In this study, a high-tenacity polyester was used to produce biaxial weft knitted fabric in three different loop densities. All of the composite samples were manufactured using the vacuum injection process. Epoxy resin was used as the matrix in the composite samples. Tensile tests in the course and wale directions were carried out on all samples. The results showed that the tensile strength and the elastic modulus of the composites were improved by increasing the loop density. On the other hand, multi-scale finite element modeling was employed to predict the elastic constants and the tensile strength of the composites. In this method, unit-cells of biaxial weft knitted fabrics with a script were modeled by ABAQUS finite element software in the meso scale. Periodic boundary conditions were applied to the unit-cells. Stiffness matrices of composites were calculated by a python code. In the macro model, a shell geometry was created and the elastic constants calculated from the meso scale were assigned to the macro model. The tensile strength of composites in the course and wale directions was predicted by the Tsai-Wu failure criterion equations. The numerical results had a good agreement with the experimental ones. According to the numerical results, the difference in the loop densities as the inputs data could be used and elastic constants and strength of composites in the course and wale directions could be obtained. So, this model is a useful method to predict the tensile behavior of biaxial weft knitted composites with different geometries.

In this study, tensile and flexural behavior of biaxial and rib weft-knitted composite is obtained numerically and experimentally. Multi-scale finite element modeling is employed to simulate the tensile and flexural behavior of composite samples. In the finite element modeling, the geometry of a unit cell of each fabric is initially modeled in ABAQUS software, and then periodic boundary conditions were applied to a unit cell. The stiffness matrix for each structure was obtained by a python code via meso scale modeling and used as input data for the macro modeling. To validate the numerical model, two types of weft-knitted fabrics (rib 1 × 1 and biaxial fabrics) are produced by a flat weft knitting machine. Epoxy resin is used to construct composite by the vacuum injection process (VIP). After that, the tensile and three-point bending tests were applied to composite samples. The experimental results showed that tensile strength and tensile modulus of biaxial composites are greater than rib composites, in both wale and course directions. Moreover, in three-point bending test, biaxial composite showed more strength and more stiffness in comparison to rib composite. Finite element results were compared to experimental results in tensile and bending tests. The results showed that good agreement with experimental results in the linear section of tensile and flexural behavior of composites. Consequently, the current multi-scale modeling can be used to predict the stiffness matrix and mechanical behavior of complex composite structures such as knitted composites.

Energy absorption capacity is of great importance in engineering applications such as bumpers, helmets and packaging. Textile-made composites have attracted world's attention due to their high energy absorption and lightweight. This study aims at evaluating energy absorption capability of composites reinforced by three-dimensional-weft knitted fabrics. To achieve this purpose, weft knitted fabrics with different structures and surface densities were prepared from nylon yarns. Having washed the fabrics, their shapes have changed to three-dimensional ones using a thermoforming process and specific casting. Three-dimensional fabrics were first covered by epoxy resin and then laid in a bed of poly vinyl chloride foam in order to improve their energy absorption capacities. Quasi-static pressure and dynamic pendulum impact tests were carried out for samples. The results were analyzed by the Minitab software and optimal sample was determined.