Microscopic in-situ measurement method for linear thermal expansion coefficient of silicon carbide

Abstract

The invention belongs to the technical field of electron microscopy, and discloses a microscopic in-situ measurement method for linear thermal expansion coefficient of silicon carbide, which comprises the following steps: (1) dispersing palladium and alumina nanoparticles on an in-situ heating chip, heating and observing to obtain a polycrystalline diffraction ring photo; (2) performing circle center identification on the obtained polycrystalline diffraction ring; (3) carrying out inverse space diffraction ring radius identification based on experimental data; and (4) obtaining a change diagram of the linear thermal expansion coefficient along with the temperature according to the obtained data. The linear thermal expansion coefficients of different coating materials and silicon carbide can be compared at the same temperature at the same time by using the position relation between the silicon carbide heating body on the heating chip and the load sample, and the linear thermal expansion coefficient can be accurately obtained by introducing standard palladium particles to calibrate picture errors caused by diffraction. And a brand-new reliable test scheme is developed for screening coating materials in the future.

Description

Technical Field

[0001] This invention relates to the field of electron microscopy, and in particular to a microscopic in-situ measurement method for the linear thermal expansion coefficient of silicon carbide. Background Technology

[0002] The pursuit of higher speeds is one of the main directions of aircraft development, and the maximum speed of aero-engines is closely related to the combustion temperature of their internal fuel. Therefore, the ability of turbine blades to withstand high temperatures directly limits the choice of combustion temperature. Currently, turbine blades mainly use high-temperature alloy materials, but metallic materials suffer from mechanical property degradation problems such as creep and softening at high temperatures. In contrast, silicon carbide fiber-reinforced silicon carbide ceramics (SiC) offer a more promising alternative. f SiC composite blades possess excellent high-temperature mechanical properties. Their tensile strength is relatively insensitive to temperature changes, and their operating temperature can reach up to 1500℃, making them a highly promising and ideal alternative for future high-performance aero-engine blades.

[0003] Due to SiC f / SiC composite materials face water and oxygen corrosion during operation and Molten salt corrosion can severely damage the substrate structure, making it crucial to design reliable environmental barrier coatings (EBCs) for material surfaces. These coatings must maintain highly matched thermal expansion characteristics with the substrate over a wide temperature range (room temperature to 1500°C service temperature)—specifically, the difference between their linear coefficients of thermal expansion must be strictly controlled within a certain range. Within this range, the coating is protected from cracking, peeling, and other failures caused by stress accumulated during thermal cycling, thus ensuring long-term stable service of the engine's hot-end components.

[0004] Methods for measuring the linear thermal expansion coefficient of materials mainly include pushrod dilatometers, laser interferometry (non-contact), high-temperature X-ray diffraction, particle suspension method, and nanoparticle electron diffraction. Electron diffraction technology has a unique advantage in measuring the linear thermal expansion coefficient of materials with atomic-level resolution. It can directly and accurately capture the thermal expansion behavior of crystalline materials (such as β-SiC nanoparticles) along different crystal orientations in situ and at the nano to atomic scale by tracking the real-time changes in interplanar spacing during heating, far exceeding traditional macroscopic dilatometers or XRD statistical averaging methods. At the same time, combined with in-situ heating chip technology, it can resolve lattice distortion dynamics in a wide temperature range of 25–1200℃, quantify the contribution of surface and interface reconstruction, grain boundaries or defects to anisotropy, and provide irreplaceable atomic-level mechanistic basis for the thermal matching design of materials in extreme environments. Summary of the Invention

[0005] [Technical Issues] The technical problem to be solved by this invention is how to accurately measure the linear thermal expansion coefficient of silicon carbide at the microscale and in situ, and effectively correct for systematic errors introduced during temperature and scanning.

[0006] [Technical Solution] To address the above problems, this invention provides a microscopic in-situ measurement method for the linear thermal expansion coefficient of silicon carbide.

