Scopus
YÖKSİS Eşleşti
Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel
Materials Science and Technology (United Kingdom) · Temmuz 2012
YÖKSİS Kayıtları
Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel
Materials Science and Technology · 2013 SCI
PROFESÖR HUMAR KAHRAMANLI ÖRNEK →
Makale Bilgileri
DergiMaterials Science and Technology (United Kingdom)
Yayın TarihiTemmuz 2012
Cilt / Sayfa28 · 980-986
Scopus ID2-s2.0-84864028941
Özet
In the present study, the prediction of cutting forces and surface roughness was carried out using neural networks and support vector regression (SVR) with six inputs, namely, three axis vibrations of the tool holder and cutting speed, feedrate and depth of cut. The data obtained by experimentation are used to construct predictive models. A feedforward backpropagation neural network and SVR have been selected for modelling. The coefficient of determination (R 2), mean absolute prediction error and root mean square error were calculated for each method, and these values served as a measure of prediction precision. We carried out comparison of the prediction accuracy of artificial neural networks and SVR. Comparison of the two models indicates that both models have successful performance. Experimental results are provided to confirm the effectiveness of this approach. © 2012 Institute of Materials, Minerals and Mining.
Yazarlar (3)
1
İlhan Asiltürk
ORCID: 0000-0002-8302-6577
2
Humar Kahramanli
3
H. El Mounayri
Anahtar Kelimeler
CNC turning
Neural network
Surface roughness
SVR prediction model
Kurumlar
Purdue School of Engineering and Technology
Indianapolis United States
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
13
Atıf
3
Yazar
4
Anahtar Kelime