Micromechanical model for rapid prediction of plain weave fabric composite strengths under biaxial tension

The biaxial properties of plain weave fabric composites are important as they are more representative of the performance under complex loading conditions. Experimental determination of these properties is difficult and Finite Element Analysis provides accurate prediction but is computationally expensive and requires skilled users. To provide a simple and rapid prediction of the strength of plain weave fabric composites under biaxial tension a novel micromechanical model is proposed in this paper. To predict the biaxial tensile strengths the minimum total complementary potential energy principle is used on a micromechanical unit cell where the orthogonally interlaced yarns are idealised as curved beams. The new model is verified with a finite element method model on three warp/weft biaxial loading ratios: 1:1, 2:1 (1:2) and 3:1 (1:3) and uniaxial experimental data. The model is verified on four types of material, ranging in mechanical properties from carbon to glass fibres, and 11 yarn specifications, including five cases compared to experimental results and six cases compared to the FE model, giving a mean error of 9.85% and a maximum error of 16.74% compared to experimental results and a mean error of 10.71% and a maximum error of 14.67% compared to the FE model, which demonstrates the effectiveness of the model. The standard deviation of prediction errors among the 11 cases is 2.66%, which demonstrates the robustness of the model for a range of applications. The proposed model is able to predict the uniaxial and biaxial tensile strengths without experimental investigations at the fabric and laminate level and only requires the yarn mechanical properties and specifications.

Automated summary

A novel micromechanical model is proposed in this study to predict the biaxial tensile strengths of plain weave fabric composites. Through validation experiments and comparisons, the model shows accurate and effective predictions for a range of material types and yarn specifications.