A Method for Predicting Powder Flowability for Selective Laser Sintering

This work investigates a method for pre-screening material systems for selective laser sintering 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...

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Bibliographic Details
Published in:JOM (1989) 2022-03, Vol.74 (3), p.1102-1110
Main Authors: Sassaman, Douglas, Phillips, Timothy, Milroy, Craig, Ide, Matthew, Beaman, Joseph
Format: Article
Language:English
Subjects:
Aluminum oxide
Carbon fiber reinforced plastics
Chemistry/Food Science
Composite materials
Density
Earth Sciences
Engineering
Environment
Interfaces
Laser sintering
Machine learning
Manufacturing
Measurement techniques
Methods
Physics
Rapid prototyping
Raw materials
Sintering (powder metallurgy)
Solid Freeform Fabrication 2021
Standard deviation
Surface roughness
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Summary:This work investigates a method for pre-screening material systems for selective laser sintering 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.
ISSN:1047-4838
1543-1851
DOI:10.1007/s11837-021-05050-w