Extreme value statistics with uncertainty to assess porosity equivalence across additively manufactured parts

Fatigue performance in powder bed fusion–laser beam additively manufactured material is influenced by the largest pore size within the stressed volume, which correlates with fatigue life in porosity-driven failures. However, single value estimates for the largest pore size are insufficient to capture the experimentally observed scatter in fatigue properties. To address this gap, in this work, we incorporate uncertainty quantification into extreme value statistics to estimate the largest pore size distribution in a given volume of material. We specifically capture uncertainty in the number of pores present and the extreme value distribution parameter estimates. We then applied this statistical framework to compare the porosity between two part geometries: a 4-point bend fatigue specimen and an axial fatigue specimen in the gauge section. The two part geometries were manufactured with the same process conditions using Ti-6Al-4V, followed by porosity characterization via X-ray micro computed tomography. The results show that the largest pore size distribution of the 4-point bend specimen is insufficient to accurately capture the largest pore size observed in the axial fatigue specimen, despite similar dimensions. Our findings highlight the need for rigorous statistical analysis to quantify the differences between porosity distributions.