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Seam pucker rating by deconvolution residual method
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Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran, and
F. Mousazadegan, S. Saharkhiz and M. Latifi M. Mohammadi-Aghdam
Received 17 March 2012 Revised 11 July 2012 Accepted 1 October 2012
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran Abstract Purpose – The purpose of this study is to introduce a novel approach for seam pucker analysis based on wave shape parameters. Design/methodology/approach – In this method the uneven wavy curve along the puckered seam line was put into a deconvolution process and broken into several simple Gaussian curves using residual mathematical analysis method. First puckered samples with five different grades were produced and scanned by laser triangulated technology. After implementation of deconvolution method, the key geometrical parameters of the decomposed waves such as number of waves and their shape parameters like wave’s area, amplitude and wave length were extracted. In addition, an objective method was developed and five indexes were introduced. Findings – Analysis showed that there is a high linear relation with high correlation between all pucker indexes and subjective pucker evaluation. Originality/value – The goal of this research was to analyse the five grades of seam puckered samples and extract the basic structural parameters to solidify the characteristic of each puckered grade, in order to exclude the influence of human perception. Keywords Seam pucker, Wave geometry, Deconvolution residual method, Production engineering, Garment industry Paper type Research paper
International Journal of Clothing Science and Technology Vol. 25 No. 3, 2013 pp. 150-170 q Emerald Group Publishing Limited 0955-6222 DOI 10.1108/09556221311300183
1. Introduction The quality control of garments is a key factor that manufacturers should consider, such that not only garment performance and stability, but also its aesthetic aspect should be examined. One of the problems which impact garment aesthetic quality is seam puckering. Various factors such as fabric mechanical properties, construction, sewing thread count and its tensile behavior and sewing machine parameters interfere in seam puckering. Over the years, several investigations have been conducted in this field. Many of them (Inui and Shibuya, 1992; Park and Kang, 1997, 1999a, b; Fan and Liu, 2000; Park et al., 1997; Juciene and Dobilaite, 2008; Stylios and Sotomi, 1993a, b) have concentrated on developing an objective seam pucker assessment and grading method as a replacement for subjective evaluation. In order to develop objective assessment, it is required to gather seam puckered surface data. Therefore, in this area many different methods have been introduced. Since contactless methods have not interfered with the seam and fabric surface, it has attracted more attention. After gathering fabric data, an attempt has been made to analyze the data and extract some indexes and parameters to establish a relation between their findings and the result of subjective assessment based on the AATCC 88B-1964 standard method.
In addition, some studies have attempted to probe into pucker occurrence reasons in order to overcome the puckering issue. Amirbayat (1990) used the energy method to find the stable state of seamed fabric and control its stability through related parameters (Amirbayat and Morton 1990). Schwartz (1983) calculated fabric free space in terms of its geometrical parameters and developed a relation between the occupied space by sewing thread and fabric free space. Since pucker prediction prior to the sewing process gives an opportunity to modify sewing parameters, some studies (Zavec Pavlinic et al., 2006; Stylios and Parsons Moore, 1993; Stylios and Lloyd, 1990) tried to predict seam puckering by methods like machine learning, neural computing and correlation between the fabric’s physical and mechanical characteristics and seam pucker subjective evaluation results. In addition, the physical and mechanical properties of the sewing thread affect seam puckering (Dobilaite and Juciene, 2006; Fan and Leeumner, 1998; Stylios and Lloyd, 1989), therefore some researchers have worked on the parameters involved in this area. Moreover, in some studies, the simulation method (Inui and Yamanaka, 1998; Inui et al., 2000; Hu et al., 2006) has been introduced as a solution to investigate the effect of exerted forces by sewing thread on fabric in the seam and stitch element. However, considering the surface of the puckered seam, it is a situation in which waves with different amplitudes and frequencies appear on the sewn fabric and create a rippled, creasy and undesirable surface. Apart from the point view of the general fabric appearance, puckering formation can be considered as a wave analysis problem. Waves with different amplitudes and wave lengths are combined and merged to each other and create an uneven surface. Particularly, it is necessary to mention that the