Yuru Fu

Papers

Total Citations

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About

Yuru Fu is a researcher in intelligent manufacturing and robotic welding systems, with a primary focus on vision-based seam tracking and adaptive control for automated welding processes. Their most cited work, "A Vision Algorithm for Robot Seam Tracking Based on Laser Ranging" (2023), addresses a critical challenge in industrial welding: the high-temperature deformation of weldments that leads to accuracy deviations and compromised weld quality. Fu’s key contribution lies in developing a laser ranging vision algorithm that enables real-time, precise seam tracking, compensating for thermal distortion without requiring post-weld corrections. This work has garnered 2 citations, reflecting its emerging relevance in the field of robotic automation. By tackling the practical limitations of current weld trackers—which are typically mounted at the welding torch tip and susceptible to heat-induced errors—Fu’s research advances the reliability of autonomous welding systems. Their work is particularly valuable for industries requiring high-precision welding, such as automotive and aerospace manufacturing. Fu’s ongoing contributions to vision-guided robotics and process control continue to support the development of more robust, adaptive manufacturing technologies.