Deep learning for automated defect detection in high-reliability electronic parts

Emily A. Donahue; Tu-Thach Quach; Kevin Potter; Cari Martinez; Matthew Smith; Christian D. Turner

doi:10.1117/12.2529584

6 September 2019 Deep learning for automated defect detection in high-reliability electronic parts

Emily A. Donahue, Tu-Thach Quach, Kevin Potter, Cari Martinez, Matthew Smith, Christian D. Turner

Proceedings Volume 11139, Applications of Machine Learning; 1113907 (2019) https://doi.org/10.1117/12.2529584
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Recent advances in deep learning have shown promising results for anomaly detection that can be applied to the problem of defect detection in electronic parts. In this work, we train a deep learning model with Generative Adversarial Networks (GANs) to detect anomalies in images of X-ray CT scans. The GANs detections can then be reviewed by an analyst to confirm the presence or absence of a defect in a scan, significantly reducing the amount of time required to analyze X-Ray CT scans. We employ a trained GAN via a system referred to in the literature as an AnoGAN. We train the AnoGAN on images of X-Ray CT scans from normal, non-defective components until it is capable of generating images that are indistinguishable from genuine part scans. Once trained, we query the AnoGAN with an image of an X-ray CT scan that is known to contain a defect, such as a crack or a void. By sampling the GANs latent space, we generate an image that is as visually close to the query image as possible. Because the AnoGAN has learned a distribution over non-defective parts, it can only produce images without defects. By taking the difference between the query image and the generated image, we are able to highlight anomalous areas in the defective part. We hypothesize that this work can be used to improve speed and accuracy for quality assurance of manufactured parts by applying machine learning to non-destructive imaging.

Conference Presentation

Citation Download Citation

Emily A. Donahue, Tu-Thach Quach, Kevin Potter, Cari Martinez, Matthew Smith, and Christian D. Turner "Deep learning for automated defect detection in high-reliability electronic parts", Proc. SPIE 11139, Applications of Machine Learning, 1113907 (6 September 2019); https://doi.org/10.1117/12.2529584

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available