Application of artificial neural networks and Monte Carlo method for predicting the reliability of RF phytosanitary treatment of wood

Bedelean, Bogdan

doi:10.1007/s00107-018-1312-1

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…In this work, an uncertainty of ± 10% was assumed for the inputs of the models. The sensitivity coefficient was calculated as the ratio of the change in the dependent variable to the corresponding change in the independent variable (Cronin and Gleeson 2006;Bedelean 2018). The sensitivity analysis revealed that the ANN and RSM models developed to predict the bending moment capacity of joints loaded in compression were more sensitive to dowel diameter and less sensitive to dowel length and adhesive consumption.…”

Section: Resultsmentioning

confidence: 99%

“…The artificial neural networks (ANN) modeling technique, which is based on the behavior of the human brain, has been applied in wood engineering to predict various outputs, such as thermal conductivity, mechanical properties, swelling and shrinkage, reliability of the phytosanitary treatment of wood, equilibrium moisture content, and wood structure (Avramidis and Iliadis 2005;Watanabe et al 2013;Tiryaki et al 2016;Bedelean 2018;Ozsahin and Murat 2018). Modeling with ANN involves gathering the experimental data, transforming and dividing data for the training and testing sets, and performing the training, testing, and validation phase of the network.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Răcășan

Bedelean

Georgescu

et al. 2020

BioRes

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The bending moment capacity of heat-treated wood dowel joints loaded in compression or tension was predicted via two artificial neural network (ANN) models. Additionally, a comparative study between similar models that were developed through response surface methodology (RSM) was performed. The joints were made of heat-treated ash (Fraxinus excelsior). The values of the ultimate failure load and the moment arms were recorded for each run via a universal testing machine. To develop the ANN models, the experimental data were randomly divided into three subsets, which were needed for the training, testing, and validation phases. The RSM models were obtained from the literature. The performances of the models were analyzed in terms of the correlation coefficient, coefficient of determination, root mean square error, mean square error, and mean absolute prediction error. A sensitivity analysis was also performed to observe potential changes in the results due to the uncertainty in the input variables. The ANN model better predicted the bending moment capacity of heat-treated wood dowel joints loaded in compression than the RSM model. In contrast, the RSM model predicted the bending moment capacity of joints loaded in tension more accurately than the ANN model.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Răcășan

Bedelean

Georgescu

et al. 2020

BioRes

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“…The coded form of the selected mathematical model is presented in Equation (13). The actual forms of regression equations are presented in Equations ( 14) and (15). The most important factor that affects the drilling torque is tooth bite, followed by the drill tip angle and drill type (flat or helical).…”

Section: Drilling Torque (Y 4 )mentioning

confidence: 99%

“…ANN and RSM have been applied in wood science for various topics such as predicting the wood moisture content, prediction of noise emission in the machining of wood materials by means of an artificial neural network, optimum CNC cutting condition, reliability of phytosanitary treatment of wood [11][12][13][14][15]. More information about the modeling process with artificial neural networks could be found in the literature [12,16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wood Particleboard Drilling Operating Parameters by Means of the Artificial Neural Network Modeling Technique and Response Surface Methodology

Bedelean

Ispas

Răcășan

et al. 2022

Forests

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Drilling is one of the oldest and most important methods of processing wood and wood-based materials. Knowing the optimum value of factors that affect the drilling process could lead both to high-quality furniture and low-energy consumption during the manufacturing process. In this work, the artificial neural network modeling technique and response surface methodology were employed to reveal the optimum value of selected factors, namely, drill tip angle, tooth bite, and drill type of the delamination factor at the inlet and outlet, thrust force, and drilling torque. The data set that was used in this work to develop and validate the ANN models was collected from the literature. The results showed that the developed ANN models could reasonably predict the analyzed responses. By using these models and the response surface methodology, the optimum values of analyzed factors were revealed. Moreover, the influences of selected factors on the drilling process of wood particleboards were analyzed.

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3D finite heat transfer method to optimize a hot air convective system for phytosanitary treatment of wood pallets

Casas

Sariol

Roca

et al. 2023

Energ. Ecol. Environ.

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Application of artificial neural networks and Monte Carlo method for predicting the reliability of RF phytosanitary treatment of wood

Cited by 4 publications

References 29 publications

Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Optimization of Wood Particleboard Drilling Operating Parameters by Means of the Artificial Neural Network Modeling Technique and Response Surface Methodology

3D finite heat transfer method to optimize a hot air convective system for phytosanitary treatment of wood pallets

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