Prediction of Optimum Veneer Drying Parameters with Artificial Neural Networks for Production of Plywood with High Mechanical Properties Primjena umjetnih neuronskih mreža za predviđanje optimalnih parametara sušenja furnira za proizvodnju furnirske ploče visokih mehaničkih svojstava


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ÖZTÜRK H.

Drvna Industrija, vol.74, no.3, pp.297-308, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 74 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.5552/drvind.2023.0074
  • Journal Name: Drvna Industrija
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Compendex, Environment Index, Geobase, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.297-308
  • Keywords: artificial neural network, moisture content, plywood, thermal conductivity, veneer drying temperature
  • Karadeniz Technical University Affiliated: Yes

Abstract

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 specified 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 artificial 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 reli-able 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.