Internal structure analysis method and system for polymeric material

Abstract

To provide an internal structure analysis method and system for a polymeric material capable of simply and more faithfully grasping an internal structure of a polymeric material using image data acquired by an electron microscope.SOLUTION: An internal structure analysis method for a polymeric material causes an arithmetic device 3 to execute preprocessing step on original image data D1 of a polymeric material acquired by an electron microscope 2. In the preprocessing step, a desired target structure in the original image data D1 is extracted based on parameters related to the desired target structure and a degree of change in pixel values between adjacent pixels in the original image data D1, and enhanced image data D2 is created by increasing the difference between a pixel value of each pixel corresponding to the extracted desired target structure and a pixel value of each other pixel. In an analysis step, appropriate enhanced image data D2 is used to calculate feature quantities of the desired target structure, and specific characteristics of the polymeric material are grasped based on the feature quantities.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method and system for analyzing the internal structure of a polymeric material, and more particularly to a method and system for analyzing the internal structure of a polymeric material that can easily and more faithfully grasp the internal structure of a polymeric material using image data acquired by an electron microscope. [Background technology]

[0002] A method for understanding the network structure of crosslinked rubber using image data acquired by an electron microscope has been proposed (see Patent Document 1). The network structure of crosslinked rubber is composed of a large number of molecular chains that intersect and branch in a complex three-dimensional manner. In image data of crosslinked rubber acquired by an electron microscope, the greater the number of overlapping molecular chains or the closer the molecular chains are to the front, the larger the pixel value (the brighter the image). On the other hand, in this image data, the fewer the number of overlapping molecular chains or the farther the molecular chains are to the back, the smaller the pixel value (the darker the image). Therefore, to accurately understand each molecular chain that makes up the network structure, it is necessary to distinguish minute differences in pixel values ​​in this image data.

[0003] When image data acquired by an electron microscope is simply subjected to known noise removal image processing such as normalization or smoothing to clarify the molecular chains of the target object, problems arise, such as objects composed of pixels with low pixel values ​​being removed as noise or the contours of the target object becoming blurred. Similar problems arise when using image data acquired by an electron microscope to understand not only the network structure of crosslinked rubber, but also the distribution of linear or granular objects randomly present in polymeric materials such as rubber or resin. When identifying the target object to be clarified in the image data and performing such image processing, an additional step of identifying the region where the target object exists is required, and this step becomes extremely cumbersome when the region to be identified is small and diverse. Therefore, there is room for improvement in easily and faithfully understanding the internal structure of polymeric materials using image data acquired by an electron microscope. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2023-85735 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide a method and system for analyzing the internal structure of a polymeric material that can easily and more faithfully grasp the internal structure of the polymeric material using image data acquired by an electron microscope. [Means for solving the problem]

[0006] The method for analyzing the internal structure of a polymeric material of the present invention, which achieves the above-mentioned object, uses original image data of the polymeric material obtained by an electron microscope to analyze the internal structure of the polymeric material by a computing device, and comprises: an image acquisition step of acquiring the original image data; a preprocessing step of obtaining enhanced image data in which a desired target structure is enhanced relative to the original image data; and an analysis step of calculating feature amounts of the target structure using the enhanced image data and grasping predetermined characteristics of the polymeric material based on the feature amounts, wherein the preprocessing step extracts the target structure from the original image data based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, and creates the enhanced image data in which the difference between the pixel values ​​of each pixel corresponding to the extracted target structure and the pixel values ​​of each of the other pixels is increased.

[0007] The system for analyzing the internal structure of a polymeric material of the present invention comprises an electron microscope that acquires original image data of the polymeric material, and a computing device that analyzes the internal structure of the polymeric material using the original image data, and the computing device executes a preprocessing step in which the computing device creates enhanced image data that emphasizes a desired target structure relative to the original image data, and an analysis step in which the computing device calculates feature values ​​of the target structure using the enhanced image data and determines predetermined characteristics of the polymeric material based on the feature values, and in the preprocessing step, the target structure in the original image data is extracted based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, and the enhanced image data is created by increasing the difference between the pixel values ​​of each pixel corresponding to the extracted target structure and the pixel values ​​of each of the other pixels. [Effects of the Invention]

[0008] According to the present invention, even if the target structure is unclear in the original image data, by performing the preprocessing process on the original image data, appropriate enhanced image data in which the target structure is enhanced can be obtained. Since the target structure is clear in the appropriate enhanced image data, the feature quantity of the target structure can be grasped with higher accuracy in the analysis process using the enhanced image data. As a result, it is advantageous for accurately grasping the characteristics of the polymer material that is the analysis target.

