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Abstract

Cotton woven fabrics were treated with three different silicone softeners. These softeners were applied on the fabric using the pad-dry-cure method. The hand value of treated fabrics was analyzed by determining bending length, crease recovery angle, and surface roughness and smoothness properties. With increased silicone softener concentration, an improvement in surface smoothness of the fabric was observed at a certain level, beyond which there were no significant changes in smoothness. Statistical analysis (ANOVA) was carried out to establish a relationship between the application of softeners at different concentrations on the bending length, smoothness, and crease recovery angle properties. A correlation was established between subjective and Kawabata analyses.

Keywords

Bending Length Digital Image Processing Hand Kawabata Softening Surface Roughness

Introduction

Fabric smoothness-roughness has been considered as one of the principal factors of clothing comfort. It is also a significant factor in today's consumer buying decisions.¹ Fabric smoothness behavior is directly proportional to the fabric surface friction behavior. Fabric friction is defined as the resistance to motion.² Subjective fabric hand assessment is definitely influenced by the static and dynamic friction among the cloth surface and the thumb or finger, although other properties are also involved in the assessment.³ The human finger is a delicate instrument equipped for distinguishing small contrasts in the frictional conduct of fabric. The results of hand tests are communicated in abstract terms (e.g., clingy, oily, soft, sleek, harsh, scratchy, sheer, sticky, and waxy) contingent on the feeling of touch. So, it is imperative to evaluate texture erosion quantitatively and, in addition, elements that may influence it.⁴

The processing of textiles to accomplish a specific hand is one of the most basic parts of finishing. Softeners improve the hand, smoothness, elasticity, and antistatic and soil release properties of textiles.⁵ The mechanism of cotton softening is well established.⁶ Among many textile quality factors, smoothness is a very important property.⁷

At the present time, standard or specified values for hand are not available. Stiffness and drape testers are used to test fabrics in terms of bending length and drape coefficient values, respectively. Choudhury⁸ studied the effect of softeners on softness and established that the bending length decreased with increased softener concentration, indicating a reduction in fabric stiffness. Chattopadhyay and Vyas⁹ found that silicone softener treatment on fabrics decreased the bending length, which was indicative of enhanced material quality.

Crease recovery refers to the ability of the fabric to return to its original shape after removing folding deformations.¹⁰ The Kawabata tester is an instrument that measures frictional properties in terms of the frictional coefficient. The estimation of total hand value made by Kawabata is known as the KES-FB framework.¹¹ It is comprised of four specific instruments: FBI, FB2, FB3, and FB4.Tese instruments measure an aggregate of 16 parameters identified with tensile, compression, shear, bending, surface roughness, and friction properties. The fabric assurance by simple testing (FAST) device is nearly as straight forward as the KES-FB system.¹¹

Hasani and Behtaz¹² have evaluated the surface roughness of weft knitted fabrics by analyzing the signals obtained from image processing using the 600 dpi resolution scanner and Matlab software. AATCC TM202-2012¹³ describes a relative hand value instrument system (RHV-IS) for the determination of relative hand value. The present study is therefore undertaken to understand the effect of various softeners on surface smoothness characteristics of cotton woven fabric using various analytical techniques.

Materials and Methods

Cotton Fabric

Greige cotton fabric (100%, plain woven) was sourced from M/s Surya Processors Pvt. Ltd., Ghaziabad, U.P., India. For processing, an enzymatic desizing agent (DXTRA conc., M/s Harris & Menuk Chemicals, Chennai, India) was used. Other chemicals used, including sodium hydroxide and hydrogen peroxide, were supplied by M/s Jai Laxmi Chemicals. The fabric was pretreated by desizing, singing, scouring, bleaching, and mercerizing in the mill using standard recipes (Table I).

