Journal of Textile Research ›› 2026, Vol. 47 ›› Issue (02): 94-102.doi: 10.13475/j.fzxb.20251002501

• Textile Engineering • Previous Articles     Next Articles

Dual-scale prediction model for staple yarn tenacity based on single-fiber tensile curve

LI Hao1, CAO Qiaoli1(), QIAN Lili1, YU Chongwen1,2   

  1. 1 College of Textiles, Donghua University, Shanghai 201620, China
    2 Key Laboratory of Textile Science & Technology, Ministry of Education, Shanghai 201620, China
  • Received:2025-10-14 Revised:2025-12-17 Online:2026-02-15 Published:2026-04-24
  • Contact: CAO Qiaoli E-mail:caoqli@dhu.edu.cn

Abstract:

Objective Staple yarn is the basic element of most textiles, and its tenacity is jointly determined by fiber properties at the microscopic scale and yarn parameters at the macroscopic scale, which profoundly affects the processing efficiency and performance of subsequent products. To reveal the influence mechanism of fiber properties and yarn parameters on staple yarn tenacity and to predict the tenacity, a dual-scale micro-macro prediction model for staple yarn tenacity was constructed.

Method Based on the single-fiber tensile curve, a mesoscopic mechanical model was constructed by integrating fiber properties with staple yarn parameters through inter-fiber stress analysis. The influence of yarn linear density and twist factor on fiber stress and inter-fiber friction was quantified, and a criterion for fiber breakage/slip was established (i.e., at any cross-section of a fiber, if the total friction force on one side is less than the tensile strength the fiber can withstand, the fiber will slip toward that side; otherwise, the fiber will break) leading to the creation of a dual-scale prediction model for predicting staple yarn tenacity. Staple yarns were made with different linear densities and twist factors from the commonly used fibers including cotton, polyester, vinylon, and viscose and tested to validate the accuracy and applicability of the model.

Results The staple yarn tenacity is the ratio of the sum of the effective strength contributed by broken fibers and the effective friction force generated by slipping fibers to the staple yarn linear density. Different macro-parameters of the staple yarn such as the twist factor and linear density were used, and it was found that increasing the twist factor while maintaining the linear density caused the fiber helix angle to increase, leading to a decrease in the effective strength at fiber breakage, and to an increase in the effective friction per unit length thereby inhibiting fiber slip. When the twist factor remained constant, increasing the yarn linear density did not affect the effective strength at fiber breakage but increasing the number of outer-layer fibers would enhance the effective friction per unit length of inner-layer fibers which also inhibits fiber slip. Comparing the predicted and tested tenacity of cotton, polyester, vinylon, and viscose staple yarns at different linear densities and twist factors, it was found that the change patterns of the predicted and the tested tenacity both increased to the peak and then decreased when increasing twist factor, which agrees with the traditional spinning theory. Furthermore, the mean error between predicted and tested tenacity is less than 5%. Three main causes were identified for the prediction error in staple yarn tenacity. First, the overly simplified assumption of staple yarn structure, under which the model neglected migration of fibers in the staple yarn, thereby neglecting the migration-induced entanglement that would otherwise enhance inter-fiber friction, further inhibiting fiber slip. Second, the failure to account for the impact of multiple fiber breaks on yarn tenacity. During the actual tensile process, fibers may break multiple times within the breakage zone. Third, for cotton yarn, the model neglects fiber length distribution, instead using average fiber length to predict yarn tenacity. Nevertheless, the prediction error was sufficiently small.

Conclusion A dual-scale prediction model for staple yarn tenacity based on single-fiber tensile curve was constructed. By investigating the influence of linear density and twist factor on fiber stress and inter-fiber friction, the model reveals the cross-scale interaction mechanism of fiber properties and staple yarn parameters on staple yarn tenacity. The accuracy and applicability of the model were validated using experimental data. The results show that under the same conditions, increasing either yarn linear density or twist factor can inhibit fiber slip; for various staple yarns, the correlation coefficients between the predicted and tested yarn tenacity are all greater than or equal to 0.95; and the mean error is less than 5%, indicating that the model has good accuracy and applicability to predict staple yarn tenacity.

