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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2006, 11(3), 163-172; https://doi.org/10.3390/mca11020163

Modelling of Microhardness Values by Means of Artificial Neural Networks of Al/Sicp Metal Matrix Composite Material Couples Processed with Diffusion Method

University of Fırat, Faculty of Technical Education, Department of Metal, 23119 Elazıg, Turkey
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Published: 1 December 2006
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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.
Keywords: Artificial Neural Network; MMCs; Diffusion Bonding; Microhardness Artificial Neural Network; MMCs; Diffusion Bonding; Microhardness
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Taşkın, M.; Çalıgülü, U. Modelling of Microhardness Values by Means of Artificial Neural Networks of Al/Sicp Metal Matrix Composite Material Couples Processed with Diffusion Method. Math. Comput. Appl. 2006, 11, 163-172.

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