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01916nma a2200397 u 4500 |
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EBX01000000000000001193031 |
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220822 ||| deu |
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|a 9783731511281
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|a 1000137690
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|a Felica Tatzel, Leonie
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|a Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens
|h Elektronische Ressource
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| 260 |
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|a Karlsruhe
|b KIT Scientific Publishing
|c 2022
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| 300 |
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|a 1 electronic resource (234 p.)
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| 653 |
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|a Laser cutting
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| 653 |
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|a machine learning
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| 653 |
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|a Maschinelles Lernen
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| 653 |
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|a Schnittqualität
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| 653 |
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|a cut quality
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| 653 |
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|a Electrical engineering / bicssc
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| 653 |
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|a convolutional neural network
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| 653 |
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|a Laserschneiden
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| 653 |
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|a stainless steel
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| 653 |
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|a Faltendes neuronales Netz
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| 653 |
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|a Edelstahl
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| 041 |
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7 |
|a deu
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
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|a Forschungsberichte aus der Industriellen Informationstechnik
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| 500 |
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|a Creative Commons (cc), by-sa/4.0, http://creativecommons.org/licenses/by-sa/4.0
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| 028 |
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|a 10.5445/KSP/1000137690
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/78418
|z DOAB: description of the publication
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| 856 |
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|u https://library.oapen.org/bitstream/20.500.12657/52956/1/9783731511281.pdf
|7 0
|x Verlag
|3 Volltext
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|a 620
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|a 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.
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