Prediction of plywood bonding quality using an artificial neural network

Abstract

The bonding quality test is one of the most important of all tests performed on plywood, because it determines the suitability of boards for use in the type of exposure they are intended for. Because this test involves aging pretreatment, results are not available in -24-97 h after manufacture, depending on the type of board, and therefore any error in the manufacturing process is not detected until 1-4 days later. To solve this time problem, an artificial neural network was developed as a predictive method to determine the suitability of board bonding through other properties that can be determined in less testing time: thickness, moisture content, density, bending strength, and modulus of elasticity. The network designed WAS a feedforward multilayer perceptron trained by supervised learning after normalization of the input data, and allowed the bonding test result to be predicted with 93% accuracy.

Evaluation of different modeling approaches for total tree-height estimation in Mediterranean Region of Turkey

“…Artificial neural network models (ANNs) have been used widely in environmental sciences including the field of forest modeling. Maier and Dandy (2000) stated a review of neural network (NN) modeling issues and applications for the prediction and forecasting of water resources variables; Liu et al (2003) used neural network models (NNs) in classification of ecological habitats, Corne et al (2004) predicted forest attributes using NNs, Özçelik et al (2008) conducted a comparative study of NNs and standard methods for estimating tree bole volume, Fernández et al (2008) handled ANNs for the prediction of standard particleboard mechanical properties, Esteban et al (2009) utilized ANNs in wood identification, while Esteban et al (2011) employed ANNs for the prediction of plywood bonding quality. It is worth noting that the back-propagation algorithms are the one-quarter of total forest land in these country (Anonymous, 2006).…”

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

“…Determination of the optimum number of hidden layers and nodes within each layer are real problem, and there is no procedure available to know this for first time. For that, a trial and error approach (multiple runs) was followed to get at the best network architecture [32,33].…”

Yangın Geciktirici Kimyasallarla Emprenye Edilmiş Kontrplakların Retensiyon Miktarları ve Mekanik Dirençlerinin Yapay Sinir Ağları ile Tahmin Edilmesi

“…Therefore, it is important to find more economic methods providing desirable results concerning technological properties (Demirkir et al, 2013). Artificial neural networks (ANNs) have been widely used in the field of wood (Esteban et al, 2011). The neural network most commonly used is the multilayer perception, whose nature as a universal function approximation makes it a powerful tool for modelling complex relations between variables (Fernandez et al, 2012).…”