Investigation of surface geometry of seersucker woven fabrics

Seersucker woven fabrics are characterized by unconventional structure. The seersucker, also known as crimp effect, is characterized by the presence of a three-dimensional wavy (puckered) effect and relatively flat sections, particularly stripes [1]. Typical seersucker woven fabrics are characterized by the presence of puckered and flat strips in the warp direction. They are manufactured from two warp sets on a loom with two loom beams [2]. One beam carries warp yarns for the flat strips and the other carries warp yarns for the puckered strips. The seersucker effect is created due to different tension of both sets of warp yarns. There are also other methods for manufacturing seersucker woven fabrics, for instance application of yarns of different elasticity [3] or by appropriate fabric finishing when the alkali-printed stripes of a cotton fabric shrink and force the non-printed stripes to take the characteristic wavy form [1]. The puckered elements create the special topography of the fabric surface and influence the parameters of the fabric, especially the surface properties. The surface geometry of the seersucker woven fabrics is created in order to achieve an attractive visual effect (Figure 1).

Figure 1

Previous investigations confirmed that the seersucker woven fabrics have good comfort-related properties due to their puckered structure [4,5]. The surface geometry of the seersucker woven fabrics influences their technological and utility properties as well as the fabrics’ appearance and aesthetic. Due to this fact, the assessment of the surface geometry of the fabrics is an important aspect of seersucker fabric measurement and design. There are not any recognized test methods for the measurement of the parameters characterizing the geometric structure of the seersucker fabric surface.

In general, the KES-FB 4 instrument is used for the measurement of surface roughness of textile materials [6–8]. It is a module of the KES-FB (Kawabata Evaluation System for Fabrics) system developed for complex evaluation of textile materials. By using the KES-FB 4 module, the following surface properties can be measured:

coefficient of friction (MIU),

The KES-FB 4 belongs to the so-called contact or stylus methods of textile surface measurement. The SMD parameter characterizing the geometrical roughness of textile surface is calculated according to the following equation [9]: (1) S M D = 1 X ∫ 0 X ( T − T av ) ⋅ d x . SMD=\hspace{.25em}\frac{1}{X}\underset{0}{\overset{X}{\int }}(T-{T}_{\text{av}})\cdot \text{d}x. The parameters T and X are shown in Figure 2.

Figure 2

In the KES-FB 4, the measuring sensor is made of a U-shaped steel wire with a diameter of 0.5 mm. The width of the sensor is 6 mm. The dimensions of the sensor influence the precision of measurement. They cause incomplete penetration of the sensor into the profile. The large dimension of the sensor eliminates the roughness components of the dimensions lower than the dimension of the sensor.

The irregularity of woven fabric surface has two main sources: irregularity of the yarn surface and irregularity caused by interlacing the threads: warp and weft (Figure 3). The dimensions of both kinds of irregularities differ significantly.

Figure 3

Typical cotton yarns applied in the woven fabrics have a linear density in the range of 8–100 Tt. Their diameter is much lower than 0.5 mm. This surface irregularity caused by the yarn irregularity cannot be detected by the KES-FB 4 roughness sensor. Moreover, the surface irregularity caused by the yarn interlacing is not fully detected. Thread spacing can vary depending on the density and thickness of warp and weft. In dense fabrics, the thread spacing p (Figure 4) is lower than 0.5 mm. Due to this fact, in such dense fabrics some irregularities of the fabric surface are omitted by the sensor. In the transverse direction, the sensor has a dimension of 6 mm. It can cause the vertical movement of the sensor to be limited by the top parts of threads (Figure 5).

Figure 4

Figure 5

The contact methods can be easily affected by environmental conditions, such as moisture. Another disadvantage of the contact measurements of fabric roughness is the flexibility of fabrics. In general, fabrics are flexible materials. They can change their shape by the movement of the sensor on their surface, which can cause measurement error. In particular, while measuring the seersucker woven fabric, its puckered area can deform due to pressure and movement of the sensor. This should be taken into consideration while assessing the surface topography of the seersucker fabrics or other fabrics of developed surface topography, such as waffle woven fabrics.

