1. Introduction
Visual attributes of textile fabrics represent a sub-group of sensory properties, perceived particularly by the 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 latter are much simpler to describe, with definitions widely accepted and measurement methods well established.
The importance of such fabric sensory attributes is indisputable. It is hard to imagine a consumer would buy a textile product without first looking at them. Therefore, it can be stated that the success of any new fiber, new finish or new textile product is largely dependent on the acceptance of its sensory traits [
1,
2,
3]. In this paper, we focus on measurement of fabric drape as one of the sensory attributes.
Fabric drape refers to the fabric shape or profile when held at the center/edge, such as used for curtains, or as a tablecloth or a skirt covering an object, often termed in the latter cases as the fabric formability, and is resulted from fabric’s response towards gravity due to its own weight [
2,
3].
The current approaches in evaluating all such attributes including drape are still quite primitive, and largely rely on human sensory judgment, which in many cases is not readily reproducible and repeatable, lagging far behind the need for fast product development from the industry, and for short product turn over time in the apparel market.
It is interesting to note that the major fabric sensory attributes, namely hand, drape and wrinkle recovery, are interconnected and governed by the same group of mechanical properties [
1,
4], thus implying a collective approach for measurement, i.e., because of this close interconnectivity, it is possible that the same instrument properly configured can measure all such fabric attributes.
Although there has been a long history of research and many attempts have been made to develop instrumental means to measure such fabric attributes, the resultant methods are not effective enough to meet the industrial requirements [
5,
6,
7,
8]. Pierce [
1], for instance, proposed several simple methods including the “cantilever” and “loop” sample shapes to measure the bending resistance as “… strictly a measure of the draping quality of a fabric”. Such approaches are reflected in several existing American Association of Textile Chemists and Colorists (AATCC) and American Society of Testing and Materials (ASTM) standard tests. However Chu et al. later pointed out that “two-dimensional distortion tests are incapable of differentiating between drape and paperiness, i.e., it is possible to select a piece of paper and a piece of fabric both of which have the same bending properties, yet it is doubtful that the paper will drape as well as the cloth” [
2]. Hearle et al. provided a comprehensive review of existing research on the subject [
9], and a very thorough study of fabric drape was done by Cusick [
3].
Then, there was a detailed review article in 1987 by Jacob and Subramaniam on the literature of fabric drape [
10]. For instance, multiple regression analysis was used to determine whether certain deformation properties (stiffness, shear, and extensibility) and structural characteristics (fabric weight, thickness, and density) can serve as reliable predictors of the drape of knitted fabrics [
11]. Several studies focused on static and dynamic drape of fabrics [
12,
13,
14]. There are also reported investigations on the draping behaviors of multi-layer textiles using digital image processing [
15,
16]. As expected, the challenging question of theoretical modeling of fabric drape phenomena has attracted much interest [
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31].
Fabric drape has been considered to be primarily determined not only by its bending but also its shear resistance, and in that order [
3,
9]. We also know that it is the shear properties that differentiate between a piece of paper and fabric.
The search and exploration for a better instrument for fabric drape test continues today, but very few commercial products are available, including notably the Fabric Drape Tester (better known as the Cusick Drapemeter [
3] or Chu method [
2]), by tracing the shadow of a draped fabric on paper. However, this test suffers from several shortcomings [
32,
33,
34] and thus has not been widely used by, for instance, the U.S. industry. It is a bit surprising that there is not even a U.S. standard for fabric drape test, a grave gap urgently required to fill. For anyone who has the experience of using the Cusick Drapemeter, several problems become immediately clear:
Poor reproducibility, i.e., multiple tests of the same sample often do not yield reasonably close results, and Chu thus recommended for more test replicas: “Five in each direction, warp and filling, is a reasonable number” [
2]. Knowing fabrics are anisotropic, one may argue why not in other directions?
Low sensitivity—only significant difference in fabric drape can be detected [
34].
It has a slow and cumbersome test process [
35].
Many fabrics tend to curl and twist when cut into specimens, which further affects the reproducibility, and even the physical meaning of the test results [
4].
A large size sample size is required: 30 cm in diameter [
36]. If five each are required in both warp and filling directions, that is too much fabric to ask for in many cases.
Although there have been variations [
32,
33,
37,
38] of the Cusick Drapemeter, attempting to eliminate or alleviate some of the problems in the original design, none of them seems to have improved to such degree that a commercial model has been widely accepted by industrial users. Imaging analysis has been [
15,
39,
40,
41,
42] applied to the test, but it can only improve the analysis of the results and can do little in dealing the problems intrinsic in the test principles.
The Japanese Kawabata’s fabric hand system (better known as the Japanese KES system) is probably among the very few attempted to address the overall relationships between fabric sensory attributes and mechanical properties [
43]. Then, there is a result by Pan and his coworkers from 1983 [
4,
44,
45], when a new instrument called PhabrOmeter fabric test system [
46] was developed and commercialized for evaluating sensory attributes of various types of fibrous sheets, as shown in
Figure 1. Unlike the Japanese KES system, no attempt is made to separately measure individual fabric properties (such as bending, compression, tensile and surface properties) deemed to be associated with fabric sensory attributes. Instead, this instrument is based on the previously proposed fabric extraction method [
4] with some critical improvements to generate comprehensive test results [
4,
44,
45]. Then, a computer algorithm will derive a series of parameters including a Relative Hand Value, the fabric Softness, Smoothness, Stiffness, etc. PhabrOmeter has been commercialized by Nu Cybertek Inc. in Davis CA [
46] and some successful applications have been reported [
47,
48]. In addition, an AATCC standard test method for the PhabrOmeter, AATCC TM202, has been devised to guide the users [
49].
It has long been established that fabric hand is closely related to its drapeability [
1,
50]. Given the fact that, during an extraction process on the PhabrOmeter, the fabric sample is going through a forced drape with complex yet low stress state, as in
Figure 1b, it is only logical to examine the potential for PhabrOmeter to be applied to fabric drape measurement. An added feature of using PhabrOmeter for fabric drape test is that, as the sample is cut into circular shape and extracted by a force exerted at the sample center, it thus “isotropicizes” the measurement process and eliminates the sample directional effect—a problem severely plaguing the results of the Cusick Drapemeter [
35].
2. A New Criterion for Fabric Classification
Before proceeding any further, one major issue has to be settled. With huge varieties of fabrics, the first challenge for any quality evaluation scheme is to categorize the products into fewer, and more homogeneous groups if a general test method is to be applicable to all fabric types. 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.
There is no formally established fabric classification scheme, and some parameters including weave types, fabric weight and thickness have often been used expediently in categorizing fabrics [
32,
51]. The Japanese Kawabata’s KES system approached this problem by first choosing fabrics for the same applications, and then grouping them based on the fabric weight to yield four major groups including the fabrics for Men’s winter and summer suits, as well as Women’s medium-thickness and thin dress [
43].
The PhabrOmeter has been applied to cover much wider range of products, including textile fabrics and paper tissues so that more general and representative schemes have to be established. Here are the rules we used to establish general parameters, by which all products can be classified into groups that both make sense and are easy to use:
As the most fundamental parameters in determining fabric performance, both fabric weight and thickness should be included in the resultant parameters.
In developing PhabrOmeter, it is known that, in the fabric extraction process, the fabric compaction density in the nozzle in
Figure 1b is the key factor [
52,
53] in generating the test results. Increasing either fabric weight or thickness will lead to an increased fabric compaction density, i.e., both fabric weight and thickness affect the resultant parameter in the same trend.
Structural differences (weaves, fiber types, etc.) can be specified afterwards within each resultant group, if necessary.
According to Rule 1, the actual fabric volumetric density
ρ (weight/volume) becomes the first logical candidate:
where
W is the fabric weight (weight/area) and
T is the fabric thickness (length). However, use of
ρ would violate the second rule, i.e., both fabric weight and thickness have to influence the result in the same trend. Alternatively, we define a new parameter λ
In this case, changing either fabric weight or thickness will alter the λ value in the same trend, thus satisfying both rules. λ has the unit of (weight/length) and is expediently termed linear density.
Table 1 provides the normal possible ranges for the three quantities W, T and λ, covering most commercial fibrous sheet products.
Although λ value is a linear function of either
W or
T, it is still questionable to classify all products by evenly dividing the λ value, as there is no proof that λ value is evenly distributed over the entire scope. It is conceivable that, over the possible fabric ranges, there are fewer extremes or exceptions; in other words, a normal distribution of λ would be a more acceptable assumption. The division shown in
Table 2 is the grouping result based on this consideration, currently used in the PhabrOmeter.
