ARTIFICIAL NEURAL NETWORKS TO ESTIMATE THE PHYSICAL-MECHANICAL PROPERTIES OF AMAZON SECOND CUTTING CYCLE WOOD

Pamella Carolline Marques dos Reis, Agostinho Lopes de Souza, Leonardo Pequeno Reis, Ana Márcia Macedo Ladeira Carvalho, Lucas Mazzei, Lyvia Julienne Sousa Rêgo, Helio Garcia Leite

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.

Full Text:

PDF

Refbacks

  • There are currently no refbacks.