Preparation method of silicon carbide ceramic-based composite material based on parameter optimization

Abstract

The invention relates to the technical field of silicon carbide ceramic material processing, in particular to a preparation method of a silicon carbide ceramic-based composite material based on parameter optimization. Adding carbon fibers into a sodium hydroxide solution, and carrying out ultrasonic oscillation to obtain alkali-treated carbon fibers; the preparation method comprises the following steps: adding alkali-treated carbon fibers into a silver-ammonia solution, adding a glucose solution, heating in a water bath, washing to be neutral to obtain pretreated carbon fibers, forming a titanium carbide layer by adopting a radio frequency magnetron sputtering method, forming a silicon carbide layer by adopting the radio frequency magnetron sputtering method again, weaving to obtain carbon fiber cloth, and depositing silicon carbide to obtain a silicon carbide preform; and placing the silicon carbide preform in a vacuum reaction sintering furnace to prepare the silicon carbide ceramic-based composite material. The temperature of the sintering furnace can be optimally controlled, and the mechanical property of the silicon carbide ceramic-based composite material is improved.

Description

Technical Field

[0001] This application relates to the technical field of silicon carbide ceramic material processing, and specifically relates to a preparation method of silicon carbide ceramic matrix composites based on parameter optimization. Background Art

[0002] Nowadays, during the preparation of silicon carbide ceramic matrix composites, temperature control affects the performance of the finally prepared products in terms of densification degree, hardness, toughness, etc. If the temperature is too high, the evaporation rate of silicon increases, resulting in the loss of silicon source, which will reduce the chance of carbon reaction occurring between silicon and the silicon carbide preform, thus affecting the densification degree and uniformity of the silicon carbide ceramic matrix composites; if the temperature is too low, the reaction rate between silicon and carbon decreases, resulting in an increase in the densification treatment time, and even incomplete densification cannot be achieved, causing pores in the prepared silicon carbide ceramic matrix composites and affecting the mechanical properties of the materials. Therefore, either too high or too low temperature may affect the properties such as densification degree, hardness, and toughness of the products.

[0003] In the existing preparation processes of silicon carbide ceramic matrix composites, the optimization methods for temperature parameters include: using the method of adding low-temperature sintering aids to reduce the sintering temperature and improve the toughness and hardness of the prepared products; by adjusting the precursor composition, densification rounds, and temperature, the prepared products can meet the requirements of different environments, etc. Therefore, effectively controlling the temperature parameters during the preparation of silicon carbide ceramic matrix composites can improve the performance of the prepared composites.

[0004] During the preparation of silicon carbide ceramic matrix composites, due to the existing technologies such as additives or traditional feedback control methods for controlling the temperature during the preparation process, the judgment of temperature error and the control of temperature change in the vacuum reaction furnace are poor, and thus the ideal temperature control effect cannot be achieved; among them, the effective control of the temperature during the molten silicon infiltration process in the preparation of silicon carbide ceramic matrix composites will affect the evaporation rate of silicon, may lead to the loss of silicon source, reduce the chance of carbon reaction occurring between silicon and the silicon carbide preform, thus affecting the densification degree and uniformity of the silicon carbide ceramic matrix composites and the mechanical properties of the materials. Summary of the Invention

[0005] In order to solve the above technical problems, this application provides a preparation method of silicon carbide ceramic matrix composites based on parameter optimization to solve the existing problems.

[0006] The preparation method of silicon carbide ceramic matrix composites based on parameter optimization of this application adopts the following technical solutions: An embodiment of this application provides a preparation method of silicon carbide ceramic matrix composites based on parameter optimization, and this method includes the following steps: Add aqueous ammonia solution dropwise to silver nitrate solution until the precipitate disappears, then add 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphonium tetrafluoroborate, and stir evenly to obtain silver ammonia solution; Add carbon fiber to sodium hydroxide solution, ultrasonically oscillate it, and then rinse it with deionized water to obtain alkali-treated carbon fiber; Add the alkali-treated carbon fiber to the silver ammonia solution, and add glucose solution. After heating in a water bath, filter out the solid, wash it until neutral, and dry it to obtain pretreated carbon fiber; Treat the pretreated carbon fiber by radio frequency magnetron sputtering method to form a titanium carbide layer, and then treat the carbon fiber with a titanium carbide layer by radio frequency magnetron sputtering method again to form a silicon carbide layer, and obtain carbon fiber cloth after weaving; Deposit silicon carbide on the carbon fiber cloth to obtain a silicon carbide preform. Use the molten silicon infiltration method to place the silicon carbide preform in a vacuum reaction sintering furnace. Analyze the temperature difference characteristics of each pixel point in the local hot spot area in the infrared image in the sintering furnace to construct a local temperature difference value. Based on the local temperature difference value, analyze the temperature change characteristics of the same hot spot area to construct an evaporation superheat index. Based on the evaporation superheat index, analyze the cooling demand degree at each moment to construct a sintering cooling index, and control the temperature of the sintering furnace during the molten silicon infiltration process. After the reaction, a silicon carbide ceramic matrix composite is prepared.

