An intelligent production process for silicon carbide ceramic-based composite materials

Abstract

The present application relates to the technical field of silicon carbide composite material preparation, and specifically to an intelligent production process of silicon carbide ceramic-based composite materials, the process comprising: obtaining pretreated carbon fibers after alkali treatment and water bath heating treatment, then obtaining composite carbon fibers by radio frequency magnetron sputtering and weaving into carbon fiber cloth, depositing silicon carbide on the carbon fiber cloth by chemical vapor deposition to obtain a silicon carbide preform, densifying the silicon carbide preform by melt siliconization, collecting infrared thermal imaging images at each moment in the melt siliconization densification process, obtaining the melt siliconization sufficiency coefficient of the images at each moment, adjusting and optimizing the densification processing time, and obtaining a silicon carbide ceramic-based composite material. The present application improves the density of silicon carbide ceramic-based composite materials.

Description

Technical Field

[0001] The present application relates to the technical field of silicon carbide composite material preparation, and specifically to an intelligent production process for silicon carbide ceramic-based composite materials. Background Art

[0002] Compared with traditional materials, silicon carbide ceramic-based composites have better advantages in terms of light weight, high temperature resistance, oxidation resistance, corrosion resistance, and radiation resistance. Research on silicon carbide ceramic-based composites has promoted the development of materials science and promoted industrial upgrading. It has a wide range of applications in aerospace, nuclear energy, and automotive industries. The intelligent production process of silicon carbide ceramic-based composites has high practical significance.

[0003] During the preparation process of silicon carbide ceramic-based composites, the melt siliconization time needs to be strictly controlled. When the melt siliconization time is too long, it may cause damage or structural changes to the carbon fibers, reducing the mechanical strength and density of the composite materials. When the melt siliconization time is too short, the silicon particles may not be completely melted or fully penetrated into the pores of the silicon carbide preform, affecting the densification degree of the prepared silicon carbide ceramic-based composite materials. A low degree of densification may also allow electromagnetic waves to propagate through the pores, reducing the electromagnetic shielding performance of the composite materials. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an intelligent production process for silicon carbide ceramic-based composite materials to solve the existing problems.

[0005] The intelligent production process of a silicon carbide ceramic-based composite material of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides an intelligent production process for a silicon carbide ceramic-based composite material, the process comprising the following steps:

[0007] S1, using a sodium hydroxide solution to perform an alkali treatment on the carbon fiber, adding the alkali-treated carbon fiber to a silver ammonia solution, adding a glucose solution to perform a water bath heating treatment, filtering out the solid, washing, and drying to obtain a pretreated carbon fiber;

[0008] S2, using radio frequency magnetron sputtering to sequentially form a titanium carbide layer and a silicon carbide layer on the surface of the pretreated carbon fiber to obtain a composite carbon fiber and weave it into a carbon fiber cloth;

[0009] S3, depositing silicon carbide on the carbon fiber cloth by chemical vapor deposition to obtain a silicon carbide preform, densifying the silicon carbide preform by melt siliconization, and adjusting and optimizing the densification processing time, specifically:

[0010] Collect infrared thermal imaging images at each moment in the process of melt siliconization and densification, analyze the distribution and difference of the temperature of the pixels in the neighborhood of each pixel in the image, and obtain the heat transfer coefficient of each pixel in the image;

[0011] Analyze the difference and distance relationship of the heat transfer coefficient of some pixels in the image to obtain the molten siliconization sufficiency coefficient of the image at each moment;

[0012] Adjusting and optimizing the densification processing time in combination with the molten siliconization sufficient coefficient;

[0013] After completing the densification treatment, a silicon carbide ceramic-based composite material is obtained.

[0014] In one embodiment, the alkali treatment comprises:

[0015] The carbon fiber was added into a sodium hydroxide solution, the weight ratio of the sodium hydroxide solution to the carbon fiber was 6.5-7.5:1, the concentration of the sodium hydroxide solution was 25-30%, and the solution was treated with 340-400W ultrasonic oscillation for 1.5-2h. After filtering, the solution was rinsed with deionized water for 2-3 times.

[0016] In one embodiment, the preparation of the silver ammonia solution comprises:

[0017] To a 0.1-0.2 mol / L silver nitrate solution, add ammonia solution with a mass concentration of 2.53% dropwise. After a brown precipitate is produced, continue to add ammonia solution dropwise until the precipitate disappears. Then, add 1-allyl-2,3-dimethylimidazolium tetrafluoroborate with a molar amount of 2% of the silver nitrate solution and ethyltriphenylphosphine tetrafluoroborate with a molar amount of 1% of the silver nitrate solution. Stir evenly to obtain a silver ammonia solution.

[0018] In one embodiment, the volume of the glucose solution is twice the volume of the silver ammonia solution, and the concentration is 0.71 times the concentration of the silver ammonia solution. The temperature of the water bath heating is 55-60°C, and the time is 90-100 minutes.

[0019] In one embodiment, the forming of the titanium carbide layer and the silicon carbide layer comprises:

[0020] The parameters of the titanium carbide layer are: the vacuum degree before sputtering is Pa, RF sputtering power is 850 1000W, deposition time 110 120min, the distance between the target and the pretreated carbon fiber is 70 80mm, argon flow rate is 35 45sccm;

[0021] The parameters of the silicon carbide layer are: vacuum degree before sputtering is Pa, RF sputtering power is 1050 1200W, deposition time 60 70min, the distance between the target and the pretreated carbon fiber is 70 80mm, argon flow rate is 35 50sccm.