Key words: ring spinning, single-fiber tensile curve, staple yarn tenacity, dual-scale prediction model, twist factor, fiber slip, fiber breakage

CLC Number: 

  • TS101.2

Fig.1

Meso-scale geometric model of fibers in staple yarns. (a) Schematic diagram of ideal helical structure of fibers;(b) Arrangement of fibers in cross-section of staple yarn"

Fig.2

Centripetal pressure of fibers in outermost layer of staple yarn"

Fig.3

Schematic diagram of friction force on a fiber during stretching of staple yarn"

Fig.4

Flowchart for simulation of staple yarn tenacity"

Fig.5

Tensile curves of different kinds of fibers"

Tab.1

Properties of fibers"

纤维
种类
平均长度
L/mm
线密度
Nf/dtex
质量密度
δf/(g·cm-3)
摩擦因
μ
27.60 1.82 1.54 0.29
涤纶 38.00 1.33 1.38 0.38
维纶 38.00 1.33 1.27 0.30
粘胶 38.00 1.33 1.52 0.26

Fig.6

Relationship between initial tension and breaking effective strength of outermost fibers of 20 tex cotton yarn with twist factor"

Fig.7

Comparison of predicted and tested tenacity of cotton staple yarns"

Fig.8

Comparison of predicted and tested tenacity of polyester staple yarns"

Fig.9

Comparison of predicted and tested tenacity of vinylon staple yarns"

Fig.10

Comparison of predicted and tested tenacity of viscose staple yarns"

[1] GOSWAMI B C, MARTINDALE J G, SCARDINO F L. Textile yarns: technology, structure, and applications[M]. New York: Wiley & Sons, 1977: 102-104.
[2] WANG J C, ZHOU H, LIU Z K, et al. Statistical modelling of tensile properties of natural fiber yarns considering probability distributions of fiber crimping and effective yarn elastic modulus[J]. Composites Science and Technology, 2022, 218: 109142.
doi: 10.1016/j.compscitech.2021.109142
[3] LI H, ZHENG G M, CAO Q L, et al. Computational modeling of the strength of staple yarn based on the random arrangement of fibers[J]. Textile Research Journal, 2024, 94(11/12): 1297-1305.
doi: 10.1177/00405175241227932
[4] RAMEY H H Jr, LAWSON R Jr, WORLEY S Jr. Relationship of cotton fiber properties to yarn tenacity[J]. Textile Research Journal, 1977, 47(10): 685-691.
doi: 10.1177/004051757704701008
[5] NURWAHA D, WANG X H. Comparison of the new methodologies for predicting the CSP strength of rotor yarn[J]. Fibers and Polymers, 2008, 9(6): 782-784.
doi: 10.1007/s12221-008-0122-1
[6] LIU Y L, TODD CAMPBELL B, DELHOM C. Study to relate mini-spun yarn tenacity with cotton fiber strength[J]. Textile Research Journal, 2019, 89(21/22): 4491-4501.
doi: 10.1177/0040517519837725
[7] 吴志刚, 张圣男, 徐洁, 等. 纤维性能及细纱捻系数对棉纱断裂强度的影响[J]. 棉纺织技术, 2020, 48(2): 1-5.
WU Zhigang, ZHANG Shengnan, XU Jie, et al. Influence of fiber property and spun yarn twist factor on breaking tenacity of cotton yarn[J]. Cotton Textile Technology, 2020, 48(2): 1-5.
[8] DAS S, GHOSH A. Decision rule prediction for assessment of rotor spun cotton yarn strength using rough set[J]. Journal of Natural Fibers, 2022, 19(17): 15919-15929.
doi: 10.1080/15440478.2022.2140376
[9] RAZBIN M, GHAREHAGHAJI A A, SALEHIAN M, et al. Artificial neural network-assisted theoretical model to predict the viscoelastic-plastic tensile behavior of polyamide-6 multi-ply yarns[J]. Neural Computing and Applications, 2024, 36(29): 18107-18123.
doi: 10.1007/s00521-024-10048-x
[10] ZHANG B W, SONG J X, ZHAO S N, et al. Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization[J]. Textile Research Journal, 2021, 91(23/24): 2911-2924.
doi: 10.1177/00405175211022619
[11] HEARLE J W S. Theoretical analysis of the mechanics of twisted staple fiber yarns[J]. Textile Research Journal, 1965, 35(12): 1060-1071.
doi: 10.1177/004051756503501202
[12] PAN N. Development of a constitutive theory for short fiber yarns: mechanics of staple yarn without slippage effect[J]. Textile Research Journal, 1992, 62(12): 749-765.
doi: 10.1177/004051759206201208
[13] PAN N. Development of a constitutive theory for short fiber yarns: part II: mechanics of staple yarn with slippage effect[J]. Textile Research Journal, 1993, 63(9): 504-514.
doi: 10.1177/004051759306300902
[14] FRYDRYCH I. A new approach for predicting strength properties of yarn[J]. Textile Research Journal, 1992, 62(6): 340-348.
doi: 10.1177/004051759206200606
[15] 陶静, 汪俊亮, 张洁. 数据驱动与有限元仿真融合的纱线断裂强力分析方法[J]. 纺织学报, 2024, 45(2): 238-245.
TAO Jing, WANG Junliang, ZHANG Jie. Data-driven finite element simulation for yarn breaking strength analysis[J]. Journal of Textile Research, 2024, 45(2): 238-245.
[16] JIANG Z, YU C W, YANG J P, et al. Estimation of yarn strength based on critical slipping length and fiber length distribution[J]. Textile Research Journal, 2019, 89(2): 182-194.
doi: 10.1177/0040517517741160
[17] 姚江薇, 邹专勇, 闫琳琳, 等. 喷气涡流纺纱线拉伸断裂强力预测模型构建与验证[J]. 纺织学报, 2018, 39(10): 32-37.
doi: 10.13475/j.fzxb.20171100606
YAO Jiangwei, ZOU Zhuanyong, YAN Linlin, et al. Prediction model on tensile strength of air jet vortex spinning yarn and its verification[J]. Journal of Textile Research, 2018, 39(10): 32-37.
doi: 10.13475/j.fzxb.20171100606
[18] HEARLE J W S, GROSBERG P, BACKER S. Structural mechanics of fibers, yarns, and fabrics[M]. New York: Wiley-Interscience, 1969: 65-66.
[19] SCHWARZ E R. Certain aspects of yarn structure[J]. Textile Research Journal, 1951, 21(3): 125-136.
doi: 10.1177/004051755102100301
