Taguchi-ANN Hybrid Approach for Evaluating Unsupported Overhang Structures in FDM-Printed Polymers

dc.contributor.authorUlkir, Osman
dc.contributor.authorBayraklilar, M. Said
dc.contributor.authorKuncan, Melih
dc.date.accessioned2025-10-03T08:57:22Z
dc.date.available2025-10-03T08:57:22Z
dc.date.issued2025
dc.departmentMuş Alparslan Üniversitesien_US
dc.description.abstractFused deposition modeling (FDM) faces significant challenges in producing unsupported overhangs, particularly regarding dimensional accuracy and surface quality. While support structures can address these issues, they increase material waste and post-processing requirements. This study investigates how process parameters affect the quality of unsupported inclined surfaces in FDM printing, aiming to optimize fabrication without support structures. Test specimens with three surface inclination angles-75 degrees (Surface-1), 60 degrees (Surface-2), and 45 degrees (Surface-3)-were produced using acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and polyethylene terephthalate glycol (PETG). The study employed an L27 Taguchi orthogonal array to evaluate five key process parameters: material type (MT), infill pattern (IP), wall thickness (WT), infill density (ID), and layer thickness (LT). Dimensional deviations and surface roughness were measured across all angled regions. Dimensional deviations remained below 2.3% across all specimens. ANOVA revealed LT and MT as the most significant factors affecting both dimensional accuracy and surface roughness (p < 0.001), with LT showing the highest contribution to surface roughness variance (F values > 500). Surface roughness improved with decreasing surface angles, while dimensional accuracy showed an inverse trend. Artificial neural network (ANN) models developed for predicting quality metrics achieved R-2 values exceeding 0.90. This study establishes a comprehensive framework integrating Taguchi design, statistical analysis, and machine learning (ML) for optimizing unsupported overhang fabrication in FDM. The findings reveal crucial relationships between process parameters and part quality, demonstrating that carefully controlled parameter selection can achieve acceptable quality without support structures. The developed predictive models offer a reliable tool for parameter optimization in FDM manufacturing of unsupported overhangs.en_US
dc.description.sponsorshipMus Alparslan University Technology Research and Project Coordination Unit [BAP-24-TBMYO-4901-03]en_US
dc.description.sponsorshipThis work was supported by the Mus Alparslan University Technology Research and Project Coordination Unit as a project numbered BAP-24-TBMYO-4901-03.en_US
dc.identifier.doi10.1002/pol.20250752
dc.identifier.issn2642-4150
dc.identifier.issn2642-4169
dc.identifier.orcid0000-0002-1095-0160
dc.identifier.scopus2-s2.0-105016833523
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1002/pol.20250752
dc.identifier.urihttps://hdl.handle.net/20.500.12639/7548
dc.identifier.wosWOS:001576968400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWileyen_US
dc.relation.ispartofJournal of Polymer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20251003
dc.subjectartificial neural networksen_US
dc.subjectdimensional accuracyen_US
dc.subjectfused deposition modelingen_US
dc.subjectsurface roughnessen_US
dc.subjectTaguchien_US
dc.subjectunsupported overhangen_US
dc.titleTaguchi-ANN Hybrid Approach for Evaluating Unsupported Overhang Structures in FDM-Printed Polymersen_US
dc.typeArticle

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