Papers
10
Total Citations
105
H-Index
6
About
Dr. Jumyung Um is a leading researcher at the intersection of intelligent manufacturing, robotics, and sustainable production systems. His work focuses on creating autonomous, self-organizing factories through the integration of digital twins, deep reinforcement learning, and artificial intelligence. Dr. Um’s most impactful contribution is his pioneering use of synthetic data and reinforcement learning to develop digital twins for autonomous collaborative robots, a paper that has garnered 29 citations and is reshaping how manufacturing systems adapt to supply chain disruptions. He has also made significant strides in logistics automation, notably with his BoxStacker system for 3D bin packing (17 citations), and in energy-efficient manufacturing, where his total energy estimation model for remote laser welding (17 citations) addresses critical energy consumption challenges in the automotive industry. His work on the GadgetArm robot gripper (10 citations) further advances autonomous object manipulation for Industry 4.0. With a growing portfolio of highly cited papers and recent explorations into LLM-based multi-robot communication, Dr. Um is a driving force in the evolution of resilient, intelligent, and energy-aware production systems.
Research Focus
Key Achievements
Top Papers
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- 3Total Energy Estimation Model for Remote Laser Welding Process17 citations · 2013
- 4Deep Learning Approach of Energy Estimation Model of Remote Laser Welding11 citations · 2019
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