Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1741
This item is licensed with a CreativeCommons licence by-nc-nd/4.0
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dc.contributor.authorRudolph, Jan-Peer-
dc.contributor.authorEmmelmann, Claus-
dc.date.accessioned2018-09-20T04:56:34Z-
dc.date.available2018-09-20T04:56:34Z-
dc.date.issued2018-03-21-
dc.identifier.citationProcedia CIRP (67): 185-190 (2018)de_DE
dc.identifier.issn2212-8271de_DE
dc.identifier.urihttp://tubdok.tub.tuhh.de/handle/11420/1744-
dc.description.abstractSelective laser melting (SLM) is increasingly used in the industrial production of metallic parts. This creates the need for an efficient and accurate quotation costing. The manufacturing costs of a part mainly result from the machine running time for coating and exposure. At the time of the offer calculation the final orientation of the part in the build chamber and the composition of the build job are typically not known. Addressing this need, this paper presents and evaluates different statistical based methods for an automated and self-learning calculation for SLM given a part’s CAD data.en
dc.language.isoende_DE
dc.publisherElsevierde_DE
dc.relation.ispartofProcedia CIRPde_DE
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.subjectCalculationde_DE
dc.subjectQuotation costingde_DE
dc.subjectSelf-learningde_DE
dc.subjectSelective laser melting (SLM)de_DE
dc.subjectAdditive manufacturing (AM)de_DE
dc.subject.ddc600: Technikde_DE
dc.titleSelf-learning calculation for selective laser meltingde_DE
dc.typeArticlede_DE
dc.identifier.urnurn:nbn:de:gbv:830-88222497-
dc.identifier.doi10.15480/882.1741-
dc.type.diniarticle-
dc.subject.ddccode600-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-88222497de_DE
tuhh.oai.showtrue-
dc.identifier.hdl11420/1744-
tuhh.abstract.englishSelective laser melting (SLM) is increasingly used in the industrial production of metallic parts. This creates the need for an efficient and accurate quotation costing. The manufacturing costs of a part mainly result from the machine running time for coating and exposure. At the time of the offer calculation the final orientation of the part in the build chamber and the composition of the build job are typically not known. Addressing this need, this paper presents and evaluates different statistical based methods for an automated and self-learning calculation for SLM given a part’s CAD data.de_DE
tuhh.publisher.doi10.1016/j.procir.2017.12.197-
tuhh.publication.instituteLaser- und Anlagensystemtechnik G-2de_DE
tuhh.identifier.doi10.15480/882.1741-
tuhh.type.opus(wissenschaftlicher) Artikelde
tuhh.institute.germanLaser- und Anlagensystemtechnik G-2de
tuhh.institute.englishLaser- und Anlagensystemtechnik G-2de_DE
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
openaire.rightsinfo:eu-repo/semantics/openAccessde_DE
dc.type.driverarticle-
dc.rights.ccby-nc-ndde_DE
dc.rights.ccversion4.0de_DE
dc.type.casraiJournal Articleen
tuhh.container.volume67de_DE
tuhh.container.startpage185de_DE
tuhh.container.endpage190de_DE
dc.relation.conference11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '17de_DE
dc.rights.nationallicensefalsede_DE
item.fulltextWith Fulltext-
item.creatorOrcidRudolph, Jan-Peer-
item.creatorOrcidEmmelmann, Claus-
item.creatorGNDRudolph, Jan-Peer-
item.creatorGNDEmmelmann, Claus-
item.grantfulltextopen-
crisitem.author.deptLaser- und Anlagensystemtechnik G-2-
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