Xuexin Zhang
Papers
8
Total Citations
139
H-Index
6
About
Xuexin Zhang is a leading researcher in intelligent robotics and digital manufacturing, with a focus on large-scale component machining and human-robot interaction. His work bridges the gap between theoretical control systems and practical industrial applications, particularly in aerospace manufacturing. Zhang’s most impactful contribution is his pioneering Digital Twin modeling framework for robotic machining of large-scale components (47 citations), which integrates knowledge graphs and function blocks to enable real-time simulation and optimization. He has also developed innovative solutions for pose measurement using binocular vision and priori data (24 citations), significantly improving precision in handling large aerospace cylindrical parts. In the domain of robot control, Zhang’s neural approximation-based adaptive variable impedance control (20 citations) enhances compliance and robustness during robot-environment interaction, while his adaptive hierarchical error compensation method (17 citations) addresses long-term industrial robot accuracy degradation. His recent work on multi-domain chatter detection in robotic milling (15 citations) employs directional attention mechanisms to improve machining stability across varied robot poses. With over 139 total citations across his most-cited papers, Zhang’s research consistently advances the frontiers of intelligent manufacturing, earning recognition for its practical impact on precision and efficiency in large-scale robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Neural approximation-based adaptive variable impedance control of robots20 citations · 2020
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