Table I

Mill Recipes

Process	Chemical/Auxiliary	Processing Temperature (°C)
Desizing (After singeing)	2 g/L Desizing enzyme 4g/L Wetting agent Acetic acid (to maintain pH 5.5-6.0)	70
Scouring	30 mL/kg Caustic lye 4 mL/kg Wetting agent	98
Bleaching	30 mL/kg Caustic lye 4 mL/kg Wetting agent 25 mL/kg Hydrogen peroxide 4 mL/kg Peroxide stabilizer 4 mL/kg Sequestering agent	98
Mercerization	Caustic lye (520 Tw)	35

Softener Treatment

Mercerized fabric samples were given softening treatments in the Northern India Textile Research Association (NITRA) pilot plant. Tree types of softeners were used in this study: a modified polysiloxane micro emulsion (Soft 1), an organo modified polysiloxane micro emulsion (Soft 2), and a hydrophilic nano silicone (Soft 3) at various concentrations (40, 60, and 80 g/L). These were pad-dry-cure applied on the mercerized fabric using a standard recipe (5–5.5 pH, 75% wet pickup at 30–35 °C, a drying temperature of 110 °C and a curing temperature of 150 °C for 40s) as recommended by the softener supplier.

A total of 10 samples were prepared using these softeners. Sample details, along with the concentration of individual softeners used are given in Table II.

Table II

Sample Codes

Sample	Code
Mercerized	M1
Soft 1 (40 g/L)	S1-40
Soft 1 (60 g/L)	S1-60
Soft 1 (80 g/L)	S1-80
Soft 2 (40 g/L)	S2-40
Soft 2 (60 g/L)	S2-60
Soft 2 (80 g/L)	S2-80
Soft 3 (40 g/L)	S3-40
Soft 3 (60 g/L)	S3-60
Soft 3 (80 g/L)	S3-80

Fabric Sample Analysis

The samples were tested for various physical properties including mass (IS 1964), EPI/PPI (IS 1963), tensile strength (ISO 13934-1), tear strength (ISO 13937-1), bending length (IS 6490), and crease recovery (IS 4681).

The change in fabric surface roughness or smoothness after softening treatments was determined by the Digital Image Processing (DIP)’ method¹⁴ using Matlab software, subjective analysis, and the Kawabata Evaluation System.¹⁵

DIP

The DIP method is based on surface height variation measurement. Fabric samples were scanned using a 600 dpi digital image processing scanner.¹⁶ After scanning, the obtained images were cropped so as to have a uniform and regular figure. Using Matlab software, simulation and surface roughness were plotted. The obtained images were first inverted so that the lighter areas represented the densely populated part of the fabric and the dark parts represented the sparsely populated region.¹⁷ The images were transformed to gray scale and then loaded into Matlab. To remove the noise, Gaussian filters were applied. The degree of smoothening was determined by the standard deviation of the Gaussian, which was generally taken as 5.¹⁷ A three-dimensional surface plot was generated. The images were then converted to a surface plot in Matlab. The color of the individual pixels was plotted on the y-axis. The difference between the color in different regions of the fabric caused a difference in the height in the plot. This difference was used to determine the surface roughness. The maximum peak height of the digital images was taken for the analysis. An appropriate computer program was written with due consideration of the various parameters of the three-dimensional surface plot of the scanned fabric images and simulation was performed using Matlab programming for obtaining the fabric surface roughness index.¹⁷ Decrease in the percent roughness index was determined using Eq. 1.

Decrease in roughness index (%) = ((peak height of mercerized fabric - peak height of softener treated fabric) / (peak height of mercerized fabric)) ×100

Eq. 1

Kawabata Evaluation System

The Kawabata system was used to determine the coefficient of friction (MIU) of the surface of fabric samples in the warp and weft directions on the sides that would be in contact with the skin during wear.¹⁸ The framework gains information from two sensors estimating the frictional coefficient and the roughness of the fabric surface at the same time in three distinct regions within a fabric sample of 20 × 20 cm. The MIU is calculated by averaging the output over the distance between 0 to 20 mm using an integrator.¹⁹

The MIU is defined in Eq. 2.

