Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9939
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dc.contributor.authorMohr, Gunther-
dc.contributor.authorAltenburg, Simon J.-
dc.contributor.authorUlbricht, Alexander-
dc.contributor.authorHeinrich, Philipp-
dc.contributor.authorBaum, Daniel-
dc.contributor.authorMaierhofer, Christiane-
dc.contributor.authorHilgenberg, Kai-
dc.date.accessioned2020-04-29T15:03:29Z-
dc.date.available2020-04-29T15:03:29Z-
dc.date.issued2020-01-09-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/11051-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9939-
dc.description.abstractAmong additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherlaser powder bed fusionen
dc.subject.otherselective laser meltingen
dc.subject.otheradditive manufacturingen
dc.subject.otherprocess monitoringen
dc.subject.otherinfrared thermographyen
dc.subject.otheroptical tomographyen
dc.subject.othercomputed tomographyen
dc.subject.otherdata fusionen
dc.subject.otherlack-of-fusionen
dc.subject.otherL-PBFen
dc.subject.otherSLMen
dc.subject.otherAMen
dc.subject.otherCTen
dc.titleIn-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomographyen
dc.typeArticleen
dc.date.updated2020-03-06T11:26:45Z-
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn2075-4701-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.3390/met10010103en
dcterms.bibliographicCitation.journaltitleMetalsen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume10en
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.issue1en
dcterms.bibliographicCitation.articlenumber103en
Appears in Collections:FG Verfahren und Technologien für hochbeanspruchte Schweißverbindungen » Publications

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