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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Journal of Physics: Conference Series

ISBN

—

ISSN

1742-6588

e-ISSN

1742-6596

Počet stran výsledku

9

Strana od-do

—

Název nakladatele

Institute of Physics Publishing Ltd.

Místo vydání

Bristol

Místo konání akce

Nová Lesná

Datum konání akce

5. 9. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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