Zhongxi Sheng
Papers
7
Total Citations
136
H-Index
5
About
Zhongxi Sheng is a leading researcher in intelligent robotic welding systems, with a focus on automation, computer vision, and adaptive path planning for complex weld seams. His major contributions include pioneering a passive vision-based seam tracking method for thin-plate closed-gap butt welding, which has garnered 77 citations and remains foundational in the field. He has also developed novel point cloud-driven frameworks for multi-layer multi-pass welding, particularly for saddle-shaped seams, achieving up to 18 citations for his 2024 work. Sheng’s innovative filling strategies and Transformer-based adaptive path generation methods (2025, 14 citations) represent cutting-edge advances in robotic welding. Notably, he designed and experimentally verified an intelligent wall-climbing welding robot system for large-scale steel structures (2014, 13 citations), demonstrating practical industrial impact. His recent work on multi-view model reconstruction and laser scanning-based path planning (2024–2025) further underscores his commitment to integrating 3D sensing and machine learning into robotic manufacturing. With a growing citation record and a series of high-impact publications, Sheng is shaping the future of automated welding through robust, adaptive, and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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