International Journal of Textile and Fashion Technology (IJTFT) ISSN(P): 2250-2378; ISSN(E): 2319-4510 Vol. 5, Issue 1, Feb 2015, 1-6 © TJPRC Pvt. Ltd. DETERMINING CREASE RECOVERY ANGLE AT DIFFERENT TIME INTERVALS AND MODELING IT IN TERMS OF GRAMS PER SQ. MT (GSM) PRAGNYA S. KANADE & MILIND V. KORANNE Department of Textile Engineering, Faculty of Tech & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara Gujarat, India ABSTRACT Creasing is a very complex phenomenon dealing with combined action of several forces like bending, flexing, tensile etc. It an aesthetic property and has direct bearing with the constituent yarn/fibers of which it is composed of and could also be direction dependent. Application of finishes is an important part of any textile manufacturing but is likely to affect the crease recovery property as it tends to fill up the empty spaces inside the fabric matrix. Nevertheless finishes have to be applied to the fabrics to improve their performance/feel/any related property. In that case it becomes necessary to study, not only its influence on the recovery behavior but also on other properties like bending, draping, permeability or whichever property is expected to be affected. Just as recovery behavior of yarns is important and is time dependent same is true for fabrics. This work reports the crease recovery of fabrics in their dry state taken at different time intervals. The greater is the recovery; greater is its resistance to crease formation. Design-expert® software has been used to develop a model to predict crease recovery angle (CRA) for different recovery times and fabric weight per unit area. Significant model could be developed with these variables and equations developed can be used to obtain the recovery angles at desired time interval (0 to 60 seconds), GSM (67 to 246) and for warp/weft. KEYWORDS: Fabric Samples, Crease Recovery, Crease Recovery Angle, Modeling, Recovery Time INTRODUCTION “The ability of fabrics to resist the formation of crease or wrinkle when lightly squeezed is termed as crease resistance of the fabric and is associated with the fabric stiffness” [1]. Cotton though is comfortable to wear; its CRA is very poor and hence is aesthetically unappealing. Cotton fabrics are most prone to crease formation since their ability to recover from the applied deformation is the least and has been explained in the mechanism of crease formation [1]. It is known that the synthetic yarns/fibers have greater resilience and are springy in nature, obviously resulting in greater CRA up to 150-155°. Wool is the most resilient natural fiber and its expected CRA is around 140-145°. Various methods are available to improve the dry and wet crease recovery of cotton fabrics. Several researchers have reported the influence of different treatments on fabric and many other have studied the recovery property in wet as well as dry conditions along with mathematical models. Studies have been carried out on the influence of reactive printing on the dry crease recovery with the intention of reducing the energy consumption in the finishing process [2]. The influence of hybrid super elastic shape memory alloy wires to improve wrinkle resistance of flax fabric was studied for four different hybrid yarns [3] but was found to have no significant relation with its bending properties. www.tjprc.org editor@tjprc.org 2 Pragnya S. Kanade & Milind V. Koranne MATERIALS AND METHODS Five different fabric samples were collected from the market whose GSM (gram/sq.mt) ranged from 67 to 246. The samples have been coded in terms of a number ranging from 1 to 5 The fabrics were tested for their structural properties as well as other properties using the standard procedures. Table -1 shows few of the structural properties tested. Table 1: Structural Properties of Fabrics Tested Sample Code 1 2 3 4 5 EPI 80 60 72 96 100 PPI 66 44 44 48 46 GSM 66.71 111.75 156.46 201.25 245.912 METHODS The crease recovery of the fabrics was tested using the Shirley crease recovery tester shown figure 1and the value reported in this instrument was crease recovery angle (CRA). Figure 1: Shows the Instrument Used for Measuring CRA The testing was carried out using the standard procedure with minor changes for measuring the recovery angles at intermediate time intervals. The fabric was cut into 2”X1”, following which it was carefully folded into half and placed into a template. A load of 2 Kg was applied to it for one minute. Thereafter the sample