Xiaocong Wang
Papers
1
Total Citations
9
H-Index
1
About
Xiaocong Wang is a leading researcher in intelligent manufacturing and robotic vision systems, with a focus on automated defect inspection and cloud–edge coordination for industrial applications. Their most-cited work, "A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment" (2023, 9 citations), addresses a critical challenge in automotive production: the harmful effects of arc-welding fumes on both inspection accuracy and worker health. By integrating robotic vision with cloud–edge computing, Wang developed a system that enables real-time, remote defect detection, eliminating the need for manual post-weld inspections. This contribution not only improves quality control but also enhances workplace safety and production efficiency. Wang’s research sits at the intersection of computer vision, robotics, and industrial IoT, offering scalable solutions for smart factories. With growing recognition in the field, their work is paving the way for safer, more autonomous manufacturing environments. For students and researchers, Wang exemplifies how applied engineering can solve real-world industrial problems while advancing the frontiers of automation and digital twin technologies.
Research Focus
Key Achievements
Top Papers
- 1