Papers
8
Total Citations
48
H-Index
4
About
Xinyong Mao is a mechanical engineering researcher specializing in robotic machining dynamics, with a particular focus on vibration characterization, chatter suppression, and modal analysis in industrial milling robots. His work addresses a critical challenge in advanced manufacturing: the dynamic instability of robotic milling systems that limits their precision and efficiency in machining large, complex surfaces. Mao's most influential contribution, "Characterization of milling robot mode shape and analysis of the weak parts causing end vibration" (2022, 21 citations), established foundational understanding of how structural weaknesses in robot bodies contribute to end-effector vibration. Building on this, he has pioneered innovative methods for identifying robot dynamic characteristics across varying poses and workspaces, including self-excitation techniques and output-only modal identification using minimal sensor configurations — approaches that are both practical and non-intrusive. A standout applied contribution is his development of tuned mass damper (TMD) solutions specifically tailored to suppress low-frequency chatter in robotic milling, leveraging modal directionality to optimize damper placement. More recently, his research has advanced autonomous and regularized methods for high-dimensional workspace dynamic identification, pushing toward fully automated vibration management in industrial settings. With growing citation impact across his portfolio, Mao is an emerging authority in intelligent robotic machining dynamics.
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