Yarn Geometry Research Articles

Jute textiles with enhanced interfacial bonding as reinforcement for cementitious composites

In fabric-cement composites, the limited impregnation of cementitious matrix products due to thick and twisted yarns leads to premature failure due to poor bonding strength. In addition, cellulosic textile reinforcements have many challenges about durability, appearance of voids at mortar-fiber interface, and rise of microcracks. Textile performances were evaluated in different conditions: coated with micro-silica powder, pretreated, and without any treatment. This study also assessed how textile weave structure and yarn geometry configuration affect the interactions of two different jute textiles (Close Weave Jute Fabric – CJF and Open Weave Jute Fabric - OJT) when used as reinforcement in mortar matrix. Textile characterization and composite analysis (by four-point bending tests, SEM/EDS, and physical tests) were conducted to assess the different textile reinforcements, the mechanical behavior of produced composites, and visual and chemical compounds analysis of the interfacial transition zone between textile and mortar matrix after silica coating. Micro silica powder coating was deemed necessary to address limited impregnation and to avoid telescope pull-off. Weave structure determined the difference between jute fabrics to reinforce mortar matrix, being only OJF (larger interstices in the weave structure) with micro silica coating allowed a better matrix interaction and stood out from the other textiles and achieved the best specific energy of all samples, (4.28 ± 0.91) kJ.m-2. Calcium and silicon inside the yarn interstices and textile-matrix interface indicate the formation of strong bonds by calcium-silicate-hydrate products. The silica coating treatment enhanced formation of strong bonds, which demonstrated future promise for natural fiber application.

Mesoscale modeling of woven composite twisted structures combining digital element embedded model and affine transform

The mesoscale model with realistic yarn geometries could improve the accuracy of property prediction of woven composite structures. This paper aims to propose a novel mesoscale modeling method for the woven composite twisted structures. Firstly, the torsional deformation of the yarns in the unit cell model is simulated with high-fidelity using digital element fibers embedded into the matrix solid meshes. Then, the solid geometry model of the twisted composite structures is reconstructed from the digital element deformation model by combining the affine transforms. Finally, the elastic responses of the twisted composite structure under cantilever loading are predicted by assigning the element information of the established mesoscale model to the macroscale model. It is shown that the geometric morphology and predicted mechanical responses of the high-fidelity model are consistent with the experimental results. Furthermore, the effect of twist angles on the stiffness properties of the twisted composite structures is analyzed using the presented numerical method. The proposed digital modeling method could provide a guide for the early design of woven composite structures.

ARTIFICIAL NEURAL NETWORKS TO ENHANCE THE RING MACHINE EFFICIENCY AND YARN QUALITY BY DETERMINATION AND OPTIMIZATION OF DYNAMIC YARN TENSION

The yarn spinning process involves the interaction of large varieties of variables. The relation between the dynamic yarn tension (DYT), yarn quality, and production efficiency of the spinning frame cannot be established conclusively. Artificial neural network (ANN) is a promising step in this filed. In this research work, ANNs simulation and modeling is applied for the optimization of the DYT n to improve the production efficiency and quality of yarn. The research to date in DYT is insufficient to meet the developmental requirement of the high-speed and efficient ring spinning frame. One of the major problems facing the effective use of the ANN is the correct selection of the input parameters to be fed for the training of ANNs. Data of various input variables such as count, traveler no., spindle speed and dynamic yarn tension etc., was used for ANN modeling and simulation. DYT plays a significant role in the determination of yarn quality and its productivity in terms of end breakage rate. However, it has never been explained in terms of displacement from the original yarn path. This work is aimed to the determination and optimization of DYT at ring spinning frame. The influence of different yarn geometry parameters on DYT, measured by the tensiometer was investigated. The optimized DYT values for the machines, running at different speed and different counts were determined using ANN modeling. It is found that the optimized values predicted from ANN resulted in better quality, high production, and decreased end-breakage at industrial ring spinning frames. By the implementation of ANNs the optimum speed and effective utilization of textile raw materials can be achieved.

Simulation of failure behavior of 2.5D SiC/SiC variable thickness dovetail joint structures based on mesoscale model

The application of ceramic matrix composites plays an important role in improving the performance of aero engines. However, mesoscopic aperiodicity causes excellent challenges for the performance analysis of ceramic matrix composite structures. It is no longer applicable to obtain the macroscopic properties of materials through experiments or RVE simulations for structural performance analysis. Here an attempt is made to develop a performance analysis method for mesoscopic aperiodic ceramic matrix composite structures based on mesoscale models and apply the method to the strength calculation of 2.5D SiC/SiC variable thickness dovetail joint structures. The established model reasonably approximates and accurately describes the structure’s real yarn geometries and orientations, reflecting the differences of yarn architectures in different regions. The yarn stress–strain curve is obtained through experiments, and the yarn constitutive model is established. The failure behavior of the 2.5D woven dovetail joint structure is simulated using the progressive damage analysis method and taking the yarn as the basic load-bearing unit. The simulated results are in good agreement with the experimental data in terms of failure load, load–displacement responses, macroscopic damage evolutions, and failure modes.

