Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens

• Autore: Felica Tatzel Leonie
• Pubblicazione: Karlsruhe, : KIT Scientific Publishing, 2022
• Descrizione fisica: 1 online resource (234 p.)
• Soggetto topico: Electrical engineering
• Soggetto non controllato: convolutional neural network; cut quality; Edelstahl; Faltendes neuronales Netz; Laser cutting; Laserschneiden; machine learning; Maschinelles Lernen; Schnittqualität; stainless steel
• Sommario/riassunto: 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.
• Titolo autorizzato: Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens
• ISBN: 1000137690
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Tedesco
• Record Nr.: 9910547687503321
• Lo trovi qui: Univ. Federico II