Tactile evaluation of down jacket fabric by the comprehensive handle evaluation system for fabrics and yarns

Abstract

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.

Keywords

handle evaluation fabrics down jacket handle characteristics objective evaluation

One always evaluates the quality of fabrics by handle. Tactile handle is used to judge the performance of the fabric by human touch, which is a narrow sense of fabric handle. In 1925, Binns^1,2 first raised the issue of fabric handle. In 1930, Peirce³ used the cantilever beam method to measure the bending length and bending stiffness of a specimen to express the fabric handle. The research of fabric handle has gradually become a hot spot. In 1951, Hoffman et al.⁴ proposed that the denier, elastic modulus and cross-sectional shape of fiber were three important factors that affect the fabric handle. They researched the effects of various physical and mechanical factors on the fabric handle, and found 13 factors that would affect fabric handle. In 1958, Howorth and Oliver⁵ used the mathematical statistics method to find the relationship between the physical characteristic value and the amount of hand feeling. The proposal of the Kawabata evaluation system (KES) in the 1970s and the fabric assurance by simple testing (FAST)-style instrument in the 1990s pushed the objective assessment of fabric handle to a higher level.

Subjective evaluation of fabrics is the traditional tactile handle test. But it cannot rule out subjective arbitrariness. Subjective evaluation requires highly skilled testers. The results vary from person to person and from time to time. It is very limited, lacking quantitative description and has poor data comparability. The fabric handle evaluation system objectively measures the physical and mechanical quantities related to the fabric through the instrument, then relates it to the handle evaluation. It can calculate the characteristics and grades of fabric styles based on measuring the physical and mechanical quantities of fabrics. The Kawabata evaluation system for fabrics (KES-F)^6,7 is a set of fabric handle testers developed in the 1970s to test the basic mechanical behavior of fabric under low load. The FAST system⁸ is a set of test systems developed in the early 1990s to test the basic mechanical behavior and the dimensional stability under low load. The above two test systems use multiple instruments to carry out multiple measurements to comprehensively evaluate the fabric handle. However, they are expensive as they require more instruments and space. Therefore, it is necessary to establish a measurement method that uses a single instrument to evaluate the texture of the fabric.

The PhabrOmeter system for fabric feel evaluation is an intelligent-style instrument developed on the basis of the nozzle extraction style instrument.^9,10 The system features three fabric touch characteristics, namely stiffness, smoothness and softness,¹¹ and the total touch value is obtained based on the Karhunen–Loeve transformation and weighted Euclidean distance.¹² The handle indexes and total handle value just give the relative feel value compared with the standard sample. The Fabric Touch Tester (FTT)¹³ and Wool HandleMeter¹⁴ are also simple and effective testers to predict the feel of the fabric. The FTT is developed by SDL Atlas, using different measuring components to test various mechanical properties of the fabric. It is essentially an instrument system that measures multiple indicators on a single machine. Wool HandleMeter uses a ring test to obtain the pushing force and the corresponding force–displacement curve.¹⁵ It focuses on the measurement of wool fabrics, and combines objective evaluation with subjective evaluation. However, the Wool HandleMeter has not been applied to the characterization of woven fabrics, and no research has been published in this regard. These methods are mainly limited to laboratory research.

The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) is a current fabric handle instrument, which is derived from the comprehensive evaluation system of fabric yarn feel.^16–18 The CHES-FY is based on the principle of three-point beam bending,^19–21 and the performance of compression, bending, friction, tension and shear of the fabric can be obtained by a single measurement through the force–displacement curve.^22–28 It realizes the in situ combination to characterize the stiffness of the fabric and other styles.^29,30 The CHES-FY, based on the three-point bending method, simulates the thumb, index finger and middle finger of a human hand, and performs a series of tests on the sample, which is closer to the subjective tactile handle.

Down jackets are needed in the cold winter and have increasingly become a necessity in winter. Down jackets are composed of fabrics and internal fillings. Down jacket fabric is an important part of down jackets, but there are few relevant studies up to now. Therefore, this paper uses the CHES-FY to test the down jacket fabrics, analyzes the experimental force–displacement curve and selects the effective evaluation index of fabric handle to express handle characteristics. Through subjective and objective evaluation, the fabric handle of the down jacket fabrics is accurately scored, and the feasibility of evaluating fabric handle is verified.

Experimental samples

Fifty-two kinds of typical down jacket fabrics from textile mills and markets were purchased, which were divided into two parts. Ten down jacket fabrics were randomly selected for verifying the theoretical model, and the remaining 42 fabrics were used to build the model. The fabrics were mainly polyester and polyester blended fabrics, and they were all woven fabrics. A YG141 thickness gauge was used to measure the thickness of the fabric, and the average value of the five measurements of different areas of the same sample was used as the final result of the thickness. The mass of fabric per unit area was measured by five samples of 100 cm² using an electronic balance with an accuracy of ±0.1 mg. The thicknesses of the fabrics were between 0.03 and 0.48 mm (the standard deviation is 0.09 mm), and the masses per unit area were between 28 and 220 g/m² (the standard deviation is 51 g/m²). The cut samples were balanced for 24 hours under standard conditions of (20 ± 2)℃ and (65 ± 3)% relative humidity (RH), and all tests were conducted under the above standard conditions.