Multiscale modeling of woven composites by deep learning neural networks and its application in design optimization

Due to complex yarn geometries and multiscale nature, it is very challenging to predict mechanical properties and strength constants of woven composites in an accuracy and efficient way. In this paper, mechanics of structure genome (MSG) is employed to predict effective material properties and strength constants of woven composites. The microscale analysis is performed to compute the effective properties and strength constants of yarns based on a hexagonal packed microstructure. Then, the mesoscale analysis is performed to compute the effective material properties and strength constants of woven composites. The accuracy and efficiency of the MSG method are compared with the computational homogenization method. In addition, simulation data using the MSG model is generated to train deep learning neural networks (DNN) models. The trained models provide ultra-efficient surrogate models with high accuracy. The DNN models are further applied to a design optimization problem to tailor geometry parameters to enhance the mechanical performance of plain woven composites, and the optimization can be efficiently solved with gradients from the DNN models. The developed computational framework can be applied to general woven composites featuring complex microscale and mesoscale features.

Influence of yarn geometry on electrical properties of silver-coated nylon filaments for e-textiles: a fundamental study

Conductive fibrous assemblies and yarns play a crucial role in wearable electronic textiles (e-textiles), through their use in flexible sensors and interconnects. This study investigated the influence of yarn twist and geometrical parameters on the electrical properties of silver-coated nylon multifilament yarns, ranging from 1-ply to 4-ply, with twist levels of 30 twists per meter (TPM) and up to 600 TPM. Increase in twist level resulted in decreasing yarn linear resistance, with a plateau at 300 TPM, along with limiting values for yarn specific volume (1.6-1.9 cc/g), and fibre orientation angle (12-18°). The increase in yarn conductivity with higher twist was explained by greater contact between the fibrous assembly, that bridges electrically conductive pathways in the yarn structure. Twisted yarns (2-ply) were fabricated into electrode structures via embroidery, and a progressive increase in contact impedance was observed, followed by a stabilization and plateau within the range of measured impedance from 210 to 300 TPM. This observation was attributed to the decrease in the yarn specific volume, and subsequently the longitudinal diameter with increasing twist level, which decreased the contact area between the skin and electrode interface. The electrodes fabricated from varying yarn twist levels were used for electrocardiogram (ECG) measurement, and demonstrated comparable signal quality to standard gel electrodes. This experimental and theoretical work forms the basis in defining relationships between established yarn twist mechanics and geometrical properties with electrical properties. This can guide materials and design parameter selection of suitable conductive yarns for e-textiles used in biopotential monitoring applications.

Simulation of an Impact on an Aramid Fabric Panel by A .357 Projectile

Abstract This paper presents a model for impact bullet – panel of woven aramid fabrics in order to use it for an initial assessment of similar panels to protect against bullets in a range of 100…800 m/s. Based on these initial simulations, the designer could select the number of layers for actual tests. The factors that could be varied in these simulations could be the shape and dimensions of the projectiles, their mass and impact velocity. It is presented only the case of impacting a panel with .357 Magnum, with an incidence velocity of 420 m/s. The panel is composed of 8 layers of plain woven fabrics (200 mm x 200 mm), with weft and warp yarns, the yarn geometry being close to Teijin CT736 phenolic fabric, with 1680 DTEX and 410 g/m2. The simulation helps to identify the stages of panel failure and to evaluate qualitatively the similarity in panel failure with that of the actual panel, tested under the same conditions as the model.

Kinematic modeling with Micro-CT slice auxiliary calibration for 2.5D woven SiO2f/SiO2 composites

An algorithm combining Micro-CT slice auxiliary calibration and kinematic modeling method is proposed to automatically generate yarns with irregular cross-sections and central paths for 2.5D woven SiO2f/SiO2 composites. The initial boundary conditions of the kinematic modelling simulation are calibrated by extracting the yarn positions from the corresponding Micro-CT slices. The simulation parameters with the real physical meanings are also determined in the kinematic modelling calculation. An explicit contact algorithm for truss elements in the kinematic modelling simulation with the calibration boundary conditions is employed to calculate nodal displacement. The virtual geometry of 2.5D woven SiO2f/SiO2 composites including detailed geometry information such as irregular yarn cross-section and yarn waviness or crimp can be reconstructed, which are in good agreement with the yarn geometry structures observed in Micro-CT scanning slices. The reconstructed geometry model can reflect the change of textile configurations during weaving and solidification process. The developed high-fidelity modelling method can also be used to reconstruct the geometry model of other textile composites with complex meso-structures.

