Shuhe Chang
Papers
1
Total Citations
45
H-Index
1
About
Shuhe Chang is a leading figure in intelligent welding and robotic automation, with a primary focus on advancing multi-layer/multi-pass welding (MLMPW) technology for heavy industrial applications. His most cited work, "A Weld Position Recognition Method Based on Directional and Structured Light Information Fusion in Multi-Layer/Multi-Pass Welding" (2018, 45 citations), addresses a critical bottleneck in automated thick-component joining for the energy sector. Chang pioneered a visual recognition method that fuses directional and structured light data, enabling robots to accurately identify weld pass positions in real time—a breakthrough that significantly enhances the precision and reliability of automatic welding in demanding environments. This contribution has been widely cited by researchers seeking to improve adaptive control in robotic welding systems. By solving the challenge of real-time seam tracking in complex, multi-pass scenarios, Chang’s work directly supports the automation of high-integrity welds in pipelines, pressure vessels, and structural steel, marking him as an innovator at the intersection of computer vision and manufacturing.
Research Focus
Key Achievements
Top Papers
- 1