Kenan Deng
Papers
16
Total Citations
426
H-Index
10
About
Kenan Deng is a leading researcher in the field of robotic machining, with a focus on enhancing the precision, stability, and intelligence of industrial robots. His work centers on three key areas: chatter prediction and detection, error compensation, and stiffness modeling. Deng’s major contributions include developing methods to predict in-process frequency response functions and chatter stability in robotic milling, accounting for pose and feedrate—work that has garnered 69 citations. He has also pioneered techniques for elasto-geometrical error and gravity model calibration, achieving 62 citations, and introduced a mutual cross-attention fusion network for surface roughness prediction, a 2025 paper with 39 citations. His impact is evident across his top-cited papers, which collectively exceed 300 citations, and includes innovations like vision-based autonomous calibration using ArUco maps and single-camera systems for large workspaces. Notable achievements include optimizing robot base positions and spacecraft cabin angles via a novel stiffness domain index, as well as in-situ joint stiffness identification using eye-in-hand cameras. Deng’s research is instrumental in advancing robotic accuracy and stability, making him a key figure in modern manufacturing and automation.
Research Focus
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