Papers
2
Total Citations
12
H-Index
2
About
Mi Liang is a researcher whose work centers on the precision measurement and performance evaluation of industrial robots, with a particular focus on developing rigorous mathematical frameworks for quantifying measurement uncertainty. Their most cited contributions address two fundamental challenges in robotics metrology: evaluating the uncertainty of positioning repeatability and orientation repeatability measurements for industrial robots. Both works tackle the inherently nonlinear nature of these evaluation procedures, proposing systematic methods to model uncertainty sources and guide practitioners in minimizing measurement error. Published in 2018 and 2020 respectively, these papers each garnered 6 citations, reflecting a focused but meaningful impact within the specialized field of industrial robot calibration and standards compliance. By bridging the gap between theoretical uncertainty analysis and practical measurement guidance, Mi Liang's research provides valuable tools for engineers and metrologists working to ensure the reliability and accuracy of robotic systems in manufacturing environments. Their contributions are particularly relevant as industries increasingly depend on high-precision robotic automation, making robust uncertainty evaluation frameworks essential for quality assurance and standardization efforts.
Research Focus
Key Achievements
Top Papers
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