Shuang Mei
Papers
2
Total Citations
88
H-Index
2
About
Shuang Mei is a leading researcher in intelligent manufacturing and industrial automation, with a focus on deep learning-driven visual inspection and robotic guidance systems. Their most impactful work introduces a novel deep convolutional neural network for detecting wafer semiconductor surface defects—including stains, burrs, scratches, and holes—that critically compromise downstream production quality in welding robots, spray robots, and unmanned material delivery vehicles. This highly cited paper (85 citations) has established a new benchmark for automated quality control in semiconductor fabrication. More recently, Mei has advanced the field of mining automation with a vision-guided strategy for accurately delivering drill pipes in horizontal directional drilling rigs, addressing the pressing need to reduce labor intensity in coal extraction. By integrating 2D vision sensors for precise pipe localization, this work demonstrates practical, cost-effective solutions for heavy industry. Mei’s contributions bridge cutting-edge computer vision with real-world manufacturing challenges, offering scalable inspection and guidance systems that enhance both product quality and worker safety. Their research is essential reading for engineers and scientists developing autonomous industrial systems.
Research Focus
Key Achievements
Top Papers
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