A method and system for quantitatively evaluating the orientation of composite material filling particles

Abstract

The present invention belongs to the field of composite material detection technology, and specifically discloses a method and system for quantitatively evaluating the orientation of composite material filling particles, which includes S1, obtaining a cross-sectional micrograph of the composite material to be tested and converting it into a grayscale image; S2, processing the grayscale image to obtain a binary image; S3, traversing all pixel blocks in the binary image, obtaining the image longitudinal continuity SSV and the image transverse continuity SSH, and then obtaining the vertical orientation ORI; S4, rotating the binary image multiple times according to a preset angle, repeating steps S2 and S3 each time the image is rotated, and obtaining a curve of the vertical orientation varying with the rotation angle, wherein the maximum value of the vertical orientation in the curve and its corresponding rotation angle are the image orientation and orientation angle. The present invention does not require other complex equipment and instruments, and is not affected by the crystal structure of the filler particles; at the same time, the present invention performs full-image processing on the binary information, and also has excellent evaluation effects on complex filler particle information.

Description

Technical Field

[0001] The present invention belongs to the technical field of composite material detection, and more specifically, relates to a method and system for quantitatively evaluating the orientation of composite material filling particles. Background Art

[0002] Composite materials consist of a polymer matrix and filler particles. Due to the polymer matrix's excellent mechanical properties, electrical insulation, chemical stability, heat resistance, cold resistance, and shock resistance, they are often used in fields such as electronics, optics, thermal engineering, mechanics, biochemistry, and food science. However, to realize the application of composite materials in specific fields, filler particles are needed to enhance certain properties of the composite. For example, due to their low thermal conductivity, they can easily lead to heat accumulation in thermal environments, causing system reliability issues. To address this thermal issue, academia and industry often add thermally conductive particles to improve the thermal conductivity of composites. In the mechanical field, fibers are used to enhance the mechanical properties of composites in a specific direction. For anisotropic filler particles, their orientation in the composite material can significantly affect the improvement in the thermal or mechanical properties of the composite material. Researchers often use magnetic fields, electric fields, and flow fields to control the orientation of filler particles in composites. Therefore, the degree of orientation of filler particles and the overall orientation angle are crucial to the thermal and mechanical properties of composites. Efficient and quantitative methods for quantitatively assessing the orientation of filler particles in composites can provide a basis for the design and regulation of filler particles in composites.

[0003] Currently, commonly used methods for measuring orientation include wide-angle X-ray diffraction, small-angle X-ray diffraction, optical birefringence, infrared dichroism, ultrasonic shear wave, and microscopic three-dimensional reconstruction. However, optical birefringence and infrared dichroism are often used to measure polymer orientation. However, due to the absorption of light signals by filler particles, they are not suitable for measuring the orientation of filler particles in composite materials. Ultrasonic shear wave detection utilizes the difference in propagation speed of ultrasonic shear waves between oriented and non-oriented methods. It is commonly used to measure polymer and fiber orientation and often requires specialized measuring instruments. Microscopic three-dimensional reconstruction reconstructs the three-dimensional coordinates of filler particles from micrographs of composite cross-sections at different angles. However, this method requires identifying the same particle in micrographs at different observation angles, making it difficult to obtain micrographs of the same position at different observation angles. Furthermore, this method is not suitable for evaluating the overall orientation of composite materials with complex filler particle structures and high filler concentrations. Wide-angle X-ray diffraction and small-angle X-ray diffraction require highly crystalline filler particles and an amorphous matrix, making quantitative assessment of filler particle orientation difficult and the results unintuitive.

