Sichen CHEN
Papers
1
Total Citations
17
H-Index
1
About
Sichen Chen is a rising researcher in advanced manufacturing, specializing in robotic milling process monitoring and chatter identification. Their work addresses a critical challenge in modern machining: the detection and suppression of self-excited chatter, which degrades surface quality and limits productivity. Chen’s most-cited paper, “Early chatter identification based on optimized VMD with multi-band information fusion and compression method in robotic milling process” (2023, 17 citations), introduces a novel approach that combines optimized variational mode decomposition with multi-band information fusion. This method enables sensitive, real-time chatter detection from processing signals, allowing operators to intervene before damage occurs. By compressing and fusing information across frequency bands, Chen’s technique improves identification accuracy while reducing computational load—a practical advancement for industrial robotic milling. Though early in their career, Chen’s work demonstrates strong potential for impact in smart manufacturing and process monitoring. Their contributions are particularly valuable for researchers and engineers seeking to enhance machining efficiency through data-driven vibration analysis.
Research Focus
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Top Papers
- 1