceedings of the 2018 International Symposium on Flexible Automation, where this paper written by PhD Student, Dan Wang, and Dr. Chen, won the “Best Paper Award In Theory”.