A composite material fatigue performance prediction method and system based on infrared thermal imaging

Abstract

The application provides a composite material fatigue performance prediction method and system based on infrared thermal imaging, belongs to the technical field of material mechanical performance testing, and comprises the following steps: determining fatigue performance characterization parameters and corresponding static strength values of a to-be-predicted composite material, building a composite material fatigue performance test system, determining step-by-step fatigue loading stress and load, applying step-by-step increasing stress level fatigue cycle load to the to-be-predicted composite material for a given cycle, obtaining a temperature field sequence, and extracting temperature characteristic parameters representing the energy dissipation state of the material; based on the temperature characteristic parameters and a composite material damage infrared feature library, obtaining the temperature-damage feature-stress relationship of the to-be-predicted composite material under each cycle loading stage; based on the relationship, obtaining the critical transition stress of the to-be-predicted composite material, determining the fatigue limit, obtaining the fatigue life-stress relationship, and performing fatigue performance prediction. The scheme improves the prediction efficiency and accuracy and avoids misjudgment of a single parameter.

Description

Technical Field

[0001] This application relates to the field of materials mechanical property testing technology, and in particular to a method and system for predicting the fatigue performance of composite materials based on infrared thermal imaging. Background Technology

[0002] Composite materials, due to their high specific strength, high specific stiffness, and excellent corrosion resistance, have been widely used in aerospace, wind power generation, and marine engineering. To achieve lightweight, long-life, and independently controllable structures for my country's aircraft, utilizing domestically produced composite materials is an inevitable path. Therefore, the application of advanced domestically produced composite materials has evolved from non-load-bearing structures and secondary load-bearing structures to gradually expanding to primary load-bearing structures. However, at the microscopic level, composite materials have complex internal laminated structures, and at the macroscopic load-bearing level, aerospace composite material structures are subjected to complex and diverse load forms, especially cyclic loading. Therefore, stress coupling forms are diverse, and typical damage modes, such as matrix cracking, fiber fracture, delamination, and interface debonding, often exhibit multi-scale, progressive evolution characteristics, posing significant challenges to the health monitoring and life prediction of in-service composite material structures. Furthermore, aerospace composite material structures must meet structural integrity requirements to ensure service safety and economy. Therefore, accurate and rapid prediction of the fatigue performance of composite materials is particularly important. The fatigue damage accumulation of composite materials is a random and complex process, accompanied by numerous damage modes, such as matrix cracking, interlaminar delamination, and fiber fracture. Different damage modes are dominated by different stress components, and the stress state affects the interaction and coupling of different stress components with the damage modes.

[0003] Currently, the fatigue properties of composite materials are mainly described by the SN curve (stress-life curve) or ε-N curve (strain-life curve) plotted together with the fatigue life determined by the group method at a given stress or strain and the fatigue limit determined by the rise-fall method. The fatigue limit, also known as the conditional fatigue limit, is the fatigue life taken at a given stress or strain (e.g., 10...). 6 Or 10 7 The stress value under cyclic stress is used as the fatigue limit. Commonly used fatigue performance testing methods for polymer-based composite materials can refer to domestic standard methods. Although domestic standard methods specify tensile-tensile fatigue, tensile-compression fatigue, compression-compression fatigue, bending fatigue, adhesive tensile-shear fatigue, general requirements, and fatigue data statistical analysis methods, they essentially mainly specify the group method to determine the fatigue life under a given load level and plot the SN or ε-N curve based on this. Considering the strong nonlinear and multi-scale characteristics of composite material fatigue behavior, accurate prediction of fatigue performance is a challenging task, and traditional test methods are often time-consuming, costly, and difficult to fully explain the internal damage development process of composite materials.

