Ruiling Yuan
Papers
1
Total Citations
37
H-Index
1
About
Ruiling Yuan is an emerging researcher in intelligent manufacturing and robotic welding, with a focus on real-time quality monitoring and deep learning. Her most cited work introduces AF-FTTSnet, an end-to-end two-stream convolutional neural network designed for online quality assessment in robotic welding processes. This contribution addresses a critical challenge in automated manufacturing: enabling machines to detect weld defects in real time without human intervention. By fusing temporal and spatial features, her model achieves robust performance, earning 37 citations since its 2024 publication. Yuan’s research bridges computer vision and industrial automation, offering practical solutions for smart factories. Her work is particularly notable for advancing non-destructive evaluation methods, reducing waste and improving safety in high-precision welding. As a rising voice in the field, she continues to explore how neural networks can transform traditional manufacturing into adaptive, data-driven systems. Her achievements underscore a commitment to making industrial processes more efficient and reliable through cutting-edge AI.
Research Focus
Key Achievements
Top Papers
- 1