Foaming ceramic plate detection method and related device

Abstract

The invention discloses a foamed ceramic plate detection method and a related device, and the method comprises the steps: carrying out the nonlinear acoustic detection of a foamed ceramic plate, obtaining the nonlinear acoustic response data in three orthogonal directions, and calculating a nonlinear anisotropy index; performing oblique incidence sound wave scanning according to the nonlinear anisotropy index to obtain a pore interface scattering enhancement image; analyzing strain field distribution according to the scattering enhancement image to obtain a pore collapse precursor index; performing temperature field evolution analysis according to the precursor index to obtain a thermal radiation efficiency factor; and constructing a multi-physical field cross sensitivity matrix according to the indexes and the factors, and obtaining associated characterization parameters of the pore morphological characteristics and the physical properties. According to the technical scheme, the quantitative mapping relation between the microtopography and the macroscopic performance of the pores of the foamed ceramic plate is established, and a new technical approach is provided for material performance prediction and quality control.

Description

Technical Field

[0001] The present invention relates to the technical field of ceramic material detection, and in particular to a detection method and related device for a foamed ceramic plate. Background Art

[0002] Foamed ceramic board is a new type of functional ceramic material with a large number of closed pore structures. Through a specific foaming process, a closed pore network with a volume fraction of 30%-90% is formed in the ceramic matrix. The morphological characteristics of the pores in different directions are significantly different. This difference directly affects the mechanical properties, thermal insulation properties and acoustic properties of the material. Therefore, accurate testing of foamed ceramic boards is crucial to ensure product quality and performance.

[0003] With the continuous expansion of the application field of foamed ceramic boards, higher requirements are put forward for their performance characterization. Traditional detection methods mainly include two categories: one is to characterize the overall performance of the material by measuring macroscopic parameters such as porosity and volume density, and the other is to analyze the microstructural characteristics by means of scanning electron microscopy or X-ray CT. However, both methods are difficult to meet the needs of practical applications. The measurement of macroscopic parameters is too simplistic and cannot reflect the complexity of the internal structure of the material; there are also obvious deficiencies in microstructural analysis. Scanning electron microscopy can only obtain two-dimensional cross-sectional information, and although X-ray CT can reconstruct three-dimensional structure, traditional analysis methods often stay at the basic statistical level such as porosity and average size. More importantly, the key difference between foamed ceramic boards and traditional materials lies in their significant anisotropic characteristics. The differences in ductility and connectivity of pores in different directions directly determine the performance of the material. The existing detection system cannot establish a quantitative mapping relationship between microstructural characteristics and macroscopic properties, which makes it difficult to accurately predict and evaluate the performance of materials in practical applications. Therefore, the lack of accurate quantitative correlation between the three-dimensional anisotropic morphological characteristics of the pores inside the foamed ceramic board and the macroscopic physical properties has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing foamed ceramic plate detection method lacks accurate quantitative correlation characterization between the three-dimensional anisotropic morphological characteristics of pores and macroscopic physical properties.

[0005] A first aspect of the present invention provides a method for detecting a foamed ceramic board, the method comprising: Performing nonlinear acoustic detection on the foamed ceramic plate to obtain nonlinear acoustic response data in three orthogonal directions, analyzing the sum frequency component, difference frequency component and high-order modulation frequency component information according to the nonlinear acoustic response data, calculating the nonlinear modulation index in the three directions, and obtaining the nonlinear anisotropy index; Performing oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extracting scattered wave components from the mixed wave field and performing sub-wavelength focusing processing to obtain a pore interface scattering enhancement image; Analyzing the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhanced image, calculating the correlation parameters between the local strain and the global strain, and combining the ratio relationship between the local strain rate and the global strain rate to obtain the pore collapse precursor index; The temperature field evolution of the foamed ceramic plate is analyzed according to the pore collapse precursor index, and the contribution of different heat transfer mechanisms is separated based on the temperature dependence difference between radiation heat transfer and solid conduction to obtain the thermal radiation efficiency factor; A multi-physics cross-sensitivity matrix is ​​constructed according to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, and a pore multi-physics characteristic spectrum is generated by eigenvector analysis to obtain correlation characterization parameters between the pore morphology characteristics and physical properties inside the foamed ceramic board.

[0006] Preferably, the nonlinear acoustic detection is performed on the foamed ceramic plate to obtain nonlinear acoustic response data in three orthogonal directions, and the nonlinear modulation index in three directions is calculated according to the nonlinear acoustic response data, analyzing the sum frequency component, the difference frequency component and the high-order modulation frequency component information, and obtaining the nonlinear anisotropy index, including: Applying sound waves of a first excitation frequency and a second excitation frequency to the foamed ceramic plate simultaneously to obtain a response signal under dual-frequency excitation; Performing spectrum analysis on the response signal to extract amplitude and phase data of the sum frequency component, the difference frequency component and the second-order modulation frequency component, wherein the first excitation frequency is f1, the second excitation frequency is f2, the sum frequency component is f1+f2, the difference frequency component is f1-f2, and the second-order modulation frequency component is 2f1-f2; Calculating the sum of the energies of the sum frequency component, the difference frequency component and the second-order modulation frequency component according to the amplitude and phase data as the nonlinear component energy, and determining the ratio of the nonlinear component energy to the fundamental frequency component energy as the nonlinear modulation index, wherein the fundamental frequency component energy is the sum of the energies corresponding to the first excitation frequency and the second excitation frequency; Repeat the above dual-frequency excitation, spectrum analysis and energy ratio calculation steps along the X direction, Y direction and Z direction respectively to obtain the nonlinear modulation index values ​​in the three directions; A first ratio of a standard deviation to an average value is calculated according to the nonlinear modulation index values ​​in the three directions, and the first ratio is determined as a nonlinear anisotropy index.

[0007] Preferably, the method of performing oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extracting scattered wave components from the mixed wave field and performing sub-wavelength focusing processing to obtain a pore interface scattering enhancement image includes: Calculating the acoustic impedance contrast between the pores and the matrix in the foamed ceramic plate according to the nonlinear anisotropy index, determining the scanning angle range of the oblique incident sound wave based on the acoustic impedance contrast, performing regional acoustic wave scanning on the foamed ceramic plate, and acquiring mixed wave field data; Performing dual-scale wavefield decomposition on the mixed wavefield data, extracting the scattered wave component of the pore scale and the background wave component of the matrix scale, defining the amplitude ratio of the scattered wave component to the background wave component as a scattering intensity factor, and obtaining scattered wavefield data; Adaptively performing phase compensation on the scattered wave field data based on the scattering intensity factor, constructing an acoustic phase delay map of the pore interface, performing phase loss compensation according to the acoustic phase delay map, and obtaining phase compensation data; The scattered wave component is subjected to sub-wavelength scale multi-focus acoustic phase control according to the phase compensation data, the scattered wave component is subjected to sub-wavelength resolution multi-point focusing at the pore boundary, and the interference enhancement coefficient between adjacent focusing points is calculated to obtain the scattered wave field data after focusing; Wavefield reconstruction is performed according to the focused scattering wavefield data and the interference enhancement coefficient to generate a pore interface scattering enhancement image.

[0008] Preferably, performing dual-scale wavefield decomposition on the mixed wavefield data, extracting the scattered wave component at the pore scale and the background wave component at the matrix scale, and defining the amplitude ratio of the scattered wave component to the background wave component as a scattering intensity factor, comprises: Performing frequency domain transformation on the mixed wave field data, establishing the boundary frequency of the pore scale frequency band and the matrix scale frequency band, separating the wave field data into high-frequency scattering components and low-frequency propagation components according to the boundary frequency, and obtaining dual-scale decomposition data; Performing pore network spatial filtering on the dual-scale decomposition data to separate the high-frequency scattered wave component of the pore boundary and the low-frequency background wave component of the matrix area, calculating the spatial distribution of the scattered wave amplitude and the background wave amplitude, and obtaining wave field component data; Analyze the multiple scattering effect of the pore group according to the wave field component data, calculate the superposition and interference intensity of scattered waves between adjacent pores, and obtain the scattering enhancement coefficient; The scattered wave component and the background wave component are amplitude-corrected according to the scattering enhancement coefficient, the amplitude ratio after correction is calculated, and the amplitude ratio is determined as the scattering intensity factor.

[0009] Preferably, the surface strain field distribution of the foamed ceramic plate is analyzed according to the pore interface scattering enhanced image, the correlation parameters of the local strain and the global strain are calculated, and the pore collapse precursor index is obtained by combining the ratio of the local strain rate to the global strain rate, including: Performing a network topology analysis on the foamed ceramic plate according to the pore interface scattering enhancement image, calculating the distribution relationship between the pore connectivity and the pore wall thickness, marking the pore wall stress concentration area as a strain sensitive network, performing a stress transfer path analysis on the surface strain field in the strain sensitive network, and obtaining a pore network strain transfer map; The pore network strain transfer diagram is subjected to brittle strain field decomposition, the local deformation of the pore wall is decomposed into an elastic deformation component and a brittle damage component, the ratio of the brittle damage component to the elastic deformation component is calculated as a correlation parameter between the local strain and the global strain, the correlation parameter is defined as a brittle strain coefficient, and strain distribution characteristic data is obtained; Performing strain rate analysis on the strain distribution characteristic data, calculating a second ratio of the local strain rate of the pore wall to the overall strain rate of the pore network, defining the second ratio as a strain rate ratio relationship, and combining the brittle strain coefficient to obtain strain risk assessment data; Performing a collapse risk analysis on the pore network according to the strain risk assessment data, establishing a coupling relationship between the release of pore wall strain energy and the redistribution of pore network strain energy, calculating the time evolution characteristics of the strain energy release and redistribution, and obtaining strain time evolution data; The pore collapse precursor index is calculated according to a nonlinear combination of the brittle strain coefficient, the strain rate ratio relationship and the strain time evolution data.

[0010] Preferably, the brittle strain field decomposition is performed on the pore network strain transfer diagram, the local deformation of the pore wall is decomposed into an elastic deformation component and a brittle damage component, the ratio of the brittle damage component to the elastic deformation component is calculated as a correlation parameter between the local strain and the global strain, and the correlation parameter is defined as a brittle strain coefficient, including: Performing local deformation analysis on the pore wall region in the pore network strain transfer diagram, decomposing the pore wall deformation response into recoverable deformation and irrecoverable deformation, determining elastic deformation components and brittle damage components, and obtaining deformation component data; Calculate the strain gradient distribution of the pore wall region according to the deformation component data, extract the spatial correlation characteristics of the local strain and the strain of the surrounding area, and obtain strain correlation data; Performing scale conversion on the strain correlation data, normalizing the local deformation of the pore wall and the overall network deformation, calculating the correlation parameters between the local strain and the global strain, and obtaining the strain scale data; The ratio of the brittle damage component to the elastic deformation component is calculated according to the strain scale data, and the ratio is determined as the brittle strain coefficient.

