Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/2907
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKazancoglu, Yigit-
dc.contributor.authorEsme, Ugur-
dc.contributor.authorKulekci, Mustafa Kemal-
dc.contributor.authorKahraman, Funda-
dc.contributor.authorSamur, Ramazan-
dc.contributor.authorAkkurt, Adnan-
dc.contributor.authorIpekci, Melih Turan-
dc.date.accessioned2023-06-16T14:50:40Z-
dc.date.available2023-06-16T14:50:40Z-
dc.date.issued2012-
dc.identifier.issn1580-2949-
dc.identifier.issn1580-3414-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/2907-
dc.description.abstractWire-electrical-discharge machining (WEDM) is a modification of electro-discharge machining (EDM) and has been widely used for a long time for cutting punches and dies, shaped pockets and other machine parts on conductive materials. WEDM erodes workpiece materials by a series of discrete electrical sparks between the workpiece and an electrode flushed or immersed in a dielectric fluid. The WEDM process is particularly suitable for machining hard materials as well as complex shapes. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the surface roughness in a WEDM process. A back-propagation neural network (BPNN) was developed to predict the surface roughness. In the development of a predictive model, machining parameters of open-circuit voltage, pulse duration, wire speed and dielectric flushing pressure were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the surface roughness (R-a). Finally, the Taguchi approach was applied to determine the optimum levels of machining parameters.en_US
dc.language.isoenen_US
dc.publisherInst Za Kovinske Materiale I In Tehnologieen_US
dc.relation.ispartofMaterıalı in Tehnologıjeen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWEDMen_US
dc.subjectTaguchi-design methoden_US
dc.subjectneural networken_US
dc.subjectsurface roughnessen_US
dc.subjectMaterial Removal Rateen_US
dc.subjectMultiobjective Optimizationen_US
dc.subjectWedm Processen_US
dc.subjectParametersen_US
dc.subjectSteelen_US
dc.titleApplication of a Taguchi-based neural network for forecasting and optimization of the surface roughness in a wire-electrical-discharge machining processen_US
dc.typeArticleen_US
dc.identifier.scopus2-s2.0-84870153816en_US
dc.departmentİzmir Ekonomi Üniversitesien_US
dc.authoridKazancoglu, Yigit/0000-0001-9199-671X-
dc.authoridKulekci, Mustafa Kemal/0000-0002-5829-3489-
dc.authoridKazancoglu, Yigit/0000-0001-9199-671X-
dc.authorwosidKazancoglu, Yigit/E-7705-2015-
dc.authorwosidKulekci, Mustafa Kemal/M-7600-2015-
dc.authorwosidKazancoglu, Yigit/AAT-5676-2021-
dc.identifier.volume46en_US
dc.identifier.issue5en_US
dc.identifier.startpage471en_US
dc.identifier.endpage476en_US
dc.identifier.wosWOS:000310039700008en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ4-
dc.identifier.wosqualityQ4-
item.grantfulltextreserved-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept03.02. Business Administration-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File SizeFormat 
2080.pdf
  Restricted Access
271.99 kBAdobe PDFView/Open    Request a copy
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

3
checked on Nov 20, 2024

WEB OF SCIENCETM
Citations

2
checked on Nov 20, 2024

Page view(s)

66
checked on Nov 18, 2024

Download(s)

6
checked on Nov 18, 2024

Google ScholarTM

Check





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