An ultrasonic fatigue experiment monitoring method based on digital twinning

Abstract

The application belongs to the field of material performance testing of mechanical structure strength, and particularly relates to an ultrasonic fatigue experiment monitoring method based on digital twinning. The method is based on an ultrasonic fatigue testing machine platform, test data such as frequency, temperature and load are collected, an ultrasonic fatigue digital twinning model of the test object is established, and real-time online monitoring of ultrasonic fatigue and life prediction are carried out in combination with a fatigue life prediction model. Compared with the traditional test method, the application combines ultrasonic detection data and digital twinning model technology to realize prediction and evaluation of the material fatigue life, improve the test efficiency, reduce the cost of long-life testing, and improve the accuracy and reliability of the test data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of material science engineering and the field of digital technology, and in particular to an ultrasonic fatigue experiment monitoring method based on digital twinning. BACKGROUND

[0002] The safe service of mechanical structures with super-long life is developing upwardly; as an important bearing form of structures, fatigue and its life are gradually developing from low-cycle fatigue to high-cycle fatigue and super-high-cycle fatigue, and the actual service life of key components such as steam turbine rotors and engine blades has reached 10 8 -10 10 times and above, which poses new challenges to the super-high-cycle fatigue strength of materials and structures; under this background, the traditional fatigue performance research cannot meet the development needs, and using a higher frequency fatigue testing machine to carry out material mechanical behavior tests is an important option, in which the working frequency of ultrasonic fatigue is 20 kHz ± 500 Hz, and it is specifically widely used in the super-high-cycle fatigue behavior of materials; but higher frequency will affect the resonance conditions, and the performance requirements for the sample and equipment are also higher; and the ultrasonic fatigue test itself takes a long time, and the accuracy of life test is very high, so it is difficult to realize data acquisition at a frequency of 20 kHz using sensors, which leads to difficulty in realizing online monitoring of the fatigue process; while laser may be an expected method, but it provides little data, and it is difficult to judge the results; and the digital twinning technology can realize online monitoring of the fatigue process and real-time display of the ultrasonic fatigue test process due to its advantages of real-time monitoring, high-precision prediction and comprehensive evaluation, which has important value for improving the test accuracy of the ultrasonic fatigue test process at home and abroad; at the same time, the traditional material fatigue life prediction method has the problems of low prediction accuracy and high test cost; therefore, the ultrasonic fatigue experiment monitoring method based on digital twinning not only can realize comprehensive monitoring, state visualization and convenient prediction of the fatigue life of the test piece in the ultrasonic fatigue test process, but also promotes the development of the material fatigue life prediction industry towards digitization and intelligentization, and has a wide application prospect;

[0003] The ultrasonic fatigue test system is a resonant system composed of an ultrasonic fatigue test mechanical device and a test piece; first, the ultrasonic generator converts voltage into an electrical signal, the amplitude horn amplifies the vibration from the transducer, thereby converting it into a larger amplitude, the tool head can transmit the high-frequency vibration of the system to the sample test, and finally to the end of the test piece, completing the ultrasonic high-frequency load loading of the test piece; the digital twinning technology is a method of combining the actual system with the virtual model, which can predict the performance and behavior of the system through simulation; at present, the digital twinning technology is increasingly widely used in the industrial field, but the research on material fatigue life prediction is still relatively less;

[0004] As a key enabling technology to solve the interaction problem between digital model and physical entity, digital twin technology will play an important role in supporting product development business full process and helping the integration of scientific research, production and management. Ultrasonic fatigue test is a new technology suitable for ultrahigh cycle fatigue performance of metal materials. The construction of digital twin model with real-time characteristics is beneficial to expand the application scope, seek real-time parameter monitoring, intelligent work and practicality, etc. SUMMARY

[0005] Based on the existing technical problems, the application provides an ultrasonic fatigue experiment monitoring method based on digital twin.

[0006] The ultrasonic fatigue experiment monitoring method based on digital twin provided by the application comprises the following steps:

[0007] S1, obtaining the geometric structure parameters of the laboratory ultrasonic fatigue test piece and the material characteristics used;

[0008] S2, obtaining the initial working condition and environmental parameter of the ultrasonic fatigue test;

[0009] S3, building a digital twin sub-model according to the data obtained in S2;

[0010] S4, constructing a complete digital twin model based on the digital twin sub-model in S3;

[0011] S5, monitoring the related data of the fatigue life of the metal material in real time during the ultrasonic fatigue test;

[0012] S6, using the signal processing and statistical tools in MATLAB to identify and extract the characteristics related to the fatigue life of the metal, and preprocessing the data obtained in S5, including data cleaning, removing outliers and data smoothing operation;

[0013] S7, based on the data processed in S6, the digital twin model updates the parameters of the digital twin model in S4 in real time and makes a prediction;

[0014] S8, calculating the deviation obtained by comparing the prediction result in S7 with the real result in S5 in real time;

[0015] S9, adjusting and correcting the digital twin model in S4 according to the deviation data calculated in S8 to obtain a new and more accurate digital twin model, and optimizing the digital twin model;

[0016] S10, using the trained digital twin model to predict the fatigue life of the new test piece, and the prediction result is the fatigue life of the material under the corresponding load under certain environmental conditions, and the optimization design is carried out accordingly.

[0017] Preferably, the performance parameters of the material to be detected in S1 include its brand and mechanical performance parameters, the ultrasonic fatigue testing machine in S2 consists of an ultrasonic fatigue testing mechanical device and a resonant system of a test piece, and the working conditions and environmental parameters in S2 include working frequency, temperature, and load during the ultrasonic fatigue test.

[0018] Preferably, the digital twin sub-model in S3 includes a test piece frequency evolution model, a stress model, a strain model, and a temperature field model, and a fatigue life prediction model.

[0019] Preferably, S3 includes the following steps:

[0020] S31, a test piece frequency evolution model is built based on the resonance frequency evolution law:

[0021] S32, a stress model is built based on the maximum stress of the middle section of the test piece;

[0022] S33, a strain model is built based on Hooke's law and the dynamic elastic modulus of the material and the stress model in S32;

[0023] S34, a temperature field model is built based on thermal analysis;

[0024] S35, a fatigue life prediction model is built based on infrared thermography.

[0025] Preferably, in S31, during the ultrasonic fatigue process of the metal material, the resonance frequency of the test piece continuously decreases with the increase of the cycle number, the real-time collected frequency image is taken as a reference, a relative frequency-f-cycle number relationship diagram is drawn, a frequency change rate df / dN is obtained, and the ultrasonic fatigue damage of the test piece is preliminarily understood.

[0026] Preferably, in S32, the ultrasonic fatigue test selects a test piece according to different test conditions, loading methods, and frequency effects to adapt to different test conditions, several commonly used ultrasonic fatigue test pieces are taken as coordinate origins, the stress amplitude is maximum in the middle of the test piece and is zero at both ends, the displacement is zero in the middle of the test piece and is maximum at both ends, and therefore, a stress model is built based on the maximum stress amplitude of the middle section of the test piece.

[0027]

[0028] In the formula, E is the elastic modulus of the material, and U0 is the maximum displacement amplitude of the test piece.

[0029] Preferably, in S33, the stress and strain of the ultrasonic fatigue test piece belong to the elastic deformation interval, the corresponding stress amplitude and strain amplitude are converted and calculated through Hooke's law and the dynamic elastic modulus of the material, and a strain model is built.

[0030]

[0031] In the formula, σ max is the maximum stress of the middle section of the test piece, E d is the dynamic elastic modulus of the material, and ε max is the maximum strain of the middle section of the test piece.

[0032] Preferably, in the step S34, based on the length-diameter ratio of the ultrasonic fatigue test sample and the temperature distribution of the test sample along the length direction, the surface temperature of the test sample can be simplified as a one-dimensional field, and the temperature is distributed in a gradient along the length direction of the test sample, with the center of the test sample as the origin, the direction close to the clamping end as the negative direction of the x-axis, and the free end as the positive direction of the x-axis, and the temperature field model of the test sample is:

[0033] θ(x)=Α1x 2 +Α2x+Α3

[0034] Wherein, Α1<0, θ is a one-dimensional temperature value, and Α2 and Α3 are fitting parameters.

[0035] Preferably, in the step S35, the fatigue life prediction model of the infrared thermal imaging method is used to predict the fatigue life of the material based on the initial temperature rise slope R, and the fatigue life prediction expression is as follows:

[0036] N f R=C

[0037] In the formula, N f is the fatigue life of the material, R is the temperature rise slope, and C is the material constant.

[0038] Preferably, in the step S4, the parameters of each digital twin sub-model are fused based on the Simulink visualization tool platform in MATLAB, and are fully debugged to construct an integrated simulation platform of multiple physical fields, and form a complete digital twin model.

[0039] The related data of the material fatigue life in the S5 include the ultrasonic fatigue test related data, and the related data include the test piece end amplitude, temperature, test frequency, load amplitude, and strain, and the related data of the test piece material fatigue life in the S5 are obtained by actually recording the ultrasonic fatigue test machine control system.

[0040] The adjustment and update method of the parameters in the S9 adopts an extended Kalman filtering algorithm.

[0041] The beneficial effects in the application are:

[0042] 1. By setting up an ultrasonic fatigue testing machine as an experimental platform to establish the digital twin model of the research object, a fatigue prediction model is established, and the fatigue life prediction and optimization are carried out based on the ultrasonic fatigue digital twin, which can effectively reduce the production and maintenance cost and improve the product reliability and safety; compared with the traditional test method, the present application combines the ultrasonic detection data with mathematical modeling and simulation technology to realize the prediction and evaluation of the material fatigue life, reduce the safety risk, and also provide more accurate basis for the design, optimization and improvement of the structure to improve the performance and economic benefit; in addition, before the actual test to understand the material fatigue performance, the system can also be used for pre-experiment to lay the foundation for formal experiment, which can improve the experimental efficiency while reducing the consumption of human and material resources, and realize low-carbon environmental protection.

[0043] 2. Improve efficiency: the ultrasonic fatigue digital twin prediction method for material fatigue life can calculate, simulate and predict the fatigue life of metal, which saves the test time and cost compared with the traditional fatigue test which consumes a lot of time and resources;

[0044] Accuracy is improved: the ultrasonic fatigue test itself takes a long time, and the fatigue life test precision is very high, the present application provides an ultrasonic fatigue digital twin prediction method for material fatigue life, which can provide more accurate fatigue life prediction results based on the initial temperature rise slope R;

[0045] Process visualization: the ultrasonic fatigue digital twin prediction method for material fatigue life can monitor the fatigue process online, realize the real-time display of the ultrasonic fatigue test process, and has important value for improving the test precision of the ultrasonic fatigue test process at home and abroad;

[0046] Optimization design: through simulating and predicting the fatigue life of metal, the ultrasonic fatigue digital twin prediction method for material fatigue life can be used for optimizing the design and manufacturing process of metal materials to improve the durability and reliability of parts.

[0047] 3. Through the application of digital twin technology, a fast and accurate fatigue prediction method is provided, which does not need to carry out a large number of tests, reduces the waste of time and resources; the technology can quickly and accurately predict the fatigue life of materials by comparing and matching the physical properties of actual materials with digital models, simulate different fatigue loading conditions and material properties, and help researchers understand the influencing factors of fatigue life; in addition, the digital twin technology improves the efficiency of structure design and optimization, helps researchers quickly evaluate the fatigue performance of materials, provides feasibility study and reference for the safety of structure, reduces the cost of trial and error and iteration; the application of digital twin technology fully embodies the new concept of data-driven manufacturing industry development in the background of new generation information technology industrial internet technology and intelligent manufacturing concept. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A step diagram of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0049] Figure 2 A flowchart of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0050] Figure 3 Several commonly used ultrasonic fatigue samples and their stress amplitudes and displacement schematic diagrams of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0051] Figure 4 A dog bone-shaped specimen size diagram of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0052] Figure 5 A transducer model perspective view of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0053] Figure 6 A variable amplitude rod model perspective view of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0054] Figure 7 A dog bone-shaped specimen model perspective view of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application;

[0055] Figure 8 An ultrasonic fatigue experiment key structure assembly model perspective view of an ultrasonic fatigue experiment monitoring method based on digital twinning proposed in the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0057] Reference Figures 1-8 An ultrasonic fatigue experiment monitoring method based on digital twinning, in order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further described in detail below with reference to the drawings and 25Cr2Ni2MoV steel welded joint ultrasonic fatigue test.

[0058] As Figure 1 shown, the laboratory ultrasonic fatigue test process digital twinning modeling method proposed in the present application includes the following steps:

[0059] S1, obtain the geometric structure parameters of the laboratory ultrasonic fatigue test piece and the material properties used, which can be obtained from the drawing file of the test piece; the material properties at least include the grade and mechanical property parameters of the material to be detected, which can be obtained through the material manual or laboratory test;

[0060] Further, common test pieces include equal cross-section cylindrical samples, variable cross-section cylindrical samples (variable cross-section cylindrical samples are also called dog bone-shaped samples, which are divided into two types of cylindrical samples: one without an intermediate equal cross-section section, and the other with an intermediate equal cross-section section), and plate-shaped samples; among them, the equal cross-section sample is the simplest sample type, but the displacement stress coefficient is relatively low, and it is not suitable for ultrasonic fatigue testing of high-strength steel, and is generally only used for displacement calibration of ultrasonic fatigue testing machine; the variable cross-section cylindrical test piece can shorten the length of the test piece and obtain a larger displacement stress coefficient, and increase the heat dissipation area of the test piece; when it is necessary to study the influence of surface treatment on the fatigue performance of the material, a section of equal stress cross-section area is required in the test piece, the variable cross-section arc-shaped test piece cannot meet the requirements, and the arc-shaped test piece with an intermediate equal cross-section section needs to be used; and when the ultrasonic fatigue of the plate-shaped test piece is needed, the ultrasonic fatigue testing machine system control software with a circular test piece cannot meet the test requirements, at this time, the plate-shaped ultrasonic fatigue test piece needs to be used.

[0061] S2, the ultrasonic fatigue testing machine is composed of an ultrasonic fatigue testing mechanical device and a test piece to form a resonant system, which simulates the fatigue failure of the material in actual use by applying high-frequency ultrasonic vibration load; when performing ultrasonic fatigue testing, it is necessary to determine the working condition parameters, mainly including the ultrasonic generator power, frequency, amplitude, temperature and load mode, by adjusting these parameters, the fatigue performance of the material under different working conditions can be simulated, providing a theoretical basis for the fatigue life prediction and performance evaluation of the material, and designing the test scheme to carry out research.

[0062] The common ultrasonic fatigue testing machine system mainly includes an ultrasonic generator, a piezoelectric transducer, an amplifier, a test piece and a cooling device; among them, the ultrasonic generator serves as an exciter, the working frequency is 20kHz±500Hz, and the frequency is generated through system resonance; the piezoelectric transducer converts high-frequency electrical signals into high-frequency resonance, which is transmitted to the ultrasonic fatigue test piece through the displacement amplifier to obtain the corresponding stress.

[0063] S3, according to the characteristics, initial working conditions and environmental parameters obtained in S2 and the physical action relationship in ultrasonic fatigue testing, a digital twin sub-model of the test bench is built; the vibration behavior of the test piece in ultrasonic fatigue testing can be described by dynamic equations and vibration theory, so as to take the dog bone-shaped sample of 25Cr2Ni2MoV steel welded joint as the test object, and build a test piece frequency evolution model, a stress model, a strain model and a temperature field model, including the following steps:

[0064] S31, building a frequency evolution model of the test piece based on the resonance frequency evolution law;

[0065] S32, building a stress model based on the maximum stress of the middle section of the test piece;

[0066] S33, building a strain model based on Hooke's law and the dynamic elastic modulus of the material and the stress model in S32;

[0067] S34, building a temperature field model based on thermal analysis;

[0068] S35, building a fatigue life prediction model based on infrared thermography.

[0069] Further, in step S31, through the resonance frequency evolution law, it can be inferred that in the ultrasonic fatigue process of the metal material, the resonance frequency of the test piece decreases continuously with the increase of the cycle number; the frequency image collected in real time can be taken as a reference to draw a relative frequency-f-cycle number relationship diagram of the relative frequency f and the cycle number N, and the frequency change rate df / dN can be obtained, so that the ultrahigh cycle fatigue damage of the test piece can be preliminarily understood.

[0070] In step S32, since the stress of the middle equal-section part of the dog-bone-shaped test piece does not change much, the stress of this part can be approximated as being at the maximum stress level all the time, so a stress model is built based on the maximum stress of the middle section of the test piece:

[0071]

[0072] wherein,

[0073]

[0074]

[0075] In the formula, σ max is the maximum stress of the middle section of the test piece, E d is the dynamic elastic modulus of the material, w is the angular frequency, c is the propagation speed of ultrasonic waves in the material, R1, R2, L1 and L2 are the sizes of the test piece, L3 is the resonance length, and U0 is the maximum displacement amplitude of the test piece.

[0076] Further, in step S33, since the stress and strain of the ultrasonic fatigue test piece are very small, which belongs to the elastic deformation interval, the corresponding stress amplitude and strain amplitude can be converted and calculated through Hooke's law and the dynamic elastic modulus of the material to build a strain model:

[0077]

[0078] In the formula, σ max is the maximum stress of the middle section of the test piece, and E dE is the dynamic modulus of elasticity of the material, ε max εmax is the maximum strain of the middle section of the test piece;

[0079] Further, in step S34, based on the length-diameter ratio of the ultrasonic fatigue test sample and the temperature distribution of the test sample along the length direction, the surface temperature of the test sample can be simplified as a one-dimensional field, and the temperature is gradiently distributed along the length direction of the test sample; further, taking the center of the test sample as the origin, the direction close to the clamping end is the negative direction of the x-axis, and the free end direction is the positive direction of the x-axis, and the temperature field model of the test sample temperature is:

[0080] θ(x) = A1x 2 + A2x + A3

[0081] Wherein, A1<0, θ is a one-dimensional temperature value, and A2 and A3 are fitting parameters.

[0082] And in step S35, based on the infrared thermal imaging method, a fatigue life prediction model is built to predict the fatigue life of the 25Cr2Ni2MoV steel welded joint; by fitting the initial temperature rise slope and the fatigue life under the corresponding loading stress, the value of the material constant C in the fatigue life prediction expression is obtained, and then the predicted fatigue life is obtained from the initial temperature rise slope.

[0083] S4, the coordination relationship and interface matching between the different digital twin sub-models obtained in S3 are considered, first of all, the data transmission and interaction mode between each sub-model needs to be clear, MATLAB Simulink visual tool platform can be used to establish a multi-physical field integrated simulation platform containing multiple sub-models, the executable sub-models are fused, and sufficient debugging is carried out to ensure the coordination matching between the interfaces of each sub-model, at the same time, the interaction and influence between different physical fields are considered, the parameters and model settings are gradually adjusted, more comprehensive simulation results are provided, and the accuracy and reliability of the integrated simulation platform are improved.

[0084] S5, the ultrasonic fatigue machine loads the fatigue load on the material to be tested, and simultaneously monitors and records the ultrasonic fatigue test related data through the ultrasonic fatigue test machine control system, mainly including test frequency, load amplitude, strain, temperature; wherein, the temperature of the test piece in the test process is analyzed and processed by using the IRSoft software of the infrared thermal imaging instrument to analyze the distribution and evolution of the temperature; the computer control software of the ultrasonic fatigue test machine can record the test resonance frequency and test time, and the strain distribution can be measured by the micro dynamic strain gauge pasted on the surface of the test piece, which together constitute the computer measurement and control device of the test, thereafter, through the processing of the data, the fatigue-life curve of different material test pieces can be drawn.

