Guangjun Zhang
Papers
17
Total Citations
860
H-Index
8
About
Guangjun Zhang is a pioneering figure in robotic wire and arc additive manufacturing (WAAM), a field he has advanced through a unique blend of process modeling, intelligent control, and multi-robot coordination. His core research focuses on predicting and controlling bead geometry—the fundamental building blocks of 3D-printed metal parts—using neural networks and regression analysis, as demonstrated in his highly cited 2012 papers (over 315 and 231 citations, respectively). These foundational works enabled the precise, repeatable fabrication of large-scale components. Zhang’s impact extends to developing closed-loop control systems, including fuzzy-logic and vision-based methods, to ensure homogeneous layers and correct geometric deviations in real time. His recent contributions tackle the complex challenge of multi-robot coordination for WAAM, optimizing deposition paths and workload allocation for manufacturing massive parts, such as the large sprocket repair detailed in his 2021 work. With over 800 cumulative citations, Zhang’s research bridges theoretical modeling and practical automation, making him a key innovator in scalable, intelligent additive manufacturing.
Research Focus
Key Achievements
Top Papers
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- 6Large-size sprocket repairing based on robotic GMAW additive manufacturing35 citations · 2021
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