Simultaneous-integrated evaluation of mechanical–thermal sensory attributes of woven fabrics in considering practical wearing states

Abstract

Tactile sensations of fabrics are the primary property determining the wearing comfort of clothing; however, comprehensive evaluation of the fabric tactile property by considering the flexural buckling of fabrics under high curvature, hysteresis performance and thermal property has not been fully studied, leading to a clear gap between the existing measurement methods and application requirements. Herein, a simultaneous-integrated testing method, namely the Touch Sensation Tester for Fabrics (TST-F) was introduced to evaluate the mechanical–thermal sensory properties of woven fabrics. The introduced instrument used one device with a single mechanical sensor to test various mechanical properties by constructing different deformations of fabrics, and the thermal property was simultaneously measured using an infrared detector array, achieving an efficient characterization of the mechanical–thermal sensation properties of textiles. The measurement capacity and repeatability of the TST-F were statistically analyzed; the measurement indices and their relation with fabric mechanical–thermal sensation properties were also exhibited. Results showed that the TST-F was promising to characterize fabric touch sensations in terms of bending stiffness, compression softness with wrinkling, stretching tightness and thermal comfort by considering the infrared transmission and heat conductivity of textiles.

Keywords

Touch sensation instrument measurement fabric handle mechanical–thermal attributes

Tactile sensation is an important aspect of human interaction with the ambient environment, and clothing is perhaps the most intimate object interplaying with our skin, which is sensitive to temperature and pressure due to millions of kinds of receptors.^1,2 Whereas the human tactile sensation response toward fabrics, generally described as “fabric handle,” has long been used to evaluate wear comfort and application areas of fabrics,^3–7 the effective characterization and accurate prediction of mechanical and thermal comfort by considering the actual wearing conditions are still pending problems.

Since the 1970s, several predominant attempts to study the sensation evaluation of fabrics have been made intensively in Japan and Australia, and as a result, the two most well-known systems, that is the Kawabata Evaluation System for Fabrics (KES-F)⁸ and Fabric Assurance by Simple Testing (FAST),⁹ were developed to evaluate fabric handle by testing different mechanical properties of fabrics. However, the two sets of handle evaluation systems adopted multiple devices to test various low-stress properties of fabrics using different measurement sensors, which easily leads to measuring errors when combining the different measurements. The high cost in time and materials is also a shortcoming of the two systems.

Some effort has recently been made to attempt to characterize the tactile sense of fabrics by simultaneous measurement methods. Pan¹⁰ used the ring test principle to develop a PhabrOmeter system for evaluating fabric sensory responses based on the computer pattern recognition technique. The PhabrOmeter system provides a convenient way to evaluate the comprehensive property of fabrics, but it cannot capture the hysteresis in the different deformation processes of fabrics by a ring extraction test.^11,12 Liao et al.¹³ introduced the Fabric Touch Tester (FTT) to characterize the touch properties of textile materials, but the FTT uses different modules to measure different properties, which is actually an integration of the separate devices.¹⁴ More recently, several emerging devices, such as the Comprehensive Handle Evaluation System for Fabrics and Yarns (CHES-FY),^15,16 Wool HandldMeter^17,18 and Leeds University Fabric Handle Evaluation System (LUFHES),¹⁹ have been designed to evaluate the touch perception performance of fabrics using one instrument. However, there are some issues that still need to be addressed by further studies, as follows.
i. Normally, fabrics are subjected to complex deformation such as compression, stretching, bending and multi-dimensional creasing and buckling caused by joint activities, and the multi-dimensional bending and folding processes are considered as important factors affecting the wearing comfort in our daily life; however, the flexural buckling of fabrics under high curvature is a less studied area in the existing research studies of handle evaluation.

ii. The hysteresis is a typical nature of fabrics in terms of viscoelasticity and weak interaction among fibers and yarns, but most present simultaneous testing methods cannot measure the hysteresis of fabrics.

iii. Although the thermal property of fabrics plays a critical role in the tactile sensations of fabrics, existing studies of fabric handle generally isolate the thermal property from mechanical performance, leading to a lack of comprehensive evaluation devices for both mechanical and thermal properties of fabrics, especially the infrared transmission property of fabrics.

This paper tries to tackle the above proposed issues by using a simultaneous-integrated testing method, namely the Touch Sensation Tester for Fabrics (TST-F). This apparatus can measure multiple mechanical and thermal properties relating to fabric touch sensory properties by buckling-induced multi-morphological deformations of a fabric sample with an acceptable temporal and spatial resolution. The prototype, testing indices and methodologies of the apparatus are reported.

Apparatus description and measurement principle

The TST-F apparatus includes the measuring module, driving module and data processing and controlling system. The measuring module is used to sense the mechanical and thermal signals, and the hardware equipment comprises a moving slab, a testing slab, an infrared detector array embedded in the testing slab and a force sensor connecting with the testing slab, as shown in Figure 1. The measuring range and accuracy of the force sensor are 500 cN and 0.01% F.S., respectively. The infrared detector array has three detection heads and can sense the heat radiating from the surface of the heat panel on the surface of the moving slab. The measuring range of each infrared detector is from 10°C to 100°C, and the accuracy rating of the infrared detector is 0.05°C. Analog signals collected by the force sensor and infrared detector array are transmitted to the data processing system via an amplifier and analog–digital (AD) converter to output the measurement results. The driving module is used to control the movement of the moving slab with respect to the testing slab with the help of the servo motor, digital–analog (DA) converter and controlling system.

Figure 1.
The hardware equipment and the measurement chain of the Touch Sensation Tester for Fabrics. IR: infrared; AD: analog–digital; DA: digital–analog.

