Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/2907
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kazancoglu, Yigit | - |
dc.contributor.author | Esme, Ugur | - |
dc.contributor.author | Kulekci, Mustafa Kemal | - |
dc.contributor.author | Kahraman, Funda | - |
dc.contributor.author | Samur, Ramazan | - |
dc.contributor.author | Akkurt, Adnan | - |
dc.contributor.author | Ipekci, Melih Turan | - |
dc.date.accessioned | 2023-06-16T14:50:40Z | - |
dc.date.available | 2023-06-16T14:50:40Z | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 1580-2949 | - |
dc.identifier.issn | 1580-3414 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14365/2907 | - |
dc.description.abstract | Wire-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.iso | en | en_US |
dc.publisher | Inst Za Kovinske Materiale I In Tehnologie | en_US |
dc.relation.ispartof | Materıalı in Tehnologıje | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | WEDM | en_US |
dc.subject | Taguchi-design method | en_US |
dc.subject | neural network | en_US |
dc.subject | surface roughness | en_US |
dc.subject | Material Removal Rate | en_US |
dc.subject | Multiobjective Optimization | en_US |
dc.subject | Wedm Process | en_US |
dc.subject | Parameters | en_US |
dc.subject | Steel | en_US |
dc.title | Application of a Taguchi-based neural network for forecasting and optimization of the surface roughness in a wire-electrical-discharge machining process | en_US |
dc.type | Article | en_US |
dc.identifier.scopus | 2-s2.0-84870153816 | en_US |
dc.department | İzmir Ekonomi Üniversitesi | en_US |
dc.authorid | Kazancoglu, Yigit/0000-0001-9199-671X | - |
dc.authorid | Kulekci, Mustafa Kemal/0000-0002-5829-3489 | - |
dc.authorid | Kazancoglu, Yigit/0000-0001-9199-671X | - |
dc.authorwosid | Kazancoglu, Yigit/E-7705-2015 | - |
dc.authorwosid | Kulekci, Mustafa Kemal/M-7600-2015 | - |
dc.authorwosid | Kazancoglu, Yigit/AAT-5676-2021 | - |
dc.identifier.volume | 46 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.startpage | 471 | en_US |
dc.identifier.endpage | 476 | en_US |
dc.identifier.wos | WOS:000310039700008 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q4 | - |
dc.identifier.wosquality | Q4 | - |
item.fulltext | With Fulltext | - |
item.grantfulltext | reserved | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 03.02. 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 | Size | Format | |
---|---|---|---|
2080.pdf Restricted Access | 271.99 kB | Adobe PDF | View/Open Request a copy |
CORE Recommender
SCOPUSTM
Citations
3
checked on Nov 6, 2024
WEB OF SCIENCETM
Citations
2
checked on Nov 6, 2024
Page view(s)
66
checked on Nov 11, 2024
Download(s)
6
checked on Nov 11, 2024
Google ScholarTM
Check
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.