Shen Junqi

Papers

Total Citations

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About

Shen Junqi is a researcher specializing in intelligent manufacturing and robotic welding automation, with a particular focus on integrating visual sensing technologies for precision control. His most-cited work, "Robotic Weld Seam Correction Control System Based on Visual Sensing" (2019, 5 citations), addresses a critical challenge in automated welding: real-time seam tracking and correction. By developing a vision-based control system that enables robots to dynamically adjust welding paths in response to workpiece variations, Shen's research enhances weld quality and reduces reliance on manual intervention. This contribution is especially valuable in industries requiring high-precision fabrication, such as automotive and aerospace manufacturing. While his citation count reflects a growing interest in his work, Shen's impact lies in bridging the gap between theoretical control algorithms and practical industrial applications. His research underscores the potential of sensor-guided robotics to improve efficiency and consistency in complex manufacturing environments. For students and researchers exploring the intersection of computer vision, robotics, and production engineering, Shen Junqi's work offers a foundational example of how real-time feedback systems can transform traditional welding processes into smarter, more adaptive operations.