Evaluation of some of the properties of particleboard as function of manufacturing parameters

Nemli, Gökay; Aydın, İsmail; Zekoviç, Emir

doi:10.1016/j.matdes.2006.01.015

Cited by 59 publications

(35 citation statements)

References 4 publications

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“…It can therefore be stated that thickness, moisture content, specific gravity, thickness swelling and water absorption all have an influence on the result of the properties of internal bond strength, MOR and MOE. These results agree with those obtained by other authors (Halligan and Schniewind, 1974;Hayashi et al, 2003;Nemli et al, 2007).…”

Section: Resultssupporting

confidence: 94%

“…This is why obtaining models capable of predicting properties such as bending strength and internal bond strength through the use of other properties which are quicker and easier to determine, such as moisture content, specific gravity, swelling and absorption, can be an extremely useful instrument in the production control process. The references consulted showed studies that relate the strength properties to the specific gravity and moisture content of the board, both of particle and fibreboard, with positive results in all cases (Halligan and Schniewind, 1974;Mc Nat, 1974;Vital et al, 1974;Kelly, 1977;Hayashi et al, 2003;Wong et al, 2003;Cai et al, 2004;Nemli et al, 2007). However, in addition to specific gravity and moisture content, the present study included thickness swelling and water absorption to determine the prediction of internal bond strength (IB), bending strength (MOR) and modulus of elasticity (MOE).…”

Section: Introductionmentioning

confidence: 99%

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

Fernández¹,

Esteban²,

Palacios³

et al. 2008

For. syst.

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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.Key words: wood-based panels, physico-mechanical properties, ANN, regression fit, predictive model. ResumenPredicción de propiedades mecánicas del tablero de partículas estándar mediante una red neuronal artificial y comparación con un modelo de regresión multivariante Se han determinado las propiedades físicas (densidad, humedad, hinchazón en espesor y absorción de agua) y mecánicas (tracción perpendicular a las caras, resistencia a flexión y módulo de elasticidad) de 93 tableros de partículas estándar de diferentes espesores, de fabricación española, elegidos aleatoriamente a la salida del proceso de producción, utilizando los métodos de ensayo recogidos en las normas EN correspondientes, excepto en los ensayos de hinchazón y absorción que se ha utilizado norma UNE (española). El espesor y los valores obtenidos de las propiedades físicas han sido introducidos en una red neuronal artificial (RNA) para predecir las propiedades mecánicas del tablero. El ajuste se ha comparado con los habituales modelos de regresión multivariante. La utilización de una red neuronal ha permitido obtener los valores de resistencia a flexión, módulo de elasticidad y resistencia a la tracción perpendicular a las caras de los tableros de partículas a través de los datos conocidos, no sólo de espesor, humedad y densidad sino también de hinchazón en espesor y absorción de agua. La red neuronal propuesta tiene una adecuación a los valores experimentales muy superior a cualquiera de los modelos de regresión multivariante obtenidos.Palabras clave: tableros derivados de la madera, propiedades físico-mecánicas, RNA, ajuste por regresión, modelo predictivo.

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Section: Resultssupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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

Fernández¹,

Esteban²,

Palacios³

et al. 2008

For. syst.

View full text Add to dashboard Cite

show abstract

“…Such size distribution indicates that the use of a denser screen produced a higher share of the finer fractions. From the literature it is commonly known that the particleboards containing high amounts of fine particles and dust exhibit lower physical and mechanical properties (Nemli et al 2007;Sackey et al 2008).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, a complex analysis of the particles is justified since their properties determine the performance of the final product-i.e. panels (Nemli et al 2007;Nazerian et al 2011). Fractional composition and bulk density are the key factors affecting not only particles blending, mat forming and pressing (Suchsland and Woodson 1991;Gamage et al 2009;Nazerian et al 2011), but also determine the size of the transportation and production equipment as well as the necessary storage space.…”

Section: Introductionmentioning

confidence: 99%

“…It was demonstrated that when glue load was constant, but the content of fine fractions and dust increased, fine particles tend to absorb a disproportionate quantity of the adhesive which reduces the quantity of adhesive available for bonding between the particles. Subsequently, physical and mechanical properties of particleboards are lowered (Nemli et al 2007;Sackey et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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Properties of particleboard produced from post-industrial UF- and PF-bonded plywood

Laskowska

Mamiński

2017

Eur. J. Wood Prod.

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An approach based on the recycling of waste plywood as a recovered wood resource for the particleboard industry is described. It is demonstrated that post-industrial urea-(UF) and phenol-formaldehyde (PF) bonded plywood can be effectively shredded to the form of the recovered particles that can be a valuable material in particleboard manufacturing. The effects of shredding conditions and binder type on the recovered particles characteristics and the mechanical properties of the particleboards were analyzed. It is shown that the substitution of 20-100% of virgin particles with the recovered material is possible without affecting the performance of the particleboard.

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Development of Binderless Composites from Different Nonwood Lignocellulosic Materials: Overview

Nitu¹,

Shams²,

Islam

et al. 2017

Handbook of Ecomaterials

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Studies about manufacturing of synthetic adhesive-free binderless particleboards from nonwood lignocellulosic materials have come under limelight as a consequence of wood supplies reduction along with the demand for formaldehyde-free panels. The overview of binderless particleboard's development from different agricultural waste using an assorted range of manufacturing processes and their self-bonding mechanisms are presented in this chapter. Also, the features of different manufacturing parameters of binderless particleboards, such as pressing temperature and pressure, pressing time, particle size, mixing ratio and additional substances, are discussed. The physical, mechanical, and biological properties of every binderless particleboard are typically evaluated. This chapter provides a big picture of previous researches of binderless particleboards from different lignocellulosic agricultural waste with better understanding of their self-bonding mechanisms, contributing factors, and the manufacturing processes in favor of future value-added environmental friendly binderless particleboards.

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Evaluation of some of the properties of particleboard as function of manufacturing parameters

Cited by 59 publications

References 4 publications

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

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

Properties of particleboard produced from post-industrial UF- and PF-bonded plywood

Development of Binderless Composites from Different Nonwood Lignocellulosic Materials: Overview

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