Yu‐Heng Lin
Papers
1
Total Citations
8
H-Index
1
About
Yu-Heng Lin is a researcher at the forefront of applying deep learning to industrial automation and quality control. His primary research focuses on computer vision and machine learning for defect detection in manufacturing processes, particularly in the finishing stages of grinding and polishing. Lin's most influential work, "Defect Detection of Grinded and Polished Workpieces Using Faster R-CNN" (2021, 8 citations), addresses a critical bottleneck in automated fabrication: while robots can polish and grind components, quality inspection still relies on skilled human workers. By adapting the Faster R-CNN object detection framework to identify surface defects on finished workpieces, Lin demonstrated that deep learning can reliably automate this final inspection step, reducing reliance on manual labor and improving consistency. This contribution is significant for industries seeking fully automated production lines. His research bridges the gap between advanced AI techniques and practical manufacturing challenges, offering scalable solutions for real-world quality assurance. Lin's work continues to inform the development of intelligent inspection systems in industrial settings.
Research Focus
Key Achievements
Top Papers
- 1Defect Detection of Grinded and Polished Workpieces Using Faster R-CNN8 citations · 2021