JP3212282B2 - Method of estimating visual sensation characteristics of fabric and method of manufacturing fabric - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for estimating a visual sensation characteristic of a woven fabric and a method for manufacturing a woven fabric using the estimation method. And a method of preliminarily establishing a correlation between unevenness characteristic values of a yarn that can be used as a warp or a weft of the woven fabric, and estimating visual sensibility characteristics of various woven fabrics based on the correlation. Or a method for producing a woven fabric having desired visual sensation characteristics using the estimation method.

[0002]

2. Description of the Related Art Generally, in a woven fabric in which a warp and a weft are interwoven, the warp and the weft form a woven fabric surface while intersecting up and down. And the visual sensibility characteristics (sensitivity rank or sensibility scale) of the woven fabric surface are as follows:
It is mainly determined by color, weaving structure, warp density, weft density, yarn thickness (count, denier, etc.), material, etc., but such sensibility characteristics have conventionally been grasped by individual subjective judgment. Has been considered a matter of personal preference.

[0003] In such a woven fabric, even if the colors, woven structure, warp density, weft density, yarn thickness, and material, which are important factors in determining the visual sensation characteristic, are the same, The expression of the fabric changes greatly depending on the shape of the yarn. That is, even if the above-described elements are fixed, many kinds of fabrics can be produced by using various yarns having different irregularities, such as uneven yarns and hemp yarns, as warps or wefts.

[0004]

As described above, there are many types of woven fabrics depending on the characteristics of the unevenness of the yarn to be used. For each of these woven fabrics, the visual sensibility characteristics are determined. Since they cannot be made or ranked, the selection of the type of yarn to be used in the woven fabric has hitherto been made depending on the intuition of a skilled person. Therefore, unless the fabric is actually manufactured, the visual sensibility characteristics of the woven fabric cannot be reliably grasped, and the difference in the visual sensibility characteristics between these various fabrics is also low. There was a problem of not knowing.

[0005] Further, even if a fabric sample is actually produced or the fabric is represented as an image by simulation using a computer, it is impossible to objectively judge a subtle difference in the fabric expression. For this reason, the evaluation of the visual sensation characteristics of such a fabric must be performed by relying on the judgment of a skilled person or by testing a large number of subjects. However, there is a problem in that, when relying on the judgment of a skilled person, it is often not possible to eliminate the bias of evaluation of each person, while on the other hand, a lot of time is required when testing a subject.

The present invention has been made in order to solve the above-mentioned conventional problems, and has been made in order to solve the above problems.
When the weft density, the thickness of the yarn, and the material are fixed, the method is capable of easily estimating or grasping the visual sensibility characteristics of a woven fabric made using any type of yarn, and thus has a desired sensibility characteristic. It is an object of the present invention to provide a method for easily producing a woven fabric.

[0007]

The method of estimating the visual sensation characteristic (kansei rank or kansei scale) of a woven fabric according to the first aspect of the present invention, which has been made to solve the above-mentioned problems, is as follows. a) A method for estimating the visual sensibility characteristics of a woven fabric in which a warp and a weft are woven, and (b) a plurality of types of yarns that can be used as a warp or a weft; Finding the fractal dimension to represent, (c) for each woven fabric having the same color, woven structure, warp density, weft density, yarn thickness and material of the woven fabric, use any one of the plurality of yarns as the warp and the weft. The visual sensibility characteristic value regarding the coolness or warmth of the used fabric is obtained by an experiment, and (d) the correlation between the sensibility characteristic value of the woven fabric and the fractal dimension of the yarn is determined by a multiple regression analysis method. Standing, (e Based on the correlation, a woven fabric using a yarn of a different type from the woven fabric whose kansei characteristic value was obtained by the above experiment, the visual sensibility characteristic value for coolness or warmth is calculated from the fractal dimension of the yarn. It is characterized by being estimated.

[0008] According to the method for estimating the sensibility characteristic, for a woven fabric whose sensibility characteristic regarding coolness or warmth is unknown,
Even without actually fabricating the fabric, the visual sensation characteristic of the coolness or warmth of the fabric can be estimated or grasped from the fractal dimension of the yarn, so that the visual sensation of the desired coolness or warmth can be obtained. Product design, for example, selection of yarn, for producing a woven fabric having excellent sensitivity characteristics is facilitated. According to the fractal dimension, the unevenness characteristics of the yarn can be easily quantified.

In the above method for estimating the visual sensation characteristic of the fabric, the correlation is established by the multiple regression analysis, so that a correlation very well matching the experimental data can be obtained.

[0010] A method for producing a woven fabric according to the second aspect of the present invention is provided with a visual sensibility characteristic of a desired coolness or warmth by using the above-mentioned method for estimating the visual sensation characteristic of a woven fabric. Yarns from which a woven fabric can be obtained, characterized in that the yarns are used to produce woven fabrics with the desired visual sensation of coolness or warmth. According to the method for manufacturing a woven fabric, a woven fabric having a desired visual sensitivity characteristic of coolness or warmth can be easily manufactured.

In the method for estimating or producing the visual sensibility characteristics of the woven fabric according to the present invention, the sensibility characteristics are estimated by the following procedure, and an appropriate combination of the warp and the weft can obtain the desired sensibility characteristics. Is determined. That is, first, the color of the fabric, the weave structure, the warp density, the weft density,
Six or more woven fabrics made using different types of yarns having the same thickness and the same material but having irregularities are prepared.

Next, these woven fabrics are ranked or scaled based on visual sensitivity characteristics using a pair comparison method or the like based on the visual evaluation of the subject. That is, the sensitivity characteristics are quantitatively grasped and quantified. Also,
Characterize the unevenness of the yarn used in the fabric by fractal dimension. The fractal dimension is a measure of the complexity of the yarn, and by using the fractal dimension, the characteristics of the unevenness of the yarn can be quantified.

The relational expression expressed by the following equation 1 is obtained by performing a multiple regression analysis on the visual sensitivity characteristics (sensitivity rank or sensitivity scale) of the woven fabric in the fractal dimensions of the warp and the weft.

S = a · D ₁ + b · D ₂ + c ····················· Equation 1 S: Kansei scale value (Kansei rank) D ₁ : Fractal dimension of warp yarn D ₂ : Fractal dimension of weft yarn a, b, c: constant

When a new woven fabric is made, the expression 1 is used to easily estimate the visual sensation characteristic (sensitivity scale or kansei rank) when woven from the fractal dimension of the yarn used. Can be. Therefore, the yarns to be used as the warp and the weft can be selected so as to obtain a woven fabric having desired sensitivity characteristics. Also, according to this choice, a fabric with the desired sensibility properties can be produced.

[0015]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be specifically described below. (Embodiment 1) Hereinafter, Embodiment 1 of the present invention will be described. In the first embodiment, using a fabric sample as shown in FIGS. 1 to 8, a scale value (sensitivity characteristic value) for “coolness”, which is one of the visual sensitivity characteristics of the textile, The correlation between the fractal dimension (irregularity characteristic value) of the yarn used in the woven fabric is established.

Each of the yarns constituting the woven fabric sample has the following properties for both the warp and the weft, and these yarns simply differ in the shape of the irregularities. Material: 100% cotton, Yarn thickness: 20th average The properties of woven fabric samples made using these yarns are as follows. Weave structure: Plain weave Warp density: 60 yarns / inch Weft yarn density: 60 yarns / inch Dyeing color: Same color as blue

As shown in FIGS. 1 to 8, a clear visual difference (a difference in appearance) is observed between these fabric samples. The visual (look) of these samples
Conventionally, coolness has been evaluated by actually making a woven fabric and allowing a skilled person such as a designer to subjectively judge the visual image of the woven fabric. For this reason, the selection or combination of yarns at the stage of fabric design prior to production has been made based on the intuition of a skilled person. Therefore, it is not possible to evaluate the coolness without actually making a woven fabric, and it is necessary to spend a great deal of time and effort.

Here, if there is any quantitative criterion for coolness when fabrics are manufactured, it is necessary to estimate or grasp the coolness of the fabrics according to the criterion without making all possible fabric samples. Therefore, the number of fabric samples can be reduced. Therefore, in the first embodiment, the correlation between the coolness of the woven fabric and the unevenness of the yarn used in the woven fabric is clarified, and the visual coolness of the woven fabric is determined based on the shape of the yarn using this correlation. We try to create criteria for control.

