Uniformity evaluation program, method, device, and anisotropic material manufacturing device, and manufacturing method

Abstract

To calculate an index for evaluating uniformity for each specific direction on a front surface of an anisotropic material.SOLUTION: An acquisition section 32 acquires a gray image expressing a concentration difference on a front surface of an anisotropic material. A conversion section 34 performs spatial frequency analysis so as to convert the gray image into a power spectrum image where each pixel has a value of a power spectrum for each spatial frequency. An average value calculation section 36 calculates an average value of the power spectrum for each spatial frequency concerning each one of a horizontal direction and a vertical direction of the power spectrum image. An index calculation section 38 calculates an inclination of a graph where a value obtained by logarithmically transforming the average value calculated for each direction is plotted in a space with the spatial frequency as a lateral axis and with the power spectrum as a longitudinal axis.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to a uniformity evaluation program, a uniformity evaluation method, a uniformity evaluation apparatus, an anisotropic material manufacturing apparatus, and an anisotropic material manufacturing method. [Background technology]

[0002] An object identification device has been proposed that calculates data that reflects the characteristics of an irregular object with high accuracy and classifies the irregular object with high accuracy. This device calculates parameters based on the power spectrum pattern of the Fraunhofer diffraction image of the irregular object, and classifies the irregular object using these parameters. In this case, the device calculates the slope or y-intercept of the regression line at an appropriate portion of the power spectrum pattern, and based on this, examines the fractal dimension of the contour of the image of the irregular object and the fractal dimension of the internal structure, and obtains parameters that reflect the characteristics of the irregular object with high accuracy. Then, this device uses the obtained parameters to identify and judge the irregular object (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2000-292747 A Summary of the Invention [Problem to be solved by the invention]

[0004] The Fraunhofer diffraction used in the technology described in Patent Document 1 is effective for measuring very small (e.g., particle diameter of 1 to 200 nm) transparent, amorphous objects present in liquid, such as white blood cells, urine, blood, etc. However, it is not suitable as an evaluation method for overlooking the uniformity of the surface of relatively large opaque materials, such as nonwoven fabrics, films, and papers.

[0005] In addition, Fraunhofer diffraction provides an isotropic power spectrum and cannot be applied to evaluate the uniformity in each specific direction on the surface of an anisotropic material.

[0006] The present disclosure has been made in consideration of the above points, and aims to provide a uniformity evaluation program, method, and apparatus that can calculate an index for evaluating the uniformity in each specific direction on the surface of an anisotropic material. [Means for solving the problem]

[0007] The uniformity evaluation program according to the first embodiment is a program for causing a computer to acquire a grayscale image showing the grayscale of the surface of an anisotropic material, convert the grayscale image into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency by performing spatial frequency analysis on the grayscale image, calculate an average value of the power spectrum for each spatial frequency for each horizontal and vertical direction of the power spectrum image, and calculate the slope of a graph in which the logarithmic transformation of the average values ​​calculated for each direction is plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis, as an index showing the uniformity of the surface of the anisotropic material.

[0008] The uniformity evaluation method according to the second embodiment is a method in which a computer acquires a grayscale image representing the grayscale of the surface of an anisotropic material, converts the grayscale image into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency by performing spatial frequency analysis on the grayscale image, calculates an average value of the power spectrum for each spatial frequency for each horizontal and vertical direction of the power spectrum image, and calculates the slope of a graph in which the logarithmic transformation of the average values ​​calculated for each direction is plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis, as an index showing the uniformity of the surface of the anisotropic material.

[0009] The uniformity evaluation device of the third aspect includes an acquisition unit that acquires a grayscale image that represents the grayscale of the surface of an anisotropic material; a conversion unit that converts the grayscale image into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency by spatial frequency analysis; an average calculation unit that calculates an average value of the power spectrum for each spatial frequency for each horizontal and vertical direction of the power spectrum image; and an index calculation unit that calculates the slope of a graph in which logarithmic transformation of the average values ​​calculated for each direction is plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis, as an index showing the uniformity of the surface of the anisotropic material.

[0010] The fourth aspect of the anisotropic material manufacturing apparatus includes the above-mentioned uniformity evaluation device, a manufacturing mechanism for manufacturing the anisotropic material, an imaging device for imaging the surface of the anisotropic material, and a control unit for controlling the operation of the manufacturing mechanism based on the index calculated by the uniformity evaluation device so that the uniformity of the surface of the anisotropic material approaches a predetermined target value.

[0011] A fifth aspect of the method for manufacturing an anisotropic material includes a manufacturing mechanism for manufacturing an anisotropic material, an imaging device for imaging the surface of the manufactured anisotropic material, a uniformity evaluation device for executing the above-mentioned uniformity evaluation method on the image captured by the imaging device, and a control unit for controlling the operation of the manufacturing mechanism based on the index calculated by the uniformity evaluation device so that the uniformity of the surface of the anisotropic material approaches a predetermined target value. Effect of the Invention

[0012] The uniformity evaluation program, method, and apparatus disclosed herein can calculate an index for evaluating the uniformity in each specific direction on the surface of an anisotropic material. [Brief description of the drawings]

[0013] [Figure 1] FIG. 2 is a block diagram showing a hardware configuration of the uniformity evaluation apparatus. [Diagram 2]1 is a functional block diagram of a uniformity evaluation apparatus according to a first embodiment. [Diagram 3] FIG. 1 is a diagram for explaining a general calculation of a stock condition index. [Figure 4] FIG. 1 is a schematic diagram of a pattern of grayscale images having the same formation index. [Diagram 5] FIG. 13 is a diagram for explaining a post-Fourier transform image. [Figure 6] FIG. 13 is a diagram for explaining conversion from a Fourier transformed image to a power spectrum image. [Figure 7] FIG. 13 is a diagram for explaining calculation of a uniformity index. [Figure 8] FIG. 13 is a diagram for explaining the relationship between the gradient of the power spectrum with respect to the spatial frequency and the texture. [Figure 9] FIG. 11 is a diagram showing a specific example of the relationship between the gradient of the power spectrum with respect to the spatial frequency and the texture. [Figure 10] 5 is a flowchart showing a flow of a uniformity evaluation process according to the first embodiment. [Figure 11] FIG. 1 shows a grayscale image and integrated uniformity index of a nonwoven sample. [Figure 12] FIG. 13 illustrates the evaluation of the uniformity index in only one direction. [Figure 13] FIG. 1 shows a grayscale image and integrated uniformity index of a paper sample. [Figure 14] FIG. 1 shows a grayscale image and integrated uniformity index of a film sample. [Figure 15] FIG. 13 is a diagram showing grayscale images generated to have the same formation index. [Figure 16] FIG. 11 is a schematic diagram of an apparatus for producing an anisotropic material according to a second embodiment. [Figure 17] FIG. 11 is a functional block diagram of a uniformity evaluation apparatus according to a second embodiment. [Figure 18] 10 is a flowchart showing a flow of a uniformity evaluation process according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an example of an embodiment of the present disclosure will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. In addition, the dimensions and ratios of the drawings are exaggerated for the convenience of explanation and may differ from the actual ratios.

[0015] First Embodiment Fig. 1 is a block diagram showing a hardware configuration of a uniformity evaluation apparatus 10 according to the first embodiment. As shown in Fig. 1, the uniformity evaluation apparatus 10 is realized by a computer including a CPU (Central Processing Unit) 12, a memory 14, a storage device 16, an input device 18, an output device 20, a storage medium reading device 22, and a communication interface (I / F) 24. Each component of the computer is connected to each other via a bus so as to be able to communicate with each other.

[0016] The storage device 16 stores a uniformity evaluation program for executing a uniformity evaluation process described later. The CPU 12 is a central processing unit, and is responsible for executing various programs and controlling each component. That is, the CPU 12 reads out a program from the storage device 16, and executes the program using the memory 14 as a working area. The CPU 12 controls each component and performs various arithmetic processing according to the program stored in the storage device 16. In the example shown in FIG. 1, the CPU 12 is a single CPU, but it may be multiple CPUs. Also, a GPU (Graphics Processing Unit) may be applied instead of the CPU 12. In this case, for example, the GPU operates under the control of the CPU 12 and is responsible for executing arithmetic processing.

[0017] The memory 14 is made up of a RAM (Random Access Memory) and serves as a working area to temporarily store programs and data. The storage device 16 is made up of a ROM (Read Only Memory) and a HDD (Hard Disk Drive) or SSD (Solid State Drive), and stores various programs including an operating system and various data.

[0018] The input device 18 is a device for performing various inputs, such as a keyboard, a mouse, etc. The output device 20 is a device for outputting various information, such as a display, a printer, etc. A touch panel display may be used as the output device 20 to function as the input device 18.

[0019] The storage medium reader 22 reads data stored in various storage media such as a CD-ROM (Compact Disc Read only memory) and a Blu-ray disc, and writes data to the storage media, etc. The communication I / F 24 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0020] Fig. 2 is a functional block diagram of the uniformity evaluation device 10 according to the first embodiment. As shown in Fig. 2, the uniformity evaluation device 10 functionally includes an acquisition unit 32, a conversion unit 34, an average calculation unit 36, and an index calculation unit 38.

[0021] The acquisition unit 32 acquires a grayscale image that represents the grayscale of the surface of an anisotropic material. An anisotropic material is a material whose rigidity varies depending on the direction. In this embodiment, the anisotropic material is more preferably a sheet material such as a nonwoven fabric or a film, and even more preferably a nonwoven fabric. Note that the sheet material is not limited to the above examples, and may be paper, canvas, woven fabric, coating, polymer film, metal foil, metal and polymer, cloth and polymer, paper and polymer composite web, etc.

[0022] Applications of the anisotropic materials include absorbent articles, packaging films, barrier films, medical products, automotive parts, agricultural materials, etc. Examples include personal care absorbent articles such as diapers, training pants, absorbent undergarments, incontinence articles, feminine hygiene products, swimwear, wet wipes, mitt wipes, etc., clothing, perforated materials, medical absorbent articles such as underpads, bed pads, bandages, absorbent drapes, medical wipes, etc. Applications are also possible in food service towels, pouches, toys, insulation materials for cooling units, sound absorbing materials, lining materials, building panels and sections, furniture and bedding, fluid storage, transfer systems, food and beverage products, containers, etc.

