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| Title | Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning |
| Creator | 羅崇銘 Lo, Chung-Ming;Lin, Ting-Yi |
| Contributor | 圖檔所 |
| Key Words | Machine learning; Deep learning; AOI; Synthetic mechanisms |
| Date | 2024-08 |
| Date Issued | 25-Oct-2024 09:39:49 (UTC+8) |
| Summary | 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 |
| Type | article |
| DOI | https://doi.org/10.1007/s10845-024-02474-4 |
| dc.contributor | 圖檔所 | |
| dc.creator (作者) | 羅崇銘 | |
| dc.creator (作者) | Lo, Chung-Ming;Lin, Ting-Yi | |
| dc.date (日期) | 2024-08 | |
| dc.date.accessioned | 25-Oct-2024 09:39:49 (UTC+8) | - |
| dc.date.available | 25-Oct-2024 09:39:49 (UTC+8) | - |
| dc.date.issued (上傳時間) | 25-Oct-2024 09:39:49 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154060 | - |
| dc.description.abstract (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. | |
| dc.format.extent | 106 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Journal of Intelligent Manufacturing | |
| dc.subject (關鍵詞) | Machine learning; Deep learning; AOI; Synthetic mechanisms | |
| dc.title (題名) | Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1007/s10845-024-02474-4 | |
| dc.doi.uri (DOI) | https://doi.org/10.1007/s10845-024-02474-4 |