Please use this identifier to cite or link to this item: https://open.ni.ac.rs/handle/123456789/3221
Title: Detailed study on fusion characteristics of rigid poly(vinyl chloride) nanocomposites: The comparison of using multiple regression analysis and artificial neural network
Authors: Moghri M.
Shamaee H.
Tavana R.
Madić, Miloš 
Shukla D.
Issue Date: 1-Jan-2015
Journal: Journal of Vinyl and Additive Technology
Abstract: © 2014 Society of Plastics Engineers. Fusion behavior of poly(vinyl chloride) (PVC) compounds plays an important role in the development of physical properties of processed material. The fusion characteristics in PVC processing are governed by material variables that affect the fusion with some interactions. In this research, the aim was to characterize the effects of formulation ingredients on fusion characteristics of PVC. Four material parameters, including the contents of nanoclay (NC), azodicarbonamide, calcium stearate, and processing aid, are proposed as affecting variables. The fusion time (FT) as well as fusion factor (FF) are considered fusion indicators and are experimentally determined in some different levels of affecting parameters. The multivariable regression analysis (MRA) and the Artificial Neural Network (ANN) modeling are considered as two analytical methods. The regression analysis result for the FT denotes, in part, significant linear and quadratic effects of NC and also its significant interactions with azodicarbonamide and calcium stearate, whereas that of FF indicates only a linear effect of NC. ANN modeling is performed with a three-layer (input, hidden, and output) neural network. The results of the comparison of the MRA and ANN predictions with experimental values are reported as the correlation coefficient (R<sup>2</sup>), mean-square error, and mean absolute percentage error for both FF and FT parameters. The obtained values clearly denote that the ANN results are more precise and especially more general than those of MRA. However, in the case of FT, improvement of the ANN modeling is much greater than that of FF.
URI: https://open.ni.ac.rs/handle/123456789/3221
ISSN: 10835601
DOI: 10.1002/vnl.21382
Appears in Collections:Naučne i umetničke publikacije

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