JP7385434B2 - Analysis method and device for reinforcing fiber bundles - Google Patents

Description

The present invention relates to a reinforcing fiber bundle analysis method and analysis device.

In recent years, reinforced resin compositions containing reinforcing fibers such as carbon fibers have attracted attention as industrially important materials because they have excellent mechanical properties such as strength and rigidity despite their low specific gravity. In particular, reinforced resin compositions are attracting attention as an alternative material to metal materials in automobile parts and aircraft parts.

The reinforcing fibers used in such reinforced resin compositions are bundles of multiple reinforcing fibers ( A reinforcing fiber bundle) impregnated with a binding agent is used.

By the way, since the state of impregnation of the sizing agent in the reinforcing fiber bundle affects the opening property (dispersibility) during the molding process of the reinforced resin composition, there is a need for a method that can analyze the state of impregnation of the sizing agent on the reinforcing fiber bundle. It is being For example, a method is known in which the state of coverage of a binding agent on the surface of a reinforcing fiber bundle is analyzed by observing the surface of the reinforcing fiber bundle using a SEM (for example, Patent Document 1).

Moreover, the strength of a molded object of the reinforced resin composition tends to vary depending on the orientation direction (meandering state) of reinforcing fibers in the molded object. Therefore, methods of analyzing the meandering state of reinforcing fibers in molded bodies are also being considered. For example, 1) a step of acquiring an X-ray CT image of a molded article of a reinforced resin composition containing carbon fibers and a matrix resin, and 2) binarizing the obtained image to identify the carbon fiber region and the matrix. An analysis method is known that includes a step of separating (separating) the resin region, and 3) a step of quantifying the meandering state of the carbon fiber from the fiber information obtained by binarization processing (for example, Patent Document 2). This document describes that when performing the binarization process in 2), filter processing such as median processing is performed in order to make the boundary between the carbon fiber and the matrix resin clear.

Special table 2017-504734 publication Japanese Patent Application Publication No. 2018-159691

However, with the analysis method shown in Patent Document 1, it is only possible to observe the adhesion state of the sizing agent on the surface of the reinforcing fiber bundle, and it is not possible to observe the distribution state or amount of the sizing agent inside the reinforcing fiber bundle. could not.

Further, Patent Document 2 targets a molded article of a reinforced resin composition as an analysis target. In a tomographic image of a molded article of a reinforced resin composition, the boundary between the reinforcing fiber region and the matrix resin region is relatively clear, and the pixel values (for example, gray values) of the pixels constituting the reinforcing fiber region and the matrix The pixel values of pixels constituting the resin region are unlikely to overlap (different from each other). Therefore, it is possible to separate the reinforcing fiber region and the convergence agent region depending on whether the pixel value is greater than or equal to the threshold value, and it is possible to divide the regions by binarization processing. However, in tomographic images of reinforcing fiber bundles, for example, when the densities of the reinforcing fibers and the focusing agent are close, or when the energy of the X-rays used for X-ray CT measurement is low and X-ray refraction tends to occur, The boundary between the fiber region and the focusing agent region is relatively unclear, and the pixel values of the pixels forming the reinforcing fiber region and the pixel values of the pixels forming the focusing agent region are partially different from each other. They may overlap (be the same). In such a case, it is not possible to separate the reinforcing fiber region and the focusing agent region depending on whether the pixel value is greater than or equal to the threshold, so it is not possible to separate the regions by binarization processing as in Patent Document 2. It is possible.

The present invention has been made in view of the above problems, and even if the boundary between the reinforcing fiber region and the focusing agent region is unclear in a tomographic image, the reinforcing fiber region and the focusing agent region can be accurately identified. It is an object of the present invention to provide a method and apparatus for analyzing reinforcing fiber bundles that can be easily separated and accurately analyzing the distribution state of a convergence agent inside the reinforcing fiber bundles.

The present invention relates to the following method and apparatus for analyzing reinforcing fiber bundles.

A method for analyzing a reinforcing fiber bundle including a plurality of reinforcing fibers and a convergence agent, the method comprising: obtaining a plurality of tomographic images of the reinforcing fiber bundle; and a differential value of a pixel value with respect to position for each of the plurality of tomographic images. and obtaining a gradient image from the calculated differential value and its corresponding position; and identifying the boundary between the reinforcing fiber and the focusing agent based on the maximum absolute value of the differential value in the gradient image. a step of specifying a region of at least one of the reinforcing fibers and the focusing agent in the tomographic image based on the boundary; and combining the identified regions of the plurality of tomographic images to determine the reinforcing fiber and the convergence agent. and obtaining a three-dimensional image of at least one of the focusing agents.

An analysis device for a reinforcing fiber bundle including a plurality of reinforcing fibers and a convergence agent, comprising: an image acquisition unit that obtains a plurality of tomographic images of the reinforcing fiber bundle; and an image acquisition unit that obtains a plurality of tomographic images of the reinforcing fiber bundle; a differential processing unit that calculates a differential value and obtains a gradient image from the calculated differential value and the position corresponding to the differential value; a boundary specifying unit that specifies a boundary; a region specifying unit that specifies a region of at least one of the reinforcing fibers and the focusing agent in the tomographic image based on the boundary; The image forming apparatus includes a three-dimensional image creation unit that synthesizes a three-dimensional image of at least one of the reinforcing fibers and the convergence agent.

According to the present invention, even if the boundary between the reinforcing fiber region and the focusing agent region is unclear in a tomographic image, the reinforcing fiber region and the focusing agent region can be precisely separated, and the reinforcing fiber region and the focusing agent region can be separated with high precision. It is possible to provide a reinforcing fiber bundle analysis method and analysis device that can accurately analyze the distribution state of a convergence agent inside the bundle.

FIG. 1 is a diagram showing an example of a tomographic image of a reinforcing fiber bundle. 2A is a partially enlarged view of region A of the tomographic image in FIG. 1, and FIG. 2B is a graph showing the gray value of each pixel on line AA of the tomographic image in FIG. 2A. 3A is a partially enlarged view of region B of the tomographic image in FIG. 1, and FIG. 3B is a graph showing the gray value of each pixel on the BB line of the tomographic image in FIG. 3A. 4A is a partially enlarged view of region B of the tomographic image in FIG. 1, FIG. 4B is a diagram showing how to specify the starting point of the focusing agent region in the tomographic image of FIG. 4A, and FIG. 4C is FIG. 4D is a diagram showing a gradient image of a tomographic image, and FIG. 4D is a diagram showing how a region is specified based on a boundary specified from the gradient image. 5A is a graph showing the gray value of each pixel on the BB line of the tomographic image in FIG. 3A, and FIG. 5B is a graph showing the differential value of the gray value in FIG. 5A. FIG. 6A is a three-dimensional representation of only the reinforcing fibers, and FIG. 6B is a three-dimensional representation of only the convergence agent. FIG. 7 is a three-dimensional diagram in which reinforcing fibers and convergence agents can be distinguished. FIG. 8 is a graph showing a histogram of the particle volume of the sizing agent in the reinforcing fiber bundle. FIG. 9 is a block diagram of a control system of the reinforcing fiber bundle analysis apparatus according to the present embodiment.

Embodiments of the present invention will be described in detail below with reference to the drawings. In this embodiment, an example will be described in which a three-dimensional distribution state of a binding agent in a reinforcing fiber bundle including a plurality of reinforcing fibers and a binding agent is analyzed.

1. Method for analyzing reinforcing fiber bundles The method for analyzing reinforcing fiber bundles according to the present embodiment includes the steps of 1) obtaining a plurality of tomographic images of the reinforcing fiber bundles.

Regarding the step 1) (step of obtaining a tomographic image) FIG. 1 is a diagram showing an example of a tomographic image of a reinforcing fiber bundle.

As shown in FIG. 1, in the tomographic image of the reinforcing fiber bundle, a plurality of reinforcing fibers, a convergence agent, and voids are confirmed. A three-dimensional image can be constructed by combining (stacking) a plurality of tomographic images.

The tomographic image can be obtained by any method, such as 3D-TEM observation, FIB-SEM observation (Focused Ion Beam), and X-ray CT observation. In this embodiment, the internal state of the reinforcing fiber bundle can be grasped relatively easily, and the sample can be observed non-destructively, especially compared to 3D-TEM, and the field of view is wide (compared to the μm order of TEM etc.). It is preferable to obtain by X-ray CT observation from the viewpoint of being able to observe on the order of mm (mm order). X-ray CT observation can be performed using, for example, a 3D micro X-ray CT imaging device.

Specifically, X-ray CT observation can be performed using the following procedure. First, as a pretreatment, a sample can be prepared by the following procedure.
1) Cut the reinforcing fiber bundle into a length of about 2 cm.
2) Fix the obtained sample to the splint with double-sided tape.
3) Set 2) above on the fixing jig (notch jig).

