Research Article Open Access

Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites

Basheer Ahmed Ahmed Ali1, Mohd Sapuan Salit1, Edi Syams Zainudin1 and Mohamed Othman1
  • 1 Universiti Putra Malaysia, Malaysia
American Journal of Applied Sciences
Volume 12 No. 3, 2015, 174-184

DOI: https://doi.org/10.3844/ajassp.2015.174.184

Submitted On: 25 November 2014 Published On: 29 April 2015

How to Cite: Ali, B. A. A., Salit, M. S., Zainudin, E. S. & Othman, M. (2015). Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites. American Journal of Applied Sciences, 12(3), 174-184. https://doi.org/10.3844/ajassp.2015.174.184

Abstract

Diversified choice of materials from natural fibre reinforced polymer composites with similar properties complicate the materials selection for engineering products. Implementation of expert system alone makes it difficult to scrutinize the vast selected materials. Hybrid of expert system with neural network technology is desired. Classification of material through neural network under various criteria influences the decision in narrowing down the selection. In this study, the integration of artificial neural network with expert system for material classification is explored. The computational tool Matlab is proposed for classification and the materials focused were natural fibre composites. Levenberg-Marquardt training algorithm, which provides faster rate of convergence, is applied for training the feed forward network. The system proves to be consistant with 93.3% classification accuracy with 15 neurons in the hidden layer. The validation of the output is compared with the target on the basis of desired mechanical properties of natural fibre reinforced polymer composites for automotive interior components.

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Keywords

  • Artificial Neural Network
  • Expert System
  • Material Classification
  • Natural Fibre Composites
  • Feed Forward Network