Statistical modelling of tensile properties of natural fiber yarns considering probability distributions of fiber crimping and effective yarn elastic modulus

Wang, Jin; Zhou, Helezi; Liu, Zhengkun; Peng, Xiongqi; Zhou, Huaming

doi:10.1016/j.compscitech.2021.109142

Cited by 21 publications

(23 citation statements)

References 23 publications

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“…They can be characterized by certain probability distribution functions. 21 The mesoscale woven fabric model combines the mixing theory 20 and probability distribution of material parameters. The model parameter identification procedure is as follows:…”

Section: Woven Fabric Modelmentioning

confidence: 99%

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A mesoscale tensile model for woven fabrics based on Timoshenko beam theory

Wang

Zhou

Ouyang

et al. 2022

Textile Research Journal

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The yarn weave architectures highly influence the mechanical response of woven fabrics. To provide an accurate prediction of the mechanical behaviors of woven fabrics under tension, a mesoscale model is developed. The model takes into account yarn properties, yarn weaving pattern, and interactions between yarns. The yarn properties such as tensile modulus and yarn strength of yarns are considered as random variables. Each yarn in fabric is modeled as a Timoshenko beam with a weaving shape modeled by a parabolic function. The yarn shape evolution and mechanical response of undulated yarn under tension are computed based on Castigliano’s second theorem. The damage initiation and propagation of a yarn in fabric is introduced through a damage variable. The model is demonstrated on woven jute fabrics. Analytical and numerical analyses are carried out and show good prediction on the macroscopic tensile response of woven jute fabric. The model provides a quantitative understanding between yarn geometrical parameters and the expected material response, and makes it helpful for designing woven structural composites with high performance.

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Section: Woven Fabric Modelmentioning

confidence: 99%

“…[26][27][28] The statistical distributions on tensile modulus E y and yarn strength r y of jute yarns are listed in Table 2. 21 The shear modulus E s of jute yarns is assumed to be 30 MPa. 29,30…”

Section: Yarn Mechanical Parametersmentioning

confidence: 99%

A mesoscale tensile model for woven fabrics based on Timoshenko beam theory

Wang

Zhou

Ouyang

et al. 2022

Textile Research Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Towards exploring a more accurate mechanical response of yarn, the properties with Weibull distribution need to be assigned to each virtual fiber based on the stochastic distribution algorithm. 5 The factors with Weibull distribution of fibers -scale and shape factor-are obtained from research, 9,10,26–28 probability of fiber can be obtained by equation (4).where m = 5.50 is the Weibull modulus or shape factor, σ = 3810 MPa is the obtained stress, σ0 is the characteristic strength or scale factor. Nf = 74 is the number of tests, i is the test serial number (ranked).…”

Section: Virtual Fiber and Virtual Yarns Modelingmentioning

confidence: 99%

Section: Geometry and Property Modelsmentioning

confidence: 99%

An efficient virtual modeling regard to the axial tensile and transverse compressive behaviors of the twisted yarns

Wang

Jiao

et al. 2022

Journal of Industrial Textiles

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The mechanical properties of yarns have a decisive effect on the performance of fiber-reinforced composite materials. Predictive simulations of the mechanical response of yarns are, thus, necessary for damage evaluation and geometric reconstruction of textiles. This paper proposed a quasi-fiber scale virtual modeling method regard to the axial tensile and transverse compressive behaviors of the twisted yarns. A stochastic properties model of the yarn was established for characterizing the statistical distribution of tensile strength. The variation of modeling parameters, including coefficient of friction, the amounts of virtual fibers per yarn and element length, versus calculation accuracy has been determined based on axial tensile and transverse compressive behavior of quartz fibers. The relationship between modeling parameters and mechanical behavior of yarn was established within the scope of this study. Axial tensile and transverse compressive behavior of yarns with different twists were predicted. The results show that balance between the modeling precision and computational efficiency can be achieved using the parameters, the COF of 0.35, virtual fiber count of 122 and Le of 0.3. This efficient modeling method is meaningful to be developed in further virtual weaving research.

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“…A textile yarn can be defined as a group of twisted fibers [ 1 ]; the shape and properties of the fibers form the overall properties of the yarn. Hence, studying and understanding the distribution and shape of the fibers across the length of the yarn is beneficial [ 2 , 3 ], and this information can be extracted from yarn cross-sectional slices or images. Analyzing the images of these sections can reveal the geometrical parameters, such as packing density, effective packing density, and ellipticity shape [ 4 , 5 , 6 ], which contribute to the yarn’s properties, such as tensile strength, elongation, appearance, compactness, and the overall fabric surface [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Techniques to Obtain the Cross-Sectional Images of Textile Yarns

2022

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In the fabric industry, textile yarns are the fundamental building blocks. Hence, visualizing and studying yarn structure is essential to understand the structure and behavior of the fibers. Obtaining the yarn’s cross-section images is crucial in the calculations of yarn’s porosity; furthermore, a more precise expansion for the fiber’s migration can be concluded from the cross-sectional images. In this paper, three different methods (microtome, micro-computed tomography, and epoxy grinding–polishing methods) to image and visualize the yarn’s cross-section are presented. The experimental techniques are compared in terms of result useability, time of preparation, and overall outcome of the cross-sectional image. The images can be used for fiber distribution, air gap calculation, and twist analysis as well. The fiber diameter distribution of polyester yarn was measured based on the images obtained by the three different methods; the average fiber diameter measured based on the combined data from the three different methods was found to be 10.90 ± 0.30 µm.

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Statistical modelling of tensile properties of natural fiber yarns considering probability distributions of fiber crimping and effective yarn elastic modulus

Cited by 21 publications

References 23 publications

A mesoscale tensile model for woven fabrics based on Timoshenko beam theory

A mesoscale tensile model for woven fabrics based on Timoshenko beam theory

An efficient virtual modeling regard to the axial tensile and transverse compressive behaviors of the twisted yarns

Experimental Techniques to Obtain the Cross-Sectional Images of Textile Yarns

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