MIU = F/P

Eq. 2

F is the frictional force and P is the sensor load.

Subjective Analysis

As per AATCC EP5-2011, a subjective analysis was carried out on four samples coded as M1, S1-80, S2-80, and S3-80. Greige fabric samples were also used along with these samples for the analysis. A questionnaire was prepared as per a 3-point Likert scale. This questionnaire was distributed to 20 respondents to get their smoothness assessment based on three ranking— smoother (Rating 1), smooth (Rating 2), and rough (Rating 3). All samples were analyzed diagonally by all respondents to average out the effect of warp and weft direction.

Statistical Analysis

Experimental data were analyzed using SPSS (IBM, version 20). Multivariate ANOVA were used to compare means. The null hypothesis (H_o) is that there was no relationship between concentration of softeners applied on the fabric and the MIU, bending length, and crease recovery values. In the alternative hypothesis, there was a relationship between concentrations of softeners applied on the fabric and MIU, bending length, and crease recovery values. The H_o hypothesis was rejected when the p-value was less than a pre-determined significance level of 0.05.

Results and Discussion

Mechanical Properties

The properties of the 10 treated fabrics, along with greige fabric, are given in Table III. Sample masses varied from 123 to 128 g/m². The mass of the greige fabric (128 g/m²) was greater than the other samples due to the presence of sizing agent. Ends/inch and picks/inch of treated fabric samples (i.e., samples having undergone mercerization and softening treatments, were greater than the greige sample due to wet shrinkage during processing. Increased ends/inch and picks/ inch also increased the tear strength of the treated fabric. On the other hand, the tensile strength of the mercerized fabric was greater than fabrics treated with various softeners. Silicon softener friction between the fibers in yarn and between the yarn within the fabric resulted in more slippage reduction in load bearing capacity with increased elongation on load.²⁰

Table III

Physical Properties

Sample	Mass (2 g/m)	End/inch	Picks/inch	Tensile Strength, (N)		Tear Strength, (g)		Crease Recovery Angle (Warp +Weft)(°)	Bending Length, (cm)		Coefficient of Friction (MIU)
Sample	Mass (2 g/m)	End/inch	Picks/inch	Warp	Weft	Warp	Weft	Crease Recovery Angle (Warp +Weft)(°)	Warp	Weft	Warp	Weft
Greige	128	120	72	576	259	1138	569	106	2.45	1.75	0.15	0.152
M1	127	136	80	681	258	977	670	156	1.40	1.38	0.149	0.136
S1-40	123	132	72	449	164	1496	940	182	1.22	1.20	0.1363	0.1413
S1-60	125	132	72	456	156	1516	947	186	1.18	1.16	0.1347	0.1353
S1-80	123	136	71	412	176	1338	913	188	1.18	1.14	0.1347	0.1350
S2-40	125	135	72	384	160	1340	849	182	1.16	1.12	0.137	0.141
S2-60	126	136	72	428	167	1404	821	184	1.20	1.16	0.135	0.135
S2-80	126	136	72	433	176	1428	959	186	1.12	1.10	0.135	0.140
S3-40	122	135	70	396	111	1342	823	173	1.26	1.16	0.1233	0.1293
S3-60	124	136	72	363	148	1380	790	178	1.22	1.12	0.1253	0.1297
S3-80	123	135	72	371	133	1340	773	180	1.18	1.10	0.1257	0.1290

Bending Length

The bending length is a major factor that determines fabric flexibility.²¹ Bending length values of treated and untreated cotton fabrics are given in Table III. The softener treated cotton fabrics showed lower bending length values when compared to mercerized fabric, indicating that the mercerized sample was stiffer then the softener-treated samples. With the increase in softener concentration from 40 g/L to 80 g/L, there was a reduction in bending length. The reduction in bending length indicates reduced rigidity/stiffness, and hence improved softness, of the fabric. The softening ability of silicones originates from siloxane backbone flexibility due to Si-O-Si bond rotation.²⁰

