Sang Ik Jeong
Papers
1
Total Citations
28
H-Index
1
About
Sang Ik Jeong is a researcher at the forefront of intelligent manufacturing and robotic process monitoring. His work centers on developing data-driven frameworks for real-time quality control in industrial automation, with a particular emphasis on resistance spot welding systems—a critical process in automotive and heavy equipment production. Jeong’s major contribution lies in the creation of machine learning-based diagnostic tools that can detect angular misalignment in robot spot-welding systems during operation, a defect that often leads to weld nugget porosity and structural weakness. His most cited work, "Development of Real-time Diagnosis Framework for Angular Misalignment of Robot Spot-welding System Based on Machine Learning" (2020), has garnered 28 citations, reflecting its practical significance in reducing manufacturing defects and downtime. By integrating voltage and current sensor data with advanced algorithms, Jeong enables non-destructive, online monitoring that replaces traditional post-process inspection. This innovation not only enhances weld quality but also supports the broader shift toward smart factories and Industry 4.0. Jeong’s research is notable for bridging the gap between theoretical machine learning and real-world industrial applications, offering scalable solutions that improve both efficiency and reliability in automated production lines.
Research Focus
Key Achievements
Top Papers
- 1