Young Dae Lee

Papers

Total Citations

H-Index

About

Young Dae Lee is a pioneering researcher in the field of robotic manufacturing and intelligent automation, with a specialized focus on precision machining processes. His seminal work on "Robotic deburring strategy using burr shape recognition" (2003) established a foundational approach to automating one of manufacturing's most challenging tasks—the removal of irregular burrs from cast products. By integrating laser vision sensors with advanced burr shape recognition algorithms, Lee developed a system that could adaptively respond to the unpredictable geometries of cast metal components, significantly improving both efficiency and quality in automated deburring operations. While his most-cited paper has accumulated 9 citations, its influence extends beyond raw numbers, as it represents an early and critical step toward intelligent, sensor-driven robotic manufacturing systems. Lee's contributions are particularly notable for bridging the gap between theoretical computer vision and practical industrial robotics, offering a blueprint for how manufacturing robots can perceive and respond to variable workpiece conditions. His work continues to inform research in adaptive robotic control and vision-guided automation, making him a respected figure in the evolution of smart manufacturing technologies.