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密度和含水率对竹基纤维复合材料抗弯性能的影响 被引量:2

Effects of Density and Moisture Content on Flexural Properties of Bamboo Based Fiber Composites
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摘要 通过试验探究不同密度和含水率对竹基纤维复合材料的静曲强度(modulus of rupture,MOR)以及弹性模量(modulus of elasticity,MOE)的影响,并建立密度和含水率对MOR以及MOE影响的预测模型。结果表明,在测试区间内试板的密度与抗弯性能呈正相关;随着含水率升高,试板抗弯性能先增大后减小。根据预测模型,压制密度1.17 g/cm^(3)、含水率11%左右的板材可以获得较高的MOR,大约为151.32 MPa;压制密度1.20 g/cm^(3)、含水率10.65%左右的板材可以获得较高的MOE,大约为19.68 GPa。该研究为提高竹基纤维复合材料的抗弯性能以及生产实践提供一定理论指导。 This paper investigated effects of different densities and moisture contents on the modulus of rupture(MOR)and modulus of elasticity(MOE)of bamboo-based fiber composite panels.A predictive model was developed to predict the effects of density and moisture content on MOR and MOE.The results showed that the density was positively correlated with the flexural properties of panels in the test range.With the increase in the moisture content,the flexural properties of the composite panels increased first and then decreased.Based on predictive model,the panels with the pressed density of 1.17 g/cm^(3) and the moisture content of 11%obtain a higher MOR,which was 151.32 MPa.When pressed density was of 1.20 g/cm^(3) and the moisture content was of 10.65%,the panel obtains a higher MOE of 19.68 GPa.The study provided reference for improving bamboo-based fiber composites flexural properties and a theoretical guidance for practical production.
作者 杨春梅 李月茹 田心池 马红霞 丁禹程 于文吉 YANG Chunmei;LI Yueru;TIAN Xinchi;MA Hongxia;DING Yucheng;YU Wenji(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,Heilongjiang,China;Research Institute of Forest Industry,Guangdong Academy of Forestry,Guangzhou 510520,Guangdong,China;Research Institute of Wood Industry,Chinese Academy of Forestry,Beijing 100091,China)
出处 《木材科学与技术》 北大核心 2023年第3期44-50,共7页 Chinese Journal of Wood Science and Technology
基金 广东省重大专项研发计划项目“竹重组材制备关键装备研发”(2020B020216001) 中央高校基本科研业务费专项资金项目(2572020DR12)。
关键词 竹基纤维复合材料 密度 含水率 抗弯性能 预测模型 bamboo-based fiber composites density moisture content flexural properties predictive models
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