In laser powder bed fusion metal additive manufacturing, insufficient shield gas flow allows accumulation of condensate and ejecta above the build plane and in the beam path. These process byproducts are associated with beam obstruction, attenuation, and thermal lensing, which then lead to lack of fusion and other defects. Furthermore, lack of gas flow can allow excessive amounts of ejecta to redeposit onto the build surface or powder bed, causing further part defects. The current investigation was a preliminary study on how gas flow velocity and direction affect laser delivery to a bare substrate of Nickel Alloy 625 (IN625) in the National Institute of Standards and Technology (NIST) Additive Manufacturing Metrology Testbed (AMMT). Melt tracks were formed under several gas flow speeds, gas flow directions, and energy densities. The tracks were then cross-sectioned and measured. The melt track aspect ratio and aspect ratio coefficient of variation (CV) were reported as a function of gas flow speed and direction. It was found that a mean gas flow velocity of 6.7 m/s from a nozzle 6.35 mm in diameter was sufficient to reduce meltpool aspect ratio CV to less than 15 %. Real-time inline hotspot area and its CV were evaluated as a process monitoring signature for identifying poor laser delivery due to inadequate gas flow. It was found that inline hotspot size could be used to distinguish between conduction mode and transition mode processes, but became diminishingly sensitive as applied laser energy density increased toward keyhole mode. Increased hotspot size CV (associated with inadequate gas flow) was associated with an increased meltpool aspect ratio CV. Finally, it was found that use of the inline hotspot CV showed a bias toward higher CV values when the laser was scanned nominally toward the gas flow, which indicates that this bias must be considered in order to use hotspot area CV as a process monitoring signature. This study concludes that gas flow speed and direction have important ramifications for both laser delivery and process monitoring.

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