Ming Yin
Papers
1
Total Citations
22
H-Index
1
About
Ming Yin is a leading researcher in intelligent manufacturing and advanced process monitoring, with a focus on laser additive manufacturing and robotic automation. Their work bridges the gap between physical process dynamics and data-driven diagnostics, particularly through the integration of deep learning with real-time sensing. Yin’s most cited paper, "Online monitoring of local defects in robotic laser additive manufacturing process based on a dynamic mapping strategy and multibranch fusion convolutional neural network" (2023, 22 citations), introduces a novel framework that transforms high-dimensional sensor data into actionable defect detection. This contribution is pivotal for improving quality control in metal additive manufacturing, where local defects can compromise structural integrity. By combining dynamic mapping with a multibranch fusion CNN, Yin’s approach enables real-time, in-situ monitoring—a critical step toward fully autonomous production. Their work has been recognized for its practical impact on reducing waste and enhancing reliability in aerospace and biomedical applications. Yin’s research continues to push the boundaries of smart manufacturing, offering scalable solutions for Industry 4.0.
Research Focus
Key Achievements
Top Papers
- 1