Investigation of the Performance of Cotton/Polyester Blend in Different Yarn Structures

For this study, cotton(CO) fiber and polyester(PES) fiber were used with different blending ratios. Cotton fiber and polyester fiber (Dacron®) properties are given in Table 1 and Table 2, respectively. Here, blending was done in the yarn spinning drawing process. Obtained drawn sliver and roving properties from the draw frame and simplex machine are given in Table 3. Rigid, core, and dual-core-spun groups of yarns were produced from these blended fibers, where rigid group indicates yarn without any core component. Yarn linear density was 328.06 dtex (Ne 18/1) for all groups. For core-spun yarn, 78 dtex Lycra® (elastane (El)), and for dual-core-spun yarn, 50 dtex polybutylene terephthalate (PBT) filament and 78 dtex Lycra® (elastane) were used as the core components. The yarn composition with the blend ratio is given in Table 4 to display the design of the experiment.

For this study, to produce core- and dual core-spun yarns, a modified ring spinning method was operated where the core components used were fed into the center of the feeding system; commercially, it is the most accepted system. Figure 1 shows the schematic view of the core- and dual core-spun yarns’ ring spinning process with the feeding system. For yarn production, all the machine settings were kept as standard for all three groups. Drafts of the PBT and elastane were 1.0 and 3.5, respectively. Here, the yarn’s strength, elongation, unevenness, hairiness, and imperfections were evaluated to determine the yarn’s performance. To determine the yarn’s performance, TS 244 EN ISO 2060, TS 2394, TS12863, and TS 245 EN ISO 2062 standard test methods were followed for yarn count, Uster unevenness, yarn hairiness, and yarn-breaking tenacity measurement, respectively. For each measurement, five tests were done, and a calculated mean was used for evaluation. Before testing the samples, yarns were conditioned according to ASTM D 1776 test method. Rather than having numerous criteria, the yarn quality index (YQI) is useful to understand the yarn quality. YQI measures how excellent the quality of the yarn is. In general, having better YQI means a higher quality of yarn. Yarn strength, elongation, and irregularity are all included in the total quality index. YQI is calculated by using the following formula [31]: YQI=Yarn tenacity (cN/tex)X Elongation %Unevenness% YQI = {{Yarn\;tenacity\;\left( {cN/tex} \right)X\;Elongation\;\% } \over {Unevenness\% }}

Figure 1

Modified ring spinning method. (a) Core-spun yarn production; (b) dual-core-spun yarn production [32,33].

A two-way ANOVA test was done using SPSS 25.0 for the yarn properties. Two-way ANOVA is a statistical test where the effect of two independent variables on a dependent variable is analyzed. ANOVA also determines whether there is any form of interaction between the two variables in addition to examining the main effect of each independent variable. Here in this study, yarn type (rigid, core, dual core) and blending ratio were considered independent variables to evaluate the yarn properties statistically. A correlation analysis was performed among the yarn type, blending ratio, and yarn properties. Pearson correlation measured the statistical relationship between the yarn type and blending ratio on yarn properties, along with giving information about the magnitude and direction of the relationship. For the correlation analysis, the coefficient value can be ranged from −1 to +1, where −1 indicates a perfect negative relationship and +1 denotes a perfect positive relation. A value of zero has no correlation. As long as the coefficient value is between ±0.50 and ±1, the association is regarded to be strong. It is considered a medium correlation if the value is between ±0.30 and ±0.49 and a small correlation if the number is below ±0.29. Furthermore, a regression model was also developed from the regression analysis for the yarn properties where yarn type (A) and blending ratio (B) acted as independent variables or predictors for predicting the yarn properties referred to as dependent variables. For correlation and regression analysis, yarn type was counted as a quantitative variable considering the presence of core component numbers in the yarn.

