Modeling Mechanical Properties of Aluminum Composite Produced Using Stir Casting Method
ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite materials have complex nature which posses the nonlinear rel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Mehran University of Engineering and Technology
2011-01-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Subjects: | |
| Online Access: | http://publications.muet.edu.pk/research_papers/pdf/pdf64.pdf |
| Summary: | ANN (Artificial Neural Networks) modeling methodology was adopted for predicting
mechanical properties of aluminum cast composite materials. For this purpose
aluminum alloy were developed using conventional foundry method.
The composite materials have complex nature which posses the nonlinear relationship
among heat treatment, processing parameters, and composition and affects their
mechanical properties. These nonlinear relation ships with properties can more
efficiently be modeled by ANNs. Neural networks modeling needs sufficient data base
consisting of mechanical properties, chemical composition and processing parameters.
Such data base is not available for modeling.
Therefore, a large range of experimental work was carried out for the development of
aluminum composite materials. Alloys containing Cu, Mg and Zn as matrix were
reinforced with 1- 15% Al2O3 particles using stir casting method. Alloys composites
were cast in a metal mold. More than eighty standard samples were prepared for
tensile tests. Sixty samples were given solution treatments at 580oC for half an hour
and tempered at 120oC for 24 hours.
The samples were characterized to investigate mechanical properties using Scanning
Electron Microscope, X-Ray Spectrometer, Optical Metallurgical Microscope, Vickers
Hardness, Universal Testing Machine and Abrasive Wear Testing Machine.
A MLP (Multilayer Perceptron) feedforward was developed and used for modeling
purpose. Training, testing and validation of the model were carried out using back
propagation learning algorithm.
The modeling results show that an architecture of 14 inputs with 9 hidden neurons and
4 outputs which includes the tensile strength, elongation, hardness and abrasive wear
resistance gives reasonably accurate results with an error within the range of 2-7 %
in training, testing and validation. |
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| ISSN: | 0254-7821 2413-7219 |