Kuang-Chyi Lee
Papers
1
Total Citations
2
H-Index
1
About
Kuang-Chyi Lee is a researcher focused on applied machine learning and industrial automation, with a particular emphasis on defect detection and classification in manufacturing components. His work bridges computer vision and precision engineering, notably through the development of deep learning models for quality control. His most-cited paper, "Classification of Guide Rail Block by Xception Model" (2022), demonstrates the use of convolutional neural networks to automate the inspection of linear guide rail blocks—critical components in milling machines, lathes, robotic arms, and automated machinery. By leveraging the Xception architecture, Lee’s approach enhances the accuracy and efficiency of identifying oil stains and defects, reducing reliance on manual inspection. This contribution has garnered 2 citations, reflecting its relevance to smart manufacturing and Industry 4.0. Lee’s research addresses practical challenges in industrial settings, offering scalable solutions for real-time quality assurance. His work is particularly valuable for students and engineers exploring the intersection of AI and mechanical systems, showcasing how deep learning can optimize traditional manufacturing processes. Through his focus on applied computer vision, Lee continues to advance the automation of precision component inspection.
Research Focus
Key Achievements
Top Papers
- 1Classification of Guide Rail Block by Xception Model2 citations · 2022