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:	Abolfazl Kargarfard, Kazem Doost hosseini, Amir Nourbakhsh
Formato:	article
Lenguaje:	FA
Publicado:	Regional Information Center for Science and Technology (RICeST) 2008
Materias:	PARTICLEBOARD regression MAT MOISTURE CONTENT GRADIENT Particle Geometry Press Temperature and Press Time Forestry SD1-669.5
Acceso en línea:	https://doaj.org/article/1ca388418db244bb83dd2e0f68c5fdf9
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spelling	oai:doaj.org-article:1ca388418db244bb83dd2e0f68c5fdf92021-12-02T02:13:42ZThe pridiction of particleboard properties with regression models application in different condition production1735-09132383-112X10.22092/ijwpr.2008.117399https://doaj.org/article/1ca388418db244bb83dd2e0f68c5fdf92008-03-01T00:00:00Zhttp://ijwpr.areeo.ac.ir/article_117399_f36d4401952758a096ba3321770c07e4.pdfhttps://doaj.org/toc/1735-0913https://doaj.org/toc/2383-112XThe 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.Abolfazl KargarfardKazem Doost hosseiniAmir NourbakhshRegional Information Center for Science and Technology (RICeST)articlePARTICLEBOARDregressionMAT MOISTURE CONTENT GRADIENTParticle GeometryPress Temperature and Press TimeForestrySD1-669.5FAتحقیقات علوم چوب و کاغذ ایران, Vol 23, Iss 1, Pp 1-11 (2008)
institution	DOAJ
collection	DOAJ
language	FA
topic	PARTICLEBOARD regression MAT MOISTURE CONTENT GRADIENT Particle Geometry Press Temperature and Press Time Forestry SD1-669.5
spellingShingle	PARTICLEBOARD regression MAT MOISTURE CONTENT GRADIENT Particle Geometry Press Temperature and Press Time Forestry SD1-669.5 Abolfazl Kargarfard Kazem Doost hosseini Amir Nourbakhsh The pridiction of particleboard properties with regression models application in different condition production
description	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.
format	article
author	Abolfazl Kargarfard Kazem Doost hosseini Amir Nourbakhsh
author_facet	Abolfazl Kargarfard Kazem Doost hosseini Amir Nourbakhsh
author_sort	Abolfazl Kargarfard
title	The pridiction of particleboard properties with regression models application in different condition production
title_short	The pridiction of particleboard properties with regression models application in different condition production
title_full	The pridiction of particleboard properties with regression models application in different condition production
title_fullStr	The pridiction of particleboard properties with regression models application in different condition production
title_full_unstemmed	The pridiction of particleboard properties with regression models application in different condition production
title_sort	pridiction of particleboard properties with regression models application in different condition production
publisher	Regional Information Center for Science and Technology (RICeST)
publishDate	2008
url	https://doaj.org/article/1ca388418db244bb83dd2e0f68c5fdf9
work_keys_str_mv	AT abolfazlkargarfard thepridictionofparticleboardpropertieswithregressionmodelsapplicationindifferentconditionproduction AT kazemdoosthosseini thepridictionofparticleboardpropertieswithregressionmodelsapplicationindifferentconditionproduction AT amirnourbakhsh thepridictionofparticleboardpropertieswithregressionmodelsapplicationindifferentconditionproduction AT abolfazlkargarfard pridictionofparticleboardpropertieswithregressionmodelsapplicationindifferentconditionproduction AT kazemdoosthosseini pridictionofparticleboardpropertieswithregressionmodelsapplicationindifferentconditionproduction AT amirnourbakhsh pridictionofparticleboardpropertieswithregressionmodelsapplicationindifferentconditionproduction
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The pridiction of particleboard properties with regression models application in different condition production

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