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Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning

Author

Listed:
  • Chung-Ming Lo

    (National Chengchi University)

  • Ting-Yi Lin

    (National Chengchi University)

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.

Suggested Citation

  • Chung-Ming Lo & Ting-Yi Lin, 2025. "Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4769-4783, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02474-4
    DOI: 10.1007/s10845-024-02474-4
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    References listed on IDEAS

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    1. Sang M. Lee & DonHee Lee & Youn Sung Kim, 2019. "The quality management ecosystem for predictive maintenance in the Industry 4.0 era," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-11, December.
    2. Javid Akhavan & Jiaqi Lyu & Souran Manoochehri, 2024. "A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1389-1406, March.
    3. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    4. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
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