Guang-Yu Nie

Papers

Total Citations

H-Index

About

Guang-Yu Nie is a leading researcher at the intersection of robotics, construction automation, and intelligent manufacturing. His work directly confronts the critical labor shortage in the welding industry—a deficit projected to reach 360,000 welders in the U.S. by 2027. Nie’s primary contributions lie in developing autonomous robotic welding systems that combine real-time joint tracking with three-dimensional scanning. His most-cited paper, "Automatic and Real-Time Joint Tracking and Three-Dimensional Scanning for a Construction Welding Robot" (2023, 14 citations), introduces a novel framework that enables robots to perceive and adapt to complex welding environments without human intervention. Complementing this, his earlier work, "Real-Time and Automatic Detection of Welding Joints Using Deep Learning" (2022, 8 citations), leverages convolutional neural networks to identify weld seams with high accuracy, even in challenging industrial settings like nuclear power plants. By integrating deep learning with robotic control, Nie has advanced the frontier of smart manufacturing, offering scalable solutions that promise to enhance productivity, safety, and quality in steel fabrication. His research is pivotal for students and engineers seeking to understand how AI and robotics can transform traditional trades into high-tech, autonomous processes.