Hongwei Zeng
Papers
2
Total Citations
19
H-Index
2
About
Hongwei Zeng is a researcher whose work focuses on the critical challenge of chatter detection and recognition in robotic drilling systems—a problem that directly impacts manufacturing efficiency and quality. His major contributions lie in developing advanced signal processing techniques to identify and predict chatter, a destructive vibration that limits precision in automated drilling. In his highly cited 2019 paper, Zeng introduced a novel method combining multi-synchrosqueezing transform with energy entropy for chatter detection, earning 13 citations for its innovative approach. Building on this, his 2023 work proposed a synchroextracting chirplet transform (SECT) for early chatter recognition, achieving 6 citations. This method systematically extracts time-frequency features to enable real-time monitoring, offering a practical solution to a long-standing industrial barrier. Zeng’s research bridges theoretical signal analysis with applied robotics, providing tools that enhance process stability and product quality. His work is particularly notable for its focus on early detection, which can prevent costly damage and downtime. For students and researchers in manufacturing, robotics, or signal processing, Zeng’s contributions offer a clear pathway from fundamental theory to impactful industrial application.
Research Focus
Key Achievements
Top Papers
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- 2