| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
|
| Presentation Title |
An Optimized Neural Network for Detecting Complex Glass Defects in Automated Inspection |
| Author(s) |
SHIHYU CHEN, Yen-Hsiang Chen, Fong-Ji Tsai |
| On-Site Speaker (Planned) |
SHIHYU CHEN |
| Abstract Scope |
Glass defect detection is critical in manufacturing, yet traditional machine vision methods struggle with complex and subtle defect patterns. This study proposes a deep learning-based approach using a concatenated hybrid network to enhance defect classification accuracy. By integrating efficient feature extraction with deep feature reuse, the model effectively captures intricate defect variations.
The proposed network is evaluated against state-of-the-art deep learning architectures using a curated glass defect dataset. Performance metrics—including accuracy, precision, recall, and confusion matrices—assess each model’s effectiveness. Experimental results demonstrate that the hybrid network outperforms conventional CNNs, achieving superior classification robustness and reliability. This research advances automated glass inspection by introducing a deep learning framework capable of handling complex defects, thereby enhancing manufacturing efficiency and quality control. |
| Proceedings Inclusion? |
Undecided |