Simulación estructural de espumas de aluminio a partir de imágenes 2D mediante la combinación de técnicas de homogeneización y machine learning
[EN] The use of resistant, rigid, low-weight materials with good both acoustic and thermal properties is very interesting intoday¿s industry. Among these materials, one can find aluminium foams, whose mechanical behaviour is necessaryfor their application. In order to obtain the geometry of an alumi...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/123279 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/123279 |
| Access Level: | acceso abierto |
| Palavra-chave: | Homogeneización Espuma de aluminio Red neuronal Machine learning Homogenization Aluminium foam Neural network INGENIERIA MECANICA |
| Resumo: | [EN] The use of resistant, rigid, low-weight materials with good both acoustic and thermal properties is very interesting intoday¿s industry. Among these materials, one can find aluminium foams, whose mechanical behaviour is necessaryfor their application. In order to obtain the geometry of an aluminium foam, several techniques can be applied, and allof them are based in the fact that information is initially obtained by a Computed Axial Tomography (CAT). One ofthese techniques, known as segmentation, involves a CAD being generated from an image in order to build the FiniteElement (FE) model. Another option is to use techniques such as CutFEM or cgFEM, in which a certain amount ofpixels, which define the properties of the material, are embedded in each element. Among the existing methods forevaluating the material properties matrix, this study proposes the use of homogenization techniques, sped up by the useof machine learning techniques. This method has been applied to real problems obtaining a high speed up, conservingprecision. |
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