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Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net
Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects.
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
Transfer learning is leveraged to reduce the training data demands and increase the prediction accuracy of the trained model.
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