Next, the obtained sample is subjected to X-ray CT observation at the highest resolution (270 to 325 nm/pixel) using, for example, a 3D micro X-ray CT imaging device. Observation conditions may be as follows.
<Observation conditions>
Equipment used: Rigaku Co., Ltd., nano3DX
Measurement conditions: Target Cu Lens used: 0270 Binning 1
Scanning angle: 1000 points Cumulative time at each angle: 45 seconds FOV: 0.9mmφ x 0.6mm
Pixel size: 0.26μm/voxel
Since the cut end of the sample is damaged, it should be removed from the field of view (FOV) during CT observation, and the area approximately 1 cm from the end should be included in the field of view (FOV) during CT observation. do.

A tomographic image is an image obtained by X-ray CT observation or the like. The tomographic image may be an original image obtained by observation, or it can be obtained by constructing a three-dimensional image based on the obtained original image and then reconstructing a cross section in an arbitrary direction (arbitrary cross-sectional reconstruction). It may be an image.

When performing arbitrary cross-sectional reconstruction, sliding (division) of the three-dimensional image can be performed using the integrated mesh function of the observation device used to obtain the three-dimensional image. Thereby, multiple tomographic images can be obtained. The number of divisions is not particularly limited, but in high-resolution CT measurement where the focusing agent can be observed, it is preferable to have a smaller number of divisions from the viewpoint of increasing the accuracy of analysis.

The tomographic image may be a tomographic image in any direction, that is, a cross-sectional image along the length direction of the reinforcing fiber bundle, or a cross-sectional image along the direction crossing the length direction. good. In this embodiment, it is a cross-sectional image along the length direction of the reinforcing fiber bundle (see FIGS. 1 and 2).

2A is a partially enlarged view of region A of the tomographic image in FIG. 1, and FIG. 2B is a graph showing the gray value of each pixel on line AA of the tomographic image in FIG. 2A. 3A is a partially enlarged view of region B of the tomographic image in FIG. 1, and FIG. 3B is a graph showing the gray value of each pixel on the BB line of the tomographic image in FIG. 3A.

In FIG. 2B, the horizontal axis indicates the position (μm) of each pixel on line AA in the tomographic image of FIG. 2A, and the vertical axis indicates the gray value (μm) of each pixel on line AA in the tomographic image of FIG. 2A. pixel value). Similarly, in FIG. 3B, the horizontal axis indicates the position (μm) of each pixel on line BB in the tomographic image of FIG. 3A, and the vertical axis indicates the position of each pixel on line BB in the tomographic image of FIG. 3A. Indicates gray value (pixel value).

As shown in FIGS. 2B and 3B, the tomographic image is displayed using pixel values (shading, brightness). Since a tomographic image is usually a grayscale image, it is displayed as a gray value (pixel value) of 8 bits and 256 gradations from pure black (0) to pure white (256).

As shown in FIG. 2B, on line AA of the tomographic image, the gray value of the pixels constituting the reinforcing fiber region (approximately 120 or more), the gray value of the pixels constituting the focusing agent region (approximately 67 (more than or equal to 120), and the gray values (less than about 67) of pixels constituting the region of the void do not overlap with each other. In such a case, as in the past, the reinforcing fiber region, sizing agent region, and void region can be separated (separated) depending on whether the gray value is above a threshold value (see Figure 2B). ).

On the other hand, as shown in FIG. 3B, on the BB line of the tomographic image, the gray value of the pixels constituting the focusing agent region (less than about 140) and the gray value of the pixels constituting the reinforcing fiber region (approximately 110 or more) partially overlap with each other. In such a case, it is not possible to separate the reinforcing fiber region, the sizing agent region, and the void region depending on whether the gray value is greater than or equal to the threshold value (see FIG. 3B).

In the present invention, by specifying the boundaries between the reinforcing fiber region, the focusing agent region, and the void region based on the gradient image of the tomographic image in FIG. 3A, the regions can be divided with high accuracy. That is, near the boundary between regions, the change (gradient) in gray value usually becomes large. Therefore, the tomographic image is converted into a gradient image (gradient image) showing the gradient of gray values, and the boundary is identified based on the maximum value of the absolute value of the gradient value (differential value) in the gradient image (differential function). .

