Papers

4

Total Citations

50

H-Index

4

About

Jangwook Lee is a pioneering researcher in robotic welding and teleoperation systems, whose work bridges the gap between intelligent automation and human-robot interaction. His primary research areas include real-time welding quality monitoring, machine vision integration, and force-feedback teleoperation. Lee’s most impactful contribution is his 2024 study on predicting internal welding penetration using infrared thermal imaging combined with machine vision and artificial neural networks—a breakthrough that addresses the longstanding challenges of objectivity, timeliness, and cost in welding quality assessment. This work has already garnered 23 citations, signaling its rapid influence in manufacturing automation. In the early 2000s, Lee established foundational concepts in teleoperation, introducing a distributed controller architecture for master arms that mitigates non-uniform time delays and enhances position command update rates. His exoskeleton-type master arm with force reflection, detailed in multiple papers from 2003, represents a significant advance in man-machine interfaces, enabling operators to feel precise forces from remote slave robots. With cumulative citations across his key works, Lee’s research continues to shape the future of intelligent robotic systems, offering practical solutions for industries requiring high-precision, real-time control.

Research Focus

Key Achievements

4
H-Index
4
Papers
50
Total Citations
13
Avg Citations/Paper
🏆 Most Cited Paper
Prediction of internal welding penetration based on IR thermal image supported by machine vision and ANN-model during automatic robot welding process
23 citations · 2024
📈 Most Prolific Year: 2003 (3 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Korea Electric Power Corporation (South Korea), Korea Institute of Science and Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago