IDEAS home Printed from https://ideas.repec.org/a/ids/ijmtma/v40y2026i1-2p138-153.html

Online recognition method for appearance defects in mechanical parts processing based on machine vision

Author

Listed:
  • Qian Meng
  • Pengfei Shi

Abstract

In order to solve the shortcomings of traditional parts appearance defect recognition methods with low recognition accuracy and long recognition time, this paper proposes an online recognition method for mechanical parts processing appearance defects based on machine vision. Firstly, the image acquisition environment of mechanical parts based on machine vision is determined. Secondly, the part image is pre-processed; thirdly, the maximum entropy segmentation method is used to complete the image segmentation. Finally, the defect texture features of the part image are extracted, and the support vector machine algorithm is combined to realise the online recognition of the appearance defects of mechanical parts. Experiments show that the recognition time of the proposed method never exceeds 600 s, the recognition accuracy is 93.75%, and the average time overhead of identifying a part is 0.5 s, which has high recognition accuracy and less time overhead, and has better application performance.

Suggested Citation

  • Qian Meng & Pengfei Shi, 2026. "Online recognition method for appearance defects in mechanical parts processing based on machine vision," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 40(1/2), pages 138-153.
    • Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:138-153
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=151521
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijmtma:v:40:y:2026:i:1/2:p:138-153. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=21 .

    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.