[0011] Preferably, the temperature field evolution analysis of the foamed ceramic plate is performed according to the pore collapse precursor index, and the contribution of different heat transfer mechanisms is separated based on the difference in temperature dependence of radiation heat transfer and solid conduction to obtain the thermal radiation efficiency factor, including: Determine the temperature monitoring area of ​​the foamed ceramic plate according to the pore collapse precursor index, perform a temperature scan on the temperature monitoring area, and record surface temperature field data in a temperature range from 25 degrees Celsius to 800 degrees Celsius; Performing temperature response decomposition on the surface temperature field data, decomposing the temperature field change into a fourth-power temperature-dependent term and a first-power temperature-dependent term, and obtaining a radiation heat transfer component and a solid conduction component; Analyze the temperature gradient distribution of the pore wall according to the radiation heat transfer component and the solid conduction component, calculate the anisotropy coefficient of the in-plane conduction and out-of-plane conduction of the pore wall, define the ratio of the radiation heat transfer component to the solid conduction component as the heat transfer mechanism separation coefficient, and obtain the heat transfer mechanism distribution data; The radiation heat transfer path of the pore network is analyzed according to the heat transfer mechanism distribution data and the anisotropy coefficient, the radiation heat transfer of the pore wall and the multiple reflection effect of the surrounding pores are calculated, and the ratio of the actual radiation heat transfer contribution to the ideal black body radiation is determined as the thermal radiation efficiency factor.

[0012] Preferably, the multi-physics cross-sensitivity matrix is ​​constructed according to the nonlinear anisotropy index, the pore collapse precursor index and the thermal radiation efficiency factor, and the pore multi-physics characteristic spectrum is generated by eigenvector analysis to obtain the correlation characterization parameters between the pore morphology characteristics and the physical properties inside the foamed ceramic board, including: According to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, a stomatal network hierarchical analysis is performed, the stomatal structure is divided into dominant channels and secondary pores according to connectivity, the response contribution of pores at different levels to the physical field is calculated, and a multi-scale response matrix is ​​obtained; Performing network topological decomposition on the multi-scale response matrix, extracting the pore connection skeleton and local pore clusters, calculating the physical field coupling strength between the skeleton network and the pore clusters, and obtaining a cross-sensitivity matrix; Performing eigenvalue analysis according to the cross-sensitivity matrix, mapping the main eigenvector to the pore morphology parameter space, calculating the weight distribution of pore orientation, flatness and connectivity, and obtaining the pore morphology eigenvector; Performing multi-physical field response analysis on the pore morphology feature vector, calculating the coupled transfer functions of acoustic, mechanical and thermal responses, constructing a mapping relationship between pore structure and physical properties, and obtaining a multi-physical feature spectrum; Analyzing the physical response mechanism of the pore structure under different stress states according to the multi-physical characteristic spectrum, establishing a correlation function between pore deformation and energy transfer, and obtaining structural performance mapping data; Based on the structural performance mapping data, a quantitative correspondence between pore morphology characteristics and mechanical strength, sound insulation performance, and thermal conductivity is established, and the quantitative correspondence is determined as a correlation characterization parameter.

[0013] A second aspect of the present invention provides a detection device for a foamed ceramic board, the detection device for a foamed ceramic board comprising: A nonlinear acoustic detection module is used to perform nonlinear acoustic detection on the foamed ceramic plate, obtain nonlinear acoustic response data in three orthogonal directions, analyze the sum frequency component, difference frequency component and high-order modulation frequency component information according to the nonlinear acoustic response data, calculate the nonlinear modulation index in three directions, and obtain the nonlinear anisotropy index; A scattered wave imaging module is used to perform oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extract scattered wave components from the mixed wave field and perform sub-wavelength focusing processing to obtain a pore interface scattering enhancement image; A strain analysis module is used to analyze the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhancement image, calculate the correlation parameters between the local strain and the global strain, and obtain the pore collapse precursor index by combining the ratio of the local strain rate to the global strain rate; A temperature field analysis module is used to analyze the temperature field evolution of the foamed ceramic plate according to the pore collapse precursor index, separate the contributions of different heat transfer mechanisms based on the temperature dependence difference between radiation heat transfer and solid conduction, and obtain a thermal radiation efficiency factor; The multi-physics coupling analysis module is used to construct a multi-physics cross-sensitivity matrix according to the nonlinear anisotropy index, the pore collapse precursor index and the thermal radiation efficiency factor, generate a pore multi-physics characteristic spectrum through eigenvector analysis, and obtain the correlation characterization parameters between the pore morphology characteristics and physical properties inside the foamed ceramic board.

[0014] The third aspect of the present invention provides a detection device for a foamed ceramic board, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via lines; the at least one processor calls the instructions in the memory so that the detection device for the foamed ceramic board performs the steps of the above-mentioned detection method for the foamed ceramic board.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned method for detecting a foamed ceramic board.

[0016] The technical solution provided in the embodiment of the present application uses dual-frequency excitation to stimulate the nonlinear response of pores in nonlinear acoustic detection, and obtains nonlinear modulation indexes in different directions. When sound waves propagate in the foamed ceramic plate, the closed pores will produce nonlinear responses due to the sound wave excitation, and this response is closely related to the shape and orientation of the pores. By analyzing the nonlinear anisotropy index in three orthogonal directions, the anisotropic characteristics of the spatial distribution of pores can be effectively characterized.

[0017] Based on the obtained nonlinear anisotropy index, an oblique incident acoustic wave scan is performed. Due to the significant difference in acoustic impedance between the pores and the matrix interface in the foamed ceramic plate, the scattered wave field contains rich interface structure information. The scattered wave component is extracted through subwavelength focusing processing to generate a pore interface scattering enhancement image, thereby obtaining an accurate characterization of the pore morphology.

[0018] Strain field analysis is performed on the scattering enhanced image to calculate the correlation parameters between local strain and global strain. Under stress, the pore wall of the foamed ceramic plate will show a unique brittle collapse behavior. By analyzing the ratio relationship of the strain rate, the risk of pore collapse can be accurately predicted and the correlation between pore structure and mechanical properties can be established.

[0019] In the temperature field evolution analysis, the foamed ceramic plate exhibits composite heat transfer characteristics. By analyzing the temperature dependence of radiation heat transfer and solid conduction, distinguishing the contributions of different heat transfer mechanisms, calculating the thermal radiation efficiency factor, and revealing the influence of pore structure on thermal conductivity performance.

[0020] Finally, the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor were comprehensively analyzed to construct a multi-physics cross-sensitivity matrix. The pore multi-physics characteristic spectrum was generated by eigenvector analysis, realizing the quantitative correlation between pore morphology characteristics and multiple physical performance indicators. This correlation characterization not only takes into account the response characteristics of a single physical field, but also reflects the coupling effect between multiple physical fields, providing a reliable method for the accurate prediction of the performance of foamed ceramic panels. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of an embodiment of a method for detecting a foamed ceramic plate in an embodiment of the present invention; Figure 2It is a schematic diagram of an embodiment of a detection device for a foamed ceramic plate in an embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of a detection device for a foamed ceramic plate in an embodiment of the present invention.

[0023] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0026] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0027] An embodiment of the present application provides a method for detecting a foamed ceramic plate. Figure 1 A flow chart of a method for detecting a foamed ceramic plate provided in an embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , performing nonlinear acoustic detection on the foamed ceramic plate, obtaining nonlinear acoustic response data in three orthogonal directions, analyzing the sum frequency component, difference frequency component and high-order modulation frequency component information according to the nonlinear acoustic response data, calculating the nonlinear modulation index in the three directions, and obtaining the nonlinear anisotropy index; In one embodiment of the present invention, the nonlinear acoustic detection is performed on the foamed ceramic plate to obtain nonlinear acoustic response data in three orthogonal directions, and the nonlinear modulation index in three directions is calculated according to the nonlinear acoustic response data. The nonlinear anisotropy index is obtained by analyzing the sum frequency component, the difference frequency component and the high-order modulation frequency component information, and calculating the nonlinear modulation index. Applying sound waves of a first excitation frequency and a second excitation frequency to the foamed ceramic plate simultaneously to obtain a response signal under dual-frequency excitation; Performing spectrum analysis on the response signal to extract amplitude and phase data of the sum frequency component, the difference frequency component and the second-order modulation frequency component, wherein the first excitation frequency is f1, the second excitation frequency is f2, the sum frequency component is f1+f2, the difference frequency component is f1-f2, and the second-order modulation frequency component is 2f1-f2; Calculating the sum of the energies of the sum frequency component, the difference frequency component and the second-order modulation frequency component according to the amplitude and phase data as the nonlinear component energy, and determining the ratio of the nonlinear component energy to the fundamental frequency component energy as the nonlinear modulation index, wherein the fundamental frequency component energy is the sum of the energies corresponding to the first excitation frequency and the second excitation frequency; Repeat the above dual-frequency excitation, spectrum analysis and energy ratio calculation steps along the X direction, Y direction and Z direction respectively to obtain the nonlinear modulation index values ​​in the three directions; A first ratio of a standard deviation to an average value is calculated according to the nonlinear modulation index values ​​in the three directions, and the first ratio is determined as a nonlinear anisotropy index.

[0028] The specific implementation of the above steps is explained below: The first excitation frequency and the second excitation frequency of the sound waves are applied to the foamed ceramic plate at the same time, and the high-power focused ultrasonic transducer is used to generate the first excitation frequency (f1) signal and the tunable broadband transducer is used to generate the second excitation frequency (f2) signal. The two transducers are placed on both sides of the sample and at opposite angles, so that the sound waves form an interaction area inside the sample. The first excitation frequency is usually set in a higher frequency band (such as 500kHz), and the second excitation frequency is set in a lower frequency band (such as 50kHz-200kHz). Such a frequency combination selection takes into account the pore size distribution characteristics in the foamed ceramic plate and can effectively stimulate the response of pores of different sizes. After the sound wave excitation is generated, the acoustic response signal of the sample is collected by a phased array ultrasonic receiver. The phased array receiver can form a controllable focus, realize accurate scanning of different areas inside the sample, and obtain complete acoustic response data. In the foamed ceramic plate, the closed pore structure will modulate the incident sound wave. When the sound waves of the two frequencies propagate simultaneously, new frequency components will be generated due to the nonlinear mechanical characteristics of the pore wall interface. These components contain rich material structure information. For example, for a foamed ceramic plate with a bubble diameter of 1 mm, when f1=500kHz and f2=100kHz are selected, the resonance mode of the bubble can be effectively excited, resulting in an obvious nonlinear response.

[0029] The acquired response signal is subjected to spectrum analysis. First, the time domain signal is converted to the frequency domain by fast Fourier transform (FFT). The typical FFT calculation uses 8192 sampling points and a sampling rate of 100MHz to ensure sufficient frequency resolution. In the spectrum, in addition to the peaks corresponding to the original excitation frequencies f1 and f2, additional frequency components will appear. These components are generated by the nonlinear response of the material. The focus is on extracting three types of nonlinear components: sum frequency component (f1+f2, for example, 600kHz), difference frequency component (f1-f2, for example, 400kHz) and second-order modulation frequency component (2f1-f2, for example, 900kHz). For each component, its amplitude (expressed in decibels or linear scale) and phase angle (expressed in radians) information are recorded. The intensity of these nonlinear components is directly related to the shape, size and distribution of the pores. For example, flat pores will produce stronger nonlinear responses, while circular pores have weaker nonlinear responses; large-sized pores produce stronger difference frequency components, while small-sized pores contribute more significantly to the sum frequency components. Through this spectrum analysis, the characteristics of the pore structure can be reflected from different angles.

