Modeling
                    constituent–property
                    relationship of polyvinylchloride composites by neural networks

Bhumi Reddy Srinivasulu Reddy; Mookala Premasudha; Bharat B. Panigrahi; Kwon‐Koo Cho; Nagireddy Gari Subba Reddy

doi:10.1002/pc.25612

journal article Apr 27, 2020

Modeling constituent–property relationship of polyvinylchloride composites by neural networks

Bhumi Reddy Srinivasulu Reddy Mookala Premasudha Bharat B. Panigrahi Kwon‐Koo Cho Nagireddy Gari Subba Reddy

Polymer Composites Vol. 41 No. 8 pp. 3208-3217 · Wiley

View at Publisher Save 10.1002/pc.25612

Abstract

Abstract

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, CaCO
3
, di‐2‐ethylhexyl phthalate, chlorinated paraffin wax, and CaCO
3
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.
R
2
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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Metrics

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Citations

36

References

Details

Published: Apr 27, 2020
Vol/Issue: 41(8)
Pages: 3208-3217
License: View

Authors

B

Bhumi Reddy Srinivasulu Reddy

Department of Materials Engineering and Convergence Technology & RIGET Gyeongsang National University Jinju South Korea

M

Mookala Premasudha

Department of Materials Engineering and Convergence Technology & RIGET Gyeongsang National University Jinju South Korea

B

Bharat B. Panigrahi

Department of Materials Science and Metallurgical Engineering Indian Institute of Technology Hyderabad Sangareddy Telangana India

K

Kwon‐Koo Cho

Department of Materials Engineering and Convergence Technology & RIGET Gyeongsang National University Jinju South Korea

N

Nagireddy Gari Subba Reddy

School of Materials Science and Engineering, Engineering Research Institute Gyeongsang National University Jinju South Korea

Funding

NSF Award: #1410461

Cite This Article

Bhumi Reddy Srinivasulu Reddy, Mookala Premasudha, Bharat B. Panigrahi, et al. (2020). Modeling constituent–property relationship of polyvinylchloride composites by neural networks. Polymer Composites, 41(8), 3208-3217. https://doi.org/10.1002/pc.25612

Modeling constituent–property relationship of polyvinylchloride composites by neural networks

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