Minjun Xu
Papers
1
Total Citations
9
H-Index
1
About
Minjun Xu is a researcher specializing in intelligent manufacturing and digital twin technology, with a particular focus on process monitoring and defect suppression in precision machining. His most cited work, "Process-oriented unstable state monitoring and strategy recommendation for burr suppression of weak rigid drilling system driven by digital twin" (2021, 9 citations), introduces a novel framework that integrates digital twin models with real-time monitoring to detect unstable drilling states and recommend corrective strategies. This contribution addresses a critical challenge in manufacturing—burr formation in weak rigid systems—by enabling adaptive process control. Xu’s research bridges the gap between virtual simulation and physical machining, offering practical solutions for improving product quality and reducing waste. His work has been recognized for its potential to enhance automation in aerospace and automotive industries, where precision drilling is essential. With a growing citation footprint, Xu is establishing himself as an emerging voice in the application of digital twins for process optimization, and his ongoing efforts continue to push the boundaries of smart manufacturing.
Research Focus
Key Achievements
Top Papers
- 1