[0009] In the preprocessing step, image processing is performed on the original image data to extract the target structure based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, thereby obtaining appropriate enhanced image data. Therefore, there is no need to perform complex image processing by complexly dividing the original image data according to the shape of the target structure, etc. Therefore, the present invention is advantageous for easily and faithfully grasping the internal structure of a polymer material. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is an explanatory diagram illustrating an embodiment of a system for analyzing the internal structure of a polymer material; [Figure 2] FIG. 1 is an explanatory diagram illustrating image data of a polymer material obtained by an electron microscope. [Figure 3] FIG. 1 is a flowchart showing an example of the procedure of a method for analyzing the internal structure of a polymer material. [Figure 4] 3 is an explanatory diagram showing an enlarged schematic example of a part of the image data in FIG. 2.

[0023] FIG. [Figure 5] FIG. 10 is an explanatory diagram illustrating an example of emphasized image data. [Figure 6] 10 is a histogram showing the number of molecular chains present for each length in the enhanced image data. [Figure 7] FIG. 1 is an explanatory diagram illustrating image data of polymer materials used in the examples. [Figure 8] 8 is an explanatory diagram illustrating enhanced image data in which the molecular chains in FIG. 7 are enhanced. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, the method and system for analyzing the internal structure of a polymer material according to the present invention will be described based on the embodiments shown in the drawings.

[0012] An embodiment of an internal structure analysis system 1 (hereinafter referred to as analysis system 1) illustrated in Fig. 1 includes an electron microscope 2 and a computing device 3. This analysis system 1 is used to implement a method for analyzing the internal structure of a polymer material illustrated in Fig. 3. In this analysis system 1, image data D1 (hereinafter referred to as original image data D1) of the polymer material illustrated in Fig. 2 is acquired by the electron microscope 2, and appropriate enhanced image data D2 illustrated in Fig. 5 is created using this enhanced image data D2. The internal structure of the polymer material is then analyzed using this enhanced image data D2.

[0013] As illustrated in FIG. 3, the internal structure analysis method includes an image acquisition step S110, a preprocessing step S120, and an analysis step S130, which are performed in sequence. In the image acquisition step S110, original image data D1 is acquired. In the preprocessing step S120, appropriate enhanced image data D2 is obtained by enhancing a desired target structure relative to the original image data D1. In the analysis step S130, feature quantities of the target structure are calculated using the appropriate enhanced image data D2, and predetermined characteristics of the polymer material are identified based on these feature quantities. The desired target structure to be enhanced in the enhanced image data D2 is specifically a linear structure (string-like structure) or granular structure in the polymer material. In this embodiment, the linear structure of the polymer material, i.e., the molecular chain of crosslinked rubber, is selected as the desired target structure.

[0014] The polymeric materials to be analyzed include, for example, crosslinked rubber (vulcanized rubber), resin, and unvulcanized rubber. These polymeric materials may contain fillers. In this embodiment, crosslinked rubber is used as the polymeric material. This crosslinked rubber is made by crosslinking natural rubber (NR) or synthetic rubbers such as styrene-butadiene rubber (SBR), butadiene rubber (BR), and isoprene rubber (IR) with crosslinking agents (vulcanizing agents) such as sulfur or organic peroxides. This crosslinked rubber may also contain fillers such as silica and carbon black, as well as additives such as vulcanization accelerators and antioxidants.

[0015] To obtain the original image data D1, an ultrathin section taken from a cross-linked rubber test piece is used as the sample P. The thickness of the sample P is, for example, 50 nm to 300 nm. The cross-linked rubber test piece is prepared by swelling the cross-linked rubber with a polymerizable monomer and then polymerizing the polymerizable monomer in the presence of a polymerization initiator, resulting in a saturated, swollen network structure that is fixed. The polymerizable monomer used to prepare the test piece may be any polymerizable monomer that can swell the cross-linked rubber to a saturated state and polymerize in the presence of the cross-linked rubber. Examples of the polymerizable monomer include methyl methacrylate, styrene monomer, and silicone. The polymerization initiator may be any polymerizable monomer that generates radicals by heat, light, or oxidation-reduction, such as benzoyl peroxide, peroxide, and triethylborane. The sample P may also be stained with a dye. Examples of the dye include osmium tetroxide and iodine. When collecting sample P from the test piece, it is advisable to set the magnification of the electron microscope 2 lower than when acquiring the original image data D1, identify a position that exhibits a representative cross-linked state of the test piece, and collect sample P from that position.

