Buyun Sheng
Papers
4
Total Citations
119
H-Index
4
About
Buyun Sheng is a researcher at the forefront of intelligent manufacturing and human-robot collaboration, with a primary focus on optimizing complex assembly systems through advanced computational methods. Their work centers on developing machine learning and metaheuristic algorithms to solve critical challenges in automotive body welding and assembly line balancing. Sheng’s most impactful contribution is a parallel machine learning strategy for predicting welded joint quality in automotive bodies, which has garnered 54 citations for its practical industrial applications. They have also pioneered multi-objective optimization approaches for robotic assembly lines, including a discrete artificial bee colony algorithm for human-robot collaborative mixed-model two-sided assembly lines (34 citations) and a simulated annealing algorithm addressing setup times and multiple constraints (24 citations). Notably, Sheng introduced an innovative anxiety model inspired by psychology to enhance multi-robot task allocation in search and rescue operations, demonstrating interdisciplinary thinking. Their work bridges theoretical algorithm development with real-world manufacturing efficiency, making significant strides toward smarter, more adaptive production systems.
Research Focus
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Top Papers
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