Yingjian Xiang
Papers
3
Total Citations
35
H-Index
3
About
Yingjian Xiang is a researcher at the forefront of intelligent manufacturing, specializing in robotic belt grinding and precision material removal for complex aerospace components. His work addresses a critical industry challenge: transitioning from traditional manual grinding to high-performance robotic automation for workpieces with intricate geometries, such as turbine blades. Xiang’s major contributions include developing predictive modeling frameworks that accurately characterize material removal behavior, enabling more consistent and efficient grinding processes. He has also pioneered multi-algorithm fusion–based intelligent decision-making methods that dynamically optimize process parameters, significantly enhancing machining quality and reducing trial-and-error. His most cited paper (2023) has garnered 18 citations, reflecting growing interest in digital processing and data-driven optimization for robotic grinding. By integrating predictive analytics with real-time control, Xiang’s research bridges the gap between theoretical modeling and practical industrial application. His work is particularly notable for its potential to improve productivity and surface integrity in high-value manufacturing, making him a rising contributor to the fields of robotic machining, process optimization, and intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
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