Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling

Shirvanimoghaddam, K, Khayyam, H, Abdizadeh, H, Akbari, M, Pakseresht, A, Ghasali, E and Naebe, M 2016, 'Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling', Materials Science and Engineering A: Structural Materials: Properties, Microstructures and Processing, vol. 658, pp. 135-149.


Document type: Journal Article
Collection: Journal Articles

Title Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling
Author(s) Shirvanimoghaddam, K
Khayyam, H
Abdizadeh, H
Akbari, M
Pakseresht, A
Ghasali, E
Naebe, M
Year 2016
Journal name Materials Science and Engineering A: Structural Materials: Properties, Microstructures and Processing
Volume number 658
Start page 135
End page 149
Total pages 15
Publisher Elsevier
Abstract This paper investigates the manufacturing of aluminium-boron carbide composites using the stir casting method. Mechanical and physical properties tests to obtain hardness, ultimate tensile strength (UTS) and density are performed after solidification of specimens. The results show that hardness and tensile strength of aluminium based composite are higher than monolithic metal. Increasing the volume fraction of B4C, enhances the tensile strength and hardness of the composite; however over-loading of B4C caused particle agglomeration, rejection from molten metal and migration to slag. This phenomenon decreases the tensile strength and hardness of the aluminium based composite samples cast at 800 °C. For Al-15 vol% B4C samples, the ultimate tensile strength and Vickers hardness of the samples that were cast at 1000 °C, are the highest among all composites. To predict the mechanical properties of aluminium matrix composites, two key prediction modelling methods including Neural Network learned by Levenberg-Marquardt Algorithm (NN-LMA) and Thin Plate Spline (TPS) models are constructed based on experimental data. Although the results revealed that both mathematical models of mechanical properties of Al-B4C are reliable with a high level of accuracy, the TPS models predict the hardness and tensile strength values with less error compared to NN-LMA models.
Subject Numerical Modelling and Mechanical Characterisation
Keyword(s) Metal matrix composite
B4C
Artificial neural network
Thin plate spline
DOI - identifier 10.1016/j.msea.2016.01.114
Copyright notice © 2016 Elsevier B.V.
ISSN 0921-5093
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