Dening Song
Papers
4
Total Citations
37
H-Index
3
About
Dening Song is a researcher specializing in advanced manufacturing and robotics, with a focus on the precision machining of thin-walled structures. His primary research areas include robot-assisted machining, toolpath planning, and real-time velocity control for multi-axis systems. Song’s major contributions center on developing innovative support strategies for machining thin-walled parts with non-uniform thickness and closed cross-sections, such as his work on auxiliary support path planning and end-supporter scheduling, which enhance stability and accuracy during complex operations. His most cited paper, "Auxiliary support path planning for robot-assisted machining of thin-walled parts" (2025, 21 citations), demonstrates significant impact in this niche field. Additionally, his "Convolution-Based Velocity-Smoothing Principle" (2025, 11 citations) introduces a novel method for real-time parametric curve interpolation, enabling smoother and faster feedrate planning without repeated calculations—a breakthrough for multi-axis systems like machine tools and robot arms. Song’s work on five-axis toolpath re-scheduling for blade machining further underscores his expertise in reducing axis sensitivity and facilitating assisted support. With a growing citation record, Song is establishing himself as a key contributor to precision manufacturing and robotics.
Research Focus
Key Achievements
Top Papers
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