Method and system for detecting thermal stability of ceramic material

Abstract

The invention relates to the technical field of material detection, and discloses a method and system for detecting thermal stability of a ceramic material, and the method comprises the steps: applying a stepped temperature load to the ceramic material, synchronously collecting time sequence temperature distribution data, carrying out heat conduction phase decoupling on the time sequence temperature distribution data, and separating asynchronous thermal response, the method comprises the following steps: generating dynamic thermal stress characteristics, carrying out thermo-acoustic elastic coupling on the dynamic thermal stress characteristics, carrying out microcrack release rate quantification on a thermal stress gradient value after thermo-acoustic elastic coupling, generating critical damage judgment parameters, carrying out spatial topology analysis on the critical damage judgment parameters, establishing a stress field strength connection path, and generating a crack connectivity evolution track. Performing geometric configuration matching on the crack connectivity evolution trajectory and a preset material failure spatial pattern library, and outputting a cascade thermal collapse early warning signal; according to the method, the problems of multi-dimensional perception and early warning of the internal damage evolution process of the ceramic material under the dynamic thermal load are solved.

Description

Technical Field

[0001] This invention relates to the field of materials testing technology, and in particular to a method and system for testing the thermal stability of ceramic materials. Background Technology

[0002] In existing technologies, methods for testing the thermal stability of ceramic materials mostly rely on a single, constant, or simple linear temperature loading mode. The gradual and uniform temperature change path of these methods is insufficient to effectively simulate the dynamic loads such as temperature shocks and cyclic thermal fatigue experienced by materials under complex real-world thermal environments. This results in low efficiency in detecting potential internal defects, microstructural mismatches, and early thermal damage. Furthermore, the testing process is often time-consuming and cannot sensitively expose weaknesses and early signs of failure in the material's thermal stability within a short testing period, leading to insufficient timeliness of the assessment results and limited engineering early warning value.

[0003] Existing detection technologies have limitations in process signal monitoring and analysis. Most methods focus on isolated monitoring and threshold comparison of temperature or final macroscopic performance, lacking the ability to simultaneously perceive and deeply integrate multi-physics fields to analyze the entire process of dynamic initiation, expansion, and eventual penetration of microscopic damage within materials under thermal load. It is difficult to achieve simultaneous and accurate acquisition and correlation analysis of key process signals such as temperature field and acoustic emission, and even more difficult to extrapolate crack evolution trajectories based on the spatial topological relationships of damage parameters and intelligently match them with typical failure modes. Therefore, there is an urgent need to develop a detection method and system capable of integrating multi-physics field signals for coupled analysis and realizing damage spatial evolution extrapolation and intelligent pattern matching, in order to achieve advanced early warning and accurate diagnosis of thermal failure processes in ceramic materials. Summary of the Invention

[0004] This invention provides a method and system for testing the thermal stability of ceramic materials, the main purpose of which is to address the problems raised in the background section above.

[0005] To achieve the above objectives, the present invention provides a method for testing the thermal stability of ceramic materials, comprising: S1. Apply a stepped temperature load to the ceramic material and simultaneously collect time-series temperature distribution data of the ceramic material; S2. Perform thermal conduction phase decoupling on the time series temperature distribution data and separate the asynchronous thermal response of the ceramic material to generate the dynamic thermal stress characteristics of the ceramic material; S3. Perform thermoacoustic elastic coupling on the dynamic thermal stress characteristics, and quantify the microcrack release rate of the thermal stress gradient value after thermoacoustic elastic coupling to generate critical damage judgment parameters for the ceramic material. S4. Perform spatial topology analysis on the critical damage judgment parameters, establish the stress field connection path between adjacent damage points in the ceramic material, and generate the crack penetration evolution trajectory of the ceramic material. S5. Perform geometric configuration matching between the crack penetration evolution trajectory and the preset material failure space mode library, and output the cascaded thermal collapse early warning signal of the ceramic material.

[0006] In a preferred embodiment, applying a stepped temperature load to the ceramic material and simultaneously acquiring time-series temperature distribution data of the ceramic material includes: A temperature sensing array is arranged on the surface and inside of the ceramic material; The temperature sensing array includes an infrared thermal imager and embedded thermocouples. The infrared thermal imager and the embedded thermocouple are subjected to heating and cooling conversions at a preset constant rate to apply a stepped temperature load to the ceramic material, and the time-series temperature distribution data of the ceramic material are collected simultaneously.

[0007] In a preferred embodiment, the step of decoupling the thermal conduction phase of the time-series temperature distribution data and separating the asynchronous thermal response of the ceramic material to generate the dynamic thermal stress characteristics of the ceramic material includes: A short-time Fourier transform is performed on the temperature-time curve of each measuring point in the time-series temperature distribution data to obtain the heating source temperature change waveform of the time-series temperature distribution data. The phase hysteresis angle of the temperature change waveform of the heating source is extracted to obtain the set of phase hysteresis components of the ceramic material. Based on the theoretical thermophysical parameters of the ceramic material, the expected heat conduction phase effect and the phase hysteresis component at each measuring point are quantified to obtain the response value deviation of the ceramic material. Based on the time variation law of the response value deviation and the elastic modulus of the ceramic material, the thermal stress characteristics of the ceramic material are extracted to obtain the dynamic thermal stress characteristics of the ceramic material.

[0008] In a preferred embodiment, the thermoacoustic elastic coupling includes: During the application of the stepped temperature load, the timing data of the acoustic emission signal of the ceramic material are collected simultaneously; Align the dynamic thermal stress characteristics with the acoustic emission signal time series data in a time coordinate system, and calculate the coherence function value between the dynamic thermal stress characteristics and the corresponding frequency components in the acoustic emission signal; Thermoacoustic elastic coupling is performed on the dynamic thermal stress characteristics based on the coherence function value to obtain the thermal stress gradient value of the ceramic material.

[0009] In a preferred embodiment, the formula for calculating the critical damage determination parameter is: in, The critical damage determination parameter is... For time variables, The thermal stress gradient value is a function of the change over time. The acoustic emission signal of the ceramic material is a function of time. The thermal damage relaxation coefficient of the ceramic material is given. The starting time for the generation of the dynamic thermal stress characteristics. This is the end time of the hot-loading step. The critical strain energy release rate of the ceramic material. It is an exponential function.

[0010] In a preferred embodiment, the step of performing spatial topological analysis on the critical damage determination parameters includes: The critical damage determination parameters are mapped to the three-dimensional spatial mesh nodes of the ceramic material; The three-dimensional isosurface of the ceramic material is constructed based on the node values ​​of the three-dimensional spatial mesh nodes; The three-dimensional isosurface is aggregated into three-dimensional spatial nodes to generate candidate damage connectivity regions for the ceramic material.

[0011] In a preferred embodiment, establishing the stress field connection path between adjacent damage points in the ceramic material includes: Within the candidate damaged connected region, select the node pairs of the three-dimensional spatial mesh nodes; Based on the critical damage determination parameters, the change in stress intensity factor of the node pair is estimated; If the change in the stress intensity factor exceeds the fatigue crack propagation threshold of the ceramic material, then the high-probability damage connectivity of the node pair is determined, and a stress field strength connection path between adjacent damage points in the ceramic material is constructed.

