Yuxiang Hong
Papers
5
Total Citations
157
H-Index
5
About
Dr. Yuxiang Hong is a leading researcher in intelligent robotic welding, specializing in vision-based seam tracking, weld quality monitoring, and automated welding guidance. His work addresses critical challenges in industrial welding, particularly for complex structures like box girders and marine engineering equipment. Dr. Hong’s major contributions include developing advanced feature extraction methods for weld gap detection in GMAW systems, enabling robots to adapt to variable gaps and noisy environments. He pioneered the AF-FTTSnet, an end-to-end two-stream convolutional neural network for real-time online quality monitoring of robotic welding, achieving 37 citations. His research on welding seam trajectory recognition using laser vision sensors has enabled automated skip welding guidance for spatially intermittent seams, while his work on 3D zigzag-line welding seam pose extraction has improved tracking accuracy for heavy equipment manufacturing. Most recently, he has applied spatial-temporal deep learning to molten pool serial images for intelligent seam tracking in foils joining. With over 157 citations across his top papers, Dr. Hong’s innovations are driving the next generation of flexible, intelligent robotic welding systems.
Research Focus
Key Achievements
Top Papers
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