[0022] In one embodiment, the carbon fiber cloth is woven by cyclically stacking a single layer of 0° non-woven cloth, a tire net, a 90° non-woven cloth, and a tire net in sequence, and a relay needle punching method is used to weave the carbon fiber cloth to obtain a density of 0.3 0.4g / Carbon fiber cloth.

[0023] In one embodiment, the parameters of the chemical vapor deposition method include:

[0024] Temperature is 1030 1080℃, time 95 110h, atmosphere pressure is 3~4Kpa, Traffic is 280 320mL / min, Ar flow rate is 320 350mL / min, trichloromethylsilane temperature is 35 40℃, The molar mass ratio of trichloromethylsilane is 8.5 10:1.

[0025] In one embodiment, the determination of the heat transfer coefficient includes:

[0026] With each pixel as the center, a pixel window of a preset size is constructed, and the fusion result of the difference in the temperature values ​​of all two pixels in the pixel window is used as the neighborhood heat difference of the pixel window; the mutation points of the temperature values ​​of all pixels in the pixel window are obtained, and the discrete degree of the positions of all the mutation points in the pixel window is used as the discreteness of the heat change point distribution of the pixel window;

[0027] Reverse mapping the neighborhood heat difference, fusing the reverse mapping result with the discreteness of the heat change point distribution, and determining it as the heat uniformity factor of the pixel window;

[0028] Obtain the two pixels with the largest temperature value difference and the farthest distance in the pixel window of each pixel, and perform reverse mapping on the fusion result of the temperature difference of the two pixels and the distance between the two pixels, as the thermal diffusion factor of the corresponding pixel window; obtain each group of symmetrical pixels that conform to the central symmetry in the pixel window of each pixel, and perform reverse mapping on the fusion result of the temperature difference between all groups of symmetrical pixels, so as to obtain the thermal imaging symmetry of the corresponding pixel window;

[0029] The fusion result of the heat diffusion factor and the thermal imaging symmetry is used as the heat transfer efficiency of the corresponding pixel window;

[0030] The fused normalized result of the heat uniformity factor and the heat transfer efficiency is used as the heat conduction coefficient of the central pixel of the corresponding pixel window.

[0031] In one embodiment, the determination of the melt siliconization sufficiency coefficient includes:

[0032] Arrange all the heat transfer coefficients in the image in descending order, obtain a preset number of partial pixels, and fuse the differences in the heat transfer coefficients between any two pixels in the partial pixels as the molten silicon infiltration deficiency factor of the image;

[0033] Obtaining groups of pixels with equal heat transfer coefficients in the image, and fusing the measured distances of the heat transfer coefficients between any two groups of pixels as the heat transfer difference of the image;

[0034] The fusion result of the molten siliconization deficiency factor and the thermal conductivity difference is reversely mapped to obtain the molten siliconization sufficiency coefficient of the image.

[0035] In one embodiment, the process of adjusting and optimizing the densification processing time in combination with the molten siliconization sufficient coefficient includes:

[0036] If the molten siliconization sufficiency coefficient is less than a preset threshold, the densification processing time is extended by a preset length from the corresponding moment of the molten siliconization sufficiency coefficient; otherwise, the densification processing time is not adjusted.

[0037] This application has at least the following beneficial effects:

[0038] The present application obtains the heat conductivity coefficient of each pixel in the image by collecting infrared thermal imaging images at each moment in the molten siliconization densification process during the preparation process of the silicon carbide ceramic-based composite material, analyzing the distribution and difference of the temperature of the pixels in the neighborhood of each pixel in the image, and comprehensively considering the temperature uniformity and heat transfer efficiency of the silicon carbide preform during the molten siliconization treatment process, more accurately evaluating the sufficient uniformity of heat conduction within the pixel neighborhood, and improving the reliability of the judgment of the sufficiency of molten siliconization; analyzing the difference and distance relationship of the heat conductivity coefficients of some pixels in the image, obtaining the molten siliconization sufficiency coefficient of the image at each moment, reflecting the sufficiency of the densification of the silicon carbide preform at the corresponding moment of the image, and using this to measure the rationality of the densification processing time of the silicon carbide preform, improving the accuracy of the densification processing time of the silicon carbide preform, and then improving the density of the silicon carbide ceramic-based composite material, preventing the silicon carbide preform from being insufficiently densified due to too short a molten siliconization treatment time, which affects the performance of the silicon carbide ceramic-based composite material. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A flowchart of the steps of an intelligent production process of a silicon carbide ceramic-based composite material provided in this application;

[0041] Figure 2 This is a schematic diagram of infrared thermal imaging image acquisition;

[0042] Figure 3 It is a schematic diagram of equal heat conduction paths;

[0043] Figure 4 Adjustment of the flow chart for the duration of the melt siliconizing treatment;

[0044] Figure 5 Schematic diagram of the test results of flexural strength and fracture toughness of silicon carbide ceramic matrix composites;

[0045] Figure 6 Schematic diagram of the test results of electromagnetic shielding performance of silicon carbide ceramic matrix composite materials. DETAILED DESCRIPTION

[0046] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the intelligent production process of a silicon carbide ceramic-based composite material proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0048] The specific scheme of the intelligent production process of a silicon carbide ceramic-based composite material provided by the present application is described in detail below with reference to the accompanying drawings.

