Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Garcia Fernandez, Francisco; García Esteban, Luis; Palacios de Palacios, Paloma de; Navarro Cano, Nieves y Conde García, Marta (2008). Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model. "Investigación Agraria: Sistemas y Recursos Forestales", v. 17 (n. 2); pp. 178-187. ISSN 1131-7965.

Descripción

Título: Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model
Autor/es:
  • Garcia Fernandez, Francisco
  • García Esteban, Luis
  • Palacios de Palacios, Paloma de
  • Navarro Cano, Nieves
  • Conde García, Marta
Tipo de Documento: Artículo
Título de Revista/Publicación: Investigación Agraria: Sistemas y Recursos Forestales
Fecha: Agosto 2008
Volumen: 17
Materias:
Escuela: E.T.S.I. Montes (UPM) [antigua denominación]
Departamento: Ingeniería Forestal [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The physical properties (specific gravity, moisture content, thickness swelling and water absorption) and mechanical properties (internal bond strength, bending strength and modulus of elasticity) were determined on 93 Spanish-manufactured standard particleboards of different thicknesses selected randomly at the end of the production process. The testing methods of the corresponding European standards (EN) were used, except in the case of the thickness swelling and absorption tests, for which the Spanish UNE standard was used. The thickness and the values obtained for the physical properties were entered into an artificial neural network in order to predict the mechanical properties of the board. The fit was compared with the usual multivariate regression models. The use of a neural network made it possible to obtain the values of bending strength, modulus of elasticity and internal bond strength of the boards utilizing the known data, not only of thickness, moisture content and specific gravity, but also of thickness swelling and water absorption. The neural network proposed is much better adapted to the observed values than any of the multivariate regression models obtained.

Más información

ID de Registro: 2336
Identificador DC: http://oa.upm.es/2336/
Identificador OAI: oai:oa.upm.es:2336
URL Oficial: http://www.inia.es/inia/contenidos/publicaciones/index.jsp?intranet=1&idcategoria=1359
Depositado por: Memoria Investigacion
Depositado el: 22 Feb 2010 12:42
Ultima Modificación: 20 Abr 2016 12:04
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