Thermal barrier coating surface CMAS thickness detection method and device and storage medium

Abstract

The invention discloses a thermal barrier coating surface CMAS thickness detection method and device, and a storage medium. The method comprises the following steps: obtaining time-domain spectral data representing the thickness of CMAS attached to the surface of a thermal barrier coating; enhancing the influence of signal values on result data by adopting methods of data cleaning, normalization processing and missing value processing; the method comprises the following steps of: measuring a time-domain spectrogram data set obtained by a CMAS fused coating with different thicknesses for multiple times, constructing a fusion mechanism and data hybrid driving model by utilizing the difference of peak positions and combining a flight time method with a mathematical model, fitting the model based on a big data set result, fitting a model algorithm for detecting the thickness of the CMAS, and determining the thickness of the CMAS. The mechanism based on the terahertz technology and a data hybrid driving model can be used for monitoring the thickness of the CMAS attached to the surface of the coating after high-temperature cooling in real time. By the adoption of the technical scheme, signal data can be precisely processed, and the thickness of the CMAS attached to the surface of the thermal barrier coating can be precisely predicted.

Description

Technical Field

[0001] The present invention belongs to the technical field of non-destructive testing, and particularly relates to a method and device for detecting the CMAS thickness on the surface of a thermal barrier coating, and a storage medium. Background Art

[0002] Thermal barrier coatings (TBCs) are an advanced matrix protection technology, which consists of an external ceramic layer (Top Coat, TC) and an intermediate metal bond coat (Bond Coat, BC), and is used as a protective layer for hot-end components such as aero-engine and gas turbine blades. At present, the TC layer widely used in aero-engines is yttria-stabilized zirconia (YSZ) stabilized with 6wt% - 8wt% yttria, and the preparation methods include air plasma spraying, electron beam physical vapor deposition, and plasma physical vapor deposition technology. The BC layer can enhance the bonding force between the TC layer and the matrix, reduce the thermal expansion mismatch between the metal matrix and the TC layer, and enhance the oxidation resistance of the matrix. This material combination is favored because of its low thermal conductivity and good thermal expansion coefficient matching with the substrate, thus effectively extending the service life of engine components and optimizing fuel efficiency.

[0003] With the continuous increase in the operating temperature of aero-engines, TBCs materials are facing a more severe service environment, and a new form of thermal barrier coating failure has attracted wide attention. In the 1990s, Kim, Borom, Stott et al. discovered a glassy deposit composed of CaO, MgO, Al2O3, Si2O, and Fe2O3 on engine blades and defined it as CMAS (Calcium-Magnesium-Alumino-Silicate). Under the influence of high temperature, CMAS will transform into a liquid glass phase, penetrate into the microstructure of the thermal barrier coating, resulting in serious damage such as cracks and spalling of the coating, and may even damage the cooling system, leading to the risk of engine overheating and even failure. They will block the pores of the coating, erode the surface of the coating, and reduce the thermodynamic and mechanical properties of the coating through penetration and chemical reactions, ultimately resulting in coating failure, seriously affecting the performance and life of aero-engines. In addition, the thickness of the CMAS layer will hinder the effective heat dissipation of the thermal barrier coating, causing the metal substrate below the coating to withstand a higher temperature, thereby accelerating the thermal fatigue and creep processes of the metal. Secondly, the thick CMAS layer may contain a high concentration of alkaline elements, which will chemically react with the coating material at high temperature, resulting in changes in the chemical stability and phase structure of the coating, and further weakening the thermal insulation performance and mechanical strength of the coating. The attachment of the thick CMAS layer may also cause stress concentration on the surface of the coating, increasing the risk of coating cracking and spalling, posing a serious threat to the overall integrity and service life of the coating. Therefore, how to detect the thickness of CMAS on the surface of thermal barrier coatings is an urgent problem to be solved in the field of aero-materials research at present. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for detecting the thickness of CMAS on the surface of a thermal barrier coating, and a storage medium.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for detecting the thickness of CMAS on the surface of a thermal barrier coating, comprising:

[0007] Obtaining time-domain spectral data characterizing the thickness of CMAS attached to the surface of the thermal barrier coating;

[0008] Performing data cleaning, normalization processing, and missing value processing on the time-domain spectral data set, and calculating the time difference between two peaks according to the gap between the peaks;

[0009] For the repeated refraction between peaks before and after, using the time-of-flight method, fitting the distance between two refractions, and fitting these two distances according to weights to obtain a model that can measure the thickness of CMSA attached to the coating surface. Substitute the data obtained multiple times for verification to obtain a hybrid-driven model of the mechanism model and data model based on terahertz technology;

[0010] The hybrid-driven model of mechanism and data based on terahertz technology is used to monitor the thickness of CMAS attached to the coating surface in real time after high-temperature cooling.

[0011] Preferably, use the thickness of CMAS measured under SEM microscopy to train the obtained hybrid-driven model of mechanism and model repeatedly so that it can characterize the thickness of CMAS attached to the surface of the thermal barrier coating with low error and high efficiency.

[0012] Preferably, fit to measure the thickness of CMAS, that is:

[0013]

[0014] where: D is the thickness of CMAS attached to the coating surface; α1 and α2 are the weights corresponding to the two time-of-flight methods; △1 and △2 are the weights of the refractive indices of CMAS and the coating; ax 2 +bx + c is the fitting degree fitted to the discount trend of the corresponding time-of-flight method, a is 8.52, b is -0.005, and c is 0.79; t1 and t2 are the time differences between two consecutive peaks; C is the speed of light.

[0015] Preferably, apply the prepared CMAS powder on the surface of the thermal barrier coating to conduct a high-temperature thermal cycle experiment. After cooling down, use terahertz spectroscopy technology to obtain the time-domain spectral data characterizing the thickness of CMAS attached to the surface of the thermal barrier coating.

[0016] Preferably, conduct a thermal cycle experiment on the CMAS attached to the surface of the thermal barrier coating, put it into an independent high-temperature furnace body and keep it at a constant temperature of 1200 °C for 20 min, cool it with a fan, and after completion, conduct a rapid cooling for 10 min to reduce the temperature to 300 °C to form a thermal cycle lasting 1 h; and continue to conduct multiple thermal cycle experiments on the thermal barrier coating sample, and finally obtain a thickness measurement experimental sample of the molten CMAS attached to the coating surface after cooling.

[0017] The present invention also provides a device for detecting the thickness of CMAS on the surface of a thermal barrier coating, including:

[0018] An acquisition module for acquiring time-domain spectral data characterizing the thickness of CMAS attached to the surface of the thermal barrier coating;

[0019] A preprocessing module for performing data cleaning, normalization, and missing value processing on a time-domain spectral data set, and calculating the time difference between two peaks according to the gap between the peaks;

[0020] A calculation module for repeatedly refracting between peaks before and after, using the time-of-flight method to fit the distance between two refractions, and fitting these two distances according to weights to obtain a model that can measure the thickness of CMSA attached to the coating surface. Substitute the data obtained multiple times for verification to obtain a hybrid-driven model of a mechanism model and a data model based on terahertz technology;

[0021] A detection module for real-time monitoring of the thickness of CMAS attached to the coating surface after high-temperature cooling based on the hybrid-driven model of the mechanism and data of terahertz technology.

[0022] Preferably, it further includes an optimization module for repeatedly training the obtained hybrid-driven model of the mechanism and the model using the thickness of CMAS measured under a SEM microscope, so that it can characterize the thickness of CMAS attached to the surface of the thermal barrier coating with low error and high efficiency.

[0023] The present invention also provides a storage medium, on which a computer program is stored, and the computer program executes a method for detecting the thickness of CMAS on the surface of a thermal barrier coating when running.

