Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

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dc.authorid/0000-0003-3300-1336
dc.contributor.authorPalavar, Osman
dc.contributor.authorOzyurek, Dursun
dc.contributor.authorKalyon, Ali
dc.date.accessioned2024-09-29T15:57:54Z
dc.date.available2024-09-29T15:57:54Z
dc.date.issued2015
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, the effect of aging parameters on wear behavior of PM Inconel 706 (IN 706) superalloy was experimentally investigated and an ANN model was developed to predict weight loss after wear tests. IN 706 superalloy powders were cold pressed (700 MPa) and sintered at 1270 degrees C for 90 min. The sintered components were gradually aged for 16 h at 730 degrees C and for 12-20 h at 620 degrees C. The samples of IN706 superalloy were subjected to wear test at a constant sliding speed of 1 m/s under three different loads (30 N, 45 N and 60 N) and for five different sliding distances (400-2000 m). The results clearly showed that delta, gamma' and gamma '' phases were observed around grain boundaries of IN 706 superalloy aged for different periods. The highest hardness was measured for the samples aged for 12 h. Weight losses were found to increase as the sliding distance increased. Moreover, the ANN modeling of weight loss values for IN 706 superalloy gave effective results and can be successfully used to predict weight loss values in the parameters that were determined by the obtained high R-2 value. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.matdes.2015.05.055
dc.identifier.endpage172en_US
dc.identifier.issn0264-1275
dc.identifier.issn1873-4197
dc.identifier.scopus2-s2.0-84941266879en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage164en_US
dc.identifier.urihttps://doi.org/10.1016/j.matdes.2015.05.055
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5081
dc.identifier.volume82en_US
dc.identifier.wosWOS:000358096700020en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMaterials & Designen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInconel 706 superalloyen_US
dc.subjectAging parametersen_US
dc.subjectWear behavioren_US
dc.subjectANN modelen_US
dc.titleArtificial neural network prediction of aging effects on the wear behavior of IN706 superalloyen_US
dc.typeArticleen_US

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