Use of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modeling

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dc.contributor.authorCerro, Azahara
dc.contributor.authorRomero, Pablo E.
dc.contributor.authorYigit, Okan
dc.contributor.authorBustillo, Andres
dc.date.accessioned2024-09-29T15:50:57Z
dc.date.available2024-09-29T15:50:57Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMachine learning algorithms for classification are employed in this study to generate different models that can predict the surface roughness of parts manufactured from polyvinyl butyral by means of Fused Deposition Modeling (FDM). Five input variables are defined (layer height, print speed, number of perimeters, wall angle, and extruder temperature), and 16 parts are 3D printed, each with three different surfaces (48 surfaces in total). The print values used to print each part were defined by a fractionated orthogonal experimental design. Using a perthometer, the average value of surface roughness, Ra, on each surface was obtained. From these experimental values, 40 models were trained and validated. The model with the best prediction results was the one generated by bagging and Multilayer Perceptron (BMLP), with a Kappa statistic of 0.9143. The input variables with the highest influence on the surface finish are the wall angle and the layer height.en_US
dc.description.sponsorshipSpanish Centro para el Desarrollo Tecnologico e Industrial (CDTI) [IDI-20191008]; Plan Propio de Investigacion of the University of Cordobaen_US
dc.description.sponsorshipThis work was partially supported by the SMART-EASY project (Reference Number IDI-20191008) funded by the Spanish Centro para el Desarrollo Tecnologico e Industrial (CDTI) and by the Plan Propio de Investigacion of the University of Cordoba.en_US
dc.identifier.doi10.1007/s00170-021-07300-2
dc.identifier.endpage2475en_US
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.issue7-8en_US
dc.identifier.scopus2-s2.0-85106506945en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2465en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-021-07300-2
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3816
dc.identifier.volume115en_US
dc.identifier.wosWOS:000654169400005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subject3d printingen_US
dc.subjectFused deposition modelingen_US
dc.subjectFused filament fabricationen_US
dc.subjectSurface roughnessen_US
dc.subjectWEKAen_US
dc.titleUse of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modelingen_US
dc.typeArticleen_US

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