Objective measurement of the comprehensive handle evaluation system for fabrics and yarns

Fabric samples were cut with three directions, namely warp (J), weft (W) and 45° directions to the warp direction. The cutting size was 500 mm × 55 mm. The side yarns on both sides were removed to set the size at 500 mm × 50 mm, as shown in Figure 1.
Figure 1.
Fabric sample size.

The test procedure of the CHES-FY can be divided into five steps, as shown in Figure 2. The first step is the thickness measurement, that is, step I. Before placing the sample in the test area, the left-hand pressure roller in Figure 2 will drop and rise once. Then the left-hand pressure roller will touch the fabric sample. The thickness is obtained by the drop difference between the two contacts. In step II, called compression, the pressure rollers slowly move down to the set value of compression force. Then the pressure rollers move up, and we enter step III, called bending. The pressure plate slowly moves down to the set values, which causes the fabric sample to bend and deform. Step IV is friction. The pressure rollers move down slowly with a certain pressure value at both ends of the sample when the pressure plate keeps the position where it stayed in step III. If the force of the pressure rollers reaches the set value, the pressure plate will continue to move downward to the set displacement. When the force of the pressure rollers on both sides increases to prevent the fabric from sliding, then the pressure plate continues to move until the stretching displacement or force reaches the limit to complete step V, called stretching. All these steps vividly imitate the subjective evaluation process when people hold the fabric between their fingers to press, bend, rub and stretch.
Figure 2.
Device and schematic diagram of the comprehensive handle evaluation system for fabrics and yarns (CHES-FY): (a) CHES-FY; (b) schematic diagram.

The system parameters are set as follows. The initial pressure value for thickness measurement is 10 cN, the maximum compression force is 200 cN and the maximum bending displacement of the pressure plate is 200 mm. The force of the pressure rollers during step IV is 50 cN, and the maximum friction displacement is 300 mm. In step V, the force of the left-hand pressure roller is 900 cN, the maximum force of the right-hand pressing roll is 400 cN and the maximum stretching displacement is 250 mm.

Typical curve and index of the CHES-FY

The typical force–displacement curve obtained by taking sample Y11 as an example by the CHES-FY is shown in Figure 3. According to the shape characteristics of the curve, it is divided into the following stages, thickness and compression, bending, friction and stretch. Through the analysis of sample deformation and mechanical characteristics, objective mechanical and physical performance indexes can be extracted from the force–displacement curve, as shown in Table 1.
Figure 3.
Force–displacement curve (sample Y11).

Table 1.
Objective mechanical and physical properties of fabric by the comprehensive handle evaluation system for fabrics and yarns

Mechanical properties Index Indicator meaning Unit Explanation

Compression ΔT Thickness difference mm The difference of fabric thickness between TL and initial pressure 10 cN.

TL Fixed load thickness mm The size of the load depends on the demand. The current default value is 200 cN.

Ac Curve area cN·mm Integral of force value to displacement.

Sc Slope cN/mm Use the least squares method to fit all the data in the bending stage, and the slope of the fitted curve.

Ltc Linearity — Lts = 2WT/ɛm * Fm

Bending Sb Slope cN/mm All the data in the bending stage are fitted by the least square method, and the slope of the fitted curve.

P Peak cN Maximum bending part.

Ab Curve area cN·mm Integral of force value to displacement.

W Half-width — Half-peak width, which refers to the full width of the band when the height of the absorption band is half the maximum, that is, the width of the transmission peak when the peak height is half.

Friction F Friction (mean) cN Friction phase, the average of all force values.

μ Coefficient of friction — —

Sf Standard deviation of friction coefficient cN/mm —

Stretch E Elongation under constant load mm The length when the force value is 400 cN.

L Load under constant elongation cN When the extension is 8 cm, the required force value.

As Curve area cN·mm Integral of force value to displacement.

Ss Slope cN/mm Use the least squares method to fit all the data in the stretching stage, and the slope of the fitted curve.