As seen in Fig. 2, it was found that 100% CO yarn has the lowest strength, while 100% PES yarn has the highest amount of strength for the rigid group. Core- and dual core groups also show the same results. From all groups, it can be stated that increasing the polyester amount gradually enhances the yarn strength. Because polyester fiber has more strength than cotton fiber, having a higher amount of polyester fiber in the yarn shows higher strength [14,22,34]. Among the group, for a particular blending ratio, it was found that rigid yarns have higher strength value than core-yarns and dual core yarns, respectively. Due to the presence of core components, the amount of cotton and polyester fiber is reduced in core- and dual core yarn, which has less strength than rigid yarn. Moreover, in the yarn cross section for core- and dual core-spun yarn, there is a high probability of spreading the fiber, which results in decreased packing density and less strength [35].

A two-way ANOVA test was conducted, and the effect of yarn type and blending ratio on yarn strength was examined. From Table 5, it can be observed that interaction between yarn type and blending ratio could not be demonstrated as p = 0.067, though a statistically significant (p < 0.001) difference in yarn strength is found by both yarn type and blending ratio. Pearson correlation analysis from Table 6 reveals a strong positive correlation for blending ratio on yarn strength but nothing about yarn type. Equation 1 in Table 7 shows the regression equivalent for the yarn strength, and it is found that independent variables are statistically significant (p < 0.001), useful for predicting yarn strength. R-squared was found to be 0.879, which reveals 87.9% of the variance in yarn strength is attributable to yarn type and blending ratio.

Figure 3 shows that increasing the PES ratio in the yarn increases the elongation amount B-work (break to work) for rigid, core, and dual core-spun yarn groups individually. 100% PES rigid, core, and dual core-spun yarns have the highest amount of elongation, while 100% cotton yarns have the lowest elongation value in the rigid, core, and dual core yarn groups, respectively. It can be said that the PES ratio has a positive relation with yarn elongation. The figure shows that having more core components in the yarn cross section reduces the B-work amount. Furthermore, the Pearson correlation analysis also validates the statement statistically, since Table 6 indicates that yarn type and blending ratio positively correlate with yarn elongation properties. Moreover, polyester fiber has a higher elongation than cotton fiber, so increasing the polyester content in the yarn helps to enhance yarn elongation [22]. For a given blending ratio, the figure reveals that dual core-spun yarns have more elongation than rigid and core-spun yarns, whereas rigid yarns have the lowest elongation value due to having no elastic material in their core.

From the two-way ANOVA analysis in Table 5, it can be observed that there is a statistically significant interaction between the effect of yarn type and blending ratio on yarn elongation (p < 0.05). The regression equation for yarn elongation has been shown in Table 7 and it can be observed that independent variables are statistically significant for predicting (p < 0.001), and 93.2% variability can be explained by the independent variables for yarn elongation.

According to Fig. 4, increasing the amount of PES reduces the unevenness of the yarns for each rigid, core, and dual core group. Here, 100% cotton rigid, core, and dual core yarns have the highest unevenness, and 100% PES rigid, core, and dual core yarns have the lowest unevenness value in their group. So, the amount of cotton or PES has a strong effect in terms of unevenness on these yarns. The Pearson correlation from Table 6 also represents a strong negative relationship between yarn unevenness and blending ratio, but a low degree of negative relationship is found for yarn type. The increased evenness of the yarn in the cross section is attributable to the staple length and consistent fineness of polyester fiber [26,27,28]. The yarn with PBT filament and elastane in the core has less unevenness than the yarn with only elastane or without any core components, due to the presence of less amount of cotton because cotton fiber has fineness variations.

The p-value from the two-way ANOVA test for the interaction between yarn type and blending ratio on yarn unevenness is p < 0.005, which indicates that the interaction is statistically significant on yarn unevenness. Equation 3 in Table 7 represents the regression model for yarn unevenness, which is statistically reliable for independent variables (p < 0.001, R-squared = 0.957).