Development of Three-Dimensional (3D) Biodegradable Polyglycolic Acid Fiber (PGA) Preforms for Scaffold Applications: Experimental Patterning and Fiber Volume Fraction-Porosity Modeling Study.

Three-dimensional (3D) biodegradable polyglycolic acid fiber (PGA) preforms were developed as temporary scaffolds for three-dimensional tissue regeneration applications. Three-dimensional biodegradable polyglycolic acid fiber (PGA) preforms including various degrees of interlaced structures called 3D plain, semi-interlaced, and orthogonal woven preforms were designed. Analytical relations and finite element model-based software (TexGen) on fiber volume fraction and porosity fraction were proposed to predict scaffolds' stiffness and strength properties considering micromechanics relations. It was revealed that yarn-to-yarn space, density, and angles of all 3D PGA fiber preforms were heterogeneous and demonstrated direction-dependent features (anisotropy). Total fiber volume fractions (Vfp) and porosity fraction (Vtpr) predicted by analytic and numerical modelling of all 3D scaffolds showed some deviations compared to the measured values. This was because yarn cross-sections in the scaffolds were changed from ideal circular yarn (fiber TOW) geometry to high-order ellipse (lenticular) due to inter-fiber pressure generated under a tensile-based macrostress environment during preform formation. Z-yarn modulus (Ez-yarn) and strength (σz-yarn) were probably critical values due to strong stiffness and strength in the through-the-thickness direction where hydrogel modulus and strengths were negligibly small. Morphology of the scaffold showed that PGA fiber sets in the preform were locally distorted, and they appeared as inconsistent and inhomogeneous continuous fiber forms. Additionally, various porosity shapes in the preform based on the virtual model featured complex shapes from nearly trapezoidal beams to partial or concave rectangular beams and ellipsoid rectangular cylinders. It was concluded that 3D polyglycolic acid fiber preforms could be a temporary supportive substrate for 3D tissue regeneration because cells in the scaffold's thickness can grow via through-the-thickness fiber (z-yarn), including various possible mechanobiology mechanisms.

Prediction of stress-strain behavior of carbon fabric woven composites by deep neural network

In this work, a novel deep neural network was proposed for predicting the mechanical behavior of plain carbon fabric reinforced woven composites. The deep neural network was trained by a pre-simulated stress-strain curve database of woven composites depending on yarn structures and the mechanical properties of the fiber and matrix. Micro-mechanics-based multi-scale analyses of woven composites were conducted for progressive damage analysis. These analyses utilized the stress amplification factor to transfer stress between the micro-scale and meso-scale simulations and the respective failure criteria were applied for micro-scale stresses of the fiber and matrix, respectively. The database of stress-strain curves under tensile, compressive and shear loading was acquired for different yarn geometries and constituent properties. These variables were used as training input and the resulting stress-strain curves were used as training output of the network. To optimize the network, hyper parameters of the neural network, such as the number of layers and nodes, were determined by the Hyperband optimization algorithm. The train and test of deep neural network model was performed by TensorFlow backend using the Keras library in Python. Mechanical tests were performed to validate the predicted mechanical behavior from both simulation and the deep neural network. As a result, the stress-strain curves under tensile, compressive and shear loading of arbitrary woven carbon composites can be successfully predicted in several seconds by the deep neural network with high accuracy.

Micro-Scale Model of rCF/PA6 Spun Yarn Composite

Recycling carbon fibers (rCF) for reuse is one approach to improve the sustainability of CFRP. However, until now, recycled carbon fiber plastics (rCFRP) had limited composite properties due to the microgeometry of the fibers, which made it difficult to use in load-bearing components. The production of hybrid yarns from rCF and PA6 fibers allows the fibers to be aligned. The geometric properties of the yarn and the individual fibers influence the mechanical properties of the composite. An approach for the modeling and simulation of hybrid yarns consisting of recycled carbon fibers and thermoplastic fibers is presented. The yarn unit cell geometry is modeled in the form of a stochastic fiber network. The fiber trajectory is modeled in form of helical curves using the idealized yarn model of Hearle et al. The variability in the fiber geometry (e.g., length) is included in form of statistical distributions. An additional compaction step ensures a realistic composite geometry. The created model is validated geometrically and by comparison with tensile tests of manufactured composites. With the validated model, multiple parameter studies investigating the influence of fiber and yarn geometry are carried out.

