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Segmentation of additive manufacturing (AM) defects in X-ray ComputedTomography (XCT) images is challenging, due to the poor contrast, small sizesand variation in appearance of defects. Automatic segmentation can, however,provide quality control for additive manufacturing. Over recent years,three-dimensional convolutional neural networks (3D CNNs) have performed wellin the volumetric segmentation of medical images. In this work, we leveragetechniques from the medical imaging domain and propose training a 3D U-Netmodel to automatically segment defects in XCT images of AM samples. This worknot only contributes to the use of machine learning for AM defect detection butalso demonstrates for the first time 3D volumetric segmentation in AM. We trainand test with three variants of the 3D U-Net on an AM dataset, achieving a meanintersection of union (IOU) value of 88.4%.

Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net