Fatigue life prediction of rough Hastelloy X specimens fabricated using laser powder bed fusion

Additive manufacturing, especially laser powder bed fusion (L-PBF), is extensively used for fabricating metal parts with intricate geometries. However, parts produced via L-PBF suffer from varied surface roughness, which affects the fatigue properties. Accurate prediction of fatigue properties as a function of surface roughness is a critical requirement for qualifying L-PBF parts. In this work, an analytical methodology was put forth to predict the fatigue life of L-PBF components having heterogeneous surface roughness. Thirty-six Hastelloy X specimens were printed using L-PBF followed by industry-standard heat treatment procedures. Half of these specimens had as-printed gauge sections and the other half were printed as cylinders from which fatigue specimens were extracted via machining. Specimens were printed in a vertical orientation and an orientation of 30° from the vertical axis. The surface roughness of the specimens was measured using computed tomography and parameters such as the maximum valley depth were used to build an extreme value distribution. Fatigue testing was conducted at an isothermal condition of 500 °F. It was observed that the rough specimens failed much earlier than the machined specimens due to the deep valleys present on the surfaces of the former ones. The valleys behaved as notches leading to high strain localization. Based on this observation, an analytical functional relationship was formulated that treated surface valleys as notches and correlated the strain localization around these notches with fatigue life, using the Coffin-Manson-Basquin and Ramberg-Osgood equations. The functional relationship was generated with the average of the extreme value distribution. The mean life curve from the functional relationship showed a maximum difference of 2 % from the experimental mean fatigue life observations for vertically built rough specimens and 10 % for 30⁰-built rough specimens. In conclusion, the proposed analytical model successfully predicted the fatigue life of L-PBF specimens at an elevated temperature undergoing different strain loadings.