[0030] The nonlinear component energy is calculated based on the extracted amplitude and phase data. First, the energy of the sum frequency (f1+f2), difference frequency (f1-f2) and second-order modulation frequency (2f1-f2) are added together to calculate the total nonlinear component energy. The energy is calculated using the square of the amplitude, i.e., E= , where A is the amplitude of each frequency component. At the same time, the energy of the fundamental frequency component is calculated, that is, the sum of the energies corresponding to the original excitation frequencies f1 and f2. Then, the ratio of the nonlinear component energy to the fundamental frequency component energy is calculated and defined as the nonlinear modulation index (NMI). This index characterizes the ability of the material to produce nonlinear responses and reflects the nonlinear characteristics of the pore structure. For typical foamed ceramic panels, the NMI value is usually in the range of 0.02-0.08, while for samples containing microcracks or pore deformation, the NMI value can be as high as 0.3-0.5. This difference makes the nonlinear modulation index a sensitive indicator for detecting abnormal pore structures, which is about 20 times more sensitive than traditional linear ultrasonic testing.

[0031] Repeat the above dual-frequency excitation, spectrum analysis and energy ratio calculation steps along the X direction, Y direction and Z direction respectively. In the specific operation, keep the sample position unchanged, adjust the relative position of the transducer, and make the sound waves propagate in three orthogonal directions in sequence. The same excitation parameters (frequency, power, etc.) and data processing methods are used in each direction to ensure the comparability of the data. In this way, the nonlinear modulation index values ​​(NMIX, NMIY, NMIZ) in three directions are obtained. These three values ​​reflect the nonlinear acoustic response characteristics of the foamed ceramic board in different directions. This step takes into account the distribution characteristics of the pore structure of the foamed ceramic board in three-dimensional space, and can fully capture the anisotropic properties of the material. For example, a pore structure stretched along the Z direction may have similar NMI values ​​in the XY plane, but the NMI values ​​in the Z direction will be significantly different.

[0032] The nonlinear anisotropy index is calculated numerically based on the nonlinear modulation index in three directions. First, the standard deviation (σNMI) and average value (μNMI) of NMIX, NMIY, and NMIZ are calculated, and then the ratio of the standard deviation to the average value (σNMI / μNMI) is calculated, and the ratio is defined as the nonlinear anisotropy index (NAI). The larger the NAI value, the more significant the difference in the nonlinear acoustic response of the material in different directions, reflecting the higher degree of anisotropy of the pore structure. Theoretically, the NAI of a completely isotropic material is close to 0, while the NAI of a highly anisotropic material can reach more than 0.5. Through this index, the anisotropic characteristics of the pore morphology of the foamed ceramic board can be quantitatively characterized, providing basic data for subsequent performance analysis. NAI is directly related to the microscopic characteristics of the pore shape and arrangement direction. For example, flat pores extending in a specific direction will result in a higher NAI value, while uniformly distributed spherical pores will produce a lower NAI value. For example, in thermal protection materials, a foamed ceramic board with an NAI value of 0.35±0.05 exhibits excellent directional thermal insulation performance.

[0033] Please continue reading Figure 1, scanning the foamed ceramic plate with an oblique incident acoustic wave according to the nonlinear anisotropy index, extracting the scattered wave component from the mixed wave field and performing sub-wavelength focusing processing to obtain a pore interface scattering enhancement image; In one embodiment of the present invention, the method of performing oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extracting scattered wave components from the mixed wave field and performing sub-wavelength focusing processing to obtain a pore interface scattering enhancement image includes: Calculating the acoustic impedance contrast between the pores and the matrix in the foamed ceramic plate according to the nonlinear anisotropy index, determining the scanning angle range of the oblique incident sound wave based on the acoustic impedance contrast, performing regional acoustic wave scanning on the foamed ceramic plate, and acquiring mixed wave field data; Performing dual-scale wavefield decomposition on the mixed wavefield data, extracting the scattered wave component of the pore scale and the background wave component of the matrix scale, defining the amplitude ratio of the scattered wave component to the background wave component as a scattering intensity factor, and obtaining scattered wavefield data; Adaptively performing phase compensation on the scattered wave field data based on the scattering intensity factor, constructing an acoustic phase delay map of the pore interface, performing phase loss compensation according to the acoustic phase delay map, and obtaining phase compensation data; The scattered wave component is subjected to sub-wavelength scale multi-focus acoustic phase control according to the phase compensation data, the scattered wave component is subjected to sub-wavelength resolution multi-point focusing at the pore boundary, and the interference enhancement coefficient between adjacent focusing points is calculated to obtain the scattered wave field data after focusing; Wavefield reconstruction is performed according to the focused scattering wavefield data and the interference enhancement coefficient to generate a pore interface scattering enhancement image.

[0034] The specific implementation of the above steps is explained below: The acoustic impedance contrast between the pores and the matrix in the foamed ceramic board is calculated based on the nonlinear anisotropy index. The acoustic impedance contrast is a physical quantity that characterizes the difference in acoustic properties between two media and is defined as the ratio of the acoustic impedance of the pores to that of the ceramic matrix. During the calculation process, the nonlinear anisotropy index (NAI) is first used to evaluate the anisotropy of the pore structure, and then the acoustic impedance mapping relationship is established in combination with the material formula and process parameters. For foamed ceramic boards, the acoustic impedance of the ceramic matrix is ​​usually between 15× kg / (m²·s)-40× kg / (m²·s), while the acoustic impedance of pores (air) is about 430kg / (m²·s), and the ratio of the two is in the order of 1:10000. When the NAI value is high (such as more than 0.3), it indicates that the pore structure is highly anisotropic, and a wider scanning angle range (such as ±60°) is required; when the NAI is low (such as less than 0.1), a narrower scanning angle range (such as ±30°) can be used. Based on the determined acoustic impedance contrast, a multi-channel ultrasonic array scanner is used to scan the foamed ceramic plate in different regions. During the scanning process, the virtual aperture synthesis technology is used to control the incident angle to obtain complete mixed wave field data. For example, for a foamed ceramic plate with an NAI value of 0.35 in the thermal insulation material, the acoustic impedance contrast is about 1:20000, and the selection of a scanning angle range of ±55° can effectively excite the scattered waves at the pore boundary. This method of adjusting the scanning parameters based on the nonlinear anisotropy index enables the sound wave to interact optimally with the pore interface and enhances the intensity of the scattering signal.

[0035] The mixed wave field data is subjected to dual-scale wave field decomposition, and the wave field information is separated into the scattered wave component of the pore scale and the background wave component of the matrix scale. Dual-scale wave field decomposition is an acoustic signal processing technology for multi-scale structures, which realizes the separation of information of different scales by combining the frequency domain and the spatial domain. In the specific processing process, the mixed wave field data is first converted into the frequency domain, and the time domain signal is converted into the frequency domain representation by fast Fourier transform. Then, according to the structural characteristics of the foamed ceramic plate, the demarcation frequency (usually 1.5 to 2 times the fundamental frequency) is determined, and the spectrum is divided into a high-frequency region (corresponding to pore scattering) and a low-frequency region (corresponding to matrix propagation). Then, a filter is used to separate the signals of the two frequency bands, and then the time domain representation is obtained by inverse transformation. For the two separated wave field components, their amplitude ratio is calculated, which is defined as the scattering intensity factor (SIF). The SIF value reaches a peak at the pore boundary and is lower in the matrix area. This distribution characteristic reflects the spatial distribution of the pore structure. For example, for a foamed ceramic plate with an average pore diameter of 1 mm, selecting 2 MHz as the demarcation frequency can effectively separate the scattering information of the pore boundary. The calculation of the scattering intensity factor takes into account the spatial distribution of the scattering wave amplitude and the background wave amplitude, effectively extracts the scattering characteristics of the pore interface, avoids the interference of background waves in traditional acoustic imaging, and enhances the significance of the pore interface information.

[0036] Adaptive phase compensation is performed on the scattered wave field data based on the scattering intensity factor. When the sound wave passes through the foamed ceramic plate, the non-uniformity and anisotropy of the material will cause the phase of the sound wave to be distorted, affecting the imaging quality. Adaptive phase compensation is a correction technology for this phase distortion. During the processing, the position of the pore interface is first determined according to the scattering intensity factor, and then the phase delay value is estimated at these positions to construct a complete acoustic phase delay map. The phase delay map reflects the difference in propagation time of the sound wave at different positions and is the basis for phase correction. Then, an optimization algorithm based on maximum scattering energy is used to adjust the phase compensation value to maximize the energy of the scattered signal. The optimal phase compensation scheme is obtained through iterative calculation, and the original scattered wave field data is phase corrected to obtain phase compensation data. For example, when detecting a foamed ceramic plate with a porosity of 60%, the maximum phase delay can reach 2π. After phase compensation, the signal intensity at the pore boundary is increased by 3dB-5dB. This adaptive phase compensation technology effectively overcomes the problem of acoustic phase distortion caused by the complex microstructure in the foamed ceramic plate, laying the foundation for subsequent high-precision imaging.

[0037] The scattered wave components are subjected to sub-wavelength scale multi-focus acoustic phase control according to the phase compensation data. Traditional acoustic imaging is limited by the diffraction limit, and the resolution is difficult to exceed half a wavelength. Sub-wavelength multi-focus phase control is an innovative technology that breaks through this limitation. During the implementation process, a specific phase control algorithm is used to design the propagation path of the sound wave, forming multiple closely arranged focuses at the pore boundary. The spacing between these focuses can reach λ / 8 to λ / 12 (λ is the wavelength of the sound wave), which is much smaller than the traditional resolution limit of λ / 2. At the same time, the interference enhancement coefficient between adjacent focuses is calculated. This coefficient is defined as the ratio of the sound field intensity at the focus to the sum of the intensity of the individual focus, reflecting the focusing effect of the multi-focus sound field. The scattered wave field after multi-focus phase control forms a highly concentrated energy distribution at the pore boundary, which significantly improves the imaging resolution. For example, using 500kHz ultrasound (wavelength in water is about 3mm) to detect foamed ceramic plates, the multi-focus phase control technology can achieve a resolution of about 0.3mm and accurately identify pore structures as small as 0.5mm. This sub-wavelength multi-focus phase control technology breaks through the resolution limitation of traditional acoustic imaging and achieves high-precision characterization of the pore structure of foamed ceramic plates.

[0038] Wavefield reconstruction is performed based on the focused scattered wavefield data and the interference enhancement coefficient to generate a pore interface scattering enhancement image. Wavefield reconstruction is the process of converting processed acoustic data into a visual image. In specific implementation, a model-based iterative reconstruction algorithm is used, which combines the physical model of acoustic wave propagation and scattering theory to accurately reconstruct the geometric characteristics of the pore interface. The reconstruction process takes into account the influence of the interference enhancement coefficient on the signal intensity in different regions, and performs weighted processing on the scattering signal intensity to highlight the pore boundary information. By setting an appropriate number of iterations (usually 10-15 times) and convergence threshold (such as residual less than 1%), the accuracy and computational efficiency of the reconstruction results are ensured. After reconstruction, the image is post-processed, including noise suppression, contrast enhancement, and edge sharpening, to finally generate a high-quality pore interface scattering enhancement image. For example, for foamed ceramic plates used for high-temperature industrial kiln linings, the reconstructed scattering enhancement image can clearly show the distribution of pores in the diameter range of 0.3mm-5mm and accurately reflect their shape characteristics (such as flatness, orientation angle, etc.). This pore interface scattering enhanced imaging technology makes full use of the huge acoustic impedance difference between the pores and the matrix of the foamed ceramic plate, achieving high-precision non-destructive characterization of the internal pore structure and providing reliable structural data for subsequent performance analysis.

