Previsão do módulo de elasticidade transversal de compósitos unidirecionais através de redes neurais mistas
The aim of this study is to create an artificial neural network (ANN) capable of modeling the transverse elasticity modulus (E2) of unidirectional composites. To that end, we used a dataset divided into two parts, one for training and the other for ANN testing. Three types of architectures from diff...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/15697 |
| Acceso en línea: | https://repositorio.ufrn.br/jspui/handle/123456789/15697 |
| Access Level: | acceso abierto |
| Palabra clave: | RNA. Módulo de elasticidade transversal. Modelo de Halpin-Tsai. Compósitos unidirecionais Composite materials. Fatigue. Goodman diagram. Damage mechanism. Artificial neural networks CNPQ::ENGENHARIAS::ENGENHARIA MECANICA |
| Sumario: | The aim of this study is to create an artificial neural network (ANN) capable of modeling the transverse elasticity modulus (E2) of unidirectional composites. To that end, we used a dataset divided into two parts, one for training and the other for ANN testing. Three types of architectures from different networks were developed, one with only two inputs, one with three inputs and the third with mixed architecture combining an ANN with a model developed by Halpin-Tsai. After algorithm training, the results demonstrate that the use of ANNs is quite promising, given that when they were compared with those of the Halpín-Tsai mathematical model, higher correlation coefficient values and lower root mean square values were observed |
|---|