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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20501 - Materials engineering
Result continuities
Project
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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
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ISSN
1742-6588
e-ISSN
1742-6596
Number of pages
9
Pages from-to
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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
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