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dc.contributor.authorFelica Tatzel, Leonie
dc.date.accessioned2022-02-18T15:02:45Z
dc.date.available2022-02-18T15:02:45Z
dc.date.issued2022
dc.identifierONIX_20220218_9783731511281_17
dc.identifier.issn2190-6629
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/52956
dc.languageGerman
dc.relation.ispartofseriesForschungsberichte aus der Industriellen Informationstechnik
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineeringen_US
dc.subject.othercut quality
dc.subject.otherconvolutional neural network
dc.subject.othermachine learning
dc.subject.otherstainless steel
dc.subject.otherLaser cutting
dc.subject.otherSchnittqualität
dc.subject.otherMaschinelles Lernen
dc.subject.otherEdelstahl
dc.subject.otherLaserschneiden
dc.subject.otherFaltendes neuronales Netz
dc.titleVerbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens
dc.typebook
oapen.abstract.otherlanguageAlthough laser cutting of metals is a well-established process, there is considerable potential for improvement with regard to various requirements for the manufacturing industry. First, this potential is identified and then it is shown how improvements could be made using machine learning. For this purpose, a database was generated. It contains the process parameters, RGB images, 3D point clouds and various quality features of almost 4000 cut edges.
oapen.identifier.doi10.5445/KSP/1000137690
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9
oapen.relation.isbn9783731511281
oapen.imprintKIT Scientific Publishing
oapen.series.number24
oapen.pages234
oapen.place.publicationKarlsruhe


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