Yarn Segments Research Articles

An investigation of woven fabric density measurement using image analysis based on RTV-SFT

To overcome the shortcomings of time-consuming manual measurement methods of fabric density, a novel density measurement method of woven fabrics using image analysis technology via the relative total variation model based on the view of structure extraction from texture (RTV-SFT) is developed. Firstly, a fabric image acquisition system of multi-zoom portable digital microscope is constructed to obtain color dyed fabric images. Secondly, creatively from the perspective of analyzing periodic structure information of image texture information, a new structure extraction from texture algorithm, namely RTV-SFT is proposed to process fabric images to obtain images without non-periodic texture information aiming to achieve accurate yarn segmentation. Finally, the woven fabric density is calculated by effectively adopting the gray level projection method with the improved Gaussian filter algorithm. In the experiment, compared with manual method, the accuracy of the proposed density measurement method is above 93.3%.

Development and Application of Information Resources for Education and Teaching of Yarn History Based on 5G Network Technology

Yarn education is a crucial step in producing high-quality textile end products. By offering quick insights into yarn quality, online yarn testing can reduce latency in critical process control, resulting in the manufacture of higher-quality yarns. This paper proposes the development and application of information resources for the education and teaching of yarn history based on fifth-generation (5G) network technologies. Firstly, the yarn database is preprocessed using greyscale transformation and image filtering methods. Secondly, yarn segmentation is implemented using a Multiresolution Markov Random Field (MRMRF) model. Fourier transform and two-scale attention models are used to extract statistical and relative characteristics, respectively. For classifying the yarn photos, we propose using Improved Support Vector Machine (ISVM) classification. The 5G network has been set up, and data is transferred using the Transmission Control Protocol. To enhance the performance, we propose an Improved Particle Swarm Optimization (IPSO) algorithm. The performance of the suggested methodology is analyzed and compared with existing methodologies using the MATLAB simulation tool. The suggested algorithm's classification performance suggests that the 5G framework could provide an accurate, rapid, reliable, and cost-effective solution to industrial automation.

An Analytical Model for the Uniaxial Tensile Modulus of Plain-Woven Fabric Composites and Its Application and Experimental Validation

With advantages in efficiency and convenience, analytical models using experimental inputs to predict the mechanical properties of plain-woven fabric (PWF) composites are reliable in guaranteeing the composites’ engineering applications. Considering the importance of the aspect above, a new analytical model for predicting the uniaxial tensile modulus of PWF is proposed in this article. The composite yarns are first simplified as the lenticular-shaped cross-sections undulate along arc-composed paths. Force analyses of the yarn segments are then carried out with the internal interactions simplified, and the analytical model is subsequently deduced from the principle of minimum potential energy and Castigliano’s second theorem. The PWF of T300/Cycom970 is chosen as the study object to which the proposed analytical model is applied. Microscopic observations and thermal ablation experiments are conducted on the specimens to obtain the necessary inputs. The uniaxial tensile modulus is calculated and tensile experiments on the laminates are performed to validate the analytical prediction. The small deviation between the experimental and analytical results indicates the feasibility of the proposed analytical model, which has good prospects in validating the effectiveness of the experimentally obtained modeling parameters and guaranteeing the accuracy of mesoscale modeling for the PWF.

Active yarn meshes for segmentation on X-ray computed tomography of textile composite materials at the mesoscopic scale

For the past decade, the composite material community has been struggling to bridge the gap between the X-ray tomography of fiber composites and the realistic modelling of their internal structure at the mesoscopic scale. This paper proposes a new yarn segmentation method relying on a variational approach with deformable 3D surfacic meshes representing the yarn envelopes. It requires the previous localisation of yarn centroids. We apply five forces directly applied on the mesh vertices to drive the mesh fitting towards the visible yarn edges on the tomographic volume. The five forces enable a compromise between the intrinsic aspect of the envelope and the attachment to the yarn contours and regions, while limiting inter-yarn overlap. Results are presented on a X-ray tomography of a non-crimp fabric composite with unidimensional fiber bundles and on a woven composite tomography.

