Papers
5
Total Citations
51
H-Index
3
About
Dr. Kihyun Kim is a leading researcher at the intersection of robotics, energy harvesting, and intelligent manufacturing. His work focuses on creating self-powered robotic systems and enhancing their precision through advanced control and machine learning. Dr. Kim’s most impactful contribution is the development of a nonlinear model for a high-power electromagnetic energy harvesting system, designed to power a transmission line vibration deicing robot, a paper that has garnered 33 citations. He has also made significant strides in industrial robotics, using iterative learning control to improve path accuracy and reduce vibration in robot arms. More recently, Dr. Kim has pioneered data-driven approaches for predictive maintenance, including a machine learning method for fault diagnosis of harmonic drives using wireless sensors (7 citations) and a technique for predicting Z-axis belt wear in wafer transfer robots, which is critical for semiconductor manufacturing. His work extends to real-time quality control, where he has developed a machine learning system to classify the quality of robotic self-piercing rivet processes. Dr. Kim’s research is distinguished by its practical application, directly addressing challenges in energy autonomy, precision, and reliability for next-generation industrial robots.
Research Focus
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Top Papers
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