Yucong Fu
Papers
3
Total Citations
10
H-Index
2
About
Yucong Fu is a rising researcher in intelligent manufacturing and robotic automation, with a focused expertise in automated spray-painting systems. His work addresses the critical challenge of replacing experience-driven manual spraying with data-driven robotic precision, aiming to improve quality consistency and reduce occupational hazards. Fu’s major contributions lie in developing advanced predictive models and trajectory planning algorithms for complex industrial components. His most cited work, “Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network” (2024, 7 citations), introduces a novel neural network approach to predict paint film quality amidst highly coupled process parameters. He further advanced the field with a multi-objective optimization algorithm for coating turbine blades using seventh-degree B-spline curves (2025, 2 citations), and an improved DEWOA-ANFIS model for film quality prediction (2025, 1 citation). These contributions demonstrate Fu’s commitment to bridging the gap between human expertise and autonomous robotic performance, laying foundational work for smarter, safer, and more efficient industrial painting processes.
Research Focus
Key Achievements
Top Papers
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