ANALYSIS OF VOLUMETRIC SWELLING AND SHRINKAGE OF HEAT TREATED WOODS: EXPERIMENTAL AND ARTIFICIAL NEURAL NETWORK MODELING APPROACH

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TİRYAKİ S., BARDAK S., AYDIN A., NEMLİ G.

MADERAS-CIENCIA Y TECNOLOGIA, cilt.18, sa.3, ss.477-492, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 3
  • Basım Tarihi: 2016
  • Doi Numarası: 10.4067/s0718-221x2016005000043
  • Dergi Adı: MADERAS-CIENCIA Y TECNOLOGIA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.477-492
  • Anahtar Kelimeler: Dimensional stability, heat treatment, hygroscopicity, neural network, swelling, wood properties, PHYSICAL-PROPERTIES, SURFACE-ROUGHNESS, MECHANICAL-PROPERTIES, THERMOWOOD PROCESS, BEECH WOOD, TEMPERATURE, PREDICTION, BEHAVIOR, PRESSURE, DENSITY
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Shrinkage and swelling characteristics of wood as a hygroscopic material affect negatively its effective utilization for a variety of applications. Heat treatment is widely used for minimizing the negative effects of volumetric swelling and shrinkage of wood. The present study aims to develop artificial neural network (ANN) models for predicting volumetric swelling and shrinkage of heat treated woods. For this purpose, wood samples were subjected to heat treatment at varying temperatures (130, 150, 170 and 190 degrees C) for varying durations (2, 4, 6 and 8 h). Experimental results have showed that volumetric swelling and shrinkage of wood decreased by heat treatment. Then, neural networks models capable of predicting the swelling and shrinkage of the treated woods were developed based on the resulting data. It was seen that ANN models allowed volumetric swelling and shrinkage of such woods to predict successfully with a limited set of experimental data. This approach was able to predict volumetric swelling and shrinkage of wood with a mean absolute percentage error equal to 2,599% and 2,647% in test phase, respectively. The developed models might thus serve as a robust tool to predict volumetric swelling and shrinkage with less number of experiments.