Journal of Textile Research ›› 2016, Vol. 37 ›› Issue (3): 25-30.

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Process optimization of superhigh draft spinning of Lyocell yarnesr

  

  • Received:2015-01-15 Revised:2015-09-24 Online:2016-03-15 Published:2016-03-16

Abstract:

In order to solve spinning difficulties of Lyocell yarns in super high drafting that is prone to worsen yarn evenness, increase more neps and hairiness, this article used fuzzy decision method to optimize the spinning key technologies of super high draft spinning lyocell fiber, we also discussed the influence of the top pin and the central-top roller for 7.4tex closely lyocell yarn qualities. The fuzzy clustering method were used to verify the decision results of intimate data method and borda method, the results show that 7.4tex closely lyocell yarn qualities can be effectively improved, under the conditions that the front area and the central area use front-extended top pin combined with rubber-covered rollers, which can satisfy yarn quality requirements of developing high-grade lyocell textile fabrics, we could also know that the center-area key process parameters have the greatest impact on Lyocell yarns.

Key words: super high draft, Lyicell yarn, fuzzy decision, fuzzy clustering, optimization process

[1] . Measurement of yarn evenness using sequence images [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(11): 26-31.
[2] . Study o yarn unevenness based on experiment of fibers acclilrated-point distribution [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(08): 32-36.
[3] . Automatic identifying of fabric weave patterns based on kernel fuzzy clustering [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(12): 131-0.
[4] SHEN Wei;LIU Wenhao. Fabric weaving defect detection algorithm based on dynamic fuzzy clustering [J]. JOURNAL OF TEXTILE RESEARCH, 2010, 31(7): 46-49.
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