Modeling constituent–property relationship of polyvinylchloride composites by neural networks

Reddy, Bhumi Reddy Srinivasulu and Premasudha, Mookala and Panigrahi, Bharat B. and Cho, Kwon‐Koo and Reddy, Nagireddy Gari Subba (2020) Modeling constituent–property relationship of polyvinylchloride composites by neural networks. Polymer Composites, 41 (8). pp. 3208-3217. ISSN 0272-8397

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The purpose of this study is to develop an artificial neural network (ANN) model to predict and analyze the relationship between properties and process parameters of polyvinyl chloride (PVC) composites. The tensile strength, ductility, and density of PVC are modeled as a function of virgin PVC, recycled PVC, CaCO3, di-2-ethylhexyl phthalate, chlorinated paraffin wax, and CaCO3 particle size. The ANN model is trained using the backpropagation algorithm. The developed model was validated with a set of unseen test data. The correlation coefficient adj. R2 values for test data were 0.95, 0.83, and 0.90 for tensile strength, density, and ductility, respectively. The relationship between constituents and properties of PVC composites were analyzed by sensitivity analysis, index of relative importance, and quantitative estimation. The study concluded that ANN modeling was a dependable tool for the optimization of constituents for the desired properties of PVCs.

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IITH Creators:
IITH CreatorsORCiD
Panigrahi, Bharat BhooshanUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: artificial neural networks; index of relative importance; process variables; PVC composite's properties; sensitivity analysis
Subjects: Others > Metallurgy Metallurgical Engineering
Materials Engineering > Materials engineering
Divisions: Department of Material Science Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 27 Jul 2021 05:16
Last Modified: 27 Jul 2021 05:16
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