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dc.title | ANNs Prediction of Input Parameters Dutiny PMMA Laser Micro-machining | en |
dc.contributor.author | Sýkorová, Libuše | |
dc.contributor.author | Sámek, David | |
dc.relation.ispartof | Development in Machining Technology | |
dc.identifier.isbn | 978-83-7242-640-6 | |
dc.date.issued | 2011 | |
dc.event.location | Krakow | |
utb.event.state-en | Poland | |
utb.event.state-cs | Polsko | |
dc.type | bookPart | |
dc.language.iso | en | |
dc.publisher | Politechnika Krakowska | |
dc.subject | laser | en |
dc.subject | micro-machining | en |
dc.subject | surface quality | en |
dc.subject | polymer material type | en |
dc.subject | artificial neural network | en |
dc.description.abstract | This paper presents usage of artificial neural networks for modelling of laser micro-machining process. Results of the laser micro-machining – surface quality of product and his utility in specific application – depend on the laser-machine parameters and the polymer material type. Commercial CO2 laser Mercury L-30 by LaserPro, USA was used for cutting specimens. This laser system has two parameters - power and feed. The article also shows optimization of the laser micro-machining using artificial neural network. In order to interpret complicated dependencies between technological characteristics of laser micro-machining and output parameters software Matlab 6.5 with Neural Network Toolbox was used. The experimental results were evaluated and depicted into the graphs. | en |
utb.faculty | Faculty of Technology | |
dc.identifier.uri | http://hdl.handle.net/10563/1005993 | |
utb.identifier.rivid | RIV/70883521:28110/11:43867587!RIV12-MSM-28110___ | |
utb.identifier.obdid | 43867871 | |
utb.source | c-riv | |
dc.date.accessioned | 2016-04-28T10:37:30Z | |
dc.date.available | 2016-04-28T10:37:30Z | |
dc.description.sponsorship | Z(MSM7088352102) | |
dc.format.extent | 155 | |
utb.contributor.internalauthor | Sýkorová, Libuše | |
utb.contributor.internalauthor | Sámek, David | |
riv.obor | JQ |
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