CN111239019A - Thermal barrier coating micropore structural characteristic characterization method based on terahertz spectrum technology - Google Patents
Thermal barrier coating micropore structural characteristic characterization method based on terahertz spectrum technology Download PDFInfo
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Abstract
The invention provides a thermal barrier coating micropore structural characteristic characterization method based on a terahertz spectrum technology, which comprises the following steps: obtaining a sample set of thermal barrier coating samples with different micropore structural characteristics; carrying out terahertz characteristic extraction on a sample set of the thermal barrier coating sample by using a reflective terahertz time-domain spectroscopy system; respectively extracting the micro-pore structure characteristics of the thermal barrier coating samples; and establishing a support vector machine model, training the support vector machine model by adopting the characteristics, and realizing the characterization of the micropore structure characteristics of the thermal barrier coating through the support vector machine model. The terahertz characteristic extraction method adopts terahertz waves to perform terahertz characteristic extraction on the thermal barrier coating, is easy to detect the signal of the tested sample on line, in a non-contact, non-destructive and non-ionizing manner, and is simple, convenient and feasible; the method adopts a support vector machine model, has good regression capability and generalization capability on small sample data, can avoid over-fitting, and is suitable for various practical applications.
Description
Technical Field
The invention relates to a method for characterizing the structural characteristics of microporosities of a thermal barrier coating, in particular to a method for characterizing the structural characteristics of the microporosities of the thermal barrier coating based on a terahertz spectrum technology.
Background
The aeroengine technology is called as the Mingzhu in the high-end manufacturing field, is a focus technology pursued by countries in the world, and only a few countries such as China, America, Russia, Law, English and the like can independently research and develop at present. With the continuous and high-speed development of the aircraft engine technology, the thrust-weight ratio of the aircraft engine is also continuously improved, which also puts higher requirements on the high-temperature corrosion resistance of the hot-end part of the engine. Taking the thrust-weight ratio of the first four generations of fighters in the United states as an example, the thrust-weight ratio is from less than 2 to more than 10 at present, the inlet temperature of a gas turbine is over 1988K, even though the most effective cooling structure technology is used, the extreme temperature of the advanced high-temperature alloy material still can not bear the high temperature of the surface of a turbine blade of an engine, and the United states national aerospace administration (NASA) originally proposed that a thermal barrier coating layer deposited on the surface of a high-temperature alloy part can play a role in effectively protecting a substrate in the past fifty years.
Thermal barrier coatings face various challenges and even premature failure when placed in service in such harsh environments. The indexes for evaluating the performance of the thermal barrier coating are many, wherein the structural characteristics of microporosities in the ceramic layer are used as the most important performance indexes for influencing the service performance of the thermal barrier coating, and the mechanical and thermodynamic performance parameters of the thermal barrier coating, such as elastic modulus, fracture toughness, thermal conductivity and the like, are directly influenced. Along with the increase of service time, the structural characteristics of internal microporosities can be changed along with the increase of service time due to the influence of sintering, erosion and the like, the service performance and the service life of the thermal barrier coating are seriously restricted, in order to ensure the service safety of the thermal barrier coating, an effective monitoring method which can be applied to the actual service working condition is urgently needed, the structural characteristics of the internal microporosities of the thermal barrier coating can be conveniently and rapidly monitored in real time, the secondary damage of a hot-end part of an aero-engine is avoided due to detection, and meanwhile, the safety of operators needs to be ensured, namely, an online, nondestructive, non-contact, high-precision and safe detection method is needed.
