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Abstract Fatigue scattering caused by inherent geometrical defects in laser powder bed fusion (LPBF) imposes a great challenge for fabricating reliable load-bearing components. However, the lack of sufficient fatigue data and the limitation of runout conditions rationalize the need to bridge the gap between limited data and fatigue reliability. This work has developed two models based on censored linear regression (CR) and censored Gaussian process regression (CGP), respectively, to predict fatigue life scattering bounds at a given confidence for both as-built and heat-treated SS 316L samples. Furthermore, fatigue life reliability is modeled under different stress amplitudes with a CGP-based reliability model. 

Abstract Laser powder bed fusion (LPBF) is an enabling process manufacture of complex metal components. However, LPBF is prone to generate geometrical defects (e.g., porosity, lack of fusion), which causes a significant fatigue scattering. However, LPBF fatigue scattering data and analysis in the literature are not only sparse and limited to tension-compression mode but also inconsistent. This article presents a robust high-frequency fatigue testing method to construct stress-cycle curves of SS 316L to understand the scattering nature and predict the scattering pattern. A series of bending fatigue tests are performed at different stress amplitudes. Two different runout criteria are used to investigate fatigue life, fatigue limits, and scattering. The endurance limit reaches around 300 MPa for the defect size distribution at the selected process space. The defect size-based fatigue limit model is found to underestimate the endurance limit by about 30 MPa when comparing with the experimental data. Fatigue scattering is further calculated by using 95% prediction intervals, showing that low fatigue scattering is present at high stresses while a large variation of fatigue life occurs at stresses near the knee point.