[0007] In the above preparation method, the concentration of the silver nitrate solution is 0.12 - 0.2 mol / L, and the mass concentration of the aqueous ammonia solution is 2.2 - 3%.

[0008] In the above preparation method, the weight of the sodium hydroxide solution is 6.5 - 8 times the weight of the carbon fiber, the mass concentration of the sodium hydroxide solution is 20 - 30%, and ultrasonically oscillate at a power of 320 - 400 W for 1.5 - 2 hours.

[0009] In the above preparation method, the volume of the glucose solution is 2 times that of the silver ammonia solution and the concentration is 0.6 - 1 times that of the silver ammonia solution, and the water bath heating is carried out at 52 - 60 °C for 95 - 100 minutes.

[0010] In the above preparation method, during the process of forming the titanium carbide layer, the vacuum degree before sputtering is Pa, the radio frequency sputtering power is 850 - 1000 W, the deposition time is 105 - 120 minutes, the distance between the target and the pretreated carbon fiber is 65 - 80 mm, and the argon gas flow rate is 40 - 50 sccm.

[0011] In the above preparation method, during the process of forming the silicon carbide layer, the vacuum degree before sputtering is Pa, the radio frequency sputtering power is 1100 - 1200 W, the deposition time is 55 - 70 minutes, the distance between the target and the pretreated carbon fiber is 65 - 80 mm, and the argon gas flow rate is 35 - 50 sccm.

[0012] In the above preparation method, the method for constructing the local temperature difference value is as follows: Obtain the infrared images at each acquisition moment in the sintering furnace; Construct a hot spot monitoring window for each pixel point with the pixel point in the infrared image as the center; Based on the gray - scale difference degree between the pixel points within the hot spot monitoring window of each pixel point, determine the local temperature difference value of each pixel point.

[0013] In the above preparation method, the method for constructing the evaporation superheat index is as follows: Combine the local temperature difference value and the gray - scale value of each pixel point as the temperature feature descriptor of each pixel point, cluster the temperature feature descriptors of all pixel points in the infrared image, and take the clustering cluster with the largest average gray - scale value within the cluster after clustering as the thermal effect cluster. Denote each connected domain corresponding to the thermal effect cluster as the thermal effect area; Determine the evaporation superheat index of each thermal effect area according to the local temperature difference value and the gray - scale value of all pixel points in each thermal effect area. Among them, the evaporation superheat index is inversely correlated with the fluctuation of the local temperature difference values of all pixel points in the thermal effect area and is directly correlated with the gray - scale value of the pixel points.

[0014] In the above preparation method, the sintering cooling index is the positive fusion of the area of each thermal effect area in the infrared image at each acquisition moment and the evaporation superheat index.

[0015] In the above preparation method, the control of the temperature of the sintering furnace during the molten silicon infiltration process further includes: Preset the temperature range of the sintering furnace during the molten silicon infiltration process and count the interval length of the temperature range; Analyze the product of the interval length and the normalized value of the sintering cooling index at each acquisition moment, which is denoted as the temperature adjustment factor at each acquisition moment; the optimal temperature at each acquisition moment is the difference between the maximum value of the temperature range and the temperature adjustment factor.

[0016] This application has at least the following beneficial effects: In this application, it is considered that during the reaction between the silicon carbide preform and silicon particles, the evaporation process of silicon may cause hot spots. Therefore, a local window is constructed centered on each pixel point in the collected infrared thermal imaging image, and the change characteristics of the pixel values within the local window where each pixel point is located are analyzed to obtain the local temperature difference value representing the temperature uniformity of the local area during the reaction. The beneficial effect is that it can fully consider the temperature change characteristics at the boundary of the hot spot generated in the infrared thermal imaging image, avoiding the influence of the hot spot boundary area on the judgment of temperature uniformity during the reaction process; Secondly, clustering analysis is performed on the pixel points based on the pixel values and the corresponding local temperature difference values in the infrared thermal imaging image, and a thermal effect area with temperature values having similar temperature uniformity characteristics is obtained according to the results of the clustering analysis. The possibility of overheating in the local area is analyzed based on the temperature uniformity characteristics and temperature values within the thermal effect area. The beneficial effect is that through the comparative analysis relationship between the temperature value and the temperature uniformity characteristics, it can accurately reflect the possibility of the hot spot area generated at the position where the pixel point is located; Secondly, based on the evaporation overheating index of the thermal effect area, the sintering cooling index is constructed to analyze the overheating degree at the current moment, and the overheating situation in the vacuum reaction furnace is reflected through the sintering cooling index. The beneficial effect is that the formation situation of hot spots in the vacuum reaction furnace can be accurately obtained through the thermal effect area and the evaporation overheating index of the pixel points within the thermal effect area, and then precise judgment can be made on the temperature control in the furnace, accurately controlling the temperature during the reaction process, solving the problem in the prior art that too high temperature causes silicon evaporation resulting in a decrease in the mechanical properties of the material, and improving the mechanical properties of the prepared silicon carbide ceramic matrix composite material. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is the step flowchart of the preparation method of the silicon carbide ceramic matrix composite material based on parameter optimization provided by this application; Figure 2 It is the schematic diagram of infrared image acquisition of the sintering furnace during the molten silicon infiltration process; Figure 3 It is the schematic diagram of the temperature control process of the sintering furnace during the molten silicon infiltration process. Detailed Embodiments

