Predicting the Elastic Modulus of Nanoparticle-Reinforced Polymer Matrix Composites Based on Digital Image Processing and Finite Elements
“Composite” means composition of two or more separate components. Composites are materials composed of two or more components that are chemically separated from each other on a macroscopic scale, and there are interfaces between these components. Conceptually, fiber-reinforced composites have existe...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7884623 |
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| Summary: | “Composite” means composition of two or more separate components. Composites are materials composed of two or more components that are chemically separated from each other on a macroscopic scale, and there are interfaces between these components. Conceptually, fiber-reinforced composites have existed since about 800 B.C. The aim of this paper is to predict the elastic modulus of nanoparticle-reinforced polymer matrix composites based on digital image processing and finite elements and to investigate the presence of interfaces in three-phase particle-reinforced composites. In this paper, the interfacial layer is designed as a spring model with the help of the effective modulus law, a statistical debonding criterion is proposed to represent the proceeding changes, and metallurgical methods are used to optimize the particles and prepare different composites with uniform dispersion. The experimental results of this paper show that the highest increase in tensile strength of the composites is about 8% and the highest increase in elastic modulus is 40% after the addition of nanoparticle materials, and the highest increase in tensile strength is 18% and the highest increase in elastic modulus is 50% after the addition of CNFs. |
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| ISSN: | 1687-8442 |