A novel method for evaluating the slurry coating characteristics of sized yarns based on the starch-iodine color reaction principle and image processing

Abstract

The slurry coating characteristics of sized yarns directly impact warp weavability. Due to the damage to sized films, the conventional methods of detecting sized-yarn coating characteristics have drawbacks of low efficiency and poor repeatability. A novel detecting method of slurry coating characteristics was proposed based on image processing. Through the starch-iodine color reaction principle, a self-made dynamic image acquisition device was developed in this paper, in which the apparent images of starch-based sized yarns after color reaction were captured consecutively. The slurry coating percentage (SCP), slurry coating depth (SCD) and slurry coating unevenness (SCU), respectively reflecting the sizing coating integrity, sizing coating thickness and thickness unevenness, were extracted by image processing. The effects of experimental parameters, including immersion time and concentration of I₂-KI solution, on slurry coating characteristics were analyzed, and central composite design was adopted to optimize the stability of the test system. Sized yarns commonly used in textile mills were characterized by the proposed method. The experimental results indicated that immersion time of 3.56 min and I₂-KI concentration of 0.11‱ (‱ represents that the mass of the solute is one ten-thousandth of solution) led to the optimal stability of slurry coating characteristics (the CV of SCP, CV of SCD and CV of SCU were 3.32%, 5.56% and 9.37%, respectively). The much lower CV of the proposed method compared with conventional ones confirmed that the method was useful for evaluating slurry coating characteristics.

Keywords

sized yarns starch-iodine color reaction principle slurry coating coefficient slurry coating depth slurry coating unevenness

Sizing is the primary phase in the overall process of weaving preparation, and the quality of sized yarns directly affects the performance of the final textile products.¹ During the process, parts of the size agents coat around the yarn surface as film after drying by oven. Sized film is not only conducive to keep the hairiness attaching to the yarns,^2,3 but is also available to protect the yarn body, thereby enhancing the abrasion resistance of yarns.⁴ The other parts of the size agents penetrate into the yarn due to the pressure of the squeezing roller.^5,6 In the recent years, with the improvement of yarn quality and the performance of size agents, the requirement of sizing has been transformed to surface coating-fiber matrix-sized yarns in a low size pick-up. The slurry coating characteristics have a significant impact on the sizing properties and weaving process. A lower slurry coating increases the hairiness, which leads to shed clinging and increased broken ends, while more slurry coating results in decreased elasticity and elongation at break, as well as a high sizing shedding rate and brittle ends being broken.^4,7,8 More than that, the requirement of slurry coating characteristics is different according to the type and fineness of yarns. Therefore, slurry coating characteristics should be controlled reasonably in the process of sizing.^9–11

Conventionally, the slurry coating characteristics were tested by the yarn cross-section test, which is a kind of visual inspection, including the steps of section preparation, microscopic image acquisition and feature extraction of the yarn cross-section by image processing technology.^12–16 Nevertheless, due to the damage of sized films, the utilization of the yarn cross-section test was limited because it is time-consuming and has poor repeatability. In addition, segments in the micron grade were far from representing the slurry coating characteristics of the whole sized yarns. Zhu et al.¹⁷ proposed a dynamic measurement for the slurry coating characteristics from yarn sequence images. The slurry coating characteristics were obtained by variation of the yarn diameter between sized yarns and greige ones. However, the influence of penetration parts and deformation of the cross-section shape caused by high squeezing pressure on the diameters of sized yarns were ignored. In the above methods, the index, including coating rate and sized-film thickness, was calculated, while the distribution and uniformity of the slurry coating were rarely reported. Therefore, it is crucial to develop an improved method to detect the slurry coating characteristics accurately and comprehensively.

