Surface roughness estimation for turning operation based on different regression models using vibration signals

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dc.contributor.authorNeşeli, Süleyman
dc.contributor.authorYalçın, Gökhan
dc.contributor.authorYaldız, Süleyman
dc.date.accessioned2020-03-26T19:45:22Z
dc.date.available2020-03-26T19:45:22Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractOn machined parts, major indication of surface quality is surface roughness and also surface quality is one of the most specifiedcustomer requirements. In the turning process, the importance of machining parameter choice is enhancing, as it controls the requiredsurface quality. To obtain the better surface quality, the most essential control parameters are tool overhang and tool geometry in turningoperations. The goal of this study was to develop an empirical multiple regression models for prediction of surface roughness (Ra) fromthe input variables in finishing turning of 42CrMo4 steel. The main input parameters of this model are tool overhang and tool geometrysuch as tool nose radius, approaching angle, and rake angle in negative direction. Regression analysis with linear, quadratic andexponential data transformation is applied so as to find the best suitable model. The best results according to comparison of modelsconsidering determination coefficient (R 2 ) are achieved with quadratic regression model. In addition, tool nose radius was determined asthe most effective parameter on turning by variance analysis (ANOVA). Cutting experiments and statistical analysis demonstrate that themodel developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in allthree models construction and verification.en_US
dc.identifier.citationNeşeli, S., Yalçın, G., Yaldız, S. (2018). Surface Roughness Estimation for Turning Operation Based on Different Regression Models Using Vibration Signals. International Journal of Intelligent Systems and Applications in Engineering, 6(4), 282-288.
dc.identifier.endpage288en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue4en_US
dc.identifier.startpage282en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TXpBM09URXhNUT09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36030
dc.identifier.volume6en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorNeşeli, Süleyman
dc.institutionauthorYaldız, Süleyman
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYapay Zekaen_US
dc.subjectCutting tool geometry
dc.subjectTool-holder overhang
dc.titleSurface roughness estimation for turning operation based on different regression models using vibration signalsen_US
dc.typeArticleen_US

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