Again, as it is less meaningful to compare samples from different groups, all data processing, calculations, transformations and comparisons should be conducted within each major group. Of course, fabric classification is a hugely complex issue and the groups shown in
Table 2 are just general yet useful results. It may be practiced to further classify products in the same major group into subgroups if deemed necessary.
For this project, 40 fabrics of various types were collected from Cotton Inc. (Raleigh, NC, USA). Based on their λ values, the 40 fabrics were found to span all four major groups (S = 2), (L = 27), (M = 10) and (H = 1), as listed in
Table 3:
3. Samples and Test Methods
Of the 40 fabrics in
Table 4, the drape for 38, with the Cusick drape data using the imaging approach of Option B in the widely recognized international standard ISO 9073-9:2008, are provided, while the drape data for Samples #9 and #10 are not available. Our textile testing lab at University of California Davis (UCD) conducted the following experiments on all 40 fabrics including: fabric thickness (ASTM D1777) and fabric weight (ASTM D3776). We also tested all 40 fabrics using Cusick method in following steps:
Option A in ISO 9073-9:2008 was followed, using 30 cm specimen diameter.
Three specimens for each fabric were tested on each side (face and back), thus six data points were obtained for one fabric.
For each of the six readings from a given fabric, a drape coefficient value D was calculated as
where
mt is the original weight of the paper ring and
ms the weight of shadowed area only. Six
D values were then averaged to yield the mean drape coefficient for the fabric.
Note that, in ISO 9073-9:2008 Option A, the area equaling to the center-plate was not included in the calculation since we only weighed the paper ring without the center-circle part. Therefore, when the specimen is very limp (the shadow area is very small), the drape coefficient could be very small (theoretically down to 0%). Thus, to be consistent with Option B used by Cotton Inc., we also rectified the UCD data by making mt equal to the weight of a paper of circular shape with the same diameter to the outer diameter of the paper ring (i.e., adding the weight of the center-part of the paper ring) in the calculation, so that all our Cusick data were increased, with a minimum for Sample #33 from 20.7% to 38.8%.
In addition, according to ISO 9073-9:2008 standard, different diameters should be adopted for certain types of fabric to make the test consistent with the real condition. However, the document also states that “Results obtained on test specimens of different diameters are not directly comparable, in all cases, tests also need to be carried out on a 30 cm diameter test specimen, regardless of the drape coefficient”. Thus, all UCD Cusick data were obtained using 30 cm specimen diameter only.
6. Conclusions
The fabric linear density λ defined in this paper is an intrinsic fabric parameter by definition, which reflects the influence of both fabric weight and thickness on fabric extraction using PhabrOmeter is related closely to a series of fabric properties, and is easy to calculate. Our results demonstrate that grouping fabrics based on this linear density indeed improves performance comparability. With the validation presented in this study, other major parameters including fabric weave and fiber type do not show any conflicts with or supersede over the fabric linear density λ. We hence recommend that it be used more widely as a primary fabric classifier.
Regarding fabric drape test using Cusick Drapemeter (ISO 9073-9:2008), our data confirm that both Option A, and Option B are consistent. This study further demonstrated that using PhabrOmeter to measure fabric drape properties is a more reliable and effective approach—it is quick, sensitive and with high repeatability [
4,
9]. In addition, it has the additional characteristics:
cutting specimens into circular shape and being extracted during test by a force exerted at the sample center actually “isotropicize” the measurement process to reduce the variation caused by fabric directionality or anisotropies;
as the samples are forced to drape, different sample sizes recommended in Cusick test are no longer necessary; and
fabric curl, a tough problem in Cusick method, is no longer a concern for PhabrOmeter when the sample is actively compressed during test.
On the other hand, this approach will not be sensitive if fabric directionality or curling is of interest to be studied, which unfortunately is not handled well using Cusick approach either.
Before conducting drape test, the test face should be identified and recorded to maintain consistence. Face/back sides can lead to significantly different results.
Comparing the drape behaviors of different fabric types is not appropriate, and dividing them into more homogeneous groups, e.g., using fabric linear density λ, can make such comparison more meaningful. Logically, once in the same group defined by fabric linear density λ, further grouping based on fabric structure and fiber types can be done, if necessary. Our results here show, however, such further division does not result in further improvement.