Lifton, J., & Liu, T. (2021). An adaptive thresholding algorithm for porosity measurement of additively manufactured metal test samples via X-ray computed tomography. Additive Manufacturing, 39, 101899. doi:10.1016/j.addma.2021.101899
Abstract:
If additively manufactured metal parts are to be used as performance critical components in the aerospace and other high-value industries, then non-destructive methods for measuring the porosity of these parts are required; X-ray computed tomography (XCT) is a promising instrument for this measurement task. One disadvantage of using XCT for this purpose is the presence of artefacts in XCT data of high-density materials (aluminium, titanium, steel and Inconel). These artefacts are problematic for segmentation algorithms that are required for porosity measurement. In this work we propose an adaptive thresholding algorithm for segmenting XCT data. The algorithm is specifically designed to overcome the influence of beam hardening (cupping) artefacts that are typically observed when XCT scanning high-density metal parts. The algorithm is also designed to have as few as possible user-defined settings in order to minimise the influence the user has on the segmentation result. The results show that the algorithm is able to segment small voids in low contrast-to-noise ratio XCT data and that the algorithm is robust to cupping artefacts. The accuracy of the method is evaluated for a set of Inconel cubes approximately 10 mm × 10 mm × 10 mm in size (H × W × D), reference porosity measurements are made using Archimedes’ principle. The mean error and standard deviation of the XCT percentage porosity measurements evaluated by four different users are found to be: −0.38 ± 0.09%, 0.09 ± 0.09% and −0.23 ± 0.22% for samples with bulk porosities of 0.23 ± 0.28%, 1.42 ± 0.28% and 3.00 ± 0.28%, respectively.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the SERC - Singapore Aerospace Programme
Grant Reference no. : 152 15 00073
Description:
Please find the link to the article at the publisher's URL: http://dx.doi.org/10.1016/j.addma.2021.101899