[0024] The present invention characterizes the evaluation of the degree of CMAS corrosion of a rare earth element-doped thermal barrier coating under high-temperature conditions based on terahertz technology, and applies the idea of machine learning combined with an actual mathematical model method to the detection of the thickness of CMAS attached to the surface of the thermal barrier coating. It is an effective method that can flexibly and accurately process signal data and accurately predict the thickness of CMAS attached to the surface of the thermal barrier coating. Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0026] Figure 1 It is a flowchart of the method for detecting the thickness of CMAS on the surface of the thermal barrier coating in the embodiment of the present invention;

[0027] Figure 2 It is a flowchart for calculating the optical constants of CMAS;

[0028] Figure 3 It is a model of the thickness of CMAS attached to the surface of the thermal barrier coating;

[0029] Figure 4 It is a line graph obtained by smoothing the fit between peaks using a mathematical model.

[0030] Figure 5 It is a terahertz time-domain spectrogram obtained by attaching CMAS to the surface of the thermal barrier coating. Specific implementation manners

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0033] Embodiment 1:

[0034] As Figure 1 shown, the embodiment of the present invention provides a method for detecting the CMAS thickness on the surface of a thermal barrier coating, including the following steps:

[0035] S1: Prepare CMAS powder, spread different amounts of CMAS powder on the coating surface multiple times, and conduct a thermal cycle experiment at high temperature, and cool down to obtain a group of thickness measurement experiment samples.

[0036] S2: Detect the obtained group of experiment samples using the reflective terahertz technology, obtain a group of terahertz time-domain spectral data for each sample, and use a SEM microscope to scan the CMAS thickness attached to the coating surface corresponding to the terahertz time-domain data of each sample from the side.

[0037] S3: Clean, normalize, and process the missing values of the obtained time-domain spectral data group, and calculate the time difference between two adjacent peaks according to the gap between the peaks.

[0038] S4: For the repeated refractions between peaks before and after, use the time-of-flight method to fit the distance between two refractions, and fit these two distances according to the weights to obtain a model that can measure the CMSA thickness attached to the coating surface. Substitute the data obtained multiple times for verification, and thus calculate the unknown variables to obtain a mechanism and data hybrid-driven model based on terahertz technology.

[0039] S5: Use the CMAS thickness measured under a SEM microscope to repeatedly train the obtained mechanism and data hybrid-driven model so that it can characterize the thickness of CMAS attached to the surface of the thermal barrier coating with low error and high efficiency;

[0040] Furthermore, based on the time-of-flight method, the calculation formula of this method is:

[0041]

[0042] where S is the thickness; C is the speed of light; Δt is the time of flight between peaks; n is the refractive index of the object passing through the terahertz;

[0043] Furthermore, for the obtained time-domain spectral data set, through data cleaning, normalization processing, and missing value processing, the data purification process is performed on the obtained spectral data, covering normalization processing and advanced interpolation techniques for missing data, and image deepening processing is performed using multi-scale image transformation and deep learning-based pixel-level fusion algorithms.

[0044] Furthermore, as Figure 2 shown, in order to obtain the refractive index and extinction coefficient of CMAS, the following steps are processed:

[0045] S6: Cold isostatically press the CMAS powder to obtain a CMAS tablet group;

[0046] S7: Use the transmission terahertz technology to detect the CMAS tablet group to obtain the time-domain spectral data set of each CMAS tablet;

[0047] S8: Extract the optical constants from the time-domain data set to obtain the refractive index and extinction coefficient of CMAS, and take the average result of the CMAS data set multiple times;

[0048] S9: Substitute the obtained CMAS data into the mechanism and data hybrid-driven model to fit the unknowns;

[0049] Use the obtained transmittance time-domain spectrogram to calculate the optical constants to obtain the refractive index and extinction coefficient of CMAS, and its formula is:

[0050]

[0051] where n(ω) is the refractive index of the sample; α(ω) is the absorption coefficient of the sample; ω is the frequency, and d is the thickness of the CMAS tablet of the sample; is the phase of the ratio of the sample signal to the reference signal;

[0052] ρ(ω) is the modulus of the ratio of the sample signal to the reference signal;

[0053] Obtain the phase of the ratio of the above signal to the reference signal. The acquisition expression of the phase spectrum is as follows:

[0054]

