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Kargarfard, A., Doost hosseini, K., Nourbakhsh, A. (2008). The pridiction of particleboard properties with regression models application in different condition production. , 23(1), 1-11. doi: 10.22092/ijwpr.2008.117399
Abolfazl Kargarfard; Kazem Doost hosseini; Amir Nourbakhsh. "The pridiction of particleboard properties with regression models application in different condition production". , 23, 1, 2008, 1-11. doi: 10.22092/ijwpr.2008.117399
Kargarfard, A., Doost hosseini, K., Nourbakhsh, A. (2008). 'The pridiction of particleboard properties with regression models application in different condition production', , 23(1), pp. 1-11. doi: 10.22092/ijwpr.2008.117399
Kargarfard, A., Doost hosseini, K., Nourbakhsh, A. The pridiction of particleboard properties with regression models application in different condition production. , 2008; 23(1): 1-11. doi: 10.22092/ijwpr.2008.117399

The pridiction of particleboard properties with regression models application in different condition production

تحقیقات علوم چوب و کاغذ ایران
Article 1, Volume 23, Issue 1 - Serial Number 28, March 2008, Pages 1-11    PDF (364.45 K)
Document Type: Research Paper
DOI: 10.22092/ijwpr.2008.117399
Authors
Abolfazl Kargarfard* 1; Kazem Doost hosseini2; Amir Nourbakhsh3
1Wood and Forest Products Science Research Division, Research Institute of Forests and Rangelands, Tehran, Iran,
2Ph. D. professor of wood and paper Dept., Tehran university , Faculty of Natural Resources.
3Wood and Paper Science Research Division, Research Institute of Forests and Rangelands , Iran,
Abstract
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. 
Keywords
Particleboard; regression; MAT MOISTURE CONTENT GRADIENT; Particle Geometry; Press Temperature and Press Time
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