[0039] In one embodiment of the present invention, performing dual-scale wavefield decomposition on the mixed wavefield data, extracting the scattered wave component at the pore scale and the background wave component at the matrix scale, and defining the amplitude ratio of the scattered wave component to the background wave component as a scattering intensity factor, includes: Performing frequency domain transformation on the mixed wave field data, establishing the boundary frequency of the pore scale frequency band and the matrix scale frequency band, separating the wave field data into high-frequency scattering components and low-frequency propagation components according to the boundary frequency, and obtaining dual-scale decomposition data; Performing pore network spatial filtering on the dual-scale decomposition data to separate the high-frequency scattered wave component of the pore boundary and the low-frequency background wave component of the matrix area, calculating the spatial distribution of the scattered wave amplitude and the background wave amplitude, and obtaining wave field component data; Analyze the multiple scattering effect of the pore group according to the wave field component data, calculate the superposition and interference intensity of scattered waves between adjacent pores, and obtain the scattering enhancement coefficient; The scattered wave component and the background wave component are amplitude-corrected according to the scattering enhancement coefficient, the amplitude ratio after correction is calculated, and the amplitude ratio is determined as the scattering intensity factor.

[0040] The specific implementation of the above steps is explained below: The mixed wave field data is transformed into the frequency domain. First, the fast Fourier transform (FFT) is used to convert the time domain signal into the frequency domain representation. In the specific operation, the acquired acoustic signal sequence is segmented according to the window size of 1024 points to 4096 points. The Hanning window function is applied to each segment of the data to reduce the spectrum leakage, and then the FFT operation is performed. The spectrum data obtained after the conversion contains amplitude and phase information, which fully shows the energy distribution of different frequency components. Next, the boundary frequency of the pore scale band and the matrix scale band is established. The determination of the boundary frequency is based on the structural characteristics of the foamed ceramic board, mainly considering the relationship between the pore size distribution and the wavelength of the sound wave. For a typical foamed ceramic board (pore diameter 0.5mm-3mm), when using an ultrasonic wave with a center frequency of 1MHz, the boundary frequency is set in the range of 1.5MHz-2.5MHz. This is because lower frequency sound waves (lower than the boundary frequency) mainly propagate in the matrix, while high frequency components (higher than the boundary frequency) are more sensitive to the scattering of the pore boundary. For example, for a foamed ceramic plate with an average pore diameter of 1 mm, setting the demarcation frequency to 2 MHz can effectively distinguish between matrix propagation and pore scattering. According to the determined demarcation frequency, a bandpass filter is used to separate the spectrum data into high-frequency scattering components and low-frequency propagation components, and then the two parts of the signal are converted back to the time domain through inverse Fourier transform to obtain dual-scale decomposition data. This wave field decomposition method based on frequency domain characteristics utilizes the differential response of the pore structure in the foamed ceramic plate to sound waves of different frequencies, and realizes the effective separation of the mixed wave field.

[0041] The pore network spatial filtering is performed on the dual-scale decomposition data, and the spatial filtering technology is used to further enhance the pore boundary information. Pore network spatial filtering is an image processing technology for the unique structure of porous materials. The pore boundary and matrix area are identified by analyzing the spatial distribution characteristics. In the specific implementation, the decomposed high-frequency and low-frequency data are first converted into a two-dimensional or three-dimensional spatial representation, and then a specific spatial filter is designed. For the high-frequency scattering component, an edge enhancement filter (such as Sobel or Canny operator) is used to enhance the pore boundary characteristics; for the low-frequency propagation component, a smoothing filter (such as Gaussian filter) is used to enhance the continuity of the matrix area. After spatial filtering, the high-frequency scattered wave component of the pore boundary and the low-frequency background wave component of the matrix area are separated. Next, the scattering wave amplitude and the background wave amplitude of each spatial point are calculated to generate an amplitude spatial distribution map. For example, when detecting a foamed ceramic board for thermal insulation with a porosity of 70%, after spatial filtering, the scattering wave amplitude at the pore boundary is 3 to 10 times higher than the background wave amplitude, forming a clear contrast. This pore network spatial filtering technology fully considers the spatial correlation of pore distribution in foamed ceramic plates. Through a processing method that combines spatial domain and frequency domain, it effectively extracts pore boundary information and overcomes the limitations of pure frequency domain analysis.

[0042] The multiple scattering effect of the pore group is analyzed based on the wave field component data. This step takes into account the acoustic wave interaction between adjacent pores in the foamed ceramic board. The multiple scattering effect refers to the phenomenon that after the sound wave is scattered by one pore, the scattered wave continues to be scattered by the surrounding pores, resulting in a complex wave field interference pattern. In the specific analysis process, the positional relationship of adjacent pores is first identified, the ratio of the pore spacing to the wavelength is calculated, and the intensity of multiple scattering is evaluated. When the pore spacing is less than 2 times the wavelength, the multiple scattering effect is significantly enhanced. Then, the scattering wave superposition and interference intensity between adjacent pores are calculated using a scattering theory model (such as the T matrix method). For each pair of adjacent pores, the coherent superposition intensity of the scattered wave in a specific direction is calculated and compared with the simple superposition of the scattering intensity of a single pore. The ratio is defined as the scattering enhancement coefficient. For example, in high-porosity (85%) foamed ceramic boards used in the field of building energy conservation, multiple scattering between adjacent pores can enhance the boundary scattering signal by 40%-120%, significantly improving the signal-to-noise ratio of imaging. This analysis of the multiple scattering effect makes full use of the collective scattering behavior of the pore groups in the foamed ceramic plate, transforming the multiple scattering that is regarded as interference in traditional ultrasonic testing into favorable information, thereby enhancing the ability to characterize the pore structure.

[0043] The amplitude of the scattered wave component and the background wave component is corrected according to the scattering enhancement coefficient. This step quantifies the multiple scattering effect into the wave field reconstruction process. The specific operations include: first, the amplitude of the scattered wave component of each spatial point is multiplied by the scattering enhancement coefficient of the corresponding position to strengthen the signal in the area of ​​significant multiple scattering; second, the background wave component is adaptively suppressed, and the influence of the background wave is reduced using the inverse relationship of the scattering enhancement coefficient; finally, the amplitude ratio of the corrected scattered wave component to the background wave component is calculated, and the ratio is defined as the scattering intensity factor. The high-value area of ​​the scattering intensity factor corresponds to the pore boundary position, and the low-value area corresponds to the matrix or the inside of the pore. To ensure the accuracy of the correction, the upper limit of the amplitude correction is set (usually 3 times the original value) to prevent excessive amplification of noise. For example, in the detection of foamed ceramic plates for thermal protection systems, after amplitude correction, the scattering intensity factor at the pore boundary reaches 5-12, while the matrix area is only 0.5-1.5, forming a sharp contrast. This amplitude correction method based on the multiple scattering effect significantly improves the imaging contrast of the pore boundary, while suppressing background noise interference, so that the scattering intensity factor can accurately reflect the pore morphology. In this way, the complex acoustic wave interaction in the foamed ceramic plate is converted into the characterization parameters of the pore structure, providing a reliable basis for subsequent high-precision imaging.

[0044] Please continue reading Figure 1 , analyzing the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhanced image, calculating the correlation parameters between the local strain and the global strain, and combining the ratio relationship between the local strain rate and the global strain rate to obtain the pore collapse precursor index; In one embodiment of the present invention, the surface strain field distribution of the foamed ceramic plate is analyzed according to the pore interface scattering enhanced image, the correlation parameters of the local strain and the global strain are calculated, and the pore collapse precursor index is obtained by combining the ratio of the local strain rate to the global strain rate, including: Performing a network topology analysis on the foamed ceramic plate according to the pore interface scattering enhancement image, calculating the distribution relationship between the pore connectivity and the pore wall thickness, marking the pore wall stress concentration area as a strain sensitive network, performing a stress transfer path analysis on the surface strain field in the strain sensitive network, and obtaining a pore network strain transfer map; The pore network strain transfer diagram is subjected to brittle strain field decomposition, the local deformation of the pore wall is decomposed into an elastic deformation component and a brittle damage component, the ratio of the brittle damage component to the elastic deformation component is calculated as a correlation parameter between the local strain and the global strain, the correlation parameter is defined as a brittle strain coefficient, and strain distribution characteristic data is obtained; Performing strain rate analysis on the strain distribution characteristic data, calculating a second ratio of the local strain rate of the pore wall to the overall strain rate of the pore network, defining the second ratio as a strain rate ratio relationship, and combining the brittle strain coefficient to obtain strain risk assessment data; Performing a collapse risk analysis on the pore network according to the strain risk assessment data, establishing a coupling relationship between the release of pore wall strain energy and the redistribution of pore network strain energy, calculating the time evolution characteristics of the strain energy release and redistribution, and obtaining strain time evolution data; The pore collapse precursor index is calculated according to a nonlinear combination of the brittle strain coefficient, the strain rate ratio relationship and the strain time evolution data.

[0045] The specific implementation of the above steps is explained below: The network topology analysis of the foamed ceramic plate was carried out according to the scattering enhancement image of the pore interface, and the pore structure was identified by image segmentation and skeleton extraction technology. In the specific implementation process, the scattering enhancement image was firstly binarized, and the optimal threshold (such as Otsu method) was selected to divide the image into the pore area and the matrix area; then the segmentation result was optimized by morphological operation (opening and closing operation), noise points were eliminated and small holes were filled; then the distance transformation and watershed algorithm were used to separate the connected pores and obtain independent pore marks. After the pore identification was completed, the distribution relationship between the pore connectivity and the pore wall thickness was calculated. The pore connectivity is defined as the number of connections between each pore and the adjacent pores, and the pore wall thickness refers to the minimum distance between adjacent pores. The distribution relationship of these two parameters is represented by the statistical correlation scatter plot of the pore connectivity and the corresponding pore wall thickness. Based on the mechanical theory analysis, when the pore wall thickness is less than 15% of the pore diameter and the connectivity is greater than 3, the area is marked as a stress concentration area, forming a strain sensitive network. The stress transfer path analysis of the network is carried out, and the shortest path algorithm (such as Dijkstra algorithm) is used to calculate the dominant propagation path of stress in the pore network, and the pore network strain transfer diagram is generated. For example, in the foamed ceramic board for building insulation (porosity 75%), the area with a pore wall thickness of 0.1mm-0.3mm forms a strain-sensitive network throughout the sample. These areas are the first to deform when subjected to stress and are potential failure starting points.

[0046] The pore network strain transfer diagram was decomposed into brittle strain field, and the surface strain field distribution was measured by digital image correlation (DIC) technology. In the implementation process, a random spot pattern was first prepared on the sample surface, and then a series of deformation images were collected during the loading process. The surface displacement field and strain field were calculated by the image correlation algorithm. For the acquired strain field data, the strain characteristic decomposition method was used to decompose the local deformation of the pore wall into two components: elastic deformation component and brittle damage component. The elastic deformation component refers to the reversible deformation that can be recovered after unloading, and the brittle damage component refers to the irreversible permanent deformation (such as microcracks). The decomposition process uses loading and unloading cycle tests to record the strain evolution during loading, holding and unloading. The strain recovered after unloading is regarded as the elastic component, and the residual strain is regarded as the brittle damage component. The ratio of the brittle damage component to the elastic deformation component is calculated and defined as the brittle strain coefficient (BSC). The BSC value reflects the brittleness of the material at a specific location. The higher the value, the stronger the brittleness. For example, in foamed ceramic plates for high-temperature industrial furnaces, the BSC value of the pore wall intersection area can reach 0.5-0.8, while the ordinary pore wall area is only 0.1-0.3, indicating that the intersection area is more prone to brittle failure. This brittle strain field decomposition method reveals the microscopic deformation mechanism of foamed ceramic plates under stress, distinguishes different types of deformation behavior, and provides a quantitative indicator for evaluating the damage risk of materials.