Starch-based size agents have been widely used due to their cost-effectiveness and biodegradation.^18–22 The principle of the starch-iodine color reaction is that the iodine molecules are embedded into the cavities of the starch helical structure and form a blue complex compound by means of Van der Waals force with starch molecules. The color reaction between starch and iodine is quite sensitive, and has become a common method for starch content quantitative determination in biochemistry, such as for the measurement of food and tobacco.^23–25 Similarly, color reaction was applied to evaluating the starch content of sized yarns in this study. In the previous research, we found that there is a significant correlation between the sizing coating thickness of starch-based sized yarns and the color depth after the starch-iodine reaction. Depending on the starch-iodine color reaction principle, this paper proposes a detecting method of slurry coating characteristics that has the benefits of time effectiveness and simplicity as well as the capability to characterize long segments. A self-made dynamic image acquisition device was creatively applied to consecutive acquisition of apparent images of sized yarns after color reaction. By means of Python image processing software, the slurry coating percentage (SCP), slurry coating depth (SCD) and slurry coating unevenness (SCU), were extracted, respectively reflecting the sizing coating integrity, sizing coating thickness and sizing coating thickness unevenness. The effects of tester parameters, including immersion time and concentration of I₂-KI solution, on test stability were studied. Based on the analysis of the variable coefficient of slurry coating characteristics, the significant factor ranges were adopted to achieve optimal stability of the test system via central composite design (CCD). To further verify the stability of the proposed method, sized yarns commonly used in textile mills were characterized, and the CV values of the measuring parameters were compared with the conventional ones.

It should be noted that our research team is committed to exploring the effect of the synergy of sizing process parameters on sizing coating and penetration, for further study of the mechanism of the sizing process on sizing performance. Therefore, this study has concentrated on the condition of the same slurry and yarn types, and the detection method is developed to enable performing subsequent research on the sizing process and sized-yarn performance.

Device design and image processing

Description of the image acquisition device

The dynamic image acquisition device is shown in Figure 1. Sized yarn runs at a constant speed under the guidance of the electric motor, and go through the unwinding device, feeding tension control device, solution tank with I₂-KI solution, drafting tension control device and winding device in turn. A digital microscope (KEYENCE VHX 5000) is positioned above the yarn to capture consecutive apparent images of sized yarns after color reaction.

Figure 1.

Dynamic image acquisition device: (a) real system inside the camera obscura; (b) sketch. 1: unwinding device; 2: feeding tension control device; 3: solution tank; 4: guide wire; 5: digital microscope (KEYENCE VHX 5000); 6: drafting tension control device; 7: winding device; 8: electric motor; 9: sample table; 10: camera obscura.

The solution tank is a cuboid without a head cover that is made of 5 mm thick acrylic plate of hollow structure; the size of the tank is 0.6 m × 0.1 m × 0.04 m. Due to the fact that the starch-iodine color reaction should be carried out in the presence of water, sized yarn is immersed into I₂-KI solution, and apparent images of sized yarn after color reaction are captured under the wet condition in the solution tank. Sized yarn is fixed on the guide wire on the two sides of the solution tank. Feeding and drafting tension control devices are installed at the inlet and outlet of the solution tank, respectively, to keep the yarn samples in the same tension during testing. The electric motor controls the winding speed of the yarn. When the device switch is on, the electric motor drives the yarn to run at a uniform and slow speed. Sized yarn passes through the solution tank for sufficient immersion time in I₂-KI solution for a few minutes. At the time that the yarn passes under the camera lens, the microscope acquires apparent images of sized yarn after color reaction automatically and continuously.

The distribution of the size agent is equiprobable on the surface of both sides of yarns, and rotation of yarns along the axis occurs during the movement in the dynamic image acquisition device. Therefore, we consider that one-sided apparent images can reveal the sizing coating of sized yarns.

Different moments of apparent images of sized yarn after color reaction are plotted in Figure 2, in which the images revealed that during consecutive image acquisition, image quality did not change obviously. We considered that the proposed device can acquire the apparent images stably.

Figure 2.

Different moments of apparent images of sized yarns after color reaction: (a) image 1; (b) image 25; (c) image 50; (d) image 75; (e) image 100.

Image processing

In this work, Python image analysis software was applied to image processing. The steps of image processing are carried out as follows.

Gray-level transformation: transformed the original image to a grayscale one.

Median filtering: 3 × 3 median filtering was utilized to eliminate noise.

Extracting of yarn areas: image binarization was applied to extract the yarn areas.

Slant correction: there may be cases in which the yarn axis is not always parallel to the image horizontal, which is due to the variation of yarn diameter and slight movement of yarn during image capture. This could increase the experimental error of the pixel calculation of yarn. Thus, slant correction was carried out to ensure the parallel alignment between the yarn axis and image horizontal.

Extraction of effective statistical areas: the yarn was an approximate cylinder. As the camera and illuminant source were positioned directly above the yarn, the central axis areas of the yarn were nearly perpendicular to the incident light, which reflected the most light; therefore, the grayscale image of these areas was brightest. Meanwhile, the edge areas of the yarn were almost parallel to the incident light, which reflected the least light and, accordingly, the grayscale image of these parts was darkest. As illustrated in Figure 3, the average gray value of rows in the central axis areas was higher than that at the edge parts of the yarn. Previous studies have shown that average gray value of 15 rows in the central axis areas of yarn hardly changed. As a consequence, to avoid the influence of light on image processing, the 15 rows in the central axis areas were extracted as effective statistical areas. A representative grayscale image of 15 rows in the central axis areas of the yarn is shown in Figure 4(a).

Calculation of gray value: the average gray value of pixels in the effective statistical areas was calculated.

Threshold segmentation: in this work, images of unsized yarns immersed in I₂-KI solution under the same condition served as the control group. Gray-level histograms of effective statistical areas of both sized yarn and unsized yarn after I₂-KI immersion were plotted in the same coordinate axis respectively. The gray-level histograms was verified to conform to the normal distribution. Because of the color reaction between starch and iodine, the color depth of sized yarn was deeper than that of the unsized one after I₂-KI immersion, that is, the gray-level histogram of sized yarn approached zero. Fitting curves were performed for the gray histograms in the effective statistical areas of both sized and unsized yarn after I₂-KI immersion. The intersection of two fitted curves served as the threshold (T). As shown in Figure 5, the areas of sized yarn where the gray value was lower than or equal to the intersection of two fitted curves were defined as slurry coating areas. The segmentation result is plotted in Figure 4(b).

Figure 3.

Average gray value of each pixel row of the yarn image.

Figure 4.

Grayscale and binarization image of 15 rows in the central axis areas of the yarn: (a) grayscale image; (b) binarization image.

Figure 5.

Fitting curves of the gray histograms in the effective statistical areas of sized and unsized yarn after I₂-KI immersion.

Slurry coating characteristics

Slurry coating coefficient

Sized film does not cover the surface of sized yarn contiguously. In this work, the slurry coating percentage (SCP) means the percentage of coating area in the effective statistical area of sized yarn. A higher SCP value indicates that sized film was more integrated on the surface of sized yarn. As aforementioned, the fitting results showed that gray histograms in the effective statistical areas conformed to the normal distribution. Here, SCP was calculated by the proportion of area that is lower than or equal to threshold (T) in whole area of normal distribution. For ease of calculation, the normal distribution was transformed to standard normal distribution by Equation (1). As the whole area of standard normal distribution was 1, the area lower than or equal to Y in the standard normal distribution was the value of SCP. The value of SCP was obtained by a look-up table of probability of less than or equal to Y in the standard normal distribution²⁶

Y = \frac{T - \bar{G}}{σ} \sim N (0, 1)

(1)

where Y denotes the threshold in standard normal distribution, T denotes the threshold in normal distribution,

\bar{G}

denotes the average gray value of effective statistical areas after color reaction and σ denotes standard deviation of the gray value of effective statistical areas after color reaction.

Slurry coating depth

Sizing coating has a significant impact on sizing performance. The thicker film enhances the abrasion resistance of the sized yarn, whereas it tends to increase the yarn diameter, which is not conducive to high-speed weaving of superfine count high-density fabric. The thinner film leads to pilling and increased broken ends during the weaving process. The gray value is an evaluation parameter of color depth. It ranges from 0 to 255, where zero denotes black and 255 denotes white. A lower gray value indicates deeper color depth, and conversely the color depth is lighter. In this paper, the slurry coating depth (SCD) was defined as 255 minus the average gray value of the effective statistical area of sized yarn after color reaction. In that case, a higher SCD value meant deeper color depth, which indicated thicker sized film. The method used for calculating SCD is shown in the following equation

SCD = 255 - \frac{\sum_{i = 1}^{N_{y}} G_{i}}{N_{y}}

(2)

where G_i denotes the gray value of the ith pixel of the effective statistical areas after color reaction and N_y denotes the number of pixels in the effective statistical areas.

Slurry coating unevenness

The sized-film thickness unevenness results in a high possibility of yarn breakage in the weaving process. Slurry coating unevenness (SCU) means the discrete degree of gray value in a unit pixel. A lower SCU value indicated that the sized-film thickness was more even. The method used for calculating SCU is shown in the following equation

(3)

where G_i means the gray value of the ith pixel of the effective statistical areas after color reaction,

\bar{G}

means the average gray value of the effective statistical areas after color reaction and N_y denotes the number of pixels in the effective statistical areas.

Experimental details

Sample preparation

Sized yarns commonly used in textile mills were selected to evaluate the slurry coating characteristics. The parameters of sized yarns are shown in Table 1. All the samples were sized by the same size agent. The sizing formula is corn oxide starch 80 kg and slurry volume 800 L; all the size agents were purchased from Yixing Junda Size Agent Technology Co., Ltd. During the experiment, it can be claimed that the conditions for all the yarns tested were the same. All the yarn samples were conditioned at 65 ± 2% relative humidity and 20 ± 2℃ for 24 h prior to testing.

Table 1.

Parameters of sized yarn samples

Samples	Material	Yarn tex	Size pick-up (%)
Control sample	Cotton	14.6	0
1^#	Cotton	14.6	7.5
2^#	Cotton	14.6	10.2
3^#	Cotton	14.6	12.3

Methods

Yarn immersion

Previous experiments have shown that when the concentration of the I₂-KI solution was lower than 0.02‱, the change of color depth after the starch-iodine reaction was not obvious. When the concentration of the I₂-KI solution was higher than 0.18‱, the yarn edge was similar to the background, which made it difficult to distinguish. Thereby, five concentrations of I₂-KI solution (the concentration of I₂ overlaid on 0.02–0.18‱, 0.04‱ distance gradient, where the concentration of KI was 20 times of that of I₂) were adopted in the experiments. The apparent images of different immersion times (immersion time was from 1 to 5 min, with 1 min as a gradient) were captured. Meanwhile, a control group of unsized yarn by the proposed method under the same condition was also conducted. Fifty images were captured for each group.

Image acquisition

The sized yarns were circled on the dynamic image acquisition device. Then, one turned on the switch and acquired the picture dynamically. During the experiment, the lighting condition of the digital microscope was ring lighting with a luminance of level 10 (the optional levels were 0–255). All the images were captured in a camera obscura to avoid interference from environment light. Images were in the size of 1600× 1200 dpi and at a magnification of 100, where each image was the actual length of 0.35 mm. Images formats were ‘tiff’ and images were in RGB color mode.

The automatic timing photography mode was adopted, and the photography interval was 15 s. The yarn speed needed to match the exposure time of the camera. Previous experiments have shown that the yarn needed to move at a slow speed within a certain range to improve image quality. Under the 20 ms camera exposure time, the determined electric motor speed was 1 r/min, that is, the yarn moved at a uniform speed of 0.105 m/min. During the experiments, all the image acquisition conditions were the same. Then, the apparent images were imported into Python to extract the slurry coating characteristics.

Single-factor experiments and central composite design

In order to optimize the stability of the proposed method, single-factor experiments were carried out to study the effects of the immersion time and concentration of I₂-KI solution on the slurry coating percentage and the variable coefficient in order to determine the ranges of significant factors. In this paper, to systematically evaluate the main and interaction variables that affected the variable coefficient of the slurry coating coefficient, a two-factor five-level CCD was employed to optimize the factors. Sample 2^# was adopted for single-factor experiments and CCD. The second-order polynomial (quadratic model) is as follows

Y_{1} = b_{0} + b_{1} X_{1} + b_{2} X_{2} + b_{11} X_{1}^{2} + b_{12} X_{2}^{2} + b_{12} X_{1} X_{2}

(4)

where b₀ represents the constant coefficient, b₁ and b₂ are linear coefficients, b₁₁ and b₁₂ are quadratic coefficients, b₁₂ denotes interaction coefficients, Y₁ denotes the predicted response value and X₁ and X₂ are coded values of variables.

In this study, an analysis of variance (ANOVA) was performed to analyze the data to investigate the interactions between the independent variables and the response. Design-Expert 8.0.6 software was introduced for experimental design analysis and data processing.

Results and discussion

Effect of experimental parameters on slurry coating characteristics

Effect of immersion time on slurry coating characteristics

Figure 6 illustrates the slurry coating characteristics and variable coefficients of sized yarns with different immersion times at 0.10‱ concentration of I₂-KI solution. It can be observed that the SCP augmented rapidly with the increase of immersion time from 1 to 2 min, then the increasing trend became gentler from 2 to 3 min until it reached the maximum at 3 min, and finally remained stable with a slight fluctuation after 3 min. Similar tendencies were visible with SCD and SCU.

Figure 6.

Slurry coating characteristics and variable coefficients of sized yarns with different immersion time: (a) SCP and CV of SCP with different immersion times; (b) SCD and CV of SCD with different immersion times; (c) SCU and CV of SCU with different immersion times.

While the CV of SCP, SCD and SCU reached the maximum at 1 min, and then decreased steeply between 1 and 2 min, after that, the decline trends tended to diminish with the increase of immersion time from 2 to 3 min, and finally hardly changed after 3 min. This might be ascribed to the fact that under the condition of 0.10‱ I₂-KI concentration, it was likely to take more than 3 min for the starch on the surface of sized yarns to fully react with iodine to form stable complex compounds. The incomplete reaction, which attributed to less immersion time, led to a higher CV of slurry coating characteristics. Conversely, when the immersion time exceeded 3 min, the steady slurry coating characteristics attributed to complete reaction resulted in a lower CV of slurry coating characteristics.

Effect of I₂-KI concentration on slurry coating characteristics

Slurry coating characteristics and variable coefficients of sized yarns with different concentrations of I₂-KI solution at 3 min immersion are displayed in Figure 7. The figure shows that as the I₂-KI increases in concentration, the starch-iodine reaction formed more complex compounds at the same time, which led to a deeper color depth of sized yarns after color reaction, and SCP, SCD and SCU were thereby enhanced.

Figure 7.

Slurry coating characteristics and variable coefficients of sized yarns with different concentration of I₂-KI solution: (a) SCP and CV of SCP with different concentrations of I₂-KI solution; (b) SCD and CV of SCD with different concentrations of I₂-KI solution; (c) SCU and CV of SCU with different concentrations of I₂-KI solution.

While the CV of SCP, SCD and SCU reached the maximum at 0.02‱, next, it showed a sharp trend of decline as the concentration of I₂-KI solution increased from 0.02‱ to 0.06‱, and then the downtrend became gentler from 0.06‱ to 0.10‱, until finally it remained relatively stable with slight fluctuations as the concentration of I₂-KI solution exceeded 0.10‱.

This may be because at the lower concentration the color reaction was relatively slower, which subsequently led to a longer reaction time. Under the condition of 3 min immersion, the starch-iodine reaction was incomplete when the concentration of I₂-KI solution was less than 0.10‱; parts of the slurry coating areas with a lower content of starch did not reach the state of full reaction, which led to a higher CV of slurry coating characteristics. However, when the concentration of I₂-KI solution was over 0.10‱, the full reaction between starch and iodine was conducive to stabilized detection of slurry coating characteristics.

Experimental results of central composite design

According to the single-factor experimental analysis, the optimization level ranges of two factors were determined: immersion time (A) 2–4 min and concentration of I₂-KI solution (B) 0.06–0.14‱. The CV of SCP (R1), CV of SCD (R2) and CV of SCU (R3) were set as response values. A two-factor and five-level factorial CCD was carried out around the optimum to locate optimum levels of immersion time and concentration of I₂-KI solution. Factors and levels of the CCD experiment are shown in Table 2, and the design table and results of the CCD experiment are shown in Table 3.

Table 2.

Factors and levels of central composite design experiments

Codes	Factors	Code level
Codes	Factors	–1.414	–1	0	1	1.414
A	Immersion time/min	1.59	2	3	4	4.41
B	Concentration of I₂-KI solution ‱	0.04	0.06	0.10	0.14	0.16

Table 3.

Design table and results of central composite design experiment

Run	Immersion time/min	Concentration of I₂-KI solution/0/00	R1/%	R2/%	R3/%
1	3	0.1	3.32	5.38	9.28
2	3	0.1	3.20	5.48	9.71
3	3	0.1	3.53	5.74	9.63
4	3	0.04	5.23	7.62	11.31
5	1.59	0.1	5.62	7.95	11.83
6	4	0.14	3.52	5.86	10.44
7	3	0.16	3.71	5.97	10.54
8	3	0.1	3.64	5.96	9.69
9	4	0.06	3.49	5.92	10.21
10	2	0.06	5.58	8.60	11.64
11	2	0.14	3.51	5.94	10.64
12	3	0.1	3.79	6.08	9.86
13	4.41	0.1	3.68	5.68	9.69

R1: CV of SCP; R2: CV of SCD; R3: CV of SCU.

Quadratic multinomial regression equations were utilized to evaluate the relationship between response values and factors. The quadratic multinomial regression equations generated by Design-Expert are described in Equations (5)–(7) in terms of coded factors

(5)

(6)

(7)

The equations represent the quantitative effect of the factors and their interactions on the response values. The levels of A and B are substituted in the equations to obtain the theoretical values of R1, R2 and R3. The model equation was tested for significance, and its variance was analyzed. ANOVA results for the response surface quadratic model are shown in Table 4. We can see that the lack of fit of the three quadratic regression equations was not significant (lack of fit > 0.05), which signified that the regression equation did not lose its fit and the model stability can be well predicted. The p-values for model R1, R2 and R3 were less than 0.05, which indicated significant effects of independent factors on the response values. Hence, it assured that the current model provided a satisfactory fit to the data. In addition, the p-values of the two factors were all less than 0.05, which meant that the two factors were significant.

Table 4.

Analysis of variance for the response surface quadratic model by central composite experimental design

	R1		R2		R3
Source	F value	p-value prob > F	F value	p-value prob > F	F value	p-value prob > F
Model	13.4	0.0018^a	33.77	< 0.0001^a	22.96	0.0003^a
A	23.79	0.0018^a	61.67	0.0001^a	40.71	0.0004^a
B	17.94	0.0039^a	44.18	0.0003^a	6.49	0.0383^a
AB	9.02	0.0199^a	23.39	0.0019^a	5.68	0.0486^a
A ²	11.18	0.0124^a	22.86	0.002 ^a	29.97	0.0009^a
B ²	7.1	0.0323^a	21.93	0.0023^a	39.91	0.0004^a
Lack of fit	3.7	0.1195	0.54	0.6815	2.02	0.2537

Significant at the 95% confidence degree (p < 0.05).

A series of three-dimensional (3D) frameworks for affecting test stability were further depicted to study the interaction and quadratic effects of the two factors on the response value. The response surfaces for the CVs of SCP, SCD and SCU are presented in Figure 8.

Figure 8.

The response surface for CV of SCP, CV of SCD and CV of SCU: (a) effects of immersion time and concentration of I₂-KI solution on CV of SCP; (b) effects of immersion time and concentration of I₂-KI solution on CV of SCD; (c) effects of immersion time and concentration of I₂-KI solution on CV of SCU.

Design-Expert 8.0.6 statistical analysis software was employed to solve the regression equation for minimum values of R1, R2 and R3. The optimal parameters were proposed: immersion time of 3.56 min and concentration of I₂-KI solution of 0.11‱. The minimum predicted response values of R1, R2 and R3 were 3.27%, 5.43% and 9.49%, respectively.

Predicted and experimental response values led by optimal parameters are shown in Table 5. According to the determined optimal parameters for actual experiments, 50 repeated experiments were conducted, where the average experimental values of R1, R2 and R3 were 3.32%, 5.56% and 9.37%, respectively. The predicted and the experimental values were in reasonably good agreement, and the relative error was less than 1%, which indicated that the optimization results can confirm the validity of the models.

Table 5.

