Modelling of microhardness values by means of artificial neural networks of Al/SiCp metal matrix composite material couples processed with diffusion method
As a result the neural network was found successful in the prediction of modelling of microhardness values of Al/SiCp metal matrix composite material couples processed with diffusion method and behavior.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, modelling of microhardness values by means of artificial neural networks of Al/SiCp metal matrix composite material couples with diffusion method and manufactured by powder metallurgy process, were obtained using a backpropagation neural network that uses gradient descent learning algorithm.
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Key Findings & Data:After diffusion bonding and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer.
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Conclusion & Impact:As a result the neural network was found successful in the prediction of modelling of microhardness values of Al/SiCp metal matrix composite material couples processed with diffusion method and behavior.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this study, modelling of microhardness values by means of artificial neural networks of Al/SiCp metal matrix composite material couples with diffusion method and manufactured by powder metallurgy process, were obtained using a backpropagation neural network that uses gradient descent learning algorithm. After diffusion bonding and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. Then, the neural network was trained using the prepared training 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 modelling of microhardness values of Al/SiCp metal matrix composite material couples processed with diffusion method and behavior.
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Cite This Article
TAŞKIN, M., ÇALIGÜLÜ, 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, Cilt: 11 - Sayı: 3, 163–172. https://ajindex.com/articles/280096
Koker, R., Altinkok, N., Modelling of The Prediction of Tensile and Density Properties in Particle Reinforced Metal Matrix Composites by Using Neural Networks, Materials and Design, 1-7, 2005
Altinkok, N. Koker, R., Neural Network Approach to Prediction of Bending Strength and Hardening Behaviour of Particulate Reinforced (Al–Si–Mg)-Aluminium Matrix Composites, Materials and Design, 25, 595–602, 2005
Avci, E., Turkoglu I., and Poyraz, M., Intelligent Target Recognition on Based Wavelet Packet Neural Network”, Elsevier Expert Systems with Applications, 29, 175-182, 2005
Avci, E., Turkoglu I., and Poyraz, M., Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System, Lecture Notes in Computer Science, Springer-Verlag, 3522, 594-601, 2005
Chun, M.S., Biglou, J., Lenard, J.G., and Kim, J.G., Using Neural Networks to Predict Parameters in The Hot Working of Aluminum Alloys, Journal of Materials Processing Technology, 86, 245–25, 1999
Ganesan,G., Raghukandan, K., Karthikeyan, R. and Pai,B.C., Development of Processing Map for 6061 Al/15% SiCp Through Neural Networks, Journal of Materials Processing Technology, 166, 423–429, 2005
Jalham, I.S., A comparative study of some network approaches to predict the effect of the reinforcement content on the hot strength of Al–base composites, Journal of Materials Processing Technology,166, 392–397, 2005
Li, H. J., Qi, L.H., Han, H.M.and Guo, L.J., Neural Network Modeling and Optimization of Semi-Solid Extrusion for Aluminum Matrix Composites, Journal of Materials Processing Technology, 151, 126–132, 2004
Caligulu, U., The Investigation of Joinability of Diffusion Bonding with Hot Pressing Manifactured AlSiMg-SiCp Reinforced Composites, Fırat Uni. Graduate School of Naturel and Applied Sciences Department of Metallurgy Education, Master Thesis, Elazig, 2005
Taskin, M., Diffusion bonding of fine grained high carbon steels in the superplasticity temperature range, Fırat University Graduate School of Naturel and Applied Sciences Department of Metallurgy Education, PhD Thesis, Elazig,2000
Anijdan, S.H.M., Bahrami, A., Hosseini, H.R.M., and Shafyei, A., Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity, Materials and Design, 2005
Okuyucu, H., Kurt, A., and Arcaklioglu, E., Artificial neural network application to the friction stir welding of aluminum plates, Materials and Design,