[0016] Various known transmission electron microscopes can be used as the electron microscope 2. The electron microscope 2 irradiates the sample P with an electron beam and acquires original image data D1 from which the spatial distribution of electrons inside the sample P can be observed based on the intensity of the transmitted or diffracted electron beam. The original image data D1 acquired by the electron microscope 2 is input to the calculation device 3. In this embodiment, two-dimensional original image data D1 is acquired by the electron microscope 2 and used for analysis, but three-dimensional original image data D1 can also be acquired and used for analysis. The electron microscope 2 may acquire a bright-field image formed by a transmitted wave as the original image data D1, or a dark-field image formed by a diffracted wave.

[0017] The arithmetic device 3 is configured as a computer, and various data are input and stored therein, and data processing is performed using these data. Various known computers can be used as the arithmetic device 3. The arithmetic device 3 has a processing unit (CPU) 4, a main storage unit (memory) 5, an auxiliary storage unit (e.g., HDD) 6, an input unit (keyboard, mouse) 7, and an output unit (display) 8. The auxiliary storage unit 6 stores original image data D1.

[0018] The original image data D1 shown in FIG. 2 is stored in the auxiliary storage unit 6 of the calculation device 3. The original image data D1 is a grayscale dark-field image of a crosslinked rubber molecular chain as the object. Specifically, the original image data D1 images diffracted waves of the crosslinked rubber molecular chain. That is, the pixel values ​​of pixels corresponding to the molecular chain in the dark-field image are larger than the pixel values ​​of pixels corresponding to the background. Note that the original image data D1 may contain noise, and the pixel values ​​of pixels corresponding to the noise are also larger than the pixel values ​​of pixels corresponding to the background.

[0019] The embedding resin (mesh part) that fixes the network structure made up of cross-linked rubber molecular chains is treated as the background because its diffracted waves do not form an image. Pixels that correspond to the background are pixels that make up the space where no molecular chains exist. The pixel value of pixels that correspond to the background takes into account the influence of scattered electrons and is, for example, a value in the range of 0 to 70.

[0020] The difference in brightness (difference in pixel values) between pixels corresponding to molecular chains in the original image data D1 represents the degree of overlap between molecular chains and differences in their positioning in the depth direction of the page in Figure 2. In the original image data D1, the more overlapping molecular chains there are or the closer the molecular chains are to the front, the larger the pixel value (the brighter the image). Also, in the original image data D1, the fewer overlapping molecular chains there are or the farther back the molecular chains are, the smaller the pixel value (the darker the image). For example, in space A surrounded by a black solid line, many molecular chains are densely overlapping, and the molecular chains at the forefront of space A are located near the surface of sample P. Space B surrounded by a white solid line contains fewer molecular chains than space A, and the molecular chains at the forefront of space B are located halfway through the depth direction of sample P. There are no molecular chains in space C surrounded by a white solid line.

[0021] In such original image data D1, the molecular chains are not clearly defined, making it difficult to accurately grasp the internal structure of the polymer material. Therefore, in this embodiment, in a preprocessing step S120 prior to the analysis step S130, enhanced image data D2 in which the molecular chains are enhanced relative to the original image data D1 is created, and the appropriate enhanced image data D2 in which the molecular chains are clearly enhanced is used in the analysis step S130.

[0022] The contents of each step (S110, S120, S130) illustrated in FIG. 3 will be described in detail below.

[0023] In the image acquisition step S110, original image data D1 of the sample P collected from a test piece of crosslinked rubber is acquired by the electron microscope 2. In this step, the acceleration voltage, magnification, etc. of the electron microscope 2 are adjusted to acquire a dark-field image, as the original image data D1, in which diffracted waves of molecular chains of the crosslinked rubber are imaged.

[0024] The pre-processing step S120 involves inputting necessary parameters via the input unit 7 (S121), extracting the desired target structure from the original image data D1 (S122), creating enhanced image data D2 that emphasizes the extracted target structure (S123), outputting the created enhanced image data D2 (S124), and determining whether the enhancement level of the output enhanced image data D2 is appropriate (S125). Steps S121 to S125 are repeated until enhanced image data D2 in which the desired target structure is appropriately emphasized is obtained.

