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Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens

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Although 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...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Päätekijä: Felica Tatzel, Leonie (auth)
Aineistotyyppi: Elektroninen Kirjan osa
Julkaistu: Karlsruhe KIT Scientific Publishing 2022
Sarja:Forschungsberichte aus der Industriellen Informationstechnik
Aiheet:
Electrical engineering
cut quality
convolutional neural network
machine learning
stainless steel
Laser cutting
Schnittqualität
Maschinelles Lernen
Edelstahl
Laserschneiden
Faltendes neuronales Netz
Linkit:DOAB: download the publication
DOAB: description of the publication
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Kuvaus
Yhteenveto:Although 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.
Ulkoasu:1 electronic resource (234 p.)
ISBN:KSP/1000137690
9783731511281
Pääsy:Open Access

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