Figure 5 illustrates that 100% cotton yarn has the highest amount of hairiness for the rigid group, and 50/50 CO/PES has the lowest amount. In the rigid group, for the presence of polyester fiber in blended yarn, hairiness is reduced between 10% and 17% as compared to 100% cotton yarn. However, it can be observed that 100% polyester core-spun yarn has the highest hairiness value for the core group, while 50/50 CO/PES has the lowest. For the core group, it can be noticed that the cotton/polyester ratio influences hairiness, while hairiness is initially reduced with an increase of polyester but increases after the 50/50 ratio. This scenario happens due to the incorporation of cotton fiber with polyester and core components. Short fiber is reduced at first due to the reduction of cotton fiber, resulting in less hairiness until the CO/PES blending ratio reaches 50/50. While the blended yarn has more polyester fiber than cotton fiber, the polyester fiber most likely holds back the cotton fiber on the yarn’s surface, resulting in a higher hairiness value. Again, 100% cotton has the highest hairiness in the dual-core group, and 75/25 CO/PES has the lowest value. For the dual-core group, there is no regular change of hairiness for the blending ratio. So, there is a fluctuating observation for the dual-core group. Cotton fiber has the tendency to migrate to the center of the yarn while blended with polyester, which could be attributed to these irregular changes of the yarn hairiness. Fiber movement after spinning and uneven fiber control during spinning are also some of the probable reasons for it [36].

Moreover, the short fiber content of cotton fiber and the uniform length of polyester fiber has influences on yarn hairiness [18]. Figure also depicts that, among the group, for a certain blending ratio, core and dual core yarn have more hairiness than the rigid group in the presence of polyester fiber. Most probably due to incorporation with the core components, cotton could not move inwards of the yarn while increasing polyester.

The two-way ANOVA test examined the effect of yarn type and blending ratio on yarn hairiness, and it was found that there is a statistically significant interaction between the two independent variables, p < 0.001. The positive correlation of yarn type and negative correlation of blending ratio came up to a significant level for yarn hairiness in Table 6. Equation 4 in Table 7 represents the regression model for yarn hairiness, which is statistically significant, but can be explained by the 30.7% variability of the yarn hairiness (p < 0.001, R-squared = 0.307).

The yarn imperfection index (IPI) is the sum of thin places (−50%), thick places (+50%), and neps (+200%) per kilometer. According to Fig. 6, there is an inverse nature of yarn IPI with an increase of polyester content for each group of yarn because of the length and fineness uniformity of the polyester fiber. When it comes to yarn imperfections, fiber properties play a major role. In all groups, 100% polyester yarn has the lowest IPI value (less than 10), while 100% cotton yarn has the highest IPI value (more than 350). There is no regular change for a certain blending ratio among the groups. From the correlation analysis in Table 6, it can be observed that there is a strong negative relationship for the yarn blending ratio, which is statistically significant, but the yarn type does not show a significant association.

The two-way ANOVA test found that the interaction between yarn type and blending ratio on IPI values is statistically significant (p < 0.001). The regression model for the IPI of yarns is shown in Table 7 with equation 5, which is a good fit for the data (p < 0.001, R-squared = 0.962).

The YQI was calculated to better understand the yarn quality; the values are shown in Fig. 7. The figure shows that by having more polyester content in the yarn, the YQI is increased, as polyester fiber has less irregularity and higher tenacity than cotton fiber. 100% cotton yarn has the lowest YQI values, and 100% polyester shows the highest YQI values for all groups. 100% polyester yarn has almost six times the YQI value of 100% cotton for all groups, and 50/50% CO/PES has more than two times the YQI value. Among the rigid, core, and dual core groups, the dual core-spun yarn has better YQI than others following core and rigid yarn. For 100% cotton, YQI is increased about 21% from rigid to core and about 36% from core- to dual core-spun yarn, but for 75/25 % CO/PES, the change is about 35% and 20%, respectively. From rigid to core, about 48% and from core to dual-core about 28% of the YQI value is increased for a 50/50 ratio. However, increasing the polyester amount for 25/75% CO/PES blending ratio, an increase of 7% and 20% YQI value is observed from rigid to core and core to the dual core group, respectively. For 100% polyester, this increase is less than 20%.

Figure 7

Effect of blending ratio on YQI.