Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining

Basit Öğe Kaydı
dc.authoridBOY, MEHMET/0000-0003-2471-8001
dc.contributor.authorKorkut, Ihsan
dc.contributor.authorAcir, Adem
dc.contributor.authorBoy, Mehmet
dc.date.accessioned2024-09-29T15:57:09Z
dc.date.available2024-09-29T15:57:09Z
dc.date.issued2011
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this paper, the regression analysis (RA) and artificial neural network (ANN) are presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. The RA and ANN model for prediction tool-chip interface temperature are developed and mathematical equations derived for tool-chip interface temperature prediction are obtained. The tool-chip interface temperature results obtained from mathematical equations with RA and ANN model and the experimental results available in the literature obtained by using AISI 1117 steel work piece with embedded K type thermocouple into the uncoated cutting tool (Korkut, Boy, Karacan, & Seker, 2007) are compared. The coefficient of determination (R-2) both training and testing data for temperature prediction in the ANN model are determined as 0.999791289 and 0.997889303 whereas; R-2 for both training and testing data in the RA model are computed as 0.999063 and 0.999427, respectively. The correlation obtained by the training ANN model are better than the one obtained by training RA model. The training ANN model with the Levenberg-Marquardt (LM) algorithm provides more accurate prediction and is quite useful in the calculation of tool-chip interface temperature when compared with the trained RA method in machining. On the other hand, prediction values obtained the testing RA model is slightly better performance than the testing ANN model. The results show that the tool-chip interface temperature equation derived from RA and ANN model can be used for prediction. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2011.03.044
dc.identifier.endpage11656en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-79955637700en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage11651en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2011.03.044
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4625
dc.identifier.volume38en_US
dc.identifier.wosWOS:000291118500104en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
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/closedAccessen_US
dc.subjectTemperature predictionen_US
dc.subjectArtificial neural networken_US
dc.subjectRegression analysisen_US
dc.subjectMachiningen_US
dc.subjectTool-chip interface temperatureen_US
dc.titleApplication of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machiningen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu