Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8253
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dc.contributor.authorGörgülüarslan, Recep Muhammet-
dc.contributor.authorAteş, G.C.-
dc.contributor.authorUtku Güngör, Olgun-
dc.contributor.authorYamaner, Y.-
dc.date.accessioned2022-01-15T13:00:45Z-
dc.date.available2022-01-15T13:00:45Z-
dc.date.issued2022-
dc.identifier.issn1530-9827-
dc.identifier.urihttps://doi.org/10.1115/1.4053001-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8253-
dc.description.abstractAdditive manufacturing (AM) introduces geometric uncertainties on the fabricated strut members of lattice structures. These uncertainties result in deviations between the modeled and fabricated geometries of struts. The use of deep neural networks (DNNs) to accurately predict the statistical parameters of the effective strut diameters to account for the AM-introduced geometric uncertainties with a small training dataset for constant process parameters is studied in this research. For the training data, struts with certain angle and diameter values are fabricated by the material extrusion process. The geometric uncertainties are quantified using the random field theory based on the spatial strut radius measurements obtained from the microscope images of the fabricated struts. The uncertainties are propagated to the effective diameters of the struts using a stochastic upscaling technique. The relationship between the modeled strut diameter and the characterized statistical parameters of the effective diameters are used as the training data to establish a DNN model. The validation results show that the DNN model can predict the statistical parameters of the effective diameters of the struts modeled with angles and diameters different from the ones used in the training data with good accuracy even if the training data set is small. Developing such a DNN model with small data will allow designers to use the fabricated results in the design optimization processes without requiring additional experimentations. © 2021 Mary Ann Liebert Inc.. All rights reserved.en_US
dc.description.sponsorship118M715en_US
dc.description.sponsorshipThe authors acknowledge the funding provided by the Turkish Science and Research Council (TUBITAK) by project number 118M715.en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Mechanical Engineers (ASME)en_US
dc.relation.ispartofJournal of Computing and Information Science in Engineeringen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject3D printersen_US
dc.subjectAdditivesen_US
dc.subjectDeep neural networksen_US
dc.subjectExtrusionen_US
dc.subjectFabricationen_US
dc.subjectForecastingen_US
dc.subjectGeometryen_US
dc.subjectStochastic systemsen_US
dc.subjectUncertainty analysisen_US
dc.subjectEffective diameteren_US
dc.subjectGeometric uncertaintiesen_US
dc.subjectLattice structuresen_US
dc.subjectNeural network modelen_US
dc.subjectProcess parametersen_US
dc.subjectSmall trainingen_US
dc.subjectStatistical parametersen_US
dc.subjectTraining dataen_US
dc.subjectTraining dataseten_US
dc.subjectUncertaintyen_US
dc.subjectStrutsen_US
dc.titleStrut Diameter Uncertainty Prediction by Deep Neural Network for Additively Manufactured Lattice Structuresen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.identifier.volume22en_US
dc.identifier.issue3en_US
dc.identifier.wosWOS:000790402800012en_US
dc.identifier.scopus2-s2.0-85121727139en_US
dc.institutionauthorGörgülüarslan, Recep Muhammet-
dc.identifier.doi10.1115/1.4053001-
dc.authorscopusid56076567200-
dc.authorscopusid57221393471-
dc.authorscopusid57222077088-
dc.authorscopusid57354674000-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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