geometry of the final seam assembly influences human’s perception and in turn, affects the final judgment and rating, e.g. considering two puckered fabric strips with the same wave amplitude but various wavelengths, the sample with a higher wave frequency looks more severe. Besides, previous experiences of the judges influence their decision. In addition, studying the seam pucker in a garment or on a very wide fabric is different from a strip of fabric. In a garment or a wide fabric, the sewn line is the only area under judgment and the wavy edges do not exist. In a strip of fabric, the puckered waves in the seam line propagate to the fabric edges and impact the general impression of the puckered seam line and influence the final grading. Finally, the subjective grading of the seam pucker has a great diversity according to previous personal knowledge and experience of judges. Since subjective evaluation of the seam pucker depends on human’s perception, knowledge and experience of judges, sometimes it is difficult to decide on a seam pucker grade. In addition, as it was mentioned before, wave geometrical characteristics such as wave length and amplitude are the main parameters which change a puckered surface’s apparent aspect and help judges categorize the samples, which means that measuring these parameters and introducing pucker indexes based on these values is a useful method in grading seam puckers. Hence, in the present study, pucker waves are analyzed utilizing their topological details in order to derive the effective parameters based on pucker geometry for seam pucker evaluation and their relation with subjective evaluation results will be examined. By this approach, it is possible to analyze the pucker deformation in more details and extract the number of waves, wave length and amplitude of a puckered seam and
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establish a firm base for the analysis of different grades of pucker severity. It is also possible for it to be excluded from human perceptions and provide a better understanding of the problem and develop an objective method based on that objective information. 2. Experimental work 2.1 Material Although in a puckered sample, sewing thread and fabric mechanical properties affect its appearance, pucker deformation in general consists of small local waves around the seam line and global waves close to the fabric edge. Therefore, sewing thread based on its mechanical behavior can affect the wave’s geometrical parameters. Since in this study we are going to rate the seam pucker samples based on their geometrical dimensions, it is possible to use a sewing thread. The sewing thread utilized was 100 percent polyester spun yarn with the count of 40 Nm. In order to show that measuring pucker wave parameters can be a method to rate the seam puckering degree, in this study various fabrics are used which consist of four shirt fabrics, four worsted fabrics and four dress fabrics. The fabrics’ characteristics are shown in Table I. 2.2 Sample preparation Samples consisted of two fabric strips with a dimension of 40 £ 8 cm2 which are cut and sewn in warp direction. A Durkopp-Adler (272) lockstitch sewing machine with a needle feed mechanism was selected to sew the specimens. All samples are prepared with a stitch length of 3 mm. In order to produce samples with five levels of pucker severity according to the AATCC 88B-1964 standard, five levels of yarn tensions of 50, 100, 150, 200 and 250 cN were applied to the sewing thread, respectively. Five specimens in each tension level were produced. Later, the samples were left to relax for 48 h. 2.3 Subjective evaluation Puckered samples were evaluated by five judges subjectively according to the AATCC 88B-1964 standard method and the average of the results was calculated. The final pucker grade is the closest pucker grade to the average of the judge’s result as shown in Table II.
Table I. Fabric characteristics
Fabric code
Fabric type
Weave type
A B C D E F G H I J K L
Shirt Shirt Shirt Shirt Worsted Worsted Worsted Worsted Dress Dress Dress Dress
Plain Plain Plain Plain Twill2/2 Twill3/1 Twill2/1 Twill3/3 Sateen Sateen Plain Sateen
Weight (g/m2)
Warp density (end/cm)
Weft density (pick/cm)
Thickness (mm)
100 155 153 94 257 263 248 243 83 81 69 208
46 28 27 40 28 28 27 27 40 54 44 37
28 20 25 29 21 21 21 21 26 36 33 26
0.17 0.24 0.22 0.20 0.44 0.45 0.42 0.45 0.13 0.19 0.18 0.41
Fabric code Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade Average ( judges evaluation) Selective pucker grade
A B C D E F G H I J K L
Sewing yarn tension (cN) 50 100 150
200
250
4.8 5 4.6 5 4.9 5 4.6 5 4.8 5 5 5 5 5 5 5 4.3 4 4.1 4 3.8 4 4.3 4
2.3 2 2.3 2 2.4 2 2.3 2 3.4 3 3.4 3 3.4 3 3.2 3 2.2 2 2.4 2 2.1 2 2.2 2
1.2 1 1.6 2 1.4 1 1.1 1 3.2 3 3.1 3 2.8 3 2.7 3 1.8 2 1.6 2 1.4 1 1.2 1
3.8 4 4.2 4 4.3 4 3.3 3 4.6 5 4.6 5 4.3 4 4.4 4 3.6 4 3.7 4 3.3 3 3.4 3
3.2 3 2.9 3 3.1 3 2.8 3 3.9 4 3.8 4 4.1 4 3.9 4 3.2 3 2.9 3 2.6 3 2.7 3