[0049] An embodiment of the present application provides an intelligent production process for a silicon carbide ceramic-based composite material. Specifically, the following intelligent production process for a silicon carbide ceramic-based composite material is provided. Figure 1 , the process comprises the following steps:

[0050] S1, using sodium hydroxide solution to alkali-treat carbon fiber to obtain alkali-treated carbon fiber, adding alkali-treated carbon fiber to silver ammonia solution, adding glucose solution to heat in water bath, filtering out solid, washing to neutrality, and drying to obtain pretreated carbon fiber. The volume of glucose solution is 2 times the volume of silver ammonia solution, and the concentration is 0.71 times the concentration of silver ammonia solution.

[0051] In Example 1 of the present application, the step of alkali treatment of carbon fiber is: adding carbon fiber to sodium hydroxide solution, the weight ratio of sodium hydroxide solution to carbon fiber is 6.5:1, the concentration of sodium hydroxide solution is 25%, using 340W ultrasonic oscillation for 1.5h, filtering out and rinsing with deionized water twice.

[0052] In Example 2 of the present application, the step of alkali treatment of carbon fiber is: adding carbon fiber to sodium hydroxide solution, the weight ratio of sodium hydroxide solution to carbon fiber is 7:1, the concentration of sodium hydroxide solution is 28%, using 370W ultrasonic oscillation for 1.8h, filtering out and rinsing with deionized water twice.

[0053] In Example 3 of the present application, the step of alkali treatment of carbon fiber is: adding carbon fiber to sodium hydroxide solution, the weight ratio of sodium hydroxide solution to carbon fiber is 7.5:1, the concentration of sodium hydroxide solution is 30%, using 400W ultrasonic oscillation for 2h, filtering out and rinsing with deionized water for 3 times.

[0054] In Example 1 of the present application, the silver ammonia solution is obtained by dropwise adding an ammonia solution having a mass concentration of 2.53% into a 0.1 mol / L silver nitrate solution, and after a brown precipitate is produced, continuing to dropwise add the ammonia solution until the precipitate disappears, and then dropwise adding 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphine tetrafluoroborate, stirring evenly to obtain a silver ammonia solution, wherein the molar amounts of 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphine tetrafluoroborate are 2% and 1% of the added silver nitrate solution.

[0055] In Example 2 of the present application, the silver ammonia solution is obtained by dropwise adding an ammonia solution having a mass concentration of 2.53% to a 0.15 mol / L silver nitrate solution, and after a brown precipitate is produced, continuing to dropwise add the ammonia solution until the precipitate disappears, and then dropwise adding 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphine tetrafluoroborate, stirring evenly to obtain a silver ammonia solution, wherein the molar amounts of 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphine tetrafluoroborate are 2% and 1% of the added silver nitrate solution.

[0056] In Example 3 of the present application, the silver ammonia solution is obtained by dropwise adding an ammonia solution having a mass concentration of 2.53% into a 0.2 mol / L silver nitrate solution, and after a brown precipitate is produced, continuing to dropwise add the ammonia solution until the precipitate disappears, and then dropwise adding 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphine tetrafluoroborate, stirring evenly to obtain a silver ammonia solution, wherein the molar amounts of 1-allyl-2,3-dimethylimidazolium tetrafluoroborate and ethyltriphenylphosphine tetrafluoroborate are 2% and 1% of the added silver nitrate solution.

[0057] In Example 1 of the present application, the temperature of the water bath heating treatment in step S1 is 55° C. and the time is 90 minutes.

[0058] In Example 2 of the present application, the temperature of the water bath heating treatment in step S1 is 58° C. and the time is 95 minutes.

[0059] In Example 3 of the present application, the temperature of the water bath heating treatment in step S1 is 60° C. and the time is 100 min.

[0060] S2, a titanium carbide layer and a silicon carbide layer are sequentially formed on the surface of the pretreated carbon fiber by radio frequency magnetron sputtering to obtain composite carbon fiber and weave it into carbon fiber cloth.

[0061] In Example 1 of the present application, the process parameters for forming the titanium carbide layer are: the vacuum degree before sputtering is Pa, the RF sputtering power was 850 W, the deposition time was 110 min, the distance between the target and the pretreated carbon fiber was 70 mm, and the argon flow rate was 35 sccm;

[0062] The process parameters for forming the silicon carbide layer are: the vacuum degree before sputtering is Pa, the RF sputtering power was 1050 W, the deposition time was 60 min, the distance between the target and the pretreated carbon fiber was 70 mm, and the argon flow rate was 35 sccm;

[0063] The weaving method of carbon fiber cloth is: a single layer of 0° non-weft cloth, tire net, 90° non-weft cloth, tire net are cyclically stacked in sequence, and a density of 0.3g / is obtained by a relay needle punching method. Carbon fiber cloth.

[0064] In Example 2 of the present application, the process parameters for forming the titanium carbide layer are: the vacuum degree before sputtering is Pa, the RF sputtering power was 920 W, the deposition time was 115 min, the distance between the target and the pretreated carbon fiber was 75 mm, and the argon flow rate was 40 sccm;

[0065] The process parameters for forming the silicon carbide layer are: the vacuum degree before sputtering is Pa, the RF sputtering power was 1125 W, the deposition time was 65 min, the distance between the target and the pretreated carbon fiber was 75 mm, and the argon flow rate was 43 sccm;

[0066] The weaving method of carbon fiber cloth is: a single layer of 0° non-weft cloth, tire net, 90° non-weft cloth, tire net are cyclically stacked in sequence, and a density of 0.35g / is obtained by a relay needle punching method. Carbon fiber cloth.