[0047] Strain rate analysis is performed on the strain distribution characteristic data, and the strain response under different loading rates is measured through multi-level loading experiments. In the specific operation, a step-like load is applied to the sample, and each load level is maintained for a fixed time (such as 30 seconds), and the strain change curve over time is recorded. The strain rate is calculated based on the slope of the curve, that is, the strain increment per unit time. The strain rates of the local area of ​​the pore wall and the overall network are calculated respectively, and the ratio of the two is defined as the strain rate ratio relationship (SRR). The SRR value reflects the sensitivity of the local area to the load change. A high SRR value indicates that the deformation rate of the area is much higher than the overall structure, and it is a potential rapid failure area. The SRR value is combined with the aforementioned brittle strain coefficient (BSC) to construct a two-dimensional risk assessment map, with the horizontal axis being the BSC value and the vertical axis being the SRR value. Each area of ​​the sample is mapped to the map to form strain risk assessment data. For example, in the test of lightweight and high-strength foamed ceramic panels, when BSC>0.4 and SRR>3, the area is very prone to pore collapse under continuous load, which requires special attention. This strain rate analysis method takes into account the dynamic load conditions faced by foamed ceramic panels in actual applications, evaluates the response characteristics of the material on different time scales, and supplements the shortcomings of traditional static testing.

[0048] According to the strain risk assessment data, the collapse risk of the pore network is analyzed by establishing a coupling relationship model between the strain energy release of the pore wall and the redistribution of the strain energy of the pore network. In the implementation process, the strain energy density (deformation energy per unit volume) of each unit is first calculated, and then the strain energy redistribution law of the surrounding area after the local unit fails is analyzed. The strain energy release process is monitored by acoustic emission technology. The elastic wave signal released when the microstructure fails is recorded by the acoustic emission sensor. The signal energy is related to the amount of strain energy released. The strain energy release and redistribution process is analyzed in time series, and three key parameters are extracted: strain energy release rate (energy released per unit time), energy redistribution speed (failure area expansion rate) and energy accumulation threshold (critical energy density triggering failure). These three parameters constitute the time evolution characteristics of strain energy and reflect the dynamic development process of pore collapse. For example, when testing a high-temperature thermal insulation foamed ceramic plate with a porosity of 60%, it was found that the strain energy release showed obvious stage characteristics. The energy release was slow in the initial microcrack formation stage, followed by rapid growth and accompanied by pore collapse. This pore collapse risk analysis method reveals the physical mechanism of failure of foamed ceramic panels and explains the relationship between microstructural changes and macroscopic performance degradation from an energy perspective.

[0049] The pore collapse precursor index (CCPI) is calculated based on the brittle strain coefficient, strain rate ratio relationship and strain time evolution data, and the failure risk of the material is comprehensively evaluated by nonlinear combination. During the calculation process, the three parameters are first normalized to the [0,1] interval to eliminate the dimension effect; then a weighted combination is performed based on the contribution weight of each parameter to the collapse risk. The specific combination form is: CCPI= × × , where BSC is the brittle strain coefficient, SRR is the strain rate ratio, TEC is the time evolution characteristic factor (derived from the strain energy time evolution data), and α, β, and γ are weight indices determined according to experimental data (typical values ​​are α=0.5, β=0.3, and γ=0.2). The CCPI value is in the range of 0-1. The higher the value, the greater the risk of collapse. CCPI<0.3 is usually defined as a safe area, 0.3≤CCPI<0.7 is a warning area, and CCPI≥0.7 is a high-risk area. For example, in the foamed ceramic board for thermal protection system, the area with CCPI=0.75 will experience pore collapse within 10 minutes to 30 minutes under load, while the area with CCPI=0.4 can work stably for several hours under the same load. This pore collapse precursor index comprehensively considers the various failure characteristics of the foamed ceramic board under stress, establishes a quantitative mapping relationship between microscopic deformation behavior and macroscopic collapse risk, and provides a scientific basis for the safe application of materials.

[0050] In one embodiment of the present invention, the brittle strain field decomposition is performed on the pore network strain transfer diagram, the local deformation of the pore wall is decomposed into an elastic deformation component and a brittle damage component, the ratio of the brittle damage component to the elastic deformation component is calculated as an association parameter between the local strain and the global strain, and the association parameter is defined as a brittle strain coefficient, including: Performing local deformation analysis on the pore wall region in the pore network strain transfer diagram, decomposing the pore wall deformation response into recoverable deformation and irrecoverable deformation, determining elastic deformation components and brittle damage components, and obtaining deformation component data; Calculate the strain gradient distribution of the pore wall region according to the deformation component data, extract the spatial correlation characteristics of the local strain and the strain of the surrounding area, and obtain strain correlation data; Performing scale conversion on the strain correlation data, normalizing the local deformation of the pore wall and the overall network deformation, calculating the correlation parameters between the local strain and the global strain, and obtaining the strain scale data; The ratio of the brittle damage component to the elastic deformation component is calculated according to the strain scale data, and the ratio is determined as the brittle strain coefficient.

[0051] The specific implementation of the above steps is explained below: The local deformation analysis of the pore wall area in the pore network strain transfer diagram was performed, and a cyclic loading-unloading test method was used to identify different types of deformation behaviors. In the specific implementation, a preset load-unloading cycle was first applied to the foamed ceramic plate by a precision piezoelectric actuator. The load levels were set to 30%, 50%, 70% and 85% of the elastic limit of the material. Each load level was maintained for 60 seconds before being completely unloaded, and the strain response during the whole process was recorded. Then, a digital image correlation system (DIC) was used to collect the full-field deformation data of the pore wall area. The resolution of the DIC system was set to 5 microns / pixel to capture small deformations. By analyzing the strain-time curves in each loading and unloading cycle, the deformation response was decomposed into two parts: the part that was completely recovered after unloading was defined as the recoverable deformation (i.e., the elastic deformation component), and the part that remained after unloading was defined as the irrecoverable deformation (i.e., the brittle damage component). For a typical foamed ceramic plate, when the load reached 50% of the elastic limit, obvious irrecoverable deformation began to appear in the cross-region of the pore wall, indicating that microcracks had formed. For example, when testing foamed ceramic plates (porosity 65%) for high-temperature furnaces, the total strain in a certain pore wall cross region was 0.12% when the load was 5MPa, and the residual strain after unloading was 0.035%, that is, the brittle damage component accounted for 29.2% of the total deformation. This cyclic loading-unloading test method can effectively distinguish between the recoverable elastic deformation and irrecoverable brittle damage of foamed ceramic plates under stress, revealing the microscopic damage mechanism of the material.

[0052] The strain gradient distribution in the pore wall area is calculated based on the deformation component data, and the displacement field data obtained by DIC is processed by the finite difference method. In the specific process, the DIC data is first converted into a regular grid representation, and the grid spacing is set to 10 microns-20 microns (considering that the thickness of the pore wall of the foamed ceramic plate is usually in the range of 50 microns-300 microns); then the first-order and second-order spatial derivatives of each grid point are calculated using the central difference format to obtain the strain gradient field; then the strain gradient field is smoothed, and a 5×5 Gaussian kernel is used to eliminate measurement noise. For the calculated strain gradient distribution, three characteristic parameters are extracted: the maximum strain gradient value, the gradient direction, and the gradient bandwidth (defined as the spatial distance required for the strain value to decrease from the peak value to 50%). By analyzing these parameters, the spatial correlation characteristics of the local strain and the strain in the surrounding area are determined, and the transmission and concentration of the strain in the pore wall are characterized. For example, in the test of thermal insulation foamed ceramic panels, the strain gradient value of the pore wall cross region reached 0.02% / μm, which is 6-7 times that of the ordinary pore wall region (0.003% / μm); and the gradient bandwidth is only 20-30 microns, indicating that the strain is highly concentrated. This strain gradient analysis method reveals the precise location and severity of the stress concentration area in the foamed ceramic panel, providing a quantitative basis for predicting potential failure locations.

[0053] The strain correlation data is scaled and the correlation relationship between microscopic local deformation and macroscopic overall deformation is established. In the specific implementation, the reference area is first defined, including the local pore wall area (10-50 pore wall intersections) and the overall network area (representative volume units containing 500-1000 pores); then the strain data of the two areas are statistically analyzed to calculate the average value, standard deviation and distribution characteristics; then the normalization method is used to convert the strain data of different scales to the same numerical range to eliminate the influence of the absolute value difference. The normalization formula uses the min-max normalization method to map the data to the [0,1] interval. Based on the normalized data, the correlation parameters between local strain and global strain are calculated, including the local-global strain ratio (the ratio of the local maximum strain to the global average strain), the local strain concentration factor (the ratio of the local strain standard deviation to the global strain standard deviation) and the local-global response lag (the time difference between the local strain reaching the peak value and the global strain reaching the peak value). For example, in the test of foamed ceramic panels for building insulation, the local to global strain ratio is as high as 5.8, indicating that the strain in the cross-region of the pore wall is much greater than the overall level; the local to global response lag is -0.3 seconds (the negative sign indicates that the local strain appears earlier than the overall strain), indicating that the local area deformation precedes the overall structure. This scale conversion method builds a bridge between microscopic local behavior and macroscopic overall performance, solving the problem that observations at different scales are difficult to directly compare.

[0054] The ratio of the brittle damage component to the elastic deformation component is calculated according to the strain scale data to determine the brittle strain coefficient. In the specific implementation process, the elastic deformation and brittle damage of each measuring point are first extracted from the aforementioned deformation component data; then the ratio of the two is calculated, that is, the brittle damage component is divided by the elastic deformation component, to obtain the original brittle strain coefficient; then the original coefficient is spatially smoothed and normalized to eliminate the influence of measurement noise and local fluctuations, and a brittle strain coefficient distribution diagram is formed. The brittle strain coefficient (BSC) is a dimensionless parameter that characterizes the brittleness of the material at a specific location. The higher the BSC value, the more likely the area is to have brittle damage and the greater the risk of failure. According to a large amount of experimental data, the BSC value is divided into three intervals: BSC < 0.2 is a low-risk area, and the material exhibits mainly elastic behavior; 0.2 ≤ BSC < 0.5 is a medium-risk area, where there is a certain amount of brittle damage but no through cracks have yet formed; BSC ≥ 0.5 is a high-risk area, where brittle damage is dominant and sudden failure is very likely to occur. For example, when testing foamed ceramic plates for high-temperature industrial furnaces, the BSC value at the pore connection node reached 0.68, which is much higher than the 0.15 in the middle area of ​​the pore wall, indicating that the node is a potential failure starting point. This definition and calculation method of the brittle strain coefficient converts the microscopic deformation behavior of the foamed ceramic plate under stress into a quantifiable risk assessment parameter, providing a scientific basis for the safe application of materials.