[0007] In a first aspect, the present invention provides a microscopic in-situ measurement method for the linear thermal expansion coefficient of silicon carbide, the method comprising: Step 1: Disperse palladium nanoparticles and alumina nanoparticles with isotropic linear thermal expansion coefficients on an in-situ heating chip; the in-situ heating chip is based on silicon, and a silicon carbide heating element is provided on the substrate, the surface of which is covered with a silicon nitride film; the in-situ heating chip is heated and observed to obtain polycrystalline diffraction ring images of silicon carbide, palladium and alumina at different temperatures; Step 2: Identify the center of the acquired polycrystalline diffraction rings and output the center coordinates of each diffraction ring; Step 3: Based on the polycrystalline diffraction ring image and the center coordinates, the radius of the reciprocal diffraction ring is read, and the read diffraction ring radius is calibrated by temperature and scanning error. At the same time, the interplanar spacing and linear thermal expansion coefficient of silicon carbide are measured to obtain the calibrated interplanar spacing data and linear thermal expansion coefficient data of silicon carbide. Step 4: Based on the calibrated silicon carbide interplane spacing data and linear thermal expansion coefficient data, calculate the linear thermal expansion coefficient of silicon carbide at different temperatures, and plot the curve of the linear thermal expansion coefficient of silicon carbide as a function of temperature with temperature as the horizontal axis and linear thermal expansion coefficient as the vertical axis.

[0008] Optionally, the specific process of step 1 is as follows: Step 1: Disperse palladium nanoparticles and alumina nanoparticles with isotropic linear thermal expansion coefficients in ethanol to obtain an ethanol dispersion. Add the ethanol dispersion dropwise onto an in-situ heating chip and dry it. Step 2: Install the dried in-situ heating chip into the in-situ heating sample rod and check whether the resistance of the in-situ heating chip is within a reasonable range; the reasonable range is between 1000Ω and 50000Ω. Step 3: Insert the in-situ heated sample rod with a resistance within a reasonable range into the transmission electron microscope and use the transmission electron microscope to capture the polycrystalline diffraction image in the initial state; Step 4: The in-situ heated chip was heated to 25℃, 100℃, 200℃, 300℃, 400℃, 500℃, 600℃, 700℃ and 800℃ respectively. Polycrystalline diffraction images were taken using a transmission electron microscope to obtain polycrystalline diffraction ring images of silicon carbide, palladium and alumina at different temperatures.

[0009] Optionally, in step 2, a Python script is used to identify the center of the polycrystalline diffraction ring image. The specific process is as follows: Step 1: Use the PyQt5 framework to create a visualization window, load the polycrystalline diffraction ring image output in Step 1 into the visualization window, and adjust the selection parameters using keyboard-assisted operation. When the space bar is pressed, the system automatically captures the center coordinates, inner diameter, and outer diameter data of the polycrystalline diffraction ring image and closes the visualization window. Step 2: Using a grid coordinate calculation and distance transformation algorithm, based on the center coordinates and inner and outer diameter data captured in Step 1, generate a binary mask matrix with the center coordinates as the center and the inner and outer diameters as the radius. Step 3: Set the pixel values ​​outside the annular selection area corresponding to the binary mask matrix to zero, and output the mask image with focused effective diffraction signal; Step 4: Robustly fit the polycrystalline diffraction rings in the mask image using the RANSAC algorithm with 1000 iterations to obtain an initial circle, and then perform sub-pixel optimization on the initial circle to obtain the preliminary center coordinates of the polycrystalline diffraction ring image. Step 5: Repeat steps 1 to 4 three times in the same polycrystalline diffraction ring image to obtain three preliminary center coordinates. Calculate the centroid of the three preliminary center coordinates and use the centroid as the final center coordinate of the polycrystalline diffraction ring image.

