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1 Archives of Computational Materials Science and Surface Engineering

1 2009

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Characterization of syndiotactic polystyrene / carbon nanofiber composites through X-ray diffraction using adaptive neuro-fuzzy interference system and artificial neural network

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

Archives of Computational Materials Science and Surface Engineering

2009

Vol. 1, nr 4

197-204

Purpose: The purpose of the paper is to characterize of syndiotactic polystyrene/carbon nanofiber composites through X-ray diffraction using adaptive neuro-fuzzy interference system and artificial neural network. Owing to their interesting mechanical, electrical and thermal properties, syndiotactic polystyrene (s-PS)/carbon nanofiber (CNF) composites have gained adequate importance in the scientific and industrial communities and as a result, characterization of s-PS/CNF is an issue of major interest to the researchers. Design/methodology/approach: In the present paper, two quantitative models, based on adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN), are developed and compared with a goal of accurately predicting the intensity values from the scattering angle values in X-ray Diffraction (XRD) of syndiotactic polystyrene (s-PS)/carbon nanofiber (CNF) composites. Findings: Results demonstrate that both the proposed models are highly effective in estimating intensity from scattering angle. However, more accurate results are obtained with the ANFIS model as compared to the ANN model. Research limitations/implications: The results of the investigations carried out in this study is suggestive of the fact that both ANFIS and ANN can be used quite effectively for prediction of intensity from scattering angle values in XRD of s-PS/CNF composites. Originality/value: The proposed ANFIS and ANN model-predicted intensity values are in very good agreement with the experimental intensity values. However, it is seen that, irrespective of the type of composite sample, the proposed ANFIS models outperform the proposed ANN models in terms of prediction accuracy.