Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/11530
Full metadata record
DC FieldValueLanguage
dc.rights.licenserestrictedAccess-
dc.contributor.authorJovic Z.-
dc.contributor.authorArsic N.-
dc.contributor.authorVukojević V.-
dc.contributor.authorAnicic O.-
dc.contributor.authorVujicic, Sladjana-
dc.date.accessioned2021-04-20T18:35:07Z-
dc.date.available2021-04-20T18:35:07Z-
dc.date.issued2017-
dc.identifier.issn0141-6359-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/11530-
dc.description.abstract© 2016 Elsevier Inc. The main goal of the study was to analyze the influence of machining parameters on the chip shape classification. Straight turning of mild steel (A500/A500M-13) and AISI 304 stainless steel were performed to monitor the chip shapes. Cutting speed, feed rate, depth of cur and surface roughness of the material were used as inputs. Adaptive neuro-fuzzy inference system (ANFIS) was used in to determine the inputs influence on the chip shape classification. The selection process was performed to estimate the most dominant factors which affect the chip shape classification. According to the results surface roughness has the highest influence on the chip shape classification. The obtained model could be used as optimal parameter settings for the best chip shape classification.-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.sourcePrecision Engineering-
dc.titleDetermination of the important machining parameters on the chip shape classification by adaptive neuro-fuzzy technique-
dc.typearticle-
dc.identifier.doi10.1016/j.precisioneng.2016.11.001-
dc.identifier.scopus2-s2.0-85006414911-
Appears in Collections:Faculty of Engineering, Kragujevac

Page views(s)

524

Downloads(s)

10

Files in This Item:
File Description SizeFormat 
PaperMissing.pdf
  Restricted Access
29.86 kBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.