Descriptive modeling of textiles using FE simulations and deep learning

In this work we propose a novel and fully automated method for extracting the yarn geometrical features in woven composites so that a direct parametrization of the textile reinforcement is achieved (e.g., FE mesh). Thus, our aim is not only to perform yarn segmentation from tomographic images but rather to provide a complete descriptive modeling of the fabric. As such, this direct approach improves on previous methods that use voxel-wise masks as intermediate representations followed by re-meshing operations (yarn envelope estimation). The proposed approach employs two deep neural network architectures (U-Net and Mask R-CNN). First, we train the U-Net to generate synthetic CT images from the corresponding FE simulations. This allows to generate large quantities of annotated data without requiring costly manual annotations. This data is then used to train the Mask R-CNN, which is focused on predicting contour points around each of the yarns in the image. Experimental results show that our method is accurate and robust for performing yarn instance segmentation on CT images, this is further validated by quantitative and qualitative analyses.

Mathematical modelling of complicated 3D woven fabrics

This paper presents a module to facilitate the design and manufacturing process of warp-wise three dimensional (3D) woven fabrics. An implanted interface was created for the users to determine the interlacement position between the warp and weft yarns. The wefts are represented by straight rods, and the path of the warp yarn could be formed by piecing together yarn segments. The selection of yarn segments is determined by the positions of yarn interlacement. According to the position of yarn interlacement, a mathematical algorithm was developed to generate weave matrix so that the lifting plan could be automatically obtained for the subsequent weaving process. If the weave matrix was provided, the module is also able to visualise the fabric by OpenGL so that people could preview and modify the structures.

Effects of Face Sheet Structure on Mechanical Properties of 3D Integrated Woven Spacer Composites

Due to the lightweight, structural integrity, superior heat and sound insulation performance, three-dimensional (3D) integrated woven spacer composites are expected to be used in many fields such as marine, automotive, electronics, and building industries. This paper reports the effects of face sheet structure on the mechanical properties of 3D integrated woven spacer composites. Three-point bending, quasi-static compression and low-velocity impact tests were conducted to compare the mechanical responses of 3D woven spacer composites with plain and complex face sheets. The floating yarn segments in complex face sheet could efficiently transfer the stress to neighboring areas and lead to a more balanced stress distribution. The existence of floats thus has positive effect on mechanical properties of composites. On the contrary, plain structure surface was dense and the stress transfer was easily hindered by numerous weaving points, resulting in stress concentration and ultimate premature failure. As a consequence, for surface-dominated properties such as warp-direction bending and impact resistance, 3D integrated woven spacer composites with complex surface is better and should be given priority during industrial applications. In terms of weft-direction bending property and quasi-static compressive performance, which more depend on the structure of core piles, show little difference between the composites with different surfaces.

A computer vision-based system for automatic detection of misarranged color warp yarns in yarn-dyed fabric. Part III: yarn layout proofing

This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns. This paper proposes a yarn layout proofing strategy, integrating with the warp yarn segmentation and fabric image stitching methods proposed in Part I and warp region segmentation method proposed in Part II, to achieve system automation. In the previous papers, the widths of warp regions and the layout of color yarns in the tested fabric stripe are extracted from the captured fabric frame images. In this paper, through analyzing different forms of misarranged color warps, a standard yarn layout-based proofing strategy is developed to detect the misarranged color warp yarns. Experiment results demonstrate that the proposed method is proposing for the layout proofing of color warp yarns in multicolor yarn-dyed fabrics of color stripes and color checks with satisfactory accuracy and good robustness.

Color measurement of single yarn based on hyperspectral imaging system

AbstractThis article proposed a novel approach to color measurement of a single yarn using hyperspectral imaging system (HIS). Due to the size of a single yarn, it is impossible for spectrophotometers to measure its color directly. The HIS can acquire the spectral reflectance of continuous bands within a region of interest on a yarn sample, which can achieve color measurement of a single yarn compared with traditional spectrophotometers. A single yarn is segmented from the background by a spectral matching method through adaptively setting threshold of Fréchet distance values. The spectral reflectance of single yarn is specified by a method that lightness of pixels used as weight. The experiment based on Pantone Cotton Chip Set shows that the interinstrument agreement between the HIS and a standard spectrophotometer Datacolor SF650 has a significant improvement after using the R‐Model, and the average percentage improvement of the color difference is up to 54.99%. The yarn segmentation comparative experimental results show that the proposed method to segment single yarn from background is better in retaining the edge information of the yarn than the modified K‐means clustering method, and the color of the yarn segmented by the proposed method is more similar to the actual color of single yarn.

Resistive network model of the weft-knitted strain sensor with the plating stitch-Part 2: Resistive network model during the elongation along course direction

Based on the static resistive network investigated in Part 1 of this series, the resistive network model of the weft-knitted strain sensor with the plating stitch is explored under the elongation along course direction, and it changes with the conductive loops’ configuration and contact situation. Since the voltage is applied at both ends of the course, under a specific stretching state, the resistive network model can be reduced to a resistance network connected in series in the course direction and parallel in the wale direction, which determines that the sensor’s equivalent resistance increases with the growth of the conductive wale number, and decreases with the raise of the conductive course number. Through experiment and model calculation, it can be obtained that in the initial stage of stretching, the contact resistances’ changes are the main factors affecting the mechanical–electrical performance of the sensor. Then as the sensor is further stretched, the length-related resistances of the conductive yarn segments begin to affect the sensor’s properties due to yarns’ slippage and self-elongation. In addition, the weft jacquard plating technology makes the strain of the sensor reach about 32% before yarns’ slippage and self-elongation, which expands the sensor’s measurable strain range, and avoids irreversible deformation of the sensor after repeated use in this range. It can be verified that the sensor’s gauge factor can be improved by reducing the conductive course number and increasing the conductive wale number. It should be noted that the ground yarn will reduce the gauge factor of the sensor during stretching, so it is necessary to choose a ground yarn with a smaller fineness than the conductive face yarn and good elasticity in practical.

Modeling and Simulation of a Moving Yarn Segment: Based on the Absolute Nodal Coordinate Formulation

A new finite element dynamic model of a moving yarn segment has been proposed in this paper based on the absolute nodal coordinate formulation (ANCF). Apart from taking into account the elastic properties of the yarn in three dimensions, the model also considers the viscosity in the longitudinal direction and takes into account the effect of gravity and air resistance. In this paper, the simulation described the movement of the yarn segment that is pulled by the fixer on the guideway. Then, a corresponding experiment was proposed to evaluate the theoretical model. The theoretical and experimental comparisons of the motion tracing exhibited good agreement, demonstrating that the new model could predict the actual moving trace of the yarn segment. Moreover, another simulation of the spatial motion of the yarn segment was presented, to elucidate the role of the model in predicting the movement of the yarn segment. After considering the parameters of the actual process and its constraints, the authors established that the proposed model could be used to predict the trajectory of a yarn segment in the actual production process, which is vital when fabricating textile products.

Experimental and numerical analysis of interfacial bonding strength of polyoxymethylene reinforced cement composites

Abstract The objective of this paper is to investigate the interfacial bonding strength of polyoxymethylene (POM) reinforced cement composites through pull-out tests and finite element model (FEM). As comparison with usual materials, the pull-out tests of polypropylene (PP) reinforced cement composites and polyethylene terephthalate (PET) reinforced cement composites are also performed. The pull-out tests are performed in forms of fiber reinforced cement and woven fabric reinforced cement. The woven fabrics contain three different structures, i.e. plain, twill and hybrid. Compared with PP and PET, the results show that POM could form a much stronger interface bonding with cement irrespective of reinforcement form (fiber or woven fabric). Besides, the interfacial bonding strength of woven fabric reinforced cement composite presents a significant structural dependency. The FEM of woven fabric reinforced cement composite is established and the internal mechanism for the structural effect is analysed. Woven fabric comprises two different yarn segments: curved segment and straight segment. During the pull-out process, the curved segment rotates and distorts the weft yarn. Owing to the rotational deformation, the cement surrounding the curved segments damages more seriously. Therefore, a woven fabric with more straight segments could form a stronger interface bonding with cement.

Modeling and analysis of friction in end-face/inner-face circular braiding processes

Friction between yarns could greatly influence braiding processes, especially using carbon/glass fibers. However, as the points of friction are various and traveling all the time, it is quite difficult to measure the total value of frictions. To solve this problem, mathematical models have been developed to predict the friction in two braiding processes of triaxial circular braiding processes, the widely used end-face braiding process and the inner-face braiding process. To analyze the influence of the two braiding processes to the interacted friction between yarns, some definitions and initial conditions were introduced. Based on these conditions, the yarn net, which is formed during the braiding process, was divided into numerous segments. With the geometrical and force equilibrium relationships, each yarn nodes and segments were analyzed. Based on these equations and boundary conditions, the models of two braiding processes were established and solved by Matlab. A practical engineering implementation was shown to verify the validation of the model. The results shows that the inner-face braiding process of braiding process does have a better performance in eliminating the friction than the widely used end-face braiding process.