The non-contact methods of surface roughness measurement of textile materials are much more precise. The non-contact methods are divided into two groups: optical methods and image analysis and fractal methods [10]. The optical methods analyse the reflection of light from the measured surface. The fractal methods are based on the determination of a fractal dimension calculated from the surface profile obtained by image analysis techniques. The non-contact methods are used rather sporadically to measure the fabric surface. Moreover, the published studies focus mostly on the flat woven fabrics of the basic or derivative weaves. Wang et al. analysed the fabric texture using computer vision techniques [11]. They applied the optical method to measure the texture characteristics of woven fabrics, such as weave repeat and yarn counts, and the surface roughness. Additionally, they determined the fractal dimension of the investigated fabrics as a surface roughness indicator. Semnani et al. applied the image analysis to measure the roughness of the knitted fabrics using the high resolution scanner [12]. Militky and Bleša proposed a special arrangement of textile bend around sharp edge and laser lighting from the top with the CCD camera for obtaining the surface profile images in the cross direction. Next, image analysis was carried out to extract the surface profile. The method was used for measurement of the cord fabrics [13].

Ajayi specified two kinds of irregularities – the systematic variation arising from uniform fabric structures, such as cord or ribs, and random variation caused by uneven threads or thread spacing [14]. In the case of the seersucker woven fabrics, we can distinguish three kinds of irregularities: the systematic variation of the seersucker effect occurring in the puckered strips of the fabric, the systematic variation resulting from the applied weave and the random variation due to unevenness of the threads and their spacing.

There are three components of surface geometry: roughness, waviness and shape [15]. The roughness is an irregularity of a fine texture, which can be defined as a deviation from an ideal surface that appears repeatedly at relatively longer intervals than the depth. The waviness refers to the uneven surfaces that appear periodically at longer intervals than the roughness (Figure 6). In the case of textile fabrics, shape need not be considered because the textile materials are flexible and can change their shape even under very low load.

Figure 6

In the case of seersucker woven fabrics, their irregularity caused by the seersucker effect should be considered as the waviness. The question is: how should the filters be established in order to separate the waviness from the roughness and to eliminate noise. In the optical measurement systems, it is possible to apply different filters to specify the spatial wavelength range to be included in measurement results. The filters make it possible to separate out the non-critical wavelengths and emphasize the critical to surface description. In the measurement of surface of the solid materials, a cut-off filter (L _c) of 0.8 mm is applied to mark the transition from the roughness to the waviness [16]. The choice of the filter values depends on the process being assessed. There are a number of guidelines available. However, the choice of filters should be made after consideration of the profile features. For the flexible materials, such as fabrics, the values of filters are not recognized. It is a significant problem because it influences the results.

The aim of the present work was to recognize the use of non-contact optical method to measure the geometric structure of surface of the seersucker woven fabrics. In the present work, recognition in the range of the measurement, data analysis, use of appropriate filters to separate the roughness and the waviness and to eliminate the noise as well as selection of parameters that can be used for characterization of the surface geometry of the seersucker woven fabrics from the point of view of quantifying the seersucker effect were carried out.

The cotton seersucker woven fabric was the object of investigation. The fabric being investigated was manufactured on the basis of the warp sets made of cotton twisted yarn of linear density 20 tex × 2. The same yarn was introduced as a weft. In the fabric, the width of the puckered and flat strips was appropriately 5 mm and 8 mm, respectively (Figure 7).

Figure 7

The basic properties of the investigated seersucker woven fabric are presented in Table 1.

In Table 1, warp I is the warp that creates the flat strips. Warp II is the warp that creates the puckered strips. Till now, the crimp of the warp creating the puckered strips (warp II) is the only parameter applied to assess the seersucker effect. In general, it is ca. 50% and more [4,5]. There are not recognized any methods to characterize the seersucker effect in order to compare different seersucker woven fabrics and to ensure repeatability of the seersucker fabric manufacturing.

The parameters characterizing the surface geometry of the fabric was measured by using the MicroSpy^® Profile profilometer by the FRT^® the art of metrology™ [17]. It is the optical measuring tool for the precise measurement of the surface topography using the principle of chromatic distance measurement. The FRT chromatic white-light sensor used in the MicroSpy^® Profile is based on the patented method, which makes use of chromatic aberration (in principle the wavelength-dependent refractive index) of the optical lenses. The profilometer cooperates with the FRT Mark III software for measurement of numerous surface parameters [18]. The picture of fabric sample was taken in the area of dimensions of 5 cm × 5 cm. On the basis of the pictures from the profilometer and the data processing using the Mark III software, the parameters and functions characterizing the surface topography were determined according to the international standards [19,20]. Additionally, the fractal dimension, histograms of height, profile graphs and other properties were analysed for an area of the whole repeat of seersucker effect and separately for the puckered and flat areas of the investigated fabric. In the case of the investigated seersucker fabric, the repeat means the repeat of the seersucker effect – a part of the fabric area containing one puckered and one flat strip. This part (repeat) is repeated across the whole width of the fabric.

