Yajie Chen
Papers
1
Total Citations
4
H-Index
1
About
Yajie Chen is a researcher specializing in advanced manufacturing and intelligent process monitoring, with a focus on high-speed robotic milling and precision machining. Her work centers on integrating signal processing and machine learning techniques to detect and mitigate process instabilities, such as vibration, in real time. Her most-cited paper, "Online Vibration Detection in High-Speed Robotic Milling Process Based on Wavelet Energy Entropy of Acoustic Emission" (2024), introduces a novel method using wavelet energy entropy from acoustic emission signals to identify chatter and vibration during machining. This contribution is critical for improving surface quality, tool life, and process efficiency in automated manufacturing. With growing interest in smart manufacturing and Industry 4.0, her research has already garnered attention, accumulating citations that underscore its relevance. Chen’s work bridges the gap between theoretical signal analysis and practical industrial application, offering a robust framework for non-invasive, real-time monitoring. Her achievements highlight her as an emerging voice in the field of cyber-physical production systems and intelligent machining.
Research Focus
Key Achievements
Top Papers
- 1