<Fabric Sample> The difference in the structure of each fabric sample is the unevenness of the warp and weft used. Although the irregularities of these yarns seem at first glance to be random, the differences between them can be visually confirmed. In addition, the features of the unevenness of these yarns can be quantified by using a fractal dimension. Here, the fractal dimension is a measure of the complexity of the unevenness, and the numerical value increases as the shape becomes more complicated. Conventionally, there is no analysis method for complicated unevenness of the yarn, and even if a difference is visually confirmed in the shape, all of them have been treated as random. However, in the first embodiment, the feature of visual complexity can be quantified or quantified by using the fractal dimension.

The fractal dimension of each yarn is determined or calculated in the following procedure. That is, in general, a fractal is a general term for a figure or a structure that does not have a characteristic length (length or size associated with a geometric feature such as a radius). In addition, a property having no characteristic length generally has self-similarity. Here, the self-similarity is a property that when a certain figure is enlarged or reduced, the same shape (pattern) as the original figure appears. The fractal dimension indicates such a degree of self-similarity and is used as a measure of the complexity of a figure.

Then, in order to express the characteristic complexity of the yarn using the fractal dimension, the shape or density of the yarn is measured, and the fractal dimension is calculated using a computer or the like. In order to make the shape or density of the yarn usable by a computer, for example, a change in the thickness of the yarn is converted into an electric signal using a photoelectric yarn unevenness tester or a capacitance-type yarn unevenness tester, and then A The digital value may be recorded by a / D converter, or the shape of the thread may be read and recorded by a scanner or a digital camera.

As a method for obtaining the fractal dimension of the yarn from the measurement data thus obtained, for example, the following method can be mentioned. (1) Method Based on Coarse-Graining Degree A plane including a graph of yarn shape data or outer shape variation data is divided into squares with one side r by a grid of intervals r.
The number of squares including the original data is set to N (r).
When the relationship of N (r) ∝r− ^D is established when r is variously changed, let ^{D be} a fractal dimension.

(2) Method of finding from the relationship of the measure The thread shape is divided into squares by a fine grid. Of these square, painted black ones including yarn, to the number and S _n. The number of black squares that are in contact with the white squares is defined as _Xn . Then, when the relationship of ^{_{S n 1/2 α X n 1 /}} D is satisfied, and the fractal dimension of D.

The method of calculating the fractal dimension is not limited to the above two methods, but other generally used methods of calculating the fractal dimension, for example, a method of obtaining from a correlation function and a method of obtaining from a distribution function Of course, a method of obtaining from a spectrum can also be used.

Table 1 shows the results of calculating the fractal dimensions of the three yarns used in the fabric samples. In addition, all of these yarns are provided with irregularities artificially. Also,
Table 2 shows the configurations of the warp and the weft of eight types of fabric samples 1 to 8 made using any of these yarns.

[0026]

[Table 1]

[0027]

[Table 2]

<Paired Comparison Method> The visual coolness of these eight types of fabric samples 1 to 8 was scaled using the paired comparison method. This scaling is intended to capture the tendency for coolness in this fabric sample. Here, the reason for using the paired comparison method is as follows. That is, in the sensory test, in general, the evaluation standard is not clear and easily fluctuates, so it is often difficult to judge. In the paired comparison method, however, the act of comparing two items and assigning ranks or scores is only repeated. Therefore, the determination can be made easily.

The pairwise comparison method obtains various information from the pairwise comparison judgment. As such a pairwise comparison method, various analysis methods such as the Scheffe method, the Thurston method, and the Bradley method are known. ing. In the first embodiment, the fabric sample is scaled using the Bradley method. The pairwise comparison method or the Bradley method is a generally known method. For example, on February 6, 1995, Nikkagiren Co., Ltd. It is described in detail in the book "New Edition Sensory Test Handbook" concerning editing.

<Experiment> Experiments were conducted on eight types of fabric samples 1 to 8 with 22 female students as subjects in order to measure coolness, which is one of the visual sensitivity characteristics. In this experiment, a tournament table as shown in Table 3 was used in order to compare the visual coolness of the fabric samples 1 to 8 by pair.

[0031]

[Table 3]

In this experiment, for each subject,
For all combinations of the eight two fabric samples of the fabric in the sample, i.e. were carried out comparative experiments on 28 pairs of combinations _{(8 C 2 = 8 × 7} /2 = 28).
In this experiment, the fabric samples (two each) of each combination were shown to the subject and asked which one felt visually “cool”. In this experiment, a bias factor due to the order of presentation is removed by randomly presenting two images at a time.

