Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

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



Vivian W. Wong, Max Ferguson, Kincho Law, Yung-Tsun Lee, Paul Witherell


Segmentation of defects in additive manufacturing 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, 3D 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 AM samples. The main contribution of this work is that it is the first to perform 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean IOU (intersection of union) value of 88.4%.
Proceedings Title
Conference Dates
March 23-25, 2020
Conference Location
(Palo Alto-VIRTUAL), CA, US
Conference Title
SSS20 (VIRTUAL) AAAI Spring Symposium on AI in Manufacturing, 3/23-24/2020


Addictive manufacturing, defect segmentation, X-ray computed tomography, Convolutional Neural Networks, 3 D U-Net architecture


Wong, V. , Ferguson, M. , Law, K. , Lee, Y. and Witherell, P. (2020), Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net, AAAI 2020 SPRING SYMPOSIUM SERIES, (Palo Alto-VIRTUAL), CA, US, [online], (Accessed July 18, 2024)


If you have any questions about this publication or are having problems accessing it, please contact

Created March 22, 2020, Updated October 12, 2021