[0067] In Example 3 of the present application, the process parameters for forming the titanium carbide layer are: the vacuum degree before sputtering is Pa, the RF sputtering power was 1000 W, the deposition time was 120 min, the distance between the target and the pretreated carbon fiber was 80 mm, and the argon flow rate was 45 sccm;

[0068] The process parameters for forming the silicon carbide layer are: the vacuum degree before sputtering is Pa, the RF sputtering power was 1200 W, the deposition time was 70 min, the distance between the target and the pretreated carbon fiber was 80 mm, and the argon flow rate was 50 sccm;

[0069] The weaving method of carbon fiber cloth is: a single layer of 0° non-weft cloth, tire net, 90° non-weft cloth, tire net are cyclically stacked in several layers in sequence, and a relay needle punching method is used to weave to obtain a density of 0.4g / Carbon fiber cloth.

[0070] S3, depositing silicon carbide on the carbon fiber cloth by CVI (chemical vapor deposition) to obtain a silicon carbide preform, and densifying the silicon carbide preform by melt siliconization;

[0071] In Example 1 of the present application, the process parameters of the CVI method are: temperature is 1030° C., time is 95 h, The flow rate is 280 mL / min, the atmosphere pressure is 3 Kpa, the Ar flow rate is 320 mL / min, the trichloromethylsilane temperature is 35 °C, The molar mass ratio of trichloromethylsilane to trichloromethylsilane is 8.5:1.

[0072] In Example 2 of the present application, the process parameters of the CVI method are: temperature is 1060°C, time is 103h, The flow rate is 300 mL / min, the atmosphere pressure is 4 KPa, the Ar flow rate is 335 mL / min, and the trichloromethylsilane temperature is 38°C. The molar mass ratio of trichloromethylsilane to trichloromethylsilane is 9.2:1.

[0073] In Example 3 of the present application, the process parameters of the CVI method are: temperature is 1080° C., time is 110 h, The flow rate is 320 mL / min, the atmosphere pressure is 4 Kpa, the Ar flow rate is 350 mL / min, the trichloromethylsilane temperature is 40 °C, The molar mass ratio of trichloromethylsilane to trichloromethylsilane is 10:1.

[0074] In Example 1 of the present application, the process of densification using the melt siliconization method is as follows: a silicon carbide preform is placed in a vacuum reaction sintering furnace, silicon particles of equal mass to the silicon carbide preform are spread on the bottom, the melt siliconization temperature is set to 1730°C, and the atmosphere is argon gas.

[0075] In Example 2 of the present application, the process of densification using the melt siliconization method is as follows: the silicon carbide preform is placed in a vacuum reaction sintering furnace, silicon particles of the same mass as the silicon carbide preform are spread on the bottom, the melt siliconization temperature is set to 1770°C, and the atmosphere is argon gas.

[0076] In Example 3 of the present application, the process of densification using the melt siliconization method is as follows: the silicon carbide preform is placed in a vacuum reaction sintering furnace, silicon particles of the same mass as the silicon carbide preform are spread on the bottom, the melt siliconization temperature is set to 1800°C, and the atmosphere is argon gas.

[0077] The densification time by melt siliconization is 60 120min, the densification treatment time, that is, the duration of molten siliconization treatment, is adjusted and optimized, specifically:

[0078] Step S001, collecting infrared thermal imaging images at each moment in the process of molten silicon infiltration and densification, and performing preprocessing.

[0079] The infrared thermal imaging image in the vacuum reaction sintering furnace during the densification process of the melt siliconization method is obtained by using the Gaode Zhigan PT870 infrared thermal imager. In one embodiment of the present application, the infrared thermal imaging image acquisition time interval is set to 1s. The implementer can set the time interval according to the actual situation, and the present application does not impose any restrictions on this. The acquisition method is the top-down angle acquisition. The schematic diagram of the infrared thermal imaging image acquisition is shown in the figure Figure 2 In order to prevent the infrared thermal imaging images obtained during the densification process of the silicon carbide preform from being disturbed by external environmental noise and seriously affecting subsequent analysis, the wavelet transform algorithm is used to denoise the infrared thermal imaging images. The wavelet transform algorithm is a well-known technology and will not be described in detail in this application. Figure 2 In the figure, 1 represents an infrared thermal imager, 2 represents a vacuum reaction sintering furnace, 3 represents a silicon carbide preform, and 4 represents silicon particles.

[0080] It should be noted that this embodiment only proposes a method for image denoising, that is, using a wavelet transform algorithm to denoise infrared thermal imaging images. Implementers can choose other existing feasible image denoising algorithms according to actual conditions, such as a mean filter algorithm, a Gaussian filter algorithm, etc. This application does not impose any restrictions on this.

[0081] The infrared thermal imaging images collected at each moment are recorded as dense images.

[0082] Step S002, analyzing the distribution and difference of the temperature of the pixels in the neighborhood of each pixel in the dense image, and obtaining the heat transfer coefficient of each pixel in the dense image.