[0025] In step S121, parameters required for extracting and highlighting a desired target structure in the original image data D1 are input by the input unit 7 to the auxiliary storage unit 6. The input parameters are selected depending on the data processing method to be used in the next step S122 and the degree of highlighting of the desired target structure in step S123. The parameters will be described in detail later.

[0026] In step S122, the calculation device 3 executes data processing to extract the desired target structure in the original image data D1 based on the input parameters related to the desired target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data D1. The degree of change in pixel value can be determined by the difference in pixel value between each pixel and its adjacent pixel (rate of change, first derivative), second derivative, or eigenvalue of the Hessian matrix of each pixel.

[0027] In this embodiment, a known enhancement filter using eigenvalues ​​of a Hessian matrix (hereinafter referred to as enhancement filter) is used as data processing to extract a desired target structure from original image data D1. This enhancement filter calculates the Hessian matrix for each pixel of original image data D1 by convolution with the second derivative of a Gaussian function for the luminance distribution. Next, the eigenvalues ​​λ1 and λ2 of the calculated Hessian matrix are calculated, and the desired target structure (molecular chain) is extracted from the original image data D1 based on the input parameters related to the desired target structure and the calculated eigenvalues ​​λ1 and λ2.

[0028] That is, the eigenvalues ​​λ1 and λ2 of the Hessian matrix are calculated for each pixel of the original image data D1, and the desired target structure (linear structure or granular structure) is extracted based on the calculated eigenvalues ​​λ1 and λ2. The magnitude (absolute value) of the eigenvalues ​​λ1 and λ2 represents the magnitude of change in pixel value (density change). The eigenvectors e1 and e2 corresponding to the respective eigenvalues ​​λ1 and λ2 are mutually orthogonal, and each eigenvector e1 and e2 indicates the direction of change in pixel value indicated by the eigenvalues ​​λ1 and λ2. In a linear structure, the change in pixel value in the direction perpendicular to the line is large and the change in pixel value in the extension direction of the line is small (almost zero). In a granular structure, the change in pixel value in each perpendicular direction is large. Note that in a planar structure, the change in pixel value in each perpendicular direction is small (almost zero). Therefore, by utilizing these properties, the desired target structure is extracted based on the eigenvalues ​​of the Hessian matrix. That is, the desired target structure (linear structure, granular structure) is determined by evaluating the change in two-dimensional pixel values ​​in the original image data D1 using the eigenvalues ​​λ1 and λ2 of the Hessian matrix. As the degree of change in pixel values, it is more preferable to use the second-order component of the change in pixel values ​​(eigenvalues ​​of the Hessian matrix or second-order differential values ​​of pixel values) as in this embodiment, which is advantageous for more accurate extraction of molecular chains.

[0029] In this embodiment, since molecular chains (linear structures) are emphasized as the desired target structures, a condition to be satisfied by the eigenvalues ​​λ1 and λ2 indicating a linear structure is input as a parameter related to the desired target structure. This condition is sufficient if the eigenvalues ​​λ1 and λ2 indicating a linear structure can be distinguished, for example, a threshold value for the magnitude of the eigenvalues ​​λ1 and λ2. Furthermore, this condition is that one of the eigenvalues ​​λ1 and λ2 is positive and the other is negative, and the ratio of the absolute values ​​of the eigenvalues ​​λ1 and λ2 (absolute value of the negative eigenvalue / absolute value of the positive eigenvalue) is greater than 0.1. When the desired target structure is a granular structure, a condition to be satisfied by the eigenvalues ​​λ1 and λ2 indicating a granular structure is input as a parameter related to the desired target structure. This condition is sufficient if the eigenvalues ​​λ1 and λ2 indicating a granular structure can be distinguished in the same way as a linear structure. Furthermore, a value for setting the width or size of the desired target structure may be input as a parameter related to the desired target structure. In this embodiment, a value for setting the width of the molecular chain is input. The width of the molecular chain can be set arbitrarily, for example, within the range of possible widths of the actual molecular chain, or multiple values ​​within that range can be input. The width of the molecular chain can be set based on the standard deviation of the Gaussian function of the brightness distribution of the original image data D1.

[0030] As shown in FIG. 4, images Fa, Fb, and Fc exist in a portion of the original image data D1. Images Fa and Fb represent images of molecular chains. Since image Fa exists in front of image Fb, the pixel values ​​of the pixels corresponding to image Fa are greater than the pixel values ​​of the pixels corresponding to image Fb. Image Fc is an image having a granular structure. In step S122, the pixels corresponding to images Fa and Fb are extracted as the target structure, and the pixels corresponding to image Fc are not extracted as the target structure.