[0004] Infrared thermal imaging technology, due to its advantages such as non-contact, real-time, full-scene monitoring, and visualization, has become a means of revealing energy change behavior during fatigue processes, and has received widespread attention in the fields of material fatigue crack propagation, fatigue limit prediction, and damage monitoring. For example, CN118191019A discloses a method for predicting the fatigue limit of carbon fiber reinforced polymer matrix composite laminates based on the goodness-of-fit criterion. This method relies on three-dimensional heat transfer simulation using heat-generating units, requires reasonable setting of heat-generating unit parameters, and combines simulation and experiment. It uses mathematical inverse solution of energy dissipation and employs over-quadratic polynomial fitting to predict the fatigue limit of carbon fiber reinforced polymer matrix composite laminates, but does not involve fatigue life prediction under a given stress or strain level. CN120927480A discloses a rapid fatigue limit prediction method based on the energy dissipation characteristics of infrared thermal imaging, which is applicable to difficult-to-machine high-performance alloy materials such as titanium alloys and stainless steel. CN112924307A discloses a rapid fatigue limit prediction method based on infrared thermal imaging. This method is based on the Luong two-line method, combining energy dissipation theory and material fatigue damage mechanism to determine the temperature rise inflection point and obtain the predicted fatigue limit value. The experiments in this method are based on GB / T3075-2008 and GB 228.1-2010, both corresponding to the test standards for metallic materials. CN103076243A discloses a system and method for predicting the fatigue limit of magnesium alloy structures based on infrared thermal imaging. This system is used for measuring fatigue hazard points and predicting fatigue limits in magnesium alloys and their welded structures, but it is only applicable to specific alloy types with uneven stress distribution.

[0005] In summary, several methods for predicting the fatigue limit of metals and composite materials based on infrared thermal imaging exist. These methods demonstrate that infrared thermal imaging can identify potential damage locations by monitoring real-time surface temperature distribution, providing an efficient means for early damage monitoring and detection. Furthermore, it can predict the fatigue limit of materials based on differences in the temperature field under different stress levels. However, existing methods primarily focus on fatigue limit prediction and do not yet address fatigue performance prediction incorporating SN curves and fatigue limits, nor do they fully utilize existing research results. Summary of the Invention

[0006] In view of this, the present application provides a method and system for predicting the fatigue performance of composite materials based on infrared thermal imaging, which at least partially solves the problems in the field of aerospace composite materials where fatigue performance testing requires consideration of factors such as material batch, environment, and load type, and the fatigue life is highly variable, resulting in long test cycles, a large number of required samples, and huge time, manpower, and material costs for determining fatigue performance.

[0007] In a first aspect, embodiments of this application provide a method for predicting the fatigue properties of composite materials based on infrared thermal imaging, the method comprising:

[0008] Determine the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted; A fatigue performance testing system for composite materials was established, including a loading system, an infrared thermal imaging detection system, and a synchronous control and data acquisition system. Based on the determined fatigue performance characterization parameters and corresponding static strength values, the stress and load of progressive fatigue loading are determined. The fatigue performance testing system is used to apply progressively increasing stress levels of fatigue cyclic loads to the composite material to be predicted for a given cycle, and the temperature field sequence is obtained. The temperature field sequence includes the location and time of the temperature acquisition point, the stress and stress ratio of the cyclic loading level. Based on the temperature field sequence, temperature characteristic parameters characterizing the energy dissipation state of the material are extracted; Based on temperature characteristic parameters and infrared feature library of composite material damage, the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level is obtained. Based on the temperature-damage characteristics-stress relationship, the critical transformation stress of the composite material to be predicted is obtained, the fatigue limit is determined, and the fatigue life-stress relationship is obtained to predict fatigue performance.

[0009] According to a specific implementation of an embodiment of this application, determining the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted includes: Step 1.1: Query the composite material performance database to determine the basic mechanical properties, preparation process, component materials and content data of the composite material to be predicted. If found, determine the corresponding static strength value and proceed to step 1.2; if not found, conduct a static test based on the most relevant basic mechanical property test standard in the composite material fatigue method database to determine the corresponding static strength value, and then proceed to step 1.2. Step 1.2: Query the composite material fatigue method database, select the specimen configuration and load application fixture, and perform specimen processing and fixture processing for the composite material to be predicted. Spray matte black heat-resistant paint on the test surface of the specimen working section to ensure that the emissivity is not less than 0.93.

[0010] According to a specific implementation of an embodiment of this application, the step of extracting temperature characteristic parameters characterizing the energy dissipation state of a material based on a temperature field sequence includes: Based on the temperature field sequence, the slope of temperature increase over time in the initial stage of loading is obtained, and this slope is used as the initial temperature rise rate. Based on the temperature field sequence, the average temperature rise value of the sample surface when it reaches a steady state is obtained, and this average temperature rise value is taken as the steady-state temperature rise.

