IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i7d10.1007_s10845-024-02474-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02474-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-024-02474-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02474-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.