[0023] The raw material of the anisotropic material of the present embodiment may be at least one resin selected from thermoplastic resins and thermosetting resins. In this case, the anisotropic material is more preferably a thermoplastic resin, and even more preferably an olefin-based polymer. The definition and manufacturing method of thermoplastic resins or thermosetting resins are well known and are described in publications such as "Practical Plastics Encyclopedia" (edited by the Practical Plastics Encyclopedia Editorial Committee, published by Sangyo Chosakai Co., Ltd.). Examples of thermosetting resins include polyurethane, epoxy resin, thermosetting unsaturated polyester resin, urea resin, melamine resin, and phenol resin. Examples of thermoplastic resins include olefin-based polymers, polyamides, polyesters, polyacetals, styrene-based resins, acrylic resins, polycarbonates, polyphenylene oxides, chlorine resins, vinyl acetate-based resins, vinyl alcohol-based resins, cellulose resins, thermoplastic elastomers, and various copolymer rubbers.

[0024] The grayscale image may be an image obtained by capturing light transmitted through the surface of the anisotropic material or light reflected by the surface of the anisotropic material. For example, the grayscale image may be an image in which the pixel value of each pixel of an image captured by an imaging device such as a camera or scanner of the surface of the anisotropic material is expressed as a grayscale value of, for example, 0 to 255.

[0025] Here, the texture index, which is an example of an index showing the uniformity of the material surface, is generally calculated by counting the number of pixels for each gray value in a grayscale image as shown in FIG. 3, obtaining the distribution of the number of pixels for the grayscale value, and using the standard deviation and average of the distribution, for example, as shown below. Market sentiment index = standard deviation / average

[0026] However, for example, as shown in Fig. 4, the general texture index is the same for grayscale images with the same number of pixels for each grayscale value, even though the grayscale value appearance patterns are different. In other words, the primary statistics such as the texture index, which counts the number of pixels for each grayscale value, cannot distinguish between grayscale images with different grayscale value appearance patterns and evaluate the uniformity, as shown in Fig. 4.

[0027] In this embodiment, we propose a method for calculating uniformity indices for both the horizontal and vertical directions of a grayscale image. Below, we will explain in detail the conversion unit 34, average calculation unit 36, and index calculation unit 38 that realize this method.

[0028] The conversion unit 34 converts the pixel value of each pixel into a power spectrum image, which is a power spectrum for each spatial frequency, by spatial frequency analysis of the grayscale image. Specifically, as shown in Fig. 5, the conversion unit 34 performs a two-dimensional Fourier transform on the grayscale image to convert the grayscale image into a post-Fourier transform image.

[0029] As shown in A of FIG. 5, the Fourier-transformed image is an image with the center of the image as the origin, the horizontal direction as the u axis, and the vertical direction as the v axis. Each pixel of the Fourier-transformed image corresponds to a combination of spatial frequencies of transverse waves whose gray values ​​change in the horizontal direction and longitudinal waves whose gray values ​​change in the vertical direction. In addition, the spatial frequency corresponding to each pixel increases according to the distance of each pixel from the origin. That is, as shown in A of FIG. 5, the pixels closer to the inside of the image correspond to lower frequencies, and the pixels closer to the outside correspond to higher frequencies. In addition, when u=0, it represents a longitudinal wave as shown in B of FIG. 5, when v=0, it represents a transverse wave as shown in C of FIG. 5, and when u≠0 and v≠0, it represents a diagonal wave as shown in D and E of FIG. 5. The pixel value of each pixel of the Fourier-transformed image corresponds to the amplitude of the wave of the spatial frequency corresponding to that pixel as shown in F of FIG. 5.

[0030] Furthermore, as shown in FIG. 6, the conversion unit 34 calculates a power spectrum by squaring the amplitude, which is the pixel value of each pixel of the Fourier-transformed image, and converts the Fourier-transformed image into a power spectrum image.

[0031] The average calculation unit 36 ​​calculates the average value of the power spectrum for each spatial frequency in each of the horizontal and vertical directions of the power spectrum image. Specifically, the average calculation unit 36 ​​calculates the average value of the power spectrum as shown in the following formula (1). Formula (1) is a formula for calculating the average value in the vertical direction, that is, the average value of the power spectrum for each x position, as shown in the upper diagram of FIG. 7.

[0032]

number

[0033] PSD x 1D is the mean value of the power spectrum of the column at horizontal position x (1D = one dimension), L y is the length of one row (number of pixels), PSD (x,y) 2Dis the power spectrum (2D) of the pixel whose horizontal position is x and whose vertical position is y. The average value in the horizontal direction, that is, the average value of the power spectrum for each y position, can also be calculated in the same way. In this case, the PSD of equation (1) x 1D PSD y 1D , L y Let L be the length of one line. x This can be done as follows.