Based on different deformation features of a fabric sample under the action of the moving slab and testing slab, the whole testing process of the TST-F can be divided into seven typical steps, and each of the steps is intensively associated with a certain fabric property (see Figure 2, marked from I to VII). The physical properties that are related to fabric touch sensations, such as bending, buckling, compression, stretching, hysteresis and thermal properties, can be simultaneously evaluated by a single device in one test, which is different from the traditional separated measurements for various properties using different devices and samples. This proposed testing method enables a quasi-clearance-free characterization in time and space, which is helpful to enhance the measuring accuracy of the time-dependent mechanical–thermal behavior of fabrics.

Figure 2.
Illustration of the mechanical–thermal testing procedure of the Touch Sensation Tester for Fabrics.

According to the testing process, the seven testing steps are interpreted as follows: in the bending/buckling step (I), the moving slab moves toward the testing slab to bend the fabric until the two wings of the fabric contact each other, and the force sensor and infrared detector respectively record the force value and infrared transmission of the fabric. After that, the folded fabric is compressed under a pre-set maximum compression force for a certain period in the compression step (II), while the heat conduction of the compressed fabric is recorded by the infrared detector at the end of the compression step. Then, the moving slab moves in reverse, gradually recovering the deformation of the compressed and bended fabric to characterize the anelasticity in the compression recovery (III) and bending recovery (IV) steps. The moving slab continues moving backwards relative to the testing slab, and the fabric is prestressed in unbending step (V) and stretched in stretching step (VI). Once the stretching force reaches the maximum value, the moving slab goes back to the testing slab to carry out the measurement of tensile recovery by the stretching recovery step (VII), and the temperature of the heat panel on the surface of the moving slab is calibrated by the infrared detected value in this step. During the whole testing process, the force value is simultaneously recorded by the force sensor versus the movement of the moving slab.

Experimental details

Materials and methods

Twenty-one woven fabric samples were collected from the market and a textile mill. The thickness was measured under a pressure of 1 kPa following the ISO 5084-1996 standard. The weight was determined by a balance with a precision scale at ±0.1 mg. The basic parameters of the fabrics are summarized in Table 1.

Table 1.
Basic parameters of fabric samples

Sample number Weight (g/m²) Thickness (mm) Weaving structure Component

1# 156 0.54 Twill Cotton

2# 120 0.38 Plain Woo/Polyester

3# 189 0.47 Plain Wool/Spandex

4# 469 1.67 Plain Acrylon/Wool

5# 559 1.84 Satin Wool

6# 188 0.56 Plain Cotton

7# 354 1.23 Twill Wool

8# 153 0.43 Plain Cotton/Polyester

9# 418 1.40 Twill Polyester/Wool

10# 161 0.43 Twill Rayon/cotton

11# 401 1.71 Twill Wool

12# 437 1.49 Twill Wool

13# 200 0.52 Plain Nylon/Polyester

14# 271 0.92 Twill Wool/Acrylic

15# 203 0.64 Twill Polyester/Wool

16# 201 0.70 Twill Wool/Acrylic

17# 356 1.48 Satin Wool/Acrylic

18# 164 0.47 Twill Cotton/Polyester

19# 406 1.55 Twill Wool/Acrylic

20# 267 0.76 Plain Wool

21# 376 1.75 Twill Wool

Each sample is also cut to the size of 3 cm × 2 cm in the warp × weft direction for the TST-F test. The apparatus parameters of the TST-F are set as follows: the gauge length between the surface of the moving slab and testing slab was set at 5 mm; the maximum force values of the compression and stretching steps were set to 300 cN; the holding time of the maximum force value during testing was set to 60 s; the speed of the moving slab was 20 mm/min; the heat source on the moving slab was set to 37 ± 0.5°C. Three samples of each fabric were tested, and the mean values of the indices were reported. Moreover, a set of square samples of 20 cm × 20 cm in the warp × weft direction was prepared and tested on the KES-F system. The tensile, bending and compression properties were measured by the tensile/shearing tester (KES-F1), bending tester (KES-F2) and compression tester (KES-F3), respectively. Specimens of all the samples were conditioned at 20 ± 2°C and 65 ± 3% relative humidity for more than 24 hours prior to the experiment.

Measurement curves and index definitions of the TST-F

The force value in the whole deformation process of each sample is recorded by the force sensor versus the displacement of the moving slab, while the temperature on the fabric surface is also captured. The typical force–displacement curve and temperature measurement curve in a test of the TST-F are shown in Figure 3. According to the fabric deformation and main features of the curve pattern, three regions in the curves are divided, which correspond to the bending and recovery process (region A), the compression and recovery process (region B) and the stretching and recovery process (region C).

Figure 3.
The typical force–displacement curve and temperature evolution curve during fabric deformation and illustration of the curve parameters.

From the measured curves, a set of indices reflecting the physical properties of the fabrics can be extracted, and the coordinates of displacement values X₀, X₁, X₂, X₃ and X₄ at the featured demarcations are also marked, as shown in Figure 2. The physical meanings and mathematical definition of each index are summarized in Table 2, in which function $F_{n} (x)$ is the force value versus displacement x in nth step. The detailed physical meanings will be discussed in the Physical interpretation of measurement indices section.