Lts Linearity — Lts = 2WT/ɛm * Fm

Subjective evaluation

Fifty-two kinds of down jacket fabrics were cut into warp and weft dimensions of 200 mm × 200 mm for subjective judgment. Five Chinese subjects aged 20–30 years old formed a subjective evaluation group and judged according to the AATCC³¹ evaluation method. The time required for each subjective measurement item and the details of the subjective evaluation are shown in Table 2. It was divided into 1–5 points by grading. Fullness and fluffiness (FF) is the ability to recover in the thickness direction of the fabric, with a fluffy feeling, making people feel warmth and comfort, and is full of elasticity. Subjects felt that greater plumpness would give a higher FF value. Softness (SF) is the fabric's resistance to compression and deformation, light and soft. The lower the SF value subjects gave, the softer the fabric was. Stiffness (ST) is the fabric's resistance to bending deformation, rigidity and stiffness, expressed in terms of force level under a fixed bending deflection. The higher the ST value was, the stiffer the fabric felt. Smoothness (SM) is the performance of the fabric against friction and deformation. The lower the SM value, the smoother the surface was. Looseness and tightness (LT) represent the fabric's resistance to tensile deformation. A fabric with a higher LT value would feel tighter. Comprehensive handle (CH) is the comprehensive resistance of the fabric to compression, bending, friction and stretching against external deformation, expressed by the comprehensive result level of fullness, softness, stiffness, slippage and elasticity. Subjects would give a higher CH value if the overall touch felt better.
Table 2.
Subjective measurement items and style description

Handle characteristics Rating level and description
Test time per fabric

1 2 3 4 5

FF Worst Bad Medium Better Best 20 s

SF Best Better Medium Bad Worst 20 s

ST Worst Bad Medium Better Best 20 s

SM Smoothest Smooth Medium Rough Roughest 20 s

LT Loosest Loose Medium Tight Tightest 20 s

CH Worst Bad Medium Better Best 20 s

FF: fullness and fluffiness; SF: softness; ST: stiffness; SM: smoothness; LT: looseness and tightness; CH: comprehensive handle.

Before the test, the subjects should wash their hands with the same soap and dry them with the same towel, and then stay in the standard condition room for 30 minutes. The subjects were asked to perform six finger contact methods on the sample, namely initial contact, press, fold, touch, clamp and pull, squeeze. According to the description of the touch methods, the samples were distributed to the subjects to perform six judgment processes, as follows. (1) Initial contact: lightly touch the sample with the fingertip. (2) Press: drag the sample with the middle finger or the index finger and press the sample with the thumb down. (3) Fold: press the sample in half, observing the degree of bending. (4) Touch: hold the fabric with one hand, touch with the other hand. (5) Clamp and pull: hold and pull both sides of the sample with both hands. (6) Squeeze: squeeze the sample with your thumb, fingers and palm to form a fist, as shown in Figure 4.
Figure 4.
The touch gestures in the subjective evaluation process: (a) initial contact; (b) press; (c) fold; (d) touch; (e) clamping; (f) squeezing.

Results and discussion

Subjective evaluation analysis

In order to analyze whether the subjective evaluation results of the five testers were consistent or not, the Kendall consistency coefficient (W) was calculated and a χ² test was performed. According to Equations (1) and (2), the obtained consistency coefficients were all significant at the 0.01 level
$W = \frac{12 \sum_{i = 1}^{n} (T_{i} - \bar{T})^{2}}{m^{2} (n^{3} - n)}$
(1)

$χ^{2} = m (n - 1) W$
(2)
where T_i is the score value of the ith fabric sample, $\bar{T}$ is the average value of the score values of all fabric samples, m is the number of testers and n is the number of fabric samples. The Kendall consistency coefficient can effectively measure the consistency of the fabric feel grade. The higher the value, the better the consistency. It can be seen from Table 3 that the Kendall consensus coefficients were quite high, which indicated that the testers' evaluations were highly consistent. The statistical values χ² were all greater than χ²(52)_0.01 = 78.62, indicating that the subjective evaluation consistency of the five evaluators was at the 0.01 level, which is reliable. Therefore, we believed that the average value of their subjective evaluation of 52 fabric samples can be used to represent the overall grade of each handle feature of the fabric.
Table 3.
Kendall consistency coefficient (W) and χ² test results

FF SF ST SM LT CH

W 0.854 0.880 0.886 0.780 0.657 0.828

χ ² 217.705 224.292 225.847 198.781 167.652 211.252

FF: fullness and fluffiness; SF: softness; ST: stiffness; SM: smoothness; LT: looseness and tightness; CH: comprehensive handle.

In order to further analyze the subjective evaluation results, the statistical results of the judges' subjective evaluation are shown in Table 4, which shows that five judges had a similar average response to the ranking of the 52 fabrics. In addition, the ranking of the fabrics was within the defined average range of 1–5. All the averages except LT and CH were close to 3.0, and the average values of LT and CH were close to 4.0 and 3.5, respectively. From the analysis of variance of each evaluation index, it can be seen that the grade distribution of SM and CH was the largest, and the variance value of FF and LT was the smallest. This showed that the tightness of down jacket fabrics was relatively large, the feeling of fullness was about the same and the subjective feeling of slippage was quite different. The comprehensive handle of down jacket fabrics may be most closely related to the smoothness.
Table 4.
Statistical results of the subjective evaluation by the five judges

1
2
3
4
5
Ave.

Ave. Var. Ave. Var. Ave. Var. Ave. Var. Ave. Var. Ave. Var.

FF 3.1 0.8 2.9 0.8 3.2 0.9 3.2 1.2 3.2 0.8 3.1 0.9

SF 3.3 1.2 3.5 1.2 3.5 1.2 3.4 1.5 3.2 1.5 3.4 1.3

ST 3.2 1.2 3.0 1.5 3.2 1.1 3.4 1.2 3.1 1.4 3.2 1.3

SM 3.3 1.7 3.0 2.2 3.2 1.8 3.2 1.6 3.2 2.6 3.2 2.0

LT 4.5 0.7 4.2 0.8 4.5 0.7 4.3 0.7 4.1 0.5 4.3 0.7

CH 3.7 1.5 3.3 1.7 3.6 1.4 3.4 1.7 3.3 2.0 3.5 1.6

FF: fullness and fluffiness; SF: softness; ST: stiffness; SM: smoothness; LT: looseness and tightness; CH: comprehensive handle.

As shown in Table 5, in order to further confirm the internal relationship between the fabric touch characteristics of subjective evaluation, a correlation analysis between the six fabric touch characteristics was conducted. FF was positively correlated with SF, ST and LT; SF was positively correlated with ST and LT; ST was positively correlated with LT; SM was negatively correlated with FF, SF, ST and LT. CH was negatively correlated with FF, SF, ST, SM and LT. From the results of the correlation coefficient, it can be seen that the highest correlation coefficient of the six main hand features was 0.961, which was the correlation coefficient between ST and SF. It meant that the softer the fabric was, the worse its stiffness was. From the point of view of comprehensive touch, people were particularly sensitive to the overall rigidity of the down jacket fabric, and the bending performance of the fabric directly affects the human handle. Harder fabrics felt worse, while softer fabrics felt better. The correlation coefficients of CH, FF and SM were negative. It can be seen that the lighter fabric with a better texture and the fabric with a certain rough texture felt better. Most of the down jacket fabrics were fabrics with higher tightness, which was generally relevant in the comprehensive evaluation of the down jacket fabrics. In addition, LT was related to the comprehensive handle, but because most of the down jacket fabrics were relatively tight fabrics, the correlation was weak. The effect of looseness and tightness can be ignored in the evaluation of down jacket fabrics, but the looseness and tightness of the fabrics should be considered as a part of the comprehensive handle. Therefore, it was feasible to divide CH into SF, ST, SM and LT.
Table 5.
Correlation coefficients of the six fabric touch characteristics

FF SF ST SM LT CH

FF 1

SF 0.931 1

ST 0.903 0.961 1

SM −0.822 −0.856 −0.847 1

LT 0.206 0.372 0.368 −0.162 1

CH −0.465 −0.550 −0.619 −0.530 −0.094 1

**
Correlation is significant at the 0.01 level (two-tailed). Correlation is significant at the 0.05 level (two-tailed).

FF: fullness and fluffiness; SF: softness; ST: stiffness; SM: smoothness; LT: looseness and tightness; CH: comprehensive handle.

Objective test analysis

Using the CHES-FY to test the warp, weft and 45° directions of 52 down jacket fabric samples, the resulting force–displacement curve is shown in Figure 5. It can be seen from Figure 5 that the force–displacement curve had the same trend as the typical curve. It can be seen from the curves of the 52 kinds of down jacket fabrics that different fabric samples had different sizes at each stage. The curves of all tested down jacket fabrics in the stages of thickness, compression and bending had very little difference in the warp, weft and 45° directions. It showed that these properties of the samples had little to do with direction. The difference between the fabrics in the friction stage was obvious, but the results in the three directions were relatively consistent, indicating that this performance can be used to distinguish different down jacket fabrics. The difference in the three directions of the stretch phase of the down jacket fabric was the most obvious, especially in the 45° direction, where the stretch slope changed greatly. Not only was there a difference in the 45° direction between different down jacket fabrics during the stretching stage, but also the slope of the same down jacket fabric in the 45° direction was smaller than that in the warp and weft directions. In addition, the slope of the curve and the area of the four stages of compression, bending, friction and stretching were very sensitive to changes in fabric properties, which can reflect the corresponding main feel characteristics of the fabric.
Figure 5.
Force–displacement curves of the 52 kinds of fabrics. (a) Warp; (b) Weft; (c) 45° direction.