[0034] As shown in the lower diagram of FIG. 7, the index calculation unit 38 calculates the slope of a graph in which the calculated average values ​​are logarithmically converted and plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis, as a uniformity index of the surface of the anisotropic material. The example of FIG. 7 shows a case where the sheet material is a nonwoven fabric, and shows an example in which the average values ​​of the power spectrum in the longitudinal direction (MD direction) of the nonwoven fabric at the time of manufacture and in the direction perpendicular to the MD direction (CD direction) are plotted. In this embodiment, the x-axis direction of the power spectrum image corresponds to the CD direction, and the y-axis direction corresponds to the MD direction. The multiple peaks that appear in the graph shown in the lower diagram of FIG. 7 are peaks that appear according to the periodicity of pixel positions corresponding to the heat fusion points of the embossing process.

[0035] In addition, in the power spectrum image, the spatial frequency corresponding to each pixel is set according to the distance from the origin of each pixel, as in the case of the Fourier transformed image shown in FIG. 5A. Therefore, the PSD x 1D The value of is symmetric around the origin, PSD y 1D The value of is symmetrical around the origin. So, in the example in the lower part of Figure 7, x 1D For the MD direction, the average value calculated from the left half of the power spectrum image, PSD y 1D For the CD direction, the graph plots the average value calculated from the upper half of the power spectrum image.

[0036] Specifically, the index calculation unit 38 may calculate the slope of the graph by applying the least squares method or the like. The index calculation unit 38 may calculate the slope from the entire graph as shown in the lower diagram of FIG. 7, or may calculate the slope from a specific range of the graph. The specific range may be determined according to what kind of uniformity is to be evaluated. For example, a relatively large unevenness in shading appears as a low-frequency component of the spatial frequency, and a small unevenness in shading due to a yarn bundle, fiber shape, or the like appears as a high-frequency component of the spatial frequency. Therefore, when a large unevenness in shading is to be evaluated, the slope may be calculated from a portion below a predetermined spatial frequency. For example, in the example of the nonwoven fabric in FIG. 7, when a change in shading larger than the interval between the heat-fused points of the embossing on the shading image is to be evaluated, the slope of a portion having a lower spatial frequency than the lowest-frequency peak that appears in the graph according to the interval between the heat-fused points may be calculated.

[0037] The gradient calculated as above corresponds to the ratio between the power spectrum of high frequency and the power spectrum of low frequency. As shown in FIG. 8, when the gradient is gentle (when the absolute value of the gradient is small), the amplitude of the high frequency components in the frequency range where the gradient is taken is large. In this case, the image is expressed by adding up the signals with large amplitudes, so the unevenness of the gray values ​​is large, which indicates poor texture. When the gradient is steep (when the absolute value of the gradient is large), the amplitude of the high frequency components in the frequency range where the gradient is taken is small. In this case, the image is expressed by adding up the signals with small amplitudes, so the unevenness of the gray values ​​is small, which indicates good texture. FIG. 9 shows an example of the gradient calculated from each of the gray images judged to have poor texture by visual observation and the gray images judged to have good texture. In the example of FIG. 9, the absolute value of the gradient calculated from the gray images with poor texture is smaller than the absolute value of the gradient calculated from the gray images with good texture.

[0038] The index calculation unit 38 outputs the uniformity index calculated for each of the horizontal direction and the vertical direction. Alternatively, the index calculation unit 38 may output a uniformity index that is an integrated uniformity index of the horizontal direction and the vertical direction.

[0039] Next, the operation of the uniformity evaluation apparatus 10 according to this embodiment will be described.

[0040] Fig. 10 is a flowchart showing the flow of the uniformity evaluation process executed by the CPU 32 of the uniformity evaluation apparatus 10. The CPU 32 reads out the uniformity evaluation program from the storage device 16, loads it into the memory 14, and executes it, whereby the CPU 32 functions as each functional component of the uniformity evaluation apparatus 10, and the uniformity evaluation process shown in Fig. 10 is executed.

[0041] In step S10, the acquisition unit 32 acquires a grayscale image that represents the grayscale of the surface of the anisotropic material. Next, in step S12, the conversion unit 34 performs a two-dimensional Fourier transform on the grayscale image, converting it into a Fourier-transformed image in which the pixel value of each pixel is the amplitude of a wave of a spatial frequency corresponding to that pixel. Next, in step S14, the conversion unit 34 calculates a power spectrum by squaring the amplitude, which is the pixel value of each pixel of the Fourier-transformed image, and converts the Fourier-transformed image into a power spectrum image.

[0042] Next, in step S16, the average calculation unit 36 ​​calculates the average value of the power spectrum for each spatial frequency in each of the horizontal and vertical directions of the power spectrum image, for example, by using equation (1). Next, in step S18, the index calculation unit 38 creates a graph in which the logarithmic conversion of the average value of the power spectrum for each spatial frequency calculated in step S16 is plotted in a space (spatial frequency-power spectrum space) with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis. Next, in step S20, the index calculation unit 38 calculates the slope of the graph created in step S18 as a uniformity index for the surface of the anisotropic material, outputs the calculated uniformity index, and the uniformity evaluation process ends.

[0043] As described above, the uniformity evaluation device according to this embodiment acquires a grayscale image showing the grayscale of the surface of the anisotropic material, converts the grayscale image into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency by performing spatial frequency analysis on the grayscale image, calculates an average value of the power spectrum for each spatial frequency in one vertical or horizontal direction of the power spectrum image, and calculates the slope of a graph in which the calculated average value is logarithmically transformed and plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis as an index showing the uniformity of the surface of the anisotropic material. This makes it possible to calculate an index for evaluating the uniformity of the surface of the anisotropic material in each specific direction.

