Performance Test of Clothing Materials and the

Prediction Model of Clothing Thermal-Wet Comfort

Yao Yu, Xiaoming Qian School of Textiles, Tianjin Polytechnic University, Tianjin, China

Email: yao_79562@yahoo.com.cn, qianxiaoming@tjpu.edu.cn

Abstract: This research focuses on the performance test of clothing materials and analyzes the relationship between the performances of knitting fabric and clothing comfort. In the field of clothing thermal-wet comfort studying, this paper is proposed that "Fiber - Yarn - Fabrics - Clothing" can be made as the research system for the first time. According to the experiments, it analyzes that these three variables— “fibers”, “yarn prop-erties” and “fabric performance”— influence clothing thermal-wet comfort. The testing includes basic per-formance, style, comfort and clothing comfort. By methods of math-statistics, it has set up a prediction model of clothing thermal-wet comfort.

Keywords: Clothing Materials, Thermal-wet Comfort, Fibers, Yarn Properties, Fabric Performance, Manikin

1. Introduction

Clothing materials play a significant role to contribute to clothing comfort. Performance and feature of fiber is the key factor, which affects fabric and clothing’s apprecia-tion of the beauty, physiology comfort, wear durableness and maintenance performance [1]. The feature of knitting fabric depends on these aspects, such as the categories of fiber raw material composed to it, yarn, fabric organiza-tion structure, and after processing technology. It should select knitting fabric corresponding and according to the performance requirement in terms of wearing purposes and functional requirement [2].The measure of comfort is the comprehensive balance of biothermodynamics among human, clothing and circumstance. And this balance mainly includes satisfactory thermal equilibrium and moisture equilibrium, as a result of integrated coordinat-ing with situation of surroundings, activity level of hu-man body and property of clothing [3].

This essay proposes a research system, which is “fi-bers-yarn-fabric-clothing” and can analysis lengthways the relationship between systems, and find out the rele-vance about yarn properties and fabric performance in-fluencing clothing thermal-wet comfort.

2. Experiment

2.1. Experimental arrangement and introduction to specimen

There are 12 fabric specimens in these three groups (fab-ric specimens ID 1～12), and yarn specimens corre-sponding to the fabric ones(yarn specimens ID a～e). Fabric parameter design divides the experiment specimen into three groups according to three variables: fiber raw material, fineness of yarn and fabric organization struc-ture. With the clothing parameter design, both the cloth-

ing style structure design and clothing size are consistent, and the whole design unify male knit T shirt with long sleeve (clothing specimens ID 1～12 ). Subject and in-troduction to fabric specimens are shown in below table 1.

2.2. Experiment testing

The testing includes basic performance, styles, fabric comfort and clothing comfort test. Fabric comfort test: heat preservation test by flat fabric heat retention tester, water vapour transmission test by M261 revolving cup water vapor transmission tester, thermal insulation and evaporative resistance test, which is tested by self-designed instrument, called clothing thermal-wet comfort tester [4], air permeability test by gurley per-meameter, and performance of stickiness to skin, which is tested by self-designed fabric stickiness tester [5]. And clothing comfort test, which is tested by Walter[6-8], through testing the thermal insulation and evaporative resistance, and can figure out Water vapour permeability index, as the index to evaluate clothing thermal-wet comfort (Water vapour permeability index, im, as the evaluation index of measuring whether the clothing is comfortable or not in thermal environment[9,10] ). In the testing, manikin is worn T shirt specimens on upper body and the same sport pants, when the microclimate between skin and clothing regard as identical, therefore, this factor of influence is constant. In the situation of experiment 20 (±1)˚C and 65(±5)%RH, skin temperature is controlled constantly at 35˚C, and it takes the mean value of three independent test results of thermal insulation and mois-ture resistance as the final outcome.

2.3. Experiment result and analysis

By means of the method of one-way ANOVA [11], it can be gained that fiber raw material is of great influence in

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clothing thermal insulation and exerts little influence on moisture resistance; So is fineness of yarn; Fabric or-ganization structure exerts little influence on both cloth-ing thermal insulation and moisture resistance. It illus-trates there is a notable difference among these diverse

fabric of fiber raw material selected in this essay; simi-larly, the selection of fineness of yarn is reasonable, and specimens differ markedly; but there no significant dif-ference among fabrics with diverse fabric organization structure.

Table 1. Subject & Introduction to fabric specimens

Subject fiber raw material fineness of yarn fabric organization structure

Specimens ID

1 2 3 4 5 6 7 8 9 10 11 12

Fabric content

100% cotton

95% cotton

5% spande

x

95% tencel

5% spand

ex

66.5% bamboo

fibre 28.5% cotton

5% spandex

100% cotton

100% cotton

100% cotton

100% cotton

100% cotton

100% cotton

100% cotton

100% cotton

Fineness of yarn

14.6tex 14.6tex 14.8te

x 14.8tex 27.8tex 18.2tex 14.6tex 18.2tex 18.2tex 18.2tex 18.2tex 18.2tex

Fabric organization structure

plain knit

fabric

plain knit

fabric

plain knit

fabric

plain knit fabric

plain knit

fabric

plain knit

fabric

plain knit

fabric

plain knit

fabric

1+1 rib stitch

small jacquard mesh

Interl-ock

fabric

jacquard

weave

Thickness (mm)

0.823 1.024 0.984 1.088 0.970 0.868 0.823 0.840 1.045 1.077 1.156 1.183

Density h×Z

(cycles/ 100cm2)

154 ×218.5

189.5 ×253

168 ×236

176 ×260

146 ×229

161.5 ×180

154 ×218.5

171 ×200

116.5 ×160

127 ×169

142.5 ×100

131.5 ×180

Loop length (cm)

0.256 0.2914 0.291 0.278 0.25 0.258 0.256 0.2326 0.496 0.22 0.277 0.245

Weight per

square meter (g/m2)

109.849

183.76 175.5

02 188.898

209.742

137.476

109.849

139.058

153.289

170.747

184.151

206.658

Yarn specimens

ID a a e d b c a c c c c c

Corresponding yarn specimen

14.6tex Combe

d Cotton

14.6tex Combe

d Cotton

14.8tex

tencel

14.8tex bamboo

fibre/cotton

27.8tex Combe

d Cotton

18.2tex Combe

d Cotton

14.6tex Combe

d Cotton

18.2tex Combe

d Cotton

18.2tex Combe

d Cotton

18.2tex Combe

d Cotton

18.2tex Combe

d Cotton

18.2tex Combe

d Cotton

2. To establish the prediction model of clothing thermal-wet comfort of yarn-fabric-clothing system [12, 13]

2.1. To analyze the yarn property and get the prediction model of yarn

To analyze four properties of these five yarns by princi-pal component analysis method, with 9 indexes. Based on factor analysis, by method of calculating the weighted total score, it can be gained the prediction model of yarn:

yarn f1 f1S = W S (1)

Where, yarnS is clothing thermal-wet comfort of yarn,

f1W is the contributing rate of factor, f1S is factor.On

the basis of the situation of Rotation Sums of Squared Loadings, contribution of variance is substituted into:

yarn 42.301% yarn primary factor 1+28.343%

yarn primary factor 2+25.125% yarn primary factor 3

S =

(2)

=0.399744× mean twist +0.38367× sliverunevenness -0.35998× actual fineness +0.353636× nubs number +0.305836× coefficient of twist +0.273227× hairness index -0.24885× twist CVRI +0.24924× fine end +0.248738× nep number (3)

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2.2. To analyze the physical performance and fabric appearance and to get the prediction model of fabric

To analyze five properties of these twelve fabrics by principal component analysis method, 18 indexes in all. On the basis of the situation of Rotation Sums of Squared Loadings, it can be got calculation formula of principal variables. After simplifying, contribution of principal variables is analyzed:

surface appearance properties primary factor 1

appearance properties primary factor

appearance properties primary factor

S =31.358%

+23.656% 2

+14.782% 3

(4)

=31.358%×(0.898× hysteretic force 5°+0.891× shearing rigidity +0.882× hysteretic force 0.5°-0.822×specific compression work -0.796×compress thickness +0.714×drape coefficient +0.606×friction co-efficient mean deviation-0.578×dynamical friction meancoefficient+0.538×surface roughness)+ 23.656%×(0.851×bending stiffness-0.816×lengthways number of stitches -0.807×crosswise Loop course+0.774×hysteretic moment)+ 14.782%×(0.938×gram weight per unit area+0.816×apparent thickness+0.667×steady thickness)

(5)

2.3. To analyze comfort ability of fabric and clothing and to get the prediction model of fab-ric comfort

To analyze seven properties of these twelve fabrics by principal component analysis method, 12 indexes in all. In a similar way, we can get the prediction model of fab-ric comfort:

comfort comfort primary factor

comfort primary factor

comfort primary factor

comfort primary factor

S =32.216% 1

+27.124% 2

+15.030% 3

+11.374% 4

(6)

=32.216%×(0.973×vapour transmission+0.973×moisture transmission +0.782×coefficient of heat trans-fer-0.755×heat preservation rate)+ 27.124%×(0.948×horizontal stickiness force per unit gram weight +0.866×thermal insulation of clothing +0.741×permeability rating of air)+ 15.030%×(0.829×moisture vapor resistance of colthing+0.673×moisture vapor resistance of fab-ric+0.617×upright stickiness force per unit gram weight)+ 11.374%×(0.930×moisture regain) (7)

3. To build model making use of regression analysis [13]

In terms of the primary - composition analysis calcula-tion formula, the calculated value of primary factor of yarn-fabric-clothing system can be gat, and clothing thermal-wet comfort value of yarn property section, fab-ric appearance section and comfort ability section can be figure out. This essay made “Water vapour permeability index” tested by Thermal manikin method and calcu-lated as the index for evaluating clothing comfort, and the water vapour permeability index of every kind of clothing specimen wearing can be shown in table 4, where Y (Water vapour permeability index of clothing), X1 (Predicted value of yarn property section), X2 (Pre-dicted value of fabric appearance section), X3 (Predicted value of fabric and clothing comfort section) indicate signs in the regression equation.