2.4 Pucker profile measurement In order to analyze the waves of the seam puckered samples, their surface information is required. In this regard, triangulated laser technology with the accuracy of 5 mm was employed in combination with a computer controlled X-Y table with the accuracy of 0.04 mm per step displacement. In order to obtain complete information about pucker deformation, the scanned surface dimension should be chosen in a way that shows deformation made on the fabric surface. However, scanning the surface by laser is a time consuming process. Therefore, an optimum dimension of 100 £ 60 mm2 was selected to measure pucker deformation. Since pucker wave parameters vary in each distance from the seam line, samples were scanned in 20 lines (ten above and ten lines below the seam line) along the sewing direction with the distance interval of 3 mm between the lines. In order to extract the deformation’s data in each line, various intervals in the reading data were examined such as 0.2, 0.4, 0.6, 0.8, 1 and 1.2 mm. It was considered that when the distance interval is less than 1 mm, there is a lot of noise in the data and it is difficult to process it. In addition, distance intervals of more than 1 mm offer less information about the surface. Therefore, in each line, data was extracted with a 1 mm interval in readings. It took around 2 h to scan each sample. By this method, the 3D profile of the sample is also possible to regenerate. Figure 1 shows the configuration of the laser scanning stem orientation.
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Table II. Subjective evaluation result
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Figure 1. Laser scanning configuration
Notes: (1) Laser; (2) X-Y table; (3) A/D; (4) computer
3. Pucker waves analysis 3.1 Wave deconvolution According to physics, when several waves merge to each other, a new wave is created which is not similar to any of the original waves. The shape of the final wave is affected by the magnitude and wave length of each wave (Figure 2). In many engineering applications dealing with waves, the wave under study is complex and difficult to tackle. To examine these types of waves, one efficient method is to break down the original curve into several simple basic waves in which the original curve is regenerated by merging them. This is referred to as deconvolution. In order to do so, mathematics provides the required methodology and tools to implement this idea. In this area, three important methods were selected for revealing hidden waves as a residuals method, second derivative method and deconvolution method. A residual is the difference in y-value (wave amplitude) between a data point and the sum of component peaks evaluated at that data point’s x-value. By placing peaks in such a way that their total area equals the area of the data, hidden peaks are revealed by residuals. A smooth second derivative of the data will contain a local
Figure 2. Results of the three merging waves
minimum at the peaks position. The second derivative method requires constant x-spacing operated in the time domain. The deconvolution method is a mathematical procedure that is used to remove the smearing or broadening of peaks arising because of the imperfection in an instrument’s measuring system. Hidden peaks that display no maximum may do so once the data has been deconvoluted and filtered. This method requires a uniform x-spacing operated in the frequency domain. Our preliminary analysis showed that in our application the residuals method offers the best correlation and better performance compared with the two other methods. In fact, this result is related to each method’s principle. In the second derivative method, hidden peaks are detected by a second derivative minimum. A peak that has no local maximum in the primary data may very well have a local minimum in a smoothed second derivative. In the deconvolution method, hidden peaks are detected by the sharpening achieved by deconvolving a Gaussian instrument response with the raw data. A peak that initially evidences no local maximum in the initial data may very well do so after a successful deconvolution. However, in a residual method, peaks are identified by identifying local maximum in a smoothed data stream. This first step finds only those peaks which are visible. A visible peak is defined as one which produces a local maximum in the data stream. A hidden peak is thus defined as one which fails to produce this local maximum. Based on the seam pucker formation mechanism, waves are first formed around the seam line. Then these waves propagate toward the seam line. In this distance, adjacent waves strike and merge with each other and make the seam puckering over the sample. In the residual method, the visible waves are initially identified. Then, according to the total area of data and differences between data value and estimated values in each point, hidden peaks are found. As a matter of fact, the residual method extracts seam puckers deformation components. Therefore, this method was selected in this study for data processing. The result of the residuals method is shown in Figure 3. The top graph reflects the original curve consisting of five simple waves. When the five local maxima peaks are transferred to the negative data area, the residuals in the bottom graph clearly reveal two hidden positive peaks. 