[0083] In the actual production process of densifying silicon carbide preforms by melt siliconization, the production environment, equipment operation status or the quality of the silicon carbide preform may affect the distribution of molten heat during the densification process of the silicon carbide preform, and thus affect the performance of the prepared silicon carbide ceramic-based composite material; the more complete the melt siliconization of the silicon carbide preform, the more consistent the temperature distribution in the infrared thermal imaging image, and the smaller the temperature gradient difference caused by the dense and uniform internal structure; at the same time, in the melt siliconization process of the fully densified silicon carbide preform, the good heat conduction characteristics are significant.

[0084] Based on the above analysis, the dense image at the tth moment is taken as an example for subsequent analysis. A pixel window is constructed with each pixel in the dense image as the center. In one embodiment of the present application, the size of the pixel window is 9×9, which can be set by the implementer according to the actual situation. The present application does not impose any restrictions on this. The temperature values ​​of all pixels in the pixel window corresponding to each pixel are recorded in ascending order as the heat sequence of each pixel; the pair of pixels with the largest temperature difference and the farthest distance in each pixel window are recorded as the heat pole of each pixel window.

[0085] The heat sequence of the pixel window of each pixel is taken as input, and the mutation points in each heat sequence are obtained by calling the sliding window segmentation (Window Sliding Segmentation) in Ruptures (Python library). The position coordinates of all mutation points in each pixel window are sequenced in ascending order of the horizontal axis and the vertical axis as the heat change point distribution sequence of each pixel window.

[0086] In one embodiment of the present application, for the pixel window of each pixel, the fusion result of the difference in temperature values ​​of all arbitrary two pixels in the pixel window is used as the neighborhood heat difference of the pixel window.

[0087] It should be noted that the difference indicates the degree of difference between two data, which can be calculated by difference, absolute value of difference, ratio, etc., and the present application does not impose any restrictions on this; fusion means combining multiple variables in a form of enhanced overall effect, which can be calculated by addition, multiplication, etc., and the present application does not impose any restrictions on this.

[0088] In one embodiment of the present application, for the pixel window of each pixel, the accumulated result of the absolute value of the difference between the temperature values ​​of all two arbitrary pixels in the pixel window is taken as the neighborhood heat difference of the pixel window.

[0089] In another embodiment of the present application, for the pixel window of each pixel, the average temperature value of all pixels remaining in the pixel window except the mutation point is calculated, and the cumulative result of the absolute value of the difference between all mutation points in the pixel window and the average value is calculated as the neighborhood heat difference of the pixel window.

[0090] The smaller the temperature difference during the molten siliconization process of the silicon carbide preform at the corresponding position in the pixel neighborhood range, the more blurred the neighborhood heat difference is, and the smaller the cumulative absolute value of the difference between each mutation point and the temperature average of all non-mutation points in the pixel window is, that is, the smaller the neighborhood heat difference is.

[0091] In one embodiment of the present application, for the pixel window of each pixel, the discreteness of the positions of all mutation points in the pixel window is calculated as the discreteness of the heat change point distribution of the pixel window.

[0092] It should be noted that the degree of discreteness indicates the discreteness of data distribution, which can be calculated specifically by variance, standard deviation, entropy, etc., and this application does not impose any restrictions on this.

[0093] In one embodiment of the present application, for a pixel window of each pixel, the entropy of the heat change point distribution sequence of the pixel window is calculated as the discreteness of the heat change point distribution of the pixel window.

[0094] In another embodiment of the present application, for the pixel window of each pixel, the cumulative sum of the Euclidean distances between all mutation points in the pixel window and the center point of the pixel window is calculated as the discreteness of the heat change point distribution of the pixel window.

[0095] When the local hot and cold spot concentration phenomenon is milder due to the uneven distribution of molten heat during the molten siliconization process of the silicon carbide preform, the discreteness of the heat change point distribution is stronger, and the entropy of the heat change point distribution sequence corresponding to the pixel window is greater.

[0096] The neighborhood heat difference is reverse mapped, and the reverse mapping result is merged with the discreteness of the heat change point distribution to determine the heat uniformity factor of each pixel window.

[0097] It should be understood that the reverse mapping indicates that the dependent variable is negatively correlated with the independent variable, the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases, such as a subtraction relationship, a division relationship, etc.

[0098] In one embodiment of the present application, for the pixel window of each pixel, the sum of the neighborhood heat difference and the adjustment factor is calculated, and the ratio of the discreteness of the heat change point distribution to the sum is used as the heat uniformity factor of the pixel window of each pixel. Among them, the role of the adjustment factor is to avoid the denominator being zero, which leads to inability to calculate. In this embodiment, the adjustment factor is artificially set to 0.1, and the implementer can set it according to the actual situation, and this application does not limit this.

[0099] When the heat distribution in the process of molten silicon infiltration of the silicon carbide preform at the corresponding position of the pixel neighborhood is more uniform, the temperature value difference within the pixel neighborhood is smaller, and the distribution of temperature anomaly points is more discrete. That is, the larger the heat uniformity factor is, the smaller the neighborhood heat difference is, and the more significant the discreteness of the heat change point distribution is.

[0100] In one embodiment of the present application, for the pixel window of each pixel, the absolute value of the temperature difference between the thermal poles in the pixel window and the Euclidean distance between the thermal poles are calculated, and the inverse of the product of the absolute value of the temperature difference between the thermal poles and the corresponding Euclidean distance is used as the thermal diffusion factor of the pixel window.

