Artificial neural network based on predictive model and analysis for main cutting force in turning

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dc.contributor.authorTasdemir, Sakir
dc.date.accessioned2020-03-26T18:23:58Z
dc.date.available2020-03-26T18:23:58Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn manufacturing technology, the foremost issue influencing the usability and cost of products is metal cutting operations. In this operation, it is very difficult to develop a model including all the cutting parameters and tool geometry. Tool geometry that will enable the most suitable cutting conditions will increase the quality of workpiece surface and so the efficiency of the process. The incredible success of Artificial Neural Networks (ANN) in classification and estimation makes it necessary to use this approach in the area. Apart from known methods, ANN, which is an artificial intelligence technique, was used to estimate main cutting force, which is the modeling of a non-linear process. In this study, a novel artificial neural network model was developed in turning operation to determine the main cutting force. The developed ANN has 3 inputs and 1 output. The three input variables were feedrate (f-mm/rev), approaching angle (chi-degrees), rake angle (gamma-degrees), respectively. The output parameter value was the main cutting force (Fc-N). The results of ANN and experimental data were compared by statistical. The study put forth that accuracy rates obtained from training and test operations can be used in determining the main cutting force in the generated model.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study was supported by Research Fund of Selcuk University. Moreover, I would like to express my heartfelt thanks for Prof. Dr. Haci Saglam, Prof. Dr. Faruk Unsacar and Prof. Dr. Suleyman Yaldiz of Selcuk University, Technology Faculty, who helped me in the evaluation of cutting data.en_US
dc.identifier.endpage1480en_US
dc.identifier.issn1308-772Xen_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1471en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27762
dc.identifier.volume29en_US
dc.identifier.wosWOS:000304512900070en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSILA SCIENCEen_US
dc.relation.ispartofENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCHen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Neural Networken_US
dc.subjectMain cutting forceen_US
dc.subjectTurningen_US
dc.titleArtificial neural network based on predictive model and analysis for main cutting force in turningen_US
dc.typeArticleen_US

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