At the same time, principal component analysis was carried out on the fabric characteristic indexes tested by the instrument. The purpose of the main factor analysis was to extract the main factors that reflected the style characteristics of the 42 down jacket fabrics from the 17 indicators obtained by the CHES-FY. The Kaiser–Meyer–Olkin (KMO) sampling suitability number obtained by the KMO and Bartlett test (Table 6) is 0.714 > 0.6, and the Bartlett sphericity test had a significance of p < 0.05, indicating a strong correlation between the data and availability for principal component analysis.
Table 6.
Kaiser–Meyer–Olkin (KMO) and Bartlett inspection

KMO sampling suitability measure 0.709

Bartlett sphericity test Approximate chi-square 1430.653

Degrees of freedom 136

Distinctiveness 0.000

It can be seen from Table 7, showing the total variance analysis, that a total of five feature values were extracted. The characteristic value of the first main factor was 6.955, which can explain 40.911% of the original 17 variables; the characteristic value of the second main factor was 2.623, which can explain 15.427% of the original 17 variables, and the cumulative contribution rate of the first two factors reached 56.338%. The cumulative contribution rate of the first five factors reached 82.784%.
Table 7.
Analysis of total variance

Component Initial eigenvalues
Extraction sums of squared loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 6.955 40.911 40.911 6.955 40.911 40.911

2 2.623 15.427 56.338 2.623 15.427 56.338

3 2.211 13.004 69.342 2.211 13.004 69.342

4 1.207 7.103 76.445 1.207 7.103 76.445

5 1.078 6.339 82.784 1.078 6.339 82.784

6 0.689 4.053 86.837

7 0.602 3.543 90.380

8 0.480 2.825 93.205

9 0.364 2.138 95.344

10 0.331 1.945 97.288

11 0.170 1.000 98.289

12 0.102 0.600 98.889

13 0.083 0.491 99.380

14 0.053 0.314 99.694

15 0.049 0.289 99.982

16 0.003 0.018 100.000

17 7.593 E-7 4.467 E-6 100.000

Extraction method: principal component analysis

Since the variables with specific gravity affected the nature of the main factor, the rotated component matrix was conducted, shown in Table 8. With the maximum variance rotation, the original variables contained in each main factor were sorted and arranged according to the basic properties and weights; the properties were close to the original, and the variables were clustered with each other. The first main factor was mainly determined by the parameters Sf, μ, F, W, Ab and P, which was related to friction and bending performance. The first main factor was defined as the smoothness (SMo). The second main factor was mainly determined by the parameters Ltc, Sc, E, P and ΔT, and was related to the compression, bending and tensile properties. The second main factor was defined as softness (SFo). The third main factor was mainly determined by the parameters Ac, TL and ΔT, mainly related to compression performance, which was defined as fullness (FFo). The fourth main factor was mainly determined by the parameters L, As and Lts, mainly related to tensile properties. The fourth main factor was defined as elastic degree (LTo). The fifth main factor was mainly determined by the parameters Sb and Ss, and was related to bending and tensile properties. The fifth main factor was defined as stiffness (STo).
Table 8.
Composition matrix after rotation

Component

1 2 3 4 5

Sf .857 −.127 −.098 .089 .341

μ .826 −.310 .007 −.139 .356

F .825 −.310 .007 −.139 .356

W .802 .035 .195 −.089 −.157

Ab .613 −.581 .445 −.084 −.118

Ltc −.162 .846 −.364 .159 .018

Sc −.071 .845 −.144 .056 −.160

E −.206 .667 .089 .369 −.213

P .518 −.623 .474 −.091 −.121

Ac −.026 −.020 .862 −.077 .102

TL −.074 .313 −.793 .127 −.144

ΔT .233 −.545 .679 −.113 −.156

L −.093 −.034 −.101 .894 −.059

As −.210 .279 −.078 .886 −.104

Lts .106 .164 −.112 .776 .119

Sb .233 −.019 .267 .065 .842

Ss .304 −.335 −.344 −.415 .566

Extraction method: principal component analysis. Rotation method: Caesar's normalized maximum variance method. The rotation has converged after 11 iterations.

Characterization of fabric handle

The five principal components obtained by objective evaluation were comprehensive variables obtained by dimensionality reduction from the 17 indicators of the original data, and correspond to the five handle characteristics in the subjective evaluation. By standardization of the 17 indicators, the relationships between each group of main factors and their related indicators by studying the relevant component matrix were found. Then, the data was divided in the Factor Load Matrix by the square root of the eigenvalues corresponding to the principal components to get the coefficients corresponding to each of the five principal components. Finally, the obtained coefficient with the normalized data (the normalized data is recorded as ZSf*, Zμ, ZF, ZW…) was multiplied to get the principal component expression, namely
$\begin{matrix} SMo = 0.325 ZSf + 0.313 Z μ + 0.313 ZF \\ + 0.304 ZW + 0.232 ZAb - 0.061 ZLtc \\ - 0.027 ZSc - 0.078 ZE + 0.196 ZP \\ - 0.010 Zac - 0.028 ZTL + 0.088 Z Δ T \\ - 0.035 ZL - 0.080 ZAs + 0.040 ZLts \\ + 0.088 ZSb + 0.115 ZSs \end{matrix}$
(3)