A parameterized and automated modelling method for 3D orthogonal woven composite RVEs considering yarn geometry variations

A parameterized and automated modelling method for 3D orthogonal woven composite RVEs considering yarn geometry variations

A novel modeling method for the mechanical behavior of 3D woven fabrics considering yarn distortion

Three-dimensional woven fabrics feature complex meso-architectures, such as yarn torsion and cross-section variation. This paper aims to developing a novel numerical modeling method to simulate the mechanical behaviors of reinforcements considering realistic yarn geometries. The initial as-woven unit cell is first simulated by the digital element analysis. A reconstruction algorithm is then used to generate mesoscale high-fidelity solid geometric models from digital chains. Fiber directional orientation within torsional yarns of the high-fidelity model is described by a digital-chain tracing method and analyzed in detail. Furthermore, based on the high-fidelity model, a hyper-elastic constitutive model is employed for the numerical analysis of the mechanical properties of reinforcements. The finite element analysis (FEA) of transverse compression, tensile deformation and in-plane shearing deformation combined with in-situ Micro-CT experiments are carried out to verify the method. The proposed method agrees well with experimental tests. Defining fiber orientations of torsional yarns using the digital-chain tracing method leads to more accurate simulation results. The developed numerical method can be extended for the composites design and structure optimization due to its high-fidelity simulation capabilities.

Probabilistic multi-scale design of 2D plain woven composites considering meso-scale uncertainties

This paper presents a novel multi-scale design strategy for 2D plain woven composites considering the manufacturing process. The design methodology integrates the meso-structural geometry of woven fabric composites to maximise the buckling load of composite stiffened panels at the macro-level. A numerical model to simulate the autoclave manufacturing process of woven composites is developed, in which a new geometric modelling method is proposed to construct a more realistic RVE model for 2D plain woven composites accounting for the compaction effects. Deterministic and robust design optimisations are conducted to find solutions with both high performances and high robustness to uncertainties. In particular, this study considers the morphological uncertainties of the meso-scale fabric structure, i.e. yarns’ waviness and porosity incurred during manufacturing and its influence on the structural integrity of the parts. The optimisation results obtained from the deterministic and robust design problems are presented. A parametric analysis is conducted to characterise the sensitivity of the fabric properties to the geometry of yarns and related uncertainties. It is shown that the space between yarns has a significant negative impact on the structural buckling load and negative impact on the cure-induced residual stresses.

New insight into the flexural rigidity of multi-filament yarn

Filament yarn is one of the most widely used soft strands in the textile industry; it exhibits excellent flexibility and is capable of being used in various productions, including knitting, weaving, braiding, sewing, and embroidery. The inherent complex interlacement between fibers, including twist, entanglement, and yarn geometry, often exhibits a nonlinear response to external loads. In addition, the interlacement of fibers also introduces difficulties when accurately measuring the flexural property of multi-filament yarn. In particular, the flexural rigidity of the yarn strongly influences the quality of end products when incorporated into novel textile technologies. In this paper, we developed a three-point bending instrument and a method for measuring the flexural rigidity of filament yarn. A representative point obeyed from Coplan’s construction on the bending constitutive curve was determined to obtain pure flexural rigidity. Furthermore, the bending process and deformations across cross-sections of filament yarns were discussed. The research provides new physical insights into the yarn bending property with the consideration of eliminating the sub-deformation variabilities. The work also paves the way for the accurate and quick measurement of the flexural rigidity of high length-to-width ratio soft materials by a three-point bending method.

A non-linear material model for progressive damage analysis of woven composites using a conformal meshing framework

A progressive damage model is presented to investigate the damage and failure behaviors of woven composites. The conformal finite element mesh for the woven composites, used for this analysis, is generated using fabric micro-geometry from DFCA (Digital Element Approach (DEA) Fabric and Composite Analyzer, Kansas State University). The mesh generation strategy is discussed briefly – it does not make any idealized assumptions about yarn geometry and thus can be used for any woven composite micro-geometry. The woven composite domain consists of homogeneous and transversely isotropic yarns, and the surrounding homogeneous isotropic matrix. A recursive stiffness reduction method that allows for combined failure modes is employed to model progressive damage behavior. Two different fabric micro-geometries are studied: a 2D plain-weave laminate and a 3D orthogonal-weave unit cell. To support the validity of the proposed model, numerical results for the mechanical behavior of the two cases are discussed and compared with numerical predictions in literature and available experimental data.

