Predicting the Size‐Dependent Fatigue Strength Distributions in Additively Manufactured AlSi10Mg Alloy Specimens Using Extreme Value Statistics

ABSTRACT The paper presents an analysis of the selected models of the size effect on fatigue behavior of additively manufactured AlSi10Mg specimens. The predicted fatigue strength versus failure probability was assessed quantitatively in relation to high‐cycle fatigue tests. The fatigue strength decreased as the specimen size increased. Analysis of the crack initiation revealed a correlation between the observed decrease in fatigue properties and increases in the effective defect size and in the specimen size. This relationship was approximated using the Shiozawa and the El‐Haddad models. The weakest link theory, extreme defect size statistics, and defect size simulation were implemented to predict the fatigue strength distributions. The probabilistic modeling using the Monte Carlo method provided the smallest mean absolute percentage error of less than 1% for the selected analysis conditions.