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About

Donggun Lee is a researcher at the forefront of smart manufacturing and Industry 4.0, specializing in the development and application of digital twins for automated systems. His work focuses on enhancing the design, verification, and operational management of complex manufacturing environments, particularly Automated Material Handling Systems (AMHSs) and human-machine collaborative assembly lines. Lee’s most-cited paper, "Development and Application of Digital Twin for the Design Verification and Operation Management of Automated Material Handling Systems" (2021, 8 citations), demonstrates his impact in enabling unmanned factories through digital twin technology. He further advances the field by integrating digital twins with reinforcement learning for dynamic path planning of Automated Guided Vehicles (AGVs), as seen in his 2024 publication. Lee’s research also addresses the practical needs of small and medium-sized manufacturers by designing applications for collaborative robot integration, as highlighted in his 2020 study. With a growing citation record and a focus on bridging theoretical digital twin concepts with real-world industrial applications, Donggun Lee is contributing to the next generation of intelligent, adaptive manufacturing systems.