[0101] When the heat diffusion during the molten siliconization process of the silicon carbide preform at the position corresponding to the pixel window is faster, the heat diffusion factor is larger, the temperature difference between the heat poles corresponding to the pixel window is smaller, and the heat transfer path length is shorter.

[0102] A two-dimensional rectangular coordinate system is constructed with the central pixel of each pixel window as the origin, and all pixels in the two-dimensional rectangular coordinate system corresponding to the pixel window that are centrally symmetrical with the origin as the symmetry center are recorded as groups of symmetrical pixels. The fusion results of the temperature differences between all groups of symmetrical pixels in the pixel window are reversely mapped to obtain the thermal imaging symmetry of the corresponding pixel window. It should be noted that the groups of symmetrical pixels obtained in this embodiment only consider the positional symmetry and do not include the temperature values ​​of the pixels.

[0103] In one embodiment of the present application, the reciprocal of the cumulative result of the absolute value of the temperature difference between all groups of symmetrical pixels in the pixel window is used as the thermal imaging symmetry of the pixel window. It should be noted that in order to avoid the denominator being zero and causing calculation failure, when calculating the thermal imaging symmetry of the pixel window, the adjustment factor is added to the cumulative result of the absolute value of the temperature difference between all groups of symmetrical pixels in the pixel window.

[0104] When the thermal symmetry of the dense image is more significant due to the smooth melt siliconization process of the silicon carbide preform at the corresponding position of the pixel window, the symmetry of the thermal imaging is stronger, and the cumulative difference in temperature values ​​between all symmetrical pixels in the pixel window is smaller.

[0105] In one embodiment of the present application, for the pixel window of each pixel, the product of the heat diffusion factor and the thermal imaging symmetry is calculated as the heat transfer efficiency of the pixel window.

[0106] The heat transfer coefficient of any pixel window can be determined based on the heat uniformity factor and the heat transfer efficiency. Specifically, in one embodiment of the present application, the heat transfer coefficient is the normalized product of the heat uniformity factor and the heat transfer efficiency. The larger the heat uniformity factor, the smaller the difference in temperature values ​​within the pixel neighborhood, the greater the probability of uniform heat distribution in the process of molten siliconization of the silicon carbide preform at the corresponding position of the pixel window, the higher the possibility of faster heat diffusion, the higher the heat transfer efficiency, and the greater the heat transfer coefficient.

[0107] At this point, the heat transfer coefficients of all pixel windows in the dense image at the tth moment can be obtained according to the above method, and assigned to the central pixel of each pixel window to obtain the heat transfer coefficient of each pixel in the dense image at the tth moment.

[0108] Step S003, analyzing the difference in heat transfer coefficients and distance relationship of some pixels in the dense image, and obtaining the molten siliconization sufficiency coefficient of the dense image at each moment.

[0109] The heat conductivity coefficients of all pixels in the dense image are arranged in descending order as the heat conduction sequence of the dense image, and the last 20% of the pixels in the heat conduction sequence are selected as molten silicon-deficient pixels, wherein the number of molten silicon-deficient pixels can be set by the implementer according to the actual situation, and this application does not impose any restrictions on this. All molten silicon-deficient pixels with the same heat conductivity coefficient in the dense image are connected in sequence to form an isothermal conduction path, and the heat conductivity coefficients of all pixels on each isothermal conduction path in the dense image are combined to form an isothermal conduction sequence for each isothermal conduction path. The schematic diagram of the isothermal conduction path is shown in FIG. Figure 3 As shown, pixels 1, 2, and 3 represent molten silicon-deficient pixels with different heat transfer coefficients, and the three line segments represent three equal heat conduction paths.

[0110] In one embodiment of the present application, for a dense image at each moment, the DTW distance between any two equal heat conduction sequences is calculated, and the cumulative result of all the DTW distances in the dense image is used as the heat conduction difference of the dense image.

[0111] It should be noted that this embodiment only proposes a method for calculating the distance between two equal heat conduction sequences, namely, the DTW distance. The implementer may select other existing feasible distance calculation methods according to actual conditions, and this application does not impose any limitation on this.

[0112] When the melt siliconization treatment of the silicon carbide preform is more complete, the heat conduction conditions on all isothermal conduction paths in the dense image are more similar, the heat conduction difference is smaller, and the cumulative result of the DTW distance between all isothermal conduction paths corresponding to isothermal conduction sequences in the dense image is smaller.

[0113] For the dense image at each moment, for all melt-silicon-deficient pixels, the fusion result of the difference between any two melt-silicon-deficient pixels is used as the melt-silicon-deficient factor of the dense image.

[0114] In one embodiment of the present application, for the dense image at each moment, the accumulated result of the absolute value of the difference in heat transfer coefficient between any two molten silicon-deficient pixels is used as the molten silicon-deficient factor of the dense image.

[0115] When the melt siliconization treatment of the silicon carbide preform is more complete, the heat conduction difference between the melt siliconization-deficient pixels is smaller, the melt siliconization-deficient factor is smaller, and the absolute value of the cumulative difference of the heat conduction coefficient between all the melt siliconization-deficient pixels in the dense image is smaller.

[0116] For the dense image at each moment, the fusion result of the molten siliconization deficiency factor and the thermal conductivity difference is reversely mapped to obtain the molten siliconization sufficiency coefficient of the dense image.

[0117] In one embodiment of the present application, for the dense image at each moment, the inverse of the product of the molten siliconization deficiency factor and the heat conduction difference is calculated as the molten siliconization sufficiency coefficient of the dense image.