Table 2.
Definition of the physical indices of the measurement curves

Index Meaning Calculation Unit

BF _mean Mean of bending force during fabric bending deformation $B F_{mean} = Mean (F_{I} (x))$ cN

W_c Compression work of fabric being compressed by moving slab and testing slab $W_{c} = \int_{X_{1}}^{X_{2}} (F_{II} (x) - F_{II} (X_{1})) d x$ cN·mm

W_cr Compression recovery work at the compression recovery step $W_{c r} = \int_{X_{1}}^{X_{2}} (F_{III} (x) - F_{III} (X_{1})) d x$ cN·mm

R_wc Recovery ratio of the compression work $R_{w c} = W_{c r} / W_{c}$ %

D_br Deflection displacement between the point X₄ and coordinate origin X₀ $D_{b r} = ‖ \vec{X_{0} X_{4}} ‖$ mm

H_br Hysteresis between the bending force and bending recovery force $H_{b r} = | F_{I} (X_{4}) - F_{IV} (X_{4}) |$ cN

R_f Residual force induced by anelasticity $R_{f} = F_{IV} (0)$ cN

W_t Stretching work of fabric being stretched by moving slab and testing slab $W_{t} = \int_{X_{0}}^{X_{3}} (F_{VI} (x) - F_{VI} (X_{3})) d x$ cN·mm

W_tr Stretching recovery work at the compression recovery step $W_{t r} = \int_{X_{0}}^{X_{3}} (F_{VII} (x) - F_{VII} (X_{3})) d x$ cN·mm

R_wt Recovery ratio of the stretching work $R_{w t} = W_{t r} / W_{t}$ %

IR _max Maximum infrared transmission $I R_{\max} = {Max (Temp) |}_{Region A}$ °C

S_IR Efficiency of infrared transmission $S_{I R} = {Mean [\frac{\partial Temp}{\partial x}] |}_{Region A}$ °C/mm

H _max Maximum heat conduction $H_{\max} = {Max (Temp) |}_{Region B}$ °C

Subjective assessment of fabric sensation properties

Human sensations toward clothing are mainly determined by the pressure and thermal performance of fabrics in our daily wearing process. Various mechanical simulations, including bending and folding of fabrics around human joints, stretching along with large amplitude motions of the body and limbs and compression, especially for hierarchical wrinkled/creased fabrics as well as thermal simulation, such as thermal radiation and heat conductivity, can be pleasing to a human’s tactile neurons, and were used to formulate our final perceptions of garments.^20–22 Therefore, the fabric tactile sensations related to bending (including high-curvature creasing), compression, stretching and thermal properties, namely bending stiffness, compression softness, stretching tightness and thermal comfort, were assessed. Each of the fabric tactile sensations was assessed by six judges. Previous study has shown that judgments show no significant origin-, gender- or age-based differences in the subjective assessments.²³ The assessments were conducted in the standard condition by referring to the American Association of Textile Chemists and Colorists (AATCC) EP5-2007 evaluation procedure. The judges were trained to be acquainted with the descriptions and ranking scales for three typical tactile feelings before carrying out the assessments. A detailed description of the tactile attributes of fabrics is given in Table 3.

Table 3.
Description of the five rating scales for the fabric touch sensations

Rating scales Bending stiffness Compression softness Stretching tightness Thermal comfort

1 Very soft, easy to bend Very fluffy Very loose, easy to stretch Very cool and dry

2 Soft Fluffy Loose Cool

3 Moderate Moderate Moderate Moderate

4 Stiff Hardened Tight Warm

5 Very stiff, hard to bend Very hardened Very tight, hard to stretch Very warm and moist

The bending stiffness is mainly assessed based on the flexural pressure on the judges’ hand during bending and folding a fabric. The judges are asked to knead and grip a fabric to feel the bulkiness of fabrics when assessing the compression softness. The stretching tightness is evaluated by the formability and tensile elasticity of fabrics. The thermal comfort is assessed by the human psychosensory intensity of thermal insulation and heat conduction of fabrics. Each judge was asked to assess the touch properties of each fabric independently, and the average value of the subjective grades was used as the final result for each fabric.

Apparatus measurement results and analysis

Measurement capacity and repeatability of the TST-F

The measurement capacity in differentiating various fabrics and repeatability of testing results of the TST-F were investigated by statistical analysis. Three replicates were conducted for each fabric, and the mean and coefficient of variation (CV) of the measurement indices for each fabric as well as the maximum and minimum of the means and CVs are summarized in Table 4. The F-value of the analysis of variance (ANOVA) on the significance of the mean difference among different fabrics is also listed in Table 4.

Table 4.
Statistical test results of the Touch Sensation Tester for Fabrics on 21 fabric samples (mean: the average of three replicates on each sample; CV: coefficient of variation of three replicates)

