Combined Use of Modal Analysis and Machine Learning for Materials Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F21%3A00008896" target="_blank" >RIV/46747885:24410/21:00008896 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24620/21:00008896

Výsledek na webu

<a href="https://www.mdpi.com/1996-1944/14/15/4270/htm" target="_blank" >https://www.mdpi.com/1996-1944/14/15/4270/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma14154270" target="_blank" >10.3390/ma14154270</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combined Use of Modal Analysis and Machine Learning for Materials Classification

Popis výsledku v původním jazyce

The present study deals with modal work that is a type of framework for structural dynamic testing of linear structures. Modal analysis is a powerful tool that works on the modal parameters to ensure the safety of materials and eliminate the failure possibilities. The concept of classification through this study is validated for isotropic and orthotropic materials, reaching up to a 100% accuracy when deploying the machine learning approach between the mode number and the associated frequency of the interrelated variables that were extracted from modal analysis performed by ANSYS. This study shows a new classification method dependent only on the knowledge of resonance frequency of a specific material and opens new directions for future developments to create a single device that can identify and classify different engineering materials.

Název v anglickém jazyce

Combined Use of Modal Analysis and Machine Learning for Materials Classification

Popis výsledku anglicky

The present study deals with modal work that is a type of framework for structural dynamic testing of linear structures. Modal analysis is a powerful tool that works on the modal parameters to ensure the safety of materials and eliminate the failure possibilities. The concept of classification through this study is validated for isotropic and orthotropic materials, reaching up to a 100% accuracy when deploying the machine learning approach between the mode number and the associated frequency of the interrelated variables that were extracted from modal analysis performed by ANSYS. This study shows a new classification method dependent only on the knowledge of resonance frequency of a specific material and opens new directions for future developments to create a single device that can identify and classify different engineering materials.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modulární platforma pro autonomní podvozky specializovaných elektrovozidel pro dopravu nákladu a zařízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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

15

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

000682101300001

EID výsledku v databázi Scopus

2-s2.0-85111702819