FIG. 4 is a diagram showing the flow of the reinforcing fiber bundle analysis method according to the present embodiment. Of these, FIG. 4A is the tomographic image in FIG. 3A (a partially enlarged view of region B in the tomographic image in FIG. 1), and FIG. 4B shows how to specify the starting point of the region of the member in the tomographic image in FIG. 4A. FIG. 4C is a diagram showing a gradient image of a tomographic image, and FIG. 4D is a diagram showing how a region is specified based on a boundary specified from the gradient image.

That is, the reinforcing fiber bundle analysis method according to the present embodiment includes 1) obtaining a plurality of tomographic images of the reinforcing fiber bundle (see FIG. 4A), and 2) obtaining a tomographic image for each of the plurality of tomographic images. (see Figure 4C), 3) identifying the boundaries of reinforcing fibers, convergence agents, and voids based on the maximum value of the absolute value of the differential value in the gradient image, 4) determining the tomographic image based on the boundaries. 5) specifying the region of reinforcing fibers, the region of convergence agent, and the region of voids in the image (see FIG. 4D); and 5) composing the identified regions for a plurality of tomographic images to determine the region of reinforcing fibers, The method further includes obtaining a three-dimensional image of at least one of the focusing agent region and the void region (see FIGS. 6A and B and 7 below).

Furthermore, from the viewpoint of making it easier to carry out the step 3) (the step of specifying the boundary), it is preferable to further perform the step 6) of specifying the starting point of the region to be analyzed before the step 5). Step 6) may be performed between steps 1) and 2), between steps 2) and 3), or between step 3) or step 4). It may be performed simultaneously with the process. From the viewpoint of enabling analysis using Avizo, which will be described later, it is more preferable to carry out the process between steps 1) and 2).

These steps can be performed using software for analyzing tomographic images of three-dimensional images (for example, Avizo 9.7, manufactured by Thermo Fisher Scientific-Japan FI Co., Ltd.).

Each step will be explained below.

Regarding step 6) (step of specifying the starting point), first, before obtaining the gradient image, specify the starting point of the region to be analyzed (for example, reinforcing fibers or focusing agent) in the tomographic image, if necessary. You can stay there. In this embodiment, the starting point of the focusing agent area is specified (see FIG. 4B).

That is, in the gradient image, since information on the gray value itself is lost, it is preferable to specify whether each region in the gradient image corresponds to reinforcing fibers, convergence agents, or voids. Therefore, it is preferable to specify the starting point of the region in advance before obtaining the gradient image.

To specify the starting point, for example, when using Avizo 9.7, select Segmentation, select Selection, and execute Brush, Lasso, Magic Wand, and Blow to specify the analysis target ( This can be done by coloring the area (binding agent or reinforcing fibers). Note that coloring does not need to be applied to the entire region to be analyzed, but only to a part of it.

Regarding step 2) (step of obtaining a gradient image) Next, differential processing is performed on the tomographic image to obtain a gradient image (see FIG. 4C).

Differential processing is an operation to find the gradient (slope) of pixel values near the pixel of interest.The differential value of the pixel value (gray value) with respect to the position is calculated, and from the calculated differential value and the corresponding position, a gradient image is obtained. get.

The differential process may be a known differential process, and is not particularly limited, but is preferably performed, for example, by the Watershed Algorithm (S. Beucher and F. Meyer. The morphological approach to segmentation: the watershed transformation. In (See E. Dougherty, editor, Mathematical morphology in image processing, chapter 12, pages 433-481. Marcel Dekker, 1993.)

When using Avizo 9.7 as the analysis software, a gradient image can be obtained, for example, by selecting Segmentation, selecting Selection, selecting watershed, and executing Create a new gradient image.

Regarding the step 3) (step of specifying the boundary), FIG. 5A is a graph showing the gray value of each pixel on the BB line in the tomographic image of FIG. 3A, and FIG. 5B is a graph showing the gray value for the position in FIG. 3A. It is a graph showing differential values. Note that FIG. 5B corresponds to the gradient image in FIG. 4C.

As shown in FIG. 5B, the boundaries between the reinforcing fibers, the focusing agent, and the voids are identified based on the maximum value of the absolute value of the differential value of the gray value in the gradient image. In this embodiment, the position corresponding to the maximum value of the absolute value of the differential value of the gray value is defined as the boundary (see FIG. 5B).

The local maximum value is the differential value when the slope of the curve changes from positive (plus) to negative (minus), or the slope of the curve changes from negative (minus) to positive (plus) in the graph shown in Figure 5B. This is the differential value when the value changes to .