[0004] Therefore, there is an urgent need for a quantitative evaluation method for the orientation of composite material filling particles that is simple in process, unaffected by the crystal structure of the matrix and filler, applicable to filling particle structures with complex structures, and does not require special measuring instruments. Summary of the Invention

[0005] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a method and system for quantitatively evaluating the orientation of composite material filler particles, the purpose of which is to simplify the orientation evaluation process, avoid the influence of the crystal structure of the filler particles, and achieve efficient and high-precision quantitative evaluation of orientation.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for quantitatively evaluating the orientation of composite material filling particles is proposed, comprising the following steps:

[0007] S1. Obtain a cross-sectional micrograph of the composite material to be tested and convert it into a grayscale image;

[0008] S2, placing the grayscale image at a certain angle and processing it to obtain a binary image;

[0009] S3. Sequentially traverse all pixel blocks in the binary image to obtain the image longitudinal continuity SSV and the image transverse continuity SSH, and then obtain the vertical orientation ORI, specifically including:

[0010] S31, set the initial values ​​of the image vertical continuity SSV and the image horizontal continuity SSH to 0; define the traversed pixel block as the j-th pixel block, and its initial value is 1;

[0011] S32. For the j-th pixel block,

[0012] If the pixel block is black, skip this pixel block;

[0013] If the pixel block is white, the vertical upward continuity SV1, vertical downward continuity SV2, horizontal left continuity SH1, and horizontal right continuity SH2 of the pixel block are calculated according to the continuity weight function F(s); and then SSV=SSV+SV1+SV2, SSH=SSH+SH1+SH2 are iteratively updated.

[0014] S33, execute j=j+1, repeat step S32 until all pixel blocks in the binary image are traversed, and the final SSV and SSH are obtained. Then the vertical orientation degree ORI of the binary image is (1-SSH / SSV)×100%;

[0015] S4. Rotate the grayscale image multiple times according to a preset angle. Repeat steps S2 and S3 each time to obtain a curve showing the vertical orientation degree changing with the rotation angle. The maximum value of the vertical orientation degree in the curve and its corresponding rotation angle are the image orientation degree and orientation angle.

[0016] As a further preferred embodiment, in step S32, the calculation method of the longitudinal upward continuity SV1 and the longitudinal downward continuity SV2 is as follows:

[0017] (1) Let the initial values ​​of the upward traversal number m and the longitudinal upward continuity SV1 be 0;

[0018] Traverse upwards based on the pixel block and determine the color of the next pixel block: if the next pixel block is white, set m=m+1 and SV1=SV1+F(m); if the next pixel block is black, stop traversing;

[0019] When the traversed pixel block reaches the edge of the image, the traversal stops; the SV1 at the time of stopping the traversal is the final vertical upward continuity;

[0020] (2) Let the initial values ​​of the downward traversal number n and the vertical downward continuity SV2 be 0;

[0021] Traverse downwards based on the pixel block to determine the color of the next pixel block: if the next pixel block is white, set n = n + 1, SV2 = SV2 + F(n); if the next pixel block is black, stop traversing;

[0022] When the traversed pixel block reaches the edge of the image, the traversal stops; the SV2 at the time of stopping the traversal is the final vertical downward continuity.

[0023] As a further preferred embodiment, in step S32, the calculation method of the horizontal left continuity SH1 and the horizontal right continuity SH2 is as follows:

[0024] (1) Let the initial values ​​of the number of leftward traversals p and the horizontal leftward continuity SH1 be 0;

[0025] Traverse the pixel blocks to the left in turn to determine the color of the next pixel block: if the next pixel block is white, set p = p + 1, SH1 = SH1 + F (p); if the next pixel block is black, stop traversing;

[0026] When the traversed pixel block reaches the edge of the image, the traversal stops; SH1 at the time of stopping the traversal is the final horizontal left continuity;

[0027] (2) Let the initial values ​​of the rightward traversal number q and the horizontal rightward continuity SH2 be 0;

[0028] Traverse the pixel blocks to the right in turn to determine the color of the next pixel block: if the next pixel block is white, set q = q + 1, SH2 = SH2 + F(q); if the next pixel block is black, stop traversing;

[0029] When the traversed pixel block reaches the edge of the image, the traversal stops; SH2 at the time of stopping the traversal is the final horizontal rightward continuity.