[0031] In step S123, the arithmetic unit 3 executes data processing to create enhanced image data D2 by increasing the difference between the pixel value of each pixel corresponding to the extracted molecular chain and the pixel value of each of the other pixels. Specifically, enhanced image data D2 is created by enhancing the desired target structure (molecular chain) based on the input parameters for the original image data D1.

[0032] The parameters used in step S123 are input in step S121 described above. These parameters are values ​​that set the degree of enhancement of the pixel values ​​of each pixel corresponding to the extracted molecular chain. The degree of enhancement of the pixel values ​​of each pixel corresponding to the extracted molecular chain, i.e., the degree to which the pixel values ​​of each pixel corresponding to the extracted target structure are increased, can be set arbitrarily. The image representing the molecular chain becomes brighter as the pixel value of each pixel is increased, but if the pixel values ​​of all pixels corresponding to the target structure are increased to their maximum value, the boundaries between overlapping molecular chains will become indistinguishable. Therefore, the degree to which the pixel values ​​of the pixels to be emphasized are increased is set to a degree that allows overlapping molecular chains to be individually distinguished by visual inspection.

[0033] In step S123, after increasing the pixel value of each pixel corresponding to the molecular chain, it is advisable to perform a known thinning process on each pixel corresponding to the molecular chain by the calculation device 3. The enhanced image data D2 in which the molecular chains have been thinned is advantageous for grasping feature quantities such as the length, number, and number of branches of the molecular chains.

[0034] Furthermore, in step S123, after increasing the pixel value of each pixel corresponding to the molecular chain, the arithmetic unit 3 may perform data processing to remove noise using a known noise removal filter. In the enhanced image data D2 in which the pixel value of each pixel corresponding to the molecular chain is increased, the molecular chain, the background, and noise can be clearly distinguished from each other, and therefore removing noise is advantageous for making the molecular chain clearer.

[0035] In step S124, data processing is performed to output the created enhanced image data D2 to the output unit 8. Next, in step S125, data processing is performed to determine whether the degree of enhancement of the enhanced image data D2 is appropriate. In appropriate enhanced image data D2, the target structure (molecular chain) in the enhanced image data D2 is clear. Clear molecular chains mean that each molecular chain present in the enhanced image data D2 is individually identifiable. The criteria for judgment can be selected arbitrarily within the allowable range in which the outline of each molecular chain is clear. Those skilled in the art can roughly estimate the state of the actual molecular chain based on accumulated data and literature, and therefore appropriate criteria for judgment can be set based on such data and literature.

[0036] If it is determined in step S125 that the molecular chains of the enhanced image data D2 are not clear, the process returns to step S121, and the numerical values ​​of the parameters related to the target structure are changed and input. Then, steps S121 to S125 are repeated until it is determined in step S125 that the molecular chains of the enhanced image data D2 are clear. By performing this preprocessing step S120 (steps S121 to S125), appropriate values ​​of the parameters that clarify (clarify) the desired target structure in the original image data D1 can be obtained.

[0037] By using the obtained appropriate parameter values, it is possible to obtain the enhanced image data D2 shown in FIG. 5, which more faithfully represents the internal structure of the crosslinked rubber (a network structure formed by molecular chains). In the enhanced image data D2 shown in FIG. 5, the white parts represent the enhanced molecular chains, and the black parts represent the background (embedded resin). In the enhanced image data D2, each molecular chain can be individually identified.

[0038] In the analysis step S130, the calculation device 3 executes data processing to analyze the internal structure of the crosslinked rubber using the highlighted image data D2. In this data processing, the feature amounts of the target structure (molecular chain) highlighted in the highlighted image data D2 are calculated, and predetermined properties of the crosslinked rubber are determined based on the feature amounts.

[0039] For example, at least one of the distribution density of molecular chains and the proportion of molecular chains by length is calculated as a feature quantity. The feature quantities that can be calculated include variations in the size of the mesh portion of the network structure formed by the molecular chains, the number of branch points in a single molecular chain, and the branch length from the branch point to the branch end. Using feature quantities such as the distribution density of molecular chains and variations in the size of the mesh portion of the network structure as indicators, the uniformity of the crosslinking state of the crosslinked rubber can be grasped. Using feature quantities such as the proportion of molecular chains by length, the number of branch points in a single molecular chain, and the branch length from the branch point to the branch end as indicators, the tensile properties (elongation) of the crosslinked rubber, for example, can be grasped.