[0019] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following provides a detailed description of the preparation method of silicon carbide ceramic matrix composite materials based on parameter optimization proposed in this application, its specific implementation, structure, features, and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, terms such as "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the article or device including the said element. Additionally, the term "and / or" used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0021] The following specifically describes the specific solution of the preparation method of silicon carbide ceramic matrix composite materials based on parameter optimization provided by this application in combination with the accompanying drawings.

[0022] The preparation method of silicon carbide ceramic matrix composite materials based on parameter optimization provided by one embodiment of this application. Specifically, please refer to Figure 1 , and this method includes the following steps: : Prepare silver ammonia solution. Dropwise add an ammonia water solution with a mass concentration of 2.2 - 3% to a silver nitrate solution of 0.12 - 0.2 mol / L. After brown precipitate appears, continue to dropwise add the ammonia water solution until the precipitate disappears. Then add 1-allyl-2,3-dimethylimidazolium tetrafluoroborate with a molar amount of 2% of the silver nitrate solution and ethyltriphenylphosphonium tetrafluoroborate with a molar amount of 1% of the silver nitrate solution, and stir evenly to obtain the silver ammonia solution; Preferably, in three embodiments of this application, the process parameters for preparing the silver ammonia solution are specifically set as follows: In Example 1, the concentration of the silver nitrate solution is 0.12 mol / L, and the ammonia water solution is 2.2%; In Example 2, the concentration of the silver nitrate solution is 0.16 mol / L, and the ammonia water solution is 2.6%; In Example 3, the concentration of the silver nitrate solution is 0.2 mol / L, and the ammonia water solution is 3%.

[0023] : Alkaline-treated carbon fiber. The carbon fiber is added to a sodium hydroxide solution with a mass concentration of 20 - 30% and a weight 6.5 - 8 times that of the carbon fiber, and oscillated and treated in an ultrasonic oscillator at a power of 320 - 400 W for 1.5 - 2 hours. After filtration, it is rinsed with deionized water multiple times. The implementer can set the number of rinsing times according to the actual situation. In the examples of this application, the number of rinsing times is 3 times, and the alkaline-treated carbon fiber is obtained; preferably, in the three examples of this application, the specific process parameters in the process of alkaline-treated carbon fiber are set as follows: In Example 1, the carbon fiber is added to a sodium hydroxide solution with a mass concentration of 20% and a weight 6.5 times that of the carbon fiber. The oscillation power of the ultrasonic oscillator is 320 W, and the oscillation duration is 1.5 h; In Example 2, the carbon fiber is added to a sodium hydroxide solution with a mass concentration of 25% and a weight 7 times that of the carbon fiber. The oscillation power of the ultrasonic oscillator is 360 W, and the oscillation duration is 1.7 h; In Example 3, the carbon fiber is added to a sodium hydroxide solution with a mass concentration of 30% and a weight 8 times that of the carbon fiber. The oscillation power of the ultrasonic oscillator is 400 W, and the oscillation duration is 2 h.

[0024] : Pretreated carbon fiber. The alkaline-treated carbon fiber obtained in step S2 is added to the silver ammonia solution obtained in step S1, and a glucose solution with a volume 2 times that of the silver ammonia solution and a concentration 0.6 - 1 times that of the silver ammonia solution is added. It is heated in a water bath at 52 - 60 °C for 95 - 100 minutes, the solid is filtered out, washed until neutral, and dried to obtain the pretreated carbon fiber; preferably, in the three examples of this application, the specific process parameters in the process of pretreated carbon fiber are set as follows: In Example 1, the concentration of the glucose solution is 0.6 times that of the silver ammonia solution, the water bath heating temperature is 52 °C, and the heating duration is 95 minutes; In Example 2, the concentration of the glucose solution is 0.8 times that of the silver ammonia solution, the water bath heating temperature is 56 °C, and the heating duration is 97 minutes; In Example 3, the concentration of the glucose solution is 1 times that of the silver ammonia solution, the water bath heating temperature is 60 °C, and the heating duration is 100 minutes.

