Application of artificial neural network to evaluation of dimensional accuracy of 3D-printed polylactic acid parts

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dc.authoridUlkir, Osman/0000-0002-1095-0160
dc.contributor.authorGunes, Seyhmus
dc.contributor.authorUlkir, Osman
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:24:21Z
dc.date.available2024-12-24T19:24:21Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractAdditive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 mu m. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R-square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method. image
dc.identifier.doi10.1002/pol.20230876
dc.identifier.endpage1889
dc.identifier.issn2642-4150
dc.identifier.issn2642-4169
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85183030229
dc.identifier.scopusqualityQ2
dc.identifier.startpage1864
dc.identifier.urihttps://doi.org/10.1002/pol.20230876
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5930
dc.identifier.volume62
dc.identifier.wosWOS:001147100000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Polymer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subject3D printing
dc.subjectadditive manufacturing
dc.subjectANOVA
dc.subjectartificial neural network
dc.subjectdimensional accuracy
dc.subjectpolylactic acid
dc.subjectTaguchi method
dc.titleApplication of artificial neural network to evaluation of dimensional accuracy of 3D-printed polylactic acid parts
dc.typeArticle

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