Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S

Basit Öğe Kaydı
dc.authorid0000-0003-2776-1195en_US
dc.authorid0000-0001-5519-2917en_US
dc.authorid0000-0003-1753-7712en_US
dc.contributor.authorKılıçkap, Erol
dc.contributor.authorYardımeden, Ahmet
dc.contributor.authorÇelik, Yahya Hışman
dc.date.accessioned2024-01-17T13:01:20Z
dc.date.available2024-01-17T13:01:20Z
dc.date.issued2017en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut.en_US
dc.identifier.citationKılıçkap, E., Yardımeden, A. ve Çelik, Y. H. (2017). Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S. Applied Sciences (Switzerland), 7(10), 1-12.en_US
dc.identifier.doi10.3390/app7101064en_US
dc.identifier.endpage12en_US
dc.identifier.issn2076-3417
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85031402572en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.mdpi.com/2076-3417/7/10/1064
dc.identifier.urihttps://hdl.handle.net/11468/13213
dc.identifier.volume7en_US
dc.identifier.wosWOS:000414457800102
dc.identifier.wosWOS:000414457800102en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKılıçkap, Erol
dc.institutionauthorYardımeden, Ahmet
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofApplied Sciences (Switzerland)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectANN-RSMen_US
dc.subjectCutting forceen_US
dc.subjectSurface roughnessen_US
dc.subjectTool wearen_US
dc.titleMathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242Sen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Mathematical Modelling and Optimization of Cutting Force, Tool Wear and Surface Roughness by Using Artificial Neural Network and Response Surface Methodology in Milling of Ti-6242S.pdf
Boyut:
2.19 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Makine Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu