Mathematical modeling and numerical simulation of yarn behavior in a modified ring spinning system

Tang, H.B.; Xu, Bingang; Tao, Xiaoming; Feng, Jie

doi:10.1016/j.apm.2010.05.013

Cited by 29 publications

(17 citation statements)

References 19 publications

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“…For example, in Tang et al (2010a), a mathematical model and a numerical simulation to analyze dynamic behavior and twist distribution in a modified ring spinning system are described (Fig. 14.4).…”

Section: 43simulating Yarn Productionmentioning

confidence: 99%

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Simulation

Gries¹,

Veit²,

Wulfhorst³

et al. 2014

Textile Technology

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Simulation is normally used in order to analyze systems that are hard or impossible to describe using systems of explicit equations. In particular, this applies to highly complex dynamic systems such as most textile machines and processes.By carrying out a simulation experiment, knowledge about the real system can be gathered comparatively easily in contrast to trials on the machine or during the actual process. This includes practical experiments as well as a simulation using a computer program. Another field of application of simulations are systems that do not yet exist. This comprises the testing of new plant design concepts or for example the development of new spacecraft and aircraft or automobiles using carbon textile reinforced composites. 14.1Simulation with and without ComputerIn general, simulation methods can be divided into those that use a computer and those that do not, as shown in Fig. 14.1. Those simulations without computer can be separated into destructive and nondestructive methods. Simulations that use a computer can either be based on technical models (for example, finite element method for structural composites, computational fluid dynamics for polymer flows), on examples taken from nature (for example, artificial neural networks, evolutionary algorithms for machine setting optimization), and those based on analytical equations (for example, warp tension during weaving). Simulation Coauthor: Y.-S. Gloy 14.3 Modeling 14.3.1Types of ModelingIn order to save time and hence money, most simulation models use a simplified description of the real process or machine. Figure 14.2 shows typical models. Modeling Gray box Parts of system known ComponentsBlack box Behavior known Inner structure known White box Inner structure know Abstraction Reduction Figure 14.2 Principles of modeling White-Box ModelThis kind of model is suitable if the inner structure of the system is known. This structure is then deliberately abstracted, modified, and reduced to the most important influencing parameters. A typical example is the modeling of a weaving machine. Black-Box ModelIf the inner structure of a system is unknown but its behavior or its interaction with other systems can be observed and modeled, this is called a black-box model. A typical example is the use of neural networks to simulate textile processes. Gray-Box ModelIn many cases, only parts of a system are fully known and only a few, but not all, interactions between its components are established. The respective model is then called a gray-box model. In order to save costs, this approach is widely used. A fuzzy model can be regarded as a gray-box model in some cases. Model SimplificationIn order to simplify a model compared to reality, the following possibilities are often used:Components that are not of crucial significance are not taken into account. A trial based on factorial design can be helpful to determine the important influencing parameters.Unimportant details are neglected.The system is split into its components and these are analyzed separatel...

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Section: 43simulating Yarn Productionmentioning

confidence: 99%

“…14.4). (Tang et al, 2010a) In general, we have (14.1) which is equivalent to (14.2) where m stands for the yarn fineness, ω denotes the angular velocity of the pin, e z coincides with the axis z in Fig. 14 .4, and R(s,t) is the yarn path with s representing the arc length and t, the time.…”

Section: 43simulating Yarn Productionmentioning

confidence: 99%

Simulation

Gries¹,

Veit²,

Wulfhorst³

et al. 2014

Textile Technology

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show abstract

“…Furthermore, the yarn tension and balloon effect were discussed from spinning machine manufacturers' perspective in [26][27][28] . The yarn tension is affected by air-drag on the ballooning yarn and the balloon shape leading to yarn breakage, which affects energy consumption and yarn productivity in ring spinning 26 .…”

mentioning

confidence: 99%

“…The yarn tension is affected by air-drag on the ballooning yarn and the balloon shape leading to yarn breakage, which affects energy consumption and yarn productivity in ring spinning 26 . In 27 , a mathematical model of yarn ballooning motion in ring spinning was established using few methods for real-time measurement of the yarn tension, whereas the dynamical behaviour of the yarn and twist distribution in a modified ring spinning system were investigated by Tang et al 28 . Moreover 29 , utilized sensors and displayed them to form a comprehensive information management system (IMS) of rapier loom in order to improve the loom's efficiency and to guarantee improved quality of the textile fabric.…”

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confidence: 99%

Yarn tension control technique for improving polyester soft winding process

Ali¹,

Ahmed

Amer

2021

Sci Rep

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The textile industry has a great role in the improvement of any country’s economy. Moreover, the ready-made garments need different coloured high yarn quality, so yarn should be rewinded on plastic cones for dyeing. However, manufacturers are facing the problem of tension variation during soft winding process that mainly affects the yarn quality. Consequently, to overcome the tension variation drawbacks, the attainment of constant optimal tension values is required in order to: (1) Increase the winding speed while maintaining the yarn quality, (2) Improve the dyeing quality, and (3) Reduce the water consumption during the dyeing process. In this paper, a proposed yarn tension control technique is introduced to upgrade the soft winding machine, thus maintain the yarn quality and improve the manufacturing capacity. The proposed technique has been tested on Polyester yarn samples classified as; fine, medium and coarse yarn counts, to cover most yarn sizes used in the industry. Arduino Mega 2560 controller is utilized to implement the proposed tension control. The results are compared to the conventional system to advocate the effectiveness and capability of the proposed technique in overcoming the trade-off between tension control and machine speed that occurs in conventional system using variable tension levels.

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“…Based on the sliver and yarn scale, Grosberg et al 1,2 and Tang et al 3 employed a mathematical model to analyze the twist change in the dynamic yarn forming process, and the mechanism of twist propagation and distribution of staple yarn were explained in detail. The twisting dynamic model of the filament was extensively explored by Yamamoto and Matsuoka.…”

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confidence: 99%

Simulation and analysis of the twist propagation process of polyester staple yarn on the fiber scale

Wang

Zhang

Ding

et al. 2020

Textile Research Journal

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The twisting process of the sliver is an important part of the yarn spinning process, but this process has not been fully characterized on the fiber scale. Herein, based on the assumption that fibers are randomly distributed in the sliver, we analyzed the simulation twisting process of the sliver model on the fiber scale. The mathematical model of the twisting process of the sliver is set up and the non-free-end twisting process is simulated using the finite element software ABAQUS®. The simulation process clearly shows the configuration changes of the sliver caused with the increase of the twist. We also divided the twisting process into 11 stages and obtained a three-dimensional model of staple yarn. Then, the relationship curve between the ring-spun yarn fineness and the number of fibers in the cross-section of the ring-spun yarn was established by spinning the yarns of different counts of 20, 25, 30, 35, 40, 45, 50, 55, 60 and 65 Ne, and the fineness of the simulated yarn was calculated. The accuracy of the simulated yarn was verified by comparing the weight of the simulated yarn and the ring-spun yarn. The model established can be used to predict yarn properties for different purposes and can also be further utilized to study other phenomena in ring-spinning technology.

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Mathematical modeling and numerical simulation of yarn behavior in a modified ring spinning system

Cited by 29 publications

References 19 publications

Simulation

Simulation

Yarn tension control technique for improving polyester soft winding process

Simulation and analysis of the twist propagation process of polyester staple yarn on the fiber scale

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