Fan Ying
Papers
1
Total Citations
8
H-Index
1
About
Fan Ying is a researcher specializing in computer vision and intelligent robotic systems, with a particular focus on automated welding technologies. Their most recognized work centers on the application of structured-light vision to welding automation — a technically demanding domain where precision and reliability are critical. In their 2015 paper, "Recognition of the type of welding joint based on line structured-light vision," Fan Ying developed a method using line laser structured-light imaging to automatically identify welding joint types, a foundational step in enabling robots to extract weld seam features and autonomously track seam paths. This contribution addresses a core challenge in industrial robotics: reducing human intervention in welding processes while maintaining accuracy and consistency. The paper has accumulated 8 citations, reflecting its relevance within the specialized community of robotic welding and machine vision researchers. Fan Ying's work sits at the intersection of image processing, pattern recognition, and manufacturing automation, contributing practical solutions that advance the intelligent capabilities of welding robots. Their research holds meaningful implications for industrial efficiency, quality control, and the broader push toward fully automated smart manufacturing environments.
Research Focus
Key Achievements
Top Papers
- 1