Xingai Peng
Papers
2
Total Citations
7
H-Index
2
About
Xingai Peng’s research focuses on the automation and precision of industrial welding robotics, particularly for large-scale structural applications. His major contributions lie in developing intelligent error compensation and calibration methods for intersection line welding robots. Notably, his 2011 work on a wavelet neural network approach (5 citations) introduced a novel technique to correct positioning inaccuracies during multi-path, multi-layer welding of big frames. This work directly addresses the challenge of teaching and playback efficiency in robotic welding. Additionally, his 2010 study on track fitting (2 citations) proposed a filling strategy with equal layer heights to optimize multi-pass welding of complex intersection lines, enhancing both accuracy and productivity. Though his citation counts are modest, Peng’s targeted innovations in welding robot calibration and trajectory planning have practical significance for heavy manufacturing industries. His research exemplifies the integration of neural network algorithms with traditional robotic control, offering a foundation for future work in adaptive welding automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2