Prediction of storage and loss modulus in dynamic mechanical analysis using adaptive neuro-fuzzy interference system and artificial neural network
by S. Bose, D. Shome, C.K. Das
International Journal of Industrial and Systems Engineering (IJISE), Vol. 6, No. 2, 2010

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.

Online publication date: Sun, 01-Aug-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Industrial and Systems Engineering (IJISE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com