Fatigue Crack Segmentation and Characterization of Additively Manufactured Ti-6Al-4V Using X-Ray Computed Tomography

Abstract

X-ray computed tomography (XCT) is extremely useful for the non-destructive analysis of additively manufactured (AM) components. AM components often show manufacturing defects such as lack-of-fusion (LoF), which are detrimental to the fatigue life of components. To better understand how cracks initiate and propagate from internal defects, we fabricated Ti-6Al-4V samples with an internal cavity using electron beam powder bed fusion. The samples were tested in high-cycle and very high-cycle fatigue regimes. XCT was used to locate crack initiation sites and to determine characteristic properties of cracks and defects with the aid of deep learning segmentation. LoF defects exposed to the outer surface of the samples after machining were found to be as detrimental to fatigue life as the internal artificial defects. This work can benefit industries that utilize the AM of high-strength, lightweight alloys, in the design and manufacturing of components to improve part reliability and fatigue life.