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政大機構典藏 > 文學院 > 圖書資訊與檔案學研究所 > 期刊論文 > Item 140.119/154060

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/154060

Title:	Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning
Authors:	羅崇銘 Lo, Chung-Ming;Lin, Ting-Yi
Contributors:	圖檔所
Keywords:	Machine learning;Deep learning;AOI;Synthetic mechanisms
Date:	2024-08
Issue Date:	2024-10-25 09:39:49 (UTC+8)
Abstract:	The quality inspection of products before delivery plays a critical role in ensuring manufacturing quality. Quick and accurate inspection of samples is realized by highly automated inspection based on pattern recognition in smart manufacturing. Conventional ensemble methods have been demonstrated to be effective for defect detection. This study further proposed synthetic mechanisms based on using various features and learning classifiers. A database of 6000 sample images of printed circuit board (PCB) connectors collected from factories was compiled. A novel confidence synthesis mechanism was proposed to prescreen images using deep learning features. Spatially connected texture features were then used to reclassify images with low reliabilities. The synthetic mechanism was found to outperform a single classifier. In particular, the highest improvement in accuracy (from 96.00 to 97.83%) was obtained using the confidence-based synthesis. The synthetic mechanism can be used to achieve high accuracy in defect detection and make automation in smart manufacturing more practicable.
Relation:	Journal of Intelligent Manufacturing
Data Type:	article
DOI 連結:	https://doi.org/10.1007/s10845-024-02474-4
DOI:	10.1007/s10845-024-02474-4
Appears in Collections:	[圖書資訊與檔案學研究所] 期刊論文

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