IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i3d10.1007_s10845-024-02326-1.html
   My bibliography  Save this article

Synthetic data generation using finite element method to pre-train an image segmentation model for defect detection using infrared thermography

Author

Listed:
  • Kaushal Arun Pareek

    (Chair Materials and Reliability of Microsystems
    Berliner Nanotest and Design GmbH)

  • Daniel May

    (Chair Materials and Reliability of Microsystems
    Berliner Nanotest and Design GmbH)

  • Peter Meszmer

    (Chair Materials and Reliability of Microsystems)

  • Mohamad Abo Ras

    (Berliner Nanotest and Design GmbH)

  • Bernhard Wunderle

    (Chair Materials and Reliability of Microsystems)

Abstract

The vision of a deep learning-empowered non-destructive evaluation technique aligns perfectly with the goal of zero-defect manufacturing, enabling manufacturers to detect and repair defects actively. However, the dearth of data in manufacturing is one of the biggest obstacles to realizing an intelligent defect detection system. This work presents a framework for bridging the data gap in manufacturing using the potential of synthetic datasets generated using the finite element method-based digital twin. The non-destructive technique under consideration is pulse infrared thermography. A large number of synthetic thermographic measurements were generated using 2D axisymmetric transient thermal simulations. The representativeness of synthetic data was thoroughly investigated at various steps of the framework, and the image segmentation model was trained separately on experimental and synthetic datasets. The study results reveal that when carefully rendered, synthetic datasets represent the experimental data well. When evaluated on real-world experimental samples, the segmentation model pre-trained on synthetic datasets generalizes well to the experimental samples. Furthermore, another advantage of synthetic datasets is the ease of labelling a large amount of data. Finally, the robustness assessment of the model was done on two new datasets: one where the complete experimental setup was changed, and the other was an open-source infrared thermography dataset

Suggested Citation

  • Kaushal Arun Pareek & Daniel May & Peter Meszmer & Mohamad Abo Ras & Bernhard Wunderle, 2025. "Synthetic data generation using finite element method to pre-train an image segmentation model for defect detection using infrared thermography," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1879-1905, March.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02326-1
    DOI: 10.1007/s10845-024-02326-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02326-1
    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/s10845-024-02326-1?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:36:y:2025:i:3:d:10.1007_s10845-024-02326-1. 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.