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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28110%2F21%3A63536818" target="_blank" >RIV/70883521:28110/21:63536818 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1996-1944/14/10/2620" target="_blank" >https://www.mdpi.com/1996-1944/14/10/2620</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma14102620" target="_blank" >10.3390/ma14102620</a>

Result language

angličtina

Original language name

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

Original language description

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&apos;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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20501 - Materials engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

Materials

ISSN

1996-1944

e-ISSN

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Volume of the periodical

14

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

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UT code for WoS article

000662586900001

EID of the result in the Scopus database

2-s2.0-85106716945