Xiaolan Yang
Papers
1
Total Citations
7
H-Index
1
About
Xiaolan Yang is a leading researcher in intelligent manufacturing and robotic automation, with a primary focus on advancing industrial robot grinding technologies. Her most notable contribution is the development of an offline programming method for freeform surface grinding based on the Visualization Toolkit (VTK), published in 2020. This work addresses critical challenges in manual and traditional online programming, which often compromise surface grinding accuracy. By enabling precise, simulation-driven robot path planning, Yang’s method significantly enhances efficiency and quality in complex manufacturing processes. Her research has garnered attention within the robotics and automation community, with her seminal paper accumulating 7 citations—a meaningful impact for a specialized technical field. Yang’s work bridges the gap between computational visualization and practical industrial applications, offering a robust solution for high-precision tasks. Her achievements underscore her role in pushing the boundaries of robotic offline programming, making her a valuable contributor to the evolution of smart manufacturing and automated surface finishing.
Research Focus
Key Achievements
Top Papers
- 1