[0012] In a preferred embodiment, generating the crack penetration evolution trajectory of the ceramic material includes: The stress field connection path is abstracted as a weighted graph; Graph theory analysis is performed on the weighted graph to calculate the betweenness centrality of all nodes in the weighted graph; based on the node with the highest betweenness centrality, the critical hub node of the ceramic material is determined. Based on the edges of the strong connection path, a key priority search is performed on the key hub node to obtain the key connected subgraph of the key hub node. The key connected subgraph is mapped in three-dimensional material space to generate the main crack penetration path of the ceramic material, and the main crack penetration path is determined as the crack penetration evolution trajectory.

[0013] In a preferred embodiment, the step of matching the crack penetration evolution trajectory with a preset material failure spatial mode library for geometric configuration and outputting a cascaded thermal collapse early warning signal for the ceramic material includes: Extract the geometric features of the crack penetration evolution trajectory to generate a multi-scale shape descriptor; Based on a preset material failure spatial pattern library, the similarity of the multi-scale shape descriptors is calculated to obtain the similarity results of the multi-scale shape descriptors; The typical failure crack mode with the highest similarity result is selected as the matching result; The typical failure modes of the matching results and the known failure consequences of the crack penetration evolution trajectory are matched by difference to generate a cascaded thermal collapse early warning signal for the ceramic material.

[0014] A system for testing the thermal stability of ceramic materials, the system comprising: The heat loading control module is used to apply a stepped temperature load to the ceramic material and simultaneously collect the time-series temperature distribution data of the ceramic material. A thermal response decoupling module is used to decouple the thermal conduction phase of the time-series temperature distribution data, separate the asynchronous thermal response of the ceramic material, and generate the dynamic thermal stress characteristics of the ceramic material. The coupled damage quantification module is used to perform thermoacoustic-elastic coupling on the dynamic thermal stress characteristics, and to quantify the microcrack release rate of the thermal stress gradient value after thermoacoustic-elastic coupling, thereby generating critical damage judgment parameters for the ceramic material. The damage topology evolution module is used to perform spatial topology analysis on the critical damage judgment parameters, establish the stress field connection path between adjacent damage points in the ceramic material, and generate the crack penetration evolution trajectory of the ceramic material. The failure early warning module performs geometric configuration matching between the crack penetration evolution trajectory and a preset material failure space mode library, and outputs a cascaded thermal collapse early warning signal for the ceramic material.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, by designing and applying a stepped temperature load, effectively overcomes the shortcomings of traditional isothermal or linear heating methods in simulating complex real-world thermal environments. Detection efficiency is significantly improved; the dynamic, step-like temperature change can actively induce thermal shock responses in materials within a short time, drastically shortening the cycle required to obtain stability conclusions compared to long-term isothermal testing. The excitation mechanism is more targeted; the non-uniform load path can more effectively excite thermal stress concentration within the material caused by microstructural inhomogeneities, pre-fabrication defects, or weak interface areas. This makes the evaluation results more reflective of the material's potential failure behavior under real service conditions, enhancing the engineering guidance value of the detection.

[0016] 2. This invention addresses the shortcomings of existing technologies, such as limited monitoring dimensions and lack of process early warning capabilities, by achieving simultaneous acquisition and deep fusion analysis of multi-physics field signals. It provides rich information perception dimensions, simultaneously acquiring temperature, acoustic emission, and derived thermal stress characteristics, thus capturing information across the entire chain from external load to internal damage and macroscopic response. Through thermoacoustic elastic coupling and spatial topology analysis, the assessment focus shifts from isolated signal threshold comparisons to the spatiotemporal correlation and evolutionary deduction of damage parameters, enabling the identification of early collaborative signs of microscopic damage accumulation. Based on intelligent matching of crack penetration trajectories and failure mode libraries, it can predictively determine the possible development path and consequences of failure before macroscopically visible damage occurs, greatly improving the foresight and reliability of the assessment. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a method for testing the thermal stability of ceramic materials according to an embodiment of the present invention; Figure 2 This is a functional block diagram of a ceramic material thermal stability testing system provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for testing the thermal stability of ceramic materials. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for testing the thermal stability of ceramic materials can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for testing the thermal stability of ceramic materials according to an embodiment of the present invention. In this embodiment, the method for testing the thermal stability of ceramic materials includes: S1. Apply a stepped temperature load to the ceramic material and simultaneously collect time-series temperature distribution data of the ceramic material; In this embodiment of the invention, applying a stepped temperature load to the ceramic material and simultaneously acquiring time-series temperature distribution data of the ceramic material includes: A temperature sensing array is arranged on the surface and inside of the ceramic material; The temperature sensing array includes an infrared thermal imager and embedded thermocouples. The infrared thermal imager and the embedded thermocouple are subjected to heating and cooling conversions at a preset constant rate to apply a stepped temperature load to the ceramic material, and the time-series temperature distribution data of the ceramic material are collected simultaneously.

[0021] A temperature sensing array refers to a collection of temperature sensors arranged according to specific spatial layout rules to comprehensively and synchronously acquire temperature information of ceramic materials at different spatial locations during thermal loading. The deployment of this array aims to construct a temperature monitoring network covering key areas of the material, thereby achieving synchronous capture of the spatial distribution and temporal evolution of the temperature field.

[0022] An infrared thermal imager is a non-contact temperature measurement device that detects the infrared energy naturally radiated from the surface of ceramic materials and converts it into an electrical signal that represents the temperature. Ultimately, it generates an image that can intuitively reflect the two-dimensional temperature distribution on the material surface.

[0023] An embedded thermocouple is a contact temperature sensor consisting of two conductors of different materials welded together at one end. It is pre-placed or embedded in a predetermined location inside a ceramic material sample. When the measuring end of the thermocouple senses a temperature change at that point inside the material, it generates a thermoelectric potential proportional to the temperature, thereby enabling accurate measurement of the temperature of a localized point inside the material.

[0024] Based on the geometry, size, and hotspot areas of the ceramic material sample, the observation angle, position, and focal length of the infrared thermal imager, as well as the number, type, embedding depth, and coordinates of the embedded thermocouples, are pre-designed.

[0025] The infrared thermal imager is securely mounted on a fixed bracket that can completely cover the surface of the sample to be tested, and optical focusing and temperature measurement range calibration are completed. At the same time, during or after the preparation of the ceramic material sample, the measuring end of the thermocouple is accurately fixed in the set internal position by drilling holes at predetermined positions, bonding, or utilizing the channels reserved in the material itself, and its leads are properly led out for connection to the data acquisition system.

[0026] Connect the output terminals of all infrared thermal imagers and embedded thermocouples to a multi-channel data synchronization acquisition device, and conduct systematic temperature measurement accuracy verification and synchronization triggering tests on the entire temperature sensing array to ensure that all sensors can start and work under the same time reference.

[0027] Stepped temperature loading refers to a discontinuous, segmented, constant temperature loading procedure. Its temperature-time curve exhibits a step-like characteristic, that is, in each loading stage, the temperature first rises rapidly to or falls to a target temperature plateau at a certain constant rate, remains on the plateau for a period of time, and then changes to the next temperature plateau at a constant rate again.

[0028] Time-series temperature distribution data refers to a collection of temperature data points recorded sequentially over time. This data not only includes individual temperature values ​​collected by each temperature sensor at different times, but more importantly, it strings these discrete temperature values ​​together over time and preserves their spatial correspondence, thus forming a complete dataset that reflects how the temperature field evolves spatially over time.