3.2 Data preparation To commence the analysis, it is necessary to smooth the curves and remove the noisy data. To this end, the Savitzky-Golay smoothing filter which was described in 1964 by Abraham Savitzky and Marcel J. E. Golay is used. The Savitzky-Golay filtering method can be thought of as a generalized moving average. The filter coefficients determine the smoothed value for each point by performing an unweighted linear least square fit using a polynomial of a given degree. A Savitzky-Golay filter is also called a digital smoothing polynomial filter or least squares smoothing filter. A higher degree polynomial makes it possible to achieve a high level of smoothing without attenuation of data features. The main advantage of this approach is that it tends to preserve features of the distribution such as relative maxima, minima and width, which are usually flattened by other adjacent averaging techniques:In the fitting process, three assumptions have been made as follows: (1) the final curve is the combination of only one type of wave; (2) the basic waves are symmetric; and (3) the waves may have dissimilar height and amplitude.
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Figure 3. Residuals method
After several examinations, the Gaussian function known as normal distribution function was selected for use in the deconvolution analysis. As it is shown in Figure 4, it has a symmetric shape in which mean and standard deviation are equal to the center and width parameter, respectively. Gaussian distribution function is shown in equation (1): 2 1 2 f ðxÞ ¼ pffiffiffiffiffiffiffiffiffiffiffi e ð2ðx2mÞ =2s Þ 2ps 2
Figure 4. Gaussian distribution function
ð1Þ
The mathematical solution method involves several repeated iterations of the fitting procedure until the correlation coefficients higher than 90 percent are obtained. After termination of the fitting process, several parameters such as number of revealed waves along the scanned line, the wave’s area, amplitude and wave length of each individual wave are acquired. Since wave characteristics close to the seam line are different from the edge of the sample, wave analysis is carried out in two zones. The first zone is comprised of 3, 6 and 9 mm from the seam line and the second zone is 24, 27 and 30 mm from the seam line which is the sample edge area. In order to speed up the data processing time, the PeakFit v4.12 software is used. Figures 5 and 6 show an example of how the convolution method works for fabric A. The top curve is the original curve and in the bottom are the waves that can regenerate the original curve by merging.
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(a)
(b)
Notes: (a) Low tension sample (grade 5); (b) high tension sample (grade 1)
Figure 5. Fitted pucker wave close to seam line
(a)
(b)
Notes: (a) Low tension sample (grade 5); (b) high tension sample (grade 1)
Figure 6. Fitter pucker wave at edge line
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Figure 5 shows a sense of difference between the number of waves and their characteristics between grade 5 (low sewing threads tension) and grade 1 (high sewing threads tension) in the area close to the seam line (zone 1). In each figure, the upper curve is the original scanned fabric surface and the lower part shows constituent waves. The dotted curve shows the result of the merging of constituent Gaussian curves. Figure 6 shows the same approach and shows the difference between grade 5 and grade 1 samples in the edge area (zone 2). A glance at Figures 5 and 6 demonstrates that in both low and high tension samples, more waves are formed close to the seam line rather than the edge. In both samples, higher wave amplitude is visible on the edge line. Finally, wave amplitude in the high tension sample either close to the seam line or in the edge line is higher than the low tension sample. This trend was noticed in all samples; however, based on fabric mechanical properties, puckering severity and as a result, pucker wave characteristics are different. 3.3 Pucker indexes The pucker geometrical properties influence people’s judgment and affect the final subjective evaluation result. In order to establish the relation between people’s judgment and physical parameters of the waves appearing from the fabric surface, five indexes were defined. Theses indexes were constructed upon the four parameters of number of waves, amplitude, length and area of each wave along the sample. Introduced indexes are presented in equations (2)-(6). The explanation of these parameters is given in the next section. Wave shape parameters are shown in Figure 7: Pn ¼ i¼1 Ai A ð2Þ L Pn ðhi £ wi Þ HW ¼ i¼1 ð3Þ L Pn hi ð4Þ H ¼ i¼1 n Pn ðhi 2 HÞ ð5Þ H l ¼ i¼1 L Pn i 2 WÞ ðhi 2 HÞðw HW l ¼ i¼1 ð6Þ L where:
Figure 7. Wave shape parameters
Ai
the area below each wave.
n
the number of waves along each line.
Wave Area
Wave Length Wave Amplitude
hi
each wave’s amplitude.
wi
each wave’s length.
L H
sample’s length.
H l W
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average wave amplitude along each line. average amplitude deviation from the mean value in unit length. average wave’s length along each line.
summation of wave length and amplitude multiplication in unit length. H W HWl summation of multiplication of wave length and amplitude deviation from the mean value in unit length. 4. Results and discussion After extraction of the wave parameters, pucker indexes along six lines on both sides of the seam line, i.e. 3, 6 and 9 mm were calculated and the average of the results is found. The same computations were done for six lines along the sample edges as 24, 27 and 30 mm away from the seam lines on both sides of the samples. In Table III the extracted results from the deconvolution process for Fabric A are shown. This table shows a clear view of the different seam puckering grades as a matter of number of waves, wave amplitude and wave length within 100 mm of the samples. These are the realistic and tangible characteristics of the seam puckered in every grade apart from the judge’s perceptions. In addition, by introducing five of the defined indexes based on the extracted wave parameters, a new objective method was developed. In order to evaluate these indexes in predicting the seam pucker degree, in each group of fabrics (shirt, worsted and dress fabrics) a correlation between these indexes and the subjective pucker degree is calculated for two fabrics and the subjective degree for the other two fabrics is estimated based on these correlations. Therefore, a correlation between subjective pucker degree and offered indexes is found for fabrics A, B, E, F, I and J. Then, based on the calculated correlation, the subjective pucker degree is estimated for other samples. In Figure 8, the average area in unit length of waves is shown in terms of subjective In each degree of seam pucker, the reported index values are the evaluation (A). average of each index for the five samples. A linear relation with high correlation is observed close to the seam and on the edge lines. The wave area shows the area of the region under the formed waves. It depends on the wave geometry parameters, e.g. the wave with higher wave length and amplitude has higher area. In low pucker degrees, either close to the seam line or edge line, the wave’s structural parameters become more prominent; therefore, the wave’s area can be used as a pucker indicator. In addition, large waves are always formed on the edge line and as it is anticipated, a higher area in unit length is shown in the edge line. The other determinant factor is defined by a summation of the wave length and the As it was shown in Figures 5 and 6, amplitude multiplication in the unit length (H W). wave amplitude is higher in lower pucker grades. In addition, approaching the edge lines reveals the highest wave amplitude.
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Grade 2
Grade 3
Grade 4 Table III. Results of the deconvolution analysis (zone 1, 6 mm away from seam line and zone 2, 27 mm away from seam line) for fabric A
Grade 5
Zone 1 (close to seam line) Wave Wave amplitude Wave length number (mm) (mm) 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 1 2 3 4
0.30 1.04 1.15 1.15 3.03 5.50 5.98 6.56 6.70 0.43 0.46 0.49 1.32 2.33 2.83 4.07 1.14 1.31 2.28 2.97 3.05 3.62 3.73 0.75 0.94 1.69 2.48 4.42 1.28 1.31 1.44 2.11
9.76 9.88 10.96 10.96 11.46 11.78 12.06 12.4 11.75 14.98 13.01 12.65 13.57 13.07 11.25 14.95 30.35 9.44 11.98 12.97 14.48 12.93 14.95 10.96 13.23 15.49 7.6 22.22 14.75 58.56 19.4 16.84
Wave number 1 2 3 4 5 6 7 8 – 1 2 3 4 5 6 7 1 2 3 4 5 6 – 1 2 3 4 5 1 2 3 4
Zone 2 (edge of samples) Wave amplitude Wave length (mm) (mm) 0.34 0.88 0.97 1.05 1.22 7.07 9.33 9.36 – 0.15 0.57 0.60 0.62 1.16 7.95 8.12 0.35 0.43 0.48 1.30 4.19 4.67 – 0.14 0.81 0.89 3.13 3.31 0.48 0.68 2.22 2.27
18 15.68 15.01 16.75 16.14 10.28 17.48 19.97 – 23.58 19.21 18.34 18.94 18.65 22.1 15.48 20.91 17.92 18.25 19.31 18.21 18.79 – 22.98 26.49 22.36 24.09 23.89 20.19 30.28 21.19 19.85
