• Lietuvių
    • English
  • English 
    • Lietuvių
    • English
  • Login
View Item 
  •   DSpace Home
  • Mokslinės publikacijos (PDB) / Scientific publications (PDB)
  • Moksliniai ir apžvalginiai straipsniai / Research and Review Articles
  • Straipsniai Web of Science ir/ar Scopus referuojamuose leidiniuose / Articles in Web of Science and/or Scopus indexed sources
  • View Item
  •   DSpace Home
  • Mokslinės publikacijos (PDB) / Scientific publications (PDB)
  • Moksliniai ir apžvalginiai straipsniai / Research and Review Articles
  • Straipsniai Web of Science ir/ar Scopus referuojamuose leidiniuose / Articles in Web of Science and/or Scopus indexed sources
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Combined use of modal analysis and machine learning for materials classification

Thumbnail
Date
2021
Author
Abdelkader, Mohamed Abdelkader Fawzy
Noman, Muhammad Tayyab
Amor, Nesrine
Petru, Michal
Mahmood, Aamir
Metadata
Show full item record
Abstract
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.
Issue date (year)
2021
URI
https://etalpykla.vilniustech.lt/handle/123456789/111744
Collections
  • Straipsniai Web of Science ir/ar Scopus referuojamuose leidiniuose / Articles in Web of Science and/or Scopus indexed sources [7946]

 

 

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjects / KeywordsInstitutionFacultyDepartment / InstituteTypeSourcePublisherType (PDB/ETD)Research fieldStudy directionVILNIUS TECH research priorities and topicsLithuanian intelligent specializationThis CollectionBy Issue DateAuthorsTitlesSubjects / KeywordsInstitutionFacultyDepartment / InstituteTypeSourcePublisherType (PDB/ETD)Research fieldStudy directionVILNIUS TECH research priorities and topicsLithuanian intelligent specialization

My Account

LoginRegister