Figure 8 presents the raw picture of the seersucker woven fabric being investigated. The picture was obtained using the profilometer. The grey point presents the invalid data. The invalid data include the points that are invisible to the sensor because they are outside its range.

Figure 8

Next, the picture was analysed using the Mark III software. First, the invalid data were corrected using an option “Area interpolation.” The picture after correction of the invalid data is presented in Figure 9. It can be seen that the grey points disappeared.

Figure 9

In the investigated area of the seersucker fabric, there are puckered and flat strips. In order to analyse the surface topography of the fabric, the area of the repeat of the seersucker effect was isolated (Figure 10). It was done in such a way in order to compare the results of the investigated seersucker fabric with the results of other seersucker fabrics of different repeats of the seersucker effect. The comparison will be presented in next publications.

Figure 10

Figure 11a presents the profiles created along the puckered and flat strips without any filters. Figure 11b shows the place of created profiles on the repeat area.

Figure 11

The following two components occur in the profiles: roughness and waviness. In order to separate the roughness and the waviness components and additionally to eliminate noise, appropriate filters should be used. The commonly known and accepted values of the cut-off filters for the textile materials are not established. For the solid materials (for instance metals), the L _c (cut-off) filter of 0.8 mm is commonly used to separate the roughness and waviness components [16]. However, the structure of the surface of the textile materials is more complex. According to the fabric structure (Table 1), the thread spacing is as follows: warp 0.8 mm and weft 0.9 mm. Even the lowest element of the fabric structure of the plain weave (including the thread thickness) has dimensions more than 0.8 mm (Figure 12).

Figure 12

Due to this fact, it was assumed that the 0.8 mm L _c filter can ship some roughness component to the waviness range. Taking it into account, the preliminary investigations were performed to compare the results determined for two values of the L _c filter: 0.8 mm and 1.4 mm. The noise filter was established to be 245 μm (minimal value available in the software applied).

Figure 13 presents the profiles of the fabric along the puckered strip created after elimination of the waviness component using the 0.8 mm L _c filter (blue line) and the 1.4 mm L _c filter (yellow line) as well as after elimination of the roughness component, also using two filter values: 0.8 mm (green line) and 1.4 mm (red line). It can be seen that the shape of the profiles is very similar. For the roughness component, the z-values (height) are higher at the 1.4 mm L _c filter (yellow line) than that at the 0.8 mm L _c filter. In the case of the waviness component both profiles: at the 1.4 mm L _c filter and at the 0.8 mm L _c filter are very similar. The differences are observed in the range of the highest picks and the deepest valleys.

Figure 13

The profiles created along the flat strip are presented in Figure 14.

Figure 14

Similar to the profiles created along the puckered strip (Figure 13), also for the profiles created along the flat strip (Figure 14), the shape of both profiles representing the roughness (yellow and blue lines) is similar. The z-value (height) is different. It results in some differences in the values of roughness parameters. However, while establishing the same value of the L _c filter different fabrics can be measured and compared using both analysed filters. Another situation is observed in the case of the waviness profiles. It is clearly seen that the 0.8 mm L _c filter is too low (green line). Not all components of roughness are eliminated (green line). Taking into account the above analysis, the 1.4 mm L _c filter was applied in further investigations.

It is necessary to mention that for the investigated seersucker woven fabric the waviness of the flat area should be considered as an error of sample placing on the instrument table. Normally, the flat area should not contain any waviness component. This area should be parallel to the table surface. However, due to the flexibility of the fabric and the structure of the seersucker fabric, the flat area does not adhere to the table surface. Additionally, some phenomena of shape memory of the fabric are observed. Due to this fact, the sample investigated shows the waviness effect while measuring along the flat strip. In the case of the profile created along the puckered strip two components of the waviness occur: one – caused by placing the sample on the instrument table, and second – caused by the seersucker effect. The first one is an error. However, it is impossible to separate the waviness caused by both sources.

Figure 15 presents a comparison of the profiles created along the flat and puckered strips. In the picture, the same scale was applied to both axes. It visualizes a real difference between the profiles created along both areas: the flat and the puckered. Additionally, it is clearly seen that the flat profile does not stick completely to the surface of the measuring table (z = 0 mm) along the whole measured distance. On the other hand, the blue profile touches the table only in two places along the measured length: at 1–2 mm and 37–38 mm.

