Qinghui Wang
Papers
17
Total Citations
413
H-Index
11
About
Qinghui Wang is a leading researcher in robotic machining, specializing in the automation of high-precision surface finishing processes such as milling, grinding, and polishing. His work addresses a critical challenge in industrial robotics: the inherent low stiffness of robots, which compromises machining quality. Wang’s major contributions include pioneering optimization frameworks that simultaneously consider robot posture and workpiece setup to enhance stiffness, as demonstrated in his highly cited 2021 paper (96 citations). He developed region-based toolpath generation methods for freeform surfaces (83 citations) and introduced an easy-to-grind region partitioning approach for robotic belt grinding (38 citations). Wang has also advanced adaptive human-robot collaboration for complex workpiece grinding (26 citations) and uncertainty-aware error modeling for surface machining (25 citations). His recent work on hierarchical redundancy optimization and profile error compensation (2023–2024) further pushes the boundaries of precision in robotic machining. With over 400 total citations, Wang’s research is instrumental in making industrial robots viable for high-accuracy applications, directly impacting manufacturing productivity and quality.
Research Focus
Key Achievements
Top Papers
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- 4A robotic belt grinding approach based on easy-to-grind region partitioning38 citations · 2020
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