[0055] Please continue reading Figure 1 , analyzing the temperature field evolution of the foamed ceramic plate according to the pore collapse precursor index, separating the contributions of different heat transfer mechanisms based on the temperature dependence of radiation heat transfer and solid conduction, and obtaining the thermal radiation efficiency factor; In one embodiment of the present invention, the temperature field evolution analysis of the foamed ceramic plate is performed according to the pore collapse precursor index, and the contribution of different heat transfer mechanisms is separated based on the temperature dependence difference between radiation heat transfer and solid conduction to obtain the thermal radiation efficiency factor, including: Determine the temperature monitoring area of ​​the foamed ceramic plate according to the pore collapse precursor index, perform a temperature scan on the temperature monitoring area, and record surface temperature field data in a temperature range from 25 degrees Celsius to 800 degrees Celsius; Performing temperature response decomposition on the surface temperature field data, decomposing the temperature field change into a fourth-power temperature-dependent term and a first-power temperature-dependent term, and obtaining a radiation heat transfer component and a solid conduction component; Analyze the temperature gradient distribution of the pore wall according to the radiation heat transfer component and the solid conduction component, calculate the anisotropy coefficient of the in-plane conduction and out-of-plane conduction of the pore wall, define the ratio of the radiation heat transfer component to the solid conduction component as the heat transfer mechanism separation coefficient, and obtain the heat transfer mechanism distribution data; The radiation heat transfer path of the pore network is analyzed according to the heat transfer mechanism distribution data and the anisotropy coefficient, the radiation heat transfer of the pore wall and the multiple reflection effect of the surrounding pores are calculated, and the ratio of the actual radiation heat transfer contribution to the ideal black body radiation is determined as the thermal radiation efficiency factor.

[0056] The specific implementation of the above steps is explained below: The temperature monitoring area of ​​the foamed ceramic plate is determined according to the pore collapse precursor index, and a high-precision infrared thermal imager is used for non-contact measurement. In the specific implementation, a risk distribution map is first constructed based on the aforementioned pore collapse precursor index (CCPI), and the CCPI value is divided into three intervals: low risk area (CCPI < 0.3), medium risk area (0.3 ≤ CCPI < 0.7) and high risk area (CCPI ≥ 0.7). The area with a CCPI value greater than 0.5 is selected as the temperature monitoring area, and representative points are selected in each risk level area to form a complete monitoring point array. The number of monitoring points is determined according to the sample size and risk distribution, usually a 9×9 point array to a 15×15 point array. Then, the sample is placed in a high-temperature furnace for a heating test, and the heating rate is controlled at 5°C / min-10°C / min, gradually rising from room temperature (25°C) to 800°C. This temperature range was selected based on the typical application environment of foamed ceramic panels, such as industrial furnace linings (600℃-800℃), building fire insulation (300℃-600℃) and thermal protection (400℃-700℃). During the entire heating process, an infrared thermal imager was used to record the temperature field distribution on the sample surface in real time through the observation window on the furnace door, with an acquisition frequency of 2 frames per second to ensure that the dynamic process of temperature change was captured. For example, when testing foamed ceramic panels for high-temperature industrial furnaces, it was found that the pore connection node area with a CCPI value of 0.68 had a significant temperature anomaly at around 550℃, while the low-risk area with a CCPI value of 0.25 maintained a uniform temperature distribution until 800℃. This selective temperature monitoring method based on risk index has greatly improved the pertinence and efficiency of thermal analysis.

[0057] The surface temperature field data is decomposed by temperature response to separate the contributions of different heat transfer mechanisms. This step is based on the principle of heat transfer physics: the heat flux density of radiation heat transfer is proportional to the fourth power of temperature (Stefan-Boltzmann law), while the heat flux density of solid conduction is proportional to the first power of temperature (Fourier law). In specific implementation, the temperature-time curve of each monitoring point at different temperatures is first extracted, and then a polynomial fitting is performed on each curve. The fitting function form is T(t)= ·t+ · + · + · +c, where T is temperature and t is time, to and c are fitting coefficients. Next, the fitting curve is decomposed into quartic terms ( · ) and the first-order term ( ·t), the former represents the contribution of radiation heat transfer, and the latter represents the contribution of solid conduction. The second and third power terms are usually small, mainly reflecting the transition state and composite effect. Through this decomposition method, the curves of the radiation heat transfer component and the solid conduction component of each monitoring point with temperature are obtained. For example, for thermal insulation foamed ceramic panels for construction (porosity 75%), solid conduction is dominant below 300°C (accounting for 65%-80% of the total heat transfer), while radiation heat transfer becomes the main mechanism above 600°C (accounting for 55%-70%). This temperature response decomposition method reveals the transformation process of the heat transfer mechanism of foamed ceramic panels at different temperatures, providing a scientific basis for optimizing the thermal insulation performance of materials.

[0058] The temperature gradient distribution of the pore wall is analyzed according to the radiation heat transfer component and the solid conduction component, and the temperature spatial derivative is calculated by the finite difference method. In the specific process, the temperature field data is first interpolated to a regular grid (the grid spacing is usually 0.5mm-1mm), and then the temperature gradient along the in-plane direction (along the extension direction of the pore wall) and the out-of-plane direction (perpendicular to the pore wall) of the pore wall is calculated. By comparing the temperature gradients in the two directions, the anisotropy coefficient is calculated, which is defined as the ratio of the in-plane temperature gradient to the out-of-plane temperature gradient. The anisotropy coefficient reflects the unevenness of the heat conduction direction. The farther the value deviates from 1, the more significant the anisotropy. At the same time, the ratio of the radiation heat transfer component to the solid conduction component at each spatial point is calculated, and the ratio is defined as the heat transfer mechanism separation coefficient. This coefficient characterizes the relative contribution of different heat transfer mechanisms. The larger the value, the more dominant the radiation heat transfer is. The spatial distribution data of the anisotropy coefficient and the heat transfer mechanism separation coefficient are combined to generate a heat transfer mechanism distribution map, which intuitively displays the heat transfer characteristics of different regions. For example, in the test of lightweight thermal insulation foamed ceramic panels, it was found that the anisotropy coefficient of the flat pore area was as high as 3.5-4.2, while that of the spherical pore area was only 0.9-1.2; at the same time, the heat transfer mechanism separation coefficient of the large-size pore area (diameter>2mm) reached 2.8 at 700℃, while that of the small pore area was only 1.3, indicating that the large pores have a more significant enhancement effect on radiation heat transfer. This heat transfer mechanism distribution analysis method reveals the complex heat transfer process in foamed ceramic panels and explains the mechanism of the influence of pore morphology on heat transfer performance.

[0059] The radiation heat transfer path of the pore network is analyzed based on the heat transfer mechanism distribution data and anisotropy coefficient, and the radiation energy transfer process is simulated using the Monte Carlo ray tracing method. In the specific implementation, a three-dimensional radiation heat transfer model is first constructed based on the pore morphology data, and the emissivity, reflectivity and absorptivity of the pore wall are set (typical values ​​are emissivity 0.7-0.9, reflectivity 0.1-0.3, and absorptivity 0.6-0.8); then a large number of (usually The emission, propagation, reflection and absorption process of radiation photons (order of magnitude) is calculated; finally, the propagation path and energy distribution of the photons are counted to identify the main radiation heat transfer channels. On this basis, the radiation heat transfer of the pore wall and the multiple reflection effect of the surrounding pores are calculated, that is, the energy transfer enhancement effect after the photons are continuously reflected between multiple pore walls. The intensity of the multiple reflection effect is closely related to the shape, size and arrangement of the pores, and is usually expressed by the energy enhancement coefficient, which is defined as the ratio of the total energy transfer when multiple reflections are considered to the ratio when only a single transfer is considered. Furthermore, the ratio of the actual radiation heat transfer contribution (including the multiple reflection effect) to the ideal blackbody radiation (assuming that the pore is a perfect blackbody with an emissivity of 1) is determined as the thermal radiation efficiency factor (HREF). The HREF value reflects the ability of the pore structure to regulate radiation heat transfer. HREF>1 means that the pore structure enhances the radiation heat transfer, and HREF<1 means that the radiation heat transfer is inhibited. For example, when testing foamed ceramic plates for high-temperature industrial furnaces, it was found that the sample with a porosity of 65% and an average pore diameter of 1.2 mm had an HREF value of 1.35 at 700°C, indicating that the pore structure enhanced the radiation heat transfer; and when the pore wall was coated with an infrared reflective coating, the HREF value dropped to 0.72, achieving effective suppression of radiation heat transfer. This definition and calculation method of the thermal radiation efficiency factor provides a new characterization parameter for quantitatively evaluating the high-temperature thermal insulation performance of foamed ceramic plates and guides the structural optimization design of materials.

[0060] Please continue reading Figure 1 According to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, a multi-physics field cross-sensitivity matrix is ​​constructed, and the pore multi-physics characteristic spectrum is generated by eigenvector analysis to obtain the correlation characterization parameters between the internal pore morphology characteristics and physical properties of the foamed ceramic board.

[0061] In one embodiment of the present invention, the multi-physics cross-sensitivity matrix is ​​constructed according to the nonlinear anisotropy index, the pore collapse precursor index and the thermal radiation efficiency factor, and the pore multi-physics characteristic spectrum is generated by eigenvector analysis to obtain the correlation characterization parameters between the pore morphology characteristics and the physical properties of the foamed ceramic board, including: According to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, a stomatal network hierarchical analysis is performed, the stomatal structure is divided into dominant channels and secondary pores according to connectivity, the response contribution of pores at different levels to the physical field is calculated, and a multi-scale response matrix is ​​obtained; Performing network topological decomposition on the multi-scale response matrix, extracting the pore connection skeleton and local pore clusters, calculating the physical field coupling strength between the skeleton network and the pore clusters, and obtaining a cross-sensitivity matrix; Performing eigenvalue analysis according to the cross-sensitivity matrix, mapping the main eigenvector to the pore morphology parameter space, calculating the weight distribution of pore orientation, flatness and connectivity, and obtaining the pore morphology eigenvector; Performing multi-physical field response analysis on the pore morphology feature vector, calculating the coupled transfer functions of acoustic, mechanical and thermal responses, constructing a mapping relationship between pore structure and physical properties, and obtaining a multi-physical feature spectrum; Analyzing the physical response mechanism of the pore structure under different stress states according to the multi-physical characteristic spectrum, establishing a correlation function between pore deformation and energy transfer, and obtaining structural performance mapping data; Based on the structural performance mapping data, a quantitative correspondence between pore morphology characteristics and mechanical strength, sound insulation performance, and thermal conductivity is established, and the quantitative correspondence is determined as a correlation characterization parameter.

[0062] The specific implementation of the above steps is explained below: According to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, the pore network is hierarchically analyzed, and the pore structure is graded using a multi-index comprehensive evaluation method. In the specific implementation, the three indicators are first normalized to unify their numerical ranges to the [0,1] interval; then the weight coefficients are set (such as nonlinear anisotropy index 0.3, pore collapse precursor index 0.4, thermal radiation efficiency factor 0.3), and the comprehensive score is calculated; then the pore structure is hierarchically divided according to the comprehensive score, and the pores and their connecting channels with the top 30% scores are defined as dominant channels, and the rest are defined as secondary pores. The dominant channel is the dominant path for the transmission of the physical field, and the secondary pores mainly play an auxiliary role. After the division is completed, the contributions of the dominant channel and secondary pores to the physical field response are calculated separately, including acoustic response (acoustic wave propagation attenuation and scattering), mechanical response (stress distribution and deformation) and thermal response (heat conduction and radiation heat transfer). Through the response intensity of the dominant channels and secondary pores in different physical fields, a multi-scale response matrix is ​​constructed, which describes the interaction between different hierarchical structures and different physical fields. For example, when testing thermal insulation foamed ceramic panels, the dominant channel contributed 78% to the mechanical response, 65% to the thermal response, and only 42% to the acoustic response, indicating that the mechanical properties are mainly controlled by the dominant channel, while the acoustic properties are more dependent on the overall pore distribution. This pore network hierarchical analysis method reveals the functional differences of different hierarchical structures in foamed ceramic panels and provides a theoretical basis for the directional regulation of material properties.

