Experimental analysis of powder layer quality as a function of feedstock and recoating strategies

The quality and uniformity of the powder layer have a direct impact on the performance of parts produced via powder bed fusion (PBF). Because powder layer properties depend on many powder- and recoating-specific variables, it is difficult to accurately predict powder bed quality across the variety of PBF processes and powders currently available. In this work, we propose a method to assess powder bed quality as a function of both powder conditions and recoating strategies. Our method relies on the powder bed scanner technology, which provides particle-level resolution images of the entire powder layer as it is recoated. Through numerical analysis of the acquired images, we define three new metrics to assess powder bed quality, namely the powder layer thickness uniformity, surface area roughness, and surface particle density. We demonstrate the efficacy of these metrics in capturing differences in powder layers across a matrix of recoating experiments using different batches of stainless steel 316 L powder. Our results clearly show how powders with different particle surface conditions, morphology, and moisture content respond to various recoating velocities and recoater blade types, resulting in layers with different quality. Owing to the high measurement throughput and versatility, our method offers the opportunity to perform systematic spreadability studies of different powdered materials to optimize PBF processes, as well as provide in situ powder bed quality assessment during part production.