[0044] (Verification results) The results of verification of the effectiveness of the uniformity index calculated by the uniformity evaluation apparatus 10 according to the first embodiment will be described.

[0045] In this verification, samples (details will be described later) were cut to a size of 210 x 297 mm, and five pieces were prepared for each type of sample. Then, an Epson scanner "GT-X980" was set to a resolution of 1200 dpi to scan each cut sample and obtain a grayscale image. In addition, each grayscale image was trimmed to 6500 x 6500 pixels so that each sample was reflected without any margins in the grayscale image. Then, using the following formula (2), a two-dimensional Fourier transform was performed on the trimmed grayscale image to obtain a two-dimensional Fourier transformed image.

[0046]

number

[0047] Here, f(m,n) is the gray value of the pixel at pixel position m in the x direction and pixel position n in the y direction of the grayscale image, F(k,l) is the signal strength (amplitude) of the pixel at pixel position k in the x direction and pixel position l in the y direction of the Fourier transformed image, M is the number of pixels in the x direction of the grayscale image, and N is the number of pixels in the y direction of the grayscale image.

[0048] In addition, the two-dimensional Fourier transformed image was converted into a power spectrum image using the following equation (3).

[0049]

number

[0050] As mentioned above, PSD is the power spectrum. Re is the real part of the complex number, and Im is the imaginary part of the complex number. Then, using the above formula (1), averaging was performed in the x direction (horizontal direction of the image) and y direction (vertical direction of the image), and then a graph was created in which the average value of the logarithmically converted power spectrum was plotted in a space with the spatial frequency on the x axis and the power spectrum on the y axis. The slope of the graph was calculated using the least squares method, and this slope was used as the uniformity index. The range of the graph for calculating the slope was set to the lower frequency range (spatial frequency < 35 lines / mm) than the frequency corresponding to the peak corresponding to the heat fusion point of the embossing process.

[0051] Table 1 shows the uniformity indexes calculated for the nonwoven fabric samples in the horizontal direction (x direction) and vertical direction (y direction) and their integrated uniformity index. The integrated uniformity index is (horizontal uniformity index) 2 +(Vertical Uniformity Index) 2 The uniformity index was calculated by taking the square root of ...

[0052] Nonwoven fabric sample Sample 1: Mitsui Chemicals nonwoven fabric "Syntex PK102 (13g / m 2 )" Sample 2: Mitsui Chemicals nonwoven fabric "Syntex PS104 (weight 20g / m 2 )" Sample 3: Mitsui Chemicals nonwoven fabric "Syntex PS105 (weight 25g / m 2 )" Sample 4: Mitsui Chemicals nonwoven fabric "Syntex PK108 (weight 40g / m 2)" Sample 5: Mitsui Chemicals nonwoven fabric "Syntex PS110 (weight 50g / m 2 )" Sample 6: Mitsui Chemicals nonwoven fabric "Syntex PS120 (weight 100g / m 2 )"

[0053] [Table 1]

[0054] Generally, in nonwoven fabrics, the basis weight is a factor that affects the surface uniformity. As shown in Table 1 and Figure 11, the uniformity index of this embodiment is calculated to be different for each sample with a different basis weight.

[0055] In addition, the uniformity index of this embodiment can be calculated for each of the horizontal and vertical directions. Therefore, as shown in Fig. 12, it is possible to compare the uniformity index in only one direction and evaluate the quality of the texture in that direction. Note that Fig. 12 shows an example of evaluating the uniformity in the horizontal direction (CD direction), and there was a high correlation between the quality of the texture evaluated by visual observation and the magnitude relationship of the uniformity index of this embodiment.

[0056] In this way, by using the uniformity index calculated in this embodiment, it is possible to evaluate the uniformity in each direction, making it possible to perform various evaluations, such as checking the correlation between the return stress when the nonwoven fabric is pulled in the MD direction and the uniformity index in the CD direction.

[0057] The uniformity index of this embodiment is also applicable to sheet materials other than nonwoven fabric. Table 2 shows the horizontal, vertical, and integrated uniformity indexes calculated for the following paper samples. For samples 7 and 8, the uniformity index was calculated using the slope in the range of spatial frequency <3250. For samples 7 and 8, one of a set of tissues (2 sheets) was peeled off and used. Figure 13 shows the grayscale image of the paper sample and the integrated uniformity index.

[0058] Paper Sample Sample 7: Nippon Paper Crecia tissue "Kay Dry" Sample 8: Kami Shoji tissue "Elmore PICO"

[0059] [Table 2]

[0060] In the above, the uniformity index of the present embodiment is used to evaluate the texture of a sheet material made of intertwined fibers such as nonwoven fabric or paper, but the present invention is not limited to this example. The uniformity index of the present embodiment can also be applied to other evaluations of the uniformity of the surface of anisotropic sheet materials.

