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6 September 2019 Deep learning for automated defect detection in high-reliability electronic parts
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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.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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