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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28110%2F22%3A63559238" target="_blank" >RIV/70883521:28110/22:63559238 - isvavai.cz</a>

  • Result on the web

    <a href="https://iopscience.iop.org/article/10.1088/1742-6596/2413/1/012003" target="_blank" >https://iopscience.iop.org/article/10.1088/1742-6596/2413/1/012003</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1088/1742-6596/2413/1/012003" target="_blank" >10.1088/1742-6596/2413/1/012003</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20501 - Materials engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Journal of Physics: Conference Series

  • ISBN

  • ISSN

    1742-6588

  • e-ISSN

    1742-6596

  • Number of pages

    9

  • Pages from-to

  • Publisher name

    Institute of Physics Publishing Ltd.

  • Place of publication

    Bristol

  • Event location

    Nová Lesná

  • Event date

    Sep 5, 2022

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article