[0030] As a further preference, the continuity weight function F(s) is a linear function of a variable s, and for any positive integer variable s, F(s) is greater than 0.

[0031] As a further preferred embodiment, step S2, obtaining a binary image, specifically includes:

[0032] S21, performing filling particle edge extraction on the grayscale image to distinguish filler particles from the polymer matrix in the composite material, and obtaining an edge-extracted image;

[0033] S22, performing image filtering processing on the image after edge extraction to filter out environmental noise in the image;

[0034] S23 , binarizing the filtered image, setting pixel blocks with grayscale values ​​greater than a preset binarization threshold as white pixel blocks, and otherwise as black pixel blocks, to obtain a binarized image, wherein the white pixel blocks are generated by the edges of the filling particles.

[0035] As a further preference, an optical microscope or a scanning electron microscope is used to obtain a cross-sectional micrograph of the composite material to be tested.

[0036] As a further preferred embodiment, the grayscale image angle is set to 0 degrees at the initial stage; when the image is rotated, it is rotated 1 degree each time, for a total of 180 rotations.

[0037] According to another aspect of the present invention, a system for quantitatively evaluating the orientation of composite material filling particles is provided, comprising a processor configured to execute the above-mentioned method for quantitatively evaluating the orientation of composite material filling particles.

[0038] In general, the above technical solutions conceived by the present invention have the following technical advantages compared with the existing technology:

[0039] 1. This invention uses image processing on microscopic images to convert the orientation information in composite materials into binary information. This information is then logically analyzed to extract the orientation information of the filler particles in the composite material. This simplified orientation information allows the determination of not only the degree of orientation of the filler particles but also the overall orientation angle of the filler particles.

[0040] 2. This method uses image processing to assess orientation, utilizing information contained in a binary image to determine orientation. This information is derived from the contrasting light and dark relationships between filler particles and the matrix in the microscopic image. Therefore, this method requires no complex equipment or instrumentation and is unaffected by the filler particle's crystal structure. Furthermore, this method processes the entire binary image, resulting in excellent assessment results even for complex filler particle data.

[0041] 3. The present invention provides a method for calculating the orientation degree based on a binary image, and determines the vertical orientation degree as (1-horizontal continuity / vertical continuity) × 100%. The more the filling particles tend to be vertical, the more vertically continuous white blocks there are, the greater the vertical continuity, and the higher the vertical orientation degree.

[0042] 4. The present invention only requires obtaining optical microscopic images or scanning electron microscopic images of the cross section of the composite material. In the development of composite materials, even if the orientation of the filler particles is not evaluated, it is still necessary to obtain such microscopic images to provide support for the development of the composite material. This evaluation method does not require additional microscopic image acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a method for quantitatively evaluating the orientation of composite material filling particles according to an embodiment of the present invention;

[0044] Figure 2 Flowchart for calculating the vertical orientation after rotation by angle i according to an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of calculating the longitudinal and lateral continuity of any white pixel block according to an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of the process of processing electron microscope scanning microscopic images according to an embodiment of the present invention;

[0047] Figure 5 (a) and (b) are schematic diagrams showing the results of solving the orientation degree and orientation angle of the deposition morphology of the asphalt-based carbon fiber composite material filler according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0049] The embodiment of the present invention provides a method for quantitatively evaluating the orientation of composite material filling particles. Figure 1 As shown, the following steps are included:

[0050] S1. Obtain a cross-sectional micrograph of the composite material to be tested: Obtain a cross-sectional micrograph of the composite material to be tested using an optical microscope or a scanning electron microscope. Filler particles and polymer matrix inside the composite material can be clearly observed, and the micrograph is converted into a grayscale image with a pixel block grayscale value of 0-255.

