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.
Highlights
The innovation in material science and technology reveals more materials than ever before and the selection menu become countless for the engineers. Ashby (2004) described the available materials for the engineers are vast and expected to something over 120,000 materials of choice
Artificial Neural Network (ANN) was a powerful computational model that simulates the neurons of a biological nervous system
In this study we focus on few properties of natural fibre composite used as an alternative material for synthetic materials in the interior component of automobile
Summary
The innovation in material science and technology reveals more materials than ever before and the selection menu become countless for the engineers. Ashby (2004) described the available materials for the engineers are vast and expected to something over 120,000 materials of choice. Artificial Neural Network (ANN) was a powerful computational model that simulates the neurons of a biological nervous system. It consists of a set of connected cells called neurons. The neurons receive impluses as input and perform transformation of input information and transmit to output neurons (Yegnanarayana, 2009). It deals with non-linear problems for an accurate analytical solution. ANN provides practical solution for pattern recognition, classification and optimization problems. It is used in signal processing, speech recognition, condition monitoring and functional approximation
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