Analysis of curve parameters to characterize multidirectional fabric wrinkling by a double extraction method

Abstract

Fabrics with good shape-retention properties are strongly expected to improve the aesthetic feeling, comfort and easy-care performance of clothing in daily life, and the efficient characterization of the wrinkle recovery property of fabrics is a necessary approach to facilitate the development of garments with good shape retention. Here, a double extraction method was presented to evaluate fabric wrinkling based on the wrinkling-induced residual force–displacement curves. The correlation analysis was used to determine applicable evaluation indices in order to cluster the wrinkle recovery property of fabrics based on a K-means clustering algorithm. Moreover, subjective judgements were conducted and compared with the objective K-means cluster method. The results show that there is good consistency between objective K-means clustering and subjective judgements, indicating that the indices featured from wrinkling-induced residual force–displacement curves can be used to evaluate the wrinkle recovery of fabrics. Therefore, the double extraction method is a starting point for the rapid identification of wrinkle recovery of fabrics by the mechanical performance of textiles.

Keywords

fabric wrinkle double extraction method curve parameters cluster analysis

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References

AATCC Test Method 66-2008. Wrinkle recovery of woven fabrics: recovery angle.

ISO 2313-1972. Textiles. Determination of the recovery from creasing of a horizontally folded specimen of fabric by measuring the angle of recovery.

AATCC Test Method 124-2011. Smoothness appearance of fabrics after repeated home laundering.

AATCC Test Method 128-2013. Wrinkle recovery of fabrics: appearance method.

Mohri

Hosseiniravandi

Youssefi

. Objective evaluation of wrinkled fabric using radon transform. J Text Inst Proc Abstr 2005; 96: 365–370.

Choi

Kim

Jin

et al.

Objective wrinkle evaluation system of fabrics based on 2D FFT. Fiber Polym 2009; 10: 260–265.

Behera

Mishra

. Objective measurement of fabric appearance using digital image processing. J Text Inst 2006; 97: 147–153.

Zaouali

Msahli

El Abed

et al.

Objective evaluation of multidirectional fabric wrinkling using image analysis. J Text Inst 2007; 98: 443–451.

Liu

. Novel measurement for multidirectional fabric wrinkling using wavelet analysis. Fiber Polym 2014; 15: 1337–1342.

10.

Pourdeyhimi

. Assessing wrinkling using image analysis and replicate standards. Text Res J 1995; 65: 149–157.

11.

Reed

. Instrumental evaluation of fabric wrinkle recovery. J Text Inst 1995; 86: 129–135.

12.

Ding

Wang

. Characterization and assessment of fabric smoothness appearance based on sparse coding. Text Res J 2018; 88: 367–378.

13.

Cuminato

Keyes

. Evaluating fabric smoothness appearance with a laser profilometer. Text Res J 1998; 68: 900–906.

14.

. Fabric wrinkle evaluation using laser triangulation and neural network classifier. Optical Eng 1999; 38: 1688–1694.

15.

Kang

Cho

Whang

. A new objective method of measuring fabric wrinkles using a 3-D projecting grid technique. Text Res J 1999; 69: 261–268.

16.

Yao

. 3-D surface reconstruction and evaluation of wrinkled fabrics by stereo vision. Text Res J 2009; 79: 36–46.

17.

Wang

Liu

Pan

et al.

Exploring the relationship between bending property and crease recovery of woven fabrics. J Text Inst 2015; 106: 1173–1179.

18.

Pan

. Quantification and evaluation of human tactile sense towards fabrics. Int J Des Nat 2006; 1: 48–60.

19.

Macqueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of Berkeley symposium on mathematical statistics and probability. California: University of California Press. 1966, 281–297.

20.

Huang

. Extensions to the K-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov 1998; 2: 283–304.

21.

Kendall

Smith

. The problem of m rankings. Ann Math Stat 1939; 10: 275–287.

22.

Koziol

. Cluster analysis of antigenic profiles of tumors: selection of number of clusters using Akaike's information criterion. Meth Info Med 1990; 29: 200–204.