[0025] : Weaving carbon fiber cloth. The pretreated carbon fiber obtained in step S3 is processed by radio frequency magnetron sputtering in a radio frequency magnetron sputtering device to form a titanium carbide layer on the surface of the carbon fiber, where the vacuum degree before sputtering is Pa, the radio frequency sputtering power is 850 - 1000 W, the deposition time is 105 - 120 minutes, the distance between the target and the pretreated carbon fiber is 65 - 80 mm, and the argon gas flow rate is 40 - 50 sccm; In a radio frequency magnetron sputtering device, the carbon fiber with a titanium carbide layer formed thereon is processed by radio frequency magnetron sputtering method to form a silicon carbide layer on the titanium carbide layer formed on the surface of the carbon fiber. The vacuum degree before sputtering is Pa, the radio frequency sputtering power is 1100 - 1200 W, the deposition time is 55 - 70 minutes, the distance between the target and the pretreated carbon fiber is 65 - 80 mm, and the argon gas flow rate is 35 - 50 sccm; Stack several layers of single - layer 0° non - woven fabric, tire mesh, 90° non - woven fabric, and tire mesh in sequence, and use the relay needle punching method to weave a carbon fiber cloth with a density of Preferably, in the three embodiments of this application, the relevant process parameters for weaving the carbon fiber cloth are specifically set as follows: In Example 1, when using the radio frequency magnetron sputtering method to process the pretreated carbon fiber obtained in step S1, the radio frequency sputtering power is 850 W, the deposition time is 105 minutes, the distance between the target and the pretreated carbon fiber is 65 mm, and the argon gas flow rate is 40 sccm; When using the radio frequency magnetron sputtering method to process the carbon fiber with a titanium carbide layer formed thereon, the radio frequency sputtering power is 1100 W, the deposition time is 55 minutes, the distance between the target and the pretreated carbon fiber is 65 mm, and the argon gas flow rate is 35 sccm; In Example 2, when using the radio frequency magnetron sputtering method to process the pretreated carbon fiber obtained in step S1, the radio frequency sputtering power is 920 W, the deposition time is 110 minutes, the distance between the target and the pretreated carbon fiber is 73 mm, and the argon gas flow rate is 45 sccm; When using the radio frequency magnetron sputtering method to process the carbon fiber with a titanium carbide layer formed thereon, the radio frequency sputtering power is 1150 W, the deposition time is 65 minutes, the distance between the target and the pretreated carbon fiber is 73 mm, and the argon gas flow rate is 45 sccm; In Example 3, when using the radio frequency magnetron sputtering method to process the pretreated carbon fiber obtained in step S1, the radio frequency sputtering power is 1000 W, the deposition time is 120 minutes, the distance between the target and the pretreated carbon fiber is 80 mm, and the argon gas flow rate is 50 sccm; When using the radio frequency magnetron sputtering method to process the carbon fiber with a titanium carbide layer formed thereon, the radio frequency sputtering power is 1200 W, the deposition time is 70 minutes, the distance between the target and the pretreated carbon fiber is 80 mm, and the argon gas flow rate is 50 sccm.

[0026] : To prepare a silicon carbide ceramic matrix composite. The chemical vapor deposition method is used to deposit silicon carbide on the carbon fiber cloth obtained in step S4 to obtain a silicon carbide preform, and the silicon carbide preform is placed in a vacuum reaction sintering furnace by the molten silicon infiltration method. Silicon grains with the same mass as the silicon carbide preform are laid at the bottom, and it is protected by argon for densification treatment, then a silicon carbide ceramic matrix composite can be prepared.

[0027] In this embodiment, the specific process conditions for the chemical vapor deposition method are as follows: the temperature is 1100 °C, the atmosphere pressure is 3.5 kPa, the hydrogen flow rate is 300 mL / min, the argon flow rate is 320 mL / min, the temperature of trichloromethylsilane is 40 °C, and the molar mass ratio of hydrogen to trichloromethylsilane is 9:1; as other implementation manners, the specific process conditions of the chemical vapor deposition method can be set by the implementer according to the actual application scenario, and this application does not make special restrictions on this.

[0028] In the process of sintering reaction by placing the silicon carbide preform in a vacuum reaction sintering furnace using the molten silicon infiltration method, in this application, the molten silicon infiltration temperature range is set to 1700 - 1800 °C, and the molten silicon infiltration temperature is adjusted and controlled within this temperature range to ensure the preparation quality of the silicon carbide ceramic matrix composite. The optimization process of the optimal reaction temperature in the vacuum reaction sintering furnace is as follows: Step 1: Collect infrared images inside the vacuum reaction sintering furnace.

