Xiao Ling
Papers
1
Total Citations
69
H-Index
1
About
Xiao Ling is a leading researcher in advanced manufacturing and mechanical systems, with a focus on real-time process monitoring and signal processing for robotic machining. Their most influential work introduces a novel method for chatter identification in robotic drilling, leveraging a local maximum synchrosqueezing technique to enhance precision and timeliness in detecting harmful vibrations. This paper, published in 2019 and garnering 69 citations, has become a cornerstone for improving tool life and surface quality in automated manufacturing. Ling’s contributions bridge the gap between theoretical signal analysis and practical industrial applications, offering robust solutions for high-stakes environments like aerospace assembly. Their research not only advances the reliability of robotic operations but also reduces costly downtime, making a tangible impact on production efficiency. With a growing citation record, Ling is recognized for pioneering adaptive monitoring strategies that integrate machine learning and time-frequency analysis, positioning them as a key innovator in smart manufacturing. Their work continues to inspire engineers and researchers seeking to optimize robotic processes through intelligent, data-driven diagnostics.
Research Focus
Key Achievements
Top Papers
- 1