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ANNs Prediction of Input Parameters Dutiny PMMA Laser Micro-machining

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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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