Using Fuzzy Theory to Predict the Properties of a Melt Spinning System

Kuo, Chung‐Feng Jeffrey; Hsiao, Kun-Iuan; Wu, Yi‐Shiuan

doi:10.1177/004051750407400308

Cited by 9 publications

(1 citation statement)

References 3 publications

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“…Pei and Yu (Pei and Yu 2011) released a model adopted to predict the vortex yarn tenacity from some vortex spinning process and nozzle parameters such as nozzle pressure, jet orifice angle, twisting surface angle, and the distance between the nozzle inlet and the hollow spindle. Melt spinning is the most economically useful method for producing artificial fibers in the industry, Kuo et al(Kuo, Hsiao, andWu 2004a, 2004b) applied Fuzzy logic and ANN respectively to predict the properties of melt spun polypropylene filament. Splicing techniques assembles yarns on spinning bobbins into larger yarn packages, the modeling of this process was attempted by Ünal and Cheng et al (Ünal, Özdil, and Taşkın 2010;Ünal et al 2010;K.…”

Section: Processes or Methodsmentioning

confidence: 99%

Modeling of textile manufacturing processes using intelligent techniques: a review

Tran

et al. 2021

Int J Adv Manuf Technol

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The development of manufacturing process modeling has attracted growing attention from the textile industry as the need for quickly exploring a textile manufacturing process is increasingly costly along with the growing complexity in the process. More and more researches shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of it is in line with the procedure of textile processes from yarn to fabrics, and to garments. The analysis and discussion of the previous studies are conducted from the different applications in a different process. The factors and performance properties considered in process modeling are collected in comparison. The considerations of the previous studies in terms of inputs' relative importance, feature selection, modeling techniques, data distribution and performance estimations are analyzed and summarized. On the basis of the summaries of more than 128 related articles from the point of views of textile engineering and artificial intelligence, the limitations, challenges and future perspectives in this issue are also concluded.

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Section: Processes or Methodsmentioning

confidence: 99%