Meiyan Feng
Papers
2
Total Citations
53
H-Index
2
About
Meiyan Feng is a leading researcher in robotic wire and arc additive manufacturing (WAAM), with a focused expertise in process modeling, bead geometry prediction, and path planning for large-scale metal additive components. Her most impactful work introduces a profile transformation-based recursive multi-bead overlapping model, which addresses a critical limitation in WAAM: the accurate prediction of overlapping bead profiles, particularly the flatten valley areas that conventional models fail to capture. This contribution, published in 2022 and garnering 39 citations, provides a robust framework for improving dimensional accuracy in WAAM parts. Feng further advanced the field by developing a method using axisymmetric drop shape analysis to recursively predict multi-bead profiles, enabling more reliable dimension control in robotic deposition. Her research bridges computational geometry and practical manufacturing, offering scalable solutions for industrial WAAM applications. With her work cited in leading additive manufacturing journals, Feng is recognized for tackling fundamental modeling challenges that directly impact part quality and process repeatability, making her a key contributor to the advancement of wire-arc additive technologies.
Research Focus
Key Achievements
Top Papers
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