[0010] Optionally, in step 3, the specific process of temperature calibration is as follows: The interplanar spacing difference is obtained by correlating the known alumina interplanar spacing with the measured alumina interplanar spacing, and then the temperature difference is obtained. The actual temperature is calibrated based on the temperature difference. The specific process of scanning error calibration is as follows: The interplanar spacing of palladium at various temperatures is calculated using the calibrated temperature, and the interplanar spacing of silicon carbide at different temperatures is calculated. Calculated using the following formula:

[0011] in, The standard interplanar spacing of the palladium crystal plane. This refers to the actual measured interplanar spacing of palladium crystals. To obtain the actual interplanar spacing of silicon carbide, the scanning error is calibrated according to the above formula.

[0012] Optionally, in step 3, the specific process of reading the radius of the reciprocal space diffraction ring is as follows: Select the diffraction rings of silicon carbide, palladium and alumina to be analyzed in the polycrystalline diffraction ring image, draw a mask to include the diffraction rings using the user interface, perform 10 repeated diffraction ring radius detections in the diffraction ring region corresponding to the mask, and take the average of the 10 detection results as the final radius of the diffraction ring. The diffraction ring radius detection is implemented as follows: The user interface is launched, and multiple ring detection regions are defined using a graphical operating environment built with PyQt. The user precisely adjusts the inner and outer radius boundaries of each ring using a scroll wheel, with a step accuracy of 1 pixel. The interface displays a semi-transparent, highlighted colored area in real time. Based on the global center coordinates and the user-defined ring boundaries, a precise ring mask region is generated using a coordinate transformation formula. Then, ultra-high resolution radial projection is performed on the isolated ring mask region, maintaining 4320 angular segments. Next, an adaptive Gaussian filter with a 5×5 convolution kernel is applied to suppress random noise. Then, Canny edge detection using a dynamic threshold algorithm is used to extract ring boundary features. Finally, the current radius value is calculated using the RANSAC algorithm after 300 iterations.

[0013] Optionally, in step 4, the linear thermal expansion coefficient of silicon carbide at different temperatures is calculated using the average linear thermal expansion coefficient formula, and a function graph is plotted with temperature on the horizontal axis and the linear thermal expansion coefficient on the vertical axis; the average linear thermal expansion coefficient... The calculation formula is:

[0014] in, For silicon carbide at temperature The interplanar spacing at that time For silicon carbide at the reference temperature The interplanar spacing at that time To measure temperature, The reference temperature is 25℃.

[0015] In a second aspect, the present invention provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the above-described method steps for center identification.

[0016] Thirdly, the present invention provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the above-described method steps for center identification.

[0017] [Beneficial Effects] (1) Step 1 of the present invention involves simultaneously loading palladium nanoparticles and alumina nanoparticles onto the chip in situ and acquiring polycrystalline diffraction ring images at different temperatures using a transmission electron microscope. This invention achieves the measurement of the microscopic in situ linear thermal expansion coefficient at nanoscale spatial resolution, overcoming the limitation of traditional macroscopic dilatometers that cannot distinguish lattice scale changes.

[0018] (2) Step 2 of the present invention uses a Python script based on the PyQt5 visualization interface and the RANSAC robust fitting algorithm to automatically identify the center of the circle and extract the mask, replacing the traditional manual operation, reducing human error, improving the identification accuracy and data processing efficiency, and ensuring the consistency of the center coordinates of diffraction rings of different temperatures and materials.

[0019] (3) Step 3 of the present invention introduces palladium nanoparticles and alumina nanoparticles with isotropic linear thermal expansion coefficients as internal standards, and combines them with the standard crystal plane spacing comparison method to effectively correct temperature error and scanning system error, and significantly improve the measurement accuracy and data comparability of silicon carbide linear thermal expansion coefficient.

[0020] (4) Step 4 of the present invention uses the standard average linear thermal expansion coefficient formula to calculate the thermal expansion behavior of silicon carbide at different temperatures, and plots a curve with temperature as the horizontal axis and linear thermal expansion coefficient as the vertical axis, so that the result has physical meaning and provides a quantitative basis for the thermal matching design of coating and substrate materials.

