Papers
4
Total Citations
439
H-Index
3
About
Xiaozhi Feng is a leading researcher in intelligent robotic machining and 3D vision-guided manufacturing, with a focus on advancing the precision and automation of complex component processing. His most impactful work, the 2020 paper "Robotic grinding of complex components: A step towards efficient and intelligent machining – challenges, solutions, and applications," has garnered over 400 citations, establishing him as a key voice in the field. Feng’s contributions address critical bottlenecks in robotic grinding, particularly for large, geometrically complex parts. He has developed innovative solutions for hand-eye calibration that account for robot kinematic errors, significantly improving measurement accuracy. His recent work introduces a novel weak feature point cloud registration algorithm (Sparse-VMICP) for robotic vision measurement, and a 3D vision-guided framework for repairing random local defects—a departure from the field’s traditional focus on global machining. These advances enable more efficient and intelligent repair and remanufacturing processes. Feng’s research is highly relevant for students and engineers working at the intersection of robotics, computer vision, and precision manufacturing, offering practical pathways toward fully autonomous, defect-adaptive machining systems.
Research Focus
Key Achievements
Top Papers
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- 43D Vision-Guided Robotic Grinding Framework for Repairing Random Defects3 citations · 2025