Utilização de RNA´s na construção do diagrama de vida constante de probabilidade de materiais compósitos
The static and cyclic assays are common to test materials in structures.. For cycling assays to assess the fatigue behavior of the material and thereby obtain the S-N curves and these are used to construct the diagrams of living constant. However, these diagrams, when constructed with small amounts...
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| Formato: | doctoralThesis |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/15596 |
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| Resumo: | The static and cyclic assays are common to test materials in structures.. For cycling assays to
assess the fatigue behavior of the material and thereby obtain the S-N curves and these are
used to construct the diagrams of living constant. However, these diagrams, when constructed
with small amounts of S-N curves underestimate or overestimate the actual behavior of the
composite, there is increasing need for more testing to obtain more accurate results.
Therewith, , a way of reducing costs is the statistical analysis of the fatigue behavior. The aim
of this research was evaluate the probabilistic fatigue behavior of composite materials. The
research was conducted in three parts. The first part consists of associating the equation of
probability Weilbull equations commonly used in modeling of composite materials S-N
curve, namely the exponential equation and power law and their generalizations. The second
part was used the results obtained by the equation which best represents the S-N curves of
probability and trained a network to the modular 5% failure. In the third part, we carried out a
comparative study of the results obtained using the nonlinear model by parts (PNL) with the
results of a modular network architecture (MN) in the analysis of fatigue behavior. For this
we used a database of ten materials obtained from the literature to assess the ability of
generalization of the modular network as well as its robustness. From the results it was found
that the power law of probability generalized probabilistic behavior better represents the
fatigue and composites that although the generalization ability of the MN that was not robust
training with 5% failure rate, but for values mean the MN showed more accurate results than
the PNL model |
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