[0040] The heterogeneity of the network structure can also be calculated as a feature quantity. For example, a dispersion index indicating the distribution and length dispersion of molecular chains can be calculated as a feature quantity. This dispersion index uses so-called arithmetic variance, standard deviation, etc. In addition, the enhanced image data D2 can be divided into multiple regions, and the dispersion index of the molecular chain distribution density and the representative value (median, average, etc.) of the molecular chain length for each region can be calculated as a feature quantity. Note that the heterogeneity of the crosslinked network structure can also be calculated as a feature quantity using known persistent homology analysis. In this way, calculating the heterogeneity of the crosslinked network structure as a feature quantity provides a useful index for improving the physical properties of vulcanized rubber, such as viscoelasticity and tensile properties.

[0041] To understand the state of particles (particle aggregates) of compounding materials in crosslinked rubber, resin, or unvulcanized rubber, a dark-field image of the diffracted waves of the particles (particle aggregates) is used as the original image data D1, and the granular structure is selected as the target structure to be emphasized. Parameters to be input include threshold values ​​for the eigenvalues ​​λ1 and λ2 that indicate the granular structure and values ​​that set the size of the particles (particle aggregates). This results in enhanced image data D2 in which the particles (particle aggregates) are emphasized relative to the original image data D1, and the appropriate enhanced image data D2 is used in the analysis step S130. For example, at least one of the distribution density of the particles (particle aggregates) in the enhanced image data D2 and the proportion of particles (particle aggregates) by outer diameter is calculated as a feature of the particles (particle aggregates) emphasized in the enhanced image data D2. Using these feature values ​​as indicators, the dispersion state of the particles (particle aggregates) in the polymer material can be understood. The dispersion state of particles (particle aggregates), i.e., whether the particles (particle aggregates) are uniformly dispersed or non-uniformly dispersed, is a useful indicator because it affects the variation in various properties of the polymer material.

[0042] In the analysis step S130, a histogram showing these feature amounts may be generated, as shown in Fig. 6. Fig. 6 shows the number of molecular chains present for each length in the enhanced image data D2. By generating a histogram showing such feature amounts, the properties (crosslinked state) of the crosslinked rubber can be grasped.

[0043] As described above, according to this embodiment, even if the desired target structure is unclear (fuzzy) in the original image data D1, appropriate enhanced image data D2 that enhances the desired target structure can be obtained by performing the preprocessing step S120 on the original image data D1. In the analysis step S130 using this appropriate enhanced image data D2, the target structure becomes clearer, so that the feature amount of the target structure can be grasped with higher accuracy, which is advantageous for accurately grasping the predetermined characteristics of the polymer material that is the analysis target.

[0044] Furthermore, in the preprocessing step S120, in order to obtain appropriate enhanced image data D2, it is not necessary to perform cumbersome image processing, such as dividing the original image data D1 into multiple regions of complex shapes according to the shape of the desired target structure (molecular chain) and then performing removal work only on regions containing unwanted noise or images. In other words, this preprocessing step S120 can be performed on the entire original image data D1 at once. Even when this preprocessing step S120 is performed by dividing the original image data D1 into multiple regions, it is sufficient to divide them into simple shapes, for example, by equally dividing the original image data D1. Therefore, this embodiment is advantageous for easily and accurately grasping the internal structure of a polymeric material.

[0045] The above-described embodiment can also be applied when the original image data D1 is three-dimensional data. When three-dimensional data is used as the original image data D1, in the pre-processing step S120, the original image data D1 is divided into multiple spatial regions. Appropriate three-dimensional enhanced image data D2 that independently enhances the desired target structure is created for each divided spatial region. The created appropriate three-dimensional enhanced image data D2 are then combined at the positions where the corresponding original image data D1 were divided. When the original image data D1 is three-dimensional data, dividing it in this way and performing the pre-processing step S120 can reduce the calculation processing load on the calculation device 3.