[0051] S2. Process the grayscale image of the cross-section micrograph to obtain a binary image, such as Figure 4 As shown, specifically including:

[0052] S21. Filling particle edge extraction: performing filling particle edge extraction on the grayscale image of the cross-sectional micrograph to distinguish the filler particles from the polymer matrix of the composite material, and obtaining an edge-extracted image.

[0053] S22, image filtering processing: performing image filtering processing on the edge-extracted graphic to filter out environmental noise in the image and obtain an image after image filtering processing.

[0054] Furthermore, image filtering processing methods include, but are not limited to, Butterworth low-pass filtering, FIR low-pass filtering, moving average filtering, median filtering, Wiener filtering, adaptive filtering, and wavelet filtering.

[0055] S23, image binarization processing: setting a binarization threshold, binarizing the filtered image, setting pixel blocks with grayscale values ​​greater than the binarization threshold as white pixel blocks, otherwise setting the pixel blocks as black pixel blocks, thereby obtaining a binary image containing only white and black pixel blocks; wherein the black and white pixel blocks are generated according to the light and dark relationship at different positions, and the white pixel blocks are generated by the edges of the filling particles.

[0056] Specifically, the binarization threshold is between 0 and 255, and is set according to actual conditions to ensure that the number of white pixel blocks is appropriate and continuous.

[0057] S3. Evaluate the vertical orientation according to the information in the binary image, that is, traverse all pixel blocks in the binary image in sequence (traversal can be performed in the order of horizontal first and then vertical), and obtain the image vertical continuity SSV and image horizontal continuity SSH, and then obtain the vertical orientation ORI, as shown in the following example: Figure 2 and Figure 3 As shown, specifically including:

[0058] S31, defining the image vertical continuity SSV and the image horizontal continuity SSH of the binary image, and initializing their values ​​to 0; defining the traversed pixel block as the j-th pixel block, and initializing its value to 1;

[0059] S32. For the j-th pixel block traversed, first determine its color:

[0060] If the jth pixel block is black, skip this step;

[0061] If the j-th pixel block is white, the pixel block is defined as a base block, and the vertical and horizontal continuity calculations of a single pixel block are performed on the base block, specifically including:

[0062] (1) Define the number of upward traversals as m and initialize its value to 0, and the vertical upward continuity of a single pixel block as SV1 and initialize its value to 0;

[0063] Traverse the base block upwards to determine the color of the next pixel block: if the next pixel block is white, set m=m+1 and SV1=SV1+F(m); if the next pixel block is black, stop traversing;

[0064] When the traversed pixel block reaches the edge of the image, the traversal stops; the SV1 at the time of stopping the traversal is the final vertical upward continuity.

[0065] (2) Define the number of downward traversals as n and initialize its value to 0, and the vertical downward continuity of a single pixel block as SV2 and initialize its value to 0;

[0066] Traverse downwards based on the pixel block to determine the color of the next pixel block: if the next pixel block is white, set n = n + 1, SV2 = SV2 + F(n); if the next pixel block is black, stop traversing;

[0067] When the traversed pixel block reaches the edge of the image, the traversal stops; the SV2 at the time of stopping the traversal is the final vertical downward continuity.

[0068] (3) Execute SSV=SSV+SV1+SV2.

[0069] (4) Let the initial values ​​of the number of leftward traversals p and the horizontal leftward continuity SH1 be 0;

[0070] Traverse the pixel blocks to the left in turn to determine the color of the next pixel block: if the next pixel block is white, set p = p + 1, SH1 = SH1 + F (p); if the next pixel block is black, stop traversing;

[0071] When the traversed pixel block reaches the edge of the image, the traversal stops; SH1 at the time of stopping the traversal is the final horizontal left continuity;

[0072] (5) Let the initial values ​​of the rightward traversal number q and the horizontal rightward continuity SH2 be 0;

[0073] Traverse the pixel blocks to the right in turn to determine the color of the next pixel block: if the next pixel block is white, set q = q + 1, SH2 = SH2 + F(q); if the next pixel block is black, stop traversing;

[0074] When the traversed pixel block reaches the edge of the image, the traversal stops; SH2 at the time of stopping the traversal is the final horizontal rightward continuity.