<Confirmation of Presence or Absence of Judgment Ability> Next, the presence or absence of judgment ability of each subject was confirmed from the information obtained by the above experiment. In order to perform this check, it is necessary to determine the coefficient of uniqueness of the experimental data. Generally, in order to determine the coefficient of uniqueness, first, the number of one-turn triangles is determined. here,
The circuit triangle is, for example, as follows. Now
It is assumed that there are three samples A, B, and C, and the quality of the two samples is sequentially compared. Where A
B is better than A in the comparison between B and B, and C is better than B in the comparison between B and C,
C would normally be rated better than A. That is, these determination results take a form as shown in FIG. However, when there is a fluctuation in the determination or when the evaluation is performed in a multi-dimensional manner, it may be determined that A is better than C as shown in FIG. In FIGS. 10A and 10B, the arrows indicate the determination results, and it is determined that the direction to which the arrow points is “good”.

Here, if the judgment ability or discrimination ability of the judge is high, the number of the open triangles is small, but there is no judgment ability.
If the pass / fail is determined based on the probability, the number of the open triangles has a certain stochastic distribution. Therefore,
If the number of the open triangles is sufficiently small according to this distribution, it is considered that the judge has the judgment ability or the discrimination ability.

[Procedure for Calculation and Test of Coefficient of Uniqueness] The procedure for calculation and test of the coefficient of uniqueness is approximately as follows. (1) Calculation of Number of One-Ring Triangles If the number of samples is k, and the number of one-circle triangles is d, the value of d can be calculated by the following equation 2. In Equation 2, a _i is the number of arrows (sides or diagonal lines of the k-sided polygon) outwardly protruding from the i-th vertex of the polygon (k-sided polygon) having k vertices. In addition,
In the first embodiment, k = 8 because the number of fabric samples is eight.

(Equation 2)

(2) Calculation of coefficient of uniqueness ζ The coefficient of uniqueness ζ is calculated by the following equations 3 and 4, respectively, depending on whether k is an even number or an odd number.

## EQU3 ## When k is an even number ζ = 1-24 · d / (k ³ -4k)... ...... Equation 3

## EQU4 ## When k is an odd number ζ = 1−24 · d / (k ³ −k)... ...... Equation 4 Note that if there is no open-circle triangle, ζ = 1.

(3) Test When k ≦ 7, a limit value d _0.05 of 5% risk is calculated from Table 4. If d ≦ d _0.05, it is determined that the discriminator has the discriminating ability or discriminating ability, or that there is a discernible difference between the samples.

[0038]

[Table 4]

On the other hand, when k> 7, x ₀ ² is first calculated by the following equation (5).

X ₀ ² = (8 / (k−4)) · {k · (k−1) · (k−2) / 24−d + 1/2} + f Equation 5 where f = k (K−1) · (k−2) / (k−4) ² Then, x ₀ ² calculated by Expression 5 is converted to a 5% point x ² (f, 0. 05). Here, if x ₀ ² ≧ x ² (f, 0.05), it is determined that the judge has judgment ability or discrimination ability, or that there is a discernable difference between samples. Note that a method of confirming the presence or absence of such determination ability is well known, and is described, for example, in the aforementioned book “New Edition Sensory Test Handbook”.

<Scaling> From the experimental data obtained as a result of the experiment using the paired comparison method described above, only the experimental data of subjects having judgment ability or discriminating ability are selected, and these experimental data are scaled. (Estimation of the scale value of coolness). As described above, examples of the scaling method include Thurston's method and Bradley's method. Here, the scaling by the Bradley's method is employed.

[Estimation of Scale Value] In the Bradley method, the coolness scale value (judgment ratio) is determined by the following procedure.
Is estimated. That is, the probability of determining that the fabric sample 1 is cooler than the fabric sample 2 is π ₁₂ ,
The probability that the fabric sample 1 is determined to be cooler than the fabric sample 3 is π _13, and similarly, the probability that one of the combinations of all fabric samples is determined to be cooler than the other is considered. However, such probabilities do not provide a measure of the absolute coolness of each fabric sample. Therefore, the following formula 6 is used to determine the scale value (decision ratio) π ₁ , π ₂ , π ₃ of the coolness of each fabric sample from the above probability.
π ₄ …… π ₈ is determined.

Π _ij = π _i / (π _i + π _j ) ······················· In Equation 6, π _ij is the probability of determining that the fabric sample i is cooler than the fabric sample j. The calculation method of the probability or the determination ratio is well known,
For example, it is described in the aforementioned book “New Edition Sensory Test Handbook”.

Table 5 shows the thus determined visual coolness of each fabric sample. In Table 5, a fabric sample having a larger scale value gives a more visually cool feeling.

[0043]

[Table 5]

<Relationship Between Fractal Dimension of Yarn and Scale Value of Woven Fabric> The following is a multiple regression analysis of the scale value of the coolness of the fabric samples 1 to 8 and the numerical value of the fractal dimension of each warp and weft. Equation 7 was obtained.

C = 0.814 · D ₁ + 0.395 · D ₂ −1.994 Expression 7 C: Scale value of coolness D ₁ : Fractal dimension of warp D ₂ : Fractal of weft dimension

The correlation coefficient in this multiple regression analysis was 0.8.
53. Therefore, it can be said that there is a strong correlation between the scale value of coolness and the fractal dimension of warp and weft. Therefore, it can be said that the relationship between the apparent coolness of the woven fabric and the fractal dimension of the warp and the weft can be clarified by the relational expression shown in Expression 7. Thus, by using the relational expression of Expression 7, for a woven fabric whose coolness, which is one of the visual sensation characteristics, is unknown, the fractal dimension (irregularity characteristic) of the yarn can be used without actually fabricating the woven fabric. Can be estimated, so that it is possible to easily select a yarn suitable for producing a woven fabric with a desired coolness, and thus easily produce a woven fabric with a desired coolness. Will be able to do that.

<Other Woven Fabrics> In the following, in order to verify the validity of the relational expression of the formula 7, a woven fabric sample shown in FIG. 9
Let's estimate coolness. Needless to say, the combinations of yarns shown in Table 6 are not included in the woven fabric samples 1 to 8.

[0047]

[Table 6]

In the woven fabric sample 9, the yarn B is used for the warp (D ₁ = 1.85) and the yarn A is used for the weft (D ₂ = 1.65). Coolness C
Is estimated as follows. C = 0.814 × 1.85 + 0.395 × 1.65-1.994 = 0.161 Therefore, referring to Table 5, the visual coolness of the fabric sample 9 is as follows. It is expected to come between.

<Confirmation Experiment> Table 7 shows that woven fabric samples 1 to 8
In the same manner as in the above case, the results of determining the coolness scale value of the nine kinds of fabric samples obtained by adding the fabric sample 9 to the above are shown in the same manner as above.

[0050]

[Table 7]

As is evident from Table 7, the coolness measure of the newly added fabric sample 9 falls between the fabric samples 8 and 7, as expected. That is,
The subject feels that the visual coolness of the fabric sample 9 is intermediate between the fabric samples 8 and 7. Therefore, it can be said that Expression 7 is sufficiently useful as an index at the time of designing a woven fabric.

Embodiment 2 Hereinafter, Embodiment 2 of the present invention will be described. Note that the basic configuration of the second embodiment is the same as that of the first embodiment. Therefore, in the following, the points that are different from the first embodiment will be mainly described to avoid redundant description.

In the second embodiment, using fabric samples 11 to 18 as shown in FIGS. 11 to 18, a scale value (sensitivity) for "warmth" which is one of the visual sensitivity characteristics of the fabrics The characteristic value) and the fractal dimension of the yarn used in the fabric are established.

Each of the yarns constituting the woven fabric sample has the following properties for both the warp and the weft, and these yarns differ only in the shape of the irregularities. Material: Cotton 100%, Yarn thickness: Average 20th Weave structure: Plain weave Warp density: 60 yarns / inch Weft yarn density: 60 yarns / inch Dyeing color: Same color as brown

As shown in FIGS. 11 to 18, a visual difference (appearance difference) is clearly observed between the respective fabric samples 11 to 18 in terms of warmth. Then, when fabrics are manufactured, if there is any standard for warmth, it is possible to estimate or grasp the warmth of the fabric based on the standard without making all possible fabric samples, The number of samples can be reduced. Therefore, in the second embodiment, the correlation between the warmth of the woven fabric and the shape of the yarn used in the woven fabric is clarified, and the visual warmth of the woven fabric is determined based on the shape of the yarn using this correlation. We try to create criteria for control.

