Yıl 2014,
, 0 - , 28.03.2014
Öz
Bu çalışmada Al/SiC kompozit malzemenin yüzey pürüzlülüğü kesme parametrelerine bağlı olarak yapay sinir ağları yaklaşımı kullanılarak yüksek doğrulukta tahmin edilmiştir. Al/SiC kompozit malzemenin TiCN+TiN kaplamalı cementide carbide kesici takımla işlenmesi sonucu deneysel olarak elde edilen yüzey pürüzlülüğü değerleri ileri beslemeli geriye yayılımlı 9 farklı YSA modelde eğitilmiştir. YSA modellerinin ağ yapılarındaki nöron sayıları: 3-5-6-1, 3-6-4-1, 3-6-6-1, 3-4-3-5-1, 3-4-5-3-1, 3-6-2-3-1, 3-7-1, 3-8-1 ve 3-9-1'dir. YSA’nın
eğitimi ve testi sonrası elde edilen değerler YSA modellerde yaygın olarak kullanılan istatistiksel analizlere tabi tutularak incelenmiştir. Deneysel çalışmaların zorluğu, analitik ifadelerin karmaşıklığı bir çok çalışmada olduğu gibi, YSA kullanımının avantajı kullanılarak kesme parametrelerine bağlı olarak yüzey pürüzlülüğünün tahmini bu çalışmada da YSA’nın kullanılabilirliğini göstermiştir.
Anahtar Kelimeler
Kaynakça
- Karayel, D., “Prediction and control of surface
- roughness in CNC lathe using artificial neural
- network”, Journal of Materials Processing
- Technology, 209, 7, 3125–3137, 2009.
- Neşeli, S., Yaldız S. ve Turkes, E., “Optimization
- of tool geometry parameters for turning
- operations based on the response surface
- methodology”, Measurement, 44, 3 580-587,
- -
- Davim, J.P., Gaitonde, V. N. ve Karnik, S. R.,
- “Investigation into the effect of cutting conditions
- on surface roughness in turning of free machining
- steel by ANN models”, Journal of Material
- Processing Technology, 205, 16-23, 2008.
- Nalbant, M., Gokkaya, H. ve Sur, G.,
- “Application of Taguchi method in the
- optimization of cutting parameters for surface
- roughness in turning”, Materials & Design, 28,
- , 1379–1385, 2007.
- Neşeli, S., Taşdemir Ş. ve Yaldız, S., “Prediction
- of surface roughness on turning with Artificıal
- Neural Network”, Journal of Engineering and
- Architecture Faculty of Eskişehir Osmangazi
- University, XXII, 3, 65-75, 2009.
- Kohli, A., Dixit, U.S., “A neural-network-based
- methodology for the prediction of surface
- roughness in a turning process”, Int. J. Adv.
- Manuf. Technol, 25, 118–129, 2005.
- Bernardos, P.G., Vosniakos, G.C., “Predicting
- surface roughness in machining: a review”, International Journal of Machine Tools &
- Manufacture 43, 8, 833–844, 2003.
- Petropoulos, G.P., Vaxevanidis, N. M.,
- Pandazaras, C.N. ve Antoniadis, A.A.,
- “Multiparameter identification and control of
- turned surface textures”, Int. J. Adv. Manuf.
- Technol., 29, 1-2, 118-128, 2006.
- Oktem, H., Erzurumlu T., Erzincanlı F.,
- “Prediction of minumum surface roughness in
- end milling mold part using neural network and
- genetic algorithm” Materials and Design, 27,
- -744, 2006.
- Vishal S. S., Suresh D., Rakesh S. ve Sharma, S.
- K., “Estimation of cutting forces and surface
- roughness for hard turning using neural
- Networks”, J. Intell Manuf,19, 473–483, 2008.
- Sanjay , C., Jyothi, C. ve Chin, W., “A study of
- surface roughness in drilling using mathematical
- analysis and Neural Networks”, Int. Adv. Man.
- Tec., 30, 9, 846-852, 2006.
- Zain A.M., Haron. H. ve Sharif, S., “Prediction of
- surface roughness in the end milling machining
- using artificial neural network”, Robotics and
- computer integrated manufacturing, 19, 189-
- , 2006.
