Deep learning-based conductive particle inspection for TFT-LCDs inspired by parametric space envelope
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DOI: 10.1007/s10845-023-02241-x
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- Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
- Xiongping Yue & Dong Mu & Chao Wang & Huanyu Ren & Pezhman Ghadimi, 2023. "Topological structure and COVID-19 related risk propagation in TFT-LCD supply networks," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2758-2778, April.
- Myeongso Kim & Minyoung Lee & Minjeong An & Hongchul Lee, 2020. "Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1165-1174, June.
- Eryun Liu & Kangping Chen & Zhiyu Xiang & Jun Zhang, 2020. "Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1037-1049, April.
- Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
- Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
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Keywords
Conductive particle inspection; Deep learning; Convolutional neural network; TFT-LCD; Intelligent manufacturing;All these keywords.
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