Yunwei Huang
Papers
2
Total Citations
89
H-Index
2
About
Yunwei Huang is a leading researcher at the intersection of intelligent manufacturing and robotic systems, with a primary focus on fault diagnosis, human-robot collaboration, and disassembly line optimization. His most influential work introduces a multiscale convolutional capsule network that learns discriminative features from attitude data, enabling highly accurate fault diagnosis for industrial robots—a contribution that has garnered 81 citations and set a new standard for reliability in automated production. Huang’s recent research advances the complex field of multi-man–robot collaborative disassembly, where he developed a mixed-integer programming model and a genetic Jaya algorithm to balance disassembly line efficiency, a critical step toward sustainable manufacturing and circular economies. His work is distinguished by its practical integration of deep learning and optimization, directly addressing real-world challenges in Industry 4.0. With a growing citation impact and a clear trajectory toward solving pressing industrial problems, Huang is recognized as an innovator whose methods are widely adopted by both academia and industry for enhancing robotic autonomy and human-robot teamwork.
Research Focus
Key Achievements
Top Papers
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