Ying‐Jun Quan
Papers
7
Total Citations
546
H-Index
7
About
Ying‐Jun Quan is a leading researcher at the intersection of smart manufacturing, advanced materials, and intelligent actuation systems. His work is defined by pioneering contributions to machine learning-driven machining processes, shape memory alloy (SMA)-based soft robotics, and sustainable Industry 4.0 transformation. Quan’s most influential work, “Smart Machining Process Using Machine Learning” (2018), has garnered 279 citations, establishing a foundational framework for integrating AI into precision manufacturing. He has also advanced soft actuation with his 2015 study on a single-SMA twisting actuator (76 citations), demonstrating how fewer mechanical components can achieve complex robotic motions. More recently, Quan has explored light-driven artificial muscles and surface-nanopatterned sensors, including a colour-tunable strain sensor (26 citations) and a lithography-free structural coloration method (22 citations), pushing the boundaries of responsive materials. His forward-looking 2023 review on smart factory transformation toward ESG perspectives (21 citations) underscores his commitment to environmentally and socially responsible manufacturing. Through this diverse body of work—spanning from nanoimprinting to composite manufacturing for future mobility—Quan continues to shape how intelligent systems and advanced materials converge for next-generation industrial applications.
Research Focus
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Top Papers
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