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Combined Use of Modal Analysis and Machine Learning for Materials Classification

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 throu...

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Detalles Bibliográficos
Autores principales: Abdelkader, Mohamed, Noman, Muhammad Tayyab, Amor, Nesrine, Petru, Michal, Mahmood, Aamir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8348414/
https://www.ncbi.nlm.nih.gov/pubmed/34361464
http://dx.doi.org/10.3390/ma14154270
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author Abdelkader, Mohamed
Noman, Muhammad Tayyab
Amor, Nesrine
Petru, Michal
Mahmood, Aamir
author_facet Abdelkader, Mohamed
Noman, Muhammad Tayyab
Amor, Nesrine
Petru, Michal
Mahmood, Aamir
author_sort Abdelkader, Mohamed
collection PubMed
description 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.
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spelling pubmed-83484142021-08-08 Combined Use of Modal Analysis and Machine Learning for Materials Classification Abdelkader, Mohamed Noman, Muhammad Tayyab Amor, Nesrine Petru, Michal Mahmood, Aamir Materials (Basel) Article 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. MDPI 2021-07-30 /pmc/articles/PMC8348414/ /pubmed/34361464 http://dx.doi.org/10.3390/ma14154270 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abdelkader, Mohamed
Noman, Muhammad Tayyab
Amor, Nesrine
Petru, Michal
Mahmood, Aamir
Combined Use of Modal Analysis and Machine Learning for Materials Classification
title Combined Use of Modal Analysis and Machine Learning for Materials Classification
title_full Combined Use of Modal Analysis and Machine Learning for Materials Classification
title_fullStr Combined Use of Modal Analysis and Machine Learning for Materials Classification
title_full_unstemmed Combined Use of Modal Analysis and Machine Learning for Materials Classification
title_short Combined Use of Modal Analysis and Machine Learning for Materials Classification
title_sort combined use of modal analysis and machine learning for materials classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8348414/
https://www.ncbi.nlm.nih.gov/pubmed/34361464
http://dx.doi.org/10.3390/ma14154270
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