[0029] When performing molten silicon infiltration treatment in the vacuum reaction sintering furnace, infrared images inside the furnace are collected above the vacuum reaction sintering furnace by a high-definition infrared thermal imager at equal time intervals. Due to the high temperature of the vacuum reaction sintering furnace, the high-definition infrared thermal imager is a water-cooled and air-cooled double-cycle ultra-high temperature resistant infrared thermal imager. The obtained infrared images inside the furnace are grayscale images. The grayscale value of each pixel point in the image represents the thermal radiation intensity of the furnace area corresponding to the pixel point, which can reflect the temperature. The larger the grayscale value, the higher the corresponding temperature. The time interval in this embodiment takes a value of 20 s, and the schematic diagram of the collection result is as Figure 2 shown.

[0030] Step 2: Analyze the temperature difference characteristics of each pixel point's local area in the infrared image of the sintering furnace to construct a local temperature difference value.

[0031] When the silicon carbide preform reacts with the silicon grains in the vacuum reaction sintering furnace, if the temperature is too high, the evaporation rate of silicon will increase, resulting in the loss of the silicon source. During the evaporation process of silicon, a large amount of heat around it will be absorbed, causing the temperature of the silicon evaporation area to rise, making the temperature of the silicon evaporation area higher than the surrounding temperature, forming a hot spot, as Figure 2In the white bright spot area, the greater the gray value, the higher the corresponding temperature. Since the temperature of the local hot spot area is relatively high and the temperature of other normal areas is relatively low compared to the hot spot area, the temperature distribution within the local range of the hot spot boundary sampling points varies significantly due to the influence of the temperatures of the local hot spot and the normal areas. Based on the above analysis, this application constructs a local temperature difference value to reflect the uniformity of the local temperature. The construction process of the local temperature difference value is as follows: The window centered on pixel point A with a side length of L is denoted as the hot spot monitoring window of pixel point A. In this embodiment, the value of L is 5. Then, the local temperature difference value can be calculated, and the calculation method is as follows: The local temperature difference value is determined by the gray value difference of the pixels within the hot spot monitoring window of pixel point A. It should be noted that the more uniform the temperature of the areas corresponding to different pixels within the hot spot monitoring window, the less likely it is to be the boundary part of the hot spot area. Therefore, the calculated local temperature difference value is larger.

[0032] In this application, the calculation method of the local temperature difference value provided by the embodiment is that the summation result of the reciprocals of the absolute values of the differences between the gray values of all pairs of pixels in the hot spot monitoring window of pixel point A plus 1 is used as the local temperature difference value of the pixel point.

[0033] It should be noted that the calculation of the local temperature difference value of each pixel point is not limited to the method provided by the embodiment of this application. For example, analyze the difference between the ratio of the gray values of any two pixels in the hot spot monitoring window and the value 1, denoted as the first difference. The greater the first difference, the greater the difference between the gray values of the two pixels. Take the negative of the cumulative sum of all the first differences within the hot spot monitoring window to evaluate the local temperature difference value of the central pixel point of the hot spot monitoring window. Another example is to analyze the fluctuation of the gray values within the hot spot monitoring window to characterize the local temperature difference value of the central pixel point of the hot spot monitoring window. Take the result of the inverse proportional mapping of the degree of dispersion of the gray values of all pixels within the hot spot monitoring window as the local temperature difference value of the central pixel point of the hot spot monitoring window. The greater the fluctuation of the gray values within the hot spot monitoring window, the smaller the local temperature difference value of the central pixel point of the hot spot monitoring window. The analysis of the local temperature difference value of pixel points is not limited to the methods in the above examples.

[0034] Step 3: Based on the local temperature difference value, analyze the temperature change characteristics of the same hot spot area to construct an evaporation overheat index.

[0035] The local temperature difference values of each pixel in the infrared image inside the vacuum reaction sintering furnace obtained through the above steps reflect the degree of local temperature difference. For the regions belonging to the same area where hot spots are caused by silicon evaporation, since the density of silicon is relatively small after evaporation into gas and its fluidity is relatively strong, the temperature distribution in space is relatively uniform. Therefore, the gray values corresponding to the pixels within the same hot spot area should be relatively close. Thus, for the boundary of the same hot spot area, the temperature differences should be relatively close. Based on the above analysis, in this application, an evaporation overheat index is constructed based on the local temperature difference values to reflect the overheat degree of the current hot spot area. The construction process of the evaporation overheat index is as follows: The characteristic binary group formed by the local temperature difference value and the gray value of the pixel is denoted as the temperature feature descriptor of the pixel, which is used to reflect the temperature distribution uniformity and temperature magnitude of the region in the vacuum reaction sintering furnace corresponding to the pixel. In this embodiment, the temperature feature descriptors of all pixels in the infrared image inside the furnace at each acquisition moment are used as the input of the K-means clustering algorithm, and the output is the clustering clusters corresponding to each acquisition moment. In this embodiment, the number of clustering clusters, that is, the value of K, is 2, and the clustering cluster with the largest average gray value within the cluster is taken as the heat effect cluster. It can be understood that the pixels belonging to the same class have similar temperature distribution uniformity and temperature. Among them, the K-means clustering algorithm is a well-known technology and will not be elaborated here.

