Modeling constituent-property relationship of polyvinylchloride composites by neural networks
- Reddy, Bhumi Reddy Srinivasulu; Premasudha, Mookala; Panigrahi, Bharat B.; Cho, Kwon-Koo; Reddy, Nagireddy Gari Subba
- Issue Date
- artificial neural networks; index of relative importance; process variables; PVC composite' s properties; sensitivity analysis
- POLYMER COMPOSITES, v.41, no.8, pp.3208 - 3217
- Journal Title
- POLYMER COMPOSITES
- Start Page
- End Page
- 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. 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
- 공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.