Table 8 shows the results of calculating the fractal dimensions of the four types of yarns used in the fabric samples. All of these yarns are artificially textured. The yarns A to C in Table 8 are the same as the yarns A to C in Table 1.
Table 9 shows the warp and weft configurations of the woven fabric samples made using any of these yarns.

[0057]

[Table 8]

[0058]

[Table 9]

<Paired Comparison Method> The visual warmth of these eight types of fabric samples 11 to 18 was scaled using the paired comparison method. This scaling is intended to capture the tendency for warmth in these fabric samples 11-18. The reason for using the paired comparison method is the same as that in the first embodiment.

<Experiment> Experiments were conducted on eighteen fabric samples 11 to 18 with 22 female students as subjects to measure the warmth, which is one of the visual responsive characteristics. In this experiment, a tournament chart as shown in Table 10 was used in order to compare the visual warmth of the fabric sample by a pairwise comparison method.

[0061]

[Table 10]

In this experiment, as in the case of the first embodiment, a comparative experiment was carried out for each subject with respect to a combination of 28 sets of fabric samples. In this experiment, the fabric samples (two each) of each combination were shown to the subject and asked which one felt "warm". In this experiment, as in the case of the first embodiment, a bias factor due to the order of presentation is removed by randomly presenting two images at a time.

In the second embodiment, as in the first embodiment, scaling and confirmation of the presence or absence of the judgment ability were performed. Table 11 shows the visual warmth scale values of the respective fabric samples thus determined. In Table 11, a fabric sample having a larger scale value gives a more visual warmth.

[0064]

[Table 11]

<Relationship between Fractal Dimension of Yarn and Scale Value of Woven Fabric> The scale value of the above woven fabric sample and the numerical value of the fractal dimension of each warp and weft are subjected to multiple regression analysis to obtain the following equation 8. I got the following relational expression.

W = −1.0567 · D ₁ −0.786 · D ₂ +3.363 Equation 8 W: Scale value of warmth D ₁ : Fractal dimension of warp D ₂ : Fractal dimension of weft

The correlation coefficient in this multiple regression analysis was 0.8.
It was 81. Therefore, it can be said that there is a strong correlation between the scale value of warmth and the fractal dimension of the warp and the weft. Therefore, it can be said that the correlation between the visual warmth of the woven fabric and the fractal dimension of the warp and the weft can be clarified by the relational expression shown in Expression 8. Thus, by using the relational expression of Expression 8, for a fabric whose warmth, which is one of the visual sensibility characteristics, is unknown, the fabric can be determined from the fractal dimension (irregularity characteristics) of the yarn without actually fabricating the fabric. The warmth of the fabric can be estimated, so that a yarn suitable for producing a fabric having a desired warmth can be easily selected, and thus a fabric having a desired warmth can be easily produced. Will be able to do that.

<Other woven fabrics> Hereinafter, in order to verify the validity of the relational expression of Expression 8, a woven fabric sample shown in FIG. Let us estimate the warmth for 19. The combinations of the yarns shown in Table 12 correspond to the woven fabric samples 11 to 18 described above.
It is of course not included.

[0068]

[Table 12]

In this woven fabric sample 19, the yarn A is used for the warp (D ₁ = 1.65) and the yarn A is also used for the weft (D ₂ = 1.65). , Its warmth W is estimated to be: W = −1.0567 × 1.65−0.786 × 1.65 + 3.363 = 0.325 Therefore, referring to Table 11, the visual warmth of the fabric sample 19 is determined by the fabric sample 18 and the fabric sample. 1
It is expected to be between 5.

<Confirmation Experiment> Table 13 shows that woven fabric sample 11
18 shows the results of again determining the scale value of the warmth based on the paired comparison method for nine types of fabric samples obtained by adding the fabric sample 19 to the fabric sample 19.

[0071]

[Table 13]

As is evident from Table 13, the warmth measure of the newly added fabric sample 19 falls between the fabric samples 18 and 16 as expected. That is, the test subject feels that the visual warmth of the fabric sample 19 is intermediate between the fabric samples 18 and 16. Therefore, it can be said that Expression 8 is sufficiently useful as an index at the time of designing a woven fabric.

[Brief description of the drawings]

FIG. 1 is a view showing a fiber shape of a woven fabric sample 1 according to a first embodiment.