[0021] In summary, this invention achieves high-precision, microscale measurement of the linear thermal expansion coefficient of silicon carbide, solving the problems of large errors and low resolution in traditional methods, and has significant scientific value and promising engineering applications. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of the measurement method provided by this invention.

[0024] Figure 2 A line graph showing the measured linear thermal expansion coefficient of silicon carbide provided for this invention.

[0025] Figure 1 Sub-figure a is the electronic optical path diagram, sub-figure b is the top view model of the heating chip, sub-figure c is the model of the relative position of the sample and the heating chip, and sub-figure d is the physical object diagram.

[0026] Figure 2 The top figure shows the instantaneous linear thermal expansion coefficient as a function of temperature, and the bottom figure shows the average linear thermal expansion coefficient as a function of temperature. S1-1, S1-2, S2-1, and S2-2 are four parallel experiments. Detailed Implementation

[0027] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The in-situ heating chip used in this invention is a commercially available product, and the equipment used is a Thermo Fisher Scientific Talos F200X transmission electron microscope. The palladium nanoparticles used were prepared in-house.

[0029] Example 1: This embodiment provides a microscopic in-situ measurement method for the linear thermal expansion coefficient of silicon carbide with temperature and scanning error correction, including: Step 1: Palladium nanoparticles and alumina nanoparticles with isotropic linear thermal expansion coefficients are dispersed in ethanol to obtain an ethanol dispersion. The ethanol dispersion is then dropped onto an in-situ heated chip and dried (see...). Figure 1 c, a model diagram showing the relative positions of the ethanol dispersion sample and the in-situ heating chip; the structure of the in-situ heating chip is based on silicon, with a silicon carbide heating element on the substrate, and the surface of the silicon carbide heating element covered with a silicon nitride film (see...). Figure 1 (b) is a top view of the in-situ heating chip model. The dried in-situ heating chip is then installed into the in-situ heating sample holder, and the resistance of the in-situ heating chip is checked to see if it is within a reasonable range. The in-situ heating sample holder with a resistance within the reasonable range is inserted into a transmission electron microscope, and a polycrystalline diffraction image in its initial state is captured using the transmission electron microscope (see...). Figure 1 a, is the electron optical path diagram used when acquiring diffraction data; the in-situ heated chip was heated to 25℃, 100℃, 200℃, 300℃, 400℃, 500℃, 600℃, 700℃, and 800℃, respectively. Polycrystalline diffraction images were captured using a transmission electron microscope to obtain polycrystalline diffraction ring images of silicon carbide, palladium, and alumina at different temperatures (see...). Figure 1 d is the real-space sample morphology diagram of the collected diffraction data.

[0030] Step 2: Use a Python script to identify the center of the polycrystalline diffraction ring image, including: Step 1: Use the PyQt5 framework to create a visualization window, load the polycrystalline diffraction ring image into the visualization window, and adjust the selection parameters using keyboard-assisted operations. When the space bar is pressed, the system automatically captures the center coordinates, inner diameter, and outer diameter data of the polycrystalline diffraction ring image and closes the visualization window. Step 2: Using a grid coordinate calculation and distance transformation algorithm, a binary mask matrix is ​​generated with the center coordinate as the center and the inner and outer diameters as the radius, based on the center coordinates and inner and outer diameters captured in Step 1. Step 3: Set the pixel values ​​outside the annular selection area corresponding to the binary mask matrix to zero, and output the mask image that focuses the effective diffraction signal; Step 4: Robustly fit the polycrystalline diffraction rings in the mask image using the RANSAC algorithm with 1000 iterations to obtain the initial circle, and then perform sub-pixel optimization on the initial circle to obtain the preliminary center coordinates of the polycrystalline diffraction ring image. Step 5: Repeat steps 1 to 4 three times in the same polycrystalline diffraction ring image to obtain three preliminary center coordinates. Calculate the centroid of the three preliminary center coordinates and use the centroid as the final center coordinate of the polycrystalline diffraction ring image. Finally, output the center coordinates of each diffraction ring.

