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Probability of Detection and Validation for Computed Tomography Processes for Additive Manufacturing (20-RD-231)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0360
Agency Tracking Number: 206355
Amount: $125,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: Z4
Solicitation Number: SBIR_20_P1
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-28
Award End Date (Contract End Date): 2021-03-01
Small Business Information
4401 Dayton-Xenia Road
Dayton, OH 45432-1894
United States
DUNS: 074689217
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Veeraraghavan Sundar
 (937) 429-6900
 vsundar@ues.com
Business Contact
 Rick Weddle
Phone: (937) 426-6900
Email: rweddle@ues.com
Research Institution
N/A
Abstract

X-ray computed tomography (CT) is a widely used nondestructive evaluation (NDE) method for quality control and post-build inspection in additively manufactured (AM) components. The limitations of such NDE methods and the need to validate the capability of these methods on an ongoing basis are increasingly recognized. Automated, metallography-based serial sectioning offers a reliable method to establish ground truth data on the flaw populations as well as microstructural variations of AM components. Such data can be used to validate, and subsequently improve the reliability of NDE methods. UES proposes a project aimed at establishing comparison methods and workflows for validating CT (and potentially other NDE data) with ground truth from serial sectioning, and developing probability of detection (POD) curves. The knowledge gained from these efforts will inform CT scan strategies for improved flaw detection in AM components, evaluate flaw detectability in CT using serial sectioning as a ground truth comparison, and quantify the risk of the flaws absent from the CT data sets. Phase II extends the work of validation into the area of in situ detection and validation of in situ sensing methodologies using thermal and visual data.

* Information listed above is at the time of submission. *

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