Predicted and experimental response values led by optimal parameters

Immersion time/min	Concentration of I₂-KI solution ‱	R1/%		R2/%		R3/%
Immersion time/min	Concentration of I₂-KI solution ‱	Predicted	Experimental	Predicted	Experimental	Predicted	Experimental
3.56	0.11	3.27	3.32 ± 0.20	5.43	5.56 ± 0.23	9.49	9.37 ± 0.36

Experimental verification

Apparent images of yarn samples after I₂-KI immersion are exhibited in Figure 9.

Figure 9.

Apparent images of yarn samples after I₂-KI immersion: (a) control sample; (b) 1^#; (c) 2^#; (d) 3^#.

The slurry coating characteristics of samples 1–3^# are exhibited for comparison in Table 6. To further verify the validity of the proposed method (M1), the dynamic measurement method¹⁷ (M2) and yarn cross-section method¹⁵ (M3) were applied to detect the slurry coating characteristics. For the dynamic measurement method, both greige yarns and sized yarns were tested 50 times for each sample. For the cross-section test, 50 sections were measured for each sample. The statistical data of the CV of slurry coating characteristics are illustrated in Table 7. The comparison results revealed that the CV value of M1 was much lower than that of M2 as well as M3, that is, the proposed method was able to characterize slurry coating with more stability and accuracy than the conventional ones.

Table 6.

Slurry coating characteristics of samples 1–3^#

Samples	SCP/%	SCD	SCU/%
1^#	78.33	110.47	13.32
2^#	94.90	143.06	21.03
3^#	98.07	170.80	26.45

Table 7.

Comparison results of CV under different methods

Yarn samples	M1			M2			M3
Yarn samples	f1 (%)	f2 (%)	f3 (%)	g1 (%)	g2 (%)	g3 (%)	h1 (%)	h2 (%)	h3 (%)
1^#	7.75	6.43	10.44	16.93	16.98	19.63	23.89	27.42	27.40
2^#	3.32	5.56	9.37	15.06	16.73	18.26	22.48	28.14	27.08
3^#	2.18	4.69	9.81	15.18	15.92	18.06	18.39	25.34	25.32

f1: SCP; f2: SCD; f3: SCU; g1: film thickness; g2: sized-yarn evenness; g3: sizing penetration rate; h1: sizing penetration rate; h2: sizing coating rate; h3: integrity rate of sized film.

Conclusion

In this research, a novel detecting method of slurry coating characteristics was reported. Based on the starch-iodine color reaction principle, a self-made dynamic image acquisition device was developed to obtain the apparent images of sized yarns after color reaction. The SCP, SCD and SCU were calculated by image processing on the basis of Python image analysis. The effects of experimental parameters, including immersion time and concentration of I₂-KI solution, on slurry coating characteristics and the variable coefficient were analyzed. The CV of SCP, CV of SCD and CV of SCU were chosen to optimize the stability of the test system through CCD. Comprehensively, the models predicted the CV of SCP was 3.27%, CV of SCD was 5.43% and CV of SCU was 9.49% under the condition of 3.56 min immersion time and 0.11‱ concentration of I₂-KI solution. The less than 1% relative error indicated that the predicted and experimental values were in reasonably good agreement. In addition, the much lower CV value of the statistical data of the proposed method compared with the dynamic measurement and yarn cross-section methods demonstrated that the developed method was able to characterize slurry coating with more stability than conventional ones. The characterization of slurry coating had instructional significance for further study of the sizing performance of sized yarns. The results of the study are very attractive for practical application in the textile industry.

Footnotes

Declaration of conflicting interests

The authors 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 by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_1793) and the Fundamental Research Funds for the Central Universities (JUSRP52007A).

ORCID iD

Wenjun Yan

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A novel method for evaluating the slurry coating characteristics of sized yarns based on the starch-iodine color reaction principle and image processing

Abstract

Keywords

Device design and image processing

Description of the image acquisition device

Image processing

Slurry coating characteristics

Slurry coating coefficient

Slurry coating depth

Slurry coating unevenness

Experimental details

Sample preparation

Methods

Yarn immersion

Image acquisition

Single-factor experiments and central composite design

Results and discussion

Effect of experimental parameters on slurry coating characteristics

Effect of immersion time on slurry coating characteristics

Effect of I2-KI concentration on slurry coating characteristics

Experimental results of central composite design

Experimental verification

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

Effect of I₂-KI concentration on slurry coating characteristics