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Solving the issue of discriminant roughness of heterogeneous surfaces using elements of artificial intelligence

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dc.title Solving the issue of discriminant roughness of heterogeneous surfaces using elements of artificial intelligence en
dc.contributor.author Kubišová, Milena
dc.contributor.author Pata, Vladimír
dc.contributor.author Měřínská, Dagmar
dc.contributor.author Škrobák, Adam
dc.contributor.author Marcaník, Miroslav
dc.relation.ispartof Materials
dc.identifier.issn 1996-1944 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2021
utb.relation.volume 14
utb.relation.issue 10
dc.type article
dc.language.iso en
dc.publisher MDPI AG
dc.identifier.doi 10.3390/ma14102620
dc.relation.uri https://www.mdpi.com/1996-1944/14/10/2620
dc.subject surface quality en
dc.subject metallic materials en
dc.subject statistical analysis of measured data en
dc.subject perceptron en
dc.description.abstract This work deals with investigative methods used for evaluation of the surface quality of selected metallic materials’ cutting plane that was created by CO2 and fiber laser machining. The surface quality expressed by Rz and Ra roughness parameters is examined depending on the sample material and the machining technology. The next part deals with the use of neural networks in the evaluation of measured data. In the last part, the measured data were statistically evaluated. Based on the conclusions of this analysis, the possibilities of using neural networks to determine the material of a given sample while knowing the roughness parameters were evaluated. The main goal of the presented paper is to demonstrate a solution capable of finding characteristic roughness values for heterogeneous surfaces. These surfaces are common in scientific as well as technical practice, and measuring their quality is challenging. This difficulty lies mainly in the fact that it is not possible to express their quality by a single statistical parameter. Thus, this paper's main aim is to demonstrate solutions using the cluster analysis methods and the hidden layer, solving the problem of discriminant and dividing the heterogeneous surface into individual zones that have characteristic parameters. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. en
utb.faculty Faculty of Technology
dc.identifier.uri http://hdl.handle.net/10563/1010366
utb.identifier.obdid 43882924
utb.identifier.scopus 2-s2.0-85106716945
utb.identifier.wok 000662586900001
utb.source j-scopus
dc.date.accessioned 2021-06-22T16:32:29Z
dc.date.available 2021-06-22T16:32:29Z
dc.description.sponsorship [IGA/FT/2021/006 TBU]
dc.rights Attribution 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Kubišová, Milena
utb.contributor.internalauthor Pata, Vladimír
utb.contributor.internalauthor Měřínská, Dagmar
utb.contributor.internalauthor Škrobák, Adam
utb.contributor.internalauthor Marcaník, Miroslav
utb.fulltext.sponsorship This article was written with the support of the project IGA/FT/2021/006 TBU in Zlín. The authors would like to thank student Radek Zaoral for their cooperation.
utb.wos.affiliation [Kubisova, Milena; Pata, Vladimir; Merinska, Dagmar; Skrobak, Adam; Marcanik, Miroslav] Tomas Bata Univ Zlin, Fac Technol, Vavreckova 275, Zlin 76001, Czech Republic
utb.scopus.affiliation Faculty of Technology, Tomas Bata University in Zlín, Vavrečkova 275, Zlín, 760 01, Czech Republic
utb.fulltext.projects IGA/FT/2021/006
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Attribution 4.0 International Except where otherwise noted, this item's license is described as Attribution 4.0 International