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

Shirvanimoghaddam, K., Khayyam, H., Abdizadeh, H., Karbalaei Akbari, M., Pakseresht, A.H., Ghasali, E. and Naebe, M. 2016, Boron carbide reinforced aluminium matrix composite : physical, mechanical characterization and mathematical modelling, Materials science and engineering A, vol. 658, pp. 135-149, doi: 10.1016/j.msea.2016.01.114.

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Title Boron carbide reinforced aluminium matrix composite : physical, mechanical characterization and mathematical modelling
Author(s) Shirvanimoghaddam, K.
Khayyam, H.
Abdizadeh, H.
Karbalaei Akbari, M.
Pakseresht, A.H.
Ghasali, E.
Naebe, M.ORCID iD for Naebe, M.
Journal name Materials science and engineering A
Volume number 658
Start page 135
End page 149
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-03-21
ISSN 0921-5093
Keyword(s) metal matrix composite
artificial neural network
thin plate spine
Summary 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.
Language eng
DOI 10.1016/j.msea.2016.01.114
Field of Research 091202 Composite and Hybrid Materials
091307 Numerical Modelling and Mechanical Characterisation
0912 Materials Engineering
0913 Mechanical Engineering
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
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Document type: Journal Article
Collections: Institute for Frontier Materials
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