Use of Artificial Neural Networks in Predicting Particleboard Quality Parameters

Abstract

-This study aims to assess Artificial Neural Networks (ANN) in predicting particleboard quality based on its physical and mechanical properties. Particleboards were manufactured using eucalyptus (Eucalyptus grandis) and bonded with urea-formaldehyde and phenol-formaldehyde resins. To characterize quality, physical (density and water absorption and thickness swelling after 24-hour immersion) and mechanical (static bending strength and internal bond) properties were assessed. For predictions, adhesive type and particleboard density were adopted as ANN input variables. Networks of multilayer Perceptron (MLP) were adopted, training 100 networks for each assessed parameter. The results pointed out ANN as effective in predicting quality parameters of particleboards. With this technique, all the assessed properties presented models with adjustments higher than 0.90.

Using artificial neural networks in estimating wood resistance

“…Neural networks currently represent a promising area for multidisciplinary research (Silva et al 2010), and the use of ANN in Brazil has become important in estimating measurements of forest stands, as it is considered an efficient and promising technique by several researchers (Leite et al 2011, Castro et al 2013, Binoti et al 2015, Miguel et al 2015. Nevertheless, studies are scarce when describing the physical and mechanical properties of wood and its engineered products to predict the quality of agglomerated panels (Melo and Miguel 2016).…”

“…The applicability of artificial neural networks in wood science has already been evaluated by several authors (Tiryaki and Hamzacebi 2014, Tiryaki and Aydin 2014, Okan et al 2015, Melo and Miguel 2016, which denotes its potentiality. The purpose of this research was to evaluate the potential of ANNs in estimating wood resistance in young individuals of Eucalyptus urograndis, one of the main species used by Brazilian silviculture.…”

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

“…But, the prediction accuracy of particle gluing operating parameters with nonlinear data characteristics in the linear prediction model was poor [5]. de Melo et al [6] constructed an artificial neural network model to predict the modulus of elasticity (MOE) and modulus of rupture (MOR) of PB through parameters such as adhesive types. Nevertheless, the ANN model may lead to the prediction results falling into locally optimal solutions [7].…”

Ahşap Kompozit Malzemelerin Mekanik ve Fiziksel Özelliklerine göre Tahmininde Radyal Temelli Fonksiyon Sinir Ağının Kullanımı

“…There are a growing number of papers in the field of wood science employing artificial neural network (ANN), such as predicting physical and mechanical properties in wood and wood composites (Fernandez et al, 2008;Fernandez et al, 2012;Melo and Miguel, 2016;Ilkucar et al, 2018;Miguel et al, 2018), calculating wood thermal conductivity (Avradimis and Iliadis, 2005;Xu et al, 2007), classifying wood defects (Marcano-Cedeño et al, 2009;Shahnorbanun et al, 2010;Qayyum et al, 2016), optimizing of bonding strength of the various wood products (Cook and Chiu, 1997;Tiryaki et al, 2014) and analysing of moisture in wood (Zhang et al, 2006;Esteban et al, 2010;Özşahin, 2012).…”