The pridiction of particleboard properties with regression models application in different condition production
The application of regressions models for pridicting physical and mechanical properties of laboratory produced particleboard was studies. In order to study the influence of mat moisture content gradient, particle geometry, press time and temperature, 108 boards were produced. Regressions model indic...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | FA |
| Publicado: |
Regional Information Center for Science and Technology (RICeST)
2008
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1ca388418db244bb83dd2e0f68c5fdf9 |
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| Sumario: | The application of regressions models for pridicting physical and mechanical properties of laboratory produced particleboard was studies. In order to study the influence of mat moisture content gradient, particle geometry, press time and temperature, 108 boards were produced.
Regressions model indicated that particle geometry significantly influenced board MOR, increasing the slender ratio of particles, improved MOR. Regressions models of MOE indicated that both particle geometry and mat moisture content gradient significantly influenced board MOE, and increasing the slender ratio of particles and mat moisture content gradient, increased MOE. regression model of IB indicated that all of the variables have significantly affected IB. However, in this case, increasing mat moisture content gradient, particle geometry reduced IB and press time and temperature increased IB, moisture content gradient and particle geometry had more effective.
The results indicated that moisture content gradient and press time significantly influenced the regression model of thickness swelling after 24 hours soaking in cold water. |
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