Rate My Professor Rui Liu

Rui Liu is an Associate Professor in the Department of Mechanical Engineering within the Kate Gleason College of Engineering at Rochester Institute of Technology. He earned a B.S. degree in Jet Propulsion from Beijing University of Aeronautics and Astronautics in 2005, an M.S. in Mechanical Engineering from Northeastern University in 2010, and a Ph.D. in Mechanical Engineering from Georgia Institute of Technology in 2014, with a focus on manufacturing. Before entering academia, he served as a process engineer in the engine services division at Aircraft Maintenance & Engineering Corporation in China from 2005 to 2008. He joined Rochester Institute of Technology as a Visiting Assistant Professor in Mechanical Engineering in 2015, advanced to Assistant Professor in 2018, and was promoted to Associate Professor. He is also affiliated with the Department of Industrial and Systems Engineering.

Dr. Liu's research specializes in advanced manufacturing, encompassing human-centered machining, tool condition monitoring using machine learning techniques and advanced sensing such as audio signals, machining process optimization to enhance productivity and surface quality, and simulation of material behavior and microstructure evolution during cutting processes. His contributions include calibration-based tool condition monitoring systems for repetitive operations, edge-based algorithms for wear monitoring in milling, and investigations into human visual and tactile perception in machined surface inspection. Key publications include 'A physically based constitutive model for simulation of segmented chip formation in orthogonal cutting of commercially pure titanium' in CIRP Annals (2015), 'An edge-based algorithm for tool wear monitoring in repetitive milling processes' in Journal of Intelligent Manufacturing (2022), 'Calibration-based tool condition monitoring for repetitive machining operations' in Journal of Manufacturing Systems (2020), 'Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning' in Journal of Manufacturing Processes (2019), and 'A survey of immersive technologies and applications for industrial product development' in Computers & Graphics (2021). In 2025, he received the National Science Foundation CAREER Award to develop advanced manufacturing workforce training integrating experiential learning and regional collaborations in the Finger Lakes area. His work impacts manufacturing automation, quality control, and skills development in industry.