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- Title
ARTIFICIAL NEURAL NETWORKS TO ESTIMATE THE PHYSICAL-MECHANICAL PROPERTIES OF AMAZON SECOND CUTTING CYCLE WOOD.
- Authors
dos Reis, Pamella Carolline Marques; de Souza, Agostinho Lopes; Pequeno Reis, Leonardo; Ladeira Carvalho, Ana Márcia Macedo; Mazzei, Lucas; Sousa Rêgo, Lyvia Julienne; Garcia Leite, Helio
- Abstract
Timber from the second cutting cycle may make up the majority of future crop volumetric. However, there are few studies of the physical and mechanical properties of this timber, which are important to support the consolidation of new species. This study aimed to use Artificial Neural Networks to estimate the physical and mechanical properties of wood from the Amazon, based on basic density. The properties were: shrinkage (tangential, radial and volumetric), static bending, parallel and perpendicular to the fiber compression, parallel and transverse to the fibers, Janka hardness, traction, splitting and shear. The estimate followed the tendency of the data observed for the tangential, radial and volumetric shrinkage. The network estimated the mechanical properties with significant accuracy. Distribution of errors, static bending, parallel compression and perpendicular to the fiber compression also showed significant accuracy. Artificial Neural Networks can be used to estimate the physical and mechanical properties of wood from Amazon species.
- Subjects
ARTIFICIAL neural networks; MECHANICAL behavior of materials; TIMBER; HARDNESS; WOOD products
- Publication
Maderas: Ciencia y Tecnología, 2018, Vol 20, Issue 3, p343
- ISSN
0717-3644
- Publication type
Article
- DOI
10.4067/S0718-221X2018005003501