VAGASKÁ, Alena, Peter MICHAL, Miroslav GOMBÁR, Erika FECHOVÁ a Ján KMEC. Simulation of technological process by usage neural networks and factorial design of experiments. MM Science Journal. MM Science Journal, 2016, Neuveden, September 2016, s. 999-1003. ISSN 1803-1269. |
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@article{34526, author = {Vagaská, Alena and Michal, Peter and Gombár, Miroslav and Fechová, Erika and Kmec, Ján}, article_number = {September 2016}, keywords = {design of experiments; neural units; predicitve model; anodizing; layer thickness; factors}, language = {eng}, issn = {1803-1269}, journal = {MM Science Journal}, title = {Simulation of technological process by usage neural networks and factorial design of experiments}, volume = {Neuveden}, year = {2016} }
TY - JOUR ID - 34526 AU - Vagaská, Alena - Michal, Peter - Gombár, Miroslav - Fechová, Erika - Kmec, Ján PY - 2016 TI - Simulation of technological process by usage neural networks and factorial design of experiments JF - MM Science Journal VL - Neuveden IS - September 2016 SP - 999-1003 EP - 999-1003 PB - MM Science Journal SN - 18031269 KW - design of experiments KW - neural units KW - predicitve model KW - anodizing KW - layer thickness KW - factors N2 - The possibilities of simulation of technological process ofaluminium anodic oxidation using the methodology of Design of Experiments (DOE) and theory of neural networks in order to monitor the anodizing process under various operating conditions are presented in this paper. The influence of chemical and physical input factors on the resulting AAO (anodic aluminium oxide) layer thickness at applied current density of 1 A x dm-2 and 6 A x dm-2 has been investigated. Based on the evaluation of experimentally obtained data, the computational predictive model describing the effect of individual input factors and their mutual interactions on the AAO layer thickness was developed in the form of cubic function. This model indicates which factors are important and how they combine to influence the response, it will enable us to optimize operating conditions. The most significant benefit of our research work in this field is the fact that all relevant factors were varied simultaneously. ER -
VAGASKÁ, Alena, Peter MICHAL, Miroslav GOMBÁR, Erika FECHOVÁ a Ján KMEC. Simulation of technological process by usage neural networks and factorial design of experiments. \textit{MM Science Journal}. MM Science Journal, 2016, Neuveden, September 2016, s.~999-1003. ISSN~1803-1269.
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