[0063] The multi-scale response matrix is ​​decomposed into a network topology, and the structural features are extracted using graph theory and network analysis methods. In the specific operation, the pore network is first represented as an undirected weighted graph, with pores as nodes, connections between pores as edges, and physical field response strength as edge weights; then the minimum spanning tree algorithm is used to extract the pore connectivity skeleton, which retains the key connection paths in the network and removes redundant connections; then the community detection algorithm (such as the Louvain method) is used to identify local pore clusters, which are pore sub-networks with strong internal connections. After obtaining the skeleton network and pore clusters, the physical field coupling strength between the two is calculated, which is defined as the sensitivity coefficient of the response change in the cluster caused by the physical field change in the skeleton network. By constructing the coupling strength matrix of the three physical fields of acoustic field, mechanical field and thermal field, a cross-sensitivity matrix is ​​formed. The matrix is ​​a 3×3 square matrix, the diagonal elements represent the self-sensitivity of the same physical field, and the non-diagonal elements represent the cross-sensitivity between different physical fields. For example, in the test of foamed ceramic panels for building energy conservation, the thermal-mechanical cross sensitivity was 0.43, indicating that thermal field changes have a significant effect on mechanical response; while the acoustic-thermal cross sensitivity was only 0.12, indicating that acoustic field changes have little effect on thermal performance. This network topology decomposition method deeply reveals the interaction mechanism between different physical fields in foamed ceramic panels and captures the essential characteristics of multi-physical field coupling effects.

[0064] According to the cross-sensitivity matrix, eigenvalue analysis is performed, and the main characteristic modes are extracted by principal component analysis. In the specific implementation, the cross-sensitivity matrix is ​​first decomposed by eigenvalue, and the eigenvalues ​​and corresponding eigenvectors are calculated; then, the eigenvalues ​​are sorted, and the first few eigenvectors with a cumulative contribution rate of more than 85% are selected as the main eigenvectors; then these main eigenvectors are mapped to the pore morphology parameter space, and the corresponding relationship between the eigenvectors and the pore morphology parameters is established. The pore morphology parameters mainly include three categories: pore orientation (the angle between the main axis of the pore and the reference direction), flatness (the ratio of the longest axis to the shortest axis of the pore), and connectivity (the average number of adjacent pores connected to each pore). By calculating the weight coefficients of these three types of parameters in the main eigenvector, the pore morphology characteristic vector is obtained. This vector represents the combination of key morphological features that have a decisive influence on the physical properties of the material. For example, in the analysis of foamed ceramic panels for high-temperature industrial kilns, it was found that the weight of the pore flatness in the first principal eigenvector was 0.65, the weight of the orientation was 0.28, and the weight of the connectivity was 0.07, indicating that the flatness is the most critical factor affecting the material performance. This eigenvalue analysis method achieves the dimensionality reduction transformation from complex multidimensional data to key characteristic parameters, and extracts the essential factors of the performance of foamed ceramic panels.

[0065] The multi-physical field response analysis of the pore morphology feature vector is carried out to establish the quantitative relationship between morphology and performance. In the specific process, a test sample library is first constructed, which contains foamed ceramic board samples with different pore morphology characteristics (different orientation, flatness and connectivity combinations); then each sample is subjected to acoustic, mechanical and thermal tests to obtain performance data; then the mapping relationship between the morphology feature vector and the performance parameter is established, and a prediction model is established using methods such as multivariate regression analysis or support vector machine. In the model training process, the coupling effect between different physical field responses is analyzed and the coupling transfer function is calculated. This function describes how a physical field change affects the response characteristics of other physical fields through the pore structure. By comprehensively analyzing the response characteristics and coupling relationship of each physical field, a multi-dimensional mapping relationship between the pore structure and the physical properties is constructed to form a multi-physical feature spectrum. For example, in the study of foamed ceramic boards for thermal protection systems, it was found that when the pore orientation increased from 0° (perpendicular to the insulation surface) to 60°, the thermal conductivity decreased by 43%, but the compressive strength also decreased by 28%, indicating that there is a trade-off relationship between the performance. This multi-physics field response analysis method comprehensively examines the comprehensive performance of foamed ceramic panels and provides a scientific basis for the balanced design of materials.

[0066] According to the multi-physical characteristic spectrum, the physical response mechanism of the pore structure under different stress states is analyzed, and a multi-condition loading test method is adopted. In the specific implementation, a multi-level loading scheme is first designed, including different stress states such as uniaxial compression, triaxial compression, temperature gradient and vibration excitation; then the acoustic, mechanical and thermal responses of the sample are measured under various stress conditions; then the energy transfer and conversion law in the pore deformation process is analyzed, and the correlation function between pore deformation and energy transfer is established. This function describes how the deformation of the pore structure affects the transfer and conversion process of acoustic energy, strain energy and thermal energy. By systematically analyzing the energy transfer characteristics under different stress conditions, the structural performance mapping data is obtained. This data set contains the complete correspondence between pore morphology parameters, stress state and performance response. For example, in the test of foamed ceramic panels for building energy conservation, it was found that when the pore flatness increased from 1.2 to 3.5, the energy absorption capacity under in-plane compression conditions increased by 2.8 times, while it only increased by 0.6 times under out-of-plane compression, showing significant anisotropic characteristics. This physical response analysis method for different stress states reveals the behavior of foamed ceramic panels under complex working conditions, providing an important reference for material reliability design and performance prediction.

[0067] Based on the structural performance mapping data, the quantitative correspondence between pore morphology characteristics and mechanical strength, sound insulation performance, and thermal conductivity is established, and a multi-objective optimization method is used for comprehensive analysis. In the specific implementation, a performance prediction model is first constructed, and machine learning methods such as random forests or neural networks are used, with pore morphology characteristics as input variables and physical performance parameters as output variables; then the model is trained and verified to ensure that the prediction accuracy meets the requirements (such as prediction error less than 10%); then the trained model is used to generate a performance response surface to intuitively display the quantitative relationship between pore morphology characteristics and performance parameters. On this basis, an associated characterization parameter is established, which comprehensively considers the impact of pore morphology characteristics on multiple properties and is a quantitative indicator for measuring the comprehensive performance of materials. The definition of the associated characterization parameter takes into account the performance demand weights of specific application scenarios. For example, the lining of high-temperature industrial kilns pays more attention to thermal insulation performance (weight 0.6) and high-temperature strength (weight 0.3), while building sound insulation materials pay more attention to acoustic performance (weight 0.7). For example, in the evaluation of foamed ceramic boards, it was found that when the porosity was 65%, the average flatness was 2.5, and the main orientation angle was 30°, the correlation characterization parameter reached the optimal value of 0.82, at which time the material's comprehensive performance was the best. This evaluation method based on correlation characterization parameters establishes a quantitative mapping relationship between the microscopic morphology characteristics and macroscopic physical properties of foamed ceramic boards, realizes cross-scale prediction from "knowing the microstructure" to "knowing the macroscopic performance", and provides a theoretical tool for the directional design and optimization of materials.

[0068] The above describes the detection method of the foamed ceramic plate in the embodiment of the present invention. The following describes the detection device of the foamed ceramic plate in the embodiment of the present invention. Figure 2 , an embodiment of the detection device of the foamed ceramic plate in the embodiment of the present invention comprises: The nonlinear acoustic detection module 101 is used to perform nonlinear acoustic detection on the foamed ceramic plate, obtain nonlinear acoustic response data in three orthogonal directions, analyze the sum frequency component, difference frequency component and high-order modulation frequency component information according to the nonlinear acoustic response data, calculate the nonlinear modulation index in the three directions, and obtain the nonlinear anisotropy index; The scattered wave imaging module 102 is used to perform oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extract the scattered wave component from the mixed wave field and perform sub-wavelength focusing processing to obtain a pore interface scattering enhancement image; The strain analysis module 103 is used to analyze the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhancement image, calculate the correlation parameters between the local strain and the global strain, and obtain the pore collapse precursor index by combining the ratio of the local strain rate to the global strain rate; The temperature field analysis module 104 is used to analyze the temperature field evolution of the foamed ceramic plate according to the pore collapse precursor index, separate the contributions of different heat transfer mechanisms based on the temperature dependence difference between radiation heat transfer and solid conduction, and obtain a thermal radiation efficiency factor; The multi-physics coupling analysis module 105 is used to construct a multi-physics cross-sensitivity matrix according to the nonlinear anisotropy index, the pore collapse precursor index and the thermal radiation efficiency factor, generate a pore multi-physics characteristic spectrum through eigenvector analysis, and obtain the correlation characterization parameters between the pore morphology characteristics and physical properties inside the foamed ceramic board.

[0069] above Figure 2 The detection device of the foamed ceramic board in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The detection device of the foamed ceramic board in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0070] Figure 3 2 is a schematic diagram of the structure of a detection device for a foamed ceramic board provided by an embodiment of the present invention. The detection device 200 for a foamed ceramic board may have relatively large differences due to different configurations or performances, and may include one or more processors 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 (for example, one or more mass storage device terminals) storing application programs 233 or data 232. Among them, the memory 220 and the storage medium 230 may be temporary storage or permanent storage. The program stored in the storage medium 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the detection device 200 for a foamed ceramic board. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, and execute a series of instruction operations in the storage medium 230 on the detection device 200 for a foamed ceramic board to implement the steps of the detection method for the foamed ceramic board.

[0071] The foamed ceramic plate detection device 200 may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the detection device for the foamed ceramic board shown does not constitute a limitation on the detection device for the foamed ceramic board provided by the present invention, and may include more or less components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0072] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for detecting the foamed ceramic board.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0074] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0075] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for detecting a foamed ceramic plate, characterized in that: include: Performing nonlinear acoustic detection on the foamed ceramic plate to obtain nonlinear acoustic response data in three orthogonal directions, analyzing the sum frequency component, difference frequency component and high-order modulation frequency component information according to the nonlinear acoustic response data, calculating the nonlinear modulation index in the three directions, and obtaining the nonlinear anisotropy index; Performing oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extracting scattered wave components from the mixed wave field and performing sub-wavelength focusing processing to obtain a pore interface scattering enhancement image; Analyzing the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhanced image, calculating the correlation parameters between the local strain and the global strain, and combining the ratio relationship between the local strain rate and the global strain rate to obtain the pore collapse precursor index; The temperature field evolution of the foamed ceramic plate is analyzed according to the pore collapse precursor index, and the contribution of different heat transfer mechanisms is separated based on the temperature dependence difference between radiation heat transfer and solid conduction to obtain the thermal radiation efficiency factor; A multi-physics cross-sensitivity matrix is ​​constructed according to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, and a pore multi-physics characteristic spectrum is generated by eigenvector analysis to obtain correlation characterization parameters between the pore morphology characteristics and physical properties inside the foamed ceramic board.

