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题名:	Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning
作者:	羅崇銘 Lo, Chung-Ming;Lin, Ting-Yi
贡献者:	圖檔所
关键词:	Machine learning;Deep learning;AOI;Synthetic mechanisms
日期:	2024-08
上传时间:	2024-10-25 09:39:49 (UTC+8)
摘要:	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.
關聯:	Journal of Intelligent Manufacturing
数据类型:	article
DOI 連結:	https://doi.org/10.1007/s10845-024-02474-4
DOI:	10.1007/s10845-024-02474-4
显示于类别:	[圖書資訊與檔案學研究所] 期刊論文

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