Defect-Based Fatigue Modeling for AlSi10Mg Produced by Laser Powder Bed Fusion Process

Abstract

Defects are inevitable in metal parts manufactured by any process; the size, shape and location of such defects play a critical role in determining the material’s fatigue strength. Due to the random nature of the defects’ distribution in the part, a statistical method must be employed for fatigue strength estimation. The laser powder bed fusion (L-PBF) process introduces two main types of porosity defects: keyhole pores and lack-of-fusion pores. A defect-based statistical fatigue strength model has been developed and validated for the L-PBF AlSi10Mg aluminum alloy containing keyhole defects with different size distributions. Artificial defects were also introduced for model validation. The approach is based on the modified Murakami’s formulation to address the material dependence and followed the Romano’s approach to consider the statistical behavior of the fatigue strength. The proposed model successfully predicts the fatigue strength of different keyhole porosity distributions but is unable to predict the fatigue strength of materials containing lack-of-fusion porosity, possibly due to the higher stress concentration induced by its morphology.