Chengjin Qin
Papers
12
Total Citations
307
H-Index
8
About
Chengjin Qin is a leading researcher in intelligent manufacturing and robotic machining, with a primary focus on solving the critical problem of chatter in robotic drilling—a key barrier to achieving high-quality, efficient automation in aerospace manufacturing. His major contributions lie in developing advanced signal processing and monitoring methods for real-time chatter identification. Qin pioneered the use of synchrosqueezing and synchroextracting transforms to detect early chatter vibrations, with seminal works like “Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method” (69 citations) and “A synchroextracting-based method for early chatter identification” (48 citations). He also introduced practical, real-time solutions such as a pre-generated matrix-based monitoring method (50 citations) and a concentrated velocity synchronous linear chirplet transform (61 citations). His recent work extends into digital twin frameworks for anomaly detection in industrial robots, reflecting a move toward AI-driven predictive maintenance. With over 300 total citations across his top papers, Qin’s research is highly influential in bridging the gap between theoretical time-frequency analysis and industrial robotic applications, making him a key figure in advancing smart, chatter-free robotic drilling for modern manufacturing.
Research Focus
Key Achievements
Top Papers
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