Conductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and Electrical Behaviors

dc.authorwosidÜlkir, Osman/AAI-2940-2020
dc.contributor.authorUlkir, Osman
dc.date.accessioned2023-11-10T21:09:58Z
dc.date.available2023-11-10T21:09:58Z
dc.date.issued2023
dc.departmentMAÜNen_US
dc.description.abstractAdditive manufacturing is a process in which digital three-dimensional (3D) design data are used to build a component in layers by accumulating materials. There are many materials used in additive manufacturing technology. The most basic features that distinguish these materials are their strength and electrical behavior. They can be strong or flexible, resistant to abrasion, depending on the application used. Recently, 3D printing filament and polymeric composite materials combined with carbon nanostructures with electrical conductivity have been used. In this study, acrylonitrile butadiene styrene (ABS), a carbon black-filled conductive material with high strength and hardness, was preferred. The aim in this study is to focus on the mechanical and electrical behavior of the material processed in filament form. Fabrication of samples was done using a fused deposition modeling-based printer that controls filament orientation. Different experimental studies were conducted: (1) mechanical tests to determine the maximum tensile strength values of the samples; and (2) electrical tests to analyze the electrical resistances of the samples. In the design of the first experiment, infill volume, layer height, infill type, and printing direction were determined as factors affecting strength. In the design of the second experiment, the length, nozzle temperature, and measurement temperature were determined as the factors affecting the electrical resistance. Statistical analysis of the measured data was performed to evaluate the overall result of the experiments. Finally, a prediction model of real-time tensile strength and resistance values was created using machine learning algorithms. These algorithms are Gaussian Process Regression and Support Vector Machine. The results confirmed the known linear dependence of electrical resistance on the length of the 3D-printed conductive ABS samples and showed how changing the fabrication settings affected the strength values.en_US
dc.identifier.doi10.1089/3dp.2022.0287
dc.identifier.issn2329-7662
dc.identifier.issn2329-7670
dc.identifier.orcid0000-0002-1095-0160
dc.identifier.scopus2-s2.0-85180285568
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1089/3dp.2022.0287
dc.identifier.urihttps://hdl.handle.net/20.500.12639/5364
dc.identifier.wosWOS:000924921500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUlkir, Osman
dc.language.isoen
dc.publisherMary Ann Liebert, Incen_US
dc.relation.ispartof3d Printing and Additive Manufacturingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdditive Manufacturingen_US
dc.subjectConductive Filamenten_US
dc.subjectFused Deposition Modelingen_US
dc.subjectAcrylonitrile Butadiene Styreneen_US
dc.subjectStrengthen_US
dc.subjectElectrical Conductivityen_US
dc.subjectMachine Learningen_US
dc.subjectTensile-Strengthen_US
dc.subjectFabricationen_US
dc.subjectCompositeen_US
dc.titleConductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and Electrical Behaviorsen_US
dc.typeArticle

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