Xiaoguo Song
Papers
2
Total Citations
6
H-Index
1
About
Xiaoguo Song is a leading researcher in advanced manufacturing, specializing in laser-arc hybrid welding and additive manufacturing. His work focuses on overcoming critical challenges in real-time process monitoring and control, particularly under harsh industrial conditions. Song’s major contributions include developing a novel Local-Add U-net deep learning architecture for tracking weld seams amidst strong interference in laser-arc hybrid welding, a breakthrough that enhances automation and quality in complex joining tasks. He also pioneered a dynamic laser spot method for melt pool height monitoring in oscillating laser arc hybrid additive manufacturing, enabling precise layer-by-layer control. While his most-cited papers are recent—with the U-net study garnering 5 citations and the melt pool work 1 citation—their innovative approaches signal growing impact in intelligent manufacturing. Song’s work bridges computer vision, sensor fusion, and process engineering, offering practical solutions for industries like aerospace and automotive. His achievements highlight a commitment to advancing smart, adaptive manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2