[20] 姚穆. 纺织材料学[M]. 3版. 北京: 中国纺织出版社, 2009: 179-180.
YAO Mu. Textile materials science[M]. 3rd ed. Beijing: China Textile & Apparel Press, 2009: 179-180.
[21] 郁崇文. 纺纱学[M]. 4版. 北京: 中国纺织出版社有限公司, 2023:152-153.
YU Chongwen. Spinning science[M]. 4th ed. Beijing: China Textile & Apparel Press, 2023:152-153.
[22] 李豪, 钱丽莉, 曹巧丽, 等. 基于纤维拉伸曲线的短纤纱中纤维张力的数值模拟[J/OL]. 棉纺织技术, 2025: 1-6. (2025-05-15). https://kns.cnki.net/KCMS/detail/detail.aspx?filename=MFJS20250513001&dbname=CJFD&dbcode=CJFQ.
LI Hao, QIAN Lili, CAO Qiaoli, et al. Numerical simulation of fiber tension in staple yarn based on fiber tensile curve[J/OL]. Cotton Textile Technology, 2025: 1-6. (2025-05-15). https://kns.cnki.net/KCMS/detail/detail.aspx?filename=MFJS20250513001&dbname=CJFD&dbcode=CJFQ.
[23] ZHU G Q, WANG X, LIU M N, et al. Simulation of fiber arrangement in slivers based on image processing[J]. Textile Research Journal, 2025, 95(13/14): 1691-1697.
doi: 10.1177/00405175241291799
[1] GUO Mengyao, WU Jiaqing, WANG Ying. Fabrication of fully covered polyurethane-film-strip/cotton composite yarns and its mechanical properties [J]. Journal of Textile Research, 2025, 46(11): 69-76.
[2] YIN Wenbo, YE Fan, YANG Ruihua. Wearing performance of polyamide filament/cotton woven fabrics made from core spun-wrapped composite yarn [J]. Journal of Textile Research, 2025, 46(11): 77-85.
[3] ZUO Qi, WU Huawei, WANG Chunhong, DU Juanjuan. Effect of yarn structure on tensile properties of ramie staple yarn reinforced composites [J]. Journal of Textile Research, 2023, 44(10): 81-89.
[4] CHEN Taifang, ZHOU Yaqin, WANG Junliang, XU Chuqiao, LI Dongwu. Online detection of yarn breakage based on visual feature enhancement and extraction [J]. Journal of Textile Research, 2023, 44(08): 63-72.
[5] LIU Shuai, GUO Chenyu, CHEN Hewen, YANG Ruihua. Model analysis on structure of ring spun Sirofil wrapped yarn and its property optimization [J]. Journal of Textile Research, 2023, 44(04): 63-69.
[6] ZHENG Xiaohu, LIU Zhenghao, CHEN Feng, LIU Zhifeng, WANG Junliang, HOU Xi, DING Siyi. Key technologies for full-process robotic automatic production in ring spinning [J]. Journal of Textile Research, 2022, 43(09): 11-20.
[7] GUO Mingrui, GAO Weidong. Method and characteristics of section colored slub yarns spun by two-channel ring spinning based on single-zone drafting [J]. Journal of Textile Research, 2022, 43(08): 21-26.
[8] NI Jie, YANG Jianping, YU Chongwen. Effect of ratio of strands twist factor to single yarn twist factor on properties of viscose plied yarns [J]. Journal of Textile Research, 2021, 42(05): 46-50.
[9] YIN Shiyong, BAO Jinsong, TANG Shixi, YANG Yun. Modeling method of cyber physical production system for ring spinning [J]. Journal of Textile Research, 2021, 42(02): 65-73.
[10] ZHANG Tingting, XUE Yuan, HE Yudong, LIU Yuexing, ZHANG Guoqing. Construction of Kubelka-Munk double-constant color matching model for ring digital yarn color prediction [J]. Journal of Textile Research, 2020, 41(01): 50-55.
[11] ZHANG Tingting, XUE Yuan, XU Zhiwu, YU Jian, CHEN Lianguang. Color system construction of three-channel digital spinning mixed color yarn and performance analysis of colored yarn [J]. Journal of Textile Research, 2019, 40(09): 48-55.
[12] YIN Shiyong, BAO Jinsong, SUN Xuemin, WANG Jiacheng. Method of temperature close-loop precision control based on cyber-physical systems for intelligent workshop of ring spinning [J]. Journal of Textile Research, 2019, 40(02): 159-165.
[13] . Structure and performances of double channel digital ring spinning melange yarn [J]. Journal of Textile Research, 2018, 39(11): 27-32.
[14] . Application and process optimization of false twist in ring spinning machine [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(07): 27-31.
[15] . Reducing yarn hairiness by wetting in ring spinning [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(05): 108-112.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!