Use of Artificial Neural Networks in Predicting Particleboard Quality Parameters

Melo, Rafael Rodolfo de; Miguel, Eder Pereira

doi:10.1590/0100-67622016000500019

Cited by 6 publications

(5 citation statements)

References 14 publications

(9 reference statements)

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“…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).…”

Section: Neural Network Validationmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks in estimating wood resistance

Miguel

Melo

Junior

et al. 2018

Maderas, Cienc. tecnol.

Self Cite

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The purpose of this research was to evaluate the potential of Artificial Neural Networks in estimating the properties of wood resistance. In order to do so, a hybrid of eucalyptus (Eucalyptus urograndis) planted in the Northern Region of the State of Mato Grosso was selected and ten trees were collected. Then, four samples of each tree were removed, totaling 40 samples, which were later subjected to non-destructive testing of apparent density, ultrasonic wave propagation velocity, dynamic modulus of elasticity obtained by ultrasound, and Janka hardness. These properties were used as estimators of resistance and compressive strength parallel to fibers, and hardness. Multilayer Perceptron networks were also employed, training 100 of them for each of the evaluated parameters. The obtained results indicated that the use of Artificial Neural Networks is an efficient tool for predicting wood resistance.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Hua²,

Cai³

et al. 2022

J Wood Sci

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Particle gluing operating parameters in particleboard (PB) production have an important influence on the mechanical properties of PBs. This study developed a multi-objective optimization model based on support vector regression (SVR) optimized by the non-dominated sorted genetic algorithm-II (NSGA2) to realize the multi-objective accurate prediction of PB mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bonding (IB) strength) by adjusting particle gluing operating parameters. The NSGA2-SVR multi-objective prediction model was trained by 496 groups of experimental data of particle gluing operating parameters and PB mechanical properties. The prediction results of the NSGA2-SVR multi-objective prediction model were evaluated by 124 groups of experimental data and compared with the prediction results of the back propagation neural network (BPNN) model, general regression neural network (GRNN) model, and SVR model. The mean absolute percentage errors (MAPEs) of the NSGA2-SVR model were 49.11%, 33.64%, and 24.20% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The Theil’s inequality coefficients (TICs) of the NSGA2-SVR model were 40.93%, 27.39%, and 18.58% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The results showed that the multi-objective prediction model based on NSGA2-SVR has a superior fitting and higher prediction accuracy for the prediction performance of particle gluing operating parameters, and the NSGA2-SVR model can be applied to the multi-objective synchronous prediction of particle gluing operating parameters in the PB production line.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Kaya¹,

Ilkuçar²,

Çifci³

2019

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Knowing the mechanical and physical properties of a material is the most important criteria for engineers and designers interested in determining the intended use of the material. The prediction of wood composite materials based on their mechanical and physical properties plays an important role in their future application. In this study, radial basis function network approach was employed for prediction according to mechanical and physical properties of wood composite materials such as particleboard, fiberboard, oriented strand board and plywood, which have widespread use in the furniture industry and construction sector. Four physical and mechanical properties were used as the board density, bending strength, bending elastic modulus and tensile strength in the prediction of the wood composite materials. This study will assist wood composite users in the selection of wood composite materials that will provide the mechanical and physical properties determined in advance for any construction. Moreover, the present study will fill this gap in literature.

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“…In the manufacturing process, there are several systems for quality control, each of them specialized in measuring the variables or attributes that need to be measured [7]; the most used ones today are electronic systems, since they have a great versatility and a wide coverage, greatly favoring the quality assurance in the industries [8]. The multiple alternatives offered by electronic systems make them a research and major improvement target; advances regarding this subject are increasingly surprising and seek to improve impact processes, making them more reliable and largely productive [9].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the quality control for pieces recognition in manufacturing flexible cells by the implementation of neural networks

Ariza¹,

Parra²,

Torrejano³

2017

ces

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The high levels of automation in the companies that use manufacturing cells for their fabrication processes guarantee a high efficiency in production with high quality standards. The main objective of this paper is to develop an optimization for the tasks performed by the quality control station through artificial vision belonging to the manufacturing cell FMS-200; the development of the proposal focuses on optimizing the inspection process and control of quality by adapting image processing techniques and adaptive resonance neural networks. For this particular case, a review of the location of the optical system and a detailed time study for each movement of the station actuators was carried out, attempting to determinate a better position for the optical detection system (artificial vision camera) and the possibility elimination of some components of the station; all of this in order to considerably reduce the operating time and increasing the efficiency for the quality control of the system.

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Use of Artificial Neural Networks in Predicting Particleboard Quality Parameters

Cited by 6 publications

References 14 publications

Using artificial neural networks in estimating wood resistance

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

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

Optimization of the quality control for pieces recognition in manufacturing flexible cells by the implementation of neural networks

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