Method of Predicting the Polymer Composites’ Properties Using Neural Network Modeling
A neural network modelling technique and its training to diagnose polymer composite materials based on tomography data is introduced. As an object of study, carbon fiber made by vacuum infusion technology using an epoxy binder is considered. X-ray microtomography was used to analyze its structure an...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN FR |
| Publicado: |
EDP Sciences
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/d6b23108c249481ca4f25e9da5ec6052 |
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| Sumario: | A neural network modelling technique and its training to diagnose polymer composite materials based on tomography data is introduced. As an object of study, carbon fiber made by vacuum infusion technology using an epoxy binder is considered. X-ray microtomography was used to analyze its structure and the provided images were used as a database for creating a neural network. A neural network modelling technique and its training was developed, including an algorithm for converting tomograph images into data on the structure of the phase composition and the physical and mechanical properties of the object under study. |
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