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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Materials

ISSN

1996-1944

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

000662586900001

EID výsledku v databázi Scopus

2-s2.0-85106716945