Using Artificial Neural Networks for Real-Time Observation of the Endurance State of a Steel Specimen Under Loading

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dc.contributor.authorSelek, Murat
dc.contributor.authorŞahin, Ömer Sinan
dc.contributor.authorKahramanlı, Sirzat
dc.date.accessioned2020-03-26T17:41:07Z
dc.date.available2020-03-26T17:41:07Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe surface temperature behavior of a steel specimen under bending fatigue is exactly divided into three stages: an initial temperature increase stage, a constant temperature stage and an abrupt temperature increase stage at the end of which the specimen fails. To obtain the endurance state of the specimen we use its thermal images (TIs). By applying artificial neural networks (ANNs) and other operations to these TIs we obtain spots with maximal, approximately medium and minimal temperatures. Then by using these temperatures we analytically obtain the temperatures all of spots of the specimen and localize the regions consisting of spots of relatively high temperatures. We consider such a region as one to be cracked firstly. This approach allows us to handle only those spots that are of interest and to work in real-time even by using an infrared (IR) camera and a computer with average technical features. We are using the result obtained in this study for fatigue testing the steel materials and for sensing the pre-fatigue state of a specific part of a machine being worked in order to take preventive measures before it breaks down.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects Coordinatorship/Konya, TurkeySelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Selcuk University Scientific Research Projects Coordinatorship/Konya, Turkey.en_US
dc.identifier.citationKahramanlı, S., Şahin, Ö. S., Selek, M., (2009). Using Artificial Neural Networks for Real-Time Observation of the Endurance State of a Steel Specimen Under Loading. Expert Systems With Applications, 36(4), 7400-7408. Doi:10.1016/j.eswa.2008.09.059
dc.identifier.doi10.1016/j.eswa.2008.09.059en_US
dc.identifier.endpage7408en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage7400en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2008.09.059
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24011
dc.identifier.volume36en_US
dc.identifier.wosWOS:000264528600008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSelek, Murat
dc.institutionauthorŞahin, Ömer Sinan
dc.institutionauthorKahramanlı, Sirzat
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectMaterial fatigueen_US
dc.subjectThermal imageen_US
dc.subjectImage processingen_US
dc.subjectInfrared thermographyen_US
dc.titleUsing Artificial Neural Networks for Real-Time Observation of the Endurance State of a Steel Specimen Under Loadingen_US
dc.typeArticleen_US

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