BibTex RIS Kaynak Göster

ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES

Yıl 2008, Cilt: 3 Sayı: 12, 1811 - 1825, 01.06.2008

Öz

Kaynakça

  • AGRAWAL, G., FROST, J.D., CHAMEAU, C.L.A., (1994), “Data Analysis and Modeling Using Articial Neural Network”, Proceedings of XIII International Conference of Soil Mechanics and Foundation Engineering , New Delhi, pp.1441–1444.
  • KOKER, R., ALTINKOK, N., (2005), “Modelling of the Prediction of Tensile and Density Properties in Particle Reinforced Metal Matrix Composites by Using Neural Networks”, Materials and Design, pp.1-7.
  • CALIGULU, U., (2005), The Investigation of Joinability of Diffusion Bonding with Hot Pressing Manifactured AlSiMg-SiCp Reinforced Composites, Master Thesis, Fırat Uni. Graduate School of Naturel and Applied Sciences Department of Metallurgy Education, Elazig.
  • TASKIN, M., (2000), Diffusion Bonding of Fine Grained High Carbon Steels in the Superplasticity Temperature Range, Doctora Thesis, Fırat University Graduate School of Naturel and Applied Sciences Department of Metallurgy Education, Elazig.
  • JONES, S.P., JANSEN, R. and FUSARO, R.L. (1997), “Preliminary Investigation of Neural Network Techniques to Predict Tribological Properties”, Tribol Trans, 40, 2, pp312.
  • TASKIN, M., CALIGULU, U., GUR, A. K., (2008), "Modeling Adhesive Wear Resistance of Al-Si-Mg-/SiCp PM Compacts Fabricated by Hot Pressing Process by Means of ANN", The International Journal of Advanced Manufacturing Technology, 37, pp.715–721.
  • TASKIN, M. and CALIGULU, U., (2006), “Modelling of Microhardness Values by Means of Artificial Neural Networks of Al/SiCp Metal Matrix Composite Material Couples Processed with Diffusion Method”, Mathematical and Computational Applications, 11, 3, pp.163- 172.
  • DURMUŞ, H., ÖZKAYA, E. and MERIÇ C., (2006), “The Use of Neural Networks for the Prediction of Wear Loss and Surface Roughness of AA6351 Aluminium Alloy”, Materials and Desing, 27, pp.156-159.
  • ALTINKOK, N. and KOKER, R., (2005), “Neural Network Approach to Prediction of Bending Strength and Hardening Behaviour of Particulate Reinforced (Al–Si–Mg)- Aluminium Matrix Composites”, Materials and Design, 25, pp.595–602.
  • CHUN, M.S., BIGLOU, J., LENARD, J.G., and KIM, J.G., (1999), “Using Neural Networks to Predict Parameters in The Hot Working of Aluminum Alloys” Journal of Materials Processing Technology, 86, pp.245–251.
  • GANESAN, G., RAGHUKANDAN, K., KARTHIKEYAN, R. and PAI, B.C., (2005), “Development of Processing Map for 6061 Al/15% SiCp Through Neural Networks”, Journal of Materials Processing Technology, 166, pp.423–429.
  • PERZYK, M., and KOCHANSKI, A.W., (2001), “Prediction of Ductile Cast Iron Quality by Artificial Neural Networks”, J. Mater Process Tech., 109, pp.305–307.
  • RAFIQ, M.Y., BUGMANN, G., and EASTERBROOK, D.J., (2001), “Neural Network Design for Engineering Applications”, Comput Struct, 79, pp.1541–1552.
  • KENIG, S., BEN-DAVID, A., OMER, M. and SADEH, A., (2001), “Control of Properties in Injection Molding by Neural Networks”, Eng Appl Artif Intel, 4, pp.819–823.
  • LIMPON, R.P., (1987), “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, pp.4–22.
  • NIELSEN, R.H., (1998), “Neurocomputing Picking the Human Brain”, IEEE Spectrum, 25, 3, pp.36–41.
  • FAUSETT, F., (1994), “Fundamentals of Neural Networks: Architectures, Algorithms and Applications”, Englewood Clis., NJ, USA., pp.155-178.
  • HAYKIN, S., (1994), “Neural Networks, A comprehensive Foundation”, McMillian College Publishing Company, New York, pp.198-203.
  • AVCI, E., TURKOGLU, I., and POYRAZ, M., (2005), “Intelligent Target Recognition on Based Wavelet Packet Neural Network”, Elsevier Expert Systems with Applications, 29, pp.175-182.
  • AVCI, E., TURKOGLU, I., and POYRAZ, M., (2005), “Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System”, Lecture Notes in Computer Science 3522Springer-Verlag, pp.594-601.

ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES

Yıl 2008, Cilt: 3 Sayı: 12, 1811 - 1825, 01.06.2008

Öz

In this study, Artificial Neural Network approach to prediction of diffusion bonding behavior of SiCP reinforced aluminum alloy metal matrix composites, manufactured by powder metallurgy process, were obtained using a back-propagation neural network that uses gradient descent learning algorithm. A powder Al-Mg-Si matrix was employed with particulate SiC at 5-10-20 (wt) % fractions. MMC’s were fabricated by powder mixing and hot pressing at 600ºC below liquation temperature. Diffusion bonding was carried out under protective atmosphere (argon) at 550, 575, 600 and 625ºC process temperatures for 20, 40 and 60 minutes with a load of 0.25 MPa, below those which would cause macrodeformation. Microstructure examination at bond interface were investigated by optical microscopy, SEM. Specimens were tested for shear strength and metallographic evaluations. After the completion of experimental process and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. In neural networks training module, different SiC reinforcement fractions (wt), different temperatures and welding periods were used as input, shear strength of bonded specimens at interface were used as outputs. Then, the neural network was trained using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the system accuracy. As a result the neural network was found successful in the prediction of diffusion bonding shear strength and behavior

Kaynakça

  • AGRAWAL, G., FROST, J.D., CHAMEAU, C.L.A., (1994), “Data Analysis and Modeling Using Articial Neural Network”, Proceedings of XIII International Conference of Soil Mechanics and Foundation Engineering , New Delhi, pp.1441–1444.
  • KOKER, R., ALTINKOK, N., (2005), “Modelling of the Prediction of Tensile and Density Properties in Particle Reinforced Metal Matrix Composites by Using Neural Networks”, Materials and Design, pp.1-7.
  • CALIGULU, U., (2005), The Investigation of Joinability of Diffusion Bonding with Hot Pressing Manifactured AlSiMg-SiCp Reinforced Composites, Master Thesis, Fırat Uni. Graduate School of Naturel and Applied Sciences Department of Metallurgy Education, Elazig.
  • TASKIN, M., (2000), Diffusion Bonding of Fine Grained High Carbon Steels in the Superplasticity Temperature Range, Doctora Thesis, Fırat University Graduate School of Naturel and Applied Sciences Department of Metallurgy Education, Elazig.
  • JONES, S.P., JANSEN, R. and FUSARO, R.L. (1997), “Preliminary Investigation of Neural Network Techniques to Predict Tribological Properties”, Tribol Trans, 40, 2, pp312.
  • TASKIN, M., CALIGULU, U., GUR, A. K., (2008), "Modeling Adhesive Wear Resistance of Al-Si-Mg-/SiCp PM Compacts Fabricated by Hot Pressing Process by Means of ANN", The International Journal of Advanced Manufacturing Technology, 37, pp.715–721.
  • TASKIN, M. and CALIGULU, U., (2006), “Modelling of Microhardness Values by Means of Artificial Neural Networks of Al/SiCp Metal Matrix Composite Material Couples Processed with Diffusion Method”, Mathematical and Computational Applications, 11, 3, pp.163- 172.
  • DURMUŞ, H., ÖZKAYA, E. and MERIÇ C., (2006), “The Use of Neural Networks for the Prediction of Wear Loss and Surface Roughness of AA6351 Aluminium Alloy”, Materials and Desing, 27, pp.156-159.
  • ALTINKOK, N. and KOKER, R., (2005), “Neural Network Approach to Prediction of Bending Strength and Hardening Behaviour of Particulate Reinforced (Al–Si–Mg)- Aluminium Matrix Composites”, Materials and Design, 25, pp.595–602.
  • CHUN, M.S., BIGLOU, J., LENARD, J.G., and KIM, J.G., (1999), “Using Neural Networks to Predict Parameters in The Hot Working of Aluminum Alloys” Journal of Materials Processing Technology, 86, pp.245–251.
  • GANESAN, G., RAGHUKANDAN, K., KARTHIKEYAN, R. and PAI, B.C., (2005), “Development of Processing Map for 6061 Al/15% SiCp Through Neural Networks”, Journal of Materials Processing Technology, 166, pp.423–429.
  • PERZYK, M., and KOCHANSKI, A.W., (2001), “Prediction of Ductile Cast Iron Quality by Artificial Neural Networks”, J. Mater Process Tech., 109, pp.305–307.
  • RAFIQ, M.Y., BUGMANN, G., and EASTERBROOK, D.J., (2001), “Neural Network Design for Engineering Applications”, Comput Struct, 79, pp.1541–1552.
  • KENIG, S., BEN-DAVID, A., OMER, M. and SADEH, A., (2001), “Control of Properties in Injection Molding by Neural Networks”, Eng Appl Artif Intel, 4, pp.819–823.
  • LIMPON, R.P., (1987), “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, pp.4–22.
  • NIELSEN, R.H., (1998), “Neurocomputing Picking the Human Brain”, IEEE Spectrum, 25, 3, pp.36–41.
  • FAUSETT, F., (1994), “Fundamentals of Neural Networks: Architectures, Algorithms and Applications”, Englewood Clis., NJ, USA., pp.155-178.
  • HAYKIN, S., (1994), “Neural Networks, A comprehensive Foundation”, McMillian College Publishing Company, New York, pp.198-203.
  • AVCI, E., TURKOGLU, I., and POYRAZ, M., (2005), “Intelligent Target Recognition on Based Wavelet Packet Neural Network”, Elsevier Expert Systems with Applications, 29, pp.175-182.
  • AVCI, E., TURKOGLU, I., and POYRAZ, M., (2005), “Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System”, Lecture Notes in Computer Science 3522Springer-Verlag, pp.594-601.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Mustafa Taskin Uğur Caligulu Halil Dıkbas Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2008
Yayımlandığı Sayı Yıl 2008 Cilt: 3 Sayı: 12

