Dongming Yang
Papers
3
Total Citations
10
H-Index
2
About
Dongming Yang is at the forefront of intelligent manufacturing, pioneering the automation of robotic spraying systems through advanced machine learning and optimization algorithms. His research centers on transforming the traditionally experience-driven, high-complexity spraying process into a precise, data-driven science. Yang’s major contributions include developing novel hybrid models that dramatically improve prediction accuracy for coating quality. His 2024 work on the KHPO-ELM neural network, which has already garnered 7 citations, established a new benchmark for predicting spraying outcomes. He further advanced the field with a multi-objective optimization approach for trajectory planning on complex geometries like combustion turbine blades, using seventh-degree non-uniform B-spline curves. Most recently, his improved DEWOA-ANFIS model demonstrates how to embed human-level expertise into automated systems, addressing critical challenges in quality consistency and occupational safety. With a growing citation impact, Yang is recognized for bridging the gap between theoretical optimization and practical industrial application, making him a key figure in the next generation of smart manufacturing and robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3