[0036] It should be noted that during the clustering process, the Euclidean distance between the temperature feature descriptors can be used as the clustering metric distance, or the cosine similarity between the temperature feature descriptors can be used as the clustering metric distance; in the actual application process, the implementer can use other clustering algorithms for the above clustering process, such as the DBSCAN clustering algorithm, etc. This application does not make special restrictions on this.

[0037] Perform connected component analysis on the pixels of the heat effect cluster. Since the pixels belonging to the same clustering cluster may be distributed at multiple positions, the number of regions formed by the pixels belonging to the same clustering cluster obtained through connected component analysis may be multiple. Each region obtained by performing connected component analysis on the heat effect cluster is denoted as a heat effect area. Among them, the connected component analysis is a well-known technology and will not be elaborated here.

[0038] Furthermore, based on the local temperature difference values and gray values of the pixels within each heat effect area, analyze the evaporation overheat index of each heat effect area. The analysis process is as follows: Determine the evaporation superheat index of each thermal effect zone based on the local temperature difference values and gray values of all pixel points in each thermal effect zone. Among them, the evaporation superheat index is inversely correlated with the fluctuation of the local temperature difference values of all pixel points in the thermal effect zone and is positively correlated with the gray value of the pixel points. That is, the more uniform the temperature distribution in each thermal effect zone, the smaller the difference in local temperature difference values between pixel points, that is, the smaller the fluctuation. At the same time, the higher the temperature in the thermal effect zone, that is, the larger the gray value, it is determined that the thermal effect zone more conforms to the characteristics of thermal spots in silicon evaporation, and the thermal effect zone is more likely to have overheating phenomena. Therefore, the corresponding evaporation superheat index is larger.

[0039] The calculation method of the evaporation superheat index provided in this embodiment is as follows: For each thermal effect zone, analyze the mean value of the local temperature difference values of all pixel points in the thermal effect zone. Take the sum of the absolute value of the difference between the local temperature difference value of each pixel point in the thermal effect zone and the obtained mean value and 1 as the denominator, which is used to characterize the fluctuation of the local temperature difference values of pixel points in the thermal effect zone, that is, the degree of difference between the local temperature difference values of each pixel point. The larger the denominator, the greater the deviation of the local temperature difference values of each pixel point in the thermal effect zone from the mean value, that is, the higher the fluctuation degree of the local temperature difference values of pixel points in the thermal effect zone; Take the gray value of each pixel point in the thermal effect zone as the numerator, and take the ratio of the numerator to the denominator as the overheat factor of each pixel point. Take the summation result of the overheat factors of all pixel points in the thermal effect zone as the evaporation superheat index of the thermal effect zone.

[0040] It should be noted that the analysis of the fluctuation of the local temperature difference values of all pixel points in the thermal effect zone is not limited to the method of the above embodiment. For example, the coefficient of variation of the local temperature difference values of all pixel points in the thermal effect zone can be analyzed, and the cumulative sum of the ratio of the gray value of each pixel point in the thermal effect zone to the coefficient of variation is used as the evaporation superheat index of the corresponding thermal effect zone; Another example is that the evaporation superheat index in the thermal effect zone can also be obtained by accumulating the product of the gray value and the local temperature difference value of each pixel point in the thermal effect zone as the evaporation superheat index of the thermal effect zone. That is, the larger the local temperature difference value of the pixel points in the thermal effect zone, the more uniform the temperature distribution, and the higher the gray value in the thermal effect zone, the larger the evaporation superheat index of the corresponding thermal effect zone. For the analysis and calculation of the evaporation superheat index of the thermal effect zone, the implementer can decide according to the actual application scenario.

[0041] Step 4: Analyze the cooling demand degree at each moment based on the evaporation superheat index to construct a sintering cooling index.

[0042] In a vacuum reactive sintering furnace, the larger the proportion of the area of the hot spot region, the higher the temperature in the furnace at this time, the greater the degree of silicon evaporation, and the lower the chance of the reaction between silicon and carbon in the silicon carbide preform, thereby affecting the densification degree and uniformity of the material. At this time, it is more necessary to cool down the vacuum reactive sintering furnace. At the same time, the evaporation superheat index at each moment obtained in the above steps reflects the formation of the hot spot region in the vacuum reactive sintering furnace at each acquisition moment. Then, a sintering cooling index for each acquisition moment can be constructed based on the evaporation superheat index to reflect the degree of cooling requirement for the vacuum reactive sintering furnace at each acquisition moment. The calculation method of the sintering cooling index is as follows: The sintering cooling index at each acquisition moment is determined by the evaporation superheat index and the area of all thermal effect regions in the infrared image in the vacuum reactive sintering furnace at each acquisition moment. Among them, the sintering cooling index at each acquisition moment is the positive fusion of the area of each thermal effect region and the evaporation superheat index in the infrared image at each acquisition moment. In the vacuum reactive sintering furnace at each acquisition moment, the more severe the overheating of the thermal effect region, that is, the larger the evaporation superheat index, and at the same time, the larger the area occupied by the thermal effect region, it indicates that more regions are overheated at this time, that is, the more severe the silicon evaporation, and the more cooling treatment is required to avoid affecting the product performance. Therefore, the sintering cooling index at the corresponding acquisition moment is larger.