Table 2. Coefficientsa Unstandard-ized Coeffi-

cients

Standard-ized Coef-

ficients

Collinearity Statistics

Model

B Std.

ErrorBeta

t Sig.

Toler-ance

VIF

(Con-

stant) -.441 .061 -7.185 .006

X1 .002 .000 .600 6.098 .009 .545 1.835

X2 -.003 .001 -.210 -2.520 .086 .762 1.313

1

X3 .0011 .000 .436 4.897 .016 .666 1.502

a. Dependent Variable: Y

3.1 To build regression equation

Regression equation is built by analyzing: Y=0.002 X1-0.003 X2+0.0011 X3-0.441 (8)

It means:

yarn surface

comfort

Clothing thermal-wet comfort=0.002 S -0.003 S

+0.0011 S -0.441

[ adjR=0.992, R 0.968 ] (9)

It means: Clothing thermal-wet comfort=0.002×（predicted value of yarn property section）-0.003×（ predicted value of fabric appearance section ）

+0.0011×（Predicted value of fabric and clothing com-fort section）-0.441（constant） Factors of every section are substituted into equation (9), and equation (3), (5), (7) also are substituted into equa-tion (9). After simplifying the form of this equation by calculating, the same coefficients are combined, and the final prediction model equation can be got:

Y=[0.8×（mean twist + sliverunevenness - hys-teretic force 5°- shearing rigidity - hysteretic force 0.5°+ specific compression work）+0.7×（- actual fineness + nubs number + compress thickness - drape coefficient）

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+0.6×（coefficient of twist - dynamical friction mean coefficient -bending stiffness+ lengthways number of stitches + crosswise Loop course - hysteretic moment）+0.5×（hairness index - rate of twist irregularity + fine end + nep number + dynamical friction mean coefficient - surface roughness）+0.4×（- gram weight per unit area - apparent thickness）+0.3（- steady thickness + moisture transmission + vapour transmission + coefficient of heat transfer - heat preservation rate + horizontal stickiness force per unit gram weight + thermal insulation of clothing）+0.2×（permeability rating of air + moisture vapor resistance of clothing）+0.1×（moisture vapor resistance of fabric + moisture regain）]×10-3-0.441

[ adjR=0.992, R 0.968 ] (10)

3.2 To test the regression model

The score and sort predicted and calculated by this model of total clothing thermal-wet comfort is: 1﹥6﹥12﹥10﹥9﹥2﹥11﹥4﹥3﹥5. This sort is comprehen-sive forecast result of total clothing thermal-wet comfort according with yarn-fabric-clothing system, showing a sort order from low to high principle of comfort.Note: No.1 and No.7 fabrics are same, so comfort property of them is shown the very same value; fabric content, fine-ness of yarn and fabric organization structure of No.6 and No.8 fabrics are same, except color, so comfort property of them is similar, therefore, No.7 and No.8 fabrics are not in the sort order. This result of the sort corresponded to the marketing survey results of knitted underwear and underlines: products made of 14.6tex 100% cotton plain knit fabric are excellent, because soft and flexibility, air permeabil-ity and glossiness of them are：preferably, moreover, no pilling and fluffing after washing, no broken wires and wire drawing for extended wear.

4. Conclusions

The prediction model of clothing thermal-wet comfort of “yarn-fabric-clothing system” is established using prin-cipal component analysis and regression analysis method. Step 1: To find out the main influencing factors affecting clothing thermal-wet comfort from these various factors which are yarn property, the physical performance, fabric appearance and comfort ability of fabric and clothing,

using principal component analysis method. On the basis of factors pick-up from data, the prediction models of yarn, fabric appearance and fabric comfort section are respectively built up. Step 2: The water vapour perme-ability indexes tested by thermal manikin and calculated are used as evaluation indexes of clothing thermal-wet comfort; and predicted values are calculated by predic-tion models of all the sections. Both of them are analyzed by linear regression analysis method, and total forecast-ing regression model can be set up. The results of scores of total clothing thermal-wet comfort calculated by this model corresponded to the marketing survey results of knitted underwear and underlines; consequently, it has certain practicability and applicability.

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