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Merging experiments and computer simulations in X-ray Computed Tomography probability of detection analysis of additive manufacturing flaws

Published

Author(s)

Felix Kim, Adam L. Pintar, Jason Fox, Jared B. Tarr, Alkan Donmez, Anne-Francoise Obaton

Abstract

X-ray Computed Tomography (XCT) is a growing industrial non-destructive testing (NDT) technique for advanced manufacturing industries such as additive manufacturing (AM). Probability of detection (POD) is a critical aspect for qualifying NDT techniques/processes. We present a methodology to empirically determine XCT POD using a two-piece phantom, and which incorporates uncertainty in the measurements of the true flaw size in the phantoms. We additionally demonstrate an application of an XCT full simulation model using simulated phantoms to supplement the experimental measurements. A signal response POD analysis (a^ vs a) was implemented, where the signal response (a^) was the number of voxels determined to be in the flaw, and the true flaw sizes (a) were the measured volumes of the flaws. Phantoms with flaws representing AM lack of fusion (LOF) pores were developed and were measured with an optical measurement system allowing the quantification of uncertainty in the measurement of the true flaw size (a). The XCT simulations considered factors not assessed by the experimental measurements. The simulated phantoms have various flaw sizes, locations, and orientations. The XCT simulation results were integrated with the experimental results using a multi-level Bayesian model, which incorporated the effects of reference measurement uncertainty, imaging thresholds, and flaw locations and orientations.
Citation
Ndt & E International
Volume
119

Keywords

X-ray computed tomography, defect, flaw, additive manufacturing, powder bed, laser melting, probability of detection, model-assisted probability of detection, uncertainty

Citation

Kim, F. , Pintar, A. , Fox, J. , Tarr, J. , Donmez, A. and Obaton, A. (2021), Merging experiments and computer simulations in X-ray Computed Tomography probability of detection analysis of additive manufacturing flaws, Ndt & E International, [online], https://doi.org/10.1016/j.ndteint.2021.102416, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931109 (Accessed May 23, 2022)
Created January 27, 2021, Updated February 23, 2022