2020
DOI: 10.1177/1558925020927837
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An adhesive-aided ring spinning for improving cotton yarn quality with the aid of sodium carboxymethyl cellulose solution

Abstract: An adhesive-aided ring spinning was developed to improve cotton yarn quality through the wetting and adhesion effect of an adhesive solution, namely, sodium carboxymethyl cellulose solution on fibrous strand in yarn formation zone during spinning process. The spinning mechanism of the adhesive-aided ring spinning with sodium carboxymethyl cellulose solution was analyzed, and the effects of two factors, that is, speed ratio (the ratio of the linear surface velocity to the output speed) and sodium carboxymethyl … Show more

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Cited by 3 publications
(5 citation statements)
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“…As shown in Figure 14, when predicting yarn strength, one layer of LSTM, two layers of LSTM, and three layers of LSTM were used, and the MSE value decreased only slightly. From the point of view of reducing computation, computing time, and saving resources, this model chooses one-layer LSTM [20][21][22] Momentum, and Adam optimization algorithms. These four optimal algorithms have their own characteristics, SGD (Stochastic Gradient Descent) trains for large samples; Adagrad (Adaptive Gradient Algorithm) is an improvement of SGD to improve its robustness; Momentum is also an improvement on SGD, which can accelerate the convergence of SGD and has a strong inhibition on convergence oscillation; Adam (Adaptive Moment Estimation) only needs to give an initial learning rate, and can adapt the learning rate according to the situation in the training process [23,24].…”
Section: Influence Of Rotor Spinning Process Parameters On Thementioning
confidence: 99%
“…As shown in Figure 14, when predicting yarn strength, one layer of LSTM, two layers of LSTM, and three layers of LSTM were used, and the MSE value decreased only slightly. From the point of view of reducing computation, computing time, and saving resources, this model chooses one-layer LSTM [20][21][22] Momentum, and Adam optimization algorithms. These four optimal algorithms have their own characteristics, SGD (Stochastic Gradient Descent) trains for large samples; Adagrad (Adaptive Gradient Algorithm) is an improvement of SGD to improve its robustness; Momentum is also an improvement on SGD, which can accelerate the convergence of SGD and has a strong inhibition on convergence oscillation; Adam (Adaptive Moment Estimation) only needs to give an initial learning rate, and can adapt the learning rate according to the situation in the training process [23,24].…”
Section: Influence Of Rotor Spinning Process Parameters On Thementioning
confidence: 99%
“…The contact area length within the range from 2.97 mm to 1.22 mm can ensure the normal process of AARS spinning. In addition, the yarn moisture regains of ring spun yarn and adhesive-aided ring spun yarn are 8.3 % and 8.2 %, respectively [17]. The similar moisture regain means that the adhesive-aided ring spun yarns have the same dry condition with ring spun yarns.…”
Section: Introductionmentioning
confidence: 83%
“…In addition, the AARS is applicable in extensive range of yarn counts [16] and the twist stop effect caused by the adhesive and the contact is relatively small. However, such twist loss rate could not affect the spinning efficiency and lead to unstable spinning process through the observation of the AARS process [17].…”
Section: Analysis Of Cmc-na Solution On Yarn Performancementioning
confidence: 99%
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