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Title Investigation of Manufacturing Parameters on the Mechanical Properties of Powder Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks
Authors Amirjan, M.
Khorsand, H.
Abdoos, H.
ORCID
Keywords Mg-Al2O3 Nanocomposite
Artificial neural network
Hardness
UTS
Powder metallurgy
Type Conference Papers
Date of Issue 2012
URI http://essuir.sumdu.edu.ua/handle/123456789/35119
Publisher Sumy State University
License
Citation Amirjan, M. Investigation of Manufacturing Parameters on the Mechanical Properties of Powder Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks / M. Amirjan, H. Khorsand, H. Abdoos // Nanomaterials: Applications & Properties (NAP-2012) : 2-nd International conference, Alushta, the Crimea, September 17-22, 2012 / Edited by: A. Pogrebnjak. - Sumy : Sumy State University, 2012. - V. 1, No3. - 03CNN16
Abstract In present study, Artificial Neural Network (ANN) approach to prediction of the ODS Magnesium matrix composite mechanical properties obtained was used. Several composition of Mg- Al2O3 composites with four different amount of Al2O3 reinforcement with four different size of nanometer to micrometer were produced in different sintering times. The specimens were characterized using metallographic observation, microhardness and strength (UTS) measurements. Then, for modeling and prediction of mentioned conditions, a multi layer perceptron back propagation feed forward neural network was constructed to evaluate and compare the experimental calculated data to predicted values. In neural network training modules, different composition, sintering time and reinforcement size were used as input (3 inputs), hardness and Ultimate Tensile Strength(UTS) were used as output. Then, the neural network was trained using the prepared training set. At the end of training process the test data were used to check the system’s accuracy. As a result, the comparison of neural network output results with the results from experiments and empirical relationship has shown good agreement with average error of 2.5%. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/35119
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China China
827436
Germany Germany
2
Iran Iran
30183
Lithuania Lithuania
1
Russia Russia
1
Turkey Turkey
6479950
Ukraine Ukraine
6479949
United Kingdom United Kingdom
1
United States United States
12959896
Unknown Country Unknown Country
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Vietnam Vietnam
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