$\begin{matrix} SFo = - 0.078 ZSf - 0.191 Z μ - 0.191 ZF \\ + 0.022 ZW - 0.359 ZAb + 0.522 ZLtc \\ + 0.522 ZSc + 0.412 ZE - 0.385 ZP \\ - 0.012 ZAc + 0.193 ZTL - 0.337 Z Δ T \\ - 0.021 ZL + 0.172 ZAs + 0.101 ZLts \\ - 0.012 ZSb - 0.207 ZSs \end{matrix}$
(4)

$\begin{matrix} FFo = - 0.066 ZSf + 0.005 Z μ + 0.005 ZF \\ + 0.131 ZW + 0.299 ZAb - 0.245 ZLtc \\ - 0.097 ZSc + 0.059 ZE + 0.319 ZP \\ + 0.580 ZAc - 0.533 ZTL + 0.457 Z Δ T \\ - 0.068 ZL - 0.052 ZAs - 0.075 ZLts \\ + 0.180 ZSb - 0.231 ZSs \end{matrix}$
(5)

$\begin{matrix} LTo = - 0.081 ZSf - 0.127 Z μ - 0.127 ZF \\ - 0.081 ZW - 0.076 ZAb + 0.145 ZLtc \\ + 0.051 ZSc + 0.336 ZE - 0.083 ZP - 0.070 Zac \\ + 0.116 ZTL - 0.103 Z Δ T + 0.814 ZL \\ + 0.806 ZAs + 0.706 ZLts + 0.059 ZSb - 0.378 ZSs \end{matrix}$
(6)

$\begin{matrix} STo = - 0.078 ZSf + 0.313 Z μ + 0.313 ZF + 0.304 ZW \\ + 0.232 ZAb - 0.061 ZLtc - 0.027 ZSc \\ - 0.078 ZE + 0.196 ZP - 0.010 ZAc \\ - 0.028 ZTL + 0.088 Z Δ T - 0.035 ZL \\ - 0.080 ZAs + 0.040 ZLts \\ + 0.088 ZSb + 0.115 ZSs \end{matrix}$
(7)

From the component matrix after rotation in Table 8 and the above principal component expression, it can be seen that a certain handle feature had a high correlation with several feature parameters and can be simply used to represent the handle feature. Table 9 shows the handle characteristics and their corresponding related indexes.
Table 9.
Handle characteristics and characteristic parameters

Handle features Characteristic Parameters

SMo Sf, μ, F, W, Ab, P

SFo Ltc, Sc, E, P, ΔT

FFo Ac, TL, ΔT

LTo L, As, Lts

STo Sb, Ss

By testing a sample, the values of the five components can be acquired. By comparing the values with the subjective evaluation values, information for the sample was extracted. The 17 index values obtained by 42 down jacket fabrics through the CHES-FY test were respectively brought into the above five principal component expressions, and five handle characteristic values were obtained. Correlation analysis was made between this and the five subjective evaluation results in the subjective evaluation, except the comprehensive handle. The five corresponding handle characteristics in the subjective and objective evaluations were all significantly correlated at the 0.01 level, and the correlation coefficients were all above 0.800. We believed that the subjective and objective evaluations of these 42 down jacket fabrics had a good consistency.

In order to further verify the reliability and validity of the five principal component relationship models of fabrics, 10 down jacket fabrics were randomly selected among the 52 down jacket fabrics for the CHES-FY, 17 indicators were obtained and standardized indicators were substituted into the above principal component expression, then compared with the subjective evaluation test results. The prediction results of SM₀, SF₀, FF₀, LT₀ and ST₀ and subjective evaluation are compared in Figure 6. It can be seen that the prediction result had a high linear correlation with the subjective scoring result. The Pearson coefficients of the five hand feel features were 0.842–0.944, and the adjusted coefficients (R²) ranged from 0.709 to 0.890.
Figure 6.
Comparisons between subjective handle evaluation and model prediction: (a) smoothness; (b) softness; (c) fullness and fluffiness; (d) looseness and tightness; (e) stiffness.

To further determine the relationship between the comprehensive handle and the five main hand characteristics, the principal component synthesis model according to the weighting method of the five principal component eigenvalues in the selected principal component total eigenvalues was calculated, wherein the comprehensive handle (CH₀) had following equation
$\begin{matrix} {CH}_{0} = \frac{λ_{1}}{λ_{1} + λ_{2} + λ_{3} + λ_{4} + λ_{5}} {SM}_{0} \\ + \frac{λ_{2}}{λ_{1} + λ_{2} + λ_{3} + λ_{4} + λ_{5}} {SF}_{0} \\ + \frac{λ_{3}}{λ_{1} + λ_{2} + λ_{3} + λ_{4} + λ_{5}} {FF}_{0} \\ + \frac{λ_{4}}{λ_{1} + λ_{2} + λ_{3} + λ_{4} + λ_{5}} {LT}_{0} \\ + \frac{λ_{1}}{λ_{1} + λ_{2} + λ_{3} + λ_{4} + λ_{5}} {ST}_{0} \end{matrix}$
(8)

