Papers
1
Total Citations
11
H-Index
1
About
Linlin Wan is a researcher specializing in robotic manufacturing and precision machining, with a particular focus on weld seam grinding systems. Their most cited work, "Quantitative grinding depth model for robotic weld seam grinding systems" (2023), has garnered 11 citations, establishing a foundational framework for controlling material removal in automated grinding processes. This contribution addresses a critical challenge in industrial robotics: achieving consistent, high-quality surface finishing through predictive modeling rather than trial-and-error adjustments. Wan’s research bridges the gap between theoretical grinding mechanics and practical robotic applications, offering engineers a quantitative tool to optimize grinding depth, reduce waste, and enhance productivity in sectors like automotive and aerospace manufacturing. By developing models that account for tool wear, force dynamics, and workpiece geometry, Wan has advanced the reliability of robotic systems in harsh production environments. Their work is particularly valuable for students and researchers exploring adaptive control in manufacturing, as it provides a clear, data-driven pathway to improving automation precision. With a growing citation record, Wan’s contributions are poised to influence future innovations in intelligent robotic machining.
Research Focus
Key Achievements
Top Papers
- 1Quantitative grinding depth model for robotic weld seam grinding systems11 citations · 2023