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Prediction of Fatigue Lives in Additively Manufactured Alloys Based on the Crack-Growth Concept
This paper aims to predict the fatigue behavior of additively manufactured alloys using crack-growth data. Among different sources of damage under cyclic loadings, fatigue due to cracks
originated from voids is the most life-limiting failure mechanism in powder-based metal additive
manufacturing (AM) parts. Hence, the ability to predict the fatigue behavior of AM materials based
on the void features is the first step toward improving AM part reliability. Test results from the
literature on AM alloys are analyzed herein to model fatigue behavior based on the semi-circular
surface flaws. The fatigue-life variations in the specimens are captured using the distribution of
defect size. The results indicate that knowing the statistical distribution of the defect size can
provide the opportunity of predicting the scatter in the fatigue-life of the AM materials, using an
appropriate fatigue analysis code.Mechanical Engineerin