Papers
1
Total Citations
2
H-Index
1
About
Chao Tan is a pioneering researcher in advanced manufacturing and intelligent inspection systems, with a focus on integrating digital twin technology with robotics to revolutionize industrial quality control. His most-cited work, "Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection" (2024), addresses critical challenges in traditional manual inspection—namely, labor intensity, time consumption, and noisy workshop environments. By developing a digital twin framework that enables remote, automated defect detection for complex diesel engine assemblies, Tan has introduced a transformative approach that enhances accuracy and efficiency while reducing human error and operational risks. This contribution has already garnered attention, with 2 citations in its first year, signaling growing impact in the field. Tan’s research bridges the gap between virtual simulation and physical robotic systems, offering scalable solutions for smart manufacturing. His work is particularly notable for its practical application in heavy industry, where precision and reliability are paramount. For students and researchers exploring Industry 4.0, Tan’s innovations exemplify how digital twins and robotics can synergize to solve real-world production challenges, making him a key figure in the evolution of automated inspection technologies.
Research Focus
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Top Papers
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