Analysis of the magnetic field and electromagnetic force of a non-striking weft insertion system for super broad-width looms, based on an electromagnetic launcher

Xu, Qiao; Shunqi, Mei; Xiaoyu, Yan; Islam, Mazharul; Chen, Zhen; Wang, Shaojun; Zhang, Zhiming

doi:10.1177/0040517519839371

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…The first part is to determine the acceleration exit speed vTarget required to achieve the weft insertion for different loom spindle speeds and width requirements. 5 The second part is to determine the energization sequence of coils according to the structure of coils and the weft inserter. Using segmented combined electromagnetic launch weft insertion can provide greater and more stable electromagnetic force for the weft inserter.…”

Section: Dynamic Prediction Model Of Segmented Combined Electromagnet...mentioning

confidence: 99%

“…For example, the loom with a spindle speed of 220 r/min is required to weave a fabric with a 12 m door width to achieve a weft insertion speed of 126 m/s. 5 At present, the weft insertion speeds of rapier looms, air-jet and water-jet looms, and projectile looms can reach 20 m/s, 66.67 m/s and 25.83 m/s, respectively. 9 The existing loom weft insertion method is difficult to achieve super-wide weft insertion.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with ordinary width fabrics, the integrally formed super-wide industrial fabrics have higher overall strength consistency and more stable performance when used in large areas. 5 The use of different functional materials for weaving allows super-wide industrial fabrics to have different characteristics and adapt to different domains. Super-wide industrial fabrics prepared by using superior-strength industrial yarn 6 is beneficial to strengthening the protection of dams, mountains and coasts, and reducing the damage caused by floods, typhoons and other disasters.…”

Section: Introductionmentioning

confidence: 99%

“…9 The existing loom weft insertion method is difficult to achieve super-wide weft insertion. 5 With the rapid development of electromagnetic launch technology and pulse power control technology of electromagnetic launch system, 10 some researchers have seen the possibility of using electromagnetic force as power source to drive and complete weft insertion. In 1975, a patent for the first electromagnetic weft insertion scheme based on a conventional shuttle loom was proposed.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic prediction model of segmented combined electromagnetic launch weft insertion for super-wide width industrial fabrics

Ding

Zhang

et al. 2023

Journal of Industrial Textiles

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Stable, reliable and high-speed weft insertion is an urgent challenge for high-speed industrial textile weaving devices. More stable and large electromagnetic forces can be provided by the segmented combined electromagnetic launch weft insertion, compared to the conventional electromagnetic launch weft insertion, which facilitates the realization of high-speed weft insertion. For digital electromagnetic launch weft insertion textile devices, it is necessary to respond quickly to the signals fed back by the sensors. Therefore, a fast and high-precision dynamical model capable of characterizing the kinematic properties of the weft inserter is extremely essential for control, which is conducive to the stable and reliable operation of the device. In this paper, we consider the electromagnetic force, weft yarn tension, air resistance, pipe wall friction, and gravity during the movement of the weft inserter, and establish a dynamic prediction model of segmented combined electromagnetic launch weft insertion (DPMSCEI) for control. Combining simulations and experiments to evaluate the performance of DPMSCEI validates the effectiveness and professionalism of DPMSCEI in solving electromagnetic weft insertion dynamics.

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Section: Dynamic Prediction Model Of Segmented Combined Electromagnet...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic prediction model of segmented combined electromagnetic launch weft insertion for super-wide width industrial fabrics

Ding

Zhang

et al. 2023

Journal of Industrial Textiles

Self Cite

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“…Based on the manufacturing characteristics of cotton spinning, it is evident that the outcomes of preceding processing stages will impact subsequent stages. Taking the relationship among the last drafting, coarse yarn spinning, and fine yarn spinning as an example, the unevenness in the weight of fine yarn and the CV value of sliver stiffness determine whether pronounced weft-wise or warp-wise streaks appear in the final cotton fabric [18]. Both of these factors are influenced by the internal fiber structure of coarse yarn.…”

Section: Cotton Yarn Quality Prediction Modelmentioning

confidence: 99%

Prediction of Cotton Yarn Quality Based on Attention-GRU

Dai,

Jin,

et al. 2023

Applied Sciences

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With the diversification of spinning order varieties and process parameters, the conventional method of determining production plans through trial spinning no longer satisfies the processing requirements of enterprises. Currently, deficiencies exist in predicting spinning quality relying on manual experience and traditional methods. The back propagation (BP) neural network within the realm of deep learning theory faces challenges in handling time series data, while the long short-term memory (LSTM) neural network, despite its intricate mechanism, exhibits an overall lower predictive accuracy. Consequently, a more precise predictive methodology is imperative to assist production personnel in efficiently ascertaining cotton-blending schemes and processing parameters, thereby elevating the production efficiency of the enterprise. In response to this challenge, we propose an attention-GRU-based cotton yarn quality prediction model. By employing the attention mechanism, the model is directed towards the input features most significantly impacting yarn quality. Real-world performance indicators of raw cotton and process parameters are utilized to predict yarn tensile strength. A comparative analysis is conducted against prediction results of BP, LSTM, and gated recurrent unit (GRU) neural networks that do not incorporate the attention mechanism. The outcomes reveal that the GRU model enhanced with the attention mechanism demonstrates reductions of 56.3%, 38.5%, and 36.4% in root mean square error (RMSE), along with 0.367%, 0.158%, and 0.190% in mean absolute percentage error (MAPE), respectively. The model attains a coefficient of determination R-squared of 0.954, indicating a high degree of fitness. This study underscores the potential of the proposed attention-GRU model in refining cotton yarn quality prediction and its consequential implications for process optimization and enhanced production efficiency within textile enterprises.

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