[0075] (6) Execute SSH=SSH+SH1+SH2.

[0076] Specifically, SV1, SV2, SH1, and SH2 are the degrees of continuity of the base blocks in the vertical upward, vertical downward, horizontal left, and horizontal right directions, respectively. m, n, p, and q are the numbers of continuous white blocks in the vertical upward, vertical downward, horizontal left, and horizontal right directions, respectively.

[0077]

[0078]

[0079]

[0080]

[0081] Then the image vertical continuity SSV and image horizontal continuity SSH are:

[0082]

[0083]

[0084] Furthermore, F(m), F(n), F(p), and F(q) in the above process are all continuity weighting functions F(s). This weighting function can amplify the difference in continuity between individual pixel blocks when they have different numbers of consecutive white blocks, such that the greater the number of consecutive white blocks, the greater the continuity value. Furthermore, for any positive integer variable s, F(s) is greater than 0. Furthermore, the continuity weighting function is a linear function of one variable, such as F(s) = a + bs.

[0085] S33, execute j=j+1, determine whether j is greater than the number of pixel blocks in the binary image, if not, repeat step S32; if so, all pixel blocks in the binary image are traversed to obtain the final SSV and SSH, and the vertical orientation index of the binary image ORI=(1-SSH / SSV)×100%; it can also be expressed as:

[0086]

[0087] S4. Rotate the grayscale image of the cross-sectional micrograph multiple times at a preset angle, repeating steps S2 and S3 each time the image is rotated to obtain the vertical orientation degree at the current angle; thereby obtaining a curve showing the vertical orientation degree changing with the rotation angle, wherein the maximum value of the vertical orientation degree in the curve and its corresponding rotation angle are the image orientation degree and orientation angle of the micrograph, respectively.

[0088] Preferably, when rotating the image, the image is rotated 1 degree each time, for a total of 180 rotations.

[0089] The following are specific embodiments:

[0090] To evaluate the orientation degree and orientation angle of the deposited morphology of asphalt-based carbon fiber composite fillers, the binarization threshold is set to 50, the continuity weight function F(x) = 2.8 + 0.052x; the initial angle of the image is 0 degrees, and when rotating the image, each rotation is 1 degree, for a total of 180 rotations. The evaluation results are as follows: Figure 5 As shown, the orientation degree of the pitch-based carbon fiber is 88.79% and the orientation angle is 141°.

[0091] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating the orientation of composite material filling particles, characterized in that: The steps include: S1. Obtain a cross-sectional micrograph of the composite material to be tested and convert it into a grayscale image; S2, placing the grayscale image at a certain angle and processing it to obtain a binary image; S3. Sequentially traverse all pixel blocks in the binary image to obtain the image longitudinal continuity SSV and the image transverse continuity SSH, and then obtain the vertical orientation ORI, specifically including: S31, set the initial values ​​of the image vertical continuity SSV and the image horizontal continuity SSH to 0; define the traversed pixel block as the j-th pixel block, and its initial value is 1; S32. For the j-th pixel block, If the pixel block is black, skip this pixel block; If the pixel block is white, the vertical upward continuity SV1, vertical downward continuity SV2, horizontal left continuity SH1, and horizontal right continuity SH2 of the pixel block are calculated according to the continuity weight function F(s); and then SSV=SSV+SV1+SV2, SSH=SSH+SH1+SH2 are iteratively updated. S33, execute j=j+1, repeat step S32 until all pixel blocks in the binary image are traversed, and the final SSV and SSH are obtained. Then the vertical orientation degree ORI of the binary image is (1-SSH / SSV)×100%; S4. Rotate the grayscale image multiple times according to a preset angle. Repeat steps S2 and S3 each time to obtain a curve showing the vertical orientation degree changing with the rotation angle. The maximum value of the vertical orientation degree in the curve and its corresponding rotation angle are the image orientation degree and orientation angle.