- Taşdemir, Ş., Neşeli S., Sarıtaş İ. ve Yaldız S.,
- “Prediction of surface roughness using artificial
- neural network in Lathe”, CompSysTech’08,
- Gabrovo, Bulgaria, 2008.
- Nalbant, M., Gökkaya, H., Toktaş, İ., Sur, G.,
- “The experimental investigation of the effects of
- uncoated, PVD- and CVD-coated cemented
- carbide inserts and cutting parameters on surface
- roughness in CNC turning and its prediction
- using artificial neural networks”, Robotics and
- Computer-Integrated Manufacturing, 25, 1,
- –223, 2009.
- Abeesh, B. C., Dabade, U. A., Joshi, S. S.,
- Bhanuprasad, V. V., & Gadre, V. M. “Modeling
- of surface roughness in precision machining of
- metal matrix composites using ANN”, Journal
- of Material Processing Technology, 197, 439–
- , 2008.
- Asiltürk İ., Cunkaş, M., “Modelling and
- Prediction of surface roughness in turning
- operations using artificial neural network and
- multiple regression method”, Expert system
- with Application, 38, 5826-5832, 2011.
- Feng C-X, Wang X-F, “Surface roughness
- prediction modelling: neural networks versus
- regression”, IIE Trans., 35, 1, 11–27, 2003.
- Turgut, Y., Çinici, H., Şahin, İ. ve Fındık, T.,
- “Study of cutting force and surface roughness in
- milling of Al/Sic metal matrix composites”,
- Scientific Research and Essays, 6, 10, 2056-
- , 2011.
- Ozdemir, V., “ Determination of Turkey's
- carbonizatıon index based on basic energy
- indicators by Artifıcial Neural Networks,
- Journal of The Faculty of Engineering and
- Architecture of Gazi University, 26, 1, 9-15,
- -
- Eker, A. M., Dikmen, M., Cambazoğlu, S.,
- Düzgün, Ş.H.S.B., "Application of artificial
- neural network and logistic regression methods to
- landslide susceptibility mapping and comparison
- of the results for the Ulus district, Bartın",
- Journal of The Faculty of Engineering and
- Architecture of Gazi University, 27, 1, 163-
- , 2012
- Kalogirou, S. A., “Artificial intelligence for the
- modeling and control”, Progress in Energy and
- Combustion Science, 29, 515–566, 2003.
- Fındık, T., Taşdemir, Ş. ve Şahin, İ., “The use of
- artificial neural network for prediction of grain
- size of 17-4 pH stainless steel powders”,
- Scientific Research and Essays, 5 , 11, 1274-
- , 2010.
- Aşkın, D., İskender, İ. ve Mamizadeh, A., "Dry
- type transformer winding thermal analysis using
- different neural network methods", Journal of
- The Faculty of Engineering and Architecture
- of Gazi University, 26, 4, 905-913, 2011.
- Karataş, Ç., Sozen, A. ve Dulek, E., “Modelling
- of residual stresses in the shot peened material C-
- by artificial neural network”, Expert
- Systems with Applications, 36, 2, 3514–3521,
- -
- Menlik, T., Özdemir, M.B. vr Kirmaci, V.,
- “Determination of freze – drying behaviors of
- apples by artificial neural network”, Expert
- system with application, 37, 7669-7677, 2010.
- Sozen, A., Future projection of the energy
- dependency of Turkey using artificial neural
- network, Energy Policy, 37, 4827-4833, 2009.
- Sözen, A., Arcaklıoğlu, E., Menlik, T., Özalp,
- M., “Determination of thermodynamic properties
- of an alternative refrigerant (R407c) using
- artificial neural network”, Expert Systems with
- Applications, 36, 3, 4346–4356, 2009.
- Lewis,. C.D., Industrial and Business
- Forecasting Methods., Butterworths Publishing,
- London, 1982.
Toplam 132 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Mart 2014 |
| Gönderilme Tarihi | 28 Mart 2014 |
| Yayımlandığı Sayı | Yıl 2014 |