Automatic measurement of yarn hairiness based on the improved MRMRF segmentation algorithm

In order to determine the yarn hairiness characteristic index more accurately, a new yarn hairiness testing strategy based on a combination of image acquisition technology is proposed. Firstly, the captured yarn images are processed with gray-scale conversion and skew correction. Secondly, yarn segmentation is implemented using a multi-resolution Markov Random Field (MRMRF) model with a variable weight in the wavelet domain and yarn stem separation is realized through iterative threshold segmentation algorithm. Thirdly, the image of yarn hairiness is extracted. Finally, the total number and actual number of yarn hairiness of different length are counted sequentially based on the segmentation lines and baseline of yarn stem edge. The baseline is obtained by calculating the average distance between yarn stem edge and yarn axis. Furthermore, the feature is analyzed. Experimental results show that, compared with visual observation method, the maximum deviation of proposed image processing algorithm is 3.88%. The proposed approach can make the results of yarn hairiness segmentation more precisely, and then the more reliable results of hairiness feature detection are obtained.

A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part I: continuous segmentation of warp yarns

In textile and garment industries, misarranged warp yarns of yarn-dyed fabrics disorganize the layout of fabrics and lead to poor product quality. This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns in terms of high efficiency and good accuracy. Four main parts are included in this series of studies: warp yarn segmentation, fabric image stitching, warp regional segmentation, and yarn layout proofing. This paper proposes a continuous segmentation method of warp yarns to detect the misarranged color warp yarns for yarn-dyed fabrics automatically, which is the foundation of the developed computer vision-based system. The proposed framework consists of two main components: warp yarn segmentation and fabric image stitching. Firstly, the sequence images of a fabric stripe are captured using a designed offline image acquisition platform. Secondly, the warp yarns in the sequence images are segmented by a sub-image projection-based method successively. Thirdly, the sequence images are stitched by a yarn-template matching method based on their warp segmentation results. Finally, the continuous segmentation result of warp yarns is saved for the further processing of warp regional segmentation and color warp layout proofing. The proposed method has been evaluated on 720 fabric images of five fabric examples with plain and 2/2 twill, and experimental results show that the proposed method can realize the continuous segmentation of warp yarns in yarn-dyed fabrics with the yarn segmentation accuracy of 97.43% and image stitching accuracy of 99.53%.

Microplane-Triad Model for Elastic and Fracturing Behavior of Woven Composites

A multiscale model based on the framework of microplane theory is developed to predict the elastic and fracturing behavior of woven composites from the mesoscale properties of the constituents and the weave architecture. The effective yarn properties are obtained by means of a simplified mesomechanical model of the yarn, based on a mixed series and parallel coupling of the fibers and of the polymer within the yarns. As a novel concept, each of the several inclined or aligned segments of an undulating fill and warp yarn is represented by a triad of orthogonal microplanes, one of which is normal to the yarn segment while another is normal to the plane of the laminate. The constitutive law is defined in terms of the microplane stress and strain vectors. The elastic and inelastic constitutive behavior is defined using the microplane strain vectors which are the projections of the continuum strain tensor. Analogous to the principle of virtual work used in previous microplane models, a strain energy density equivalence principle is employed here to obtain the continuum level elastic and inelastic stiffness tensors, which in turn yield the continuum level stress tensor. The use of strain vectors rather than tensors makes the modeling conceptually clearer as it allows capturing the orientation of fiber failures, yarn cracking, matrix microcracking, and interface slip. Application of the new microplane-triad model for a twill woven composite shows that it can realistically predict all the orthotropic elastic constants and the strength limits for various layups. In contrast with the previous (nonmicroplane) models, the formulation can capture the size effect of quasi-brittle fracture with a finite fracture process zone (FPZ). Explicit finite-element analysis gives a realistic picture of progressive axial crushing of a composite tubular crush can initiated by a divergent plug. The formulation is applicable to widely different weaves, including plain, twill, and satin weaves, and is easily extensible to more complex architectures such as hybrid weaves as well as two- and three-dimensional braids.

