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dc.contributor.authorMolero, Esther
dc.contributor.authorFernández, Juan Jesús
dc.contributor.authorRodríguez-Alabanda, O.
dc.contributor.authorGuerrero-Vaca, G.
dc.contributor.authorRomero, Pablo E.
dc.date.accessioned2020-04-06T19:33:31Z
dc.date.available2020-04-06T19:33:31Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/10396/19897
dc.description.abstractIn the present work, ten data mining algorithms have been used to generate models capable of predicting the surface roughness of parts printed on polylactic acid (PLA) by using fused deposition modeling (FDM). The models have been trained using experimental data measured on 27 horizontal (XY) and 27 vertical (XZ) specimens, printed using different values for the parameters studied (layer height, extrusion temperature, print speed, print acceleration and flow). The models generated by multilayer perceptron (MLP) and logistic model trees (LMT) have obtained the best results in a cross-validation. Although it does not obtain such optimal results, the J48 algorithm (C4.5) allows the generation of models in the form of a decision tree. These trees permit to determine which print parameters have an influence on the surface roughness. For XY specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XY ) depends on the flow, the print temperature and the layer height; in the direction perpendicular to the extrusion path, the surface roughness (Ra,90,XY) depends only on the flow. For XZ specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XZ) depends only on the print speed; in the direction perpendicular to the extrusion path (Ra,90,XZ), it depends on the layer height and the extrusion temperature. According to the study carried out, the most suitable set up provides values of Ra,0,XY, Ra,90,XY, Ra,0,XZ and Ra,90,XZ equal to 0.46, 1.18, 0.45 and 11.54, respectively. A practical application of this work is the manufacture of PLA frame glasses using FDM.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.sourcePolymers 12(4), 840 (2020)es_ES
dc.subjectFused deposition modelinges_ES
dc.subjectFDMes_ES
dc.subjectFFFes_ES
dc.subjectData mininges_ES
dc.subjectMachine learninges_ES
dc.subjectPLAes_ES
dc.subjectSurface roughnesses_ES
dc.subjectWEKAes_ES
dc.subjectDecision treeses_ES
dc.subjectC4.5es_ES
dc.subjectNeural networkses_ES
dc.subjectANNes_ES
dc.subjectFrame glasseses_ES
dc.titleUse of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/polym12040840es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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