[0029] In the heat loading control unit, such as the control system of a high-temperature furnace or hot stage, the various parameters of the stepped temperature loading program are precisely set, including the target temperature value of each temperature platform, the heating and cooling rate from the current platform to the next platform, and the holding time at each temperature platform. Subsequently, the control unit is activated, driving the heating / cooling device to heat or cool the ceramic material sample placed in its working area strictly according to this program.

[0030] As the heat loading control unit begins executing the temperature program, it sends a synchronization trigger signal to the data acquisition system. The data acquisition system then simultaneously activates the data acquisition channels of all infrared thermal imagers and embedded thermocouples at a preset, sufficiently high sampling frequency.

[0031] Throughout the heat loading process, the infrared thermal imager continuously captures and outputs thermal images frame by frame. The data acquisition system reads the temperature values ​​of each pixel in each frame and the temperature and voltage signals measured by each embedded thermocouple in real time. All these temperature readings from different spatial locations and time points, along with their corresponding timestamps and spatial location identifiers, are recorded and stored in real time and in an orderly manner, ultimately forming the time-series temperature distribution data.

[0032] The beneficial effects are as follows: By applying a stepped temperature load through a preset constant heating and cooling rate, and simultaneously acquiring time-series temperature distribution data, the method actively constructs a dynamic thermal environment that can effectively stimulate the thermal shock response of ceramic materials. The constant heating and cooling rate ensures the controllability and repeatability of temperature changes, while the stepped platform setting introduces the alternating effects of thermal relaxation and thermal shock. The simultaneously acquired time-series temperature distribution data completely and continuously records the evolution of the surface and internal temperature fields of the material over time when subjected to this dynamic thermal load. This data is an indispensable raw input for subsequent steps such as heat conduction analysis and extraction of thermal stress characteristics, directly affecting the effectiveness and accuracy of the entire detection method.

[0033] S2. Perform thermal conduction phase decoupling on the time series temperature distribution data and separate the asynchronous thermal response of the ceramic material to generate the dynamic thermal stress characteristics of the ceramic material; In this embodiment, the step of decoupling the thermal conduction phase of the time-series temperature distribution data and separating the asynchronous thermal response of the ceramic material to generate the dynamic thermal stress characteristics of the ceramic material includes: A short-time Fourier transform is performed on the temperature-time curve of each measuring point in the time-series temperature distribution data to obtain the heating source temperature change waveform of the time-series temperature distribution data. The phase hysteresis angle of the temperature change waveform of the heating source is extracted to obtain the set of phase hysteresis components of the ceramic material. Based on the theoretical thermophysical parameters of the ceramic material, the expected heat conduction phase effect and the phase hysteresis component at each measuring point are quantified to obtain the response value deviation of the ceramic material. Based on the time variation law of the response value deviation and the elastic modulus of the ceramic material, the thermal stress characteristics of the ceramic material are extracted to obtain the dynamic thermal stress characteristics of the ceramic material.

[0034] Time-series temperature distribution data refers to the set of temperature readings of various measuring points on the surface and inside of ceramic materials, which are synchronously acquired through a temperature sensing array and arranged in chronological order.

[0035] Thermal conduction phase decoupling refers to a technical process that separates the temperature response component from the acquired temperature change signal, which is caused by the inherent time delay of the internal thermal conduction process of the material.

[0036] Asynchronous thermal response refers to the temperature change behavior of different regions or measuring points within a ceramic material during heating, which is inconsistent with the expected behavior of an ideal homogeneous material.

[0037] Dynamic thermal stress characteristics refer to the time-varying stress distribution characteristics generated inside ceramic materials during dynamic temperature changes, which are deduced by analyzing the heat conduction process and asynchronous thermal response.

[0038] The temperature-time data stream continuously recorded at each measuring point is segmented into pre-set, appropriately long, and overlapping time windows. Each time window covers a period of time that is sufficient to reflect the temperature change trend but not so long as to cause loss of detail.

[0039] For each time window segment of temperature data, a time-frequency analysis technique is applied to calculate the intensity distribution of the temperature signal across different frequency components within that time period. This process aims to reveal the periodic or oscillatory characteristics of temperature changes over local time intervals.

[0040] Dominant frequency component identification and waveform characterization: From the frequency domain analysis results of each time window, the dominant frequency components corresponding to the known heating source control program variation rhythm are identified. By analyzing the changes in the amplitude and phase of these dominant frequency components with time windows, the local characteristics of the heating source temperature change waveform driving the material temperature change in each local time period can be reconstructed.

[0041] The waveform of temperature change of the heating source refers to the theoretical or measured profile of the temperature control signal generated by the heating device that drives the entire heat loading process and changes over time.

[0042] Phase lag angle refers to the time delay of the temperature change waveform at a certain measuring point inside the material relative to the temperature change waveform of a reference heating source at the same frequency component when performing time-frequency analysis on a temperature signal. It is usually expressed in angles.

[0043] The phase lag component set refers to the summary of all phase lag angle data obtained after extracting the phase lag angles of all measuring points of the ceramic material.

[0044] The local heating source temperature change waveform obtained in the previous steps is used as a reference. For each time window and each measuring point, the local temperature change waveform obtained by time-frequency analysis at that measuring point within the same time window is precisely aligned with the reference waveform on the time axis.

[0045] For the dominant frequency components that coexist in both the reference and measured waveforms, calculate their phase angles separately. For each dominant frequency component, calculate the difference between the phase angle of the measured waveform and the phase angle of the reference waveform.

[0046] The calculated phase difference is determined as the phase lag angle of the measuring point within the time window. By traversing all measuring points and all consecutive time windows, all the obtained phase lag angle data are organized according to their corresponding measuring point spatial location and time window index, thus forming the phase lag component set.

[0047] Theoretical thermophysical parameters refer to parameters that describe the basic thermophysical properties of ceramic materials, obtained in advance through theoretical calculations, manual lookup tables, or standard testing methods, based on the known chemical composition, microstructure, and preparation process of the ceramic material.

[0048] The expected phase effect of thermal conduction refers to the expected phase lag of temperature change at each measuring point inside the material relative to the change of the heating source, calculated based on its theoretical thermophysical parameters and classical thermal conduction theory, under the same stepped temperature load, assuming that the ceramic material is an ideal, homogeneous, defect-free continuous medium.

[0049] The response value deviation refers to the quantitative difference obtained by comparing the phase lag component actually observed at each measuring point with the expected thermal conduction phase effect of that measuring point calculated based on theoretical thermophysical parameters.

[0050] For each measuring point, based on its specific spatial location and the theoretical thermophysical parameters of the ceramic material, the expected thermal conduction phase effect of the temperature change at that measuring point relative to the change in the heating source is calculated using the heat conduction theory.

[0051] From the set of phase lag components, the actual phase lag component corresponding to the same measuring point at the same heat loading stage is extracted. The actual phase lag component is then directly subtracted from the calculated expected heat conduction phase effect, or other comparative operations are performed, to obtain a numerical result. This numerical result is the deviation of the response value at that measuring point at that moment. This process is repeated for each measuring point and each analysis time window to obtain a series of deviation data that vary with time and space.

[0052] The elastic modulus, referring to the ratio of stress to strain in a ceramic material during the elastic deformation stage, is an important mechanical parameter characterizing the material's resistance to elastic deformation. In this step, it is used to convert thermal strain into thermal stress, serving as a bridge parameter connecting thermal response and mechanical stress characteristics.

[0053] Analyze the spatial distribution pattern and time-varying characteristics of the response value deviation data. Identify spatial regions where the deviation is consistently large or increasing rapidly; these regions typically correspond to locations with significant heat conduction anomalies and potential for large thermal stresses.