[0046] To extract the desired target structure (molecular chain) in step S122, various known edge extraction filters such as a Prewitt filter, a Sobel filter, or a Laplacian filter, or known edge detection methods such as the Canny edge detector, can also be used. These edge extraction filters and edge detection methods determine the region of the original image data D1 excluding the background as a molecular chain and extract the edge (contour). For example, when extracting the desired target structure using the Canny detector, two thresholds are input as parameters related to the desired target structure. These edge extraction filters and edge detection methods impose a lower computational load on the calculation device 3 than the above-mentioned enhancement filter, but are more likely to enhance noise. Therefore, it is desirable to apply these edge extraction filters and edge detection methods to original image data D1 that is less affected by noise. [Example]

[0047] A cross-linked rubber test piece was used as the analysis target, and the original image data D1 shown in Figure 7 was obtained. The molecular chains of the cross-linked rubber in this original image data D1 were then emphasized as the desired target structure to create the enhanced image data D2 shown in Figure 8. A test piece of isoprene rubber was swollen in styrene for 48 hours, to which 1 wt / % benzoyl peroxide was added. The test piece was then left to stand at room temperature for 3 hours, and then heated at 60°C for 24 hours. Ultrathin sections of 50 nm were prepared from the test piece using an ultramicrotome, and stained with osmium tetroxide to create sample P. The transmission electron microscope 2 was set to an accelerating voltage of 200 kV and a magnification of 60,000 times, and sample P was photographed to obtain the two-dimensional original image data D1 of sample P, as shown in Figure 7.

[0048] The original image data D1 shown in Figure 7 is a dark-field image obtained by imaging diffracted waves of molecular chains that make up the network structure of crosslinked rubber. Based on this original image data D1, the pre-processing step S120 described above was performed to create the enhanced image data D2 shown in Figure 8. For data processing in step S122 of the pre-processing step S120, a known enhancement filter using the eigenvalues ​​of the Hessian matrix was used, as described in the above embodiment. The input parameters were such that one of the eigenvalues ​​λ1 and λ2 was positive and the other was negative, and the ratio of the absolute values ​​of the eigenvalues ​​λ1 and λ2 (absolute value of the negative eigenvalue / absolute value of the positive eigenvalue) was 0.2.

[0049] It can be seen that the molecular chains are clearly distinguishable from one another in the enhanced image data D2 shown in Fig. 8 compared to the original image data D1 shown in Fig. 7. In the analysis step S130 using this enhanced image data D2, various feature quantities of the molecular chains can be grasped with higher accuracy than when the original image data D1 is used, which is advantageous for grasping various properties of this crosslinked rubber with high accuracy.

[0050] The present invention is not limited to a specific embodiment, and various modifications and variations are possible within the scope of the gist of the present invention.

[0051] The present disclosure includes the following inventions. Invention 1: 1. A method for analyzing the internal structure of a polymer material, using original image data of the polymer material acquired by an electron microscope, by a computing device to analyze the internal structure of the polymer material, an image acquisition step of acquiring the original image data; a pre-processing step of obtaining enhanced image data by enhancing a desired target structure with respect to the original image data; an analysis step of calculating a feature amount of the target structure using the enhanced image data and grasping a predetermined property of the polymer material based on the feature amount; In the pre-processing step, the target structure in the original image data is extracted based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, and the enhanced image data is created by increasing the difference between the pixel values ​​of each pixel corresponding to the extracted target structure and the pixel values ​​of each of the other pixels. Invention 2: The method for analyzing the internal structure of a polymer material according to Invention 1, wherein the target structure is a linear structure or a granular structure, and in the preprocessing step, an eigenvalue of a Hessian matrix is ​​calculated for each pixel of the original image data, and the calculated eigenvalue is used as a degree of change in the pixel value to extract the target structure in the original image data. Invention 3: The method for analyzing the internal structure of a polymer material according to invention 2, wherein a linear structure is extracted as the target structure, and thinning processing is performed on each of the pixels corresponding to the extracted target structure. Invention 4: A method for analyzing the internal structure of a polymer material according to any one of Inventions 1 to 3, in which a crosslinked rubber is swelled to fix a network structure formed by molecular chains, and this crosslinked rubber is used as the polymer material, and the molecular chains are extracted as the target structure. Invention 5: A method for analyzing the internal structure of a polymeric material according to any one of Inventions 1 to 4, wherein in the analysis step, at least one of the distribution density of the target structure and the presence ratio of the target structure by size is calculated as the feature. Invention 6: A method for analyzing the internal structure of a polymeric material according to any one of Inventions 1 to 5, wherein the analysis step calculates a dispersion index indicating the dispersion state of at least one of the distribution of the target structures and the size of the target structures as the feature. Invention 7: 7. The method for analyzing the internal structure of a polymer material according to claim 5 or 6, wherein the analyzing step generates a histogram showing the feature amount. Invention 8: 8. The method for analyzing the internal structure of a polymer material according to any one of inventions 1 to 7, wherein three-dimensional data is used as the original image data. Invention 9: The method for analyzing the internal structure of a polymer material according to Invention 8, wherein in the preprocessing step, the original image data is divided into a plurality of spatial regions, and three-dimensional enhanced image data in which the target structure is enhanced is created independently for each of the spatial regions, and each of the created three-dimensional enhanced image data is combined at a position where corresponding original image data is divided. Invention 10: 1. A system for analyzing the internal structure of a polymer material, comprising: an electron microscope for acquiring original image data of a polymer material; and a computing device for analyzing the internal structure of the polymer material using the original image data, The arithmetic device executes a preprocessing step of creating enhanced image data in which a desired target structure is enhanced with respect to the original image data, and an analysis step of calculating a feature amount of the target structure using the enhanced image data and grasping predetermined properties of the polymer material based on the feature amount, In the pre-processing step, the target structure in the original image data is extracted based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, and the enhanced image data is created by increasing the difference between the pixel values ​​of each pixel corresponding to the extracted target structure and the pixel values ​​of each of the other pixels. [Explanation of symbols]

