Yuchun Xu
Papers
7
Total Citations
97
H-Index
5
About
Yuchun Xu is a researcher whose work spans the intersection of intelligent manufacturing, robotics, and industrial automation, with particular focus on remanufacturing systems, fault diagnosis, and human-robot collaboration. His most cited contribution, a comprehensive 2023 review on few-shot learning approaches for fault diagnosis using vibration data (35 citations), positions him as a notable voice in applying machine learning to industrial reliability challenges — a critical concern where labeled fault data is scarce and safety is paramount. Equally impactful is his 2024 work on ontology and rule-based methods for human-robot collaborative disassembly planning in smart remanufacturing (32 citations), addressing the growing need for flexible, efficient end-of-life product processing. Xu's research consistently bridges theoretical innovation and practical application: he has developed robotic platforms for ultrasonic inspection in remanufacturing, automated rail tunnel inspection systems, and multi-objective welding parameter optimization using small datasets. His 2019 survey on cloud robotics (11 citations) reflects an early engagement with Industry 4.0 technologies. Across his portfolio, Xu demonstrates a sustained commitment to advancing sustainable manufacturing through intelligent robotics, making his work highly relevant for researchers and students in smart manufacturing and industrial AI.
Research Focus
Key Achievements
Top Papers
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- 3Summary of Cloud Robot Research11 citations · 2019
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