Yang Kang
Papers
1
Total Citations
11
H-Index
1
About
Yang Kang is a pioneering researcher in the field of digital twin technology and industrial robotics, with a focus on multi-level, multi-domain modeling methods. Their most-cited work, "A multi-level multi-domain digital twin modeling method for industrial robots" (2025, 11 citations), introduces a novel framework that integrates physical and virtual systems across various operational layers, enabling real-time monitoring, simulation, and optimization of robotic performance. This contribution addresses critical challenges in manufacturing, such as system interoperability and predictive maintenance, by bridging the gap between theoretical modeling and practical industrial applications. Kang’s research has garnered attention for its potential to enhance efficiency and reduce downtime in automated production lines, making it a cornerstone for future smart factory developments. With a growing citation count reflecting its relevance, this work underscores Kang’s role as an emerging leader in digital twin innovation. Their achievements highlight a commitment to advancing Industry 4.0 paradigms, offering tangible solutions for complex robotic systems.
Research Focus
Key Achievements
Top Papers
- 1