Prediction of surface roughness in longitudinal turning process by a genetic learning algorithm

dc.authoridOzkul, iskender -- 0000-0003-4255-0564
dc.contributor.authorAldaş, Kemal
dc.contributor.authorÖzkul, iskender
dc.contributor.authorEskil, Murat
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:15:00Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:15:00Z
dc.date.issued2014
dc.departmentMühendislik Fakültesi
dc.description.abstractThe surface roughness is one of the major parameters for determining the level of machining quality. The cutting parameters and conditions have great importance to achieve the desired values during the turning process. In the present work, a new approach was considered for modelling the effect of various turning process parameters and conditions on surface roughness. The experimental studies about the surface roughness after the turning process documented in the literature were collected and compiled into a model based on a genetic learning algorithm. As input parameters for modeling the work piece alloy type, tool type, tool tip radius, tool coating type, cooling conditions, cutting speed, feed rate, and cut depth were used in the study and were comprehensivly compiled.
dc.identifier.doi10.3139/120.110570
dc.identifier.endpage380en_US
dc.identifier.issn0025-5300
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage375en_US
dc.identifier.urihttps://doi.org/10.3139/120.110570
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4248
dc.identifier.volume56en_US
dc.identifier.wosWOS:000339719600004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherCarl Hanser Verlag
dc.relation.ispartofMaterialpruefung/Materials Testing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMaterials Testing
dc.subjectGenetic Algorithm
dc.subjectTurning
dc.subjectSurface Roughness
dc.titlePrediction of surface roughness in longitudinal turning process by a genetic learning algorithm
dc.typeArticle

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