No. BF_mean W_c R_wc D_br H_br R_f W_t R_wt IR _max S_IR H _max

1 Mean 5.9 42.0 53.5 0.91 3.64 3.07 50.0 51.5 26.3 2.24 36.6

　 CV 8.4% 3.9% 2.8% 11.1% 0.8% 4.3% 5.2% 1.6% 2.1% 1.7% 1.0%

2 Mean 22.6 40.0 49.5 0.03 0.38 1.84 26.5 91.0 28.0 3.04 35.5

　 CV 2.6% 3.4% 2.6% 13.8% 2.7% 5.1% 4.3% 3.3% 2.7% 1.8% 2.2%

3 Mean 5.5 32.5 69.2 0.34 1.66 1.31 79.0 67.4 26.5 2.48 36.5

　 CV 8.7% 6.3% 4.6% 5.5% 7.2% 7.4% 4.7% 2.8% 3.1% 2.5% 0.9%

4 Mean 59.2 80.0 40.7 0.29 21.07 11.20 39.0 61.2 21.0 0.71 31.8

　 CV 10.5% 5.7% 4.4% 16.5% 1.0% 7.3% 4.9% 1.5% 1.9% 1.4% 1.5%

5 Mean 117.1 142.5 26.0 0.44 51.8 31.53 62.0 47.9 21.9 0.67 30.0

　 CV 7.9% 5.8% 3.7% 11.3% 4.4% 1.1% 4.2% 2.6% 2.5% 1.2% 2.3%

6 Mean 9.4 34.0 60.9 0.08 1.21 1.94 49.0 51.0 29.6 3.37 37.0

　 CV 6.2% 6.1% 5.6% 9.3% 5.4% 2.4% 4.8% 2.8% 1.8% 1.2% 0.3%

7 Mean 40.5 75.5 40.8 0.42 14.24 8.46 48.0 56.0 22.3 1.10 33.7

　 CV 10.9% 5.6% 3.6% 10.5% 4.3% 7.6% 5.3% 2.0% 3.1% 2.0% 2.1%

8 Mean 13.0 47.0 49.3 0.21 5.85 4.55 38.8 59.9 27.0 2.51 36.9

　 CV 11.1% 5.6% 4.8% 12.0% 3.6% 4.0% 3.0% 1.2% 2.9% 1.7% 1.1%

9 Mean 80.6 138.5 26.7 0.08 6.59 8.38 40.5 58.2 22.7 1.28 34.0

　 CV 9.2% 7.5% 6.6% 18.5% 3.8% 4.4% 4.7% 2.2% 3.0% 1.8% 0.8%

10 Mean 6.4 45.0 51.3 0.45 3.07 2.21 37.0 66.5 27.6 2.79 37.0

　 CV 7.3% 6.1% 5.8% 14.5% 4.4% 3.0% 4.0% 2.5% 3.2% 2.2% 0.3%

11 Mean 63.8 114.0 31.2 0.78 23.27 17.28 57.5 51.8 23.0 1.18 33.6

　 CV 6.6% 6.2% 5.0% 6.6% 4.5% 5.0% 5.0% 1.9% 1.7% 1.6% 0.8%

12 Mean 95.4 125.0 26.5 0.29 31.36 18.65 50.5 49.0 22.2 0.96 35.0

　 CV 10.6% 5.8% 4.2% 13.5% 7.7% 7.6% 5.0% 1.9% 3.4% 2.3% 2.3%

13 Mean 12.2 64.0 36.9 0.49 5.75 4.06 39.0 59.0 26.0 2.47 37.0

　 CV 6.9% 6.1% 4.5% 16.3% 4.1% 2.8% 4.6% 3.1% 2.0% 1.1% 0.7%

14 Mean 22.2 67.0 42.3 0.44 7.57 4.88 47.0 56.1 23.8 1.28 35.4

　 CV 3.5% 3.1% 4.1% 15.2% 5.4% 3.0% 2.6% 2.0% 3.1% 2.5% 1.6%

15 Mean 9.6 49.0 50.6 0.45 3.19 2.35 49.5 64.2 23.4 1.54 36.3

　 CV 9.7% 6.0% 4.7% 11.4% 3.7% 4.2% 4.2% 2.0% 1.9% 1.6% 0.7%

16 Mean 13.5 56.5 46.3 0.42 5.41 4.29 38.0 61.4 24.9 2.02 37.0

　 CV 5.5% 6.5% 4.2% 18.3% 3.7% 5.6% 3.8% 2.4% 2.2% 1.7% 1.0%

17 Mean 96.0 152.0 21.4 0.12 22.12 15.42 42.0 56.8 22.5 0.92 34.5

　 CV 13.3% 1.7% 5.6% 19.1% 1.8% 5.7% 5.7% 4.5% 2.6% 2.3% 2.2%

18 Mean 13.5 50.0 46.5 0.31 7.08 5.69 44.5 51.9 27.3 2.62 37.0

　 CV 10.6% 6.8% 5.2% 19.9% 2.8% 3.3% 3.8% 1.9% 2.2% 1.4% 0.6%

19 Mean 67.6 101.5 31.5 0.29 26.38 14.97 66.5 46.1 22.6 1.25 35.1

　 CV 11.1% 7.3% 4.7% 19.5% 5.4% 1.5% 4.4% 0.7% 2.6% 1.4% 1.3%

20 Mean 20.0 63.5 38.9 0.52 7.98 4.84 45.5 51.1 24.7 1.67 35.0

　 CV 6.6% 5.8% 3.6% 13.2% 2.5% 3.0% 5.8% 1.9% 2.2% 1.7% 1.5%

21 Mean 48.2 102.0 29.1 0.42 17.33 10.05 57.0 58.9 22.3 1.25 34.2

CV 8.9% 6.1% 5.2% 14.5% 3.4% 4.2% 4.2% 1.7% 2.5% 1.2% 1.8%

Mean_max 117.1 152.0 69.2 0.9 51.8 31.5 79.0 91.0 29.6 3.4 37.0

Mean_min 5.5 32.5 21.4 0.0 0.4 1.3 26.5 46.1 21.0 0.7 30.0

CV_max 13.3% 7.5% 6.6% 19.9% 7.7% 7.6% 5.8% 4.5% 3.4% 2.5% 2.3%

CV_min 2.6% 1.7% 2.6% 5.5% 0.8% 1.1% 2.6% 1.0% 1.7% 1.1% 0.3%

F-value (difference between samples) 4651.6 961.7 102.9** 1.9* 1238.8 549.2 96.3 73.4 49.7 17.0 39.4

Difference is significant at the p < 0.001 level; * difference is significant at the p < 0.05 level.

The mean of different fabrics shows remarkable variations; it is verified by the ANOVA that the difference of all the indices between 21 fabrics is significant at the p < 0.001 level except that of D_br, which is significant at the p < 0.05 level. This indicates that the TST-T has a good ability to distinguish the tested fabrics and can be used to compare the physical properties of different fabrics. The large difference between the maximum and minimum means of the indices further expresses the discernibility of the TST-F for the selected fabrics. The repeatability is checked by the CV of three replicates for each type of fabric. As shown in Table 4, all the CVs of the indices are lower than 10%, except those of the BF_mean and D_br; however, the BF_mean also shows low CV (around 7%) for most cases. The relatively high fluctuation of the D_br may be caused by the folding position variations between different samples, but most CVs are still acceptable. This means that the difference caused by different replicates for the same fabric is reasonably low, which confirms the repeatability of the testing results by the TST-F.

