Regression Modeling of Surface Roughness in Grinding

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dc.contributor.authorAsilturk, Ilhan
dc.contributor.authorCelik, Levent
dc.contributor.authorCanli, Eyub
dc.contributor.authorOnal, Gurol
dc.date.accessioned2020-03-26T18:15:56Z
dc.date.available2020-03-26T18:15:56Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Conference on Advanced Materials and Information Technology Processing (AMITP 2011) -- APR 17-18, 2011 -- Guangzhou, PEOPLES R CHINAen_US
dc.description.abstractGrinding is a widely used manufacturing method in state of art industry. By realizing needs of manufacturers, grinding parameters must be carefully selected in order to maintain an optimum point for sustainable process. Surface roughness is generally accepted as an important indicator for grinding parameters. In this study, effects of grinding parameters to surface roughness were experimentally and statistically investigated. A complete factorial experimental flow was designed for three level and three variable. 62 HRC AISI 8620 cementation steel was used in grinding process with 95-96% Al2O3 grinding wheel. Surface roughness values (Ra, Rz) were measured at the end of process by using depth of cut, feed rate and workpiece speed as input parameters. Experimental results were used for modeling surface roughness values with linear, quadric and logarithmic regressions by the help of MINITAB 14 and SPSS 16 software. The best results according to comparison of models considering determination coefficient were achieved with quadric regression model (84.6% for Ra and 89% for Rz). As a result, a reliable model was developed in grinding process which is a highly complex machining operation and depth of cut was determined as the most effective parameter on grinding by variance analysis (ANOVA). Obtained theoretical and practical acquisitions can be used in various areas of manufacturing sector in the future.en_US
dc.description.sponsorshipHainan Univ, China & Asia Pacific Human Comp Interact Res Ctren_US
dc.description.sponsorshipScientific Research Projects Coordinators (BAP) of Selcuk University and TUBITAKen_US
dc.description.sponsorshipThis study is supported by Scientific Research Projects Coordinators (BAP) of Selcuk University and TUBITAK. This support is greatly appreciated.en_US
dc.identifier.doi10.4028/www.scientific.net/AMR.271-273.34en_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-3-03785-157-9
dc.identifier.issn1022-6680en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage34en_US
dc.identifier.urihttps://dx.doi.org/10.4028/www.scientific.net/AMR.271-273.34
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26778
dc.identifier.volume271-273en_US
dc.identifier.wosWOS:000303363500007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTRANS TECH PUBLICATIONS LTDen_US
dc.relation.ispartofADVANCED MATERIALS AND INFORMATION TECHNOLOGY PROCESSING, PTS 1-3en_US
dc.relation.ispartofseriesAdvanced Materials Research
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectCNC grindingen_US
dc.subjectsurface roughnessen_US
dc.subjectregression modelingen_US
dc.subjectgrinding parametersen_US
dc.titleRegression Modeling of Surface Roughness in Grindingen_US
dc.typeConference Objecten_US

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