Transferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Deposition

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dc.contributor.authorMarko, Angelina
dc.contributor.authorBähring, Stefan
dc.contributor.authorRaute, Julius
dc.contributor.authorBiegler, Max
dc.contributor.authorRethmeier, Michael
dc.date.accessioned2023-03-03T12:28:03Z
dc.date.available2023-03-03T12:28:03Z
dc.date.issued2022-11-04
dc.description.abstractDirected energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings, walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.en
dc.identifier.eissn2195-8572
dc.identifier.issn0025-5300
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18275
dc.identifier.urihttps://doi.org/10.14279/depositonce-17069
dc.language.isoen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
dc.subject.otheradditive manufacturingen
dc.subject.otherartificial neural networken
dc.subject.otherDEDen
dc.subject.otherquality assuranceen
dc.subject.otherwelding parameteren
dc.titleTransferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Depositionen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.1515/mt-2022-0054
dcterms.bibliographicCitation.issue11
dcterms.bibliographicCitation.journaltitleMaterials Testing
dcterms.bibliographicCitation.originalpublishernameDe Gruyter
dcterms.bibliographicCitation.originalpublisherplaceBerlin
dcterms.bibliographicCitation.pageend1596
dcterms.bibliographicCitation.pagestart1586
dcterms.bibliographicCitation.volume64
dcterms.rightsHolder.referenceVerlagspolicy
dcterms.rightsHolder.urlhttps://web.archive.org/web/20221207165037/https://www.degruyter.com/publishing/services/rights-and-permissions/repositorypolicy?lang=en
tub.accessrights.dnbdomain*
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Werkzeugmaschinen und Fabrikbetrieb::FG Fügetechnik
tub.publisher.universityorinstitutionTechnische Universität Berlin

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