Additive Manufacturing of Cranial Implants: Experimental Evaluation and Machine Learning Prediction of Mechanical and Dimensional Performance

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dc.contributor.authorYildirim, Yagmur Akin
dc.contributor.authorYildirim, Burak
dc.contributor.authorUlkir, Osman
dc.contributor.authorKuncan, Melih
dc.date.accessioned2026-10-23T18:14:15Z
dc.date.available2026-10-23T18:14:15Z
dc.date.issued2026
dc.departmentSiirt Üniversitesi
dc.description.abstractThis study investigates the additive manufacturing (AM) of defect-specific cranial implants produced by fused deposition modeling (FDM) using three different polymeric materials, namely polylactic acid (PLA), polyamide (PA), and polyether ether ketone (PEEK). The primary objective is to evaluate the effects of key printing parameters on the dimensional accuracy and mechanical performance of the fabricated implants as well as to develop machine learning (ML) models to predict the main output responses. For this purpose, material type (MT), printing speed (PS), infill density (ID), layer height (LH), and build orientation (BO) were selected as input parameters and organized according to an L27 Taguchi experimental design. The fabricated implants were evaluated in terms of maximum length, thickness, maximum compressive load, and impact resistance. Analysis of variance revealed that MT, ID, LH, and BO affected maximum compressive load, whereas PS was not significant for this response within the investigated range. Gaussian process regression (GPR), support vector regression (SVR), and decision tree regression (DTR) models were used to predict the mechanical responses of the implants. Among them, GPR showed the highest predictive accuracy, achieving for compressive load and for impact resistance.
dc.identifier.doi10.1002/app.71356
dc.identifier.issn0021-8995
dc.identifier.issn1097-4628
dc.identifier.issue41
dc.identifier.orcid0000-0001-6263-1025
dc.identifier.orcid0000-0002-1095-0160
dc.identifier.scopus2-s2.0-105047598904
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1002/app.71356
dc.identifier.urihttps://hdl.handle.net/20.500.12604/12352
dc.identifier.volume143
dc.identifier.wosWOS:001850220300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Applied Polymer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20261017
dc.subjectAdditive Manufacturing
dc.subjectCranial Implants
dc.subjectDimensional Accuracy
dc.subjectFused Deposition Modeling
dc.subjectMachine Learning
dc.subjectProcess Parameter Optimization
dc.titleAdditive Manufacturing of Cranial Implants: Experimental Evaluation and Machine Learning Prediction of Mechanical and Dimensional Performance
dc.typeArticle

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