Application of artificial neural networks for estimation of the mass of the waste powder during selective laser sintering

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dc.contributor.authorŠoškić, Zlatan
dc.contributor.authorTomić, Jelena
dc.contributor.authorDrvar, Nend
dc.contributor.authorMonti, Michele
dc.contributor.authorCardu, Marco
dc.date.accessioned2022-02-22T22:43:17Z
dc.date.available2022-02-22T22:43:17Z
dc.date.issued2019
dc.description.abstractThe paper presents results of the efforts to improve the accuracy of cost calculation of the selective laser sintering technology using an artificial neural network as a tool for estimation of the mass of the waste powder, based on the masses of the powder that will be, and the powder that will not be, built into products during a production process.en_US
dc.description.sponsorshipThe authors wish to acknowledge the support of European Commission through the project “Advanced design rules for optimal dynamic properties of additive manufacturing products – A_MADAM”, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 734455.en_US
dc.description.versionPublisheden_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/14231
dc.language.isoenen_US
dc.relationA_MADAM - Advanced design rules for optimal dynamic properties of additive manufacturing productsen_US
dc.relation.conferenceProceedings of 8th International Conference “Mechanics and Materials in Design - M2D 2019”, Bologna /Italy/, 4 - 6 September 2019,en_US
dc.rightsopenAccess
dc.rights.licenseBY-NC-ND
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectadditive manufacturingen_US
dc.subjectselective laser sinteringen_US
dc.subjectmanufacturing costsen_US
dc.titleApplication of artificial neural networks for estimation of the mass of the waste powder during selective laser sinteringen_US
dc.typeconferenceObjecten_US
dc.type.versionPublishedVersionen_US

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