Segmentation of Addictive Manufacturing Defects Using U-Net

Published

August 17, 2021

Author(s)

Vivian 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 deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive 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 paper 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. 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 work demonstrates that, using XCT images, U-Net can be effectively applied for automatic segmentation of AM porosity with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.

Proceedings Title

ASME 2021
International Design Engineering Technical Conferences and
Computers and Information in Engineering Conference
IDETC/CIE2021

Conference Dates

August 17-20, 2021

Conference Location

Online, Virtual, MD, US

Conference Title

ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE 2021

Pub Type

Conferences

Download Paper

https://doi.org/10.1115/DETC2021-68885

Local Download

Keywords

Smart Manufacturing, Defect Detection, Additive Manufacturing, Convolutional Neural Networks

Citation

Wong, V. , Ferguson, M. , Law, K. , Lee, Y. and Witherell, P. (2021), Segmentation of Addictive Manufacturing Defects Using U-Net, ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2021 , Online, Virtual, MD, US, [online], https://doi.org/10.1115/DETC2021-68885, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932117 (Accessed April 26, 2024)