Abstract

Additive manufacturing (AM) enables fabrication of net-shape components with high geometric complexity, however mechanical properties of parts with as-fabricated surfaces are limited by surface roughness and subsurface porosity that may initiate fatigue cracks. This study aims to improve the utility of parts with as-fabricated surfaces by quantifying processing-surface condition-fatigue performance relationships, optimizing surface contour parameters for fatigue performance, and investigating the application of surface profilometry as a non-destructive quality control method. Eleven contour parameter sets of varying energy densities were produced in three classifications: no contour passes, single contour passes, and contours with secondary healing passes. Surface roughness was evaluated using line scan profilometry and subsurface porosity by optical microscopy. Contouring reduced the average surface roughness from Ra ≅ 25 μm to Ra ≅ 5 μm, and the layer of subsurface porosity introduced by this process could be partially eliminated by a secondary healing pass. One set of processing conditions from each contour class was selected for fatigue studies (R = -1), and their results were compared with the fatigue data from machined and polished specimens to evaluate the relative influence of surface condition. Relative to machined and polished specimens, fatigue lives are approximately five times lower for contoured specimens (with and without a healing pass) and twenty times lower for non-contoured specimens at equivalent stress levels up to 107 cycles to failure. Fractographic measurements of initial defect √area of all specimens reveal that fatigue behavior is well characterized by stress intensity factor amplitude across all surface conditions. An extreme value statistical method is developed using profilometry data to predict S-N curves, and it is concluded that non-destructive surface roughness measurements may be suitable for estimating the fatigue lives of components with as-fabricated surfaces as a rapid method for quality control.

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