Crease Recovery

Crease recovery is an important fabric property of the fabric, generally caused by a fold due to pressure on the fabrics. The tendency to crease depends on the structural characteristics of cellulose fibers. With a high degree of fiber orientation, a pronounced tendency to crease diminishes. The crease recovery angle was determined using a Shirley crease recovery angle tester. For this purpose, rectangular test specimens (14 × 15 mm) were cut both in warp and weft directions. Before testing, the sample was conditioned as per IS 4681 standard test conditions. Crease recovery angle values of mercerized and softener treated cotton fabrics are given in Table III. It is evident from the Table III that the crease recovery angle increased from mercerized to softener treated samples. This may be due to swelling of fiber in the fabric upon softening, leading to better fabric recovery from deformation. The fabric crease recovery angle also increased as the softener concentration used increased from 40 g/L to 80 g/L.^22,23

Roughness

The roughness properties of all fabric samples were evaluated in terms of MIU values using the Kawabata evaluation system. The results of mercerized, as well as softener-treated fabric samples, are shown in Table III. MIU values were higher for mercerized fabric samples in both warp and weft direction than for softener-treated fabric samples.

Statistical Analysis

Results are given for the multivariant ANOVA analysis between softener concentrations and MIU values in Table IV. The null hypothesis (H_o) was rejected as the p-value was less than a predetermined significance level (0.05). The regression coefficient (R²) value was 0.91, which indicated a very strong relationship between the softener concentration and MIU values.

Table IV

Multivariant ANOVA among Softener Concentrations and Coefficient of Friction (MIU)

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	880024.000^a	7	125717.714	44.001	0.000
Intercept	11652483.266	1	11652483.266	4078.369	0.000
Friction Warp	106138.000	3	35379.333	12.383	0.000
Friction Weft	160000.000	2	80000.000	28.000	0.000
frictionwarp × frictionweft	0.000	0	—	—	—
Error	80000.000	28	2857.143	—	—
Total	14006568.000	36	—	—	—
Corrected Total	960024.000	35	—	—	—

R Squared = 0.917 (Adjusted R Squared = 0.896).

The multivariant ANOVA results for studying the relationship between softener concentration and bending length and crease recovery are shown in Table V. The null hypothesis (H_o) was rejected p-value was less than a pre-determined significance level (0.05). The regression coefficient (R) value was 0.87, which indicated a very strong relationship between the softener concentration, bending length, and crease recovery.

Table V

Multivariant ANOVA among various Softener Concentrations and Bending Length and Crease Recovery

Source	Dependent Variable	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	Bending Length	0.218^a	2	0.109	96.073	0.000
	Crease Recovery	504.800	2	252.400	46.046	0.000
Intercept	Bending Length	168.460	1	168.460	148204.930	0.000
Intercept	Crease Recovery	959083.200	1	959083.200	174967.881	0.000
Concentration	Bending Length	0.218	2	0.109	96.073	0.000
Concentration	Crease Recovery	504.800	2	252.400	46.046	0.000
Error	Bending Length	0.031	27	0.001	—	—
Error	Crease Recovery	148.000	27	5.481	—	—
Total	Bending Length	168.709	30	—	—	—
Total	Crease Recovery	959736.000	30	—	—	—
Corrected Total	Bending Length	0.249	29	—	—	—
Corrected Total	Crease Recovery	652.800	29	—	—	—

R Squared = 0.877 (Adjusted R Squared = 0.868).

Table VI shows the correlation between subjective and Kawabata analysis. The R² value was 0.78, showing good correlation between them.

