  This work investigates a method for pre-screening material systems for
Selective Laser Sintering (SLS) using a combination of Revolution Powder
Analysis (RPA) and machine learning. To develop this method, nylon was mixed
with alumina or carbon fibers in different wt.% to form material systems with
varying flowability. The materials were measured in a custom RPA device and the
results compared with as-spread layer density and surface roughness. Machine
learning was used to attempt classification of all powders for each method.
Ultimately, it was found that the RPA method is able to reliably classify
powders based on their flowability, but as-spread layer density and surface
roughness were not able to be classified.