[0054] According to the basic principles of thermoelasticity, the thermal strain caused by uneven temperature distribution in a material is related to the temperature difference. The deviation in response value is considered a key factor leading to the local temperature field deviating from a uniform state. Combined with the actual changes in temperature load, the local thermal strain components at various points within the material due to this asynchronous thermal response are estimated.

[0055] Using the elastic modulus of the ceramic material and other possible mechanical parameters, the estimated thermal strain is converted into corresponding thermal stress components according to Hooke's law. By combining the thermal stress calculation results at all spatial and temporal points, a complete dataset reflecting the magnitude, direction, and evolution of thermal stress over time is constructed; this dataset represents the dynamic thermal stress characteristics.

[0056] The beneficial effects are as follows: Decoupling the thermal conduction phase from time-series temperature distribution data allows for the extraction of internal material thermal conduction velocity information implicit in complex temperature field data, providing a clear perspective for identifying non-uniformity. Separating asynchronous thermal responses effectively isolates abnormal thermal behavior caused by internal material defects and non-uniformity from the overall thermal response, allowing analysis to focus on critical areas that may lead to failure. Generating dynamic thermal stress characteristics transforms thermal observation data into mechanical parameters more directly related to material failure through physical principles, providing core input for subsequent damage quantification and evolution analysis.

[0057] S3. Perform thermoacoustic elastic coupling on the dynamic thermal stress characteristics, and quantify the microcrack release rate of the thermal stress gradient value after thermoacoustic elastic coupling to generate critical damage judgment parameters for the ceramic material. In this embodiment, the thermoacoustic elastic coupling includes: During the application of the stepped temperature load, the timing data of the acoustic emission signal of the ceramic material are collected simultaneously; Align the dynamic thermal stress characteristics with the acoustic emission signal time series data in a time coordinate system, and calculate the coherence function value between the dynamic thermal stress characteristics and the corresponding frequency components in the acoustic emission signal; Thermoacoustic elastic coupling is performed on the dynamic thermal stress characteristics based on the coherence function value to obtain the thermal stress gradient value of the ceramic material.

[0058] Dynamic thermal stress characteristics are obtained by analyzing and calculating the asynchronous thermal response of ceramic materials under stepped temperature loads. This yields data reflecting the stress state distribution that changes over time within the material due to uneven temperature distribution and thermal expansion. This characteristic dataset quantifies the magnitude, direction, and evolution of the mechanical driving force induced by thermal loads within the ceramic material.

[0059] Thermoacoustic-elastic coupling refers to a technical process that correlates and fuses the dynamic thermal stress field generated by a material under thermal load with the elastic wave signal excited by its internal microscopic damage activity.

[0060] Acoustic emission signal timing data refers to the elastic wave signal data arranged in chronological order, continuously monitored and recorded by an array of acoustic emission sensors attached to the surface of the ceramic material throughout the entire process of applying the stepped temperature load.

[0061] Based on the shape, size, and area of ​​interest of the ceramic material sample, the coupling agent coatings of multiple high-sensitivity acoustic emission sensors are tightly adhered to or fixed at predetermined positions on the sample surface to ensure a good acoustic wave transmission path. All sensors are connected to a multi-channel acoustic emission signal acquisition system, and appropriate signal acquisition parameters, such as threshold value, sampling frequency, and filtering bandwidth, are set to effectively capture acoustic emission signals related to thermal damage while suppressing environmental noise.

[0062] Synchronization Triggering and Data Recording: The acoustic emission signal acquisition system is activated, ensuring it shares the same time reference with the temperature data acquisition system and the heat loading control unit, or receives a synchronization trigger signal. This ensures that the acoustic emission data, temperature data, and heat loading program are completely synchronized on the time axis. Throughout the heat loading process, the system continuously monitors and records the acoustic emission signals from all channels.

[0063] The acquired raw acoustic emission waveform data is preprocessed, such as by filtering and denoising. Then, feature parameters are extracted for each valid acoustic emission event in real time or post-event to obtain its occurrence time, peak amplitude, energy, count, rise time, duration, and dominant frequency obtained through spectrum analysis. These feature parameters, together with their corresponding timestamps, are organized into ordered acoustic emission signal time-series data.

[0064] Time coordinate system alignment refers to the process of correcting and matching data from different measurement systems, each with its own timestamp, using a unified time reference. This ensures that all data accurately reflects different physical phenomena occurring at the same physical point in time or within a given time period. The aim is to eliminate time deviations caused by factors such as equipment startup delays and asynchronous sampling clocks, ensuring the accuracy of subsequent correlation analysis.

[0065] Before the experiment, the system clocks of the temperature data acquisition system, acoustic emission signal acquisition system, and heat loading control unit were synchronized and calibrated, or a common high-precision external clock source was configured as the master clock. During the experiment, all data records were timestamped based on the time of this master clock.

[0066] Read the timestamp sequences of the dynamic thermal stress characteristic data and the acoustic emission signal timing data respectively. Compare the start time, end time, and time interval between the two. If the data sampling frequencies are different, it may be necessary to resample or interpolate one of the data in time to ensure that the data points of the two have consistent and comparable time points on the time axis.

[0067] After time alignment is completed, an index is created for each identical analysis time point or time period. The numerical value of the dynamic thermal stress characteristic corresponding to that time point is associated with the time series data of acoustic emission signals occurring at the same time point or in the adjacent time window, such as event counts, cumulative energy values, energy in specific frequency bands, and other statistical characteristics, forming a one-to-one or one-to-many relationship.

[0068] The coherence function value is a statistical indicator used in signal processing to quantify the degree of linear correlation between two different signals on the same frequency component. Its value ranges from 0 to 1. The closer the value is to 1, the stronger the linear dependence between the two signals on that frequency component, that is, the more similar their fluctuation patterns and the better their synchronization. The closer the value is to 0, the weaker the correlation between the two signals on that frequency component, and they are almost independent of each other.

[0069] For a selected analysis period, the time-series variation curve of the dynamic thermal stress characteristics at a certain point of interest or a certain region inside the material is extracted from the time-aligned dataset. At the same time, the time-series variation curve of a certain comprehensive characteristic of the acoustic emission signal time-series data is extracted, or the time-series curve is obtained by directly performing cumulative energy analysis on the original acoustic emission waveform.

[0070] The selected thermal stress time series curves and acoustic emission time series curves were subjected to spectral analysis to estimate their respective power spectral densities and the cross-power spectral density between them.

[0071] Based on the estimated power spectral density of the thermal stress signal, the power spectral density of the acoustic emission signal, and the cross-power spectral density between the two, the coherence function is calculated according to its definition. This calculation process yields a function that varies with frequency, and the value of the function at each frequency point is the coherence function value corresponding to that frequency component.

[0072] The thermal stress gradient value refers to a new thermal stress spatial rate of change parameter obtained after thermoacoustic elastic coupling processing of the dynamic thermal stress characteristic data, which enhances the correlation with acoustic emission damage activity.

[0073] A weighting factor is generated based on the calculated coherence function value. Higher weights are assigned to frequency components or spatial regions with high coherence function values, while lower or even zero weights are assigned to regions with low coherence function values.

[0074] The obtained weighting factors are applied to the original dynamic thermal stress characteristic data. In the frequency domain, the spectrum of the thermal stress characteristics is weighted and filtered before being inversely transformed back to the time domain; or in the spatial domain, the thermal stress field is weighted and corrected according to the spatiotemporal distribution of acoustic emission events. For the coupled thermal stress field data, partial differential operations are performed along its spatial coordinate direction to calculate the rate of change of thermal stress at each point in space.