2. The method for quantitatively evaluating the orientation of composite material filling particles according to claim 1, wherein: In step S32, the calculation method of the longitudinal upward continuity SV1 and the longitudinal downward continuity SV2 is as follows: (1) Let the initial values ​​of the upward traversal number m and the longitudinal upward continuity SV1 be 0; Traverse upwards based on the pixel block and determine the color of the next pixel block: if the next pixel block is white, set m=m+1 and SV1=SV1+F(m); if the next pixel block is black, stop traversing; When the traversed pixel block reaches the edge of the image, the traversal stops; the SV1 at the time of stopping the traversal is the final vertical upward continuity; (2) Let the initial values ​​of the downward traversal number n and the vertical downward continuity SV2 be 0; Traverse downwards based on the pixel block to determine the color of the next pixel block: if the next pixel block is white, set n = n + 1, SV2 = SV2 + F(n); if the next pixel block is black, stop traversing; When the traversed pixel block reaches the edge of the image, the traversal stops; the SV2 at the time of stopping the traversal is the final vertical downward continuity.

3. The method for quantitatively evaluating the orientation of composite material filling particles according to claim 1, wherein: In step S32, the calculation method of the horizontal left continuity SH1 and the horizontal right continuity SH2 is as follows: (1) Let the initial values ​​of the number of leftward traversals p and the horizontal leftward continuity SH1 be 0; Traverse the pixel blocks to the left in turn to determine the color of the next pixel block: if the next pixel block is white, set p = p + 1, SH1 = SH1 + F (p); if the next pixel block is black, stop traversing; When the traversed pixel block reaches the edge of the image, the traversal stops; SH1 at the time of stopping the traversal is the final horizontal left continuity; (2) Let the initial values ​​of the rightward traversal number q and the horizontal rightward continuity SH2 be 0; Traverse the pixel blocks to the right in turn to determine the color of the next pixel block: if the next pixel block is white, set q = q + 1, SH2 = SH2 + F(q); if the next pixel block is black, stop traversing; When the traversed pixel block reaches the edge of the image, the traversal stops; SH2 at the time of stopping the traversal is the final horizontal rightward continuity.

4. The method for quantitatively evaluating the orientation of composite material filling particles according to claim 1, wherein: The continuity weight function F(s) is a linear function of a variable s, and for any positive integer variable s, F(s) is greater than 0.

5. The method for quantitatively evaluating the orientation of composite material filling particles according to claim 1, wherein: Step S2, obtaining a binary image, specifically includes: S21, performing filling particle edge extraction on the grayscale image to distinguish filler particles from the polymer matrix in the composite material, and obtaining an edge-extracted image; S22, performing image filtering processing on the image after edge extraction to filter out environmental noise in the image; S23 , binarizing the filtered image, setting pixel blocks with grayscale values ​​greater than a preset binarization threshold as white pixel blocks, and otherwise as black pixel blocks, to obtain a binarized image, wherein the white pixel blocks are generated by the edges of the filling particles.

6. The method for quantitatively evaluating the orientation of composite material filling particles according to claim 1, wherein: Use an optical microscope or a scanning electron microscope to obtain a cross-sectional micrograph of the composite material to be tested.

7. The method for quantitatively evaluating the orientation of composite material filling particles according to any one of claims 1 to 6, characterized in that: When setting the initial angle, the grayscale image is 0 degrees; when rotating the image, rotate 1 degree each time, for a total of 180 times.

8. A quantitative evaluation system for the orientation of composite material filling particles, characterized in that: The method comprises a processor configured to execute the method for quantitatively evaluating the orientation degree of filling particles in a composite material according to any one of claims 1 to 7.

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