Table VI

Correlation between Kawabata and Subjective Analysis by Tests of Between-Subjects Effects

Source	Dependent Variable	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	subjectiveanalysis	20.467^a	4	5.117	41.169	0.000
Corrected Model	kawabataanalysis	0.003	4	0.001	39.789	0.000
Intercept	subjectiveanalysis	805.610	1	805.610	6481.752	0.000
Intercept	kawabataanalysis	0.955	1	0.955	48576.934	0.000
Sample	subjectiveanalysis	20.467	4	5.117	41.169	0.000
Sample	kawabataanalysis	0.003	4	0.001	39.789	0.000
Error	subjectiveanalysis	5.593	45	0.124	—	—
Error	kawabataanalysis	0.001	45	1.966 ×10^–5	—	—
Total	subjectiveanalysis	831.670	50	—	—	—
Total	kawabataanalysis	0.959	50	—	—	—
Corrected Total	subjectiveanalysis	26.060	49	—	—	—
Corrected Total	kawabataanalysis	0.004	49	—	—	—

R Squared = 0.785 (Adjusted R Squared = 0.766).

Subjective Analysis

Samples were analyzed by respondents for smoothness as previously described using one of three ratings: 1 (smoother), 2 (smooth), and 3 (rough). The results obtained by subjective analysis were compared with the MIU values obtained from the Kawabata system. The results are shown in Fig. 1. These showed similar trends. Higher MIU values led to less smoothness. The S3-80 sample was smoother (2.7) than the others in subjective analysis. This sample has lowest MIU value (0.27 × 10). The greige (G1) sample was rougher (1.4) and had the highest MIU value (0.5 × 10).

Fig. 1

Graph of subjective analyses vs. Kawabata analysis.

Fabric Surface Peak Height and Roughness Index

Digital image processing methods were used to obtain surface plots for mercerized and softener- treated fabric samples. These plots are shown in Fig. 2. The mercerized fabric sample had a greater numbers of peaks with greater peak heights (Fig. 2a and Table VII) than softener-treated fabric samples (Figs. 2b–j). The surface of the mercerized fabric was rougher than that for the softener-treated fabric samples. With the increase of softener concentration from 40 g/L to 80 g/L, fabric samples became smoother (Figs. 2b–j). The values of peak height and roughness index are given in Table VII. Of the three softeners tested, S-3 provided a smoother surface than the others. A maximum decrease in roughness was found when fabric was treated with softener S-3 at 80 g/L.

Figs. 2a–j

Surface plots obtained by the DIP method for mercerized and 40, 60, and 80 g/L silicone-treated fabric samples.

Conclusions

It was observed that the softener-treated cotton fabrics tested gave a lower bending length value as compared to the mercerized test fabric, indicating that the mercerized sample was stiffer then the softener-treated samples. A reduction in bending length was noted when the softener concentration was increased from 40 g/L to 80 g/L. Reduction in bending length indicated reduced rigidity/stiffness, and hence improved fabric softness due to use of the silicone softener.

The mercerized test fabric had the smallest crease recovery angle, which increased after the treatment with softener. The crease recovery angle (dry state) of various softener-treated samples was greater than for the mercerized fabric sample. Swelling of fiber in the fabric due to the presence of softener may be responsible for the ability of these treated fabrics to recover from deformation, as well as providing excellent soft hand. The crease recovery angle increased as the softener concentration of increased from 40 g/L to 80 g/L.

The Kawabata evaluation study showed that the coefficient of friction (MIU) value was higher for mercerized fabric in both warp and weft direction than for the softener-treated fabric samples, and had good correlation with the subjective analysis.

The digital image processing (DIP) method indicated that the mercerized fabric sample had a greater numbers of peaks with greater peak heights than softener-treated fabrics. This indicates that the surface of the mercerized fabric was rougher than the softener-treated fabric samples. The increase of softener concentration from 40 g/L to 80 g/L resulted in smoother (less rough) fabric samples. Softener S-3 provided the smoothest surface of the softeners tested. The greatest decrease in roughness was found when the fabric was treated with softener S-3 at 80 g/L.

