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An adage within the Additive Manufacturing (AM) community is that-complexity is free‖. Complicated geometric features that normally drive manufacturing cost and limit design options are not typically problematic in AM. While geometric complexity is usually viewed from the perspective of part design, this advantage of AM also opens up new options in rapid, efficient material property evaluation and qualification. In the current work, an array of 100 miniature tensile bars are produced and tested for a comparable cost and in comparable time to a few conventional tensile bars. With this technique, it is possible to evaluate the stochastic nature of mechanical behavior. The current study focuses on stochastic yield strength, ultimate strength, and ductility as measured by strain at failure (elongation). However, this method can be used to capture the statistical nature of many mechanical properties including the full stress-strain constitutive response, elastic modulus, work hardening, and fracture toughness. Moreover, the technique could extend to strain-rate and temperature dependent behavior. As a proof of concept, the technique is demonstrated on a precipitation hardened stainless steel alloy, commonly known as 17-4PH, produced by two commercial AM vendors using a laser powder bed fusion process, also commonly known as selective laser melting. Using two different commercial powder bed platforms, the vendors produced material that exhibited slightly lower strength and markedly lower ductility compared to wrought sheet. Moreover, the properties were much less repeatable in the AM materials as analyzed in the context of a Weibull distribution, and the properties did not consistently meet minimum allowable requirements for the alloy as established by AMS. The diminished, stochastic properties were examined in the context of major contributing factors such as surface roughness and internal lack-of-fusion porosity. This high-throughput capability is expected to be useful for follow-on extensive parametric studies of factors that affect the statistical reliability of AM components.

Additive manufacturing enables the rapid, cost effective production of customized structural components. To fully capitalize on the agility of additive manufacturing, it is necessary to develop complementary high-throughput materials evaluation techniques. In this study, over 1000 nominally identical tensile tests are used to explore the effect of process variability on the mechanical property distributions of a precipitation hardened stainless steel produced by a laser powder bed fusion process, also known as direct metal laser sintering or selective laser melting. With this large dataset, rare defects are revealed that affect only %2% of the population, stemming from a single build lot of material. The rare defects cause a substantial loss in ductility and are associated with an interconnected network of porosity. The adoption of streamlined test methods will be paramount to diagnosing and mitigating such dangerous anomalies in future structural components.

Additively manufactured austenitic stainless steels exhibit numerous microstructural and morphological differences compared to their wrought counterparts that will influence the metals corrosion resistance. The characteristic as-printed surface roughness of powder bed fusion (PBF) stainless steel parts is one of these morphological differences that increases the parts susceptibility to localized corrosion. This study experimentally determines the average surface roughness and breakdown potential (E b) for PBF 316L in 6 surface finished states: as-printed, ground with SiC paper, tumble polished in abrasive media, electro-polished, chemically passivated, and the application of a contour/re-melt scan strategy. In general, a smaller average surface roughness led to a larger E b. The smoothest surface treatments, ground and electro-polished conditions, led to E b near the materials limit (~+1.0 V Ag/AgCl) while all other surface treatments exhibited significantly lower E b (~+0.3 V Ag/AgCl) The build angle was also shown to impact surface roughness, where surfaces at high angles from the build direction resulted in larger roughness values, hence lower E b .