was removed and using tweezers was placed into the clamp on the instrument. At the same instant the CRA was noted which has been taken as zero time. Similarly readings were also noted after 20, 40 and 60 seconds, unlike the standard method in which CRA is reported after it is allowed recovery time of 60 seconds. This would give the instantaneous recovery of the given samples and the recovery pattern/trend could be observed in case of various samples. RESULTS AND DISCUSSIONS The results from table 2 shows that irrespective of the weight per sq.mt of the fabric as the allowed recovery time increases, improvement is the recovery angle is observed. Table 2 shows the CRA noted at different time intervals. Similarly the warp CRA was greater than the weft CRA; this could be attributed to the higher number of ends than picks found in each of the case. Impact Factor (JCC): 2.9594 Index Copernicus Value (ICV): 3.0 3 Determining Crease Recovery Angle at Different Time Intervals and Modeling it in Terms of Grams per Sq. Mt (GSM) Table 2: Time Wise Recovery of Different Fabric Samples Sample Code 1 Direction warp Weft warp weft warp weft warp weft warp weft 2 3 4 5 0 114 107 128 107 127 121 124 128 137 131 Time in Seconds 20 40 60 117 119 120 112 116 119 128 128.5 135 108 112 113.5 131 133 134 128 129 130 124.5 125.5 125.5 130 136 138 139 144 147 134 138 141 Greater the number of threads in the given strip, greater will be their resistance to being bent, which is done while putting crease into the fabric sample. Such samples when allowed to recovery will encourage the threads to come back to their original position though complete recovery may not observed. It also true that the CRA finally reached is a function of the constituent yarns and the fibers from which they are composed of but here an attempt has been made to model the crease recovery angle measured at different time intervals with the weight per unit area of fabric samples. MODELING CRA IN TERMS OF GSM It is known that the recovery property is a complex interaction between different fabric properties. In the present case the fabric weight per sq.mt has been considered for the model build-up. A fabric may be heavy either due to number of ends/picks, count or crimp of warp/weft or the thickness of the fabric. Design-Expert® software has been used to carry out the modeling of fabric variables. Design-Expert® Software CRA wp Design-Expert® Software Factor Coding: Actual CRA wp (degree) Design points above predicted value Design points below predicted value Predicted vs. Actual Color points by value of CRA wp: 147 150 X1 = B: time X2 = C: C 107 140 150 Actual Factor A: gsm = 156.5 C R A w p (d e g re e ) P re d ic te d 140 130 120 130 120 110 110 100 100 60 40 100 110 120 130 140 150 B: time (seconds) warp 20 0 weft C: C Actual Figure 2: Plot of Actual CRA vs Predicted CRA Figure 3: 3-D plot of CRA, Time and Direction The figure 2 shows that most of the points lie very close to the line implying that it is a close fitting model. Response surface technique has been used to obtain the quadratic model for the fabric variables and figure 3 shows 3-D plot of the variables namely the crease recovery, time intervals at which the recovery is measured for warp or weft. Analysis of variance for the same has been tabulated in table 3. www.tjprc.org editor@tjprc.org 4 Pragnya S. Kanade & Milind V. Koranne Table 3: Response Surface Model to Predict CRA at 95% Confidence ANOVA for Response Surface Quadratic model Analysis of Variance Table [Classical sum of Squares - Type II Sum of Mean F p-Value Source Squares df Square Value Prob > F Model 4738.57 13 364.51 10.77 < 0.0001 significant A-gsm 3043.36 1 3043.36 89.96 < 0.0001 B-time 728.36 3 242.79 7.18 0.0005 C-C 279.02 1 279.02 8.25 0.0064 AB 38.65 3 12.88 0.38 0.7673 AC 198.34 1 198.34 5.86 0.0199 BC 6.05 3 2.02 0.060 0.9806 A^2 444.79 1 444.79 13.15 0.0008 Residual 1420.86 42 33.83 not Lack of Fit 1018.61 26 39.18 1.56 0.1790 significant Pure Error 402.25 16 25.14 The Model F-value of 10.77 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C, AC, A2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. The "Lack of Fit F-value" of 1.56 implies the Lack of Fit is not significant relative to the pure error. There is a 17.90% chance that a "Lack of Fit F-value" this large could occur due to noise. Table 4: Statistics of the Model Developed at 95% Confidence Std. Dev Mean C.V. % PRESS 5.82 125.96 4.62 2479.75 R-Squared Adj R-Squared Pred R-Squared Adeq Precision 0.7693 0.6979 0.5974 11.707 The "Pred R-Squared" of 0.5974 is in reasonable agreement with the "Adj R-Squared" of 0.6979; i.e. the difference is less than 0.2. This shows good correlation between the variables chosen for this model. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable while the obtained ratio is 11.707 that indicates an adequate signal. The error values calculated for different terms have been tabulated in table 5. Table 5: Error Calculation of the Developed Model Term Intercept A-gsm B[1] B[2] B[3] C-C AB[1] AB[2] AB[3] AC B[1]C B[2]C B[3]C A^2 Impact Factor (JCC): 2.9594 Coefficient Estimate 130.28 9.19 -4.75 -2.04 2.32 -2.23 -0.47 -1.47 1.06 2.35 -0.55 0.30 0.089 -6.72 df 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Standard Error 1.42 0.97 1.35 1.35 1.35 0.78 1.68 1.68 1.68 0.97 1.35 1.35 1.35 1.85 95% CI Low 127.41 7.24 -7.47 -4.75 -0.40 -3.80 -3.86 -4.86 -2.33 0.39 -3.27 -2.41 -2.63 -10.46 95% CI High 133.15 11.15 -2.03 0.68 5.04 -0.66 2.92 1.92 4.44 4.30 2.16 3.02 2.81 -2.98 VIF 1.00 1.00 1.00 1.00 Index Copernicus Value (ICV): 3.0 5 Determining Crease Recovery Angle at Different Time Intervals and Modeling it in Terms of Grams per Sq. Mt (GSM) Equation 1 is a generalized equation to calculate the Crease Recovery Angle (CRA) for different time intervals and that too for a given fabric GSM. Now if data related to warp is substituted in Eq.1 then CRA in warp direction can be found while if data related to weft is substituted then CRA for weft can be obtained. CRA=+130.28+9.19*A-4.75*B[1]-2.04B[2]+2.32*B[3]-2.23*C-0.47*AB[1]- .47*AB[2]+1.06*AB[3]+2.35*AC0.55*B[1]C+0.30*B[2]+0.089*B[3]-6.72*A2, Eq. 1 Where A stands for GSM, B stands for time interval, [1], [2] and [3] stands for time interval of 20 seconds, 40 seconds and 60 seconds, C stands for warp or weft. The equation in terms of coded factors can be used to make predictions about the response for given levels of each factor. By default, the high levels of the factors are coded as +1 and the low levels of the factors are coded as -1. The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients. The equation in terms of actual factors can be used to make predictions about the response for given levels of each factor. Here, the levels should be specified in the original units for each factor. This equation should not be used to determine the relative impact of each factor because the coefficients are scaled to accommodate the units of each factor and the intercept is not at the center of the design space. The CRA values for warp can be calculated for given values of GSM and this can be done for any of the time interval chosen from Eq. 2 to 9 as shown in Appendix 1. CONCLUSIONS The crease recovery angle is an important fabric property and can be associated with the aesthetic property of fabric. Fabrics are available in different weights measured per unit area. This model developed will be useful to find the CRA of fabric in the given weight/unit area range without having to use the instrument. This work may be extended further to study the behavior when the fabric is in wet condition. ACKNOWLEDGEMENTS The authors are greatful to S. A. Agrawal and T. A. Desai for helping in experimetal and computational work. REFERENCES 1. Technology of textile finishing Dr. V. A. Shenai and Dr. N. M. Saraf, Second edition 1995 2. Reactive printing and crease resistance finishing of cotton fabrics Part – I Study of influential factors by an experimental design approach Fareha Asim and Muzaffar Mahmood, Journal of textile and apparel, management and technology, Vol. 7, issue 1, spring 2011, pp 1-10 3. Wrinkle Recovery of Flax Fabrics with Embedded Super elastic Shape Memory Alloys Wires, Simona Vasile, Izabela Luiza Ciesielska-Wróbel, Lieva Van Langenhove, Fibres & Textiles in Eastern Europe 2012; 20, 4(93): 56-61. APPENDICES APPENDIX-1 CRA(warp,0 sec) = 96.63179+0.33371*GSM-8.386E-004*GSM2 Eq. 2 CRA(weft, 0sec) = 82.852+0.3862*GSM-8.386E-004*GSM2 Eq. 3 www.tjprc.org editor@tjprc.org 6 Pragnya S. Kanade & Milind V. Koranne CRA(warp, 20sec) = 100.237+0.323*GSM-8.386E-004*GSM2 Eq. 4 CRA(weft, 20sec) = 88.172+0.3751*GSM-8.386E-004*GSM2 Eq. 5 CRA(warp, 40sec) = 100.389+0.351*GSM-8.386E-004*GSM2 Eq. 6 CRA(weft, 40sec) = 87.894+0.403*GSM-8.386E-004*GSM2 Eq. 7 CRA(warp, 60sec) = 102.752+0.349*GSM-8.386E-004*GSM2 Eq. 8 CRA(weft, 60sec) = 90.4+0.40137*GSM-8.386E-004*GSM2 Impact Factor (JCC): 2.9594 Eq. 9 Index Copernicus Value (ICV): 3.0