[0055] In the above formula, x(t) is the time-domain signal, and X(f) is the time-domain signal after Fourier transform. is the phase spectrum;

[0056] Furthermore, as Figure 3 shown, after attaching a thermal barrier coating to the surface of the metal substrate, make CMAS powder, spread different contents of CMAS powder on the coating surface multiple times, and conduct a thermal cycle experiment at high temperature on it. After cooling, obtain a group of thickness measurement experimental samples; among them: conduct a thermal cycle experiment on the entire model, put it into an independent high-temperature furnace body and keep it at a constant temperature of 1200 °C for 20 min, use a fan for cooling, and conduct rapid cooling for 10 min after the end to reduce the temperature to 300 °C, forming a thermal cycle lasting for 1 h; and continue to conduct multiple thermal cycle experiments on the thermal barrier coating samples. Finally, obtain the thickness measurement experimental samples after the molten CMAS cools and adheres to the coating surface. Corrode using CMAS for thermal cycle experiments, and according to the detected terahertz time-domain spectrogram. As Figure 4 shown, according to the linear relationship between the time difference between peaks corresponding to its flight time and its thickness, use a mathematical model fitting to measure the thickness of CMAS, that is:

[0057]

[0058] where: D is the thickness of CMAS attached to the coating surface; α1 and α2 are the weights corresponding to the two time-of-flight methods; △1 and △2 are the weights of the refractive indices of CMAS and the coating; ax 2 + bx + c is the fitting degree fitted to the discount trend of the corresponding time-of-flight method, where a is 8.52, b is -0.005, and c is 0.79; t1 and t2 are the time differences between two consecutive peaks; C is the speed of light;

[0059] As Figure 5 shown, for Figure 4 the obtained terahertz time-domain signal spectrogram, according to the linear relationship between the time difference between its peaks and its thickness, fit out the Figure 5 shown curve graph, and obtain the linear function of its curve as ax 2 + bx + c, which is the obtained fitting degree. For the different terahertz time-domain spectrograms obtained, according to the time distance between different peaks in the time-domain spectrogram, use its thickness-time relationship to calculate its fitting degree according to the peak trend;

[0060] The entire obtained model is brought into the calculation based on big data to obtain α1 and α2 (the weights corresponding to the two-time time-of-flight method) and △1 and △2 (the weights of CMAS and the coating refractive index). The obtained mathematical model is a mechanism and data hybrid-driven model, which is used as a method for detecting the thickness of CMAS attached to the surface of a thermal barrier coating by using terahertz non-destructive testing technology.

[0061] Example 2:

[0062] The embodiment of the present invention also provides a device for detecting the thickness of CMAS on the surface of a thermal barrier coating, including:

[0063] An acquisition module for acquiring time-domain spectral data characterizing the thickness of CMAS attached to the surface of the thermal barrier coating;

[0064] A preprocessing module for performing data cleaning, normalization processing, and missing value processing on the time-domain spectral data set, and calculating the time difference between two peaks according to the gap between the peaks;

[0065] A calculation module for repeatedly refracting between peaks before and after, using the time-of-flight method to fit the distance between the two refractions, and fitting these two distances according to the weights to obtain a model that can measure the thickness of CMSA attached to the coating surface. Substitute the data obtained multiple times for verification to obtain a mechanism model and a data model hybrid-driven model based on terahertz technology;

[0066] A detection module for real-time monitoring of the thickness of CMAS attached to the coating surface after high-temperature cooling based on the mechanism and data hybrid-driven model of terahertz technology.

[0067] As an implementation manner of the embodiment of the present invention, it further includes: an optimization module for repeatedly training the obtained mechanism and model hybrid-driven model by using the thickness of CMAS measured under a SEM microscope, so that it can characterize the thickness of CMAS attached to the surface of the thermal barrier coating with low error and high efficiency.

[0068] Example 3:

[0069] The embodiment of the present invention also provides a storage medium, on which a computer program is stored, and the computer program executes the method for detecting the thickness of CMAS on the surface of a thermal barrier coating when running.