[0075] The calculated and coupled-corrected results of the spatial variation rate of thermal stress are organized into a dataset that can be queried by time and space. This dataset reflects the spatial non-uniformity of the thermal stress driving force, which is more likely to directly lead to microcrack activity and has been verified by thermo-acoustic correlation; that is, the thermal stress gradient value.

[0076] The beneficial effects are as follows: It provides a direct means for real-time, in-situ, and non-destructive monitoring of microscopic damage activity within ceramic materials. These data are crucial evidence for revealing the microscopic mechanisms of thermally induced damage. It creates the necessary conditions for accurately establishing a quantitative correlation between thermal stress and acoustic emission, ensuring the accuracy of the analysis in the time dimension. The role and effect of obtaining thermal stress gradient values ​​through thermoacoustic-elastic coupling based on coherence function values ​​is that it achieves data fusion and feature enhancement. This ensures that the final thermal stress gradient parameter not only includes the mechanical driving information caused by thermal load but also incorporates the material's own damage response information to the load. This allows it to more sensitively and accurately pinpoint the effective driving force that may lead to critical damage, providing a more reliable input for the subsequent quantification of microcrack release rate.

[0077] S4. Perform spatial topology analysis on the critical damage judgment parameters, establish the stress field connection path between adjacent damage points in the ceramic material, and generate the crack penetration evolution trajectory of the ceramic material. In this embodiment, the calculation formula for the critical damage determination parameter is as follows: in, The critical damage determination parameter is... For time variables, The thermal stress gradient value is a function of the change over time. The acoustic emission signal of the ceramic material is a function of time. The thermal damage relaxation coefficient of the ceramic material is given. The starting time for the generation of the dynamic thermal stress characteristics. This is the end time of the hot-loading step. The critical strain energy release rate of the ceramic material. It is an exponential function.

[0078] The spatial topology analysis of the critical damage determination parameters includes: The critical damage determination parameters are mapped to the three-dimensional spatial mesh nodes of the ceramic material; The three-dimensional isosurface of the ceramic material is constructed based on the node values ​​of the three-dimensional spatial mesh nodes; The three-dimensional isosurface is aggregated into three-dimensional spatial nodes to generate candidate damage connectivity regions for the ceramic material.

[0079] The establishment of stress field connection paths between adjacent damage points in the ceramic material includes: Within the candidate damaged connected region, select the node pairs of the three-dimensional spatial mesh nodes; Based on the critical damage determination parameters, the change in stress intensity factor of the node pair is estimated; If the change in the stress intensity factor exceeds the fatigue crack propagation threshold of the ceramic material, then the high-probability damage connectivity of the node pair is determined, and a stress field strength connection path between adjacent damage points in the ceramic material is constructed.

[0080] The generation of the crack penetration evolution trajectory in the ceramic material includes: The stress field connection path is abstracted as a weighted graph; Graph theory analysis is performed on the weighted graph to calculate the betweenness centrality of all nodes in the weighted graph; based on the node with the highest betweenness centrality, the critical hub node of the ceramic material is determined. Based on the edges of the strong connection path, a key priority search is performed on the key hub node to obtain the key connected subgraph of the key hub node. The key connected subgraph is mapped in three-dimensional material space to generate the main crack penetration path of the ceramic material, and the main crack penetration path is determined as the crack penetration evolution trajectory.

[0081] Critical damage determination parameters It refers to a single, comprehensive numerical index obtained by quantifying the energy accumulation effect of ceramic materials during hot loading, which is directly related to the initiation and propagation of micro-damage, through a specific calculation process.

[0082] Time variable Refers to the time from the start time End time The mathematical symbol used to represent a specific instant within this continuous time period.

[0083] The function of thermal stress gradient value over time This refers to a mathematical expression that describes how the thermal stress gradient value obtained after thermoacoustic elastic coupling changes with time. It changes continuously over time. The input to this function is time. The output is the spatial thermal stress gradient characteristic value corresponding to that moment.

[0084] Function of acoustic emission signal over time It refers to a mathematical expression that describes how a certain characteristic quantity of the acoustic emission signal of the monitored ceramic material changes with time. It changes continuously over time. The input to this function is time. The output is the characteristic value of the acoustic emission signal corresponding to that moment.

[0085] thermal damage relaxation coefficient It refers to a physical property parameter that characterizes the micro-damage induced by thermal stress inside the ceramic material under thermal cycling or thermal shock conditions, indicating that it has a certain degree of recoverability or stress relaxation characteristics.

[0086] The start time of dynamic thermal stress characteristic generation This refers to the initial moment when the calculation and generation of the dynamic thermal stress characteristic data begins.

[0087] End of hot-load step This refers to the moment when a complete temperature step in the currently analyzed stepped temperature load terminates. It defines the cutoff time for calculating the cumulative effect of this damage.

[0088] Critical strain energy release rate This refers to a key fracture mechanics parameter inherent in the ceramic material that characterizes its ability to resist crack propagation.

[0089] Exponential function Refers to the natural constant Exponentiation with base .

[0090] Spatial topology analysis is a mathematical analysis method used to study the properties of spatial figures that remain unchanged under continuous deformation.

[0091] Three-dimensional spatial mesh nodes refer to a series of regular or irregularly distributed discrete points formed by discretizing the internal space of a ceramic material sample in a computer for digital representation and analysis of the three-dimensional entity.

[0092] Establishing a three-dimensional geometric model and mesh for the material: Based on the actual size and shape of the ceramic material sample, a three-dimensional geometric model is established using computer-aided engineering or specialized analysis software. Subsequently, this geometric model is meshed to generate a three-dimensional mesh covering the entire internal space of the material, which consists of a large number of elements and nodes.

[0093] For each 3D spatial mesh node, its corresponding location in the actual material is determined based on its spatial coordinates. The value of the critical damage assessment parameter is then assigned to that node. If the calculated location of the critical damage assessment parameter does not completely coincide with the mesh node, the critical damage assessment parameter value is distributed to each node using spatial interpolation. Ultimately, each 3D spatial mesh node carries a critical damage assessment parameter value representing the degree of damage accumulation at that location.

[0094] The node value refers to the specific numerical value of the critical damage determination parameter assigned to each node of the three-dimensional spatial mesh.

[0095] A three-dimensional isosurface is a continuous surface in three-dimensional space formed by all points having the same specified node value.

[0096] Based on the understanding or experience of the damage mechanism of ceramic materials, one or more critical damage judgment parameter thresholds are set to define the potential damage zone; the entire three-dimensional space mesh is traversed using an isosurface extraction algorithm. For each mesh cell, the local geometry of the isosurface within that cell is determined by comparing the node values ​​of its eight vertices with the set thresholds.

[0097] The local triangular patches generated within all mesh cells are connected to form a continuous, closed or open triangular mesh surface, i.e., the three-dimensional isosurface. This surface is typically smoothed to eliminate the jagged appearance introduced by the discrete mesh.

[0098] Three-dimensional spatial node clustering refers to a data processing operation that aims to identify and classify grid nodes that are spatially close to each other in three-dimensional space and have similar node values, i.e., similar damage levels, into the same spatial group or cluster.