[0011] According to a specific implementation of an embodiment of this application, obtaining the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level includes: Obtain the initial temperature rise rate function of the composite material to be predicted under each cyclic loading level with respect to stress level, stress ratio, and damage type. ; Obtain the steady-state temperature rise function of the composite material to be predicted under each cyclic loading level with respect to stress level, stress ratio, and damage type. ; in, Let be the initial temperature rise rate function. Let σ be the steady-state temperature rise function. i R represents the stress under cyclic loading, R is the stress ratio, and D is the damage type.

[0012] According to one specific implementation of the embodiments of this application, the damage types include matrix damage, interlaminar damage, fiber damage, and coupling damage.

[0013] According to a specific implementation of this application, the step of obtaining the critical transition stress of the composite material to be predicted based on the temperature-damage characteristics-stress relationship, determining the fatigue limit, and obtaining the fatigue life-stress relationship to predict fatigue performance includes: Based on the temperature-damage characteristics-stress relationship, the critical transition stress σ at a given stress ratio R, where the thermal response of the composite material to be predicted changes from being dominated by reversible thermoelastic effects to being dominated by irreversible damage dissipation effects, is determined. c,R Determine the fatigue limit; Based on the traditional logarithmic and double logarithmic fatigue life relationships, a fatigue life-temperature-damage characteristic relationship is established for the composite material to be predicted. The initial temperature rise rate function Or steady-state temperature rise function Substituting the fatigue life-temperature-damage characteristic relationship, we obtain the fatigue life-stress relationship. To predict fatigue performance, where N f Let σ represent fatigue life and σ represent stress.

[0014] Secondly, embodiments of this application also provide a composite material fatigue performance prediction system based on infrared thermal imaging, used to execute the composite material fatigue performance prediction method based on infrared thermal imaging as described in any embodiment of the first aspect, the system comprising: The parameter determination module is used to determine the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted. The test system construction module is used to build a fatigue performance test system for composite materials, including a loading system, an infrared thermal imaging detection system, and a synchronous control and data acquisition system. The temperature field sequence acquisition module is used to determine the stress and load of progressive fatigue loading based on the determined fatigue performance characterization parameters and corresponding static strength values. The fatigue performance testing system is used to apply fatigue cyclic loads with progressively increasing stress levels to the composite material to be predicted for a given cycle, and the temperature field sequence is obtained. The temperature field sequence includes the location and time of the temperature acquisition point, the stress and stress ratio of the cyclic loading level. The temperature feature parameter extraction module is used to extract temperature feature parameters that characterize the energy dissipation state of materials based on temperature field sequences. The correspondence establishment module is used to obtain the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level based on temperature characteristic parameters and composite material damage infrared characteristic library; The fatigue limit determination and fatigue performance prediction module is used to obtain the critical transition stress of the composite material to be predicted based on the temperature-damage characteristics-stress relationship, determine the fatigue limit, obtain the fatigue life-stress relationship, and predict the fatigue performance.

[0015] According to one specific implementation of the embodiments of this application, the loading system adopts a servo hydraulic fatigue testing machine or an electric servo fatigue testing machine, and the loading system operates stably at 0.1Hz~20Hz.

[0016] According to a specific implementation of an embodiment of this application, the infrared thermal imaging detection system uses a cooled infrared thermal imager, the noise equivalent temperature difference of the cooled infrared thermal imager is better than 20mK, and the frame rate of the cooled infrared thermal imager is not less than 800 times the loading frequency of the loading system.

[0017] According to a specific implementation of an embodiment of this application, the synchronous control and data acquisition system is equipped with a synchronous trigger box or uses a data acquisition system with synchronous trigger function. The controller of the loading system outputs a TTL trigger pulse synchronized with the load signal directly through the external trigger port of the infrared thermal imaging detection system.