[0061] Table 3 shows the horizontal, vertical and integrated uniformity indices calculated for the following film samples. For samples 9 and 10, the uniformity index was calculated using the slope in the range of spatial frequency < 3250. Figure 14 shows the grayscale image and integrated uniformity index of the film sample.

[0062] Film Samples Sample 9: Toray film "Lumirror 50to60" Sample 10: Sample 9 with intentional wrinkles

[0063] [Table 3]

[0064] In the case of a sheet material that is uniformly composed of the same components, such as a film, when wrinkles appear on the surface, the slope of the power spectrum in the spatial frequency-power spectrum space becomes steeper. In other words, the absolute values ​​of the uniformity indices in the horizontal and vertical directions, as well as the integrated uniformity index, become larger. Therefore, by using the uniformity index of this embodiment, it is possible to detect wrinkles occurring in the film.

[0065] Next, the results of verifying the effectiveness of the uniformity index of this embodiment in comparison with the above-mentioned general formation index (=standard deviation / average) will be described.

[0066] As shown in FIG. 15, grayscale images of patterns 1 to 8 with the same texture index were generated despite the different shading patterns (appearance). In this verification, an image generation algorithm (Reference: Thomas R. Etherington, Perlin noise as a hierarchical neutral landscape model, short communication 22, 1 (2022) p.1-6.) was used to create grayscale images of patterns 1 to 8 with 180 × 180 pixels, which were then converted to gray images to generate grayscale images. In addition, for each grayscale image, a histogram of the grayscale value representing the number of pixels for each grayscale value was created, and the histograms were matched to match the texture index of all grayscale images at 0.46. In addition, in the graph of the spatial frequency-power spectrum space, the range for calculating the slope was set to the range of spatial frequency < 90, and the horizontal, vertical, and integrated uniformity indices were calculated in the same manner as above. Table 4 shows the uniformity indices calculated from the grayscale images of each pattern.

[0067] [Table 4]

[0068] As shown in Table 4, even if the texture index is the same for two grayscale images, if the grayscale patterns are different, the uniformity index of this embodiment makes it possible to quantitatively evaluate each pattern as if they were different.

[0069] <Second embodiment> In the second embodiment, a manufacturing apparatus for manufacturing a sheet-shaped anisotropic material will be described. In the second embodiment, the same components as those in the first embodiment will be denoted by the same reference numerals and detailed description will be omitted. In the second embodiment, the case where the anisotropic material is a nonwoven fabric will be described as an example.

[0070] Fig. 16 is a schematic diagram of an anisotropic material manufacturing apparatus 200 according to the second embodiment. As shown in Fig. 16, the manufacturing apparatus 200 includes an imaging device 50, an extruder 52, a molding machine 54, a net 56, a winding machine 58, and a uniformity evaluation device 210. The extruder 52, the molding machine 54, the net 56, and the winding machine 58 are an example of the "manufacturing mechanism" of the present disclosure.

[0071] The imaging device 50 is manufactured and installed at a position where it can capture an image of the surface of the anisotropic material (in this embodiment, a nonwoven fabric) before it is wound by the winding machine 58. The imaging device 50 captures an image of the surface of the nonwoven fabric at regular time intervals or at a timing when imaging is instructed, and inputs the captured image to the uniformity evaluation device 210.

[0072] The extruder 52 melts the resin that is the raw material of the nonwoven fabric and extrudes it into the molding machine 54. The molding machine 54 melts the molten resin, extrudes the molten resin as fine fibers through a nozzle, and accumulates the fibers on a net 56 that rotates at a predetermined speed by rollers while blowing air onto the extruded fibers, thereby continuously forming a nonwoven fabric. The winding machine 58 winds up the formed nonwoven fabric by rollers.

[0073] Fig. 17 is a functional block diagram of a uniformity evaluation device 210 according to the second embodiment. As shown in Fig. 17, the uniformity evaluation device 210 functionally includes an acquisition unit 232, a conversion unit 34, an average calculation unit 36, an index calculation unit 38, and a control unit 40.

[0074] The acquisition unit 232 acquires an image captured by the imaging device 50, and performs processing such as trimming to a predetermined size and grayscaling to acquire a grayscale image.

[0075] The control unit 40 controls the operation of the manufacturing mechanism based on the uniformity index calculated by the index calculation unit 38 so that the uniformity of the surface of the anisotropic material approaches a predetermined target value.

[0076] Specifically, the control unit 40 generates control signals for adjusting parameters related to the amount of resin extruded by the extruder 52, parameters related to the blowing of air by the molding machine 54, and the like, and outputs the control signals to the extruder 52 or the molding machine 54. As a result, the parameters are adjusted in the extruder 52 and the molding machine 54 according to the control signals. Note that the control unit 40 may also control the rotation speed of the rollers that rotate the net 56, and the like.

[0077] The hardware configuration of the uniformity evaluation apparatus 210 according to the second embodiment is similar to the hardware configuration of the uniformity evaluation apparatus 10 according to the first embodiment shown in FIG. 1, and therefore will not be described.

[0078] Next, the operation of the apparatus 200 for producing an anisotropic material according to the second embodiment will be described.