Figure 15

The parameters characterizing the surface geometry of the fabric was measured for the whole repeat and separately for the flat and puckered areas. In order to do this, the flat and puckered areas of the repeat have been separated. The full range of parameters has been determined for the investigated areas of the fabric. In Table 2, only the selected basic parameters are presented:

R _a , W _a – arithmetic mean of the absolute of the ordinate values within a defined area. It is a height parameter (arithmetical mean height of a line). It expresses, as an absolute value, the difference in height of each point compared to the arithmetical mean of the surface;

The aforementioned parameters marked by the R letter mean the roughness parameter determined after excluding the waviness component, whereas the parameters marked by the W letter are the waviness parameters determined after excluding the roughness component.

In general, the values of the so-called height parameters (R _a , W _a , R _q , W _q ) present clear tendency – the highest values are observed for the puckered area, whereas the lowest for the flat area of the fabric (Figure 16). It is according expectation taking into consideration the structure of the investigated areas of the fabric.

Figure 16

Similar tendency occurs also for the W _z (DIN) parameter. On the other hand, a different trend was found for the R _z (DIN) parameter. Here, the highest value was stated for the whole repeat. It is easy to understand because the highest picks and the deepest valleys observed both in the flat and puckered areas occur in the whole repeat, and are taken into consideration while determining the R _z(DIN) parameter. The final values of the R _z (DIN) and W _z (DIN) parameters depend on the distribution of the height (z-value) in the defined area.

The values of the R _sk parameter are positive for the repeat as well as for the individual (puckered and flat) strips. It means that the deviation in height is beneath the mean line. It includes the data after excluding the waviness component. In the case of the W _sk parameter determined after excluding the roughness component, the values for the repeat as well as for the flat and puckered areas are negative. This means the deviation in height is above the mean line.

The R _ku values are higher than 3.0. This means that the roughness component shows spiked distribution of height. In the case of waviness component, the kurtosis (W _ku) for the whole repeat is higher than 3.0, whereas separately for the puckered and flat strips the W _ku values are lower than 3.0. It shows that the height distribution of waviness component is skewed above the mean plane. The histograms in Figure 17–19 show the height (z-value) distribution for primary, unfiltered data. It is clearly seen that the height distribution in the flat area (Figure 17) differs significantly from the height distribution in the puckered area (Figure 18). Histogram for the flat area is sharp with the maximum of 18.384% at height 0.588 mm, whereas for the puckered strip the histogram is flat with the maximum of 7.078% at height 1.261 mm. For the whole repeat, the histogram of z-value is a sum of histograms for both the flat and the puckered areas (Figure 19). For the whole repeat, the most frequent (16.776%) height is 7.010 mm.

Figure 17

Figure 18

Figure 19

Figure 20 presents the autocorrelation function for the whole repeat (Figure 20a) and separately for the flat (Figure 20b) and the puckered (Figure 20c) strips.

Figure 20

The autocorrelation is the cross-correlation of a signal with itself and thus gives information about the self-similarity. The autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of periodic structures, which has been buried under noise (roughness). On the basis of the autocorrelation function, the following two spatial parameters are determined according to DIN EN ISO 25178 [19]:

autocorrelation length (S _al) – it is a horizontal distance of the autocorrelation function that has the fastest decay to a specified value s (0 ≤ s < 1).

The values of spatial parameters are presented in Table 3. On the basis of the results it can be stated that the surface geometry of the investigated seersucker woven fabric is strongly anisotropic. It includes both whole repeat and separately the puckered and flat strips. It is according to expectations because generally the woven fabrics are anisotropic. Majority of their properties are determined separately in the warp and weft directions. In particular, the seersucker woven fabrics are anisotropic as the puckered strips occur regularly in the determined distance in the warp direction. Additionally, the seersucker effect is also regular and repeating.

The present investigations confirmed that the non-contact method using the optical profilometer can be successfully applied to assess the geometry of the surface of seersucker woven fabrics. The profilometer together with the co-operating software provides a wide range of data characterizing the surface of the selected area of the investigated fabrics. All parameters and functions defined in the standards can be determined using the applied method. The question is how can the individual parameters be used in practice for design and manufacturing of the seersucker woven fabrics. In particular, which of the determined parameters can be applied to quantify the seersucker effect and to ensure the repeatability of the seersucker effect.

The obtained results show that parameters such as arithmetic mean deviation of height, root mean square deviation of height and mean roughness depth have different values for the puckered and the flat areas. Similarly, the histograms of the height and the autocorrelation function have different shapes for both separated areas. It means that the aforementioned parameters and functions can be used for characterizing the seersucker effect. However, it needs further investigations.

In future work, different variants of the seersucker fabrics will be investigated to check the influence of the structure of the seersucker woven fabrics on the surface characteristics of the fabrics.