Combined Use of Modal Analysis and Machine Learning for Materials Classification

Simple item page
dc.contributor.organizationTechnická univerzita v Libercics
dc.contributor.otherCXIcs
dc.creatorAbdelkader, Mohamed
dc.creatorNoman, Muhammad Tayyab
dc.creatorAmor, Nesrine
dc.creatorPetru, Michal
dc.creatorMahmood, Aamir
dc.date.accessioned2021-09-24T13:40:33Z
dc.date.available2021-09-24T13:40:33Z
dc.date.issued2021-07-30
dc.description.abstractThe 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.cs
dc.identifier.citationAbdelkader, M.; Noman, M.T.; Amor, N.; Petru, M.; Mahmood, A. Combined Use of Modal Analysis and Machine Learning for Materials Classification. Materials 2021, 14, 4270. https://doi.org/10.3390/ma14154270cs
dc.identifier.doi10.3390/ma14154270
dc.identifier.urihttps://dspace.tul.cz/handle/15240/160976
dc.language.isoencs
dc.publisherMultidisciplinary Digital Publishing Institutecs
dc.publisher.abbreviationMDPIcs
dc.relation.ispartofseries14/15;Materials (MDPI)
dc.relation.isrefereedtruecs
dc.subjectisotropic; anisotropic; orthotropic; modal analysis; resonance frequency; mode shapescs
dc.titleCombined Use of Modal Analysis and Machine Learning for Materials Classificationcs
dc.typeArticlecs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
materials-14-04270-v2.pdf
Size:
55.67 MB
Format:
Adobe Portable Document Format
Description:
Research article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections