Method and system for visualizing lifetime prediction based on transducer glue layer thermal fatigue

Abstract

This application provides a method and system for visually predicting the lifespan of transducer adhesive layers based on thermal fatigue, belonging to the field of visualization prediction technology. The method includes: establishing a thermo-mechanical coupling simulation model of the transducer and performing simulation analysis; introducing environmental parameters and performing high-low temperature alternating fatigue analysis based on the thermal stress simulation results; identifying fatigue inducing parameters based on monitoring change signals; mapping the thermal stress simulation results to fatigue life data; predicting the thermal fatigue of the transducer adhesive layer based on a stress-life correlation data table; and transmitting the prediction results to a remote terminal for verification. This application solves the technical problem in the prior art where the prediction of the thermal fatigue life of the transducer adhesive layer heavily relies on high-low temperature cycle testing of the actual product after prototyping, making it impossible to intervene in the product design stage, and resulting in long testing costs and cycles, which affects the accuracy of lifespan prediction. It achieves rapid and accurate prediction of the thermal fatigue life of the transducer adhesive layer and visualizes the location of weak points during the design stage.

Description

Technical Field

[0001] This application relates to the field of visualization prediction technology, specifically to a method and system for visualization prediction of transducer adhesive layer lifespan based on thermal fatigue. Background Technology

[0002] The transducer is the core component of an ultrasonic flow meter. It utilizes the forward and reverse piezoelectric effects of piezoelectric ceramics to achieve electromechanical-acoustic conversion. The specific structural design relies on adhesive bonding to connect and fix the piezoelectric ceramic and the matching layer, thus packaging the transducer product. Because the piezoelectric ceramic is fixed, the adhesive undergoes thermal expansion and contraction deformation during product application due to the temperature of the working environment. Under prolonged thermal cycling, the adhesive will experience fatigue damage, eventually leading to cracking between the piezoelectric ceramic and the matching layer, thus causing the transducer to lose its normal operating capability.

[0003] Current methods for predicting the thermal fatigue life of transducer adhesive layers still rely on high and low temperature cycling tests, which are typically conducted only after product design is complete. Therefore, it's impossible to intervene in life prediction during the product development phase, impacting the product design optimization process. High and low temperature cycling tests require lengthy testing periods, usually several months, and each test is costly, severely restricting product development speed and hindering the timely detection of adhesive layer damage. Because the piezoelectric ceramic fixing adhesive is encapsulated inside the transducer, it's impossible to accurately determine the initial location of adhesive cracking or deformation during thermal expansion and contraction, further increasing the difficulty of monitoring adhesive layer damage.

[0004] Existing simulation methods can intervene in the product development stage, predicting fatigue life through the 3D structure of the transducer and visually predicting the locations where the adhesive layer is prone to cracking. However, most current simulation methods rely on mathematical statistical models, and the accuracy of the results heavily depends on the stress-life curve of a specific adhesive layer material under specific experimental conditions. However, the mechanical properties of the adhesive layer are affected by factors such as ambient temperature, humidity, substrate differences, and product structure, leading to significant variations in its mechanical properties under different application scenarios. This makes existing simulation methods unable to accurately reflect the fatigue behavior of the adhesive layer. Therefore, simulations based on traditional stress-life curves often exhibit instability and large errors under different structures and operating conditions, failing to meet the requirement for accurate prediction of transducer fatigue life during the development stage.

[0005] In summary, existing technologies suffer from the technical problem that the prediction of the thermal fatigue life of the transducer adhesive layer relies heavily on high and low temperature cycling tests of the actual product after prototyping, making it impossible to intervene in the product design stage. Furthermore, the testing costs and cycles are long, which affects the accuracy of life prediction. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for visually predicting the life of transducer adhesive layer based on thermal fatigue, in order to solve the technical problems in the prior art where the prediction of the thermal fatigue life of transducer adhesive layer relies heavily on the high and low temperature cycle test of the actual product after prototyping, which makes it impossible to intervene in the product design stage, and the test cost and cycle are long, affecting the accuracy of life prediction.

[0007] To achieve the above objectives, this application provides a method and system for visually predicting the lifespan of transducer adhesive layers based on thermal fatigue.

[0008] Firstly, this application provides a life visualization prediction method based on the thermal fatigue of a transducer adhesive layer. This method is implemented through a life visualization prediction system based on the thermal fatigue of a transducer adhesive layer. The method includes: establishing a thermo-mechanical coupling simulation model of the transducer; performing simulation analysis based on the thermo-mechanical coupling simulation model to obtain thermal stress simulation results; introducing environmental parameters and performing alternating high and low temperature fatigue analysis according to the thermal stress simulation results to extract monitoring change signals; identifying fatigue inducing parameters based on the monitoring change signals to generate fatigue life data; mapping the thermal stress simulation results to the fatigue life data to construct a stress-life correlation data table; predicting the thermal fatigue of the transducer adhesive layer based on the stress-life correlation data table; and transmitting the prediction results to a remote terminal for verification.

[0009] Optionally, the thermophysical and mechanical parameters of the transducer adhesive layer are introduced; a three-dimensional analysis of the transducer is performed to construct a three-dimensional structural model of the transducer; the thermophysical and mechanical parameters are mapped to the three-dimensional structural model of the transducer to establish a thermo-mechanical coupling simulation model; simulation calculations are performed based on the thermo-mechanical coupling simulation model to obtain multi-temperature environment stress simulation distribution data; and the multi-temperature environment stress simulation distribution data is added to the thermal stress simulation results.

[0010] Optionally, environmental parameters are introduced to analyze temperature field changes and extract time parameters of temperature field changes; temperature holding is divided according to the temperature field change time parameters, and low temperature holding time parameters and high temperature holding time parameters are set; high and low temperature alternating fatigue records are performed on the transducer according to the low temperature holding time parameters and the high temperature holding time to generate a fatigue record parameter set; multiple peak signals are constructed by oscilloscope processing based on the fatigue record parameter set; and monitoring changes are fused according to the multiple peak signals to construct a monitoring change signal.

[0011] Optionally, the plurality of peak signals are continuously monitored, and adjacent peak signals are subtracted according to the monitoring results to generate a plurality of signal difference values; the plurality of signal difference values ​​are compared to see if they exceed a signal deviation threshold; when the signal difference value exceeds the signal deviation threshold, fatigue inducing parameters are identified, and a recording instruction is generated according to the fatigue inducing parameters; high and low temperature cycle recording is performed through the recording instruction to generate the number of high and low temperature cycles; the maximum stress value of the adhesive layer is retrieved, and fatigue analysis is performed based on the number of high and low temperature cycles and the maximum stress value of the adhesive layer to generate the fatigue life data.

[0012] Optionally, multiple sets of maximum stress values ​​are extracted based on the thermal stress simulation results; the multiple sets of maximum stress values ​​are curve-fitted with fatigue life data to generate a curve fitting graph; the goodness of fit is calculated according to the curve fitting graph; a two-dimensional null data table is constructed, and the multiple sets of maximum stress values ​​and the fatigue life data are filled into the null two-dimensional data table according to the goodness of fit, wherein the first column is the maximum stress value of the adhesive layer, the second column is the fatigue life data, and a two-dimensional data table is constructed; outliers are removed by traversing the two-dimensional data table, and the stress-life correlation data table is constructed.

[0013] Optionally, based on the stress-life correlation data table, color gradient rendering is performed on the transducer adhesive layer to construct an adhesive layer stress cloud map; the adhesive layer stress cloud map is traversed to perform adhesive layer fatigue analysis on the transducer to identify multiple crack-prone areas; thermal fatigue visualization prediction analysis is performed according to the multiple crack-prone areas to construct a life-stress curve, which includes simulated stress points and predicted life points; the simulated stress points and predicted life points are encapsulated to construct the prediction result.

[0014] Optionally, the prediction result is received via the remote terminal, and error analysis is performed on the prediction result to generate a data error value; the data error value is verified to be less than a preset error threshold; if the data error value is less than the preset error threshold, the transducer is subjected to adhesive layer structure analysis based on the simulated stress point to determine the weak structure parameters; optimization analysis is performed according to the predicted lifetime point and the weak structure parameters to generate optimization suggestions; if the data error value is greater than or equal to the preset error threshold, the prediction result is determined to be invalid, and a step backtracking check is performed.

[0015] Secondly, this application also provides a life visualization prediction system based on the thermal fatigue of transducer adhesive layers, used to execute the life visualization prediction method based on the thermal fatigue of transducer adhesive layers as described in the first aspect. The life visualization prediction system based on the thermal fatigue of transducer adhesive layers includes: a simulation analysis module for establishing a thermo-mechanical coupling simulation model of the transducer, performing simulation analysis based on the thermo-mechanical coupling simulation model, and obtaining thermal stress simulation results; a high-low temperature alternating fatigue analysis module for introducing environmental parameters and performing high-low temperature alternating fatigue analysis according to the thermal stress simulation results, and extracting monitoring change signals; a causative parameter identification module for identifying fatigue causative parameters based on the monitoring change signals and generating fatigue life data; a data mapping module for mapping the thermal stress simulation results to the fatigue life data, constructing a stress-life correlation data table; and a thermal fatigue prediction module for performing thermal fatigue prediction of the transducer adhesive layer based on the stress-life correlation data table, and transmitting the prediction results to a remote terminal for verification.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] By establishing a thermo-mechanical coupling simulation model of the transducer, simulation analysis is performed based on the model to obtain thermal stress simulation results. Environmental parameters are introduced, and high-low temperature alternating fatigue analysis is conducted according to the thermal stress simulation results to extract monitoring change signals. Fatigue inducing parameters are identified based on the monitoring change signals, and fatigue life data is generated. The thermal stress simulation results are mapped to the fatigue life data to construct a stress-life correlation data table. Thermal fatigue prediction of the transducer adhesive layer is performed based on the stress-life correlation data table, and the prediction results are transmitted to a remote terminal for verification. In other words, by establishing a thermo-mechanical coupling simulation model, mapping thermal stress simulation results to fatigue life data, constructing a stress-life correlation data table, and predicting the thermal fatigue of the transducer adhesive layer, rapid and accurate prediction of the thermal fatigue life of the transducer adhesive layer during the product design stage is achieved, shortening the product development cycle and reducing product development costs. Simultaneously, the location affecting the thermal fatigue life of the transducer adhesive layer is visualized, improving the accuracy of thermal fatigue life prediction.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the lifetime visualization prediction method based on thermal fatigue of transducer adhesive layer in this application.

[0021] Figure 2 This is a schematic diagram of the life visualization prediction system based on thermal fatigue of transducer adhesive layer according to this application.

[0022] Figure labeling: Simulation analysis module 11, high and low temperature alternating fatigue analysis module 12, causative parameter identification module 13, data mapping module 14, thermal fatigue prediction module 15. Detailed Implementation

[0023] This application provides a method and system for visually predicting the thermal fatigue life of transducer adhesive layers. This addresses the technical problem in existing technologies where transducer adhesive layer thermal fatigue life prediction heavily relies on high- and low-temperature cycling tests of prototypes, preventing intervention during the product design phase and resulting in long testing costs and cycles, thus affecting the accuracy of life prediction. By establishing a thermo-mechanical coupling simulation model, mapping thermal stress simulation results to fatigue life data, and constructing a stress-life correlation data table, the thermal fatigue of the transducer adhesive layer is predicted. This enables rapid and accurate prediction of the thermal fatigue life of the transducer adhesive layer during the product design phase, shortening the product development cycle and reducing R&D costs. Furthermore, the visualization of locations affecting the thermal fatigue life of the transducer adhesive layer improves the accuracy of thermal fatigue life prediction.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1This application provides a life visualization prediction method based on the thermal fatigue of transducer adhesive layers. The method is applied to a life visualization prediction system based on the thermal fatigue of transducer adhesive layers. The specific steps of the method are as follows:

[0026] S100: Establish a thermo-mechanical coupling simulation model of the transducer, perform simulation analysis based on the thermo-mechanical coupling simulation model, and obtain the thermal stress simulation results.

[0027] Furthermore, S100 of this application includes: introducing the material thermophysical parameters and material mechanical parameters of the transducer adhesive layer; performing three-dimensional analysis on the transducer to construct a three-dimensional structural model of the transducer; mapping the material thermophysical parameters and the material mechanical parameters to the three-dimensional structural model of the transducer to establish a thermo-mechanical coupling simulation model; performing simulation calculations based on the thermo-mechanical coupling simulation model to obtain multi-temperature environment stress simulation distribution data; and adding the multi-temperature environment stress simulation distribution data to the thermal stress simulation results.

[0028] Specifically, the transducer is the core component of an ultrasonic flow meter, utilizing the direct and inverse piezoelectric effects of piezoelectric ceramics to achieve electromechanical-acoustic conversion. Piezoelectric ceramics generate mechanical vibrations under the influence of an electric field (direct piezoelectric effect) or generate electrical signals under mechanical vibrations (inverse piezoelectric effect). Electromechanical-acoustic conversion involves converting electrical signals into ultrasonic waves (emission) or ultrasonic waves into electrical signals (reception). In ultrasonic gas meters based on the time-of-pass method, the transducer is used to transmit and receive ultrasonic signals, calculating the flow rate by measuring the time difference of ultrasonic wave propagation in the fluid. In short, the transducer is the key device for realizing the mutual conversion between electrical and ultrasonic signals.

[0029] Key material parameters of the transducer adhesive layer are collected, including thermophysical and mechanical parameters. Thermophysical parameters describe the inherent properties of a material's physical behavior under thermal conditions, including density, specific heat capacity, thermal conductivity, and coefficient of thermal expansion. Mechanical parameters describe the inherent properties of a material's deformation and failure behavior under external forces, including elastic modulus and Poisson's ratio. Density is the mass per unit volume, affecting the volume change of a material during thermal expansion and contraction; specific heat capacity is the amount of heat absorbed or released per unit mass of material during a unit temperature change, affecting heat transfer during temperature changes; thermal conductivity indicates a material's ability to transfer heat, with higher values ​​indicating faster heat conduction; the coefficient of thermal expansion is the degree of volume or length change of a material under temperature changes. Elastic modulus is the stiffness of a material, reflecting the degree of deformation under stress; a higher modulus indicates less deformation; Poisson's ratio is the ratio of deformation perpendicular to the direction of tension or compression to deformation in the original direction, commonly used to describe the deformation characteristics of a material.

[0030] A 3D analysis of the transducer was performed, and a geometric model of the transducer was created using computer-aided design software. The dimensions, shapes, and relative positions of each component (such as the piezoelectric ceramic, adhesive layer, and matching layer) were considered to construct the 3D structural model. The outer diameter, inner diameter, and thickness of the piezoelectric ceramic, the diameter and thickness of the matching layer, and the inner cavity dimensions and wall thickness of the housing were determined based on the product design drawings. Modeling the adhesive layer is a crucial step. Thin, uniformly thick solid layers were generated by stretching or rotating between the piezoelectric ceramic and the matching layer, and between the piezoelectric ceramic and the housing sidewalls. The adhesive layer thickness was taken as the nominal value specified in the process specifications, such as 0.10 mm, and rounded corners were retained to eliminate singular stresses in subsequent simulations. All components were assembled according to the actual assembly sequence, ensuring a tight fit without penetration at the contact interfaces. After the model was completed, geometric simplification was performed, removing minute features with minimal impact on the thermo-mechanical response, such as chamfers, fillets with radii less than 0.2 mm, lead solder joints, and markings. The simplified model underwent an interference check to confirm the absence of geometric conflicts. Finally, the model is exported to a common 3D exchange format, such as STEP, for import into finite element preprocessing software. The transducer's 3D structural model is a solid model containing all components, including the piezoelectric ceramic sheet, matching layer, metal shell, adhesive layer, and electrode leads. Each component is positioned according to the actual assembly sequence, and the adhesive layer is precisely modeled as an independent geometric body, with its thickness and coverage area set according to process specifications.

[0031] The collected thermophysical and mechanical parameters of the material are imported into the three-dimensional structural model of the transducer, and temperature dependence is set for thermo-mechanical coupling analysis. The thermophysical parameters of the material affect the distribution of thermal stress under temperature changes, while the mechanical parameters determine the deformation behavior of the material under thermal stress. Through mapping, a thermo-mechanical coupling simulation model is obtained, which is a finite element model with both heat conduction analysis and structural stress analysis capabilities. The model includes mesh elements, material properties, contact definitions, boundary conditions, and load step settings, and can solve the temperature field and stress field sequentially.

[0032] Simulation calculations were performed using a thermo-mechanical coupling simulation model. The multi-temperature environment encompassed multiple characteristic temperature points or continuous temperature cycles covering the transducer's service conditions and accelerated testing conditions. Typical conditions included a -40℃ low-temperature limit, a 25℃ room-temperature limit, an 85℃ high-temperature limit, and a linear heating / cooling transient process. Steady-state thermal analysis was performed to calculate the temperature field distribution at ambient temperatures of -40℃, 25℃, and 85℃ when thermal equilibrium was reached, confirming the absence of a significant temperature gradient within the transducer. Subsequently, each steady-state temperature field was imported as a load into the static analysis module to solve for the corresponding thermal expansion / contraction stress field, focusing on extracting the equivalent Mises stress and the first principal stress in the adhesive layer region. Transient thermo-mechanical coupling analysis was performed, simulating a complete temperature cycle: starting at 25℃, cooling to -40℃ at a rate of 15℃ / min and holding for 30 minutes, then heating to 85℃ at a rate of 15℃ / min and holding for 30 minutes, finally cooling back to 25℃. The stress-time curve at the point of maximum stress in the adhesive layer was recorded throughout the process, and transient stress contour maps were extracted at the -40℃ endpoint, the 85℃ endpoint, and the moment of maximum temperature change. All calculation results were exported in tabular or contour map form. Key data included the maximum, minimum, and average values ​​of the global stress in the adhesive layer, as well as the characteristic stress value located in the central region of the piezoelectric ceramic-matching layer interface. The stress simulation distribution data represents the full-field stress results of the adhesive layer and interface region under a given temperature load, typically output as nodal stress values, element stress components, or equivalent Mises stress, and includes corresponding spatial coordinate information.

[0033] Thermo-mechanical coupling simulation revealed that the transducer undergoes thermal expansion deformation in a high-temperature environment. The stress in the adhesive layer reaches its maximum when the ambient temperature reaches ±85°C. This maximum stress occurs in the adhesive layer between the adhesive layer and the transducer sidewall, not in the adhesive layer between the piezoelectric ceramic and the matching layer. This indicates that the high-temperature environment is not the cause of fatigue cracking in the adhesive layer between the piezoelectric ceramic and the matching layer. Thermo-mechanical coupling simulation also revealed that the transducer undergoes thermal contraction deformation in a low-temperature environment. The stress in the adhesive layer reaches its maximum when the ambient temperature reaches -40°C. This maximum stress occurs in the adhesive layer between the piezoelectric ceramic and the matching layer. This indicates that the -40°C environment is the primary cause of fatigue cracking in the adhesive layer between the piezoelectric ceramic and the matching layer. By fixing the -40°C environment and varying the thickness of the adhesive layer and other parameters, thermo-mechanical coupling simulations were performed to obtain the thermal stress values ​​of the adhesive layer in multiple segments under different operating conditions.

[0034] The stress results of the adhesive layer under all operating conditions were exported as a structured data table, namely, a multi-temperature environment stress distribution. The data table uses the adhesive layer element number as the row index, and the column fields include the three-dimensional coordinates of the element centroid, the temperature condition identifier, the equivalent Mises stress value, the first principal stress value, and the shear stress component. Simulation results from different batches and with different design parameters were summarized into the same database to establish a stress characteristic value-structural parameter comparison table. The multi-temperature environment stress simulation distribution obtains the stress distribution of the material under different temperatures through simulation calculations, reflecting the stress situation of the transducer adhesive layer under different temperature conditions during actual use.

[0035] By merging and archiving the multi-temperature environment stress simulation distribution with previous simulation results, thermal stress simulation results are obtained. By establishing a thermo-mechanical coupling simulation model and performing three-dimensional analysis of the transducer, the stress distribution of the adhesive layer under different temperature environments can be accurately predicted, thereby effectively predicting its thermal fatigue life.

[0036] S200: Introduce environmental parameters and perform high and low temperature alternating fatigue analysis according to the thermal stress simulation results, and extract monitoring change signals.

[0037] Furthermore, S200 of this application includes: introducing environmental parameters to analyze temperature field changes and extracting temperature field change time parameters; dividing the temperature holding period according to the temperature field change time parameters, and setting low temperature holding time parameters and high temperature holding time; performing high and low temperature alternating fatigue recording on the transducer according to the low temperature holding time parameters and the high temperature holding time, and generating a fatigue recording parameter set; performing oscilloscope processing based on the fatigue recording parameter set to construct multiple peak signals; and performing monitoring change fusion according to the multiple peak signals to construct a monitoring change signal.

[0038] Specifically, the actual transducer sample corresponding to the thermo-mechanical coupling simulation model was subjected to alternating high and low temperature fatigue tests every half hour. The environmental parameters were the temperature conditions under which the transducer was in service or during accelerated testing, specifically including the target low temperature, target high temperature, temperature change rate, and number of cycles, set to -40℃ to +85℃ based on the actual application conditions of the product. Through transient thermo-mechanical coupling simulation, the time required for the internal adhesive layer and piezoelectric ceramic-matching layer interface region of the transducer to reach a new thermal equilibrium state during the transition from low to high ambient temperature was calculated, yielding the temperature field change time parameter. The temperature field change time parameter is the length of time from the start of the ambient temperature change to when the temperature of the area of ​​interest inside the transducer (i.e., the interface between the adhesive layer and the piezoelectric ceramic-matching layer) reaches the target temperature value with a fluctuation of less than ±1℃. Similarly, the time parameter for switching from high to low temperature can also be calculated; however, since low temperature has been identified as the main cause of failure, maintaining the low-temperature segment is more critical, therefore, the focus is on capturing the heating phase time.

[0039] Low-temperature environments are the primary cause of thermal fatigue damage to the transducer adhesive layer. The transducer temperature field is raised from -40°C to +85°C, switching between different temperature fields via a lifting platform, completing the process in a maximum of 30 seconds. Temperature holding periods are determined based on the time parameters of the temperature field changes. Actual transducer samples corresponding to the simulation model are subjected to alternating high and low temperature fatigue tests, holding at -40°C for half an hour and at +85°C for half an hour, to shorten the testing time. Alternating between low and high temperatures, the temperature and stress changes in each cycle are recorded to generate a fatigue record parameter set, containing all stress changes experienced by the adhesive layer under different temperature conditions. This fatigue record parameter set is a complete time-series dataset generated collaboratively by the test chamber controller, data acquisition card, and oscilloscope during long-term testing over several weeks to months. It includes structured information such as timestamps, current cycle number, current ambient temperature, transducer real-time sensitivity (peak-to-peak voltage), and drive voltage waveform, and is typically stored in CSV or TDMS format.

[0040] During the high and low temperature experiments, the transducer was not powered on. After the experiments, it was left to stand at room temperature for three hours before being measured with an oscilloscope. Oscilloscope processing was performed based on the fatigue recording parameter set. The echo signal continuously output by the transducer during the high and low temperature cycles was input into a digital oscilloscope, and appropriate time base and voltage levels were set to ensure a stable display of the ultrasonic echo envelope waveform on the oscilloscope screen. The oscilloscope was typically set to rising edge trigger mode, with the transducer's transmit pulse synchronization signal as the trigger source. Since the raw waveform captured by the oscilloscope may contain noise, a 5-point moving average filter was applied to the peak region of each waveform, and the maximum value after filtering was taken as the peak signal for that cycle. The peak signals from all cycles were arranged in chronological order to form an independent peak signal sequence for each sample. The peak signal is the maximum amplitude value in the ultrasonic echo envelope waveform captured by the oscilloscope, i.e., the peak-to-peak voltage, reflecting the electromechanical conversion efficiency of the transducer and is a key indicator parameter for determining whether interfacial debonding failure of the adhesive layer has occurred.

[0041] By monitoring and fusing multiple peak signals, all peak signal data from the start of the test to the failure point are arranged chronologically to form a degradation trajectory curve with the number of thermal cycles as the x-axis and the normalized peak-to-peak voltage as the y-axis. This yields a single monitoring change signal, fully presenting the entire process of adhesive layer damage from initiation to expansion and penetration. Environmental parameters are introduced for temperature field change analysis, combined with alternating high and low temperature fatigue testing, simulating the fatigue behavior of the transducer adhesive layer during temperature changes. The fusion of oscilloscope processing and monitoring change signals not only helps improve the accuracy of fatigue prediction but also enables early design optimization, shortening product development cycles and improving transducer reliability.

[0042] S300: Based on the monitored change signals, identify fatigue-inducing parameters and generate fatigue life data.

[0043] Furthermore, S300 of this application includes: continuously monitoring the plurality of peak signals; subtracting adjacent peak signals based on the monitoring results to generate a plurality of signal difference values; comparing whether the plurality of signal difference values ​​exceed a signal deviation threshold; when the signal difference value exceeds the signal deviation threshold, identifying fatigue inducing parameters and generating a recording instruction based on the fatigue inducing parameters; recording high and low temperature cycles using the recording instruction to generate the number of high and low temperature cycles; retrieving the maximum stress value of the adhesive layer; performing fatigue analysis based on the number of high and low temperature cycles combined with the maximum stress value of the adhesive layer to generate the fatigue life data.

[0044] Specifically, the peak-to-peak signal curve changes on the oscilloscope are observed and recorded. When the peak difference of the signal curves exceeds 3dB, it is determined that the adhesive layer between the piezoelectric ceramic and the matching layer of the transducer has cracked and failed. At this point, the fatigue life of the transducer adhesive layer is recorded. Multiple peak signals obtained during the fatigue test are continuously monitored, and the output signal of the transducer is repeatedly measured and recorded at a fixed frequency or fixed cycle interval. Each peak signal value is subtracted from the previous peak signal value to obtain multiple signal differences. The signal deviation threshold is a pre-set state boundary value used to determine whether the transducer has lost its specified function. Industry practice and prior failure physical analysis typically fix this threshold at 3dB.

[0045] When the signal difference exceeds the signal deviation threshold, it means that the adhesive layer between the piezoelectric ceramic and the matching layer of the transducer has cracked and failed. The fatigue inducing parameters are then identified, which are the key physical quantities that lead to the final failure of the transducer. Based on the fatigue inducing parameters, a recording instruction is generated, i.e., an instruction is issued to record key data during the fatigue process.

[0046] Under the recording instructions, the high and low temperature alternation cycle recording continues. Data is recorded after each cycle. By accumulating these data, the number of high and low temperature alternation cycles is obtained, that is, the total number of complete temperature cycles that the transducer has withstood from the start of the test until it is judged to have failed.

[0047] The maximum stress value of the adhesive layer obtained from thermo-mechanical coupling simulation is retrieved, which represents the maximum stress value borne by the adhesive layer during alternating high and low temperatures. Based on the number of high and low temperature cycles and the maximum stress value of the adhesive layer, fatigue analysis is performed to obtain fatigue life data from actual transducer sample experiments, representing the expected life of the adhesive layer under given high and low temperature cycle numbers and stress conditions. Through continuous monitoring and signal difference analysis of the adhesive layer under alternating high and low temperature cycles, accurate prediction and anomaly monitoring of fatigue life are achieved.

[0048] S400: Map the thermal stress simulation results with the fatigue life data to construct a stress-life correlation data table.

[0049] Further, S400 of this application includes: extracting multiple sets of maximum stress values ​​based on the thermal stress simulation results; performing curve fitting between the multiple sets of maximum stress values ​​and fatigue life data to generate a curve fitting graph; calculating the goodness of fit according to the curve fitting graph; constructing a null two-dimensional data table, filling the multiple sets of maximum stress values ​​and the fatigue life data into the null two-dimensional data table according to the goodness of fit, wherein the first column is the maximum stress value of the adhesive layer, the second column is the fatigue life data, and constructing a two-dimensional data table; traversing the two-dimensional data table to remove outliers, and constructing the stress-life correlation data table.

[0050] Specifically, the maximum stress value for each cycle is extracted from the thermal stress simulation results; that is, the highest stress the adhesive layer withstands under specific temperature and load conditions. Multiple sets of extracted maximum stress values ​​are then curve-fitted with the corresponding fatigue life data to find the mathematical relationship between stress value and fatigue life. A scatter plot of the original data is created with stress value on the x-axis and fatigue life value on the y-axis, and the fitted curve is plotted on the same coordinate system. A legend, axis labels, and a fitting equation display box are added to the chart to generate a vector-formatted curve fitting graph. The curve fitting graph is a visual chart that plots the original data points and the fitted curve together on the same coordinate system. The x-axis represents the maximum stress value of the adhesive layer, and the y-axis represents fatigue life. Typically, the y-axis uses a logarithmic scale to display data over a wide life range, intuitively presenting the correlation between stress and life and the quality of the fit.

[0051] The goodness of fit is calculated based on the curve fitting plot, measuring the degree to which the fitted curve explains the original data points. The value ranges from 0 to 1; the closer to 1, the higher the fit between the curve and the data points, and the stronger the model's interpretability. The null value two-dimensional data table is an empty table framework created before data fitting and cleaning, containing predefined columns and fields, including the maximum stress value of the adhesive layer, fatigue life data, data source, whether it participates in the fitting, and outlier markers. After confirming that the goodness of fit meets the standard, multiple sets of maximum stress values ​​and fatigue life data are filled into the null value two-dimensional data table one by one, with the corresponding data source and fitting participation markers simultaneously filled in. All data points in the two-dimensional data table are traversed to identify and remove outliers. Outliers are usually caused by data recording errors or abnormal test conditions. After removing outliers, the final stress-life correlation data table is obtained, with each row clearly recording the representative value of the thermal fatigue life of the transducer adhesive layer corresponding to a specific stress value. By correlating the fitted curve and the stress-life data, the fatigue life of the transducer adhesive layer under different operating conditions can be accurately predicted.

[0052] S500: Based on the stress-life correlation data table, perform thermal fatigue prediction on the transducer adhesive layer, and transmit the prediction results to a remote terminal for verification.

[0053] Furthermore, S500 of this application includes: performing color gradient rendering on the transducer adhesive layer based on the stress-life correlation data table to construct an adhesive layer stress cloud map; traversing the adhesive layer stress cloud map to perform adhesive layer fatigue analysis on the transducer and identify multiple crack-prone areas; performing thermal fatigue visualization prediction analysis according to the multiple crack-prone areas to construct a life-stress curve, wherein the life-stress curve includes simulated stress points and predicted life points; and encapsulating the simulated stress points and the predicted life points to construct a prediction result.

[0054] Specifically, the transducer adhesive layer is rendered with a color gradient based on the stress-life correlation data table, using color changes to represent different data values. Colors typically range from light to dark or from cool to warm to indicate data changes, often used in fatigue analysis to visually display stress distribution in different areas. A red-yellow-green-blue spectrum is commonly used, with red areas representing high stress (dangerous) and blue areas representing low stress (safe). The rendering results are presented as a contour map, allowing designers to visually identify stress concentration areas through color distribution. The upper and lower limits of the color bar are dynamically adjusted based on the current stress range of the adhesive layer: the upper limit is the maximum stress value of the adhesive layer obtained from the current simulation calculation, and the lower limit is 0 MPa. Simultaneously, several key stress values ​​already marked in the stress-life correlation data table are annotated on the color bar with corresponding life values. The contour map is checked for continuity and the absence of mesh artifacts. Lighting, transparency, and viewing angle are adjusted to ensure clear visibility of stress distribution at the edges of the adhesive layer and in interface transition areas. Annotation boxes are added to the contour map, directly marking the location of the maximum stress node in the adhesive layer, the stress value, and the corresponding life obtained from the correlation data table.

[0055] By traversing the stress cloud map of the adhesive layer, fatigue analysis of the adhesive layer of the transducer is performed. By identifying areas of stress concentration or areas of drastic stress change, areas with fatigue risk that may lead to cracking are identified and marked as crack-prone areas. These areas may develop cracks due to excessive fatigue.

[0056] Thermal fatigue prediction analysis is performed based on the adhesive layer stress cloud map and crack-prone areas. This identifies potential fatigue zones in the adhesive layer during operation. For each crack-prone area, its maximum stress value is used as input, and the corresponding predicted life is obtained from the stress-life correlation data table. The life-stress curve is a two-dimensional curve with stress on the x-axis and life on the y-axis. It displays the SN curve fitted based on historical calibration data, highlighting the stress points obtained from the current simulation calculation and the predicted life points obtained through curve interpolation. The life-stress curve includes simulated stress points and predicted life points. The simulated stress points are the maximum stress values ​​of the adhesive layer extracted from a specific crack-prone area of ​​the transducer, marked as scatter points on the curve, usually in red. The predicted life points are the life values ​​read on the y-axis after the simulated stress points are vertically mapped to the fitted curve, usually marked with a blue diamond and connected to the simulated stress points with a dashed line, visually demonstrating the mapping path between stress and life.

[0057] The simulated stress points and predicted life points in the life-stress curve, along with their associated metadata (coordinates, confidence intervals, data version, timestamps), are packaged according to a predetermined data structure to generate digital objects that designers can directly parse. By encapsulating this data, a complete prediction result dataset is formed. The prediction results are a structured data package that fully describes the current transducer adhesive layer thermal fatigue life prediction information, including input parameters, output results, intermediate mapping links, and quality labels. The stress cloud map generated by color gradient rendering technology can intuitively display the stress state of different regions of the adhesive layer, helping to quickly identify potential fatigue areas. By analyzing the crack-prone areas of the stress cloud map, more accurate basis for fatigue life prediction can be provided, thereby identifying potential structural problems at an early stage. By encapsulating simulated stress points and predicted life points, complete prediction result data can be formed, improving product reliability and R&D efficiency.

[0058] Furthermore, this application also includes the following steps: receiving the prediction result through the remote terminal, performing error analysis on the prediction result, and generating a data error value; verifying whether the data error value is less than a preset error threshold; if the data error value is less than the preset error threshold, performing adhesive layer structure analysis on the transducer based on the simulated stress point to determine the weak structure parameters; performing optimization analysis according to the predicted lifetime point and the weak structure parameters to generate optimization suggestions; if the data error value is greater than or equal to the preset error threshold, determining that the prediction result is invalid, and performing a step backtracking check.

[0059] Specifically, the remote terminal receives prediction results from the thermal stress simulation and fatigue analysis system. The remote terminal is a computer terminal device deployed in R&D departments, testing laboratories, or production quality centers that can access the company's internal network. It is typically equipped with a large display screen and dedicated client software, used to receive simulation prediction results, retrieve experimental measurement data, and perform comparative analysis.

[0060] The remote terminal performs error analysis between the predicted results and the actual test data, calculating the data error value. It reads the preset error threshold for this product series, the maximum acceptable error boundary, typically ranging from 10% to 20%. If the data error value is less than the preset error threshold, the prediction result is considered valid, and the system automatically jumps to the optimization analysis branch. If the data error value is greater than or equal to the preset error threshold, the prediction result is considered invalid, and the system automatically jumps to the step backtracking verification branch.

[0061] When the data error value is less than the preset error threshold, the crack-prone areas in the prediction results are highlighted using the transducer's 3D model as a reference, and the coordinates and stress values ​​of the simulated stress points in those areas are automatically retrieved. A local parameter sensitivity analysis is performed on these areas, the stress changes are recalculated, and the sensitivity coefficient of stress to each parameter is calculated. The parameter with the largest absolute sensitivity value is identified as a weak structural parameter. Weak structural parameters are geometric or material design variables that lead to high stress and low lifespan in the transducer's adhesive layer, including but not limited to adhesive layer thickness, piezoelectric ceramic diameter, matching layer thickness, matching layer elastic modulus, and shell sidewall clearance. By performing sensitivity analysis on the structural characteristics of the area where the simulated stress point is located, the design parameter that contributes the most to the stress at that location can be identified.

[0062] After identifying the weak structural parameters, the optimization algorithm module is invoked. Starting with the current stress value, constrained by the adjustment range allowed by the product design specifications, and guided by the target lifespan, the required structural parameter modifications are calculated in reverse. The goal of the optimization analysis is to improve the fatigue life of the product by reducing high-stress areas through improved adhesive layer design. The optimization results will generate specific design optimization suggestions to help engineers improve the transducer structure and extend its service life.

[0063] If error analysis reveals that the data error value is greater than or equal to the preset error threshold, it indicates that the prediction results may have a significant deviation. Initiate a backtracking verification process to re-examine the entire analysis and prediction process and identify potential sources of error or deviation. The backtracking process may involve re-examining the simulation model, input data, fatigue analysis methods, etc.

[0064] Error analysis and backtesting effectively improve the accuracy and reliability of transducer adhesive layer thermal fatigue life prediction. Real-time feedback from remote terminals and prediction results allows for rapid adjustment and optimization of design schemes, shortening product development cycles. Effective optimization analysis and suggestion generation reduce the number of experimental verifications, improving R&D efficiency and lowering costs.

[0065] In summary, the lifetime visualization prediction method based on transducer adhesive layer thermal fatigue provided in this application has the following technical advantages:

[0066] By establishing a thermo-mechanical coupling simulation model of the transducer, simulation analysis is performed based on the model to obtain thermal stress simulation results. Environmental parameters are introduced, and high-low temperature alternating fatigue analysis is conducted according to the thermal stress simulation results to extract monitoring change signals. Fatigue inducing parameters are identified based on the monitoring change signals, and fatigue life data is generated. The thermal stress simulation results are mapped to the fatigue life data to construct a stress-life correlation data table. Thermal fatigue prediction of the transducer adhesive layer is performed based on the stress-life correlation data table, and the prediction results are transmitted to a remote terminal for verification. In other words, by establishing a thermo-mechanical coupling simulation model, mapping thermal stress simulation results to fatigue life data, constructing a stress-life correlation data table, and predicting the thermal fatigue of the transducer adhesive layer, rapid and accurate prediction of the thermal fatigue life of the transducer adhesive layer during the product design stage is achieved, shortening the product development cycle and reducing product development costs. Simultaneously, the location affecting the thermal fatigue life of the transducer adhesive layer is visualized, improving the accuracy of thermal fatigue life prediction.

[0067] Example 2: Based on the same inventive concept as the transducer adhesive layer thermal fatigue-based lifetime visualization prediction method in Example 1, this application also provides a transducer adhesive layer thermal fatigue-based lifetime visualization prediction system. Please refer to the appendix. Figure 2 The lifetime visualization prediction system based on transducer adhesive layer thermal fatigue includes:

[0068] The simulation analysis module 11 is used to establish a thermo-mechanical coupling simulation model of the transducer, perform simulation analysis based on the thermo-mechanical coupling simulation model, and obtain thermal stress simulation results; the high and low temperature alternating fatigue analysis module 12 is used to introduce environmental parameters and perform high and low temperature alternating fatigue analysis according to the thermal stress simulation results, and extract monitoring change signals; the induced parameter identification module 13 is used to identify fatigue induced parameters based on the monitoring change signals and generate fatigue life data; the data mapping module 14 is used to map the thermal stress simulation results with the fatigue life data and construct a stress-life correlation data table; the thermal fatigue prediction module 15 is used to predict the thermal fatigue of the transducer adhesive layer based on the stress-life correlation data table and transmit the prediction results to a remote terminal for verification.

[0069] Furthermore, the simulation analysis module 11 in the life visualization prediction system based on the thermal fatigue of the transducer adhesive layer is also used for: introducing the material thermophysical parameters and material mechanical parameters of the transducer adhesive layer; performing three-dimensional analysis on the transducer to construct a three-dimensional structural model of the transducer; mapping the material thermophysical parameters and the material mechanical parameters to the three-dimensional structural model of the transducer to establish a thermo-mechanical coupling simulation model; performing simulation calculations based on the thermo-mechanical coupling simulation model to obtain multi-temperature environment stress simulation distribution data; and adding the multi-temperature environment stress simulation distribution data to the thermal stress simulation results.

[0070] Furthermore, the high and low temperature alternating fatigue analysis module 12 in the life visualization prediction system based on transducer adhesive layer thermal fatigue is also used for: introducing environmental parameters to perform temperature field change analysis and extracting temperature field change time parameters; dividing the temperature holding period according to the temperature field change time parameters, and setting low temperature holding time parameters and high temperature holding time; recording high and low temperature alternating fatigue of the transducer according to the low temperature holding time parameters and the high temperature holding time, and generating a fatigue record parameter set; performing oscilloscope processing based on the fatigue record parameter set to construct multiple peak signals; and performing monitoring change fusion according to the multiple peak signals to construct a monitoring change signal.

[0071] Furthermore, the causative parameter identification module 13 in the life visualization prediction system based on transducer adhesive layer thermal fatigue is also used for: continuously monitoring the multiple peak signals; subtracting adjacent peak signals based on the monitoring results to generate multiple signal difference values; comparing whether the multiple signal difference values ​​exceed a signal deviation threshold; when the signal difference value exceeds the signal deviation threshold, identifying fatigue causative parameters and generating a recording instruction based on the fatigue causative parameters; recording high and low temperature cycles through the recording instruction to generate the number of high and low temperature cycles; retrieving the maximum stress value of the adhesive layer; performing fatigue analysis based on the number of high and low temperature cycles combined with the maximum stress value of the adhesive layer to generate the fatigue life data.

[0072] Furthermore, the data mapping module 14 in the life visualization prediction system based on transducer adhesive layer thermal fatigue is also used for: extracting multiple sets of maximum stress values ​​based on the thermal stress simulation results; performing curve fitting between the multiple sets of maximum stress values ​​and fatigue life data to generate a curve fitting graph; calculating the goodness of fit according to the curve fitting graph; constructing a null two-dimensional data table, filling the multiple sets of maximum stress values ​​and the fatigue life data into the null two-dimensional data table according to the goodness of fit, wherein the first column is the maximum stress value of the adhesive layer, the second column is the fatigue life data, and constructing a two-dimensional data table; traversing the two-dimensional data table to remove outliers, and constructing the stress-life correlation data table.

[0073] Furthermore, the thermal fatigue prediction module 15 in the life visualization prediction system based on transducer adhesive layer thermal fatigue is also used for: performing color gradient rendering on the transducer adhesive layer based on the stress-life correlation data table to construct an adhesive layer stress cloud map; traversing the adhesive layer stress cloud map to perform adhesive layer fatigue analysis on the transducer and identify multiple crack-prone areas; performing thermal fatigue visualization prediction analysis according to the multiple crack-prone areas to construct a life-stress curve, the life-stress curve including simulated stress points and predicted life points; and encapsulating the simulated stress points and predicted life points to construct a prediction result.

[0074] Furthermore, the thermal fatigue prediction module 15 in the life visualization prediction system based on transducer adhesive layer thermal fatigue is also used for: receiving the prediction result through the remote terminal, performing error analysis on the prediction result, and generating a data error value; verifying whether the data error value is less than a preset error threshold; if the data error value is less than the preset error threshold, performing adhesive layer structure analysis on the transducer based on the simulated stress point to determine the weak structure parameters; performing optimization analysis according to the predicted life point and the weak structure parameters to generate optimization suggestions; if the data error value is greater than or equal to the preset error threshold, determining that the prediction result is invalid and performing a step backtracking check.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The lifetime visualization prediction method and specific examples based on transducer adhesive layer thermal fatigue in the foregoing embodiment 1 are also applicable to the lifetime visualization prediction system based on transducer adhesive layer thermal fatigue in this embodiment. Through the foregoing detailed description of the lifetime visualization prediction method based on transducer adhesive layer thermal fatigue, those skilled in the art can clearly understand the lifetime visualization prediction system based on transducer adhesive layer thermal fatigue in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for visually predicting the lifespan of a transducer adhesive layer based on thermal fatigue, characterized in that, include: A thermo-mechanical coupling simulation model of the transducer is established, and simulation analysis is performed based on the thermo-mechanical coupling simulation model to obtain the thermal stress simulation results; Environmental parameters were introduced to perform high and low temperature alternating fatigue analysis based on the thermal stress simulation results, and monitoring change signals were extracted. Based on the monitored change signals, fatigue inducing parameters are identified, and fatigue life data is generated. The thermal stress simulation results are mapped to the fatigue life data to construct a stress-life correlation data table; Based on the stress-life correlation data table, thermal fatigue prediction is performed on the transducer adhesive layer, and the prediction results are transmitted to a remote terminal for verification.

2. The lifetime visualization prediction method based on transducer adhesive layer thermal fatigue as described in claim 1, characterized in that, A thermo-mechanical coupling simulation model of the transducer is established, and simulation analysis is performed based on the thermo-mechanical coupling simulation model to obtain thermal stress simulation results. The method includes: The thermophysical and mechanical parameters of the material introduced into the transducer adhesive layer; Perform three-dimensional analysis on the transducer and construct a three-dimensional structural model of the transducer; The thermophysical parameters and mechanical parameters of the material are mapped to the three-dimensional structural model of the transducer to establish a thermo-mechanical coupling simulation model. Simulation calculations are performed based on the aforementioned thermo-mechanical coupling simulation model to obtain stress simulation distribution data for multi-temperature environments. The stress distribution data from the multi-temperature environment simulation is added to the thermal stress simulation results.

3. The lifetime visualization prediction method based on thermal fatigue of transducer adhesive layer as described in claim 1, characterized in that, Based on the thermal stress simulation results, environmental parameters are introduced to perform high and low temperature alternating fatigue analysis, and monitoring change signals are extracted. The method includes: Environmental parameters are introduced to analyze temperature field changes, and time parameters of temperature field changes are extracted. Temperature holding time is divided according to the temperature field change time parameter, and low temperature holding time parameter and high temperature holding time parameter are set. According to the low temperature holding time parameter and the high temperature holding time, the transducer is subjected to high and low temperature alternating fatigue recording to generate a fatigue recording parameter set; Multiple peak signals are constructed by oscilloscope processing based on the fatigue recording parameter set; The monitoring changes are fused based on the multiple peak signals to construct a monitoring change signal.

4. The lifetime visualization prediction method based on thermal fatigue of transducer adhesive layer as described in claim 3, characterized in that, The method for identifying fatigue-inducing parameters based on the monitored change signals and generating fatigue life data includes: The multiple peak signals are continuously monitored, and the multiple peak signals are subtracted from each other according to the monitoring results to generate multiple signal difference values. Compare whether the differences between the multiple signals exceed the signal deviation threshold; When the signal difference exceeds the signal deviation threshold, fatigue induced parameters are identified, and a recording instruction is generated based on the fatigue induced parameters. The high and low temperature cycle is recorded using the recording instruction, generating the number of high and low temperature cycles; The maximum stress value of the adhesive layer is retrieved, and fatigue analysis is performed based on the number of high and low temperature cycles and the maximum stress value of the adhesive layer to generate the fatigue life data.

5. The lifetime visualization prediction method based on transducer adhesive layer thermal fatigue as described in claim 1, characterized in that, The thermal stress simulation results are mapped to the fatigue life data to construct a stress-life correlation data table. The method includes: Multiple sets of maximum stress values ​​were extracted based on the thermal stress simulation results. The multiple sets of maximum stress values ​​are curve-fitted with fatigue life data to generate a curve fitting graph. Calculate the goodness of fit based on the curve fitting graph; Construct a two-dimensional data table with null values. Fill the multiple sets of maximum stress values ​​and fatigue life data into the two-dimensional data table with null values ​​according to the goodness of fit. The first column is the maximum stress value of the adhesive layer, and the second column is the fatigue life data. Construct a two-dimensional data table. The two-dimensional data table is traversed to remove outliers, and the stress-life correlation data table is constructed.

6. The lifetime visualization prediction method based on thermal fatigue of transducer adhesive layer as described in claim 1, characterized in that, Based on the stress-life correlation data table, the method for predicting the thermal fatigue of the transducer adhesive layer includes: Based on the stress-life correlation data table, the transducer adhesive layer is rendered with color gradient to construct an adhesive layer stress cloud map. By traversing the stress cloud diagram of the adhesive layer, fatigue analysis of the adhesive layer of the transducer was performed to identify multiple areas prone to cracking. Based on the multiple crack-prone identification areas, a thermal fatigue visualization prediction analysis is performed to construct a life-stress curve, which includes simulated stress points and predicted life points. The simulated stress points and the predicted life points are encapsulated to construct the prediction results.

7. The lifetime visualization prediction method based on thermal fatigue of transducer adhesive layer as described in claim 6, characterized in that, Methods for transmitting prediction results to a remote terminal for verification include: The prediction results are received through the remote terminal, and error analysis is performed on the prediction results to generate data error values. Verify whether the data error value is less than a preset error threshold; If the data error value is less than the preset error threshold, the transducer is subjected to adhesive layer structure analysis based on the simulated stress point to determine the weak structural parameters. Based on the predicted lifetime points and the parameters of the weak structures, an optimization analysis is performed to generate optimization suggestions. If the data error value is greater than or equal to the preset error threshold, the prediction result is determined to be invalid, and a step backtracking test is performed.

8. A lifespan visualization prediction system based on thermal fatigue of transducer adhesive layer, characterized in that, The step of implementing the lifetime visualization prediction method based on thermal fatigue of transducer adhesive layer according to any one of claims 1 to 7, wherein the lifetime visualization prediction system based on thermal fatigue of transducer adhesive layer comprises: The simulation analysis module is used to establish a thermo-mechanical coupling simulation model of the transducer, perform simulation analysis based on the thermo-mechanical coupling simulation model, and obtain thermal stress simulation results. The high and low temperature alternating fatigue analysis module is used to introduce environmental parameters and perform high and low temperature alternating fatigue analysis according to the thermal stress simulation results, and extract monitoring change signals. The fatigue induced parameter identification module is used to identify fatigue induced parameter based on the monitored change signal and generate fatigue life data. The data mapping module is used to map the thermal stress simulation results with the fatigue life data to construct a stress-life correlation data table. The thermal fatigue prediction module is used to predict the thermal fatigue of the transducer adhesive layer based on the stress-life correlation data table, and transmit the prediction results to a remote terminal for verification.

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