Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens
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...
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| Format: | Electronic Book Chapter |
| Published: |
Karlsruhe
KIT Scientific Publishing
2022
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| Series: | Forschungsberichte aus der Industriellen Informationstechnik
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 100 | 1 | |a Felica Tatzel, Leonie |4 auth | |
| 245 | 1 | 0 | |a Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| 260 | |a Karlsruhe |b KIT Scientific Publishing |c 2022 | ||
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| 520 | |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. | ||
| 540 | |a Creative Commons |f by-sa/4.0 |2 cc |4 http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a German | ||
| 650 | 7 | |a Electrical engineering |2 bicssc | |
| 653 | |a cut quality | ||
| 653 | |a convolutional neural network | ||
| 653 | |a machine learning | ||
| 653 | |a stainless steel | ||
| 653 | |a Laser cutting | ||
| 653 | |a Schnittqualität | ||
| 653 | |a Maschinelles Lernen | ||
| 653 | |a Edelstahl | ||
| 653 | |a Laserschneiden | ||
| 653 | |a Faltendes neuronales Netz | ||
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| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/78418 |7 0 |z DOAB: description of the publication |