LEADER 01906nam  2200457z- 450 
001 9910547687503321
005 20220219
010   $a1000137690
035   $a(CKB)5840000000005224
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/78418
035   $a(oapen)doab78418
035   $a(EXLCZ)995840000000005224
100   $a20202202d2022      |y         0
101 0 $ager
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aVerbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens
210   $aKarlsruhe$cKIT Scientific Publishing$d2022
215   $a1 online resource (234 p.)
225 1 $aForschungsberichte aus der Industriellen Informationstechnik
311 08$a3-7315-1128-2 
330   $aAlthough 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.
606   $aElectrical engineering$2bicssc
610   $aconvolutional neural network
610   $acut quality
610   $aEdelstahl
610   $aFaltendes neuronales Netz
610   $aLaser cutting
610   $aLaserschneiden
610   $amachine learning
610   $aMaschinelles Lernen
610   $aSchnittqualita?t
610   $astainless steel
615  7$aElectrical engineering
700   $aFelica Tatzel$b Leonie$4auth$01330397
906   $aBOOK
912   $a9910547687503321
996   $aVerbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens$93039821
997   $aUNINA