Note that in a graph of differential values, a plurality of local maximum values, that is, not only local maximum values that correspond to boundaries, but also local maximum values that do not correspond to boundaries are usually detected. In FIG. 5B, four local maxima (P1 to P4) are detected. Among these local maximum values, local maximum values (P1 and P4 in FIG. 5B) whose absolute value is greater than or equal to the threshold (for example, differential value is 40 or more) are defined as "local maximum values corresponding to the boundary."

When using Avizo 9.7, the boundary can be specified, for example, during the process of expanding from a specified starting point to an area. That is, in Avizo 9.7, this can be done by selecting Segmentation, selecting Selection, selecting watershed, and executing Apply and create a new label field.

Specifically, by executing the Apply and create a new label field, in the process of expanding the colored starting point (Fig. 4B) to a region in the tomographic image, if the maximum value of the absolute value of the differential value is encountered. , the speed of expansion slows down, and when it encounters a larger maximum value, the speed slows down even more.Finally, the expansion stops when it encounters a maximum value that is greater than the threshold, and that position becomes the boundary, making it possible to separate the regions. becomes. That is, among the local maximum values of the absolute values of the plurality of differential values, the local maximum value that is equal to or greater than the threshold value is specified as the "boundary local maximum value."

Regarding the step 4) (step of specifying the region) Next, the reinforcing fiber region, the convergence agent region, and the void region are identified in the tomographic image based on the identified boundaries (FIGS. 4D and 5B). reference).

As described above, the area can be specified by selecting Segmentation, selecting Selection, selecting watershed, and executing Apply and create a new label field in Avizo 9.7.

Regarding step 5) (obtaining a three-dimensional image), the regions identified in step 4) above are combined with multiple tomographic images to determine the reinforcing fiber region, convergence agent region, and void region. At least one is displayed in three dimensions. Thereby, a three-dimensional image of at least one of the reinforcing fiber region, the focusing agent region and the void region can be obtained (see FIGS. 6A and B and 7).

Specifically, compositing multiple tomographic images means performing the steps 3) and 4) above for each of the multiple tomographic images, identifying the regions of reinforcing fibers, convergence agents, and voids, and then adding them together. This can be done by combining (ie, stacking in the slicing direction).

Converting a region to 3D (extending it from a specified starting point to a 3D region) can be done using the Selection command (Apply and create a new label field button) in the Segmentation tool of Avizo 9.7, as described above. can.

In addition, if the boundary cannot be identified as a closed line, in the process of expanding the reinforcing fibers, sizing agent, and voids from their respective starting points to a three-dimensional area, the boundary can be defined as the point where three or two of the above contact. It can be approximated.

FIG. 6A is a diagram displaying only a three-dimensional image of reinforcing fibers, and FIG. 6B is a diagram displaying only a three-dimensional image of a convergence agent. FIG. 7 is a three-dimensional diagram in which reinforcing fibers and convergence agents can be distinguished.

That is, it is possible to extract and display only the three-dimensional distribution state of the reinforcing fibers (see FIG. 6A), or to extract and display only the three-dimensional distribution state of the sizing agent (see FIG. 6B). Further, as shown in FIG. 7, the three-dimensional distribution state of the reinforcing fibers and the three-dimensional distribution state of the sizing agent can be displayed separately. Thereby, it is possible to confirm the three-dimensional distribution state (spatial distribution state) of the focusing agent inside the reinforcing fiber bundle, which could not be analyzed by the conventional analysis method using SEM observation.

Furthermore, the reinforcing fiber bundle analysis method according to the present embodiment may further include steps other than those described above. For example, between steps 1) and 2), the step 7) of removing noise from the tomographic image may be further performed, or based on the data such as the three-dimensional image obtained in step 5), ) A step of obtaining the distribution of particle volumes of the sizing agent may be further performed.

Regarding the step 7) (noise removal step) Noise removal (default setting) specifically refers to a process of amplifying the gray value gap at the boundary between the reinforcing fiber and the focusing agent in the tomographic image. Noise can be removed by, for example, a non-local means filter.

Regarding the step 8) (step of obtaining a histogram) FIG. 8 is a graph showing a histogram of the particle volume of the sizing agent.

That is, in the present invention, from the data obtained in any one or more of the steps 1) to 5) above, it is possible to analyze the volume of each particle of the focusing agent, which was impossible with conventional SEM observation. Become. As a result, as shown in FIG. 8, it is possible to obtain a histogram indicating the volume and proportion of particles present.

