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Image Data-Centric Visual Feature Selection on Roll-to-Roll Slot-Die Coating Systems for Edge Wave Coating Defect
Yoonjae Lee1, Junyoung Yun1, Sangbin Lee1
1Department of Mechanical Design and Production Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
Polymers
|April 27, 2024
Summary
This study introduces a primary color selection (PCS) method for precise edge defect detection in roll-to-roll (R2R) manufacturing. The PCS method enhances accuracy and efficiency in identifying coating flaws, improving quality control.
Area of Science:
- Manufacturing Engineering
- Computer Vision
- Materials Science
Background:
- Roll-to-roll (R2R) manufacturing requires high-quality coatings with precise specifications.
- Accurate detection of coating defects is crucial for consistent maintenance and quality control in R2R systems.
Purpose of the Study:
- To propose and evaluate a novel primary color selection (PCS) method for detecting edge defects in R2R manufacturing.
- To address challenges in defect detection, including training data demands, complexity, and adaptability.
Main Methods:
- A vision data-centric approach utilizing color information for defect detection.
- Selection of the primary color channel based on color variability to distinguish coated and non-coated regions.
- Implementation of the PCS method for precise edge defect identification.
Main Results:
- The PCS method achieved a superior accuracy of 95.8% in detecting edge defects.
- This accuracy significantly outperformed the traditional weighted sum method, which achieved 78.3%.
- The method minimized data capacity requirements and processing time.
Conclusions:
- The PCS method offers a highly accurate and efficient solution for real-time edge defect detection in R2R manufacturing.
- This approach facilitates improved quality control and production optimization by mitigating edge coating defects.