Footnotes

Acknowledgement

We are grateful to the Ministry of Textiles, Govt. of India, for sponsoring the project entitled “Development of Fabric Smoothness Tester.”

References

Xiao

; Junyan

; Yi

; Quanhai

; Xinxing

Journal of Fiber Bioengineering & Informatics 2011, 105–114.

http://drmsparmar.blogspot.com/2017/02/nitra-developed-smoothness-tester-for.html (accessed March 2020).

Bhuvana;

D. G.

; Raghunathan

AUTEX Research Journal 2006, 6 (4), 216–221.

Das

; Kothari

V.K.

; Vandana

AUTEX Research Journal 2005, 5 (3), 133–140.

Parveenzadeh

; Hajiraissi

Tenside Surfactants Detergents 2008, 45 (5), 254–257.

Igarashi

; Nakamura

Journal of Materials Science Research 2019, 8 (1), 35–42.

Turner

; Sari-Sarraf

; Zhu

; Hequet

; Lee

Automatic assessment of fabric smoothness, 2002 45th Midwest Symposium on Circuits and Systems, IEEE, Tulsa, Oklahoma, USA, Vol 2, 2002.

Roy Choudhury

A.K.

; Chatterjee

; Saha

; Shaw

Journal of Textile Institute 2012, 103 (9), 1–9.

Chattopadhyay

D.P.

; Vyas

D. D.

Indian Journal of Fibre and Textile Research 2010, 35, 68–71.

10.

Ceven

E. K.

Textile Research Journal 2013, 83 (20), 2118–2128.

11.

Pattanaik

M. N

; Ray

S.C.

Indian Journal of Fibre and Textile Research 2016, 41, 298–305.

12.

Hasani

; Behtaz

Indian Journal of Fibre and Textile Research 2013, 38, 101.

13.

AATCC TM202-2012, Relative Hand Value of Textiles; AATCC: Research Triangle Park, NC, USA.

14.

Drobina

; Mieczysław

AUTEX Research Journal 2006, 6 (1), 40–48.

15.

Behra

B.K.

; Hari

P.K.

Indian Journal of Fibre & Textile Research 1994, 19, 168–171.

16.

Behera

B.K.

; Pattanayak

A.K.

Indian Journal of Fibre and Textile Research 2008, 33.

17.

Kawabata

The standardization and analysis of hand evaluation, Textile Machinery Society of Japan in Oska, 1980.

18.

Jadhav

A.A.

, Ph.D. Thesis, RMIT University, Melbourne, Australia, 2014.

19.

Nawaza

; Troynikov

; Watson

Physics Procedia 2011, 22, 478–486.

20.

https://pdfs.semanticscholar.org/24bc/428253b21025506903f51793be14e4f060fc.pdf (accessed March 2020).

21.

http://textileinsight.blogspot.com/2015/03/determination-of-bending-length-of.html (accessed March 2020).

22.

Parmar

M.S.

; Sisodia

; Singh

Development of smoothness tester for finished fabrics, 57th Joint Technological Conference, BITRA, Mumbai, India, February 2017.

23.

Parmar

M.S.

; Sisodia

NITRA fabric smoothness tester for testing surface smoothness of fabrics and garments, 13th International Conference on Apparel & Home Textiles, India Habitate Center, New Delhi, India, September 2017.

Effect of Softeners on Smoothness Behavior of Cotton Fabrics

Abstract

Keywords

Introduction

Materials and Methods

Cotton Fabric

Softener Treatment

Fabric Sample Analysis

DIP

Kawabata Evaluation System

Subjective Analysis

Statistical Analysis

Results and Discussion

Mechanical Properties

Bending Length

Crease Recovery

Roughness

Statistical Analysis

Subjective Analysis

Fabric Surface Peak Height and Roughness Index

Conclusions

Footnotes

Acknowledgement

References