[0031] Step 3: Select the diffraction rings of silicon carbide, palladium, and alumina to be analyzed in the polycrystalline diffraction ring image. Use the user interface to draw a mask to include the diffraction rings. Perform 10 repeated diffraction ring radius detections within the diffraction ring region corresponding to the mask. Take the average of the 10 detection results as the final radius of the diffraction ring. Perform temperature calibration and scanning error calibration on the read diffraction ring radius. At the same time, measure the interplanar spacing and linear thermal expansion coefficient of silicon carbide to obtain the calibrated interplanar spacing data and linear thermal expansion coefficient data of silicon carbide. Furthermore, the implementation of diffraction ring radius detection is as follows: The user interface is launched, and multiple ring detection regions are defined through a graphical operating environment built with PyQt. The user precisely adjusts the inner and outer radius boundaries of each ring using a scroll wheel, with a step accuracy of 1 pixel. The interface displays a semi-transparent, color-highlighted area in real time. Based on the global center coordinates and the user-defined ring boundaries, a precise ring mask region is generated using a coordinate transformation formula. Then, ultra-high resolution radial projection is performed on the isolated ring mask region, maintaining 4320 angular segments. Next, an adaptive Gaussian filter with a 5×5 convolution kernel is applied to suppress random noise. Then, Canny edge detection using a dynamic threshold algorithm is used to extract ring boundary features. Finally, the current radius value is calculated using the RANSAC algorithm after 300 iterations. The specific process of temperature calibration is as follows: the interplanar spacing difference is obtained by corresponding the known interplanar spacing of alumina crystals and the measured interplanar spacing of alumina crystals, and then the temperature difference is obtained. The actual temperature is calibrated based on the temperature difference. The specific process of scanning error calibration is as follows: The interplanar spacing of palladium at various temperatures is calculated using the calibrated temperature, and the interplanar spacing of silicon carbide at different temperatures is also calculated. Calculated using the following formula:

[0032] in, The standard interplanar spacing of the palladium crystal plane. This refers to the actual measured interplanar spacing of palladium crystals. To obtain the actual interplanar spacing of silicon carbide, the scanning error is calibrated according to the above formula.

[0033] Step 4: Based on the calibrated silicon carbide interplanar spacing data and linear thermal expansion coefficient data, calculate the linear thermal expansion coefficient of silicon carbide at different temperatures using the average linear thermal expansion coefficient formula, and plot the curve of the linear thermal expansion coefficient of silicon carbide versus temperature with temperature as the horizontal axis and linear thermal expansion coefficient as the vertical axis (see...). Figure 2 Average linear thermal expansion coefficient The calculation formula is:

[0034] in, For silicon carbide at temperature The interplanar spacing at that time For silicon carbide at the reference temperature The interplanar spacing at that time To measure temperature, The reference temperature is 25℃.

[0035] Set up comparison example 1: Compared to Example 1, the only difference is that the circle center recognition is done manually using the imageJ software.

[0036] Set up comparison example 2: Compared to Example 1, the only difference is that a single-crystal diffraction photograph of palladium is obtained instead of a polycrystalline diffraction ring photograph.

[0037] Experimental results showed that Comparative Example 1 had high labor costs and large errors in center identification; Comparative Example 2 had difficulty determining the linear thermal expansion coefficient of standard palladium nanoparticles, resulting in inaccurate results; while this embodiment successfully obtained an accurate linear thermal expansion coefficient of silicon carbide, and compared with the control group experiment, this embodiment had the highest accuracy.