Orientation-guided two-scale approach for the segmentation and quantitative description of woven bundles of fibers from three-dimensional tomographic images

This paper proposes a two-scale approach for the description of fibrous materials from tomographic data. It operates at two scales: coarse scale to describe weaving patterns and fine scale to depict fiber layout within yarns. At both scales, the proposed approach starts with the segmentation of yarns and fibers. Then, the fibrous structure (fiber diameters, fiber and yarn orientations, fiber density within yarns) is described. The segmentation algorithms are applied to a chunk of a woven ceramic-matrix composite observed at yarn and fiber scales using tomographic data from the European synchrotron radiation facility. The fiber and yarn segmentation results allow investigation of intrayarn fiber layout. The analysis of intrayarn fiber density and orientations shows the effects of the weaving process on fiber organization, in particular fiber compaction or yarn shearing. These results pave the way toward a deeper analysis of such materials. Indeed, the data collected with the proposed methods are a key starting point for realistic image synthesis. Such images may in turn be used to validate the fiber and yarn segmentation algorithms. Besides, and above all, they will allow material behavior simulation, aiming at the evaluation of the material’s strengths and weaknesses inferred from its fibrous architecture.

A novel method for weft and warp yarn segmentation in multicolour yarn‐dyed fabric images

This paper proposes a novel method for segmentation of weft and warp yarns in multicolour yarn‐dyed fabric images. A multicolour yarn‐dyed fabric is cross‐woven by weft and warp yarns with different colours. When a multispectral imaging system is used to measure the colour of a multicolour yarn‐dyed fabric image, its weft and warp yarns need to be detected before analysing their colours. Detection of interstices between weft and warp yarns is firstly conducted. A modified K‐means clustering approach is then utilised to separate weft and warp yarns. The number of clusters is fixed to 2. The metric to measure the distance between a pixel and the mean of a cluster is the CIELAB colour difference. The initial means are determined by the expected values of fitted Gaussian distributions to CIExyY colour histograms. Experimental results show that the proposed method is promising for the segmentation of weft and warp yarns in multicolour yarn‐dyed fabrics, with an improved segmentation accuracy and much faster processing speed than K‐means clustering in CIEXYZ and CIELAB spaces.

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 1: Single‐system‐mélange color fabrics

AbstractTo recognize the layout of color yarns of single‐system‐mélange color fabric automatically, a novel FCM‐based stepwise classification method is proposed in this article. This method consists of three main steps: (1) warp yarn segmentation, (2) weft color recognition, and (3) the layout of color warps recognition. In the first step, the yarn segmentation method based on mathematical statistics of subimages is adopted to localize warp yarns preliminarily; and then the segmentation results of warp yarn are corrected by misrecognized‐boundary remove and missing‐boundary interpolation. In the second step, the weft color is extracted based on RGB color histograms of whole fabric image. In the third step, the pixels in each warp yarn are classified into two clusters by fuzzy C‐means clustering (FCM) algorithm in CIELAB color model separately, and the preliminary recognized layout of color warps is obtained. All warp colors are clustered by FCM algorithm in CIELAB color model again and the precise layout of color warps is output. The experimental and theoretical analysis proved that the proposed method can recognize the layout of color yarns of single‐system‐ mélange color fabrics with satisfactory accuracy and good robustness. © 2015 Wiley Periodicals, Inc. Col Res Appl, 40, 626–636, 2015

Simulation on fiber random arrangement in the yarn

Fiber random arrangement in the yarn exerts considerable influence on yarn properties, such as yarn irregularity. In this study, fiber random arrangement in the yarn was simulated to form a yarn segment with a given length according to the uniform distribution of the positions of fiber left heads along yarn axis. Irregularity of the total length of fibers within each yarn subsection was calculated as yarn limit irregularity. It can be seen from the results that when all fibers have the same linear density, yarn limit irregularities calculated by the simulated yarns grow from rapidly to slowly with the increase of fiber lengths. The results are not only in good agreement with the results calculated by equations of classic theories, but also in coincident with experimental results. Therefore, the simulation on fiber random arrangement is reasonable and, to some extent, can be applied to yarn limit irregularity calculation.