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Additive manufacturing (AM) is a developing manufacturing technology, which provides excellent attributes compared to other manufacturing techniques. However, one of the critical challenges is the presence of defects that hinder the mechanical properties of the parts, particularly the fatigue life. Experimental understanding of fatigue is a cumbersome process. Therefore, numerical prediction based on specified conditions (such as porosity and applied load) can be an alternative to experimental analysis at the design stage of AM parts. In this study, elastic–plastic finite element analysis (FEA) is performed to obtain the stress distribution around pores, and their resultant effect on fatigue life for L-PBF (laser powder bed fusion) produced AlSi10Mg alloy samples. The stress field is calculated for both single and multiple pore models, where stress concentration is evaluated as a function of the pore’s location and its size. It is found that both pore location and size affect the stress field; however, location effects dominate over pore size. For the same remote applied stress level, the stress concentration around the pore increases with an increase in pore size, and the local maximum stress occurs near the pores that are closest to the surface. The current study also evaluates the relative effect of porosity fraction, average pore size, and location. It is found that the magnitude and sensitivity of stress concentration are hierarchically controlled by porosity location, pore size, and porosity density. A multi-scale finite element (FE) model is proposed based on inherent porosity data measured using Computed Tomography (CT) to predict overall fatigue life. The fatigue cycles are calculated using the rainflow counting algorithm in FE Safe using the stress–strain data obtained from the proposed FEA model. Using the proposed model, it is possible to generate S–N curves for any loading condition for a given porosity condition (porosity density and average pore size). The S–N curve results obtained from the FE model are compared to the experimental observations. The predicted fatigue life shows approximately 5% error with experimental results at high stress loading conditions. However, the proposed model overpredicts the fatigue life at low stress loading by almost 30%.

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