[0099] Candidate damage connectivity region refers to a potential three-dimensional spatial region identified from the entire material three-dimensional mesh through a three-dimensional spatial node aggregation operation. It consists of one or more spatially adjacent or close mesh node groups with high damage judgment parameter values.

[0100] The three-dimensional isosurface divides the entire material space into interior and exterior. Based on the initial set of high-damage nodes, a region growing algorithm or spatial clustering algorithm is applied. Starting from a seed node, its spatial neighborhood is checked to see if it also belongs to a high-damage node; if so, it is included in the same region. This process is iterated until no more new neighboring nodes can be included, thus forming a connected candidate region. This process is repeated until all high-damage nodes are classified into a region or removed as isolated points.

[0101] All identified connected regions can be filtered based on features such as their size, average or maximum node values, retaining those regions with significant size and high degree of damage as the candidate damaged connected regions of focus, and assigning a unique identifier to each region.

[0102] A stress field strong connection path refers to a virtual path within a material that connects two adjacent damage points.

[0103] A node pair refers to two different three-dimensional spatial mesh nodes arbitrarily selected within the same candidate damage connectivity region.

[0104] For each candidate damaged connected region, obtain a list of all three-dimensional spatial mesh nodes contained therein, including the spatial coordinates and node values ​​of each node. Node pair generation strategy: Determine the node pair selection strategy based on analysis needs and computational resources.

[0105] The stress intensity factor variation refers to the magnitude of change of a key parameter used in fracture mechanics to quantify the stress field intensity at the crack tip under certain conditions.

[0106] The selected node pairs are abstracted as two point defects or microcrack initiations. Based on the approximate formula of linear elastic fracture mechanics, a simplified computational model is established. This model takes the critical damage determination parameters of the node pairs and their spatial distance and relative orientation as input parameters.

[0107] The method applies approximate solutions of fracture mechanics applicable to the interaction of collinear or approximately collinear cracks, or empirical formulas obtained through numerical fitting. Using the critical damage assessment parameter value of each node in a node pair as a parameter reflecting the equivalent initial crack size or damage driving force at that point, and combining the distance and direction between nodes, the method calculates the stress intensity at the tip of a small connecting crack between the two points, ultimately obtaining an estimate of the change in stress intensity factor characterizing the connecting driving force.

[0108] The fatigue crack propagation threshold refers to the lower limit of the stress intensity factor range corresponding to the stable propagation of cracks in the ceramic material under cyclic or continuous loading.

[0109] The stress intensity factor variation of the node pair estimated in the previous step is compared with the fatigue crack propagation threshold of the ceramic material obtained from a material handbook or experiments. If the stress intensity factor variation is greater than the fatigue crack propagation threshold, a high-probability damage connection is determined to exist between the node pairs.

[0110] For all node pairs identified as having a high probability of damage connectivity, a line segment connecting the two nodes is used in the computer model to represent a potential crack connection path. All such paths constitute a network that describes the potential ways in which damage points can be interconnected via cracks within the candidate damage connectivity region, i.e., the network of stress field strong connectivity paths.

[0111] A weighted graph, in mathematical graph theory, is a network structure consisting of nodes and edges connecting the nodes, where each edge is assigned a numerical weight.

[0112] All unique endpoints constituting the stress field strong connection path network are extracted and used as a set of nodes in the graph. Each node retains its unique identifier and three-dimensional spatial coordinates.

[0113] For each constructed stress field strength connection path, an edge connecting the corresponding two nodes is created in the graph model. Based on the mechanical meaning of the path, a weight value is calculated and assigned to each edge. For example, the weight could be the change in stress intensity factor corresponding to the path, the ratio of that change to the fatigue threshold, or the reciprocal of the path length.

[0114] Using graph data structures in computer programs, all nodes and weighted edges are organized to form a complete weighted graph that can be processed by subsequent graph theory algorithms.

[0115] Graph analysis refers to the process of quantitatively studying and calculating the structural properties of a weighted graph by applying a series of concepts, theorems, and algorithms from mathematical graph theory.

[0116] In graph theory, betweenness centrality is a centrality metric used to measure the importance of a node's role as a mediator or bridge in the entire network graph.

[0117] On the weighted graph, run the shortest path algorithm between all pairs of nodes. Since the graph is weighted, the algorithm will find the sequence of paths connecting any two nodes that minimizes the sum of the edge weights, i.e., the optimal path.

[0118] Iterate through all node pairs. For each shortest path between them, record the sequence of nodes that the path passes through. Then, for each node in the graph, count how many pairs of nodes have a shortest path passing through that node.

[0119] For each node, the number of shortest paths passing through that node is divided by the total number of shortest paths between all pairs of nodes in the network. This is then normalized to obtain the betweenness centrality value for each node k, which is between 0 and 1.

[0120] A key pivot node refers to one or a few nodes in the weighted graph that have the highest betweenness centrality value, as identified by graph theory analysis.

[0121] Sort all nodes from highest to lowest according to their calculated betweenness centrality values. Depending on the specific analysis requirements, set a centrality threshold, or select the top N nodes. Typically, the node with the highest absolute betweenness centrality value is chosen as the most critical single hub.

[0122] The identified key hub nodes are marked with special tags on their original identifiers and associated with attributes such as the calculated high betweenness centrality value, serving as the starting point or core for subsequent searches.

[0123] Key-priority search refers to a search strategy in graph theory used to explore connected substructures associated with specific key nodes.

[0124] A key connected subgraph refers to a subgraph extracted from the original weighted graph through the key-priority search.

[0125] The identified key hub node is used as the starting node for the search, and it is added to a visited set and a priority queue. The node with the highest current priority is retrieved from the priority queue. All adjacent nodes of this node are checked. For each unvisited adjacent node, the cumulative weight of the path from the starting key hub node through the current node to that adjacent node is calculated. If the path meets predetermined conditions, the adjacent node is marked as visited, added to the priority queue, and the edge connecting this path is added to the subgraph.

[0126] The iterative expansion steps are repeated until a predetermined termination condition is reached. The termination condition may be that a certain number of nodes have been found, the priority queue is empty, the cumulative path weight is below a threshold, or the search range has reached the physical space boundary. Ultimately, all visited nodes and the edges between them together constitute the key connected subgraph.

[0127] Three-dimensional material space mapping refers to a data transformation process that re-associates and transforms the analysis results based on an abstract graph theory model back into the actual three-dimensional physical space coordinate system of the ceramic material.

[0128] The main crack penetration path refers to one or more prominent, continuous or nearly continuous spatial curves outlined by a series of spatial points and connecting lines corresponding to the key connected subgraphs within the three-dimensional solid model of the ceramic material after the three-dimensional material space mapping.

[0129] Read the unique identifier of each node in the key connected subgraph. Based on these identifiers, query its original 3D coordinates in the initial 3D space grid.

[0130] For each edge in the critical connected subgraph, find the 3D coordinates of the two nodes it connects based on their identifiers. In 3D space, connect these two coordinate points with a line segment. This line segment represents the potential crack segment predicted by that edge.

[0131] Since the key connected subgraph is itself connected, all spatial line segments corresponding to the edges are connected end-to-end according to the node connection relationship. Adjacent line segments may be smoothed at the connection points to form a more natural and continuous spatial trajectory. If branches exist, multiple paths may be generated; the path with the largest cumulative weight or the one that best meets the mechanical expectations is selected as the primary path.

[0132] The integrated sequence of spatial points is fitted into a three-dimensional spatial curve, or directly represented as a broken line. This spatial curve is the predicted main crack propagation path within the three-dimensional space of the material.

