Statistical analysis to assess porosity equivalence with uncertainty across additively manufactured parts for fatigue applications

Justin P. Miner; Sneha Prabha Narra

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arXiv · 2411.03401

Statistical analysis to assess porosity equivalence with uncertainty across additively manufactured parts for fatigue applications

Justin P. Miner·Sneha Prabha Narra

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Abstract

Previous work on fatigue prediction in Powder Bed Fusion - Laser Beam has shown that the estimate of the largest pore size within the stressed volume is correlated with the resulting fatigue behavior 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 by capturing uncertainty in the number of pores present and the upper tail parameters. We then applied this statistical framework to compare the porosity equivalence between two geometries: a 4-point bend fatigue specimen and an axial fatigue specimen in the gauge section. Both geometries were manufactured with the same process conditions using Ti-6Al-4V, followed by porosity characterization via X-ray Micro CT. 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. Based on our findings, we provide insight into the design of witness coupons that exhibit part-to-coupon porosity equivalence for fatigue.

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Justin P. Miner, Sneha Prabha Narra. 2024-11-05. Statistical analysis to assess porosity equivalence with uncertainty across additively manufactured parts for fatigue applications. https://arxiv.org/abs/2411.03401

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