Use of Span Lengths Extracted from the HVI Fibrogram to Predict Yarn Quality

ABSTRACT

Cotton fiber properties play a significant role in determining the quality of yarns and fabrics. Information on fiber length distribution is crucial throughout many textile processing steps to transform fibers into yarn. The current High Volume Instrument (HVI) provides only two traditional length parameters, the upper half mean length (UHML) and uniformity index (UI). The UI is the mean length (ML) divided by the UHML expressed as a percentage. Both UHML and ML are extracted from the fibrogram, are highly correlated, and characterize only the longer fibers in a sample. Yet, the whole fibrogram contains more descriptive information than the two length measurements provided by the HVI. In addition, the fibrogram is a stable measurement on an HVI, and differences in fibrogram measurements across instruments can be reduced using a recently proposed calibration method. However, the use of the complete fibrogram by the textile industry is complicated and almost impossible in practice. This study aimed to investigate a simplified method to identify key span lengths extracted from the HVI fibrogram that can improve yarn prediction compared to the current method. The results obtained from a set of 60 commercial-like samples covering a wide range of length parameters are promising.

摘要

棉纤维的性能在决定纱线和织物的质量方面起着重要作用. 在将纤维转化为纱线的许多纺织加工步骤中，纤维长度分布信息至关重要. 目前的大容量仪器（HVI）只提供两个传统的长度参数，上半平均长度（UHML）和均匀度指数（UI）. UI是平均长度（ML）除以以百分比表示的UHML. UHML和ML都是从纤维图中提取的，高度相关，并且仅表征样品中较长的纤维. 然而，整个纤维图比HVI提供的两个长度测量包含更多的描述性信息. 此外，纤维图是HVI上的稳定测量，并且可以使用最近提出的校准方法来减少仪器之间纤维图测量的差异. 然而，纺织工业使用完整的纤维图是复杂的，在实践中几乎是不可能的. 本研究旨在研究一种简化的方法来识别从HVI纤维图中提取的关键跨距，与目前的方法相比，该方法可以改进纱线预测. 从一组覆盖广泛长度参数的60个商业类样品中获得的结果是有希望的.

KEYWORDS:

• Cotton fiber
• fiber length distribution
• fibrogram
• span length
• yarn quality
• yarn prediction

关键词:

• 棉纤维
• 纤维长度分布
• 纤维图
• 跨度长度
• 纱线质量
• 纱线预测

Highlights

• The current length parameters, Upper Half Mean Length (UHML) and Uniformity Index (UI) do not adequately characterize within sample variability of cotton fibers length.

• Other key length parameters from the fibrogram that can improve yarn prediction models are identified.

• Yarn models with new length information performed better in predicting important yarn quality parameters than the current High Volume Instrument (HVI) and Advanced Fiber Information System (AFIS) outputs.

• Yarn models developed with a large set of commercial cotton bales could be an effective tool for the spinners to predict the quality of yarn and fabrics.

Acknowledgements

We would like to thank Cotton Incorporated for funding this research and Plains Cotton Growers for providing the PCIC cotton samples. We appreciate the help of the phenomics research laboratory technicians and staff at the Fiber and Biopolymer Research Institute, Department of Plant and Soil Science, Texas Tech University for testing our samples.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Cotton Incorporated under Grant number 17-533.