Shiming Zhuo
Papers
1
Total Citations
22
H-Index
1
About
Shiming Zhuo is a researcher advancing the field of intelligent manufacturing through the integration of machine learning and real-time process monitoring. His work centers on robotic laser additive manufacturing, where he addresses the critical challenge of detecting local defects during production. In his most-cited paper, published in 2023, Zhuo introduced a novel dynamic mapping strategy combined with a multibranch fusion convolutional neural network, enabling online monitoring of defects with high accuracy. This contribution has garnered 22 citations, reflecting its immediate relevance to quality control in additive manufacturing. By bridging deep learning with robotic systems, Zhuo’s research offers practical solutions for improving reliability and efficiency in metal additive processes, a key concern for aerospace, automotive, and biomedical industries. His work stands out for its focus on real-time, in-situ detection, moving beyond post-process inspection to enable adaptive manufacturing. Zhuo’s achievements highlight his role in shaping the next generation of smart manufacturing systems, where data-driven algorithms directly enhance production outcomes.
Research Focus
Key Achievements
Top Papers
- 1