Junqi Chen
Papers
1
Total Citations
85
H-Index
1
About
Junqi Chen is a researcher whose work sits at the intersection of advanced manufacturing, intelligent sensing, and machine learning-driven process optimization. Chen's most recognized contribution centers on developing innovative approaches to robotic belt grinding, particularly for challenging aerospace-grade superalloys such as Inconel 718. In a highly cited 2019 study that has accumulated 85 citations, Chen introduced a novel material removal prediction framework that combines acoustic sensing with an ensemble XGBoost learning algorithm — a method that significantly advances the precision and intelligence of automated grinding processes. This work addresses a longstanding challenge in manufacturing: accurately predicting and controlling material removal in real time during complex robotic machining operations. By integrating non-intrusive acoustic signal monitoring with powerful ensemble machine learning, Chen's approach offers a practical, data-driven solution that enhances both surface quality and process efficiency. The strong citation impact of this research underscores its relevance to the broader robotics, smart manufacturing, and industrial automation communities, positioning Chen as a contributor to the growing field of AI-enabled precision machining.
Research Focus
Key Achievements
Top Papers
- 1