For example, when using Avizo 9.7, a histogram is created by labeling each particle using the Labeling module, analyzing the volume of each particle using the Label Analysis module, and displaying the results. be able to.

(effect)
As explained above, in a tomographic image of a reinforcing fiber bundle, the gray values of the pixels that make up the reinforcing fiber region and the gray values of the pixels that make up the focusing agent region may partially overlap. However, depending on the gray value threshold, it was not possible to separate the reinforcing fiber region and the sizing agent region.

On the other hand, according to the reinforcing fiber bundle analysis method of the present invention, there is no case where the gray values of pixels forming the reinforcing fiber region and the gray values of pixels forming the focusing agent region partially overlap. Even if the tomographic image is converted into a gradient image showing the gradient of gray values, the boundary can be specified based on the maximum value or minimum value of the differential value of the pixel value in the obtained gradient image. Thereby, in the tomographic image, the region of reinforcing fibers and the region of focusing agent can be precisely separated.

2. Reinforcing fiber bundle analysis device FIG. 9 is a block diagram of a control system of the reinforcing fiber bundle analysis device 100 according to the present embodiment.

As shown in FIG. 9, the analysis device 100 includes an imaging section 110, a processing section 120, and a display section 130.

The imaging unit 110 is not particularly limited, and may be an imaging unit such as an X-ray CT observation device.

The processing unit 120 acquires image information (tomographic image) captured by the imaging unit 110, and processes and analyzes it. Specifically, the processing section 120 includes an image acquisition section 121 , a starting point specifying section 122 , a differential processing section 123 , a boundary specifying section 124 , a region specifying section 125 , and a three-dimensional image creating section 126 .

The image acquisition unit 121 acquires image information (a plurality of tomographic images forming a three-dimensional image) captured by the imaging unit 110. The image acquisition unit 121 constructs a three-dimensional image from the obtained image information as necessary, and then slides (divides) it in an arbitrary direction to reconstruct a cross section in an arbitrary direction to obtain a tomographic image. It's okay.

The starting point specifying unit 122 specifies the starting point of a region to be analyzed in the tomographic image. For example, in a tomographic image, the starting point is specified by coloring the region corresponding to the focusing agent.

The differential processing unit 123 performs differential processing on each of the plurality of tomographic images obtained by the image acquisition unit 121 to obtain a gradient image.

Based on the gradient image obtained by the differential processing unit 123, the boundary identifying unit 124 identifies the boundaries between the reinforcing fibers, the focusing agent, and the voids based on the maximum value of the absolute value of the differential value of the pixel value in the gradient image. .

The area specifying unit 125 specifies the reinforcing fiber area, the focusing agent area, and the void area in the tomographic image based on the boundary specified by the boundary specifying unit 124.

The three-dimensional image creation unit 126 synthesizes the information obtained by the area identification unit 125 for a plurality of tomographic images (stacks them in the slice direction), and determines the areas of reinforcing fibers, convergence agents, and voids. Make at least one of them tertiary. Thereby, a three-dimensional image of at least one of the reinforcing fiber region, the focusing agent region, and the void region can be obtained.

Further, the processing unit 120 analyzes the volume of each particle of the convergence agent, based on the three-dimensional image data obtained by the three-dimensional image creation unit 126, as necessary, and analyzes the volume of each particle of the convergence agent contained in the reinforcing fiber bundle. The device may further include a histogram creation unit (not shown) that creates a histogram of particle volumes of the agent.

Note that the processing unit 120 may be realized by software using a processing device such as a CPU (Central Processing Unit), or may be realized by hardware such as an IC (Integrated Circuit), or may be realized by a combination of software and hardware. It may also be realized by using hardware.

The display unit 130 is a display screen of an image display device such as a personal computer, and displays a three-dimensional image (data) obtained by the three-dimensional image creation unit 126 or the like.

(Modified example)
Note that in the above embodiment, an example was shown in which step 6) (step of specifying the starting point of the region) is performed between step 1) and step 2) (before obtaining the gradient image). It is not limited to this. For example, in the step 6) (the step of specifying the starting point of the region), between the steps 2) and 3) (after obtaining the gradient image), the tomographic image and the gradient image are compared, and each This may be done by specifying whether the region corresponds to reinforcing fibers, sizing agents, or voids. Furthermore, the step 6) may be performed simultaneously with the step 3) (the step of specifying the boundary) or the step 4) (the step of specifying the region).