Physical interpretation of measurement indices

The KES-F is a set of common testing systems for mechanical properties in low stress. Therefore, the correlation analysis between measurement results of the TST-F and KES-F was used in this study to analyze the physical meaning of the measurement indices of the TST-F. The mechanical properties, including bending rigidity (B), bending hysteresis (2HB), tensile work (WT), tensile resilience (RT), compression work (WC) and compression resilience (RC), were measured by the KES-F (KES-F testing results can be found in Table S1 of the supplemental information). The correlation of the measurement indices by TST-F and the physical properties tested by the KES-F as well as the basic specification parameters of fabrics, namely mass (M) and thickness (T), are listed in Table 5.

Table 5.
Correlation analysis between measurement indices of the Touch Sensation Tester for Fabrics and physical properties of fabrics

BF_mean W_c R_wc D_br H_br R_f W_t R_wt IR _max S_IR H _max

B .796 .654 –.553 –.158 .861 .843 .165 –.239 –.601 –.637 –.770

2HB .799 .649 –.566 –.119 .881 .852 .178 –.273 –.643 –.669 –.871

WC .756 .751 –.735 .083 .790 .769** .363 –.434* –.789 –.770 –.744**

RC .211 .231 –.169 .044 .167 .193 .031 .182 –.350 –.311 –.309

WT .357 .374 –.249 .250 .485* .455* .870 –.663 –.371 –.406 –.250

RT –.243 –.219 .186 –.314 –.391 –.375 –.165 .837 .128 .232 .235

M .905 .862 –.771 –.013 .872 .873 .337 –.484* –.870 –.882 –.889

T .880 .871 –.802 .029 .831 .838 .329 –.445* –.877 –.879 –.840**

*Correlation is significant at the 0.05 level;

** correlation is significant at the 0.01 level.

It can be seen from Table 5 that three measurement indices (BF_mean, H_br, R_f) of the bending and recovery region (region A) of the TST-F testing curve show significant relations with bending properties at the 0.01 level, as indicated by the correlation coefficients ranging from 0.796 to 0.861 with bending rigidity B and from 0.799 to 0.852 with bending hysteresis 2HB. This indicates that the measurement indices extracted from the TST-F testing curve are able to evaluate the bending property of fabrics. The W_c is correlated (0.751) with the compression work WC, which shows the efficiency of the indices to characterize the compression property, while the R_wc is not related to the compression resilience tested by the KES-F. This can be explained by the fact that the KES-F only tests the compression recovery property along the fabric thickness direction, but the TST-F characterizes the compression recovery property of fabrics that are under the wrinkling or creasing states, which is considered useful in evaluating the wearing comfort, as clothes are often folded and creased due to joint motion in our daily life. Creases and wrinkles have an intensive action on human sensory neurons in uncomfortable feelings, and thus the measurement index R_wc is meaningful and helpful to fill the gap between the actual application and existing testing methods. The indices W_t and R_wt respectively show the strongest correlation with the tensile work WT and tensile resilience with the coefficients of correlation r = 0.870 and 0.837. This confirms that the two indices can be used to characterize the tensile properties of fabrics effectively. It has been reported that heat conductivity, infrared transmission and air permeability are three main methods of thermal management of textiles.²⁴ Therefore, the maximum of the infrared transmission IR_max and efficiency of infrared transmission S_IR expressing the infrared transmission performance were defined. These two indices are measured by the infrared detector array when a fabric covers the heat source but does not contact with the heat source on the moving slab. Moreover, the maximum heat conductivity H_max was measured to characterize the heat conductivity of the fabrics by testing the temperature after the fabric contacted with hear source for a certain time (60 seconds in this study). These three thermal indices (IR_max, S_IR, H_max) show good correlation with the fabric thickness (r = 0.877, 0.879 and 0.840) and mass (r = 0.870, 0.882 and 0.889), which implies that the fabric specification plays an important role in the thermal property of fabrics. The scatters of the H_max and IR_max evolution against fabric thickness are plotted in Figure 4. As can be seen, the temperature shows a typical exponential relationship with the thickness, and the infrared transmission becomes slow, while the heat conductivity tends to be prevented with the increase of the thickness. Moreover, the insignificant index, such as D_br, is removed from the index set by referring to the correlation analysis and index quality.

Figure 4.
The evolution of H_max and IR_max versus fabric thickness.

Relationships between subjective sensations and objective measurement

Table 6 shows the correlation analysis between the measurement indices of the TST-F and subjective sensations. The subjective sensations in terms of bending stiffness show significant correlation with measurement indices BF_mean (coefficient of correlation = 0.742), H_br (0.661) and R_f (0.652) at the 0.01 level. It is interesting to find that the bending stiffness sensation is also highly correlated with indices W_c and R_wc. This is because a fabric is compressed in a bending/creasing state. It is expected that significant correlations are also found between compression softness sensation and measurement indices W_c (–0.610) at the 0.01 level, and R_wc (0.517) and H_br (–0.462) at the 0.05 level. It should be pointed out that all the W_c, R_wc and H_br indices are tested by constructing a folded state of fabrics by the TST-F. This to a large extent clarifies that fabrics under wrinkling and folding/creasing states due to joint motion have an important effect on the human sensation of compression comfort, and further indicates that the designed method can help to evaluate the effect of wrinkled or hierarchical folded fabrics on compression comfort, filling the gaps between present testing methods and actual applications. The W_t showed significant correlation with the stretching tightness sensation, as indicated by the coefficient of correlation r = –0.882 at a significant level of 0.01. Thermal comfort shows the strongest correlation with the indices relating to the thermal property, such as IR_max (0.955), S_IR (0.939) and H_max (0.863), at a significant level of 0.01, and also show good correlation with some mechanical indices relating to the bending and compression properties. This implies that the thermal property of fabrics is associated with fabric mechanics, which is consistent with the previous reports that structural and mechanical parameters of fabrics are closely related to the tactile sensation and play a very dominant role in the thermal property of fabrics.^7,25,26 Therefore, it can be concluded that the measured indices tested by the TST-F have a close relationship with subjective sensations toward fabrics, and thus can be used to characterize human touch sensations in terms of bending stiffness, compression softness, stretching tightness and thermal comfort.