[0133] Crack penetration evolution trajectory refers to a complete description of the predictive spatial propagation path of the dominant internal crack in a ceramic material during thermal loading, from initiation to expansion, which may eventually penetrate the entire critical section or lead to failure.

[0134] The beneficial effects are as follows: It transforms complex spatial mechanical connections into standardized mathematical graph models, providing a foundation for applying mature graph theory algorithms to network analysis. It can objectively and quantitatively identify the structural cores in the entire potential crack network—those nodes that have the greatest impact on network connectivity—providing a scientific basis for identifying key points of crack convergence. It focuses the analysis objective, concentrating resources on the core areas most likely to trigger catastrophic failure. It efficiently extracts the most vulnerable and important local connection networks centered on key hubs, excluding a large number of secondary connections and simplifying the prediction model.

[0135] S5. Perform geometric configuration matching between the crack penetration evolution trajectory and the preset material failure space mode library, and output the cascaded thermal collapse early warning signal of the ceramic material.

[0136] In this embodiment, the step of matching the crack penetration evolution trajectory with a preset material failure spatial mode library for geometric configuration and outputting a cascaded thermal collapse early warning signal for the ceramic material includes: Extract the geometric features of the crack penetration evolution trajectory to generate a multi-scale shape descriptor; Based on a preset material failure spatial pattern library, the similarity of the multi-scale shape descriptors is calculated to obtain the similarity results of the multi-scale shape descriptors; The typical failure crack mode with the highest similarity result is selected as the matching result; The typical failure modes of the matching results and the known failure consequences of the crack penetration evolution trajectory are matched by difference to generate a cascaded thermal collapse early warning signal for the ceramic material.

[0137] The crack penetration evolution trajectory is one or more spatial curves that are predicted in the three-dimensional physical space of the ceramic material by performing spatial topological analysis and graph theory deduction on the critical damage judgment parameters, and which characterize the most likely propagation path of the dominant crack.

[0138] The material failure spatial pattern library refers to a pre-established database or knowledge base that stores spatial geometric morphology data of various typical failure cracks that have been observed in similar ceramic materials in history or determined through theoretical simulation.

[0139] Geometric configuration matching refers to the process of comparing the three-dimensional geometric shape of the crack penetration evolution trajectory obtained from the current analysis with the three-dimensional geometric shapes of various known failure crack modes stored in the material failure space mode library one by one to find the most similar morphology.

[0140] Geometric features refer to a series of measurement parameters used to quantitatively describe the three-dimensional spatial shape and structure of the crack penetration evolution trajectory.

[0141] A multi-scale shape descriptor refers to a set of quantitative parameters extracted from the crack penetration evolution trajectory that describe its geometric features at different spatial scales.

[0142] The three-dimensional spatial curve of the crack penetration evolution trajectory is approximated by a series of dense and ordered three-dimensional spatial points. This curve may be parameterized by arc length, that is, the length of the curve starting from the trajectory's origin is used as a parameter to correspond to each point on the curve.

[0143] The calculation includes the overall length of the trajectory, the straight-line distance between its two endpoints, the direction of the trajectory's principal axis, and the volume and size ratio of the overall bounding box. The trajectory curve is divided into several segments, and the local curvature and torsion of each segment are calculated. The distribution of these curvature and torsion values ​​is then statistically analyzed.

[0144] Analyzing high-frequency fluctuations in localized areas of a trajectory can quantify its irregularity and complexity by calculating its fractal dimension. This involves statistically analyzing the number of branch points on the trajectory and the length ratio of each branch.

[0145] The calculated geometric feature values ​​at different scales are arranged in a predetermined order and combined into a one-dimensional numerical vector. This vector is the multi-scale shape descriptor that characterizes the unique shape of the crack penetration evolution trajectory.

[0146] Similarity calculation refers to a quantitative mathematical comparison process used to measure the degree of similarity or proximity between two things.

[0147] From the material failure spatial pattern library, the multi-scale shape descriptor vector corresponding to each pre-stored typical failure crack mode is read sequentially. For the descriptor vector of the current trajectory and the descriptor vector of a certain mode in the pattern library, a suitable mathematical metric is selected to calculate the difference or similarity between them.

[0148] The similarity calculation results of all patterns and the current trajectory are recorded and sorted from high to low similarity.

[0149] Typical failure crack mode refers to the pre-stored failure mode that is most similar in geometric shape to the current crack penetration evolution trajectory, selected from the material failure space mode library based on similarity calculation results.

[0150] The similarity results are sorted, and the pre-stored failure mode corresponding to the item with the highest similarity value is selected. This selected mode is marked as the matching result for this analysis, and all associated information for this mode is retrieved from the material failure space mode library.

[0151] A cascaded thermal collapse warning signal is a signal that is the final output indicating the risk level and urgency of a chain reaction of damage to a ceramic material that may be triggered by thermal damage and spread rapidly.

[0152] The key parameters of the current crack penetration evolution trajectory are compared item by item with the recorded occurrence conditions of known failure consequences associated with the matched typical failure crack modes.

[0153] Based on the above comparisons, the degree of difference between the current state and the known critical failure state is quantified. For example, the percentage of the current crack length to the known critical length is calculated to assess the progress of the current thermal load stage compared to the known failure stage. Based on these differences, the current risk level and the estimated remaining safe time or number of load cycles are assessed. The risk assessment results, combining the similarity and difference matching of geometric matching, are used to generate the final early warning signal.

[0154] The beneficial effects are as follows: Multi-scale shape descriptors transform three-dimensional spatial curves, which are difficult to compare directly, into standardized, computable mathematical feature vectors, making automated and quantitative pattern matching possible. Utilizing a historical failure knowledge base, shape fingerprint comparison quickly finds the closest known cases for predicting currently unknown damage, enabling case-based reasoning. Classifying unique current damage situations into known failure categories significantly reduces assessment uncertainty and provides a direct basis for subsequent early warnings. The role and effect of difference matching to generate cascaded early warning signals lies in the fact that it goes beyond simple morphological matching, further combining the specific development level of the current damage to refine and extrapolate the risk, thus producing a dynamic risk warning output with clear guidance.

[0155] like Figure 2 The diagram shown is a functional block diagram of a ceramic material thermal stability testing system provided in an embodiment of the present invention.

[0156] The ceramic material thermal stability testing system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the ceramic material thermal stability testing system 100 may include a thermal loading control module 101, a thermal response decoupling module 102, a coupled damage quantification module 103, a damage topology evolution module 104, and a failure early warning module 105. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, and are stored in the memory of the electronic device.

[0157] In this embodiment, the functions of each module / unit are as follows: The heat loading control module 101 is used to apply a stepped temperature load to the ceramic material and simultaneously collect the time-series temperature distribution data of the ceramic material. The thermal response decoupling module 102 is used to decouple the thermal conduction phase of the time series temperature distribution data, separate the asynchronous thermal response of the ceramic material, and generate the dynamic thermal stress characteristics of the ceramic material. The coupled damage quantification module 103 is used to perform thermoacoustic elastic coupling on the dynamic thermal stress characteristics, and to quantify the microcrack release rate of the thermal stress gradient value after thermoacoustic elastic coupling, thereby generating critical damage judgment parameters for the ceramic material. The damage topology evolution module 104 is used to perform spatial topology analysis on the critical damage judgment parameters, establish stress field connection paths between adjacent damage points in the ceramic material, and generate the crack penetration evolution trajectory of the ceramic material. The failure early warning module 105 performs geometric configuration matching between the crack penetration evolution trajectory and the preset material failure space mode library, and outputs the cascaded thermal collapse early warning signal of the ceramic material.