The matching relation analysis of yarn pre-tension in weaving 3D bi-directional angle-interlock fabrics

Yarn pre-tension is an important technological parameter in the weaving process and significantly affects the geometry and properties of woven composites. 3D bi-directional angle-interlock (3DBA) fabrics have the same yarn crimp in the warp and weft directions, which requires a special matching relationship between the warp and weft yarn tensions applied in the weaving process. This study focused on obtaining the pre-tension matching relationship that applied between the warp and weft yarns in the weaving of 3DBA fabrics. A quasi-static mechanical model of yarn interactions in the weaving of 3DBA fabrics was developed based on the 2D woven fabric yarn interaction model. The weaving process of 3DBA fabrics could be divided into two interweaves as one cycle. In a given interweaving of a single group of yarns, the pre-tension applied to the warp yarns tends to increase irregularly with the position of the interweaving point and varies linearly with the weft pre-tension. The weft crimps distribution of 3DBA fabrics was compared when yarns were applied to the different proportions of pre-tension in the weaving process. The experiment result shows that the sample with the best uniform distribution of yarn geometry is the one to which the yarn pre-tension calculated by the model is applied in the weaving process. The result provides theoretical support for warp and weft pre-tension applied in the 3DBA fabrics weaving process.

Engineering the geometry of novel yarns for flexible, hybrid composites Part II: Maximising the tensile properties

Part I of this study established the concept and built the theoretical basis for a novel composite yarn. Part II deals with practical aspects of producing the yarn structural parameters necessary for flexible textile composite uses. This study investigates how to maximise the number of breaks of the core or foundation component of the yarn by using the design of experiment method. The results have shown that the number of these breaks vary between 1.4 and 6 times in comparison with only one break that would result from a typical conventional ply yarn. The analysis of the results indicates that a low overfeed ratio of 121% of the undulating component can lead to ∼4.7 breaks of the foundation component in comparison with only ∼2.6 breaks that would result using an overfeed ratio of 158%. To increase the number of breaks of the foundation component, it is necessary to use a high number of wraps, a low overfeed ratio, more than one input yarn for the foundation component and an undulating component of high tensile strength, which enhances its synergy with the foundation component by self-locking during breaking. Increasing the number of breaks of the yarn enhances the performance and safety of flexible composites.

High-fidelity modeling of 3D woven composites considering inhomogeneous intra-yarn fiber volume fractions

The accuracy of strength prediction of 3D woven composites is strongly dependent on the quality of the utilized mesoscale geometry model. This paper aims at presenting a novel high-fidelity modeling approach for 3D woven composites considering both realistic mesoscale architectures and inhomogeneous intra-yarn fiber volume fractions (iy-FVF) along the yarn path. The unit cell model is numerically reconstructed from simulated results of digital element analysis. An Alpha-shapes algorithm was utilized to extract the shape of yarn cross-sections, and a digital-element-chain tracing method was established to avoid artificial inaccurate torsion deformation in the yarn geometry. The inhomogeneity of iy-FVF along the yarn path was specially characterized, and its effect on the mechanical performance of the composite was analyzed based on the progressive damage model. Furthermore, the proposed numerical method was employed to investigate the influence of the weft tow size on the tensile properties of the composite. It is shown that the predicted results of the high-fidelity model agree well with experiments. Iy-FVF varies significantly in warp yarns of the investigated 3D woven composite. Considering the inhomogeneous iy-FVF, predicted warp stiffness and strength increase about 10%, leading to higher predictive accuracy.

Engineering the geometry of novel yarns for flexible, hybrid composites Part I: Multiple breaks

This study reports on the development of a novel form of yarn reinforcement for making super tough, flexible composites. The concept is based on the rupturing of components of the composite yarn many times before totally breaking. The composite yarn structure has a configuration of three main components − helical, straight and sinusoidal. The components are not restricted by the type of raw material, and any type of fibre or blend can be used. Experimentally, the hollow-spindle spinning system was used to make nine variants of this composite yarn. These variants were tested for their tensile behaviour at an extension rate of 250 mm/min. It was found that the core component of two of the nine variants had eight breakages, while the core component of the remaining variants had four to five breakages before the final break of the core component. The main mechanism that allows the core component yarn to break so many times is a locking applied to it by the wrapping yarn during stretching. The gain in strength for these variants was 21–81% in comparison with a typical twistless composite yarn. Flexible composites made of such yarn configurations can find numerous uses in construction, geotextiles, ballistic protective garments, aerospace, automotive and shipping industries.