Kaynak Göster

APA Dıkbas, M. T. . U. C. . H. (2008). ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES. Yaşar Üniversitesi E-Dergisi, 3(12), 1811-1825. https://doi.org/10.19168/jyu.36124
AMA Dıkbas MTUCH. ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES. Yaşar Üniversitesi E-Dergisi. Haziran 2008;3(12):1811-1825. doi:10.19168/jyu.36124
Chicago Dıkbas, Mustafa Taskin Uğur Caligulu Halil. “ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES”. Yaşar Üniversitesi E-Dergisi 3, sy. 12 (Haziran 2008): 1811-25. https://doi.org/10.19168/jyu.36124.
EndNote Dıkbas MTUCH (01 Haziran 2008) ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES. Yaşar Üniversitesi E-Dergisi 3 12 1811–1825.
IEEE M. T. . U. C. . H. Dıkbas, “ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES”, Yaşar Üniversitesi E-Dergisi, c. 3, sy. 12, ss. 1811–1825, 2008, doi: 10.19168/jyu.36124.
ISNAD Dıkbas, Mustafa Taskin Uğur Caligulu Halil. “ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES”. Yaşar Üniversitesi E-Dergisi 3/12 (Haziran 2008), 1811-1825. https://doi.org/10.19168/jyu.36124.
JAMA Dıkbas MTUCH. ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES. Yaşar Üniversitesi E-Dergisi. 2008;3:1811–1825.
MLA Dıkbas, Mustafa Taskin Uğur Caligulu Halil. “ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES”. Yaşar Üniversitesi E-Dergisi, c. 3, sy. 12, 2008, ss. 1811-25, doi:10.19168/jyu.36124.
Vancouver Dıkbas MTUCH. ARTIFICIAL NEURAL NETWORK (ANN) APPROACH TO PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF SiCP  REINFORCED ALUMINUM  METAL MATRIX COMPOSITES. Yaşar Üniversitesi E-Dergisi. 2008;3(12):1811-25.