Similarly, the processing section 120 of the reinforcing fiber bundle analysis device 100 is not limited to that shown in FIG. 9, and may include a starting point specifying section 122 between the differential processing section 123 and the boundary specifying section 124. Alternatively, the processing unit 120 may not include the starting point specifying unit 122.

Further, in the above embodiment, an example has been described in which the reinforcing fiber bundle further includes voids (in addition to the reinforcing fibers and the sizing agent), but the present invention is not limited to this. For example, if the reinforcing fiber bundle does not contain any voids (other than the reinforcing fibers and the sizing agent), the boundary between the voids (the boundary between the void and the reinforcing fibers or the sizing agent) in step 3). ) and the step of identifying the void region in step 4) are unnecessary. In addition, if the reinforcing fiber bundle further includes other members (other than reinforcing fibers and a sizing agent), in step 3), the boundaries between the reinforcing fibers and the other members are determined based on the gradient image, and , the boundary between the focusing agent and the other member may be further specified, and in step 4), a step of specifying the region of the other member may be further performed.

Furthermore, in this embodiment, step 3) (step of identifying boundaries), step 4) (step of specifying region), and step 5) (step of obtaining three-dimensional images) are performed sequentially (separately). Although an example is shown in which the step 3) is performed in the step 3), the step 3), the step 4), and the step 5) may be performed at the same time without being limited to this.

3. Reinforcing Fiber Bundle The reinforcing fiber bundle analysis method according to the present embodiment can be applied to an object that contains a fiber component and a resin component and has a small content ratio of the resin component to the fiber component (such as a reinforcing fiber bundle or prepreg). ), preferably applicable to the analysis of any reinforcing fiber bundle. A reinforcing fiber bundle usually includes a plurality of reinforcing fibers and a sizing agent.

(reinforced fiber)
Examples of reinforcing fibers include glass fibers, carbon fibers, graphite fibers, aramid fibers, boron fibers, alumina fibers and silicon carbide fibers. Among these, carbon fibers and graphite fibers are preferred because they are lightweight and can easily produce highly durable molded bodies.

From the viewpoint of impact resistance, the carbon fiber preferably has a tensile modulus of 400 GPa or more, and from the viewpoints of rigidity and mechanical strength, it preferably has a tensile strength of 4.4 to 6.5 GPa. Further, the tensile elongation of the carbon fiber is preferably 1.7 to 2.3%. Therefore, carbon fibers having a tensile modulus of at least 230 GPa, a tensile strength of at least 4.4 GPa, and a tensile elongation of at least 1.7% are most preferred.

Examples of commercially available carbon fibers include Torayca® T800G-24K, Torayca® T800S-24K, Torayca® T700G-24K, Torayca® T300-3K, and Torayca® Trademark) T700S-12K (manufactured by Toray Industries, Inc.).

The average fiber diameter (diameter of fiber cross section) of the reinforcing fibers may be, for example, 1 to 20 μm, preferably 4 to 15 μm, from the viewpoint of formability and mechanical strength.

The average fiber diameter and average fiber length of the reinforcing fibers can be determined by measuring the fiber diameter and fiber length of 10 arbitrarily selected reinforcing fibers using an electron microscope and calculating the average value thereof. Note that the fiber diameter of the reinforcing fiber is the maximum Feret diameter of the cross section of a single fiber.

(convergence agent)
The type of binding agent is not particularly limited; for example, when the reinforcing fiber is carbon fiber, urethane resin, epoxy resin, Water-dispersible resins such as polyamide resins, olefin resins, acrylic resins, and butadiene resins are preferred. Among these, urethane resins and epoxy resins are preferred, and epoxy resins are more preferred from the viewpoint of further improving mechanical strength.

The content of these resins may be 1 to 20% by weight based on the total sizing agent.

The sizing agent may further contain additives other than those mentioned above, if necessary. Examples of additives include surfactants, leveling agents, emulsifiers, and the like.

The adhesion amount of the sizing agent is, for example, 0.1 to 10 parts by mass, preferably 0.2 to 3 parts by mass, based on 100 parts by mass of reinforcing fibers. In this way, the amount of adhesion of the sizing agent is very small with respect to all the reinforcing fibers constituting the reinforcing fiber bundle, and voids are likely to be included between the reinforcing fibers. The present invention is particularly effective for such analysis targets.