[0038] Example 2 This embodiment provides another microscopic in-situ measurement method for the linear thermal expansion coefficient of silicon carbide with temperature and scanning error correction. The only difference from Embodiment 1 is step 3, as detailed below: After obtaining the center coordinates of each diffraction ring in step 2, an intensity distribution spectrum is generated by performing radial integration based on the optimized center. The pixel distance is converted into the reciprocal of the interplanar spacing using a scale factor of 0.5 / 775, and the output is a 1 / d-intensity data pair that can be directly used for the calculation of the linear thermal expansion coefficient. Then, the linear thermal expansion coefficient of silicon carbide at different temperatures is calculated using the formula for the average linear thermal expansion coefficient, and a curve of the linear thermal expansion coefficient of silicon carbide versus temperature is plotted with temperature as the horizontal axis and the linear thermal expansion coefficient as the vertical axis.

[0039] This embodiment successfully obtained an accurate linear thermal expansion coefficient of silicon carbide, and the degree of automation was high.

[0040] In summary, this invention designs a method to simultaneously obtain the linear thermal expansion coefficients of the loaded sample and silicon carbide at different temperatures using an in-situ chip, creating the most convenient experimental method for achieving thermal expansion adaptation in practical applications.

[0041] Example 3 This embodiment provides an electronic device, including: Memory, used to store computer programs; A processor is used to execute computer programs to implement the above-described method steps for center identification.

[0042] Example 4 This embodiment provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the above-described method steps for center identification.

[0043] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for microscale in-situ measurement of linear thermal expansion coefficient of silicon carbide, characterized in that, The method comprises: Step 1: disperse palladium nanoparticles and aluminum oxide nanoparticles with isotropic linear thermal expansion coefficient on an in-situ heating chip; the in-situ heating chip has a silicon substrate, a silicon carbide heating body on the substrate, and a silicon nitride film covering the surface of the silicon carbide heating body; heat the in-situ heating chip and observe to obtain polycrystalline diffraction ring images of silicon carbide, palladium and aluminum oxide at different temperatures; Step 2: identify the center of the polycrystalline diffraction ring image and output the center coordinates of each diffraction ring; Step 3: read the inverse space diffraction ring radius based on the polycrystalline diffraction ring image and the center coordinates, and calibrate the read diffraction ring radius with temperature and scanning error, while measuring the interplanar spacing and linear thermal expansion coefficient of silicon carbide to obtain calibrated interplanar spacing data and linear thermal expansion coefficient data of silicon carbide; Step 4: calculate the linear thermal expansion coefficient of silicon carbide at different temperatures according to the calibrated interplanar spacing data and linear thermal expansion coefficient data of silicon carbide, and draw a curve of the linear thermal expansion coefficient of silicon carbide changing with temperature with temperature as the horizontal axis and the linear thermal expansion coefficient as the vertical axis.

2. The measurement method according to claim 1, characterized in that, The specific process of step 1 is as follows: First step: disperse palladium nanoparticles and aluminum oxide nanoparticles with isotropic linear thermal expansion coefficient in ethanol to obtain an ethanol dispersion, and drop the ethanol dispersion onto the in-situ heating chip and dry it; Second step: put the dried in-situ heating chip into an in-situ heating sample rod, and detect whether the resistance of the in-situ heating chip is within a reasonable range; the reasonable range is between 1000Ω and 50000Ω; Third step: insert the in-situ heating sample rod with resistance within the reasonable range into a transmission electron microscope, and take polycrystalline diffraction images in the initial state using the transmission electron microscope; Fourth step: heat the in-situ heating chip, and take polycrystalline diffraction images at temperatures of 25℃, 100℃, 200℃, 300℃, 400℃, 500℃, 600℃, 700℃ and 800℃ respectively using the transmission electron microscope to obtain polycrystalline diffraction ring images of silicon carbide, palladium and aluminum oxide at different temperatures.

