Papers
19
Total Citations
332
H-Index
7
About
Zhaoyang Liao is a leading researcher in robotic machining, whose work addresses the critical challenge of enabling industrial robots to perform high-precision manufacturing tasks. His research focuses on robotic milling, grinding, and surface machining, with key contributions in stiffness optimization, toolpath planning, and error compensation. Liao’s most cited work, "Optimization of Robot Posture and Workpiece Setup in Robotic Milling With Stiffness Threshold" (2021, 96 citations), pioneered a method to simultaneously optimize robot posture and workpiece placement, overcoming the inherent low stiffness of industrial robots. His 2020 paper on region-based toolpath generation (83 citations) introduced a novel approach that partitions freeform surfaces to optimize stiffness during milling. Liao has also made significant advances in robotic belt grinding, developing interference-free and posture-smooth toolpath generation methods. His recent work on uncertainty-aware error modeling and hierarchical compensation (2023–2024) provides a comprehensive framework for predicting and correcting profile errors in robotic surface machining. With over 300 total citations, Liao’s research is directly impacting the automation of high-precision manufacturing, offering practical solutions for industries requiring complex surface machining with industrial robots.
Research Focus
Key Achievements
Top Papers
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- 3A robotic belt grinding approach based on easy-to-grind region partitioning38 citations · 2020
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