mlsanityData to 7 Oct 2026

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

arXiv 2101.08993 ↗First posted 22 Jan 2021eess.IV cs.CVcited by 4

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Abstract (arXiv)

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. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CNNs) have performed well in the volumetric segmentation of medical images. In this work, we leverage techniques from the medical imaging domain and propose training a 3D U-Net model to automatically segment defects in XCT images of AM samples. This work not only contributes to the use of machine learning for AM defect detection but also demonstrates for the first time 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean intersection of union (IOU) value of 88.4%.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 37 references that are in the map. First posted on arXiv 22 Jan 2021.

Builds on

12 of 37 references.

References · 12

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2022Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Surveycited by 6
  2. 2024Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturingcited by 4
  3. 2022Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturingcited by 1
  4. 2023An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anythingcited by 1