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Descripción
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| One of the most important properties of particleboard for its structural use is modulus of rupture (MOR). However, the test for this property lacks the immediacy necessary for real-time application to production line control. To reduce the delay in obtaining results, which affects the entire industry, it would be beneficial to have modelling tools to enable determination of mechanical properties using parameters taken directly from the production line. In relation to this, the use of artificial neural networks (ANN) has grown in recent years in various fields of science and technology. The nature of ANNs as universal function approximators makes them powerful modelling tools, particularly when it is important to obtain a high degree of reliability rather than determining the relations between the variables involved. These mathematical models have been successfully applied to obtain the mechanical properties of wood-based products using more easily-measured properties or manufacturing parameters. In this study, an ANN was developed to model particleboard MOR using manufacturing parameters, obtaining sufficient reliability for application in the factory. | |
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Internacional
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Si |
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Nombre congreso
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CompWood 2017 |
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Tipo de participación
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960 |
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Lugar del congreso
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Viena |
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Revisores
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Si |
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ISBN o ISSN
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978-3-903024-49-6 |
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DOI
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Fecha inicio congreso
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07/06/2017 |
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Fecha fin congreso
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09/06/2017 |
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Desde la página
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33 |
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Hasta la página
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33 |
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Título de las actas
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Computational methods in Wood mechanics for material properties to timber structures |