IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i5d10.1007_s10845-023-02163-8.html
   My bibliography  Save this article

Anomaly detection in additive manufacturing processes using supervised classification with imbalanced sensor data based on generative adversarial network

Author

Listed:
  • Jihoon Chung

    (Virginia Tech)

  • Bo Shen

    (NJIT)

  • Zhenyu James Kong

    (Virginia Tech)

Abstract

Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defects) detection. However, since abnormal states (with defects) occur much less frequently than normal ones (without defects) in a manufacturing process, the number of sensor data samples collected from a normal state is usually much more than that from an abnormal state. This issue causes imbalanced training data for classification analysis, thus deteriorating the performance of detecting abnormal states in the process. It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set. To achieve this goal, this paper proposes a novel data augmentation method based on a generative adversarial network (GAN) using additive manufacturing process image sensor data. The novelty of our approach is that a standard GAN and classifier are jointly optimized with techniques to stabilize the learning process of standard GAN. The diverse and high-quality generated samples provide balanced training data to the classifier. The iterative optimization between GAN and classifier provides the high-performance classifier. The effectiveness of the proposed method is validated by both open-source data and real-world case studies in polymer and metal AM processes.

Suggested Citation

  • Jihoon Chung & Bo Shen & Zhenyu James Kong, 2024. "Anomaly detection in additive manufacturing processes using supervised classification with imbalanced sensor data based on generative adversarial network," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 2387-2406, June.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:5:d:10.1007_s10845-023-02163-8
    DOI: 10.1007/s10845-023-02163-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02163-8
    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/s10845-023-02163-8?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 search for a different version of it.

    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:35:y:2024:i:5:d:10.1007_s10845-023-02163-8. 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.