[0079] Each of the extruder 52, the molding machine 54, the rollers for rotating the net 56, and the winding machine 58 starts operating based on preset parameters. As a result, the extruder 52 melts the resin that is the raw material of the nonwoven fabric, and extrudes it into the molding machine 54. The molding machine 54 then melts the molten resin, extrudes the molten resin as fine fibers through a nozzle, and accumulates the fibers on the net 56 rotating at a predetermined speed by the rollers while blowing air onto the extruded fibers, thereby continuously forming a nonwoven fabric. Furthermore, the winding machine 58 winds up the formed nonwoven fabric by the rollers.

[0080] When the production of nonwoven fabric is started as described above, the imaging device 50 captures images of the surface of the nonwoven fabric before it is wound by the winding machine 58 at regular intervals or at the timing instructed to capture images, and inputs the captured images to the uniformity evaluation device 210.

[0081] When the operation of the manufacturing mechanism starts, a uniformity evaluation process is executed in the uniformity evaluation device 210. Fig. 18 is a flowchart showing the flow of the uniformity evaluation process executed by the CPU 32 of the uniformity evaluation device 210. The CPU 32 reads out a uniformity evaluation program from the storage device 16, loads it into the memory 14, and executes it, whereby the CPU 32 functions as each functional component of the uniformity evaluation device 210, and the uniformity evaluation process shown in Fig. 18 is executed.

[0082] In step S30, the acquisition unit 232 acquires an image captured by the imaging device 50, and acquires a grayscale image by performing processing such as trimming to a predetermined size and grayscaling. Next, in step S32, a uniformity index calculation process is executed on the acquired grayscale image. The uniformity index calculation process is similar to steps S12 to S20 of the uniformity evaluation process (FIG. 10) in the first embodiment.

[0083] Next, in step S34, the control unit 40 generates control signals for controlling the operations of the extruder 52, the molding machine 54, and the rollers that rotate the net 56 based on the uniformity index calculated by the index calculation unit 38 so that the uniformity of the surface of the nonwoven fabric approaches a predetermined target value, and feeds back the control signals to the extruder 52, the molding machine 54, and the rollers. Then, the process returns to step S30.

[0084] As described above, the anisotropic material manufacturing apparatus according to the second embodiment can adjust the parameters of the manufacturing mechanism of the anisotropic material based on the uniformity index calculated by the uniformity evaluation device so that the uniformity of the manufactured anisotropic material becomes a target value. Also, as described in the first embodiment, the second embodiment can calculate the uniformity evaluation index for a specific direction. This makes it possible to adjust the operation of the manufacturing mechanism in detail, such as adjusting the parameters of the manufacturing mechanism for that specific direction, and control the uniformity of the anisotropic material so that it approaches a desired state.

[0085] In the second embodiment, the manufacturing of nonwoven fabric has been described as an example, but the present invention can also be applied to the manufacturing of paper, film, etc. In this case, the manufacturing apparatus 200 for anisotropic material has a manufacturing mechanism corresponding to each anisotropic material, and the control unit 40 of the uniformity evaluation apparatus 210 controls the manufacturing mechanism for each anisotropic material. For example, in the case of a film, parameters related to the scraping angle and the like in the process of forming a film by scraping off the discharged resin can be controlled.

[0086] Furthermore, the control unit 40 may be provided on the side of the manufacturing mechanism such as the extruder 52 and the molding machine 54, or may be configured as a device separate from the uniformity evaluation device 210 and the manufacturing mechanism.

[0087] In addition, the uniformity evaluation process executed by the CPU by reading the software (program) in each of the above embodiments may be executed by various processors other than the CPU. In this case, examples of the processor include a PLD (Programmable Logic Device) such as an FPGA (Field-Programmable Gate Array) whose circuit configuration can be changed after manufacture, and a dedicated electric circuit such as an ASIC (Application Specific Integrated Circuit) which is a processor having a circuit configuration designed exclusively for executing a specific process. The uniformity evaluation process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (for example, a plurality of FPGAs, a combination of a CPU and an FPGA, etc.). The hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0088] In addition, in each of the above embodiments, the uniformity evaluation program is described as being pre-stored (installed) in the storage device, but the present invention is not limited to this. The program may be provided in a form recorded on a recording medium such as a CD-ROM, a DVD-ROM (Digital Versatile Disc Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0089] The following supplementary information is disclosed below.

[0090] (Additional note 1) On the computer, Obtaining a grayscale image representing the grayscale of a surface of an anisotropic material; A spatial frequency analysis is performed on the grayscale image to convert it into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency; Calculating an average value of the power spectrum for each spatial frequency in each of the horizontal and vertical directions of the power spectrum image; The average values ​​calculated for each direction are logarithmically converted and plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis. The slope of the graph is calculated as an index of the uniformity of the surface of the anisotropic material. A uniformity assessment program to achieve this.

[0091] (Additional note 2) The process of converting the grayscale image into the power spectrum image includes the steps of: performing a two-dimensional Fourier transform on the grayscale image to convert the grayscale image into a Fourier transformed image; and calculating a power spectrum by squaring an amplitude, which is a pixel value of each pixel of the Fourier-transformed image, and converting the Fourier-transformed image into a power spectrum image; The uniformity evaluation program according to claim 1,

[0092] (Additional note 3) The uniformity evaluation program according to claim 1 or 2, wherein calculating the index includes calculating an index for the horizontal direction, an index for the vertical direction, and an index that is an integration of the index for the horizontal direction and the index for the vertical direction.