The reinforcing fiber bundle may be a plurality of reinforcing fibers impregnated with a binding agent liquid. The sizing agent liquid may be an emulsion containing the water-dispersible resin. The sizing agent liquid may further contain the above additives in addition to the water-dispersible resin as the main component.

According to the present invention, even if the boundary between the reinforcing fiber region and the focusing agent region is unclear in a tomographic image, the reinforcing fiber region and the focusing agent region can be precisely separated, and the reinforcing fiber region and the focusing agent region can be separated with high precision. It is possible to provide a reinforcing fiber bundle analysis method and an analysis device that can accurately analyze the distribution state of the convergence agent in the bundle.

Reference Signs List 100 Analysis device 110 Imaging unit 120 Processing unit 121 Image acquisition unit 122 Starting point specification unit 123 Differential processing unit 124 Boundary identification unit 125 Area identification unit 126 Three-dimensional image creation unit 130 Display unit

Claims (10)

A method for analyzing a reinforcing fiber bundle including a plurality of reinforcing fibers and a convergence agent, the method comprising:
Obtaining a plurality of tomographic images of the reinforcing fiber bundle;
For each of the plurality of tomographic images, calculating a differential value of a pixel value with respect to a position, and obtaining a gradient image from the calculated differential value and the corresponding position;
identifying a boundary between the reinforcing fiber and the convergence agent based on the maximum value of the absolute value of the differential value in the gradient image;
identifying a region of at least one of the reinforcing fibers and the focusing agent in the tomographic image based on the boundary;
composing the identified areas of the plurality of tomographic images to obtain a three-dimensional image of the identified area ;
Analysis method for reinforcing fiber bundles. In the step of identifying the boundary,
further identifying boundaries between the voids and the reinforcing fibers and boundaries between the voids and the focusing agent based on the maximum value of the absolute value of the differential value in the gradient image;
A method for analyzing a reinforcing fiber bundle according to claim 1. Between the step of obtaining the plurality of tomographic images and the step of obtaining the gradient image,
In each of the plurality of tomographic images, the method further includes the step of specifying a starting point of the region of the reinforcing fiber or the focusing agent,
In the step of identifying the boundary, identifying the boundary of the area in which the starting point is specified;
In the step of specifying the area, specifying the area in which the starting point is specified based on the boundary;
A method for analyzing a reinforcing fiber bundle according to claim 1 or 2. The tomographic image is an image obtained by X-ray CT imaging,
A method for analyzing a reinforcing fiber bundle according to any one of claims 1 to 3. The three-dimensional image is a three-dimensional image of the focusing agent,
A method for analyzing a reinforcing fiber bundle according to any one of claims 1 to 4. The sizing agent is in particulate form,
The reinforcing fiber bundle analysis method further includes the step of obtaining a histogram of particle volumes of the convergence agent contained in the reinforcing fiber bundle from the three-dimensional image.
A method for analyzing a reinforcing fiber bundle according to any one of claims 1 to 5. The reinforcing fiber is carbon fiber,
A method for analyzing a reinforcing fiber bundle according to any one of claims 1 to 6. The range of pixel values of pixels constituting the reinforcing fiber region and the range of pixel values of pixels constituting the focusing agent region partially overlap,
A method for analyzing a reinforcing fiber bundle according to any one of claims 1 to 7. An analysis device for a reinforcing fiber bundle including a plurality of reinforcing fibers and a convergence agent,
an image acquisition unit that obtains a plurality of tomographic images of the reinforcing fiber bundle;
a differential processing unit that calculates a differential value of a pixel value with respect to a position for each of the plurality of tomographic images, and obtains a gradient image from the calculated differential value and the position corresponding to the calculated differential value;
a boundary identifying unit that identifies a boundary between the reinforcing fibers and the convergence agent based on the maximum value of the absolute value of the differential value of the gradient image;
an area specifying unit that specifies an area of at least one of the reinforcing fibers and the focusing agent in the tomographic image based on the boundary;
a three-dimensional image creation unit that synthesizes the specified areas of the plurality of tomographic images to obtain a three-dimensional image of the specified area ;
Reinforced fiber bundle analysis device. The boundary specifying unit further specifies a boundary between the void and the reinforcing fiber, and a boundary between the void and the convergence agent, based on a maximum value of the absolute value of the differential value in the gradient image.
The reinforcing fiber bundle analysis device according to claim 9.

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