Papers
2
Total Citations
19
H-Index
2
About
Dr. Yunan Shan is a rising researcher in intelligent manufacturing and robotic machining, with a focus on integrating machine learning and digital twin technologies to enhance precision and adaptability in automated processes. Their most cited work, "Feature fusion and distillation embedded sparse Bayesian learning model for in-situ foreknowledge of robotic machining errors" (2023, 17 citations), introduces a novel framework that combines feature fusion and knowledge distillation within a sparse Bayesian learning model to predict and mitigate machining errors in real time. This contribution is pivotal for improving the reliability of robotic systems in high-stakes manufacturing environments. More recently, Dr. Shan proposed the "RMDTs: Process-oriented function-triggered robotic milling digital twin system for service-expansion" (2025), which advances the concept of digital twins by enabling dynamic, service-oriented expansions for robotic milling operations. Though early in their career, Dr. Shan’s work demonstrates a clear trajectory toward bridging theoretical machine learning with practical industrial applications, earning recognition for its potential to transform error prediction and system flexibility in smart manufacturing.
Research Focus
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Top Papers
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