2. The method for detecting a foamed ceramic plate according to claim 1, characterized in that: The nonlinear acoustic detection is performed on the foamed ceramic plate to obtain nonlinear acoustic response data in three orthogonal directions, and the nonlinear modulation index in three directions is calculated according to the nonlinear acoustic response data, by analyzing the sum frequency component, the difference frequency component and the high-order modulation frequency component information, and obtaining the nonlinear anisotropy index, including: Applying sound waves of a first excitation frequency and a second excitation frequency to the foamed ceramic plate simultaneously to obtain a response signal under dual-frequency excitation; Performing spectrum analysis on the response signal to extract amplitude and phase data of the sum frequency component, the difference frequency component and the second-order modulation frequency component, wherein the first excitation frequency is f1, the second excitation frequency is f2, the sum frequency component is f1+f2, the difference frequency component is f1-f2, and the second-order modulation frequency component is 2f1-f2; Calculating the sum of the energies of the sum frequency component, the difference frequency component and the second-order modulation frequency component according to the amplitude and phase data as the nonlinear component energy, and determining the ratio of the nonlinear component energy to the fundamental frequency component energy as the nonlinear modulation index, wherein the fundamental frequency component energy is the sum of the energies corresponding to the first excitation frequency and the second excitation frequency; Repeat the above dual-frequency excitation, spectrum analysis and energy ratio calculation steps along the X direction, Y direction and Z direction respectively to obtain the nonlinear modulation index values ​​in the three directions; A first ratio of a standard deviation to an average value is calculated according to the nonlinear modulation index values ​​in the three directions, and the first ratio is determined as a nonlinear anisotropy index.

3. The method for detecting a foamed ceramic plate according to claim 1, characterized in that: The method of scanning the foamed ceramic plate with an oblique incident acoustic wave according to the nonlinear anisotropy index, extracting scattered wave components from the mixed wave field and performing sub-wavelength focusing processing to obtain a pore interface scattering enhancement image includes: Calculating the acoustic impedance contrast between the pores and the matrix in the foamed ceramic plate according to the nonlinear anisotropy index, determining the scanning angle range of the oblique incident sound wave based on the acoustic impedance contrast, performing regional acoustic wave scanning on the foamed ceramic plate, and acquiring mixed wave field data; Performing dual-scale wavefield decomposition on the mixed wavefield data, extracting the scattered wave component of the pore scale and the background wave component of the matrix scale, defining the amplitude ratio of the scattered wave component to the background wave component as a scattering intensity factor, and obtaining scattered wavefield data; Adaptively performing phase compensation on the scattered wave field data based on the scattering intensity factor, constructing an acoustic phase delay map of the pore interface, performing phase loss compensation according to the acoustic phase delay map, and obtaining phase compensation data; The scattered wave component is subjected to sub-wavelength scale multi-focus acoustic phase control according to the phase compensation data, the scattered wave component is subjected to sub-wavelength resolution multi-point focusing at the pore boundary, and the interference enhancement coefficient between adjacent focusing points is calculated to obtain the scattered wave field data after focusing; Wavefield reconstruction is performed according to the focused scattering wavefield data and the interference enhancement coefficient to generate a pore interface scattering enhancement image.

4. The method for detecting a foamed ceramic plate according to claim 3, characterized in that: The dual-scale wave field decomposition is performed on the mixed wave field data to extract the pore-scale scattered wave component and the matrix-scale background wave component, and the amplitude ratio of the scattered wave component to the background wave component is defined as a scattering intensity factor, including: Performing frequency domain transformation on the mixed wave field data, establishing the boundary frequency of the pore scale frequency band and the matrix scale frequency band, separating the wave field data into high-frequency scattering components and low-frequency propagation components according to the boundary frequency, and obtaining dual-scale decomposition data; Performing pore network spatial filtering on the dual-scale decomposition data to separate the high-frequency scattered wave component of the pore boundary and the low-frequency background wave component of the matrix area, calculating the spatial distribution of the scattered wave amplitude and the background wave amplitude, and obtaining wave field component data; Analyze the multiple scattering effect of the pore group according to the wave field component data, calculate the superposition and interference intensity of scattered waves between adjacent pores, and obtain the scattering enhancement coefficient; The scattered wave component and the background wave component are amplitude-corrected according to the scattering enhancement coefficient, the amplitude ratio after correction is calculated, and the amplitude ratio is determined as the scattering intensity factor.

5. The method for detecting a foamed ceramic plate according to claim 1, characterized in that: The method of analyzing the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhanced image, calculating the correlation parameters between the local strain and the global strain, and combining the ratio of the local strain rate to the global strain rate to obtain the pore collapse precursor index includes: Performing a network topology analysis on the foamed ceramic plate according to the pore interface scattering enhancement image, calculating the distribution relationship between the pore connectivity and the pore wall thickness, marking the pore wall stress concentration area as a strain sensitive network, performing a stress transfer path analysis on the surface strain field in the strain sensitive network, and obtaining a pore network strain transfer map; The pore network strain transfer diagram is subjected to brittle strain field decomposition, the local deformation of the pore wall is decomposed into an elastic deformation component and a brittle damage component, the ratio of the brittle damage component to the elastic deformation component is calculated as a correlation parameter between the local strain and the global strain, the correlation parameter is defined as a brittle strain coefficient, and strain distribution characteristic data is obtained; Performing strain rate analysis on the strain distribution characteristic data, calculating a second ratio of the local strain rate of the pore wall to the overall strain rate of the pore network, defining the second ratio as a strain rate ratio relationship, and combining the brittle strain coefficient to obtain strain risk assessment data; Performing a collapse risk analysis on the pore network according to the strain risk assessment data, establishing a coupling relationship between the release of pore wall strain energy and the redistribution of pore network strain energy, calculating the time evolution characteristics of the strain energy release and redistribution, and obtaining strain time evolution data; The pore collapse precursor index is calculated according to a nonlinear combination of the brittle strain coefficient, the strain rate ratio relationship and the strain time evolution data.

6. The method for detecting a foamed ceramic plate according to claim 5, characterized in that: The brittle strain field decomposition is performed on the pore network strain transfer diagram, the local deformation of the pore wall is decomposed into an elastic deformation component and a brittle damage component, the ratio of the brittle damage component to the elastic deformation component is calculated as the correlation parameter between the local strain and the global strain, and the correlation parameter is defined as the brittle strain coefficient, including: Performing local deformation analysis on the pore wall region in the pore network strain transfer diagram, decomposing the pore wall deformation response into recoverable deformation and irrecoverable deformation, determining elastic deformation components and brittle damage components, and obtaining deformation component data; Calculate the strain gradient distribution of the pore wall region according to the deformation component data, extract the spatial correlation characteristics of the local strain and the strain of the surrounding area, and obtain strain correlation data; Performing scale conversion on the strain correlation data, normalizing the local deformation of the pore wall and the overall network deformation, calculating the correlation parameters between the local strain and the global strain, and obtaining the strain scale data; The ratio of the brittle damage component to the elastic deformation component is calculated according to the strain scale data, and the ratio is determined as the brittle strain coefficient.

7. The method for detecting a foamed ceramic plate according to claim 1, characterized in that: The temperature field evolution analysis of the foamed ceramic plate is performed according to the pore collapse precursor index, and the contribution of different heat transfer mechanisms is separated based on the temperature dependence difference between radiation heat transfer and solid conduction to obtain the thermal radiation efficiency factor, including: Determine the temperature monitoring area of ​​the foamed ceramic plate according to the pore collapse precursor index, perform a temperature scan on the temperature monitoring area, and record surface temperature field data in a temperature range from 25 degrees Celsius to 800 degrees Celsius; Performing temperature response decomposition on the surface temperature field data, decomposing the temperature field change into a fourth-power temperature-dependent term and a first-power temperature-dependent term, and obtaining a radiation heat transfer component and a solid conduction component; Analyze the temperature gradient distribution of the pore wall according to the radiation heat transfer component and the solid conduction component, calculate the anisotropy coefficient of the in-plane conduction and out-of-plane conduction of the pore wall, define the ratio of the radiation heat transfer component to the solid conduction component as the heat transfer mechanism separation coefficient, and obtain the heat transfer mechanism distribution data; The radiation heat transfer path of the pore network is analyzed according to the heat transfer mechanism distribution data and the anisotropy coefficient, the radiation heat transfer of the pore wall and the multiple reflection effect of the surrounding pores are calculated, and the ratio of the actual radiation heat transfer contribution to the ideal black body radiation is determined as the thermal radiation efficiency factor.

8. The method for detecting a foamed ceramic plate according to claim 1, characterized in that: The multi-physics cross-sensitivity matrix is ​​constructed according to the nonlinear anisotropy index, the pore collapse precursor index and the thermal radiation efficiency factor, and the pore multi-physics characteristic spectrum is generated by eigenvector analysis to obtain the correlation characterization parameters between the pore morphology characteristics and the physical properties of the foamed ceramic board, including: According to the nonlinear anisotropy index, pore collapse precursor index and thermal radiation efficiency factor, a stomatal network hierarchical analysis is performed, the stomatal structure is divided into dominant channels and secondary pores according to connectivity, the response contribution of pores at different levels to the physical field is calculated, and a multi-scale response matrix is ​​obtained; Performing network topological decomposition on the multi-scale response matrix, extracting the pore connection skeleton and local pore clusters, calculating the physical field coupling strength between the skeleton network and the pore clusters, and obtaining a cross-sensitivity matrix; Performing eigenvalue analysis according to the cross-sensitivity matrix, mapping the main eigenvector to the pore morphology parameter space, calculating the weight distribution of pore orientation, flatness and connectivity, and obtaining the pore morphology eigenvector; Performing multi-physical field response analysis on the pore morphology feature vector, calculating the coupled transfer functions of acoustic, mechanical and thermal responses, constructing a mapping relationship between pore structure and physical properties, and obtaining a multi-physical feature spectrum; Analyzing the physical response mechanism of the pore structure under different stress states according to the multi-physical characteristic spectrum, establishing a correlation function between pore deformation and energy transfer, and obtaining structural performance mapping data; Based on the structural performance mapping data, a quantitative correspondence between pore morphology characteristics and mechanical strength, sound insulation performance, and thermal conductivity is established, and the quantitative correspondence is determined as a correlation characterization parameter.

9. A detection device for a foamed ceramic plate, characterized in that: The detection device of the foamed ceramic plate comprises: A nonlinear acoustic detection module is used to perform nonlinear acoustic detection on the foamed ceramic plate, obtain nonlinear acoustic response data in three orthogonal directions, analyze the sum frequency component, difference frequency component and high-order modulation frequency component information according to the nonlinear acoustic response data, calculate the nonlinear modulation index in three directions, and obtain the nonlinear anisotropy index; A scattered wave imaging module is used to perform oblique incident acoustic wave scanning on the foamed ceramic plate according to the nonlinear anisotropy index, extract scattered wave components from the mixed wave field and perform sub-wavelength focusing processing to obtain a pore interface scattering enhancement image; A strain analysis module is used to analyze the surface strain field distribution of the foamed ceramic plate according to the pore interface scattering enhancement image, calculate the correlation parameters between the local strain and the global strain, and obtain the pore collapse precursor index by combining the ratio of the local strain rate to the global strain rate; A temperature field analysis module is used to analyze the temperature field evolution of the foamed ceramic plate according to the pore collapse precursor index, separate the contributions of different heat transfer mechanisms based on the temperature dependence difference between radiation heat transfer and solid conduction, and obtain a thermal radiation efficiency factor; The multi-physics coupling analysis module is used to construct a multi-physics cross-sensitivity matrix according to the nonlinear anisotropy index, the pore collapse precursor index and the thermal radiation efficiency factor, generate a pore multi-physics characteristic spectrum through eigenvector analysis, and obtain the correlation characterization parameters between the pore morphology characteristics and physical properties inside the foamed ceramic board.

10. A detection device for a foamed ceramic plate, characterized in that: The detection device of the foamed ceramic plate comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the foamed ceramic board detection device to perform the steps of the foamed ceramic board detection method according to any one of claims 1 to 8.

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