The resulting comprehensive style value expression is as follows
$\begin{matrix} {CH}_{0} = 2 . {471 SM}_{0} + 0 . {932 SF}_{0} + 0 . {785 FF}_{0} \\ + 0 . {429 LT}_{0} + 0 . {383 ST}_{0} \end{matrix}$
(9)

The SM₀, SF₀, FF₀, LT₀ and ST₀ values were brought into the integrated style value expression, and the comprehensive handle value theory model based on the CHES-FY and subjective hand feel in Figure 7 to explain relationship between the CHES-FY and the subjective test. It can be seen from Figure 7 that they had a good effect with a Pearson coefficient of 0.908 and an adjusted R² of 0.824. These results indicated that the theoretical model was highly linearly correlated with the subjectively tested CH. These results also indicated that the principal component expression can better predict the hand feel characteristics of down jacket fabrics. The objective test of the CHES-FY can evaluate the hand feel of fabrics.
Figure 7.
The relationship of comprehensive handle between the objective test and the subjective test.

Conclusion

This paper discussed the principle of the CHES-FY, the subjective test results and comprehensive evaluation results based on the five main hand feel characteristics of down jacket fabrics, and the correlation between the objective test and subjective evaluation. Through the statistical analysis of the subjective handle evaluation results, the effectiveness of using the average values of the subjective evaluation results to rank the fabric's feel characteristics was confirmed. By analyzing 17 parameters extracted from the force and distance curve by the CHES-FY, the main component analysis of 42 down jacket fabrics can be divided into five handle characteristics, namely fullness, softness, stiffness, smoothness and elasticity, which were similar to the subjective five handle characteristics correspondingly. Through the subjective and objective evaluation results, we have respectively established five principal component expressions and a comprehensive handle prediction model. We believed that the great impacts on the evaluation of the handle for the down jacket fabric were the smoothness and stiffness, and the effect of the tightness was small. The results showed that the established models can reliably predict the fabric handle characteristics. For different down jacket fabrics, the five handle characteristics can be better distinguished. At the same time, the CHES-FY is an effective method to evaluate down jacket fabric’s handle characteristics. The CHES-FY together with the developed regression models provided an efficient method to evaluate the handle characteristics of down jacket fabrics.

Mechanical properties	Index	Indicator meaning	Unit	Explanation
Compression	ΔT	Thickness difference	mm	The difference of fabric thickness between TL and initial pressure 10 cN.
TL	Fixed load thickness	mm	The size of the load depends on the demand. The current default value is 200 cN.
Ac	Curve area	cN·mm	Integral of force value to displacement.
Sc	Slope	cN/mm	Use the least squares method to fit all the data in the bending stage, and the slope of the fitted curve.
Ltc	Linearity	—	Lts = 2WT/ɛm * Fm
Bending	Sb	Slope	cN/mm	All the data in the bending stage are fitted by the least square method, and the slope of the fitted curve.
P	Peak	cN	Maximum bending part.
Ab	Curve area	cN·mm	Integral of force value to displacement.
W	Half-width	—	Half-peak width, which refers to the full width of the band when the height of the absorption band is half the maximum, that is, the width of the transmission peak when the peak height is half.
Friction	F	Friction (mean)	cN	Friction phase, the average of all force values.
μ	Coefficient of friction	—	—
Sf	Standard deviation of friction coefficient	cN/mm	—
Stretch	E	Elongation under constant load	mm	The length when the force value is 400 cN.
L	Load under constant elongation	cN	When the extension is 8 cm, the required force value.
As	Curve area	cN·mm	Integral of force value to displacement.
Ss	Slope	cN/mm	Use the least squares method to fit all the data in the stretching stage, and the slope of the fitted curve.
Lts	Linearity	—	Lts = 2WT/ɛm * Fm

Handle characteristics	Rating level and description	Test time per fabric
FF	Worst	Bad	Medium	Better	Best	20 s
SF	Best	Better	Medium	Bad	Worst	20 s
ST	Worst	Bad	Medium	Better	Best	20 s
SM	Smoothest	Smooth	Medium	Rough	Roughest	20 s
LT	Loosest	Loose	Medium	Tight	Tightest	20 s
CH	Worst	Bad	Medium	Better	Best	20 s

