The dynamics of fabric drape

Postle, R.; Postle, Jacqueline R.

doi:10.1108/09556229810222043

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Numerous scientists and investigators have been engaged for many years in determining the mechanical properties of fabrics because of the importance of predicting the fabric behavior during production processes and the final appearance of fabric and clothes [8][9][10][11]. Over the years many complex measurement systems have been established.…”

Section: Introductionmentioning

confidence: 99%

Determining Pseudo Poisson’s Ratio of Woven Fabric with a Digital Image Correlation Method

Hursa

Rolich

Ražić

2009

Textile Research Journal

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In this paper the determination of pseudo Poisson’s ratio of woven fabric with a digital image correlation method is presented. Measurements were performed on three cotton woven fabric samples which were prepared according the standard ISO 13934-1:1999. The fabric sample was exposed to tensile loading of 1% strain on a tensile test machine. Testing was simultaneous recording with a digital video camera. These video recordings were afterwards processed in the MATLAB program and the pseudo Poisson’s ratio determined according to the displacement in the x and y axis directions. The pseudo Poisson’s ratio was determined for three woven fabrics in the warp and weft directions. The results of the investigation show that the values of the pseudo Poisson’s ratio are in the range from 0.2 to 0.5, which agrees with results also found in the literature. According to the investigation it can be concluded that the value of the pseudo Poisson’s ratio depends on the weave type and the number of yarns in the fabric. For woven fabric in plain weave a higher pseudo Poisson’s ratio is determined (warp direction from 0.335 to 0.500, weft direction from 0.392 to 0.484). The pseudo Poisson’s ratio for woven fabric in twill weave has values in the warp direction from 0.281 to 0.329, and in the weft direction from 0.183 to 0.214.

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Section: Introductionmentioning

confidence: 99%

Determining Pseudo Poisson’s Ratio of Woven Fabric with a Digital Image Correlation Method

Hursa

Rolich

Ražić

2009

Textile Research Journal

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“…The pixel cosine similarity between filling results and real grayscale images reaches 0.97. (2) The filled single grayscale images contain more shadow information than the corresponding binary image. With a uniform grayscale range, different filled grayscale images contain stronger comparability than binary or color images of draped fabric.…”

Section: Discussionmentioning

confidence: 99%

“…The research of fabric drape is not only beneficial to the design of fabric but also provides a significant reference for fabric inquiry. Most of the research on fabric drape performance so far has focused on the characterization of drape capability, 1 the relationship between static drape performance and dynamic draping, 2,3 the relationship between drape performance and mechanical properties, 4,5 the relationship between drape performance and structural parameters, predicting drape coefficient, 6,7 predicting drape morphology, [8][9][10][11][12] new methods to capture three-dimensional (3D) model of draped fabric [13][14][15][16] and so on. However, there are rare reports about the comprehensive comparative analysis of fabric drape performance.…”

Section: Introductionmentioning

confidence: 99%

Filling the binary images of draped fabric with pix2pix convolutional neural network

Zhong

Gong

et al. 2020

Journal of Engineered Fibers and Fabrics

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To fill the binary image of draped fabric into a comparable grayscale image with detailed shade information, the three-dimensional point cloud of draped fabric was obtained with a self-built three-dimensional scanning device. The three-dimensional point cloud of drape fabric is encapsulated into a triangular mesh, and the binary and grayscale images of draped fabric were rendered in virtual environments separately. A pix2pix convolutional neural network with the binary image of draped fabric as input and the grayscale image of draped fabric as output was constructed and trained. The relationship between the binary image and the grayscale image was established. The results show that the trained pix2pix neural network can fill unknown binary top view images of draped fabric to grayscale images. The average pixel cosine similarity between filling results and ground truth could reach 0.97.

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“…For instance, multiple regression analysis was used to determine whether certain deformation properties (stiffness, shear, extensibility) and structural characteristics (fabric weight, thickness, and density) can serve as reliable predictors of the drape of knitted fabrics [7]. Several studies focused on static and dynamic drape of fabrics [8][9][10]. There are also reported investigations on the draping behaviors of multi-layer textiles using digital image processing [11,12].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Measuring Fabric Drape with a Novel Parameter for Classifying Fabrics<strong> </strong>

Pan¹,

Lin²,

Xu³

et al. 2018

Preprint

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With huge varieties of fabrics, the first challenge for any performance evaluation is to categorize the vast types of the products into fewer, more homogeneous and thus akin groups. Classification or sorting is arguably the first step of any scientific investigation, and comparison of product quality is meaningful only when conducted within a group of comparable products. A new criterion termed fabric linear density λ is first proposed in this paper so that fabrics can in general be divided into 4 groups. The derivation and validation of this parameter are provided.The importance of fabric drape is almost self-evident, but there is still no effective ways to measure this fabric attribute. The Cusick Drapemeter suffers from its low repeatability and low sensitivity, and is hence not widely or frequently used. The PhabrOmeter, along with the fabric linear density λ, is proposed and demonstrated in this study as a much more efficient alternative for fabric drape test. By actually testing 40 various fabrics, the principle, procedure and results of this method is presented in this paper. IntroductionVisual attributes of textile fabrics represent a sub-group of sensory properties, perceived particularly by visual sense of consumers. Such visual qualities include fabric drape, surface state and luster, and, by logical extension, the fabric wrinkle recovery, fabric surface retention and stain resistance, i.e., all fabric appearance related esthetic characteristics. We call them fabric attributes to differentiate from the term fabric properties such as the fabric weight and fabric strength, for the latters are much easier to describe and with definitions widely accepted, and measurement methods well established.

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The dynamics of fabric drape

Cited by 5 publications

References 2 publications

Determining Pseudo Poisson’s Ratio of Woven Fabric with a Digital Image Correlation Method

Determining Pseudo Poisson’s Ratio of Woven Fabric with a Digital Image Correlation Method

Filling the binary images of draped fabric with pix2pix convolutional neural network

A New Method for Measuring Fabric Drape with a Novel Parameter for Classifying Fabrics<strong> </strong>

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