Publications

Automatic detection of defects in high reliability as-built parts using x-ray CT

Potter, Kevin M.; Donohoe, Brendan D.; Greene, Benjamin G.; Pribisova, Abigail; Donahue, Emily D.

Automatic detection of defects in as-built parts is a challenging task due to the large number of potential manufacturing flaws that can occur. X-Ray computed tomography (CT) can produce high-quality images of the parts in a non-destructive manner. The images, however, are grayscale valued, often have artifacts and noise, and require expert interpretation to spot flaws. In order for anomaly detection to be reproducible and cost effective, an automated method is needed to find potential defects. Traditional supervised machine learning techniques fail in the high reliability parts regime due to large class imbalance: there are often many more examples of well-built parts than there are defective parts. This, coupled with the time expense of obtaining labeled data, motivates research into unsupervised techniques. In particular, we build upon the AnoGAN and f-AnoGAN work by T. Schlegl et al. and created a new architecture called PandaNet. PandaNet learns an encoding function to a latent space of defect-free components and a decoding function to reconstruct the original image. We restrict the training data to defect-free components so that the encode-decode operation cannot learn to reproduce defects well. The difference between the reconstruction and the original image highlights anomalies that can be used for defect detection. In our work with CT images, PandaNet successfully identifies cracks, voids, and high z inclusions. Beyond CT, we demonstrate PandaNet working successfully with little to no modifications on a variety of common 2-D defect datasets both in color and grayscale.