[0093] (Additional note 4) 4. The uniformity evaluation program according to any one of supplementary items 1 to 3, wherein calculating the index includes calculating an index obtained by integrating the index for the horizontal direction and the index for the vertical direction.

[0094] (Additional note 5) The uniformity evaluation program according to any one of Supplementary Items 1 to 4, wherein the grayscale image is an image obtained by capturing light transmitted through a surface of the anisotropic material or light reflected at the surface of the anisotropic material.

[0095] (Additional note 6) 6. The uniformity evaluation program according to any one of supplementary items 1 to 5, wherein the anisotropic material is a sheet material.

[0096] (Additional note 7) The uniformity evaluation program according to claim 6, wherein the sheet material is a nonwoven fabric or a film. [Explanation of symbols]

[0097] 10, 210 Uniformity evaluation device 12 CPU 14. Memory 16 Storage device 18 Input Devices 20 Output Devices 22 Storage media reader 24 Communication I / F 32, 232 Acquisition Department 34 Conversion section 36 Average calculation section 38 Indicator calculation section 40 Control section 50 Imaging device 52 Extruder 54 Molding machine 56 Net 58 Winder 200 Anisotropic material manufacturing equipment

Claims

1. On the computer, Obtaining a grayscale image representing the grayscale of a surface of an anisotropic material; A spatial frequency analysis is performed on the grayscale image to convert it into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency; Calculating an average value of the power spectrum for each spatial frequency in each of the horizontal and vertical directions of the power spectrum image; The average values ​​calculated for each direction are logarithmically converted and plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis. The slope of the graph is calculated as an index of the uniformity of the surface of the anisotropic material. A uniformity assessment program to achieve this.

2. The process of converting the grayscale image into the power spectrum image includes the steps of: performing a two-dimensional Fourier transform on the grayscale image to convert the grayscale image into a Fourier transformed image; and calculating a power spectrum by squaring an amplitude, which is a pixel value of each pixel of the Fourier-transformed image, and converting the Fourier-transformed image into a power spectrum image; The uniformity evaluation program according to claim 1 ,

3. 3. The uniformity evaluation program according to claim 1, wherein calculating the index includes calculating an index for the horizontal direction, an index for the vertical direction, and an index that is an integration of the index for the horizontal direction and the index for the vertical direction.

4. 3. The uniformity evaluation program according to claim 1, wherein the calculating of the index includes calculating an index obtained by integrating the index for the horizontal direction and the index for the vertical direction.

5. 3. The uniformity evaluation program according to claim 1, wherein the grayscale image is an image obtained by capturing an image of light transmitted through a surface of the anisotropic material or light reflected by the surface of the anisotropic material.

6. 3. The uniformity evaluation program according to claim 1, wherein the anisotropic material is a sheet material.

7. The uniformity evaluation program according to claim 6 , wherein the sheet material is a nonwoven fabric or a film.

8. The computer Obtaining a grayscale image representing the grayscale of a surface of an anisotropic material; A spatial frequency analysis is performed on the grayscale image to convert it into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency; Calculating an average value of the power spectrum for each spatial frequency in each of the horizontal and vertical directions of the power spectrum image; The average values ​​calculated for each direction are logarithmically converted and plotted in a space with the spatial frequency on the horizontal axis and the power spectrum on the vertical axis. The slope of the graph is calculated as an index of the uniformity of the surface of the anisotropic material. A uniformity evaluation method that performs the above.

9. an acquisition unit that acquires a grayscale image representing the grayscale of a surface of an anisotropic material; a conversion unit that converts the grayscale image into a power spectrum image in which each pixel has a power spectrum value for each spatial frequency by performing spatial frequency analysis on the grayscale image; an average calculation unit that calculates an average value of the power spectrum for each spatial frequency in each of the horizontal and vertical directions of the power spectrum image; an index calculation unit that calculates a slope of a graph obtained by plotting logarithmically converted values ​​of the average values ​​calculated for each direction in a space having a spatial frequency on the horizontal axis and a power spectrum on the vertical axis, as an index showing the uniformity of the surface of the anisotropic material; A uniformity assessment device comprising:

10. The uniformity evaluation device according to claim 9 ; A manufacturing mechanism for manufacturing the anisotropic material; an imaging device for imaging a surface of the anisotropic material; a control unit that controls an operation of the manufacturing mechanism based on the index calculated by the uniformity evaluation device so that the uniformity of the surface of the anisotropic material approaches a predetermined target value; An apparatus for manufacturing an anisotropic material comprising:

11. A manufacturing mechanism produces an anisotropic material; an imaging device imaging a surface of the manufactured anisotropic material; a uniformity evaluation device that performs the uniformity evaluation method according to claim 8 on the image captured by the imaging device; A control unit controls the operation of the manufacturing mechanism based on the index calculated by the uniformity evaluation device so that the uniformity of the surface of the anisotropic material approaches a predetermined target value. Methods for producing anisotropic materials.

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