[0070] The above embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method and device for detecting CMAS thickness on the surface of a thermal barrier coating, and a storage medium, characterized in that: include: Obtain time-domain spectral data characterizing the thickness of CMAS attached to the surface of thermal barrier coatings; The time domain spectral data set is cleaned, normalized and processed for missing values, and the time difference between two peaks is calculated based on the difference between the peaks. For repeated refractions between multiple peaks, the distance between two refractions is fitted according to the time-of-flight method. The two distances are fitted according to the weights to obtain a model that can measure the thickness of CMSA attached to the coating surface. The data obtained multiple times are brought in for verification to obtain a hybrid driving model based on the mechanism model and data model of terahertz technology. The mechanism and data hybrid driven model based on terahertz technology is used to monitor the thickness of CMAS attached to the coating surface after high-temperature cooling in real time.

2. The method and device for detecting CMAS thickness on the surface of thermal barrier coating and the storage medium according to claim 1, characterized in that: Using the CMAS thickness measured under a SEM microscope, the obtained mechanism and model hybrid driving model was trained repeatedly, so that it can characterize the thickness of CMAS attached to the surface of the thermal barrier coating with low error and high efficiency.

3. The method and device for detecting CMAS thickness on the surface of thermal barrier coating and the storage medium according to claim 2, characterized in that: The CMAS thickness is measured by fitting, namely: Where: D is the thickness of CMAS attached to the coating surface; α1 and α2 are the weights corresponding to the two time-of-flight methods; △1 and △2 are the weights of CMAS and the refractive index of the coating; ax 2 +bx+c is the goodness of fit of the corresponding time-of-flight method discounted trend, a is 8.52, b is -0.005, and c is 0.79; t1 and t2 are the time differences between two consecutive peaks; C is the speed of light.

4. The method and device for detecting CMAS thickness on the surface of thermal barrier coating and the storage medium according to claim 3, characterized in that: The prepared CMAS powder was applied on the surface of the thermal barrier coating to carry out a high-temperature thermal cycle experiment. After cooling down, terahertz spectroscopy technology was used to obtain time-domain spectral data characterizing the thickness of CMAS attached to the surface of the thermal barrier coating.

5. The method and device for detecting CMAS thickness on the surface of thermal barrier coating and the storage medium according to claim 4, characterized in that: The CMAS attached to the surface of the thermal barrier coating was subjected to a thermal cycle experiment. It was placed in an independent high-temperature furnace and kept at a constant temperature of 1200°C for 20 minutes. It was cooled by a fan and then quickly cooled for 10 minutes to reduce the temperature to 300°C, forming a thermal cycle lasting 1 hour. The thermal barrier coating sample was subjected to multiple thermal cycle experiments. Finally, a thickness measurement test sample was obtained after the molten CMAS was cooled and attached to the coating surface.

6. A CMAS thickness detection device for thermal barrier coating surface, characterized in that: include: An acquisition module is used to acquire time-domain spectral data characterizing the thickness of CMAS attached to the surface of the thermal barrier coating; The preprocessing module is used to clean, normalize and process the time domain spectral data set, and calculate the time difference between two peaks according to the difference between the peaks; The calculation module is used to fit the distance between two refractions according to the time-of-flight method for repeated refractions between multiple peaks. The two distances are fitted according to the weights to obtain a model that can measure the thickness of the CMSA attached to the coating surface. The data obtained multiple times are brought in for verification to obtain a hybrid driving model based on the mechanism model and data model of terahertz technology. The detection module is used to monitor the thickness of CMAS attached to the coating surface after high-temperature cooling in real time based on the mechanism of terahertz technology and the data hybrid driven model.

7. The device for detecting CMAS thickness of thermal barrier coating surface according to claim 6, characterized in that: Also includes: The optimization module is used to use the CMAS thickness measured under the SEM microscope to repeatedly train the obtained mechanism and model hybrid driving model so that it can characterize the thickness of the CMAS attached to the surface of the thermal barrier coating with low error and high efficiency.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when running, executes the method for detecting CMAS thickness of a thermal barrier coating surface according to any one of claims 1 to 5.

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