[0052] 1. Internal structure analysis system 2. Electron microscope 3 Computing device 4. Processing unit 5 Main memory 6 Auxiliary storage 7 Input section 8 Output section D1 Original image data D2 Enhanced image data

Claims

1. 1. A method for analyzing the internal structure of a polymer material, using original image data of the polymer material acquired by an electron microscope, by a computing device to analyze the internal structure of the polymer material, an image acquisition step of acquiring the original image data; a pre-processing step of obtaining enhanced image data by enhancing a desired target structure with respect to the original image data; an analysis step of calculating a feature amount of the target structure using the enhanced image data and grasping a predetermined property of the polymer material based on the feature amount; In the pre-processing step, the target structure in the original image data is extracted based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, and the enhanced image data is created by increasing the difference between the pixel values ​​of each pixel corresponding to the extracted target structure and the pixel values ​​of each of the other pixels.

2. 2. The method for analyzing the internal structure of a polymer material according to claim 1, wherein the target structure is a linear structure or a granular structure, and in the preprocessing step, an eigenvalue of a Hessian matrix is ​​calculated for each pixel of the original image data, and the calculated eigenvalue is used as a degree of change in the pixel value to extract the target structure in the original image data.

3. 3. The method for analyzing the internal structure of a polymer material according to claim 2, wherein a linear structure is extracted as the target structure, and thinning processing is performed on each of the pixels corresponding to the extracted target structure.

4. 4. A method for analyzing the internal structure of a polymer material according to claim 1 or 3, wherein a crosslinked rubber is swelled to fix a network structure formed by molecular chains, and this crosslinked rubber is used as the polymer material, and the molecular chains are extracted as the target structure.

5. 3. The method for analyzing the internal structure of a polymer material according to claim 1, wherein the analysis step calculates at least one of the distribution density of the target structure and the proportion of the target structure by size as the feature amount.

6. 3. The method for analyzing the internal structure of a polymer material according to claim 1, wherein the analysis step calculates a dispersion index indicating the dispersion state of at least one of the distribution of the target structures and the size of the target structures as the feature amount.

7. The method for analyzing the internal structure of a polymer material according to claim 5 , wherein the analyzing step generates a histogram showing the feature amount.

8. 3. The method for analyzing the internal structure of a polymer material according to claim 1, wherein three-dimensional data is used as the original image data.

9. 8. The method for analyzing the internal structure of a polymer material according to claim 7, wherein the pre-processing step comprises dividing the original image data into a plurality of spatial regions, creating three-dimensional enhanced image data in which the target structure is enhanced independently for each of the spatial regions, and combining the three-dimensional enhanced image data created at the positions where corresponding original image data are divided.

10. 1. A system for analyzing the internal structure of a polymer material, comprising: an electron microscope for acquiring original image data of a polymer material; and a computing device for analyzing the internal structure of the polymer material using the original image data, The arithmetic device executes a preprocessing step of creating enhanced image data in which a desired target structure is enhanced with respect to the original image data, and an analysis step of calculating a feature amount of the target structure using the enhanced image data and grasping predetermined properties of the polymer material based on the feature amount, In the pre-processing step, the target structure in the original image data is extracted based on parameters related to the target structure and the degree of change in pixel values ​​between adjacent pixels in the original image data, and the enhanced image data is created by increasing the difference between the pixel values ​​of each pixel corresponding to the extracted target structure and the pixel values ​​of each of the other pixels.

Images