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Quantitative analysis of thin metal powder layers via transmission X-ray imaging and discrete element simulation: Blade-based spreading approaches

Spreading uniform and dense layers is of paramount importance to creating high-quality components using powder bed additive manufacturing (AM). Blade-like tools are often employed for spreading powder metal feedstocks, especially in laser powder bed fusion and electron beam melting, where powders are characterized by a D50 of 30 microns or greater. Along with variations in boundary conditions introduced by the layer-wise geometry and surface topography of the printed component, stochastic interactions between the spreading tool and powder result in spatial variations of layer quality that are still not well understood. Here, to study powder spreading under conditions representative of powder bed AM, we employ a modular, mechanized apparatus to create powder layers from moderately and highly cohesive powders with a selection of blade-like spreading tools. Powder layer effective depth is spatially mapped using transmission X-ray imaging, and uniformity is quantified via a statistical approach. We first compare layer density, or the effective depth of powder layer, and show that blade geometries with a curved profile lead to increased material deposition. Second, this approach enables quantification of local fluctuations, or layer defect severity. For example, we observe that the primary benefit of a V-shaped rubber blade, as compared to a 45 degree rigid blade, lies in enabling local deflection of the blade edge to eliminate streaking from large particles, while also increasing deposition. Additionally, we employ a custom DEM simulation to elucidate the opposing roles of particle density and surface energy with a pseudo-material approach, where the balance of inertial and cohesive forces determine macro-scale powder flowability. For specific alloy densities, we find a critical surface energy beyond which layer density is greatly impaired when powder spreading is performed using a blade.

preprint2022arXivOpen access

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