Table 6.
Correlation analysis between the Touch Sensation Tester for Fabrics indices and subjective sensations

BF_mean W_c R_wc H_br R_f W_t R_wt IR _max S_IR H _max

Bending stiffness .742 .660 –.656 .661 .652** .058 –.348 –.464* –.478* –.610

Compression softness –.583 –.610** .517* –.462* –.466* –.289 .399 .576* .659** .504*

Stretching tightness –.452* –.460* .272 –.499* –.501* –.882** .493* .534* .531* .424

Thermal comfort .786 .711 –.777 .739 .735 .335 –.417 –.955 –.939 –.863

*Correlation is significant at the 0.05 level;

**correlation is significant at the 0.01 level.

To further study the relationships between subjective sensations and measurement results, a stepwise regression analysis was carried out to select the significant indices by using p-values < 0.05 as the criterion of indices entering the regression equation and p-values >0.10 as the criterion of removing the indices from the regression equation, respectively. The measurement indices were used as independent variables and the grades (in the range of 1–5) of fabric subjective sensations were used as dependent variables. The regression results are listed in Table 7. The four subjective sensations of fabrics are well predicted by the measurement indices of the TST-F with coefficients of determination of R² = 0.742 for bending stiffness, 0.617 for compression softness, 0.883 for stretching tightness and 0.957 for thermal comfort, indicating the measurement indices can reflect the main information of mechanical–thermal touch properties of fabrics. Therefore, prediction models with the designed instrument can be an alternative way to characterize the touch sensation of fabrics.

Table 7.
Prediction models for touch sensations of fabrics

Prediction models R ² p-values

$Bending stiffness = 0.060 B F_{mean} - 0.052 W_{c} - 0.078 R_{c} + 8.105$ 0.742 <0.001

$Compression softness = - 0.027 W_{c} + 0.030 R_{f} + 4.857$ 0.617 0.003

$Stretching tightness = - 0.094 W_{t} + 0.004 R_{t} + 7.395$ 0.883 <0.001

$Thermal comfort = - 0.558 I R_{\max} - 0.080 H_{\max} + 19.563$ 0.957 <0.001

Conclusions

The introduced instrument, named the TST-F, equipped with noncontact an infrared detector array provides an efficient and comprehensive method to measure the mechanical–thermal sensation properties of fabrics by a simultaneous-integrated test, providing a proper approach to conduct the measurement of textiles with typical time-dependent physical properties by a small temporal resolution. Four categories of touch sensations toward fabrics were defined and investigated. The measurement capacity and repeatability of the TST-F for measuring various woven fabrics were confirmed by the significant difference among different samples and reasonably low CV in replicate tests for the same fabric. The good correlation between the TST-F measurement indices and physical properties of fabrics as well as subjective sensations was also verified by correlation analysis. Results showed that the measurement indices from the TST-F involved sufficient information in terms of bending, folding, compression and stretching properties, as well as the infrared transmission and heat conductivity performance, and were able to characterize the related mechanical–thermal sensation properties of fabrics. Therefore, the introduced instrument provides a potential method to evaluate fabric tactile sensations by addressing the actual deformation features and application requirement in wearing garments.

Supplemental Material

sj-pdf-1-trj-10.1177_00405175211019903 - Supplemental material for Simultaneous-integrated evaluation of mechanical–thermal sensory attributes of woven fabrics in considering practical wearing states

Supplemental material, sj-pdf-1-trj-10.1177_00405175211019903 for Simultaneous-integrated evaluation of mechanical–thermal sensory attributes of woven fabrics in considering practical wearing states by Ling Liu, Li Wei and Fengxin Sun in Textile Research Journal

Sample number	Weight (g/m²)	Thickness (mm)	Weaving structure	Component
1#	156	0.54	Twill	Cotton
2#	120	0.38	Plain	Woo/Polyester
3#	189	0.47	Plain	Wool/Spandex
4#	469	1.67	Plain	Acrylon/Wool
5#	559	1.84	Satin	Wool
6#	188	0.56	Plain	Cotton
7#	354	1.23	Twill	Wool
8#	153	0.43	Plain	Cotton/Polyester
9#	418	1.40	Twill	Polyester/Wool
10#	161	0.43	Twill	Rayon/cotton
11#	401	1.71	Twill	Wool
12#	437	1.49	Twill	Wool
13#	200	0.52	Plain	Nylon/Polyester
14#	271	0.92	Twill	Wool/Acrylic
15#	203	0.64	Twill	Polyester/Wool
16#	201	0.70	Twill	Wool/Acrylic
17#	356	1.48	Satin	Wool/Acrylic
18#	164	0.47	Twill	Cotton/Polyester
19#	406	1.55	Twill	Wool/Acrylic
20#	267	0.76	Plain	Wool
21#	376	1.75	Twill	Wool

