塑料科技2024，Vol.52Issue(7)：12-17,6.DOI:10.15925/j.cnki.issn1005-3360.2024.07.003
基于细观力学方法的混杂纤维复合材料抗拉强度预测模型
Predictive Model of Tensile Strength for Hybrid Fiber Composites Based on Micromechanics Method
摘要
Abstract
Based on the integral equations of internal strain fields in elastic bodies containing inclusions,the deformation coordination tensor of inclusions in three-phase composites is derived.Compared with the traditional Mori-Tanaka method(M-T method),the deformation coordination tensor takes into account the distribution characteristics and interactions of inclusions from the perspective of micromechanics,leading to a modified M-T method for predicting the effective elastic properties of composites.Combining with the two-step homogenization model,a method for predicting the effective performance of hybrid fiber composites is derived.Additionally,a prediction model for the tensile strength of hybrid fiber composites is established based on the damage mechanics theory.This method is used to predict the effective elastic properties and tensile strength for coconut husk fiber/glass fiber reinforced phenolic resin composites reported in the literature,and compared the predicted results with experimental data.The results show that the proposed modified M-T method has better predictive ability than the traditional M-T method,with the predicted results and experimental data differing by less than 10.485% .The method is used to quantitatively analyze the effect of the content of coconut husk fiber and glass fiber on the effective elastic properties and tensile strength of hybrid fiber composites.The results show that the effective elastic properties and tensile strength of hybrid fiber composites increase with the increase of glass fiber content.关键词
细观力学方法/混杂纤维复合材料/有效弹性性能/抗拉强度Key words
Micromechanics method/Hybrid fiber composites/Effective elastic properties/Tensile strength分类
化学化工引用本文复制引用
秦飞飞,盛冬发..基于细观力学方法的混杂纤维复合材料抗拉强度预测模型[J].塑料科技,2024,52(7):12-17,6.基金项目
国家自然科学基金(11862023) （11862023）
云南省教育厅科学研究基金项目(2023Y0774) （2023Y0774）