Published papers and related published patent documents describe: chinese patent publication Nos. CN105758777A and CN102564914A, B.Rogue et al published in Journal of Thermal Spray Technology, volume 12, volume 4, a paper innovative structural measure of position in Thermal barrier coatings, Fan Yang et al published in International Journal of Applied Ceramic Technology, volume 6, volume 3, a paper innovative structural Evaluation of Thermal barrier coatings Using Impedance Spectroscopy, Xun Zhang et al published in Journal of the Applied Ceramic Technology, volume 6, volume 3, a paper of Thermal barrier coatings, and Xun Zhang et al published in Journal of the Applied Ceramic Technology, volume 101, volume 8, volume 6, a paper of Thermal barrier coatings, and various methods of Thermal barrier coating and Thermal barrier coating including: metallography, mercury porosimetry, electrochemical impedance, ultrasound, capacitance, eddy current, thermogravimetry, X-ray, etc. The metallographic method belongs to destructive evaluation, and the mercury porosimetry method, the electrochemical alternating impedance method, the ultrasonic method and the capacitance method need direct contact or need a coupling agent and the like, so that the automatic detection is inconvenient. The eddy current method is more suitable for conductive materials and is not suitable for non-metallic ceramic coating detection. The thermal weight loss method cannot be applied to the surface of a hot end part of an aircraft engine. The detection of the internal micropore structural characteristics of the conventional thermal barrier coating mainly focuses on the detection of internal porosity, and almost few reports are made on methods for monitoring the proportion of holes and cracks and the size of the holes. Although the microstructure characteristics can be resolved to a certain degree by the X-ray method, the method is harmful to human health, and excessive X-ray irradiation can cause defects of crystals in the thermal barrier coating. The above methods either involve porosity detection alone, do not involve further quantitative characterization of the internal microporous structure, or are not suitable for practical engineering applications, or do not provide a safe detection method, and neither involve terahertz technology.
The wavelength range of terahertz waves is between 30 μm and 3000 μm, and the position in the spectrogram of electromagnetic waves is between radio waves and light waves. The terahertz wave has the advantages of high precision, non-ionization, non-contact, no damage and the like when being used for nondestructive detection, and is very suitable for structural integrity detection of a thermal barrier coating system with a ceramic structure and a metal structure, especially for characteristic representation of a micropore structure inside the thermal barrier coating, because the terahertz wave has good penetrability on a dielectric material, cannot penetrate through the metal material, and has strong reflectivity on the surface of the metal structure.
Disclosure of Invention
The invention aims to provide a method for characterizing the micropore structure of a thermal barrier coating based on a terahertz spectrum technology, so as to further characterize the quantitative characteristics of the micropore structure and realize high-precision non-contact online nondestructive detection.
In order to achieve the aim, the invention provides a thermal barrier coating micropore structural characteristic characterization method based on a terahertz spectrum technology, which comprises the following steps:
s1: obtaining a sample set of thermal barrier coating samples with different micropore structural characteristics by adjusting the preparation process parameters of the thermal barrier coating;
s2: performing terahertz characteristic extraction on the sample set of the thermal barrier coating sample in the step S1 by using a reflective terahertz time-domain spectroscopy system;
s3: respectively extracting microporosity structure characteristics of thermal barrier coating samples with different microporosity structure characteristics in a sample set, wherein the microporosity structure characteristics comprise porosity, proportion of holes and cracks and size of holes;
s4: and establishing a support vector machine model, training the support vector machine model by adopting the terahertz characteristics in the step S2 and the micropore structure characteristics in the step S3, and realizing the characterization of the micropore structure characteristics of the thermal barrier coating through the support vector machine model.
The number of the terahertz features is 4-8.
The terahertz features include first, second, third, fourth and fifth terahertz features, and the step S2 includes:
s21: utilizing a terahertz time-domain spectroscopy system to respectively obtain a plurality of sample reflection type time-domain signals G corresponding to all thermal barrier coating samples of a sample setsampleAnd a reference reflection time domain signal G of a standard reference samplereferenceSubsequently reflecting the plurality of sample time domain signals G using principal component analysissampleCarrying out dimensionality reduction on each sample reflection type time domain signal, and selecting a principal component with the sum of accumulated contribution rates exceeding 95% as a first terahertz characteristic;
s22: reflecting the plurality of samples of the step S21 by Fourier transform to obtain a time-domain signal GsampleAnd a reference reflected time-domain signal GreferenceFourier transform is carried out to respectively obtain a plurality of sample reflection type frequency spectrum signals FsampleAnd a single reference reflected spectral signal FreferenceSubsequently, a plurality of samples of the reflected spectrum signal F are analyzed by principal component analysissamplePerforming dimensionality reduction treatment, and selecting a principal component with the sum of the accumulated contribution rates exceeding 95% as a second terahertz characteristic;
s23: according to the plurality of sample reflective spectrum signals F in the step S22sampleAnd a single reference reflected spectral signal FreferenceObtaining a sample effective reflectivity signal RsampleAnd then using principal component analysis to obtain effective reflectivity signal R of samplesamplePerforming dimensionality reduction treatment, and selecting a principal component with the sum of the accumulated contribution rates exceeding 95% as a third terahertz characteristic;
s24: extracting each sample reflected time-domain signal GsampleOf the first peak of the first reflected waveform of (1)1-sampleTo calculate the sample reflection energy and extract the reference reflection time domain signal GreferenceOf the first peak of the first reflected waveform of (1)1-referenceTo calculate the reference reflection energyCalculating an effective refractive index n according to the sample reflection energy and the reference reflection energy, and adopting the effective refractive index n as a fourth terahertz characteristic;
s25: respectively extracting sample reflection type time domain signals GsampleIs detected by the second and third reflected waveforms2And Δ t3Combined with a reference reflected time-domain signal GreferenceIs measured by the time difference at between the peak and the trough of the first reflected waveformreferenceAnd obtaining a second and a third relative aspect ratio as a fifth terahertz characteristic.