[0018] Beneficial effects: The infrared thermal imaging-based composite material fatigue performance prediction method and system in this application have the following significant advantages compared to existing grouping and elevation methods for composite material fatigue performance: 1. Fast and efficient: The entire prediction process takes only a few days, which is significantly more efficient than the traditional testing cycle of several months, greatly accelerating the material research and development and selection process; 2. Non-destructive and accurate: Based on the physical nature of energy dissipation mechanisms, a fatigue performance prediction model for composite materials is constructed by identifying critical transformation stresses. The physical meaning is clear and the prediction accuracy is high. 3. Rich in information: It comprehensively utilizes multiple temperature characteristics, avoiding misjudgments that may be caused by a single parameter, resulting in more reliable and robust results; 4. Data / Model Utilization and Accumulation: In the process of using this method to determine the fatigue performance of composite materials, existing historical data can be fully utilized, and the database related to composite materials can be continuously enriched and expanded. This also accumulates rich and detailed data and models for the construction of composite material fatigue performance prediction methods and models. Attached Figure Description

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

[0020] Figure 1 This is a flowchart of a method for predicting the fatigue properties of composite materials based on infrared thermal imaging according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the temperature sequence for determining the position of the working section of a specimen under a given load according to an embodiment of the present invention; Figure 3 To illustrate the k under different loads in the post-impact compression-compression cycle according to an embodiment of the present invention T (σ i Schematic diagram of the trend of R change; Figure 4 This is a schematic diagram of temperature-damage characteristics under a given load in a post-impact compression-compression cycle according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the temperature change of composite material B according to an embodiment of the present invention under various load levels in a post-impact compression-compression cycle with a stress ratio of 10 and a loading frequency of 0.1Hz. Figure 6 To illustrate the k values ​​of composite material B according to an embodiment of the present invention under various load levels in a post-impact compression-compression cycle with a stress ratio of 10 and a loading frequency of 0.1 Hz. T (σ i Schematic diagram of the trend of R change; Figure 7 This is a schematic diagram comparing the infrared prediction and test life of composite material B under impact-compression cycling with a stress ratio of 10 and a loading frequency of 0.1Hz, according to an embodiment of the present invention. Detailed Implementation

[0021] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0022] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0024] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0025] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0026] Currently, the determination of fatigue limits in the fatigue performance assessment of composite materials lacks standardized specifications and mainly relies on the fatigue limit testing methods for metallic materials, using a rise-fall method. Existing standards for composite material fatigue performance testing do not address fatigue limits. Within the domestic composite material performance testing industry, fatigue performance testing faces numerous challenges, including: 1) The test cycle is long and the cost is high. A single prediction usually requires 15 to 20 samples and takes 2 to 3 weeks or even several months. 2) Existing methods mostly rely on the macroscopic correlation of "stress-cycle number-fracture", without establishing a direct link between energy dissipation, microscopic damage and macroscopic performance during fatigue process. They are easily affected by batch and microscopic defects, resulting in high errors.

[0027] To address the aforementioned issues, this application proposes a method for predicting the fatigue performance of composite materials based on infrared thermal imaging technology. This method, based on infrared thermal imaging technology and combined with a material database, fatigue method library, and damage feature library, uses a single specimen for a single batch, environment, and load. Through in-depth analysis of temperature and energy dissipation changes during the fatigue process of the composite material, it establishes a system that processes the specimen surface temperature and energy dissipation during fatigue to determine the energy dissipation relationship under different load levels and locate the critical point of damage rate transition. This accurately predicts the fatigue life (SN curve) and limit of the composite material, and uses the constructed temperature-energy dissipation relationship to estimate the fatigue life under different stress or strain cycles, ultimately completing the fatigue performance prediction of the composite material. The following provides a detailed description of the infrared thermal imaging-based composite material fatigue performance prediction method and system of this application.

[0028] In a first aspect, embodiments of this application provide a method for predicting the fatigue properties of composite materials based on infrared thermal imaging, referring to... Figure 1 The method includes: Step 1: Determine the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted; Step 2: Build a fatigue performance testing system for composite materials, including a loading system, an infrared thermal imaging detection system, and a synchronous control and data acquisition system; Step 3: Based on the determined fatigue performance characterization parameters and corresponding static strength values, determine the stress and load of the progressive fatigue loading. Use a fatigue performance testing system to apply fatigue cyclic loads with progressively increasing stress levels to the composite material to be predicted for a given cycle, and obtain the temperature field sequence. The temperature field sequence includes the location and time of the temperature acquisition point, the stress and stress ratio of the cyclic loading level. Step 4: Based on the temperature field sequence, extract the temperature characteristic parameters that characterize the energy dissipation state of the material; Step 5: Based on temperature characteristic parameters and the infrared feature library of composite material damage, obtain the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level; Step 6: Based on the temperature-damage characteristics-stress relationship, obtain the critical transformation stress of the composite material to be predicted, determine the fatigue limit, obtain the fatigue life-stress relationship, and predict the fatigue performance.