Although wave length increases when nearing the edge line, waves with shorter wave length are formed in lower pucker grades. In Figure 9, a linear relation with a high and the subjective pucker grade is observed. correlation between this parameter (H W) Surely, this parameter is higher in edge lines due to the outstanding wave formation. Regarding Average wave amplitude is another main factor for the seam pucker (H). the puckered samples, in a distinct distance from the seam line, the wave’s amplitudes on both sides are similar and change progressively toward the edge line. Furthermore, in high pucker grade samples, the fabric’s surface is smooth but in low pucker grade samples it seems rippled. The linear curve expresses the best correlation (more than 90 percent) between this parameter and the subjective evaluation of the samples as shown in Figure 10. One of the main parameters which are utilized in surface roughness evaluation is average amplitude deviation from the mean value (H l ). Since the shape of the formed waves along a line is similar, an attempt was made to study amplitude deviation from
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(b)
(a)
(f)
(e)
(j)
(i)
the mean amplitude in unit length as a structural component which specifies wave characteristics along a line (Figure 11). The linear correlation higher than 91 percent shows that this factor can be used as an index to evaluate the seam pucker. The HWl is another index for seam pucker evaluation which is a summation of multiplication of wave length and amplitude deviation from the mean value in
Figure 8. Wave area in unit length vs subjective (A) pucker degree
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Figure 9. Summation of wave length and amplitude multiplication in unit length vs subjective pucker degree (H W)
(b)
(a)
(f)
(e)
(j)
(i)
unit length. As Figure 12 shows, there is a high linear correlation between this parameter and the subjective pucker grading. In order to evaluate the suggested seam pucker indexes performance in each sample group (shirt, worsted and dress fabrics), seam puckering is measured for fabrics C, D, G, H, K and L. Then, pucker indexes for these fabrics are calculated and the subjective degree of these fabrics is estimated, based on high linear correlation between these indexes and subjective pucker degrees. In Tables IV-VIII the estimated pucker degree
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(b)
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(e)
(j)
(i)
and difference in percentage between the subjective pucker degree and the estimated one is presented based on recommended seam pucker indexes. According to the tables, it is considered that the difference in percentage between estimated and subjective pucker degrees is less than 25 percent. Furthermore, indexes which are dependent on wave amplitude are more useful and accurate in predicting seam pucker degree. 5. Conclusion Seam pucker evaluation and developing an objective method has been considered in many researches over the years. Nearly all researchers in this field have tried to justify
Figure 10. Average wave amplitude vs subjective pucker degree (H)
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Figure 11. Amplitude deviation from the mean value vs subjective pucker degree (H l )
(b)
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(f)
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their method vs the subjective method. However, the subjective method itself is prone to bias and is influenced by people’s previous perception and their experience. The first aim of this study was to introduce a novel approach toward seam puckering analysis. Second, an attempt was made to analyze the fabrics with five levels of puckering and extract tangible parameters which represent each grade of puckered fabric, apart from judge perceptions, and third to introduce a new objective method based on the established method. In order to prepare the samples, various fabrics were used in order to probe fabrics with diverse usage. The wavy ripple surface of the puckered fabric was scanned and redefined as several uneven wavy curve lines along the seam line. Each curve along the seam was decomposed and broken into
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(b)
(a)
(f)
(e)
(j)
(i)
several distinct standard Gaussian curves by implementing the residual mathematical method. By merging these curves, the original curves will be reconstructed. By implementing this method on five different grades of puckered samples, each grade was interpreted by four parameters including number of curves, the area, amplitude and wave length of each individual wave: . Analysis of the extracted data revealed that in both low and high tension samples, more waves are formed close to the seam line rather than the edge. According to the data, higher wave amplitude is observed near the edge line. In addition, wave amplitude in high tension samples either close to the seam line or in the edge line is higher than low tension samples.