In the step S23, the terahertz effective reflectance signal RsampleComprises the following steps:
in the formula, FFT (G)sample) For the sample reflected spectrum signal, FFT (G)reference) Is a reference reflected spectrum signal.
In step S24, the effective refractive index n is calculated by combining fresnel equations based on the relationship between the reflected energy and incident energy ratio of the terahertz wave signal, and is:
wherein n is effective refractive index, n is greater than 1, H1-sampleIs a sample reflection time domain signal GsampleOf the first peak of the first reflected waveform, H1-referenceIs a reference reflection time domain signal GreferenceThe intensity value of the first peak of the first reflected waveform.
In the step S3, 5 thermal barrier coating samples with the same thermal barrier coating preparation process parameters are used, the thermal barrier coating samples are ground and polished by sand paper, each thermal barrier coating sample selects 10 different cross sections, each cross section selects 10 SEM images of non-metallic ceramic layers that are not communicated with each other, and then the microstructure picture is subjected to micropore structure feature statistics by imag J image analysis statistics software to extract the micropore structure features of each thermal barrier coating sample with different micropore structure features.
The processing flow of the imag J image analysis and statistics software comprises the following steps: and performing threshold conversion on the SEM image to obtain a pore microstructure schematic diagram, performing image corrosion on the pore microstructure schematic diagram, performing expansion treatment, extracting micro-pores and micro-cracks respectively, and further performing statistics to obtain the porosity, the proportion of the pores and the cracks and the size of the pores and obtain a statistical average value of the porosity, the proportion of the pores and the cracks and the size of the pores respectively to serve as the micro-pore structure characteristic of each thermal barrier coating sample.
In the step S4, when the support vector machine model is built, the kernel function of the support vector machine model is a radial basis function, and the penalty parameter and the kernel function of the support vector machine model are obtained by optimizing with a cross validation method.
In step S4, when a support vector machine model is trained, the terahertz features are used as input features of the support vector machine model, and the internal micropore structure features are used as output features of the support vector machine model.
In the step S4, sample features are composed of the terahertz features and the internal micropore structure features, the support vector machine model is trained using 80% of the total number of the sample features as a training set to obtain optimal parameters, and 20% of the total number of the sample features as a verification set to determine the regression accuracy of the support vector machine model.
The method for characterizing the structure characteristics of the microporosity of the thermal barrier coating based on the terahertz spectrum technology adopts terahertz waves to extract the terahertz characteristics of the thermal barrier coating, and because the terahertz waves have strong penetrating power to a ceramic layer in the thermal barrier coating, the method is easy to detect the signals of a tested sample on line, in a non-contact, non-destructive and non-ionizing manner, and is simple, convenient and feasible; the method adopts the support vector machine model, so the method has good regression capability and generalization capability on small sample data, can avoid over-fitting, can be used for establishing the support vector machine model by using data acquired in a laboratory or actual engineering data, and is suitable for various practical applications. In addition, the terahertz characteristic extraction adopts a principal component analysis method to perform dimensionality reduction processing on the time domain spectrum signal, the frequency domain spectrum signal and the effective reflectivity spectrum signal so as to ensure that the mutual influence among different input characteristic parameters is reduced to the minimum, the respective characterization capabilities are fully exerted, the detection speed is improved while the actual detection precision is ensured, the comprehensive prediction performance is superior to the regression prediction effect of the traditional neural network, wherein the effective refractive index estimation is performed by directly utilizing the time domain signal of the thermal barrier coating sample and the time domain signal of the reference sample, and the dispersion effect is ignored.