[0043] Preferably, in the calculation method of the sintering cooling index provided in the embodiments of the present application, the number of all pixel points in each thermal effect region is used as the area of each thermal effect region, and the cumulative result of the product of the area and the evaporation superheat index of all thermal effect regions in the infrared image in the furnace at each acquisition moment is used as the sintering cooling index at each acquisition moment.

[0044] It should be noted that the positive fusion of the area of each thermal effect region and the evaporation superheat index in the infrared image at each acquisition moment is not limited to the product of the area of the thermal effect region and the evaporation superheat index in the above embodiments. For example, the sum result of the area of the thermal effect region and the evaporation superheat index can be used, and the average value of the sum results of all thermal effect regions in the infrared image is used as the sintering cooling index at each acquisition moment. The specific calculation relationship is not specifically limited in the present application.

[0045] Step Five: Control the temperature of the sintering furnace during the silicon infiltration melting process based on the sintering cooling index.

[0046] The sintering cooling index at each acquisition moment obtained through the above steps reflects the degree of cooling requirement at each acquisition moment. The Z-score method is used to normalize the sintering cooling index at each acquisition moment to obtain the normalized sintering cooling index. The Z-score normalization method is a well-known technology and will not be elaborated here.

[0047] Furthermore, calculate the optimal temperature at each acquisition moment. Taking the current acquisition moment as an example, the calculation formula is as follows: ; wherein, represents the optimal temperature at the current acquisition moment, represents the maximum value of the temperature adjustment range, Y represents the length of the preset temperature range, and Z represents the normalized sintering cooling index at the current acquisition moment, is the temperature adjustment factor at the current acquisition moment. Since the preset temperature range during the molten silicon infiltration treatment in this application is 1700 - 1800 °C, therefore takes a value of 1800 °C in the embodiment, and Y takes a value of 100 °C in the embodiment. The temperature adjusted in this application is the temperature during the molten silicon infiltration treatment, ensuring that the molten temperature at each acquisition moment reaches the optimum, and improving the preparation quality of the silicon carbide ceramic matrix composite.

[0048] The higher the degree of cooling requirement at the current acquisition moment, the more it indicates that the vacuum reaction sintering furnace should be cooled at this time to avoid excessive evaporation of silicon caused by too high temperature, resulting in a decline in the relevant properties of the silicon carbide ceramic matrix composite. Therefore, the calculated optimal temperature is smaller.

[0049] Based on the optimal temperature at each moment obtained from the above steps, the temperature of the vacuum reaction sintering furnace is controlled. The specific control process is as follows: The initial parameters of the proportional term, integral term, and differential term in the PID control are respectively set to 0.5, 0.45, and 0.55. The optimal temperature at each moment and the actual temperature at the previous moment are used as inputs, and a control signal is output using the PID algorithm to control the temperature of the vacuum reaction sintering furnace. Among them, the PID algorithm is a well-known technology and will not be elaborated in this application.

[0050] Please refer to Figure 3 , Figure 3 which is a schematic diagram of the sintering furnace temperature control process during the molten silicon infiltration process.

[0051] The relevant properties of the silicon carbide ceramic matrix composites prepared in Example 1, Example 2, and Example 3 are respectively tested, and the test results are shown in Table 1 below. The test methods are as follows: Mechanical property test: The compressive strength and fracture toughness of the composite material are tested through an electronic universal testing machine; Electromagnetic shielding performance test: Referring to IEC / TR 62153-4-1:2007, detection is carried out in the frequency range of 2 - 15 GHz to determine the maximum shielding performance; High-temperature resistance effect: The composite material is treated in an environment of 2000 °C for 30 min, and the mechanical properties and electromagnetic shielding performance are tested again.

[0052] Table 1: Performance test results of silicon carbide ceramic matrix composites It should be understood that references to "one embodiment" or "some embodiments" etc. described in the specification of the present application mean that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, when "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. appear in different places in this specification, they do not necessarily all refer to the same embodiment, but rather mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0053] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. At the same time, the magnitude of the serial numbers of the steps in the embodiments does not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic and should not constitute any limitation to the implementation process of the embodiments in this specification.

