Modelling the shear-tension coupling of woven engineering fabrics

An approach to incorporate the coupling between the shear compliance and in-plane tension of woven engineering fabrics, in finite-element-based numerical simulations, is described. The method involves the use of multiple input curves that are selectively fed into a hypoelastic constitutive model that has been developed previously for engineering fabrics. The selection process is controlled by the current value of the in-plane strain along the two fibre directions using a simple algorithm. Model parameters are determined from actual experimental data, measured using the Biaxial Bias Extension test. An iterative process involving finite element simulations of the experimental test is used to normalise the test data for use in the code. Finally, the effectiveness of the method is evaluated and shown to provide qualitatively good predictions

Effect of tow meander on the shear compliance of woven engineering fabrics measured using the biaxial bias extension test

In this paper, a non-orthogonal constitutive model [1] is used to investigate the effect of sample misalignment due to ‘tow meander’, across the initial blank sheet on the shear compliance of a woven glass fabric, as measured using the biaxial bias extension test with various transverse loads applied [2]. The same statistical distribution and spatial correlations of shear angles observed in the woven glass fabric have been automatically reproduced using ‘VarifabGA’ [3]. The effect of realistic tow directional variability is investigated by generating blanks using VarifabGA and then simulating the biaxial bias extension test using the finite element software, Abaqus ExplicitTM. In order to assign the initial fiber orientation to each element in the mesh, a unique element set is assigned to each element. A MatlabTM code 'InitialAngle.m' has been written to produce two input files; the first 'Mat.inp' includes the material property parameters of each element and the second 'Sec.inp' includes the sections of those elements. Finally, a comparison between the experimental and predicted shear compliance shows the effect of tow directional variability

Characterising the shear–tension coupling and wrinkling behaviour of woven engineering fabrics

Modelling the forming process for engineering fabrics and textile composites using a mechanical approach, such as the finite element method, requires characterisation of the material’s behaviour under large shear deformation. For woven engineering fabrics, a coupling between in-plane tension and both shear compliance and the onset of wrinkling is to be expected. This paper focuses on a novel testing technique, the biaxial bias extension test, as a means to investigate this shear–tension coupling and fabric wrinkling. Novel methods of determining the wrinkling behaviour are demonstrated. The main difficulty with the technique lies in extracting the material contribution to the recorded signal. To do this, an experimental method is proposed and demonstrated using a plain weave glass fabric. Biaxial bias extension test results are compared against picture frame and uniaxial bias extension results

Measuring the shear-tension coupling of engineering fabrics

Modelling the forming process of engineering fabrics and textile composites using a mechanical approach, such as FEM, requires characterisation of material behaviour. Using Picture Frame (PF) tests, several previous studies have reported a coupling between in-plane tension and fabric shear compliance. However, characterising this behaviour accurately has proven problematic due to the sensitivity of the PF test to small fabric misalignments in the test rig, prompting innovative solutions such as the use of load-cells mounted on the side bars of the PF rig to measure in-plane tension during testing. This paper focuses on an alternative testing technique, the Biaxial Bias Extension test, as a means to investigate this coupling. The approach has several benefits including simple equipment requirements, the ability to vary sample dimensions and boundary conditions. The main difficulty lies in extracting the material contribution to the recorded signal. To do this, an experimental method is demonstrated using two very different textiles; glass fabric and self-reinforced polypropylene both plain weaves. The latter is challenging to characterise and was chosen due to its high propensity to wrinkle at room temperature

Characterising and modelling variability of tow orientation in engineering fabrics and textile composites

Variability of tow orientation is unavoidable for biaxial engineering fabrics and their composites. Since the mechanical behaviour of these materials is strongly dependent on the fibre direction, variability should be considered and modelled as exactly as possible for more realistic estimation of their forming and infusion behaviour and their final composite mechanical properties. In this study, a numerical code, ‘VariFab’, has been written to model realistic full-field variability of the tow directions across flat sheets of biaxial engineering fabrics and woven textile composites. The algorithm is based on pin-jointed net kinematics and can produce a mesh of arbitrary perimeter shape, suitable for subsequent computational analysis such as finite element forming simulations. While the shear angle in each element is varied, the side-length of all unit cells within the mesh is constant. This simplification ensures that spurious tensile stresses are not generated during deformation of the mesh during forming simulations. Variability is controlled using six parameters that can take on arbitrary values within certain ranges, allowing flexibility in mesh generation. The distribution of tow angles within a pre-consolidated glass–polypropylene composite and self-reinforced polypropylene and glass fabrics has been characterised over various length scales. Reproduction of the same statistical variability of tow orientation as in these experiments is successfully achieved by combining the VariFab code with a simple genetic algorithm