Yisen Zeng

Papers

Total Citations

H-Index

About

Yisen Zeng is a researcher focused on intelligent manufacturing and robotic precision machining, with particular expertise in aircraft engine component finishing and real-time tool condition monitoring. His work addresses critical challenges in automated polishing processes, where maintaining consistent quality is essential for high-performance aerospace components. In his highly cited 2023 study on vibration stability during robotic pneumatic grinding wheel polishing of aircraft engine blades, Zeng developed methods to suppress chatter and ensure surface integrity—a contribution that has already garnered 20 citations for its practical relevance to advanced manufacturing. His subsequent research introduced a novel approach combining neural ordinary differential equations (neural ODEs) with a backpropagation-genetic algorithm (BP-GA) to predict tool wear in robotic machining. This work directly tackles the problem of erratic polishing quality caused by tool degradation, offering a predictive framework to optimize tool replacement timing. By integrating cutting-edge deep learning with evolutionary optimization, Zeng’s research provides actionable solutions for improving automation reliability in high-stakes manufacturing environments. His contributions are shaping the future of intelligent, self-correcting robotic systems for aerospace and precision engineering.