Index	Meaning	Calculation	Unit
BF _mean	Mean of bending force during fabric bending deformation	$B F_{mean} = Mean (F_{I} (x))$	cN
W_c	Compression work of fabric being compressed by moving slab and testing slab	$W_{c} = \int_{X_{1}}^{X_{2}} (F_{II} (x) - F_{II} (X_{1})) d x$	cN·mm
W_cr	Compression recovery work at the compression recovery step	$W_{c r} = \int_{X_{1}}^{X_{2}} (F_{III} (x) - F_{III} (X_{1})) d x$	cN·mm
R_wc	Recovery ratio of the compression work	$R_{w c} = W_{c r} / W_{c}$	%
D_br	Deflection displacement between the point X₄ and coordinate origin X₀	$D_{b r} = ‖ \vec{X_{0} X_{4}} ‖$	mm
H_br	Hysteresis between the bending force and bending recovery force	$H_{b r} = \| F_{I} (X_{4}) - F_{IV} (X_{4}) \|$	cN
R_f	Residual force induced by anelasticity	$R_{f} = F_{IV} (0)$	cN
W_t	Stretching work of fabric being stretched by moving slab and testing slab	$W_{t} = \int_{X_{0}}^{X_{3}} (F_{VI} (x) - F_{VI} (X_{3})) d x$	cN·mm
W_tr	Stretching recovery work at the compression recovery step	$W_{t r} = \int_{X_{0}}^{X_{3}} (F_{VII} (x) - F_{VII} (X_{3})) d x$	cN·mm
R_wt	Recovery ratio of the stretching work	$R_{w t} = W_{t r} / W_{t}$	%
IR _max	Maximum infrared transmission	$I R_{\max} = {Max (Temp) \|}_{Region A}$	°C
S_IR	Efficiency of infrared transmission	$S_{I R} = {Mean [\frac{\partial Temp}{\partial x}] \|}_{Region A}$	°C/mm
H _max	Maximum heat conduction	$H_{\max} = {Max (Temp) \|}_{Region B}$	°C

Rating scales	Bending stiffness	Compression softness	Stretching tightness	Thermal comfort
1	Very soft, easy to bend	Very fluffy	Very loose, easy to stretch	Very cool and dry
2	Soft	Fluffy	Loose	Cool
3	Moderate	Moderate	Moderate	Moderate
4	Stiff	Hardened	Tight	Warm
5	Very stiff, hard to bend	Very hardened	Very tight, hard to stretch	Very warm and moist

No.		BF_mean	W_c	R_wc	D_br	H_br	R_f	W_t	R_wt	IR _max	S_IR	H _max
1	Mean	5.9	42.0	53.5	0.91	3.64	3.07	50.0	51.5	26.3	2.24	36.6
	CV	8.4%	3.9%	2.8%	11.1%	0.8%	4.3%	5.2%	1.6%	2.1%	1.7%	1.0%
2	Mean	22.6	40.0	49.5	0.03	0.38	1.84	26.5	91.0	28.0	3.04	35.5
	CV	2.6%	3.4%	2.6%	13.8%	2.7%	5.1%	4.3%	3.3%	2.7%	1.8%	2.2%
3	Mean	5.5	32.5	69.2	0.34	1.66	1.31	79.0	67.4	26.5	2.48	36.5
	CV	8.7%	6.3%	4.6%	5.5%	7.2%	7.4%	4.7%	2.8%	3.1%	2.5%	0.9%
4	Mean	59.2	80.0	40.7	0.29	21.07	11.20	39.0	61.2	21.0	0.71	31.8
	CV	10.5%	5.7%	4.4%	16.5%	1.0%	7.3%	4.9%	1.5%	1.9%	1.4%	1.5%
5	Mean	117.1	142.5	26.0	0.44	51.8	31.53	62.0	47.9	21.9	0.67	30.0
	CV	7.9%	5.8%	3.7%	11.3%	4.4%	1.1%	4.2%	2.6%	2.5%	1.2%	2.3%
6	Mean	9.4	34.0	60.9	0.08	1.21	1.94	49.0	51.0	29.6	3.37	37.0
	CV	6.2%	6.1%	5.6%	9.3%	5.4%	2.4%	4.8%	2.8%	1.8%	1.2%	0.3%
7	Mean	40.5	75.5	40.8	0.42	14.24	8.46	48.0	56.0	22.3	1.10	33.7
	CV	10.9%	5.6%	3.6%	10.5%	4.3%	7.6%	5.3%	2.0%	3.1%	2.0%	2.1%
8	Mean	13.0	47.0	49.3	0.21	5.85	4.55	38.8	59.9	27.0	2.51	36.9
	CV	11.1%	5.6%	4.8%	12.0%	3.6%	4.0%	3.0%	1.2%	2.9%	1.7%	1.1%
9	Mean	80.6	138.5	26.7	0.08	6.59	8.38	40.5	58.2	22.7	1.28	34.0
	CV	9.2%	7.5%	6.6%	18.5%	3.8%	4.4%	4.7%	2.2%	3.0%	1.8%	0.8%
10	Mean	6.4	45.0	51.3	0.45	3.07	2.21	37.0	66.5	27.6	2.79	37.0
	CV	7.3%	6.1%	5.8%	14.5%	4.4%	3.0%	4.0%	2.5%	3.2%	2.2%	0.3%
11	Mean	63.8	114.0	31.2	0.78	23.27	17.28	57.5	51.8	23.0	1.18	33.6
	CV	6.6%	6.2%	5.0%	6.6%	4.5%	5.0%	5.0%	1.9%	1.7%	1.6%	0.8%
12	Mean	95.4	125.0	26.5	0.29	31.36	18.65	50.5	49.0	22.2	0.96	35.0
	CV	10.6%	5.8%	4.2%	13.5%	7.7%	7.6%	5.0%	1.9%	3.4%	2.3%	2.3%
13	Mean	12.2	64.0	36.9	0.49	5.75	4.06	39.0	59.0	26.0	2.47	37.0
	CV	6.9%	6.1%	4.5%	16.3%	4.1%	2.8%	4.6%	3.1%	2.0%	1.1%	0.7%
14	Mean	22.2	67.0	42.3	0.44	7.57	4.88	47.0	56.1	23.8	1.28	35.4
	CV	3.5%	3.1%	4.1%	15.2%	5.4%	3.0%	2.6%	2.0%	3.1%	2.5%	1.6%
15	Mean	9.6	49.0	50.6	0.45	3.19	2.35	49.5	64.2	23.4	1.54	36.3
	CV	9.7%	6.0%	4.7%	11.4%	3.7%	4.2%	4.2%	2.0%	1.9%	1.6%	0.7%
16	Mean	13.5	56.5	46.3	0.42	5.41	4.29	38.0	61.4	24.9	2.02	37.0
	CV	5.5%	6.5%	4.2%	18.3%	3.7%	5.6%	3.8%	2.4%	2.2%	1.7%	1.0%
17	Mean	96.0	152.0	21.4	0.12	22.12	15.42	42.0	56.8	22.5	0.92	34.5
	CV	13.3%	1.7%	5.6%	19.1%	1.8%	5.7%	5.7%	4.5%	2.6%	2.3%	2.2%
18	Mean	13.5	50.0	46.5	0.31	7.08	5.69	44.5	51.9	27.3	2.62	37.0
	CV	10.6%	6.8%	5.2%	19.9%	2.8%	3.3%	3.8%	1.9%	2.2%	1.4%	0.6%
19	Mean	67.6	101.5	31.5	0.29	26.38	14.97	66.5	46.1	22.6	1.25	35.1
	CV	11.1%	7.3%	4.7%	19.5%	5.4%	1.5%	4.4%	0.7%	2.6%	1.4%	1.3%
20	Mean	20.0	63.5	38.9	0.52	7.98	4.84	45.5	51.1	24.7	1.67	35.0
	CV	6.6%	5.8%	3.6%	13.2%	2.5%	3.0%	5.8%	1.9%	2.2%	1.7%	1.5%
21	Mean	48.2	102.0	29.1	0.42	17.33	10.05	57.0	58.9	22.3	1.25	34.2
	CV	8.9%	6.1%	5.2%	14.5%	3.4%	4.2%	4.2%	1.7%	2.5%	1.2%	1.8%
Mean_max	117.1	152.0	69.2	0.9	51.8	31.5	79.0	91.0	29.6	3.4	37.0
Mean_min	5.5	32.5	21.4	0.0	0.4	1.3	26.5	46.1	21.0	0.7	30.0
CV_max	13.3%	7.5%	6.6%	19.9%	7.7%	7.6%	5.8%	4.5%	3.4%	2.5%	2.3%
CV_min	2.6%	1.7%	2.6%	5.5%	0.8%	1.1%	2.6%	1.0%	1.7%	1.1%	0.3%
F-value (difference between samples)	4651.6**	961.7**	102.9**	1.9*	1238.8**	549.2**	96.3**	73.4**	49.7**	17.0**	39.4**

