A Method of Predicting Powder Flowability for Selective Laser Sintering

dc.creatorSassaman, D.
dc.creatorPhillips, T.
dc.creatorBeaman, J.
dc.creatorMilroy, C.
dc.creatorIde, M.
dc.date.accessioned2021-12-07T17:56:42Z
dc.date.available2021-12-07T17:56:42Z
dc.date.issued2021
dc.description.abstractThis 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.en_US
dc.description.departmentMechanical Engineeringen_US
dc.identifier.urihttps://hdl.handle.net/2152/90737
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/17656
dc.language.isoengen_US
dc.publisherUniversity of Texas at Austinen_US
dc.relation.ispartof2021 International Solid Freeform Fabrication Symposiumen_US
dc.rights.restrictionOpenen_US
dc.subjectpowder flowabilityen_US
dc.subjectpre-screeningen_US
dc.subjectrevolution powder analysisen_US
dc.subjectmachine learningen_US
dc.subjectselective laser sinteringen_US
dc.titleA Method of Predicting Powder Flowability for Selective Laser Sinteringen_US
dc.typeConference paperen_US

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