Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties
Veneer drying is the manufacturing process in the plywood industry that most affects energy consumption and panel properties such as bonding and bending. Therefore, the veneer drying temperature and moisture content should be accurately adjusted. Moreover, the determination of veneer thermal conduct...
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Format: | Article |
Language: | English |
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University of Zagreb, Faculty of Forestry and Wood Technology
2023-01-01
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Series: | Drvna Industrija |
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Online Access: | https://hrcak.srce.hr/file/445263 |
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author | Hasan Ozturk |
author_facet | Hasan Ozturk |
author_sort | Hasan Ozturk |
collection | DOAJ |
description | Veneer drying is the manufacturing process in the plywood industry that most affects energy consumption and panel properties such as bonding and bending. Therefore, the veneer drying temperature and moisture content should be accurately adjusted. Moreover, the determination of veneer thermal conductivity is as important as these two parameters and the thermal conductivity values should also be specifi ed when forming the drying programs. This study aimed to predict the optimum values of the veneer drying temperatures, moisture content and thermal conductivity, which gave the best mechanical properties, by artifi cial neural network (ANN) analysis. Poplar (Populus deltoidesI-77/51) and spruce (Picea orientalis L.) veneers and urea formaldehyde (UF) resin were used in the production of plywood. The thermal conductivity of veneer and the bonding, bending strength and elasticity modulus of the panels were tested by the relevant standards. The most accurate and reliable prediction models were obtained by analyzing the experimental data with ANN. The optimum veneer drying temperature, moisture content and thermal conductivity values that gave the best values for all three mechanical properties were 149 °C, 6.2 % and 0.02668 W/mK for poplar and 116 °C, 4.4 % and 0.02534 W/mK for spruce. |
first_indexed | 2024-03-11T19:31:54Z |
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id | doaj.art-7cc7868fb2f74da99a0bf8e6c53c7aee |
institution | Directory Open Access Journal |
issn | 0012-6772 1847-1153 |
language | English |
last_indexed | 2024-03-11T19:31:54Z |
publishDate | 2023-01-01 |
publisher | University of Zagreb, Faculty of Forestry and Wood Technology |
record_format | Article |
series | Drvna Industrija |
spelling | doaj.art-7cc7868fb2f74da99a0bf8e6c53c7aee2023-10-06T13:16:54ZengUniversity of Zagreb, Faculty of Forestry and Wood TechnologyDrvna Industrija0012-67721847-11532023-01-0174329730810.5552/drvind.2023.007415Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical PropertiesHasan Ozturk0Karadeniz Teknik University, Trabzon, TurkeyVeneer drying is the manufacturing process in the plywood industry that most affects energy consumption and panel properties such as bonding and bending. Therefore, the veneer drying temperature and moisture content should be accurately adjusted. Moreover, the determination of veneer thermal conductivity is as important as these two parameters and the thermal conductivity values should also be specifi ed when forming the drying programs. This study aimed to predict the optimum values of the veneer drying temperatures, moisture content and thermal conductivity, which gave the best mechanical properties, by artifi cial neural network (ANN) analysis. Poplar (Populus deltoidesI-77/51) and spruce (Picea orientalis L.) veneers and urea formaldehyde (UF) resin were used in the production of plywood. The thermal conductivity of veneer and the bonding, bending strength and elasticity modulus of the panels were tested by the relevant standards. The most accurate and reliable prediction models were obtained by analyzing the experimental data with ANN. The optimum veneer drying temperature, moisture content and thermal conductivity values that gave the best values for all three mechanical properties were 149 °C, 6.2 % and 0.02668 W/mK for poplar and 116 °C, 4.4 % and 0.02534 W/mK for spruce.https://hrcak.srce.hr/file/445263veneer drying temperaturemoisture contentthermal conductivityartificial neural networkplywood |
spellingShingle | Hasan Ozturk Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties Drvna Industrija veneer drying temperature moisture content thermal conductivity artificial neural network plywood |
title | Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties |
title_full | Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties |
title_fullStr | Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties |
title_full_unstemmed | Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties |
title_short | Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties |
title_sort | prediction of optimum veneer drying parameters with artifi cial neural networks for production of plywood with high mechanical properties |
topic | veneer drying temperature moisture content thermal conductivity artificial neural network plywood |
url | https://hrcak.srce.hr/file/445263 |
work_keys_str_mv | AT hasanozturk predictionofoptimumveneerdryingparameterswithartificialneuralnetworksforproductionofplywoodwithhighmechanicalproperties |