FIG. 2 is a diagram showing a fiber shape of a woven fabric sample 2 according to the first exemplary embodiment;

FIG. 3 is a diagram illustrating a shape of a fiber of a fabric sample 3 according to the first exemplary embodiment.

FIG. 4 is a diagram showing a fiber shape of a woven fabric sample 4 according to the first exemplary embodiment;

FIG. 5 is a diagram illustrating a shape of a fiber of a woven fabric sample 5 according to the first exemplary embodiment;

FIG. 6 is a diagram illustrating a shape of a fiber of a fabric sample 6 according to the first exemplary embodiment.

FIG. 7 is a diagram illustrating a shape of a fiber of a fabric sample 7 according to the first exemplary embodiment.

FIG. 8 is a diagram showing a fiber shape of a woven fabric sample 8 according to the first exemplary embodiment;

FIG. 9 is a view showing a fiber shape of a woven fabric sample 9 according to the first exemplary embodiment;

FIG. 10 (a) is a diagram showing a determination mode of a sample when the determination ability of the sampler is high, and FIG. 10 (b) is a diagram showing an evaluation mode of the sample when the determination ability of the sampler is low. Figure.

FIG. 11 is a diagram illustrating a fiber shape of a woven fabric sample 11 according to the second exemplary embodiment.

FIG. 12 is a view showing a fiber shape of a woven fabric sample 12 according to the second exemplary embodiment;

FIG. 13 is a diagram illustrating a fiber shape of a fabric sample 13 according to the second exemplary embodiment.

FIG. 14 is a diagram illustrating a fiber shape of a woven fabric sample 14 according to the second exemplary embodiment.

FIG. 15 is a view showing a fiber shape of a woven fabric sample 15 according to the second exemplary embodiment;

FIG. 16 is a diagram illustrating a shape of a fiber of a fabric sample 16 according to the second exemplary embodiment.

FIG. 17 is a diagram illustrating a fiber shape of a woven fabric sample 17 according to the second exemplary embodiment;

FIG. 18 is a diagram illustrating a fiber shape of a fabric sample 18 according to the second exemplary embodiment;

FIG. 19 is a diagram showing a fiber shape of a fabric sample 19 according to the second exemplary embodiment;

────────────────────────────────────────────────── ─── Continued on the front page (72) Susumu Katsuen, Inventor 2-4-1, Kutaro-cho, Chuo-ku, Osaka-shi, Osaka Kurashiki Spinning Co., Ltd. Osaka Head Office (72) Inventor Ippei Yamauchi Kutaro, Chuo-ku, Osaka-shi, Osaka 2-4-1 Cho Kurashiki Spinning Co., Ltd., Osaka Head Office (72) Inventor Kenichi Ota 3-14-82 Migattadai, Nishi-ku, Kobe-shi, Hyogo (56) References Textile Engineering (IV) Fabric Production / Performance and Properties ”(S63) Japan Textile Machinery Society 368-371, 181, 158-164 (58) Field surveyed (Int. Cl. ⁷ , DB name) D06H 3 / 00-3/02 G01N 21/898 G01B 11/30

Claims (2)

(57) [Claims] 1. A method for estimating a visual sensation characteristic of a woven fabric in which a warp and a weft are woven, wherein a plurality of types of yarns that can be used as a warp or a weft are characterized by their unevenness characteristics. The fractal dimension to be represented is determined, and for each woven fabric having the same color, woven structure, warp density, weft density, thickness and material of the woven fabric, a woven fabric using any of the plurality of yarns as the warp and the weft The coolness
Experimentally determines a visual sensitivity characteristic value of warmth, establishes a correlation between the sensitivity characteristic value of the fabric and the fractal dimension of the yarn by a multiple regression analysis method , and based on the correlation, The coolness of the woven fabric using a different type of yarn from the woven fabric whose sensitivity characteristic value was determined by the above experiment
Alternatively , a method for estimating a visual sensation characteristic of a woven fabric, wherein a visual sensation characteristic value regarding warmth is estimated from a fractal dimension of the yarn. 2. A method for estimating a desired coolness or warmth using the method for estimating a visual sensitivity characteristic of a woven fabric according to claim 1.
Yarns that can produce a fabric with all the visual sensation characteristics, and use the yarns to achieve the desired coolness or warmth
A woven fabric having a visual sensation characteristic.