Prediction of storage and loss modulus in dynamic mechanical analysis using adaptive neuro-fuzzy interference system and artificial neural network

S. Bose, D. Shome and C.K. Das

International Journal of Industrial and Systems Engineering, 2010, vol. 6, issue 2, 207-226

Abstract: Interfacial bonding for load transfer across the Carbon Nanotube (CNT)-matrix interface and the amount of heat build-up play a predominant role in effective utilisation of CNTs in composite applications and these factors may be effectively assessed from the values of storage and loss modulus of the concerned composites. In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) and Artificial Neural Network (ANN) models are proposed for accurately predicting storage and loss modulus from temperature in Dynamic Mechanical Analysis (DMA) of polymer nanocomposites. Results demonstrate that both ANFIS and ANN are highly effective in accurately estimating storage and loss modulus from temperature. However, more accurate results are obtained with the ANFIS models as compared to the ANN models.

Keywords: carbon nanotubes; CNT; adaptive neuro-fuzzy interference system; ANFIS; artificial neural network; ANNs; polymer nanocomposites; dynamic mechanical analysis; storage modulus; loss modulus; nanotechnology. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:6:y:2010:i:2:p:207-226

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