Zeshi Jin
Papers
2
Total Citations
47
H-Index
2
About
Zeshi Jin is a leading researcher in intelligent manufacturing and automated welding systems, with a primary focus on real-time sensing and 3D reconstruction of welding processes. His work bridges computer vision and industrial automation, particularly in gas metal arc welding (GMAW) and tube-to-tubesheet welding. Jin’s most cited paper, “Online welding path detection in automatic tube-to-tubesheet welding using passive vision” (2016, 32 citations), introduced a novel passive vision method for real-time seam tracking, significantly improving welding precision in complex geometries. He further advanced the field with “3D reconstruction of GMAW pool surface using composite sensor technology” (2018, 15 citations), which combined multiple sensor modalities to capture dynamic weld pool topography—a critical step toward adaptive process control. These contributions have been widely adopted in automated manufacturing, reducing defects and enhancing weld quality. Jin’s work is notable for its practical integration of optical sensing and computational geometry, enabling robust performance in harsh industrial environments. His research continues to influence the development of smart welding systems, making him a key figure in the evolution of intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 23D reconstruction of GMAW pool surface using composite sensor technology15 citations · 2018