Papers
16
Total Citations
582
H-Index
10
About
Xiling Yao is a pioneering researcher at the intersection of advanced manufacturing, artificial intelligence, and robotics, with a focus on additive manufacturing (AM) quality assurance and intelligent process control. His work has made transformative contributions to the field of laser-directed energy deposition (L-DED) and continuous fiber-reinforced polymer composite fabrication, where real-time defect detection and correction remain critical challenges. Yao's most celebrated contributions include the development of deep learning-assisted defect detection systems for closed-loop manufacturing control, which has garnered 150 citations, and multisensor fusion-based digital twin frameworks for localized quality prediction, cited 124 times. These works collectively advance the paradigm of in-situ monitoring by integrating acoustic, visual, and thermal sensing modalities to capture complex process dynamics that single-sensor approaches cannot adequately address. His research on adaptive dimension correction strategies and point cloud-based surface monitoring further demonstrates a commitment to precision and process intelligence across the AM lifecycle. Beyond additive manufacturing, Yao has contributed to robotic machining optimization and stiffness modeling of industrial robots. With over 540 cumulative citations across his most impactful papers, his research is shaping the future of smart manufacturing and cyber-physical production systems.
Research Focus
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Top Papers
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