[0118] The more fully the melt siliconization treatment of the silicon carbide preform is, the smaller the difference in heat conduction within the neighborhood of the melt-deficient pixel in the dense image corresponding to the melt siliconization treatment process of the silicon carbide preform is, the more similar the heat conduction conditions between the equal heat conduction paths are, the larger the melt siliconization sufficiency coefficient is, and further, the smaller the difference between the melt siliconization deficiency factor and the heat conduction is, and at this time there is no need to extend the melt siliconization treatment time of the silicon carbide preform.

[0119] At this point, the melt siliconization sufficiency coefficient of the dense image at any time can be obtained according to the above method.

[0120] Step S004, adjusting and optimizing the densification processing time in combination with the molten siliconization sufficient coefficient, and obtaining a silicon carbide ceramic-based composite material after completing the densification process.

[0121] When the molten siliconization sufficiency coefficient obtained in real time is less than the molten siliconization sufficiency threshold W, it is determined that the molten siliconization treatment of the silicon carbide preform is insufficient at this time, and the molten siliconization treatment time needs to be extended by Ymin; when the molten siliconization sufficiency coefficient obtained in real time is greater than or equal to the molten siliconization sufficiency threshold W, it is determined that the molten siliconization treatment of the silicon carbide preform is sufficient at this time, and there is no need to adjust the molten siliconization treatment time. The value range of the molten siliconization sufficiency threshold W is [0,1]. The larger W is, the stricter the requirements for the molten siliconization treatment of the silicon carbide preform are. In this application, W is taken as 0.6, and the adjustment time Y is 5. The implementer can set it according to the actual situation, and this application does not limit it.

[0122] After adjusting the melt siliconization treatment time of the silicon carbide preform, the densification treatment of the silicon carbide preform is completed to obtain a silicon carbide ceramic matrix composite material. Figure 4 In addition, the same method for preparing the silicon carbide ceramic matrix composite material as in Example 1 of the present application is adopted, except that during the densification treatment by the melt siliconizing method, the treatment time is kept constant at 80 minutes, which is used as a comparative example.

[0123] The silicon carbide ceramic-based composite materials prepared in the examples of the present application and the comparative examples were subjected to performance tests, and the test methods and results are as follows:

[0124] Mechanical properties: An electronic universal testing machine was used to test the flexural strength and fracture toughness of the composite materials.

[0125] Electromagnetic shielding performance: Refer to IEC / TR 6215341:2007, test in the 2~18GHz frequency range to determine the maximum shielding performance.

[0126] High temperature resistance: The composite material was treated at 2000°C for 30 minutes, and the mechanical properties and electromagnetic shielding properties were tested again. The test results are shown in Table 1.

[0127] Table 1 Test results of silicon carbide ceramic matrix composites

[0128]

[0129] As can be seen from Table 1, compared with the comparative example, after adjusting the melt siliconization treatment time, the performance of the prepared silicon carbide ceramic matrix composite material can be significantly improved in the embodiment of the present application, which indirectly shows that the present application improves the accuracy of the densification treatment time. Figure 5 As shown, the left ordinate is the bending strength, the right ordinate is the fracture toughness, the horizontal axis is the embodiment, the star-shaped data points represent the data before high temperature treatment, and the square data points represent the data after high temperature treatment. Figure 6 As shown, the ordinate is the maximum shielding effectiveness, the abscissa is the embodiment, the star-shaped data points represent before high-temperature treatment, and the square data points represent after high-temperature treatment.

[0130] In summary, the present application obtains the heat conductivity coefficient of each pixel in the image by collecting infrared thermal imaging images at each moment in the molten siliconization densification process during the preparation process of the silicon carbide ceramic-based composite material, analyzing the distribution and difference of the temperature of the pixels in the neighborhood of each pixel in the image, and comprehensively considering the temperature uniformity and heat transfer efficiency of the silicon carbide preform during the molten siliconization treatment process, more accurately evaluating the sufficient uniformity of heat conduction within the pixel neighborhood, and improving the reliability of the judgment of the sufficiency of molten siliconization; analyzing the difference and distance relationship of the heat conductivity coefficients of some pixels in the image, obtaining the molten siliconization sufficiency coefficient of the image at each moment, reflecting the degree of densification of the silicon carbide preform at the corresponding moment of the image, and using this to measure the rationality of the densification processing time of the silicon carbide preform, improving the accuracy of the densification processing time of the silicon carbide preform, and then improving the density of the silicon carbide ceramic-based composite material, preventing the silicon carbide preform from being insufficiently densified due to too short a molten siliconization treatment time, which affects the performance of the silicon carbide ceramic-based composite material.

[0131] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0133] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, 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 in the protection scope of the present application.

