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Prediction of Laser Drilling Qualities for Optical Grade PM..:
, In:
2023 International Conference on Machine Learning and Cybernetics (ICMLC)
,
Tsai, Ming-Jong
;
Wu, Lung-Fa
;
Li, Chen-Hao
- p. 428-433 , 2023
Link:
https://doi.org/10.1109/ICMLC58545.2023.10327926
RT T1 <cTitle>2023 International Conference on Machine Learning and Cybernetics (ICMLC)</cTitle>: T1 <Title>Prediction of Laser Drilling Qualities for Optical Grade PMMA Using Artificial Neural Network</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10327926&Exemplar=1&LAN=DE A1 Tsai, Ming-Jong A1 Wu, Lung-Fa A1 Li, Chen-Hao YR 2023 SN 2160-1348 K1 Drilling K1 Artificial neural networks K1 Optical computing K1 Predictive models K1 Optical fiber networks K1 Laser modes K1 Adaptive optics K1 Laser beam machining (LBM) K1 Laser drilling K1 Neodymium-doped yttrium aluminum garnet (Nd: YAG) K1 optical-grade Acrylic (PMMA) K1 Heat-affected zone (HAZ) K1 Artificial Neural network (ANN) K1 Levenberg-Marquardt (LM) Algorithm SP 428 OP 433 LK http://dx.doi.org/https://doi.org/10.1109/ICMLC58545.2023.10327926 DO https://doi.org/10.1109/ICMLC58545.2023.10327926 SF ELIB - SuUB Bremen
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