Application of artificial neural networks to the prediction of critical buckling loads of helical compression springs

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dc.authorscopusid25121107400
dc.authorscopusid36184052600
dc.authorscopusid56283646500
dc.authorscopusid36243748600
dc.contributor.authorIbrikçi T.
dc.contributor.authorSaçma S.
dc.contributor.authorYildirim V.
dc.contributor.authorKoca T.
dc.date.accessioned2024-08-04T19:59:13Z
dc.date.available2024-08-04T19:59:13Z
dc.date.issued2010
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper proposes the use of artificial neural networks (ANN) to predict perfectly the critical buckling loads of cylindrical isotropic helical spring with fixed ends and with circular sections, and also having large pitch angles. The buckling equations of cylindrical isotropic helical springs loaded axially consist of a set of twelve linear differential equations. As finding a solution in an analytical manner is too difficult, they are solved numerically in an exact manner based on the transfer-matrix method to collect consistent dimensionless numerical data for the training process. Then almost perfect weight values are obtained to predict the non-dimensional buckling loads. A good agreement is observed with the data available in the literature. © 20xx Journal of Mechanical Engineering. All rights reserved.en_US
dc.identifier.issn0039-2480
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-77954880749en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://hdl.handle.net/11616/90469
dc.identifier.volume56en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofStrojniski Vestnik/Journal of Mechanical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectBucklingen_US
dc.subjectCoil springen_US
dc.subjectCritical buckling loaden_US
dc.subjectDesignen_US
dc.subjectHelical springen_US
dc.titleApplication of artificial neural networks to the prediction of critical buckling loads of helical compression springsen_US
dc.typeArticleen_US

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