Chang Won Lee
Papers
1
Total Citations
4
H-Index
1
About
Chang Won Lee is a researcher whose work centers on intelligent manufacturing and process optimization, with a particular focus on robotic arc welding and predictive modeling. His key contributions lie in the development of data-driven algorithms to enhance welding precision and efficiency. In his most cited work, "A study on prediction of bead height in robotic arc welding using a neural network" (2002), Lee pioneered the use of neural networks and multiple regression methods to model the complex relationships between process parameters and bead geometry. This research enabled accurate prediction of bead height in multi-pass welding, offering a significant step toward automated quality control in industrial robotics. With 4 citations, this foundational study has informed subsequent advances in intelligent welding systems. Lee’s work demonstrates a commitment to bridging computational intelligence and manufacturing engineering, providing practical tools for improving weld consistency and reducing trial-and-error in production settings. His contributions continue to be relevant for researchers exploring machine learning applications in additive and joining processes.
Research Focus
Key Achievements
Top Papers
- 1