High Dielectric Design of Polymer Composites by Using Artificial Neural Network
Polymer-based composites with a high dielectric property have shown great potential in electrical energy storage applications. It is important to predict the dielectric constant in designing polymer composites, but it is costly and time consuming. In this study, dielectric properties of various poly...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12592 |
| _version_ | 1797461649661100032 |
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| author | Sungyub Ji Dae-Yong Jeong Cheolhee Kim Sung Yi |
| author_facet | Sungyub Ji Dae-Yong Jeong Cheolhee Kim Sung Yi |
| author_sort | Sungyub Ji |
| collection | DOAJ |
| description | Polymer-based composites with a high dielectric property have shown great potential in electrical energy storage applications. It is important to predict the dielectric constant in designing polymer composites, but it is costly and time consuming. In this study, dielectric properties of various polymer composites have been predicted by using an artificial neural network (ANN) model trained with hundreds of experimentally measured data. Eight variables such as the dielectric constant of matrix, filler, and shell, the diameter of filler, the volume fraction of filler, the dimension of filler, the thickness of shell, and the frequency were considered. To improve the prediction accuracy, hyper parameters of the ANN model were optimized through the hyperband method. Using the ANN model, we demonstrated the correlation between the dielectric constant of polymer composites and the variables. The ANN model predicted the dielectric constant with a coefficient of determination (R<sup>2</sup>) of 0.97. Furthermore, the ANN model shows good performance to predict dielectric constant at various frequencies (spanning from 100 Hz to 100 kHz). Hence, we present that the AI-based prediction model using ANN method can be helpful in designing the polymer composites with desired properties. |
| first_indexed | 2024-03-09T17:23:21Z |
| format | Article |
| id | doaj.art-c30b8b2a58bf4fc2a7209733222c79ec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:23:21Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c30b8b2a58bf4fc2a7209733222c79ec2023-11-24T13:01:21ZengMDPI AGApplied Sciences2076-34172022-12-0112241259210.3390/app122412592High Dielectric Design of Polymer Composites by Using Artificial Neural NetworkSungyub Ji0Dae-Yong Jeong1Cheolhee Kim2Sung Yi3Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97227, USADepartment of Materials Science and Engineering, Inha University, Incheon 22212, Republic of KoreaDepartment of Mechanical and Materials Engineering, Portland State University, Portland, OR 97227, USADepartment of Mechanical and Materials Engineering, Portland State University, Portland, OR 97227, USAPolymer-based composites with a high dielectric property have shown great potential in electrical energy storage applications. It is important to predict the dielectric constant in designing polymer composites, but it is costly and time consuming. In this study, dielectric properties of various polymer composites have been predicted by using an artificial neural network (ANN) model trained with hundreds of experimentally measured data. Eight variables such as the dielectric constant of matrix, filler, and shell, the diameter of filler, the volume fraction of filler, the dimension of filler, the thickness of shell, and the frequency were considered. To improve the prediction accuracy, hyper parameters of the ANN model were optimized through the hyperband method. Using the ANN model, we demonstrated the correlation between the dielectric constant of polymer composites and the variables. The ANN model predicted the dielectric constant with a coefficient of determination (R<sup>2</sup>) of 0.97. Furthermore, the ANN model shows good performance to predict dielectric constant at various frequencies (spanning from 100 Hz to 100 kHz). Hence, we present that the AI-based prediction model using ANN method can be helpful in designing the polymer composites with desired properties.https://www.mdpi.com/2076-3417/12/24/12592dielectricpolymer matrixfillercompositeneural network |
| spellingShingle | Sungyub Ji Dae-Yong Jeong Cheolhee Kim Sung Yi High Dielectric Design of Polymer Composites by Using Artificial Neural Network Applied Sciences dielectric polymer matrix filler composite neural network |
| title | High Dielectric Design of Polymer Composites by Using Artificial Neural Network |
| title_full | High Dielectric Design of Polymer Composites by Using Artificial Neural Network |
| title_fullStr | High Dielectric Design of Polymer Composites by Using Artificial Neural Network |
| title_full_unstemmed | High Dielectric Design of Polymer Composites by Using Artificial Neural Network |
| title_short | High Dielectric Design of Polymer Composites by Using Artificial Neural Network |
| title_sort | high dielectric design of polymer composites by using artificial neural network |
| topic | dielectric polymer matrix filler composite neural network |
| url | https://www.mdpi.com/2076-3417/12/24/12592 |
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