Probabilistic and defect tolerant fatigue assessment of AM materials under size effect

Abstract

Basic fatigue properties captured by laboratory testing of small specimens are usually applied to safety design of actual components. Nevertheless, their fatigue performances are significantly different due to size effect attributing a fault to possibility of large defects occur. Accordingly, extreme value statistics and weakest-link theory are applicable to defect tolerant fatigue assessment under size effect in this study. Firstly, the basic theory of extreme value statistics and weakest-link are explicated. Then, a probabilistic framework is developed to estimate the fatigue strength/life of Additive manufacturing (AM) material, wherein size effect and fatigue scatter from statistical viewpoint are illustrated and quantitative analysis. In particular, different types of size effect are simplified and unified as defect size variation, and the quantile of the critical defect derived by extreme value statistics is implemented to estimate the bounds of fatigue performance. Finally, three groups of additive manufactured specimens with different volume and processing parameters are utilized for method validation and comparison, which show great capability to estimate the fatigue scatter of AM material under size effect. The goal of this study is also to clarify the potential application of extreme value statistics and weakest-link theory from the probability and defect tolerant perspective.