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oapen-20.500.12657-529562022-02-19T02:52:38Z 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 bic Book Industry Communication::T Technology, engineering, agriculture::TH Energy technology & 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. 2022-02-18T15:02:45Z 2022-02-18T15:02:45Z 2022 book ONIX_20220218_9783731511281_17 2190-6629 9783731511281 https://library.oapen.org/handle/20.500.12657/52956 ger Forschungsberichte aus der Industriellen Informationstechnik application/pdf n/a 9783731511281.pdf https://doi.org/10.5445/KSP/1000137690 KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000137690 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. 10.5445/KSP/1000137690 44e29711-8d53-496b-85cc-3d10c9469be9 9783731511281 KIT Scientific Publishing 24 234 Karlsruhe open access
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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 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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