Papers
2
Total Citations
15
H-Index
2
About
Yujie Ye is a researcher in advanced manufacturing and welding engineering, with a focus on robotic gas metal arc welding (GMAW) and process optimization. Their work addresses critical challenges in automated welding, particularly for complex geometries like overhang and out-of-position structures. Ye’s major contributions include the application of Grey Relational Analysis (GRA) integrated with the Taguchi method to achieve multi-objective optimization of weld bead quality—balancing parameters such as penetration, bead width, and reinforcement. Their 2024 study on the effect of welding position on bead quality, with 10 citations, demonstrates practical relevance for robotic welding in construction and shipbuilding. Another highly cited work (5 citations) extends this optimization to short-circuit GMAW for overhang structures, offering industry-ready solutions for defect reduction. Ye’s research bridges theoretical optimization with real-world manufacturing constraints, making their work valuable for students and engineers seeking to improve weld quality in automated production lines. Their systematic approach to parameter tuning and multi-response optimization has been recognized as a key reference in the field.
Research Focus
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Top Papers
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