[0029] The method in this embodiment involves conducting experiments in a standard laboratory environment. It combines data from a composite material performance database, a composite material fatigue testing method library, and a composite material damage infrared characteristic library with pre-experimental methods to design a specific implementation plan for progressive cyclic loading and an infrared acquisition scheme for fatigue testing, constructing a temperature-damage characteristic-stress relationship. Based on the physical mechanism of the transition from "thermoelastic effect" to "damage dissipation effect," the critical transition stress is determined. Combining the critical transition stress and a small amount of stress-fatigue life data from group methods, a fatigue life-stress relationship for composite materials is constructed for fatigue performance prediction. The entire prediction process takes only a few days, significantly improving efficiency compared to the traditional testing cycle of several months, greatly accelerating the material research and development and selection process. Based on the physical essence of the energy dissipation mechanism, by identifying the critical transition stress, a composite material fatigue performance prediction model is constructed, with clear physical meaning and high prediction accuracy. It is information-rich: comprehensively utilizing multiple temperature characteristics avoids misjudgments that may arise from a single parameter, resulting in more reliable and robust results. In determining the fatigue performance of composite materials using this method, existing historical data can be fully utilized, while simultaneously enriching and expanding the composite material-related database. This also accumulates rich and detailed data and models for the construction of composite material fatigue performance prediction methods and models.

[0030] In one embodiment, determining the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted includes: Step 1.1: Query the composite material performance database to determine the basic mechanical properties, preparation process, component materials and content data of the composite material to be predicted. If found, determine the corresponding static strength value and proceed to step 1.2; if not found, conduct a static test based on the most relevant basic mechanical property test standard in the composite material fatigue method database to determine the corresponding static strength value, and then proceed to step 1.2. Step 1.2: Query the composite material fatigue method database, select the specimen configuration and load application fixture, and perform specimen processing and fixture processing for the composite material to be predicted. Spray matte black heat-resistant paint on the test surface of the specimen working section to ensure that the emissivity is not less than 0.93.

[0031] Furthermore, the construction of a fatigue performance testing system for composite materials specifically includes: The fatigue loading system uses a standard servo hydraulic fatigue testing machine or an electric servo fatigue testing machine, capable of stable operation from 0.1Hz to 20Hz with low waveform distortion, and equipped with a high-precision load sensor to ensure accurate load application. The infrared thermal imaging detection system uses a medium-wave or long-wave cooled infrared thermal imager to ensure sufficient sensitivity to temperature changes during composite material fatigue testing in a standard laboratory environment. The noise equivalent temperature difference is better than 20mK, and the spatial resolution is sufficient to clearly distinguish the thermal field details of the specimen's working section. The frame rate is no less than 800 times the loading frequency. An appropriate lens is selected based on the working distance and the size of the specimen's working section to ensure clear imaging of the entire working section. The synchronous control and data acquisition system must be equipped with a synchronous trigger box or use a data acquisition system with synchronous triggering function. The controller of the loading system outputs a TTL (Transistor-Transistor Logic) trigger pulse synchronized with the load signal, directly through the external trigger port of the infrared thermal imager. This system must be placed in a standard laboratory environment with no airflow to prevent background radiation reflection from windows, people, etc., from reaching the specimen surface.

[0032] Furthermore, the step of applying progressively increasing stress levels of fatigue cyclic loading to the composite material to be predicted using a fatigue performance testing system to obtain a temperature field sequence includes: Based on the fundamental mechanical property values ​​related to the fatigue performance of the composite material to be predicted obtained in step 1, determine the stress and load for progressive fatigue loading. For example, it is necessary to predict the tensile-tensile fatigue of a pure 0° layup unidirectional tape laminate of composite material A under a stress ratio of 0.1 containing 10 6 The fatigue limit SN curve can be obtained from the longitudinal tensile strength X of composite material A obtained in step 1. t The cross-sectional area of ​​the working section is calculated based on the specific configuration of the specimen to determine the cyclic load that needs to be applied at each stage.

