| Revista: | Maderas : ciencia y tecnología |
| Base de datos: | |
| Número de sistema: | 000544763 |
| ISSN: | 0718-221X |
| Autores: | Tiryaki, Sebahattin1 Bardak, Selahattin2 Aydin, Aytac1 Nemli, Gökay1 |
| Instituciones: | 1Karadeniz Technical University, Faculty of Forestry, Trabzon. Turquía 2Sinop University, Faculty of Engineering and Architecture, Sinop. Turquía |
| Año: | 2016 |
| Periodo: | Jul |
| Volumen: | 18 |
| Número: | 3 |
| Paginación: | 477-492 |
| País: | Chile |
| Idioma: | Inglés |
| Resumen en inglés | 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 ºC) 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. |
| Keyword: | Dimensional stability, Heat treatment, Hygroscopicity, Neural network, Swelling, Wood properties |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