Drawings
FIG. 1 is a schematic flow chart of a method for characterizing the microporosity structure of a thermal barrier coating based on a terahertz spectroscopy technology;
FIG. 2 is a schematic diagram of a typical structure of a tested thermal barrier coating of a thermal barrier coating microporosity structural feature characterization method based on terahertz spectroscopy of the present invention;
FIGS. 3A and 3B are waveform diagrams of a sample reflective time domain signal and a reference reflective time domain signal of a thermal barrier coating micropore structure characteristic characterization method based on a terahertz spectrum technology, respectively;
FIG. 4 is a processing flow chart of imagJ image analysis statistical software of the thermal barrier coating micropore structural feature characterization method based on the terahertz spectrum technology.
Detailed Description
The invention is further illustrated with reference to the accompanying drawings and specific examples. Technical solutions in the embodiments of the present invention are described in full detail, and the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by other technicians without invasive labor based on the embodiments of the present invention are within the protection scope of the present invention.
FIG. 1 shows a schematic flow chart of a method for characterizing a thermal barrier coating micropore structure based on a terahertz spectroscopy technology.
A thermal barrier coating micropore structure characteristic characterization method based on a terahertz spectrum technology comprises the following specific steps:
step S1: obtaining a sample set of thermal barrier coating samples with different micropore structural characteristics by adjusting the preparation process parameters of the thermal barrier coating;
as shown in fig. 2, the thermal barrier coating sample can be used for terahertz testing, and includes a non-metal ceramic layer 1 ', a metal bonding layer 2 ' and a metal substrate layer 3 ' which are sequentially arranged from top to bottom. The preparation process parameters of the thermal barrier coating comprise spraying distance, powder feeding speed, spraying power, powder size specification and other process parameters.
Step S2: performing terahertz characteristic extraction on the sample set of the thermal barrier coating sample in the step S1 by using a reflective terahertz time-domain spectroscopy system; in this embodiment, the terahertz features include first, second, third, fourth and fifth terahertz features, and the step S2 includes:
step S21: vertically incidence on the sample set of the thermal barrier coating test sample and a standard reference test sample at an incidence angle of 0 DEG by utilizing a terahertz time-domain spectroscopy system, and respectively obtaining a plurality of sample reflection type time-domain signals G corresponding to all the thermal barrier coating test samples in the sample setsampleAnd a reference reflection time domain signal G of a standard reference samplereference(ii) a The plurality of sample reflected time domain signals G are then analyzed by Principal Component Analysis (PCA)samplePerforming dimensionality reduction processing to obtain the accumulated contribution rates of the principal components, and selecting the principal components with the accumulated contribution rate sum exceeding 95% (the number of the principal components may be one or more, because the accumulated contribution rate sum reaching 95% needs one principal component, or needs the first two, or needs the first three or even more accumulation) as the first terahertz feature in modeling of the support vector machine.
Wherein the standard reference sample has no thermal barrier coating, is only metal, and is only an ideal smooth complete reflection surface.
In this embodiment, the terahertz time-domain spectroscopy system is a reflective terahertz time-domain spectroscopy system, for example, a reflective terahertz time-domain spectroscopy system of the type TeraView EOTPR5000, but in other embodiments, other terahertz time-domain spectroscopy systems may be substituted. The terahertz wave has the advantages of high precision, non-ionization, non-contact, non-damage and the like when being used for nondestructive detection, and the terahertz wave has good penetrability on dielectric materials, can not penetrate through metal materials and has strong reflectivity on the surface of a metal structure, so that the terahertz time-domain spectroscopy system is very suitable for structural integrity detection of a thermal barrier coating system with a ceramic structure and a metal structure, particularly for structural characteristic representation of microporosities of the thermal barrier coating.
The principal component analysis method can be obtained by calling functions by using the existing software MATLAB, Python and the like.
Because the sample set of the thermal barrier coating test samples comprises a plurality of thermal barrier coating test samples with different micropore structural characteristics, the obtained sample reflection type time domain signal set GsampleIs a set of many samples of the reflected time domain signal, and the obtained reference reflected time domain signal GreferenceOnly a single reflected time domain signal. Sample reflection type time domain signal set G of obtained sample set of thermal barrier coating samplesampleAnd a reference reflected time-domain signal G of a standard reference samplereferenceAs shown in fig. 3A and 3B, respectively.
