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| Autores principales: | , , , , |
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| Formato: | Artículo Open Access |
| Publicado: |
Wiley
2025
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| Materias: | |
| Acceso en línea: | https://sid.onlinelibrary.wiley.com/doi/10.1002/sdtp.18513 |
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- P‐235: Late‐News Poster: Improving Automated Inspection and Repair Performance in Display Manufacturing through Diffusion‐based Generative AI Seung-Gi Kim Eun-Chul Ko Haneul Kim DongGon Yoo SangRock Son SID Symposium Digest of Technical Papers This study presents a diffusion‐based generative AI approach to address dataset imbalances in display manufacturing automation. By generating realistic synthetic data for defects and repair failures, we achieved high‐performance classification AI and detection AI, enabling automation previously reliant on human operators. The proposed method demonstrates the potential of diffusion models to expand automation in manufacturing processes utilizing computer vision. 10.1002/sdtp.18513 http://onlinelibrary.wiley.com/termsAndConditions#vor