Enpei Liang
Papers
2
Total Citations
9
H-Index
2
About
Enpei Liang is a leading researcher in intelligent manufacturing and robotic automation, with a primary focus on advancing spray coating technologies for industrial applications. His work addresses critical challenges in automated spraying systems, particularly in optimizing process parameters and trajectory planning to enhance quality and efficiency. Liang’s major contributions include developing a novel KHPO-ELM neural network algorithm for spraying quality prediction, which integrates hybrid optimization techniques to handle the complex, coupled parameters inherent in robotic spraying—a method that has already garnered 7 citations since its 2024 publication. He has also pioneered a multi-objective optimization framework for coating trajectory planning of combustion turbine blades, employing seventh-degree non-uniform B-spline curves to achieve superior surface coverage and uniformity. This work, published in 2025, demonstrates his commitment to solving real-world manufacturing challenges with advanced mathematical modeling. Liang’s research is notable for bridging the gap between empirical, experience-driven processes and data-driven automation, offering scalable solutions for intelligent manufacturing. His achievements highlight a promising trajectory in robotics and process optimization, with growing impact in both academic and industrial sectors.
Research Focus
Key Achievements
Top Papers
- 1
- 2