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Metrological evaluation of heterogeneous surfaces obtained by water jet cutting technology using artificial intelligence elements

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dc.title Metrological evaluation of heterogeneous surfaces obtained by water jet cutting technology using artificial intelligence elements en
dc.contributor.author Marcaník, Miroslav
dc.contributor.author Kubišová, Milena
dc.contributor.author Pata, Vladimír
dc.contributor.author Novák, Martin
dc.contributor.author Vrbová, Hana
dc.contributor.author Knedlová, Jana
dc.relation.ispartof Journal of Physics: Conference Series
dc.identifier.issn 1742-6588 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2022
utb.relation.volume 2413
utb.relation.issue 1
dc.event.title 31st Joint Seminar on the Development of Materials Science in Research and Education, DMSRE 2022
dc.event.location Nová Lesná
utb.event.state-en Slovakia
utb.event.state-cs Slovensko
dc.event.sdate 2022-09-05
dc.event.edate 2022-09-09
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Physics
dc.identifier.doi 10.1088/1742-6596/2413/1/012003
dc.relation.uri https://iopscience.iop.org/article/10.1088/1742-6596/2413/1/012003/pdf
dc.description.abstract This paper deals with the design and construction of a neural network for predicting the results of roughness parameters for heterogeneous surfaces. At the same time, it demonstrates that other statistical methods, especially regression analysis, fail in this respect, and their results cannot be used reliably. The samples produced using waterjet cutting were used to obtain the necessary data for constructing the neural network. Its heterogeneity characterizes this surface. This paper describes these samples, the parameters of their creation, the laboratory measurements, the complete construction of the neural network and the subsequent comparison of the results with regression functions. This paper aims to design a functional neural network that will best describe the roughness pattern of the surface under study. This neural network will predict this waveform based on the input variables and prove that this advanced statistical method completely exceeds the capabilities and predictive value of conventional regression analyses. © Published under licence by IOP Publishing Ltd. en
utb.faculty Faculty of Technology
dc.identifier.uri http://hdl.handle.net/10563/1011326
utb.identifier.obdid 43884302
utb.identifier.scopus 2-s2.0-85145441804
utb.source d-scopus
dc.date.accessioned 2023-02-15T08:06:27Z
dc.date.available 2023-02-15T08:06:27Z
dc.rights Attribution 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.rights.access openAccess
utb.contributor.internalauthor Marcaník, Miroslav
utb.contributor.internalauthor Kubišová, Milena
utb.contributor.internalauthor Pata, Vladimír
utb.contributor.internalauthor Novák, Martin
utb.contributor.internalauthor Vrbová, Hana
utb.contributor.internalauthor Knedlová, Jana
utb.fulltext.sponsorship This article was written with the support of the project IGA/FT/2022/007 Tomas Bata University in Zlin.
utb.scopus.affiliation Tomas Bata University in Zlín, Faculty of Technology, Vavrečkova 275, Zlín, 760 01, Czech Republic
utb.fulltext.projects IGA/FT/2022/007
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Attribution 3.0 Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Attribution 3.0