Seungbeom Jang
Papers
1
Total Citations
23
H-Index
1
About
Seungbeom Jang is a researcher advancing the field of intelligent manufacturing, with a primary focus on real-time welding quality monitoring and automation. His work integrates machine vision, infrared thermal imaging, and artificial neural networks (ANNs) to address longstanding challenges in welding evaluation. Jang’s 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” (2024, 23 citations), introduces a novel, non-invasive method for predicting weld penetration depth in real time. By combining IR thermal data with machine vision, his model overcomes the subjectivity, delays, and high costs of traditional inspection techniques, enabling more objective and timely quality control. This contribution is particularly significant for robotic welding, where precision and automation are paramount. Jang’s work has quickly garnered attention, with his top paper accumulating citations shortly after publication, reflecting its relevance to both academic research and industrial application. His research stands at the intersection of sensor fusion, deep learning, and manufacturing, offering a pathway toward fully autonomous, high-quality welding processes.
Research Focus
Key Achievements
Top Papers
- 1