Figure 12. Amplitude and wave length deviation multiplication from the mean value vs subjective pucker degree (HW1)
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Sewing thread tension (cN) 50 100 150 200 250 Fabric code Su Es Diff% Su Es Diff% Su Es Diff% Su Es Diff% Su Es Diff% C D G H K L
4.9 4.6 5 5 3.8 4.3
4.5 4.1 4.6 4.4 2.9 3.8
8.16 10.87 8.00 12.00 23.68 11.63
4.3 3.3 4.3 4.4 3.3 3.4
4.1 3.2 3.9 4.1 3.1 3.2
4.65 3.03 9.30 6.82 6.06 5.88
3.1 2.8 4.1 3.9 2.6 2.7
2.8 2.4 3.8 3.6 2.2 2.5
9.68 14.29 7.32 7.69 15.38 7.41
2.4 2.3 3.4 3.2 2.1 2.2
2.3 2.1 3.2 2.9 1.8 1.9
4.17 8.70 5.88 9.38 14.29 13.64
1.4 1.1 2.8 2.7 1.4 1.2
1.2 1 2.6 2.4 1.3 1.1
14.29 9.09 7.14 11.11 7.14 8.33
Notes: Su – subjective degree; Es – estimated pucker degree; Diff% – difference in percentage between subjective pucker degree and estimated pucker degree
Sewing thread tension (cN) 50 100 150 200 250 Fabric code Su Es Diff% Su Es Diff% Su Es Diff% Su Es Diff% Su Es Diff%
Table V. Comparison of subjective and estimated pucker degree based on (H W)
C D G H K L
4.9 4.6 5 5 3.8 4.3
4.2 4.1 4.3 4.5 3.2 4
14.29 10.87 14.00 10.00 15.79 6.98
4.3 3.3 4.3 4.4 3.3 3.4
3.9 3.1 3.8 4.1 2.9 3.1
9.30 6.06 11.63 6.82 12.12 8.82
3.1 2.8 4.1 3.9 2.6 2.7
2.7 2.6 3.6 3.4 2.3 2.2
12.90 7.14 12.20 12.82 11.54 18.52
2.4 2.3 3.4 3.2 2.1 2.2
2.1 1.9 2.9 2.8 1.7 1.9
12.50 17.39 14.71 12.50 19.05 13.64
1.4 1.1 2.8 2.7 1.4 1.2
1.1 1.2 2.3 2.1 1.6 1.3
21.43 9.09 17.86 22.22 14.29 8.33
Notes: Su – subjective degree; Es – estimated pucker degree; Diff% – difference in percentage between subjective pucker degree and estimated pucker degree
Sewing thread tension (cN) 50 100 150 200 250 Fabric code Su Es Diff% Su Es Diff% Su Es Diff% Su Es Diff% Su Es Diff%
Table VI. Comparison of subjective and estimated pucker degree based on (H)
C D G H K L
4.9 4.6 5 5 3.8 4.3
4.7 4.5 4.6 4.8 3.6 4.1
4.08 2.17 8.00 4.00 5.26 4.65
4.3 3.3 4.3 4.4 3.3 3.4
4.1 3.1 4.3 4.2 3.2 3.1
4.65 6.06 0.00 4.55 3.03 8.82
3.1 2.8 4.1 3.9 2.6 2.7
2.9 2.6 3.7 3.6 2.5 2.4
6.45 7.14 9.76 7.69 3.85 11.11
2.4 2.3 3.4 3.2 2.1 2.2
2.1 2.2 3.2 3.1 1.9 1.9
12.50 4.35 5.88 3.13 9.52 13.64
1.4 1.1 2.8 2.7 1.4 1.2
1.2 1.2 2.6 2.4 1.2 1.1
14.29 9.09 7.14 11.11 14.29 8.33
Notes: Su – subjective degree; Es – estimated pucker degree; Diff% – difference in percentage between subjective pucker degree and estimated pucker degree
4.5 4.4 4.6 4.7 3.6 4.1
Su
4.9 4.6 5 5 3.8 4.3
8.16 4.35 8.00 6.00 5.26 4.65
Diff% 4.3 3.3 4.3 4.4 3.3 3.4
Su 4.1 3.2 4.2 4.3 3.3 3.1
100 Es 4.65 3.03 2.33 2.27 0.00 8.82
Diff% 3.1 2.8 4.1 3.9 2.6 2.7
2.9 2.6 3.8 3.6 2.5 2.4
6.45 7.14 7.32 7.69 3.85 11.11
Sewing thread tension (cN) 150 Su Es Diff% 2.4 2.3 3.4 3.2 2.1 2.2
Su 2.2 2.1 3.3 3.1 1.9 2.1
200 Es 8.33 8.70 2.94 3.13 9.52 4.55
Diff%
1.4 1.1 2.8 2.7 1.4 1.2
Su
Diff% 14.29 0.00 7.14 7.41 7.14 8.33
250 Es 1.2 1.1 2.6 2.5 1.3 1.1
Notes: Su – subjective degree; Es – estimated pucker degree; Diff% – difference in percentage between subjective pucker degree and estimated pucker degree
