  In this study, the effects of surface roughness and pore characteristics on
fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were
investigated. The 197 fatigue bars were printed using the same laser power but
with varied scanning speeds. These actions led to variations in the geometries
of microscale pores, and such variations were characterized using
micro-computed tomography. To generate differences in surface roughness in
fatigue bars, half of the samples were grit-blasted and the other half
machined. Fatigue behaviors were analyzed with respect to surface roughness and
statistics of the pores. For the grit-blasted samples, the contour laser scan
in the LPBF strategy led to a pore-depletion zone isolating surface and
internal pores with different features. For the machined samples, where surface
pores resemble internal pores, the fatigue life was highly correlated with the
average pore size and projected pore area in the plane perpendicular to the
stress direction. Finally, a machine learning model using a drop-out neural
network (DONN) was employed to establish a link between surface and pore
features to the fatigue data (logN), and good prediction accuracy was
demonstrated. Besides predicting fatigue lives, the DONN can also estimate the
prediction uncertainty.
