Chang Hyeon Mun

Ulsan National Institute of Science and Technology

Papers

Total Citations

H-Index

About

Chang Hyeon Mun is a researcher advancing the field of intelligent manufacturing and robotic machining, with a focus on sensor fusion and machine learning for process monitoring. His most cited work, "Indirect measurement of cutting forces during robotic milling using multiple sensors and a machine learning-based system identifier" (2022), has garnered 22 citations, establishing a foundation for non-invasive force estimation in robotic milling. This contribution addresses a critical challenge in precision manufacturing: the accurate prediction of cutting forces without direct measurement, enabling safer, more efficient robotic operations. By integrating multiple sensor inputs with a learning-based identifier, Mun’s approach enhances real-time process control and tool condition monitoring, with implications for aerospace, automotive, and high-value component fabrication. His work bridges the gap between traditional machining and Industry 4.0, offering a scalable solution for smart factories. Mun’s research is notable for its practical impact, providing a pathway to reduce downtime and improve product quality in automated manufacturing environments. As a rising voice in cyber-physical production systems, he continues to explore how data-driven methods can transform robotic machining into a more adaptive, resilient process.