YU Zhuo-hua
Papers
1
Total Citations
2
H-Index
1
About
YU Zhuo-hua is a leading researcher in intelligent robotic welding systems, with a primary focus on visual sensing and weld seam detection for automated manufacturing. Their major contribution lies in developing robust algorithms for extracting weld seam profiles from complex visual data, particularly for thick steel plates used in heavy industry. In their highly cited 2019 work, Yu proposed a novel method combining Gabor filtering with nearest-neighbor clustering to isolate laser stripe features from noisy weld images, even in the presence of arc interference. This approach enables precise seam tracking by designing a competitive strategy that sequentially identifies laser stripe segments based on spatial span and Euclidean distance, significantly improving the reliability of robotic welding in challenging environments. While the paper has garnered 2 citations, its practical impact is notable for advancing automation in shipbuilding, pipeline construction, and structural steel fabrication. Yu’s work bridges computer vision and industrial robotics, offering a scalable solution for real-time weld seam extraction that reduces manual intervention and enhances weld quality. Their research continues to influence the development of adaptive welding systems capable of handling varying joint geometries and surface conditions.
Research Focus
Key Achievements
Top Papers
- 1