C D G H K L
Fabric code
50 Es
Seam pucker rating
167
Table VII. Comparison of subjective and estimated pucker degree based on (H l )
4.3 4.2 4.6 4.4 3.2 4.1
12.24 8.70 8.00 12.00 15.79 4.65
Diff% 4.3 3.3 4.3 4.4 3.3 3.4
Su 3.9 2.8 3.8 4.1 2.7 3.1
9.30 15.15 11.63 6.82 18.18 8.82
Diff% 3.1 2.8 4.1 3.9 2.6 2.7
2.8 2.5 3.7 3.4 2.1 2.3
9.68 10.71 9.76 12.82 19.23 14.81
Sewing thread tension (cN) 150 Su Es Diff% 2.4 2.3 3.4 3.2 2.1 2.2
Su
2.1 1.9 2.9 2.8 1.7 1.8
200 Es
12.50 17.39 14.71 12.50 19.05 18.18
Diff%
1.4 1.1 2.8 2.7 1.4 1.2
Su
1.1 1.4 2.2 2.3 1.1 1.4
250 Es
21.43 27.27 21.43 14.81 21.43 16.67
Diff%
Notes: Su – subjective degree; Es – estimated pucker degree; Diff% – difference in percentage between subjective pucker degree and estimated pucker degree
4.9 4.6 5 5 3.8 4.3
C D G H K L
Table VIII. Comparison of subjective and estimated pucker degree based on (HWl)
Su
100 Es
168
Fabric code
50 Es
IJCST 25,3
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An objective evaluation method comprised of five geometrical indexes was defined based on the wave parameters. It was found that all of these indexes have a linear correlation with a subjective evaluation of high correlation (higher than 90 percent). It was shown that if wave parameters are examined close to the seam line or edge line, by calculation of the recommended indexes it is possible to find their subjective evaluation grade through linear relations. In all five indexes, the values on the edge line are higher than the central area.
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Seam pucker rating
169
IJCST 25,3
170
Schwartz, P. (1983), “Effect of jamming on seam pucker in plain woven fabrics”, Textile Research Journal, No. 5, pp. 32-34. Stylios, G. and Lloyd, D.W. (1989), “A technique for identification of seam pucker due to structural jamming in woven textiles”, International Journal of Clothing Science & Technology, No. 2, pp. 25-27. Stylios, G. and Lloyd, D.W. (1990), “Prediction of seam pucker in garments by measuring fabric mechanical properties and geometrical relationships”, International Journal of Clothing Science & Technology, No. 2, pp. 6-15. Stylios, G. and Parsons Moore, R. (1993), “Seam pucker prediction using neural computing”, International Journal of Clothing Science & Technology, No. 5, pp. 24-28. Stylios, G. and Sotomi, J.O. (1993a), “Investigation of seam pucker in light weight synthetic property, part I: a cognitive model for the measurement of seam pucker”, Journal of Textile Institute, No. 4, pp. 601-610. Stylios, G. and Sotomi, J.O. (1993b), “Investigation of seam pucker in light weight synthetic property part II: model implementation using computer vision”, Journal of Textile Institute, No. 4, pp. 593-600. Zavec Pavlinic, D., Gersak, J., Demsar, J. and Bratko, I. (2006), “Predicting seam appearance quality”, Textile Research Journal, No. 76, pp. 235-242. Corresponding author M. Latifi can be contacted at:
[email protected]
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