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Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm

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dc.contributor.authorAKGÜN, GAZİ
dc.contributor.authorsUlkir O., AKGÜN G.
dc.date.accessioned2023-05-02T08:15:34Z
dc.date.accessioned2026-01-11T19:14:03Z
dc.date.available2023-05-02T08:15:34Z
dc.date.issued2023-01-01
dc.description.abstractThe selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box–Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.
dc.identifier.citationUlkir O., AKGÜN G., "Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm", Science and Technology of Welding and Joining, 2023
dc.identifier.doi10.1080/13621718.2023.2200572
dc.identifier.issn1362-1718
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85153235583&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/289016
dc.language.isoeng
dc.relation.ispartofScience and Technology of Welding and Joining
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFizik
dc.subjectYoğun Madde 1:Yapısal, Mekanik ve Termal Özellikler
dc.subjectTemel Bilimler
dc.subjectMühendislik ve Teknoloji
dc.subjectPhysics
dc.subjectCondensed Matter 1: Structural, Mechanical and Thermal Properties
dc.subjectNatural Sciences
dc.subjectEngineering and Technology
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectTemel Bilimler (SCI)
dc.subjectMalzeme Bilimi
dc.subjectFİZİK, YOĞUN MADDE
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectNatural Sciences (SCI)
dc.subjectMATERIALS SCIENCE
dc.subjectPHYSICS
dc.subjectPHYSICS, CONDENSED MATTER
dc.subjectGenel Malzeme Bilimi
dc.subjectFizik Bilimleri
dc.subjectYoğun Madde Fiziği
dc.subjectGeneral Materials Science
dc.subjectPhysical Sciences
dc.subjectCondensed Matter Physics
dc.subjectAdditive manufacturing
dc.subjectbox-benken design
dc.subjectcascade forward artificial neural network
dc.subjectfused deposition modelling
dc.subjectgenetic algorithm
dc.subjectsurface roughness
dc.subjectAdditive manufacturing
dc.subjectsurface roughness
dc.subjectfused deposition modelling
dc.subjectbox-benken design
dc.subjectcascade forward artificial neural network
dc.subjectgenetic algorithm
dc.titlePredicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm
dc.typearticle
dspace.entity.typePublication

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