Step S22: reflecting the plurality of samples of the step S21 by Fourier transform to obtain a time-domain signal GsampleAnd a reference reflected time-domain signal GreferenceFourier transform is carried out to respectively obtain a plurality of sample reflection type frequency spectrum signals FsampleAnd a single reference reflected spectral signal Freference(ii) a Subsequently, a plurality of samples of the reflected spectrum signal F are analyzed by principal component analysissamplePerforming dimensionality reduction treatment to obtain the cumulative contribution rate of the main component, wherein the sum of the cumulative contribution rates is more than 95 percentThe principal component of the terahertz wave is used as a second terahertz characteristic when the support vector machine is modeled;
step S23: according to the plurality of sample reflective spectrum signals F in the step S22sampleAnd a single reference reflected spectral signal FreferenceObtaining sample effective reflectivity signals R of a plurality of thermal barrier coating samplessample(ii) a Then using principal component analysis method to obtain effective reflectivity signal R of samplesamplePerforming line dimensionality reduction processing to obtain the accumulated contribution rate of the principal component, and selecting the principal component with the sum of the accumulated contribution rate exceeding 95% as a third terahertz characteristic when the support vector machine is modeled;
wherein, the terahertz effective reflectivity signal RsampleComprises the following steps:
in the formula, FFT (G)sample) For the sample reflected spectrum signal, FFT (G)reference) Is a reference reflected spectrum signal.
Step S24: extracting each sample reflected time-domain signal GsampleOf the first peak of the first reflected waveform of (1)1-sampleTo calculate the sample reflection energy and extract the reference reflection time domain signal GreferenceOf the first peak of the first reflected waveform of (1)1-referenceCalculating reference reflection energy, calculating an effective refractive index n according to the sample reflection energy and the reference reflection energy, and adopting the effective refractive index n as a fourth terahertz characteristic in modeling of a support vector machine.
The effective refractive index n is calculated by combining a Fresnel equation according to the relationship between the reflected energy and the incident energy ratio of the terahertz wave signal, and can be obtained by solving the equation:
wherein n is the effective refractive index, n is a root of more than 1, H1-sampleIs a sample reflection time domainSignal GsampleOf the first peak of the first reflected waveform, H1-referenceIs a reference reflection time domain signal GreferenceThe intensity value of the first peak of the first reflected waveform.
Step S25: as shown in FIG. 3A, sample reflection type time domain signals G are respectively extractedsampleIs detected by the time difference at between the peak and the trough of the second and third reflected waveforms2And Δ t3That is, the time domain broadening of the terahertz signal caused by the sample due to the micro-pores inside the thermal barrier coating, as shown in fig. 3(b), in combination with a reference reflective time domain signal GreferenceIs measured by the time difference at between the peak and the trough of the first reflected waveformreferenceObtaining a second and a third relative aspect ratio as a fifth terahertz characteristic when the support vector machine is modeled;
wherein the second and third relative aspect ratios are respectively:
wherein, Δ t2-relative to each otherAnd Δ t3-relative to each otherSecond and third relative aspect ratios, Δ t2 and Δ t3, respectively, are sample reflected time-domain signals GsampleTime difference between peaks and troughs, Δ t, of the second and third reflected waveforms in (1)referenceIs a reference reflection time domain signal GreferenceThe time difference between the peak and the trough of the first reflected waveform of (a).
Step S3: respectively extracting microporosity structure characteristics of thermal barrier coating samples with different microporosity structure characteristics in a sample set by utilizing scanning electron microscope SEM and imag J image analysis statistical software, wherein the microporosity structure characteristics comprise porosity, proportion of holes and cracks and size of holes;
specifically, 5 thermal barrier coating samples with the same technological parameters are prepared by adopting the same thermal barrier coating, the thermal barrier coating samples are continuously ground and polished by sand paper, each thermal barrier coating sample is 10 different sections, each section is 10 SEM (scanning electron microscope) images of non-metal ceramic layers which are not mutually communicated, and then the SEM images are subjected to micropore structural feature statistics by using imag J image analysis statistical software to respectively extract the micropore structural features of the thermal barrier coating samples with different micropore structural features. As shown in fig. 4, the processing flow of the imag J image analysis statistical software includes: firstly, performing threshold conversion on the SEM image to obtain a pore microstructure schematic diagram, then performing image corrosion on the pore microstructure schematic diagram, then performing expansion (namely opening operation) treatment, respectively extracting micro-pores and micro-cracks, further respectively counting 3 characteristic variables of porosity, proportion of the pores and the cracks and size of the pores, and obtaining a statistical average value of the characteristic variables, wherein the 3 characteristic variables are used as the micro-pore structure characteristics of each thermal barrier coating sample and further used as output variables during modeling of a support vector machine.