3. The measurement method according to claim 1, characterized in that, In step 2, the center of the polycrystalline diffraction ring image is identified using a Python script, and the specific process is as follows: First step: call the PyQt5 framework to create a visualization window, load the polycrystalline diffraction ring image output in step 1 into the visualization window, adjust the selection parameters with keyboard assistance, and when the space bar is pressed, the system automatically captures the center coordinates, inner diameter and outer diameter data of the polycrystalline diffraction ring image, and closes the visualization window; Second step: use grid coordinate calculation and distance transformation algorithm to generate a binary mask matrix with the center coordinates as the center and the inner diameter and outer diameter as the radius range according to the center coordinates and inner diameter and outer diameter data captured in the first step; Third step: set the pixel values outside the ring-shaped selection area of the binary mask matrix to zero, and output the mask image of the focused diffraction signal; Fourth step: robust fitting of the polycrystalline diffraction ring in the mask image through the RANSAC algorithm of 1000 iterations to obtain the initial circle, and then sub-pixel optimization of the initial circle to obtain the preliminary center coordinates of the polycrystalline diffraction ring image; Fifth step: repeating the first to fourth steps three times in the same polycrystalline diffraction ring image to obtain three preliminary center coordinates, calculating the centroid of the three preliminary center coordinates, and taking the centroid as the final center coordinates of the polycrystalline diffraction ring image.

4. The measurement method according to claim 1, characterized by, In step 3, the specific process of temperature calibration is as follows: the difference of crystal face spacing is obtained by using the known crystal face spacing of alumina and the measured crystal face spacing of alumina, and then the temperature difference is obtained, and the actual temperature is calibrated according to the temperature difference; the specific process of scanning error calibration is as follows: the crystal face spacing of palladium at each temperature is calculated by using the calibrated temperature, and the crystal face spacing of silicon carbide at different temperatures is calculated by the following formula: wherein is the standard interplanar spacing of the palladium crystal plane, is the actually measured interplanar spacing of the palladium crystal plane, is the actually measured interplanar spacing of the silicon carbide crystal plane, the calibration of the scanning error is completed according to the above formula.

5. The measurement method according to claim 1, characterized by, In step 3, the specific process of reading the backspace diffraction ring radius is as follows: Select the diffraction ring of silicon carbide, palladium and aluminum oxide to be analyzed in the polycrystalline diffraction ring image, draw a mask to contain the diffraction ring using a user interface, and perform 10 repeated diffraction ring radius detection in the diffraction ring area corresponding to the mask, and take the average of the 10 detection results as the final radius of the diffraction ring; The implementation of the diffraction ring radius detection is as follows: start the user interface, define multiple ring detection areas through the graphical operation environment built by PyQt; the user accurately adjusts the inner and outer radius boundaries of each ring through the scroll wheel, with a step accuracy of 1 pixel, and the interface displays a semi-transparent color highlight area in real time; Based on the global center coordinates and the user-set ring boundary, an accurate ring mask area is generated through a coordinate transformation formula; then, perform ultra-high resolution radial projection on the isolated ring mask area, maintaining 4320 angle segments; then apply adaptive Gaussian filtering with a 5x5 convolution kernel to suppress random noise; then extract the ring boundary features through Canny edge detection with a dynamic threshold algorithm; Finally, use the RANSAC algorithm with 300 iterations to calculate the current radius value.

6. The measurement method of claim 1, wherein, In the step 4, the linear thermal expansion coefficient of silicon carbide at different temperatures is calculated by using the average linear thermal expansion coefficient formula, and a function graph is drawn with temperature as the horizontal axis and the linear thermal expansion coefficient as the vertical axis; the calculation formula of the average linear thermal expansion coefficient is: wherein, is the interplanar spacing of silicon carbide at a temperature , is the interplanar spacing of silicon carbide at a reference temperature , is the measurement temperature, is the reference temperature, taken as 25°C.

7. An electronic device, comprising: Comprise: A memory for storing a computer program; A processor for executing the computer program to implement the method steps of the center identification as described in step 2 of claim 1.

8. A computer-readable storage medium, characterized in that, For storing a computer program; wherein the computer program is executed by the processor to implement the method steps of the center identification as described in step 2 of claim 1. For storing a computer program; wherein the computer program is executed by the processor to implement the method steps of the center identification as described in step 2 of claim 1.

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