[0085] Wherein, the material sample fatigue process is accompanied by heat dissipation, which cannot be directly obtained and can be indirectly obtained from the temperature field detection of the material sample; At present, non-contact measurement means has been widely used, and infrared thermal imager can accurately record the temperature evolution of the specimen surface during the entire fatigue process; It can directly observe the target to be measured at a certain distance and monitor and record the temperature evolution of the specimen surface during the fatigue process in real time.

[0086] S6, using the signal processing and statistical tools in MATLAB to identify and extract the features related to the metal fatigue life, and pre-processing the data obtained in S5, including data cleaning, removing outliers, and data smoothing operation.

[0087] In MATLAB, signal processing toolbox and statistical toolbox functions can be used to identify and extract features related to metal fatigue life; The signal processing toolbox can include filtering, spectrum analysis, waveform processing and other functions, which can be used to process signal information in experimental data, such as denoising, smoothing, feature extraction, etc.; The statistical toolbox can realize functions including statistical analysis, regression analysis, hypothesis testing, which can be used for statistical analysis and feature extraction of experimental data, such as calculating mean, variance, correlation coefficient statistics; The data preprocessing tool can include data cleaning, outlier processing, and data smoothing functions, which can be used for preprocessing of raw data to improve data quality and reliability; When data preprocessing, Min-Max normalization method can be used for data normalization; Min-Max normalization is a linear transformation method that scales data to a specified range, commonly used to map data to the [0, 1] interval; This helps to eliminate the dimensional differences between different features, improve the comparability of data and the stability of the model; By using various tools and methods in MATLAB, the experimental data can be processed and features extracted in detail, which can more comprehensively analyze the features related to metal fatigue life, and provide scientific basis for further research and application.

[0088] S7, based on the data processed in S6, the digital twin model updates the parameters of the digital twin model in S4 in real time and makes predictions.

[0089] S8, real-time calculation of the deviation obtained by comparing the prediction results in S7 with the true results in S5;

[0090] S9, according to the deviation data calculated in S8, adjust and correct the digital twin model in S4 to obtain a new and more accurate digital twin model, so as to realize the optimization of the digital twin model;

[0091] If the prediction error is large, the numerical simulation method can be combined to introduce an ultrasonic fatigue correction coefficient to correct the test data to optimize the model selection and other steps to further improve the prediction accuracy, so that the prediction results of the model output are consistent with the actual monitoring data, so as to realize the optimization of the digital twin model, wherein the parameter adjustment and updating method can adopt a suitable nonlinear system modeling method and state estimation algorithm, such as extended Kalman filter (EKF) or particle filter, to more accurately predict the fatigue life.

[0092] The trained digital twin model is used to predict the fatigue life of new test pieces, and the prediction result is the fatigue life of the material under a certain environmental condition under the corresponding load, which can be used for optimization design, thereby improving the service life of the metal material in the actual working environment.

[0093] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A digital-twin-based ultrasonic fatigue experiment monitoring method, characterized in that: It comprises the following steps: S1, obtaining the geometric structure parameters of the laboratory ultrasonic fatigue test piece and the material properties used; S2, obtaining the initial working condition and environmental parameter of the ultrasonic fatigue test; S3, building a digital twin sub-model according to the data obtained in S2; S4, building a complete digital twin model based on the digital twin sub-model in S3; S5, monitoring the related data of metal material fatigue life in real time during the ultrasonic fatigue test; S6, using the signal processing and statistical tools in MATLAB to identify and extract the characteristics related to metal fatigue life, and preprocessing the data obtained in S5, including data cleaning, removing outliers and data smoothing operation; S7, based on the data processed in S6, the digital twin model updates the parameters of the digital twin model in S4 in real time, and makes prediction; S8, real-time calculation of the deviation obtained by comparing the prediction result in S7 with the real result in S5; S9, adjusting and correcting the digital twin model in S4 according to the deviation data calculated in S8 to obtain a new and more accurate digital twin model, and optimizing the digital twin model; S10, using the trained digital twin model to predict the fatigue life of new test pieces, and the prediction result is the fatigue life of the material under certain environmental conditions and corresponding load, which is used for optimization design.

2. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In S1, the performance parameters of the material to be detected are queried, including its grade and mechanical property parameters. In S2, the ultrasonic fatigue testing machine consists of an ultrasonic fatigue testing mechanical device and a test piece to form a resonant system. In S2, the working condition and environmental parameter include the working frequency, temperature and load during the ultrasonic fatigue test.

3. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: The digital twin sub-model in S3 includes building a test piece frequency evolution model, a stress model, a strain model, a temperature field model and a fatigue life prediction model.

4. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: S3 comprises the following steps: S31, building a test piece frequency evolution model based on the resonance frequency evolution law; S32, building a stress model based on the maximum stress of the middle section of the test piece; S33, building a strain model based on Hooke's law and the dynamic elastic modulus of the material and the stress model in S32; S34, building a temperature field model based on thermal analysis; S35, building a fatigue life prediction model based on infrared thermography.

5. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In step S31, during the ultrasonic fatigue process of the metal material, the resonance frequency of the test sample continues to decrease with the increase of the cycle number. The real-time collected frequency image is used as a reference to draw a relative frequency-f-cycle relationship diagram, and the frequency change rate df / dN is obtained, so as to preliminarily understand the ultrahigh cycle fatigue damage of the test sample.

6. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In step S32, the ultrasonic fatigue test selects the test sample according to different test conditions, loading methods and frequency effects to adapt to different test conditions. Several commonly used ultrasonic fatigue test samples are taken as the coordinate origin. During the test, the stress amplitude is maximum in the middle of the test sample and zero at both ends. The displacement is zero in the middle of the test sample and maximum at both ends. Therefore, a stress model is built based on the maximum stress amplitude of the middle section of the test piece: In the formula, E is the elastic modulus of the material, and U0 is the maximum displacement amplitude of the test sample.

7. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In the step S33, the stress and strain of the ultrasonic fatigue sample belong to the elastic deformation interval, and the corresponding stress amplitude and strain amplitude are calculated by Hooke's law and the dynamic elastic modulus of the material to build a strain model: In the formula, σ max is the maximum stress of the middle section of the test piece, E d is the dynamic modulus of elasticity of the material, and ε max is the maximum strain of the middle section of the test piece.

8. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In the step S34, based on the length-diameter ratio of the ultrasonic fatigue sample and the temperature distribution of the sample along the length direction caused by heat conduction, the surface temperature of the sample can be simplified as a one-dimensional field, and the temperature along the length direction of the sample is gradiently distributed. Taking the center of the sample as the origin, the direction close to the clamping end is the negative direction of the x-axis, and the direction close to the free end is the positive direction of the x-axis. The temperature field model of the sample is: θ(x) = A1x + A2x + A3 2 + A2x + A3 Wherein, A1<0, θ is a one-dimensional temperature value, A2 and A3 are fitting parameters.

9. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In the step S35, the fatigue life prediction model of the infrared thermal imaging method is used to predict the fatigue life of the material based on the initial temperature rise slope R. The fatigue life prediction expression is as follows: N f R = C In the formula, N f is the material fatigue life, R is the temperature rise slope, and C is a material constant.

10. The ultrasonic fatigue test monitoring method based on digital twinning according to claim 1, characterized in that: In the step S4, based on the Simulink visual tool platform in MATLAB, the parameters of each digital twin sub-model are fused, and sufficient debugging is performed to construct an integrated simulation platform of multiple physical fields, and a complete digital twin model is formed; The related data of the material fatigue life in the S5 includes the related data of the ultrasonic fatigue test, and the related data includes the amplitude of the end of the test piece, the temperature, the test frequency, the load amplitude and the strain. The related data of the material fatigue life of the test piece in the S5 is obtained by the actual recording of the ultrasonic fatigue test machine control system; The adjustment and updating method of the parameters in the S9 adopts the extended Kalman filtering algorithm.

Images