Papers
6
Total Citations
101
H-Index
4
About
Songlin Wan is a leading researcher in ultra-precision optical manufacturing, specializing in industrial robotic polishing and surface error control. His work addresses the critical challenge of improving the accuracy and efficiency of robotic polishing systems for large optics, where conventional methods fall short. Wan’s major contributions include pioneering region-adaptive path planning for precision polishing (29 citations), which enhances robotic control to reduce midspatial frequency errors, and developing space-variant deconvolution techniques for edge control (27 citations). He also introduced high-efficiency pseudo-random path planning to suppress ripple errors (23 citations), a key innovation for optical system performance. His research on pad wear effects on tool influence functions (16 citations) and plug-and-play error compensation models (4 citations) further advances industrial robot reliability. Notably, Wan has explored statistical perception of chaotic fabrication errors, proposing self-adaptive processing decisions to mitigate unpredictable defects in ultra-precision optics. With a cumulative impact of over 100 citations, his work bridges robotics and optical manufacturing, offering cost-effective, intelligent solutions for high-performance optics. Wan’s achievements position him as a pivotal figure in advancing robotic polishing from laboratory concepts to industrial applications.
Research Focus
Key Achievements
Top Papers
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