	FF	SF	ST	SM	LT	CH
W	0.854	0.880	0.886	0.780	0.657	0.828
χ ²	217.705	224.292	225.847	198.781	167.652	211.252

	1	2	3	4	5	Ave.
FF	3.1	0.8	2.9	0.8	3.2	0.9	3.2	1.2	3.2	0.8	3.1	0.9
SF	3.3	1.2	3.5	1.2	3.5	1.2	3.4	1.5	3.2	1.5	3.4	1.3
ST	3.2	1.2	3.0	1.5	3.2	1.1	3.4	1.2	3.1	1.4	3.2	1.3
SM	3.3	1.7	3.0	2.2	3.2	1.8	3.2	1.6	3.2	2.6	3.2	2.0
LT	4.5	0.7	4.2	0.8	4.5	0.7	4.3	0.7	4.1	0.5	4.3	0.7
CH	3.7	1.5	3.3	1.7	3.6	1.4	3.4	1.7	3.3	2.0	3.5	1.6

	FF	SF	ST	SM	LT	CH
FF	1
SF	0.931**	1
ST	0.903**	0.961**	1
SM	−0.822**	−0.856**	−0.847**	1
LT	0.206	0.372**	0.368**	−0.162	1
CH	−0.465**	−0.550**	−0.619**	−0.530**	−0.094	1

KMO sampling suitability measure	0.709
Bartlett sphericity test	Approximate chi-square	1430.653
Degrees of freedom	136
Distinctiveness	0.000

Component	Initial eigenvalues	Extraction sums of squared loadings
1	6.955	40.911	40.911	6.955	40.911	40.911
2	2.623	15.427	56.338	2.623	15.427	56.338
3	2.211	13.004	69.342	2.211	13.004	69.342
4	1.207	7.103	76.445	1.207	7.103	76.445
5	1.078	6.339	82.784	1.078	6.339	82.784
6	0.689	4.053	86.837
7	0.602	3.543	90.380
8	0.480	2.825	93.205
9	0.364	2.138	95.344
10	0.331	1.945	97.288
11	0.170	1.000	98.289
12	0.102	0.600	98.889
13	0.083	0.491	99.380
14	0.053	0.314	99.694
15	0.049	0.289	99.982
16	0.003	0.018	100.000
17	7.593 E-7	4.467 E-6	100.000

Component
Sf	.857	−.127	−.098	.089	.341
μ	.826	−.310	.007	−.139	.356
F	.825	−.310	.007	−.139	.356
W	.802	.035	.195	−.089	−.157
Ab	.613	−.581	.445	−.084	−.118
Ltc	−.162	.846	−.364	.159	.018
Sc	−.071	.845	−.144	.056	−.160
E	−.206	.667	.089	.369	−.213
P	.518	−.623	.474	−.091	−.121
Ac	−.026	−.020	.862	−.077	.102
TL	−.074	.313	−.793	.127	−.144
ΔT	.233	−.545	.679	−.113	−.156
L	−.093	−.034	−.101	.894	−.059
As	−.210	.279	−.078	.886	−.104
Lts	.106	.164	−.112	.776	.119
Sb	.233	−.019	.267	.065	.842
Ss	.304	−.335	−.344	−.415	.566

Handle features	Characteristic Parameters
SMo	Sf, μ, F, W, Ab, P
SFo	Ltc, Sc, E, P, ΔT
FFo	Ac, TL, ΔT
LTo	L, As, Lts
STo	Sb, Ss

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Yuan Tian

Yi Sun

Zhaoqun DU

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	1		2		3		4		5		Ave.
	Ave.	Var.	Ave.	Var.	Ave.	Var.	Ave.	Var.	Ave.	Var.	Ave.	Var.
FF	3.1	0.8	2.9	0.8	3.2	0.9	3.2	1.2	3.2	0.8	3.1	0.9
SF	3.3	1.2	3.5	1.2	3.5	1.2	3.4	1.5	3.2	1.5	3.4	1.3
ST	3.2	1.2	3.0	1.5	3.2	1.1	3.4	1.2	3.1	1.4	3.2	1.3
SM	3.3	1.7	3.0	2.2	3.2	1.8	3.2	1.6	3.2	2.6	3.2	2.0
LT	4.5	0.7	4.2	0.8	4.5	0.7	4.3	0.7	4.1	0.5	4.3	0.7
CH	3.7	1.5	3.3	1.7	3.6	1.4	3.4	1.7	3.3	2.0	3.5	1.6

Component	Initial eigenvalues			Extraction sums of squared loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	6.955	40.911	40.911	6.955	40.911	40.911
2	2.623	15.427	56.338	2.623	15.427	56.338
3	2.211	13.004	69.342	2.211	13.004	69.342
4	1.207	7.103	76.445	1.207	7.103	76.445
5	1.078	6.339	82.784	1.078	6.339	82.784
6	0.689	4.053	86.837
7	0.602	3.543	90.380
8	0.480	2.825	93.205
9	0.364	2.138	95.344
10	0.331	1.945	97.288
11	0.170	1.000	98.289
12	0.102	0.600	98.889
13	0.083	0.491	99.380
14	0.053	0.314	99.694
15	0.049	0.289	99.982
16	0.003	0.018	100.000
17	7.593 E-7	4.467 E-6	100.000