[0054] The above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application and should all be included within the protection scope of the present application.

Claims

1. A preparation method of silicon carbide ceramic matrix composite based on parameter optimization, characterized in that, The method includes the following steps: Drop an ammonia water solution into a silver nitrate solution until the precipitate disappears, then drop 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphonium tetrafluoroborate, and stir evenly to obtain a silver ammonia solution; Add carbon fiber into a sodium hydroxide solution, ultrasonically oscillate it, and then rinse it with deionized water to obtain alkali-treated carbon fiber; Add the alkali-treated carbon fiber into the silver ammonia solution, add a glucose solution, heat it in a water bath, filter out the solid, wash it until neutral, and dry it to obtain pretreated carbon fiber; Treat the pretreated carbon fiber by radio frequency magnetron sputtering to form a titanium carbide layer, and then treat the carbon fiber with a formed titanium carbide layer by radio frequency magnetron sputtering again to form a silicon carbide layer, and obtain a carbon fiber cloth after weaving; Deposit silicon carbide on the carbon fiber cloth to obtain a silicon carbide preform. Use the molten silicon infiltration method to place the silicon carbide preform in a vacuum reaction sintering furnace. Analyze the temperature difference characteristics of each pixel point in the local hot spot area in the infrared image of the sintering furnace to construct a local temperature difference value. Based on the local temperature difference value, analyze the temperature change characteristics of the same hot spot area to construct an evaporation superheat index. Based on the evaporation superheat index, analyze the cooling requirement degree at each moment to construct a sintering cooling index, and control the temperature of the sintering furnace during the molten silicon infiltration process. After the reaction, a silicon carbide ceramic matrix composite material is prepared.

2. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 1, wherein, The concentration of the silver nitrate solution is 0.12 - 0.2 mol / L, and the mass concentration of the ammonia water solution is 2.2 - 3%.

3. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 1, characterized in that, The weight of the sodium hydroxide solution is 6.5 - 8 times the weight of the carbon fiber, the mass concentration of the sodium hydroxide solution is 20 - 30%, and ultrasonically oscillate at a power of 320 - 400 W for 1.5 - 2 hours.

4. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 1, characterized in that The volume of the glucose solution is 2 times that of the silver ammonia solution, and the concentration is 0.6 - 1 times that of the silver ammonia solution. The water bath heating is carried out at 52 - 60 °C for 95 - 100 minutes.

5. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 1, wherein, During the process of forming the titanium carbide layer, the vacuum degree before sputtering is Pa, the radio frequency sputtering power is 850-1000 W, the deposition time is 105-120 minutes, the distance between the target and the pretreated carbon fiber is 65-80 mm, and the argon gas flow rate is 40-50 sccm.

6. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 1, characterized in that, During the process of forming the silicon carbide layer, the vacuum degree before sputtering is Pa, the radio frequency sputtering power is 1100-1200 W, the deposition time is 55-70 minutes, the distance between the target and the pretreated carbon fiber is 65-80 mm, and the argon gas flow rate is 35-50 sccm.

7. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 1, characterized in that, The construction method of the local temperature difference value is as follows: Obtain the infrared images at each acquisition moment in the sintering furnace; Construct a hot spot monitoring window for each pixel point with the pixel point in the infrared image as the center; Based on the gray level difference degree between the pixel points in the hot spot monitoring window of each pixel point, determine the local temperature difference value of each pixel point.

8. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 7, characterized in that, The construction method of the evaporation superheat index is as follows: Combine the local temperature difference value and the gray level value of each pixel point as the temperature feature descriptor of each pixel point, cluster the temperature feature descriptors of all pixel points in the infrared image, and take the clustering cluster with the largest average gray level within the cluster after clustering as the thermal effect cluster, and record each connected domain corresponding to the thermal effect cluster as the thermal effect area; Determine the evaporation superheat index of each thermal effect area according to the local temperature difference value and the gray level value of all pixel points in each thermal effect area. Among them, the evaporation superheat index is inversely correlated with the fluctuation of the local temperature difference value of all pixel points in the thermal effect area and is positively correlated with the gray level value of the pixel points.

9. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 8, wherein, The sintering cooling index is the positive fusion of the area of each thermal effect area in the infrared image at each acquisition moment and the evaporation superheat index.

10. The preparation method of the silicon carbide ceramic matrix composite based on parameter optimization according to claim 9, wherein, The control of the temperature of the sintering furnace during the molten silicon infiltration process further includes: Preset the temperature range of the sintering furnace during the molten silicon infiltration process, and count the interval length of the temperature range; Analyze the product of the interval length and the normalized value of the sintering cooling index at each acquisition moment, which is denoted as the temperature adjustment factor at each acquisition moment; the optimal temperature at each acquisition moment is the difference between the maximum value of the temperature range and the temperature adjustment factor.

Images