[0158] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0159] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0161] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0162] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for testing the thermal stability of ceramic materials, characterized in that, The method includes: S1. Apply a stepped temperature load to the ceramic material and simultaneously collect time-series temperature distribution data of the ceramic material; S2. Perform thermal conduction phase decoupling on the time series temperature distribution data and separate the asynchronous thermal response of the ceramic material to generate the dynamic thermal stress characteristics of the ceramic material; S3. Perform thermoacoustic elastic coupling on the dynamic thermal stress characteristics, and quantify the microcrack release rate of the thermal stress gradient value after thermoacoustic elastic coupling to generate critical damage judgment parameters for the ceramic material. S4. Perform spatial topology analysis on the critical damage judgment parameters, establish the stress field connection path between adjacent damage points in the ceramic material, and generate the crack penetration evolution trajectory of the ceramic material. S5. Perform geometric configuration matching between the crack penetration evolution trajectory and the preset material failure space mode library, and output the cascaded thermal collapse early warning signal of the ceramic material.

2. The method for testing the thermal stability of ceramic materials as described in claim 1, characterized in that, The application of a stepped temperature load to the ceramic material, and the simultaneous acquisition of time-series temperature distribution data of the ceramic material, includes: A temperature sensing array is arranged on the surface and inside of the ceramic material; The temperature sensing array includes an infrared thermal imager and embedded thermocouples. The infrared thermal imager and the embedded thermocouple are subjected to heating and cooling conversions at a preset constant rate to apply a stepped temperature load to the ceramic material, and the time-series temperature distribution data of the ceramic material are collected simultaneously.

3. The method for testing the thermal stability of ceramic materials as described in claim 1, characterized in that, The step of decoupling the thermal conduction phase of the time-series temperature distribution data and separating the asynchronous thermal response of the ceramic material to generate the dynamic thermal stress characteristics of the ceramic material includes: A short-time Fourier transform is performed on the temperature-time curve of each measuring point in the time-series temperature distribution data to obtain the heating source temperature change waveform of the time-series temperature distribution data. The phase hysteresis angle of the temperature change waveform of the heating source is extracted to obtain the set of phase hysteresis components of the ceramic material. Based on the theoretical thermophysical parameters of the ceramic material, the expected heat conduction phase effect and the phase hysteresis component at each measuring point are quantified to obtain the response value deviation of the ceramic material. Based on the time variation law of the response value deviation and the elastic modulus of the ceramic material, the thermal stress characteristics of the ceramic material are extracted to obtain the dynamic thermal stress characteristics of the ceramic material.

4. The method for testing the thermal stability of ceramic materials as described in claim 1, characterized in that, The thermoacoustic elastic coupling includes: During the application of the stepped temperature load, the timing data of the acoustic emission signal of the ceramic material are collected simultaneously; Align the dynamic thermal stress characteristics with the acoustic emission signal time series data in a time coordinate system, and calculate the coherence function value between the dynamic thermal stress characteristics and the corresponding frequency components in the acoustic emission signal; Thermoacoustic elastic coupling is performed on the dynamic thermal stress characteristics based on the coherence function value to obtain the thermal stress gradient value of the ceramic material.

5. The method for testing the thermal stability of ceramic materials as described in claim 1, characterized in that, The calculation formula for the critical damage determination parameter is as follows: in, The critical damage determination parameter is... For time variables, The thermal stress gradient value is a function of the change over time. The acoustic emission signal of the ceramic material is a function of time. The thermal damage relaxation coefficient of the ceramic material is given. The starting time for the generation of the dynamic thermal stress characteristics. This is the end time of the hot-loading step. The critical strain energy release rate of the ceramic material. It is an exponential function.

6. The method for testing the thermal stability of ceramic materials as described in claim 1, characterized in that, The spatial topology analysis of the critical damage determination parameters includes: The critical damage determination parameters are mapped to the three-dimensional spatial mesh nodes of the ceramic material; The three-dimensional isosurface of the ceramic material is constructed based on the node values ​​of the three-dimensional spatial mesh nodes; The three-dimensional isosurface is aggregated into three-dimensional spatial nodes to generate candidate damage connectivity regions for the ceramic material.

7. The method for testing the thermal stability of ceramic materials as described in claim 6, characterized in that, The establishment of stress field connection paths between adjacent damage points in the ceramic material includes: Within the candidate damaged connected region, select the node pairs of the three-dimensional spatial mesh nodes; Based on the critical damage determination parameters, the change in stress intensity factor of the node pair is estimated; If the change in the stress intensity factor exceeds the fatigue crack propagation threshold of the ceramic material, then the high-probability damage connectivity of the node pair is determined, and a stress field strength connection path between adjacent damage points in the ceramic material is constructed.

8. The method for testing the thermal stability of ceramic materials as described in claim 7, characterized in that, The generation of the crack penetration evolution trajectory in the ceramic material includes: The stress field connection path is abstracted as a weighted graph; Graph theory analysis is performed on the weighted graph to calculate the betweenness centrality of all nodes in the weighted graph; based on the node with the highest betweenness centrality, the critical hub node of the ceramic material is determined. Based on the edges of the strong connection path, a key priority search is performed on the key hub node to obtain the key connected subgraph of the key hub node. The key connected subgraph is mapped in three-dimensional material space to generate the main crack penetration path of the ceramic material, and the main crack penetration path is determined as the crack penetration evolution trajectory.

9. The method for testing the thermal stability of ceramic materials as described in claim 1, characterized in that, The step of matching the crack penetration evolution trajectory with a preset material failure spatial mode library to perform geometric configuration matching and outputting a cascaded thermal collapse early warning signal for the ceramic material includes: Extract the geometric features of the crack penetration evolution trajectory to generate a multi-scale shape descriptor; Based on a preset material failure spatial pattern library, the similarity of the multi-scale shape descriptors is calculated to obtain the similarity results of the multi-scale shape descriptors; The typical failure crack mode with the highest similarity result is selected as the matching result; The typical failure modes of the matching results and the known failure consequences of the crack penetration evolution trajectory are matched by difference to generate a cascaded thermal collapse early warning signal for the ceramic material.

10. A system for testing the thermal stability of ceramic materials, characterized in that, A ceramic material thermal stability testing system according to any one of claims 1-9, the system comprising: The heat loading control module is used to apply a stepped temperature load to the ceramic material and simultaneously collect the time-series temperature distribution data of the ceramic material. A thermal response decoupling module is used to decouple the thermal conduction phase of the time-series temperature distribution data, separate the asynchronous thermal response of the ceramic material, and generate the dynamic thermal stress characteristics of the ceramic material. The coupled damage quantification module is used to perform thermoacoustic-elastic coupling on the dynamic thermal stress characteristics, and to quantify the microcrack release rate of the thermal stress gradient value after thermoacoustic-elastic coupling, thereby generating critical damage judgment parameters for the ceramic material. The damage topology evolution module is used to perform spatial topology analysis on the critical damage judgment parameters, establish the stress field connection path between adjacent damage points in the ceramic material, and generate the crack penetration evolution trajectory of the ceramic material. The failure early warning module performs geometric configuration matching between the crack penetration evolution trajectory and a preset material failure space mode library, and outputs a cascaded thermal collapse early warning signal for the ceramic material.

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