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
Yükleniyor...
Tarih
2017-10-15
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Applied Sciences-Basel
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
In 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.
Açıklama
Anahtar Kelimeler
Cutting Force, Tool Wear, Surface Roughness, ANN-RSM
Kaynak
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
7
Sayı
10
Künye
Çelik, Y.H., Kılıçkap, E., Yardımeden, A. (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-Basel, 7 (10), pp.1-12. DOI:https://dx.doi.org/10.3390/app7101064
