Development and optimisation of image analysis technique for fabric buckling evaluation

Abstract

Purpose -The purpose of this paper is to optimise parameters of digital image analysis to investigate the deformation behaviour of woven sample and to detect the onset and variation of wrinkling that occurs due to bias-tensioned fabric buckling. Design/methodology/approach -Using models of predescribed shape, the relationship between the digitized gray scale intensities and wrinkles of the surface are analysed and conditions of specimen illumination and filtering procedures are chosen. Findings -It is proposed to convert acquired images to binary to record the onset of buckling and to estimate critical buckling parameters of stretched woven samples. The threshold value is determined as mean value of approximated histogram of stretched specimen centre line. It is defined that profile curve and gray scale disperse presented by parameter CV can be used to obtain additional information and to compare behaviour of different samples during bias tension. Research limitations/implications -Proposed image analysis technique allows detection of the onset of buckling wave formation and evaluation of surface waviness changes in woven samples different in colour and weave type tension. However, the behaviour of fabric samples with sharp multicoloured and complicated patterns cannot be assessed by gray scale imaging. Originality/value -The proposed approach can be adjusted to investigate different wrinkling problems -buckling during simple shearing or picture frame test, seam puckering, draping.

Image-based method for the investigation of low flexure rigidity materials

“…Non-contact methods such as holography, speckle [1,2], Moiré interferometry [3], and digital image analysis [4][5][6][7][8][9][10][11][12][13][14][15] are proposed for the evaluation of strain dis-Corresponding author, jovita.dargiene@stud.ktu.lt tribution. With different techniques and equipments it is possible to record real-time strain distribution fields.…”

Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

“…Jurgita Domskienė et al. 6 used a pre-set shape model to study the relationship between digital gray intensity and surface wrinkles with uniform sample illumination conditions and filtering procedures, and finally converted the acquired image into a binary image, which is to record the initial state of wrinkles and predict the critical parameters of wrinkles. In 2014, Liu et al.…”

“…Among the five texture features, the best correlation with subjective vision is the inverse difference moment. Jurgita Domskiene _ et al 6 used a pre-set shape model to study the relationship between digital gray intensity and surface wrinkles with uniform sample illumination conditions and filtering procedures, and finally converted the acquired image into a binary image, which is to record the initial state of wrinkles and predict the critical parameters of wrinkles. In 2014, Liu et al 7 proposed a multi-directional fabric wrinkle measurement method in which the standard deviation coefficients of wavelet decomposition and GLCM were used to characterize fabric wrinkle features.…”

Experimental measurement of wrinkle formation during draping of non-crimp fabric

“…Although the information gained about the wrinkle geometry is somewhat limited in this approach, the method nevertheless provides a quantitative way of demonstrating the reduction of wrinkling with increased blank holder force. Domskienė et al [16] described a more rudimentary approach, again using the effect of wrinkles on scattering of light, to quantify wrinkle formation. More recently Christ et al [17] used a laser triangulation sensor and camera set-up to measure the shape of the draped fabric, in a system which is available commercially by Textechno.…”