Claims

1. An intelligent production process for silicon carbide ceramic-based composite materials, characterized in that: The process includes the following steps: S1, using a sodium hydroxide solution to perform an alkali treatment on the carbon fiber, adding the alkali-treated carbon fiber to a silver ammonia solution, adding a glucose solution to perform a water bath heating treatment, filtering out the solid, washing, and drying to obtain a pretreated carbon fiber; S2, using radio frequency magnetron sputtering to sequentially form a titanium carbide layer and a silicon carbide layer on the surface of the pretreated carbon fiber to obtain a composite carbon fiber and weave it into a carbon fiber cloth; S3, depositing silicon carbide on the carbon fiber cloth by chemical vapor deposition to obtain a silicon carbide preform, densifying the silicon carbide preform by melt siliconization, and adjusting and optimizing the densification processing time, specifically: Collect infrared thermal imaging images at each moment in the process of melt siliconization and densification, analyze the distribution and difference of the temperature of the pixels in the neighborhood of each pixel in the image, and obtain the heat transfer coefficient of each pixel in the image; Analyze the difference and distance relationship of the heat transfer coefficient of some pixels in the image to obtain the molten siliconization sufficiency coefficient of the image at each moment; Adjusting and optimizing the densification processing time in combination with the molten siliconization sufficient coefficient; After completing the densification treatment, a silicon carbide ceramic-based composite material is obtained.

2. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The alkali treatment comprises: The carbon fiber was added into a sodium hydroxide solution, the weight ratio of the sodium hydroxide solution to the carbon fiber was 6.5-7.5:1, the concentration of the sodium hydroxide solution was 25-30%, and the solution was treated with 340-400W ultrasonic oscillation for 1.5-2h. After filtering, the solution was rinsed with deionized water for 2-3 times.

3. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The preparation of the silver ammonia solution comprises: To a 0.1-0.2 mol / L silver nitrate solution, add ammonia solution with a mass concentration of 2.53% dropwise. After a brown precipitate is produced, continue to add ammonia solution dropwise until the precipitate disappears. Then, add 1-allyl-2,3-dimethylimidazolium tetrafluoroborate with a molar amount of 2% of the silver nitrate solution and ethyltriphenylphosphine tetrafluoroborate with a molar amount of 1% of the silver nitrate solution. Stir evenly to obtain a silver ammonia solution.

4. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The volume of the glucose solution is twice that of the silver ammonia solution, and the concentration is 0.71 times that of the silver ammonia solution. The water bath heating temperature is 55-60° C., and the heating time is 90-100 min.

5. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The forming of the titanium carbide layer and the silicon carbide layer comprises: The parameters of the titanium carbide layer are: vacuum degree before sputtering is 1.0*10 -4 ~1.0*10 -3 Pa, RF sputtering power is 850~1000W, deposition time is 110~120min, the distance between the target and the pretreated carbon fiber is 70~80mm, and the argon flow rate is 35~45sccm; The parameters of the silicon carbide layer are: vacuum degree before sputtering is 1.0*10 -4 ~1.0*10 -3 Pa, the RF sputtering power is 1050~1200W, the deposition time is 60~70min, the distance between the target and the pretreated carbon fiber is 70~80mm, and the argon flow rate is 35~50sccm.

6. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The parameters of the chemical vapor deposition method include: The temperature is 1030~1080℃, the time is 95~110h, the atmosphere pressure is 3~4KPa, the H2 flow rate is 280~320mL / min, the Ar flow rate is 320~350mL / min, the trichloromethylsilane temperature is 35~40℃, and the molar mass ratio of H2 to trichloromethylsilane is 8.5~10:

1.

7. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The determination of the heat transfer coefficient includes: With each pixel as the center, a pixel window of a preset size is constructed, and the fusion result of the difference in the temperature values ​​of all two pixels in the pixel window is used as the neighborhood heat difference of the pixel window; the mutation points of the temperature values ​​of all pixels in the pixel window are obtained, and the discrete degree of the positions of all the mutation points in the pixel window is used as the discreteness of the heat change point distribution of the pixel window; Reverse mapping the neighborhood heat difference, fusing the reverse mapping result with the discreteness of the heat change point distribution, and determining it as the heat uniformity factor of the pixel window; Obtain the two pixels with the largest temperature value difference and the farthest distance in the pixel window of each pixel, and perform reverse mapping on the fusion result of the temperature difference of the two pixels and the distance between the two pixels, as the thermal diffusion factor of the corresponding pixel window; obtain each group of symmetrical pixels that conform to the central symmetry in the pixel window of each pixel, and perform reverse mapping on the fusion result of the temperature difference between all groups of symmetrical pixels, so as to obtain the thermal imaging symmetry of the corresponding pixel window; The fusion result of the heat diffusion factor and the thermal imaging symmetry is used as the heat transfer efficiency of the corresponding pixel window; The fused normalized result of the heat uniformity factor and the heat transfer efficiency is used as the heat conduction coefficient of the central pixel of the corresponding pixel window.

8. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The determination of the sufficiency coefficient of melt siliconization includes: Arrange all the heat transfer coefficients in the image in descending order, obtain a preset number of partial pixels, and fuse the differences in the heat transfer coefficients between any two pixels in the partial pixels as the molten silicon infiltration deficiency factor of the image; Obtaining groups of pixels with equal heat transfer coefficients in the image, and fusing the measured distances of the heat transfer coefficients between any two groups of pixels as the heat transfer difference of the image; The fusion result of the molten siliconization deficiency factor and the thermal conductivity difference is reversely mapped to obtain the molten siliconization sufficiency coefficient of the image.

9. The intelligent production process of a silicon carbide ceramic-based composite material according to claim 1, characterized in that: The step of adjusting and optimizing the densification processing time in combination with the molten siliconization sufficient coefficient includes: If the molten siliconization sufficiency coefficient is less than a preset threshold, the densification processing time is extended by a preset length from the corresponding moment of the molten siliconization sufficiency coefficient; otherwise, the densification processing time is not adjusted.

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