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Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens

dc.contributor.authorFelica Tatzel, Leonie
dc.date.accessioned2022-02-19T04:02:13Z
dc.date.available2022-02-19T04:02:13Z
dc.date.issued2022
dc.date.submitted2022-02-18T15:02:45Z
dc.identifierONIX_20220218_9783731511281_17
dc.identifier2190-6629
dc.identifierhttps://library.oapen.org/handle/20.500.12657/52956
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/78418
dc.description.abstractAlthough 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.
dc.languageGerman
dc.relation.ispartofseriesForschungsberichte aus der Industriellen Informationstechnik
dc.rightsopen access
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.subject.otherthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
dc.titleVerbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens
dc.typebook
oapen.identifier.doi10.5445/KSP/1000137690
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.relation.isbn9783731511281
oapen.imprintKIT Scientific Publishing
oapen.pages234
oapen.place.publicationKarlsruhe
dc.seriesnumber24
dc.abstractotherlanguageAlthough 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.


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