Step S4: and establishing a support vector machine model, training the support vector machine model by adopting the terahertz characteristics in the step S2 and the micropore structure characteristics in the step S3, and realizing the characterization of the micropore structure characteristics of the thermal barrier coating through the support vector machine model.
When a support vector machine model is established, a kernel function of the support vector machine model is a radial basis function, and a penalty parameter c and a kernel function g of the support vector machine model are obtained by optimizing through a cross validation method.
When a support vector machine model is trained, the terahertz features are used as input features of the support vector machine model, the internal micropore structure features are used as output features of the support vector machine model, the terahertz features and the internal micropore structure features form sample features, 80% of the total number of the sample features are used as a training set to train the support vector machine model to obtain optimal parameters, and the rest 20% of the total number of the sample features are used as a verification set to determine the regression accuracy of the support vector machine model.
Therefore, the method is based on a support vector machine algorithm, the time domain signal, the frequency spectrum signal and the effective reflectivity spectrum signal which are acquired by the terahertz reflection type spectrum system are subjected to dimensionality reduction by using a principal component analysis method, the data of the spread width ratio of the time domain waveform of the sample and the effective refractive index of the sample are combined as characteristic input, the characteristic input is combined with the micropore structure characteristic of the thermal barrier coating to perform regression model training, and a model for predicting the micropore structure characteristic of the thermal barrier coating is acquired.
The support vector machine model obtained by training can provide a novel online, non-contact, nondestructive, non-ionizing and quantifiable safety automatic detection method for the representation of the micropore structural characteristics of the thermal barrier coating, has a wide application range, and can be also applied to the detection of the microstructure characteristics of other non-ferromagnetic materials.
As mentioned above, the preferred embodiment of the present invention is only used, and is not intended to limit the scope of the present invention, and the above-mentioned embodiments of the present invention may also be varied, for example, the number of the terahertz features may also be 4 or 6-8, the effective absorption coefficient of the sample may be used as one of the terahertz features, or the method may also be generalized to the characterization of the internal microstructure features of other non-ferromagnetic materials. All simple and equivalent changes and modifications made according to the claims and the content of the specification of the present application fall within the scope of the claims of the present patent application. The invention has not been described in detail in order to avoid obscuring the invention.
Claims (10)
1. A thermal barrier coating micropore structural feature characterization method based on a terahertz spectrum technology is characterized by comprising the following steps:
step S1: obtaining a sample set of thermal barrier coating samples with different micropore structural characteristics by adjusting the preparation process parameters of the thermal barrier coating;
step S2: performing terahertz characteristic extraction on the sample set of the thermal barrier coating sample in the step S1 by using a reflective terahertz time-domain spectroscopy system;
step S3: respectively extracting microporosity structure characteristics of thermal barrier coating samples with different microporosity structure characteristics in a sample set, wherein the microporosity structure characteristics comprise porosity, proportion of holes and cracks and size of holes;
step S4: and establishing a support vector machine model, training the support vector machine model by adopting the terahertz characteristics in the step S2 and the micropore structure characteristics in the step S3, and realizing the characterization of the micropore structure characteristics of the thermal barrier coating through the support vector machine model.
2. The method for characterizing the microporosity structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 1, wherein the number of the terahertz features is 4-8.
