Mechanical Properties of Multiwalled Carbon Nanotubes Toughened Quartz Nanocomposite Using Artificial Neural Network (ANN)

Yusuf Tijjani, Maharaz Mohammed Nasir

Abstract


Sequel to the experimental results acquisition using the established test procedures as outlined in the previous study on the manufacturing and mechanical characterization of multiwalled carbon nanotubes/quartz nanocomposite, the present research has been centred on the implementation and evaluation of an artificial intelligence model for the characterization of the multiwalled carbon nanotubes (MWNTs) reinforced quartz ceramic nanocomposite. A multi input and multi output Artificial Neural Network (ANN) model was developed using the Levenberg Marquardt Back Propagation (LMBP) algorithm to predict the mechanical properties of the MWNTs/quartz nanocomposite bricks developed in the previous study. The predicted model was compared with the experimental test results in order to evaluate the power and the accuracy of the artificial intelligence model for the characterization of the entire series of the nanocomposite bricks developed. The developed model adequately predicts the mechanical properties; specifically tensile and compressive strengths of MWNTs/quartz nanocomposite with a coefficient of determination (R2) in excess of 0.92.


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References


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