	BF_mean	W_c	R_wc	D_br	H_br	R_f	W_t	R_wt	IR _max	S_IR	H _max
B	.796**	.654**	–.553**	–.158	.861**	.843**	.165	–.239	–.601**	–.637**	–.770**
2HB	.799**	.649**	–.566**	–.119	.881**	.852**	.178	–.273	–.643**	–.669**	–.871**
WC	.756**	.751**	–.735**	.083	.790**	.769**	.363	–.434*	–.789**	–.770**	–.744**
RC	.211	.231	–.169	.044	.167	.193	.031	.182	–.350	–.311	–.309
WT	.357	.374	–.249	.250	.485*	.455*	.870**	–.663**	–.371	–.406	–.250
RT	–.243	–.219	.186	–.314	–.391	–.375	–.165	.837**	.128	.232	.235
M	.905**	.862**	–.771**	–.013	.872**	.873**	.337	–.484*	–.870**	–.882**	–.889**
T	.880**	.871**	–.802**	.029	.831**	.838**	.329	–.445*	–.877**	–.879**	–.840**

	BF_mean	W_c	R_wc	H_br	R_f	W_t	R_wt	IR _max	S_IR	H _max
Bending stiffness	.742**	.660**	–.656**	.661**	.652**	.058	–.348	–.464*	–.478*	–.610**
Compression softness	–.583**	–.610**	.517*	–.462*	–.466*	–.289	.399	.576*	.659**	.504*
Stretching tightness	–.452*	–.460*	.272	–.499*	–.501*	–.882**	.493*	.534*	.531*	.424
Thermal comfort	.786**	.711**	–.777**	.739**	.735**	.335	–.417	–.955**	–.939**	–.863**

Prediction models	R ²	p-values
$Bending stiffness = 0.060 B F_{mean} - 0.052 W_{c} - 0.078 R_{c} + 8.105$	0.742	<0.001
$Compression softness = - 0.027 W_{c} + 0.030 R_{f} + 4.857$	0.617	0.003
$Stretching tightness = - 0.094 W_{t} + 0.004 R_{t} + 7.395$	0.883	<0.001
$Thermal comfort = - 0.558 I R_{\max} - 0.080 H_{\max} + 19.563$	0.957	<0.001

Footnotes

Declaration of conflicting interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported in part by the Industry-University-Research Cooperation Project of Jiangsu Provincial Science and Technology Department (BY2020360); in part by the Integrated Vocational-educational Platform Construction Project for Jiangsu Higher Vocational Education (SJZH2019-no.26); the National Natural Science Foundation of China (11802104); the Natural Science Foundation of Jiangsu Province (BK20180589); in part by the Deep Integrated Vocational-educational Platform Construction Project for Jiangsu Higher Vocational Education (SJG2016-no.19) and the Project of Yancheng Polytechnic College (2019HX-14 and 2020HX-02).

ORCID iD

Fengxin Sun

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