Jeong Eun Jeon
Papers
2
Total Citations
9
H-Index
2
About
Jeong Eun Jeon is a leading researcher in intelligent manufacturing systems, specializing in machine learning and explainable artificial intelligence (XAI) for industrial robotics. Her work focuses on enhancing the reliability of wafer transfer robots (WTRs)—critical equipment in semiconductor fabrication—through advanced fault prediction and diagnosis. Jeon’s major contributions include developing data-driven models that identify failures in high-risk components such as bearing motors, ball screws, and robot hands, using acceleration sensor data and fast Fourier transformation (FFT) for real-time anomaly detection. Her 2024 study on fault prediction and diagnosis in WTRs has already garnered 7 citations, demonstrating its immediate impact on predictive maintenance research. A subsequent paper on condition-based monitoring, with 2 citations, further refines failure detection and cause identification using XAI algorithms, making her work both technically rigorous and practically applicable. By integrating machine learning with transparent, interpretable AI, Jeon is advancing the field of smart manufacturing, offering solutions that reduce downtime and improve productivity in high-stakes semiconductor environments. Her research is a vital resource for engineers and scientists seeking to implement robust, explainable AI in industrial automation.
Research Focus
Key Achievements
Top Papers
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