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Segmentation of Additive Manufacturing Defects Using U-Net

Published

Author(s)

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

Abstract

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to the deficit performance of the fabricated part. X-ray computed tomography (XCT) is a nondestructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This article describes an automatic defect segmentation method using U-Net-based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. The performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This article demonstrates that U-Net can be effectively applied for automatic segmentation of AM porosity from XCT images with high accuracy. The method can potentially help improve the quality control of AM parts in an industry setting.
Citation
Journal of Computers and Information in Engineering
Volume
22

Keywords

smart manufacturing, defect detection, additive manufacturing, convolutional neural networks, X-ray computed tomography (XCT) images, machine learning, artificial intelligence, data-driven engineering, machine learning for engineering applications

Citation

Wong, V. , Ferguson, M. , Law, K. , Lee, Y. and Witherell, P. (2022), Segmentation of Additive Manufacturing Defects Using U-Net, Journal of Computers and Information in Engineering, [online], https://doi.org/10.1115/1.4053078 (Accessed May 30, 2024)

Issues

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Created June 30, 2022, Updated January 7, 2023