About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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Additive Manufacturing of Metals: Microstructure, Properties and Alloy Development
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Presentation Title |
Automated Serial Sectioning for Validation of X-ray Computed Tomography of Additively Manufactured Alloys |
Author(s) |
Veeraraghavan Sundar, Griffin Jones, Rachel Reed, Jayme Keist |
On-Site Speaker (Planned) |
Veeraraghavan Sundar |
Abstract Scope |
X-ray computed tomography (CT) is used for inspection of additively manufactured (AM) components, but is a technique with limits which are not always well understood. This work explores the capabilities of CT by validating its findings against those of an automated serial sectioning (Robo-Met.3D) technique. The flaw types targeted here include stochastic flaws in laser powder bed fusion (LPBF) Ti-6Al-4V; cracks in LPBF AF-9628; and lack of fusion (LOF) flaws in laser DED Ti-6Al-4V. Void populations in both the CT and Robo-Met.3D data are identified using an automated defect recognition (ADR) algorithm and then contrasted with one another to determine the true detection limits of the CT technique. Probability of detection statistics are developed and related to normalized measures of CT spatial resolution and contrast, such as voxel size, modulation transfer function (MTF) and contrast discrimination function (CDF). This work is supported by NASA SBIR Phase II, Z4.05-6355, Contract: 80NSSC21C0586. |