01906nam 2200457z- 450 9910547687503321202202191000137690(CKB)5840000000005224(oapen)https://directory.doabooks.org/handle/20.500.12854/78418(oapen)doab78418(EXLCZ)99584000000000522420202202d2022 |y 0gerurmn|---annantxtrdacontentcrdamediacrrdacarrierVerbesserungen beim Laserschneiden mit Methoden des maschinellen LernensKarlsruheKIT Scientific Publishing20221 online resource (234 p.)Forschungsberichte aus der Industriellen Informationstechnik3-7315-1128-2 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.Electrical engineeringbicsscconvolutional neural networkcut qualityEdelstahlFaltendes neuronales NetzLaser cuttingLaserschneidenmachine learningMaschinelles LernenSchnittqualitätstainless steelElectrical engineeringFelica Tatzel Leonieauth1330397BOOK9910547687503321Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens3039821UNINA