@article {10.3844/ajassp.2015.174.184,
article_type = {journal},
title = {Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites},
author = {Ali, Basheer Ahmed Ahmed and Salit, Mohd Sapuan and Zainudin, Edi Syams and Othman, Mohamed},
volume = {12},
year = {2015},
month = {Apr},
pages = {174-184},
doi = {10.3844/ajassp.2015.174.184},
url = {https://thescipub.com/abstract/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.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}