Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks

2017 ◽  
Vol 76 (2) ◽  
pp. 563-572 ◽  
Author(s):  
Sukru Ozsahin ◽  
Mirac Murat
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Specific Gravity ◽  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Treated Wood ◽  
Heat Treated ◽  
Artificial Neural

scholarly journals ARTIFICIAL NEURAL NETWORKS AS A NEW TOOL FOR ASSESSING AND MONITORING WOOD MOISTURE CONTENT

2016 ◽  
Vol 40 (3) ◽  
pp. 543-549
Author(s):  
Antônio José Vinha Zanuncio ◽  
Amélia Guimarães Carvalho ◽  
Liniker Fernandes da Silva ◽  
Angélica de Cássia Oliveira Carneiro ◽  
Jorge Luiz Colodette
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Basic Density ◽  
High Accuracy ◽  
Wood Moisture Content ◽  
Wood Moisture ◽  
The Neural Network ◽  
Artificial Neural

ABSTRACT Drying of wood is necessary for its use and moisture control is important during this process. The aim of this study was to use artificial neural networks to evaluate and monitor the wood moisture content during drying. Wood samples of 2 × 2 × 4 cm were taken at 1.3 m above the ground, outside of radial direction, from seven 2-year-old materials and three 7-year-old materials. These samples were saturated and drying was evaluated until the equilibrium moisture content, then, the artificial neural networks were created. The materials with higher initial moisture reached equilibrium moisture content faster due to its higher drying rate. The basic density of all wood materials was inversely proportional at the beginning and directly proportional to the moisture at the end of drying. All artificial neural networks used in this work showed high accuracy to estimate the moisture, however, the neural network based on the basic density and drying days was the best. Therefore, artificial neural networks can be used to control the moisture content of wood during drying.

scholarly journals Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

2017 ◽  
Vol 45 (113) ◽  
Author(s):  
Antonio Jose Vinha Zanuncio ◽  
Amélia Guimarães Carvalho ◽  
Liniker Fernandes da Silva ◽  
Marcela Gomes da Silva ◽  
Angelica de Cassia Oliveira Carneiro ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eucalyptus Grandis ◽  
Treated Wood ◽  
Heat Treated ◽  
Artificial Neural

Artificial Neural Networks Approach for Hardness Prediction of Copper Cold Spray Laser Heat Treated Coatings

2022 ◽  
Author(s):  
Maryam Razavipour ◽  
Jean-Gabriel Legoux ◽  
Dominique Poirier ◽  
Bruno Guerreiro ◽  
Jason D. Giallonardo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cold Spray ◽  
Laser Heat ◽  
Heat Treated ◽  
Hardness Prediction ◽  
Artificial Neural

Estimation of the moisture content of tropical soils using colour images and artificial neural networks

CATENA ◽  
2015 ◽  
Vol 135 ◽  
pp. 100-106 ◽  
Author(s):  
Sidney Sara Zanetti ◽  
Roberto Avelino Cecílio ◽  
Estevão Giacomin Alves ◽  
Vitor Heringer Silva ◽  
Elias Fernandes Sousa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Tropical Soils ◽  
Artificial Neural

Ann Modelling for the Analysis of the Green Moulding Sands Properties

2013 ◽  
Vol 58 (3) ◽  
pp. 961-963 ◽  
Author(s):  
J. Jakubski ◽  
P. Malinowski ◽  
St.M. Dobosz ◽  
G. Major-Gabrýs
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Environmental Humidity ◽  
Sand Temperature ◽  
Artificial Neural ◽  
Moulding Sand

Abstract Application of modern technological solutions, as well as the economic and ecological solutions, is for foundries one of the main aspects of the competitiveness on the market for castings. IT solutions can significantly support technological processes. This article presents neural networks with different structures that have been used to determine the moisture content of the moulding sand based on the moulding sand selected properties research results. Neural networks were built using Matlab software. Moulding sand properties chosen for quality control processes were selected based on wide previous results. For the proposed moulding sand properties, neural networks can be a useful tool for predicting moisture content. The structure of artificial neural network do not have a significant influence on the obtained results. In subsequent studies on the use of neural networks as an application to support the green moulding sand rebonding process, it must be determined how factors such as environmental humidity and moulding sand temperature will affect the accuracy of data obtained with the use of artificial neural networks.

scholarly journals Herstellung und Eigenschaften von thermisch vergütetem Holz – eine Übersicht | Production and properties of heat-treated wood: an overview

2005 ◽  
Vol 156 (11) ◽  
pp. 408-410
Author(s):  
Peter Niemz
Keyword(s):  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Treated Wood ◽  
Wood Industry ◽  
Heat Treated ◽  
New Material

For many years already, the thermic modification of wood has been employed in the wood industry. Numerous new or improved properties such as low equilibrium moisture content and thus lower swelling or improved durability and exotic colour tints characterise this material. Nevertheless, it is certainly open to further improvements, above all with regard to reproducibility and quality guarantees. Regulations for this relatively new material, for which only little experience values exist until now, are to be expected.

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