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: | Online |
| Sprog: | tysk |
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KIT Scientific Publishing
2022
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| Online adgang: | ONIX_20220218_9783731511281_17 |
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| _version_ | 1869525362083364864 |
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| author | Felica Tatzel, Leonie |
| author_browse | Felica Tatzel, Leonie |
| author_facet | Felica Tatzel, Leonie |
| author_sort | Felica Tatzel, Leonie |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-78418 |
| institution | Directory of Open Access Books |
| language | ger |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-784182025-07-30T11:56:43Z Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens Felica Tatzel, Leonie cut quality convolutional neural network machine learning stainless steel Laser cutting Schnittqualität Maschinelles Lernen Edelstahl Laserschneiden Faltendes neuronales Netz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering 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. 2022-02-19T04:02:13Z 2022-02-19T04:02:13Z 2022-02-18T15:02:45Z 2022 book ONIX_20220218_9783731511281_17 OCN: 1308751182 2190-6629 https://library.oapen.org/handle/20.500.12657/52956 9783731511281 https://directory.doabooks.org/handle/20.500.12854/78418 ger Forschungsberichte aus der Industriellen Informationstechnik open access image/jpeg image/jpeg image/jpeg image/jpeg n/a n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/52956/1/9783731511281.pdf https://library.oapen.org/bitstream/20.500.12657/52956/1/9783731511281.pdf https://library.oapen.org/bitstream/20.500.12657/52956/1/9783731511281.pdf https://library.oapen.org/bitstream/20.500.12657/52956/1/9783731511281.pdf KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000137690 10.5445/KSP/1000137690 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731511281 AG Universitätsverlage KIT Scientific Publishing 234 Karlsruhe open access |
| spellingShingle | cut quality convolutional neural network machine learning stainless steel Laser cutting Schnittqualität Maschinelles Lernen Edelstahl Laserschneiden Faltendes neuronales Netz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering Felica Tatzel, Leonie Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| title | Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| title_full | Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| title_fullStr | Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| title_full_unstemmed | Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| title_short | Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens |
| title_sort | verbesserungen beim laserschneiden mit methoden des maschinellen lernens |
| topic | cut quality convolutional neural network machine learning stainless steel Laser cutting Schnittqualität Maschinelles Lernen Edelstahl Laserschneiden Faltendes neuronales Netz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering |
| topic_facet | cut quality convolutional neural network machine learning stainless steel Laser cutting Schnittqualität Maschinelles Lernen Edelstahl Laserschneiden Faltendes neuronales Netz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering |
| url | ONIX_20220218_9783731511281_17 |
| work_keys_str_mv | AT felicatatzelleonie verbesserungenbeimlaserschneidenmitmethodendesmaschinellenlernens |