About this Abstract |
Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
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Symposium
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Materials
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Presentation Title |
Evaluating the Efficacy of X-ray Computed Tomography for Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing using Automated Optical Serial Sectioning |
Author(s) |
Zackary K. Snow, Edward William Reutzel, Abdalla Nassar, Griffin Jones, Rachel Reed, Veeraraghavan Sundar, Jayme S. Keist |
On-Site Speaker (Planned) |
Zackary K. Snow |
Abstract Scope |
Flaws in additively manufactured (AM) components contribute to variability in fatigue properties. X-ray computed tomography (XCT) is often used to quantify flaw populations in AM material. However, we show that XCT flaw detectability can be as low as 3.3%. A custom, automated defect recognition algorithm was used to identify flaws in XCT and automated optical serial sectioning (AOSS) data of a 0.63 mm thick, cylindrical section of AM Ti-6Al-4V built on a commercial laser powder bed fusion system. Over 1000 flaws ranging from 8.5-65.2 µm in diameter were identified in the AOSS data. Only 33 flaws were identified in the corresponding region of the XCT data. Our results show that flaws absent from the XCT data do not only correspond to flaws below the XCT resolution. We conclude that the voxel size alone is not sufficient for determining XCT flaw detectability and recommend standardized XCT image quality metrics. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |