Dan LIANG
Papers
1
Total Citations
5
H-Index
1
About
Dan Liang has made significant contributions to the field of intelligent manufacturing and robotic automation, with a primary focus on computer vision and precision welding technologies. His research addresses critical challenges in automatic welding systems, particularly in the accurate identification and tracking of weld seams under complex industrial conditions. Liang's most cited work, "Weld seam track identification for industrial robot based on illumination correction and center point extraction" (2022), introduces an innovative method that combines illumination correction techniques with center point extraction algorithms to reliably detect welds of varying shapes despite non-uniform lighting—a persistent problem in real-world manufacturing environments. This approach has garnered 5 citations, demonstrating its relevance to ongoing research in robotic welding automation. Liang's contributions are particularly valuable for advancing the precision and adaptability of industrial robots, enabling more robust performance in tasks that require high-accuracy visual guidance. His work stands at the intersection of image processing, robotics, and manufacturing engineering, offering practical solutions that bridge the gap between theoretical computer vision and industrial application.
Research Focus
Key Achievements
Top Papers
- 1