3. The method for characterizing the microporosity structure of a thermal barrier coating based on the terahertz spectroscopy as claimed in claim 1, wherein the terahertz features comprise first, second, third, fourth and fifth terahertz features, and the step S2 comprises:
step S21: utilizing a terahertz time-domain spectroscopy system to respectively obtain a plurality of sample reflection type time-domain signals G corresponding to all thermal barrier coating samples of a sample setsampleAnd a reference reflection time domain signal G of a standard reference samplereferenceSubsequently reflecting the plurality of sample time domain signals G using principal component analysissampleCarrying out dimensionality reduction on each sample reflection type time domain signal, and selecting a principal component with the sum of accumulated contribution rates exceeding 95% as a first terahertz characteristic;
step S22: reflecting the plurality of samples of the step S21 by Fourier transform to obtain a time-domain signal GsampleAnd a reference reflected time-domain signal GreferenceFourier transform is carried out to respectively obtain a plurality of sample reflection type frequency spectrum signals FsampleAnd a single reference reflected spectral signal FreferenceSubsequently, a plurality of samples of the reflected spectrum signal F are analyzed by principal component analysissamplePerforming dimensionality reduction treatment, and selecting a principal component with the sum of the accumulated contribution rates exceeding 95% as a second terahertz characteristic;
step S23: according to the plurality of sample reflective spectrum signals F in the step S22sampleAnd a single reference reflected spectral signal FreferenceObtaining a sample effective reflectivity signal RsampleAnd then using principal component analysis to obtain effective reflectivity signal R of samplesamplePerforming dimensionality reduction treatment, and selecting a principal component with the sum of the accumulated contribution rates exceeding 95% as a third terahertz characteristic;
step S24: extracting each sample reflected time-domain signal GsampleOf the first peak of the first reflected waveform of (1)1-sampleTo calculate the sample reflection energy and extract the reference reflection time domain signal GreferenceOf the first peak of the first reflected waveform of (1)1-referenceCalculating reference reflection energy, calculating an effective refractive index n according to the sample reflection energy and the reference reflection energy, and adopting the effective refractive index n as a fourth terahertz characteristic;
step S25: respectively extracting sample reflection type time domain signals GsampleIs detected by the second and third reflected waveforms2And Δ t3Combined with a reference reflected time-domain signal GreferenceIs measured by the time difference at between the peak and the trough of the first reflected waveformreferenceAnd obtaining a second and a third relative aspect ratio as a fifth terahertz characteristic.
4. The method for characterizing the microporosity structure of a thermal barrier coating based on the terahertz spectroscopy as claimed in claim 3, wherein in the step S23, the terahertz effective reflectivity signal RsampleComprises the following steps:
in the formula, FFT (G)sample) For the sample reflected spectrum signal, FFT (G)reference) Is a reference reflected spectrum signal.
5. The method for characterizing the microporosity structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 3, wherein in the step S24, the effective refractive index n is calculated by combining fresnel equations according to the relationship between the reflected energy and incident energy ratio of the terahertz wave signal, wherein the effective refractive index n is:
wherein n is effective refractive index, n is greater than 1, H1-sampleIs a sample reflection time domain signal GsampleOf the first peak of the first reflected waveform, H1-referenceIs a reference reflection time domain signal GreferenceThe intensity value of the first peak of the first reflected waveform.
6. The method for characterizing the microporosity structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 1, wherein in step S3, 5 samples of the thermal barrier coating with the same process parameters are prepared, the samples of the thermal barrier coating are sanded and polished, each sample of the thermal barrier coating is 10 different cross sections, each cross section is 10 SEM images of non-metallic ceramic layers which are not connected with each other, and then the microstructure photo is subjected to statistics of the microporosity structure characteristics by using imag J image analysis statistics software to perform the extraction of the microporosity structure characteristics for each sample of the thermal barrier coating with different microporosity structure characteristics.
7. The method for characterizing the microporosity structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 6, wherein the processing flow of the imag J image analysis statistical software comprises: and performing threshold conversion on the SEM image to obtain a pore microstructure schematic diagram, performing image corrosion on the pore microstructure schematic diagram, performing expansion treatment, extracting micro-pores and micro-cracks respectively, and further performing statistics to obtain the porosity, the proportion of the pores and the cracks and the size of the pores and obtain a statistical average value of the porosity, the proportion of the pores and the cracks and the size of the pores respectively to serve as the micro-pore structure characteristic of each thermal barrier coating sample.
8. The method for characterizing the micropore structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 1, wherein in step S4, when a support vector machine model is built, a radial basis function is used as a kernel function of the support vector machine model, and penalty parameters and the kernel function of the support vector machine model are obtained by cross validation.
9. The method for characterizing the microporosity structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 1, wherein in step S4, when a support vector machine model is trained, the terahertz features are used as input features of the support vector machine model, and the internal microporosity structure features are used as output features of the support vector machine model.
10. The method for characterizing the microporosity structure of the thermal barrier coating based on the terahertz spectroscopy as claimed in claim 1, wherein in step S4, the sample features are composed of the terahertz features and the internal microporosity structure features, the support vector machine model is trained using 80% of the total number of the sample features as a training set to obtain optimal parameters, and 20% of the total number of the sample features as a verification set to determine the regression accuracy of the support vector machine model.
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