[0033] In a standard laboratory environment, fatigue cyclic loading with progressively increasing stress levels is applied using a standard fatigue testing machine that has passed rigorous testing. This given number of cycles is determined through pre-experiments. Under moderate stress levels, temperature rise curves are recorded to determine the number of cycles required to reach steady state. Based on this, the number of cycles should be sufficient to ensure the specimen's temperature field reaches a quasi-steady state, while avoiding excessive damage accumulation at low stress levels. Typically, 1000 to 10000 cycles are used, with round numbers of up to 10000 cycles recommended. Simultaneously, based on the loading system, infrared thermal imaging detection system, and synchronous control and data acquisition system established in step 2, load-displacement records, stress-strain records, and full-frame thermal image sequences are collected and saved progressively throughout the fatigue loading process, forming a precise timestamp-based temperature field sequence T(x,y,t,σ). i,R), where x and y represent the x-coordinate and y-coordinate positions of the temperature acquisition point on the specimen, respectively, and t represents time, σ i R and R represent the applied cyclic loading level stress and stress ratio, respectively, and the recording frequency is no less than 200 times the fatigue loading frequency. Figure 2 Given the location coordinates and temperature sequence under load for the working section, the horizontal axis n represents the cycle number, and S-1 represents the highest stress level. S2 represents the second highest stress level. S3 represents the second lowest stress level. S4 represents the lowest stress level. .

[0034] In one embodiment, extracting temperature characteristic parameters characterizing the energy dissipation state of a material based on a temperature field sequence includes: Based on the temperature field sequence, the slope of temperature increase over time in the initial stage of loading is obtained, and this slope is used as the initial temperature rise rate. Based on the temperature field sequence, the average temperature rise value of the sample surface when it reaches a steady state is obtained, and this average temperature rise value is taken as the steady-state temperature rise.

[0035] In specific implementation, based on the load-displacement record and temperature field sequence T(x,y,t,σ) obtained above... i ,R), for each cyclic load level The temperature field sequence is processed to extract temperature characteristic parameters that characterize the energy dissipation state of the material, including: 1) The slope k of temperature increase over time during the initial loading stage T (σ i R (i.e., initial temperature rise rate); , In the formula The slope of the temperature rise over time during the initial loading stage (a function of the cyclic load) is given by T, which is the average temperature of the working area of ​​the specimen or the temperature of a specified location or region of the specimen. t0 corresponds to the start of the fatigue test loading time, and t is the time.

[0036] 2) The average temperature rise ΔT when the sample surface temperature reaches a steady state. s (σ i R) (i.e., steady-state temperature rise), corresponding to Figure 2 Medium temperature constant region.

[0037] Furthermore, obtaining the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level includes: Obtain the initial temperature rise rate function of the composite material to be predicted under each cyclic loading level with respect to stress level, stress ratio, and damage type. ; Obtain the steady-state temperature rise function of the composite material to be predicted under each cyclic loading level with respect to stress level, stress ratio, and damage type. ; in, Let be the initial temperature rise rate function. Let σ be the steady-state temperature rise function. i R represents the stress under cyclic loading, R is the stress ratio, and D is the damage type.

[0038] Furthermore, the damage types include matrix damage, interlaminar damage, fiber damage, and coupling damage.

[0039] Specifically, based on the obtained k T (σ i ,R) and ΔT s (σ i Based on temperature characteristic parameters such as temperature (R), combined with infrared data of different damage types under different stress level cycles from the composite material damage characteristic library, a composite material is constructed for each cyclic loading level. Temperature-damage characteristics-stress relationship, such as and Where D represents the damage type, defined as D=1 for matrix damage, D=2 for interlaminar damage, D=3 for fiber damage, and D=4 for coupled damage. For impact-induced compression-compression cyclic loading, k T (σ i The change in R shows a bilinear trend, such as Figure 3 As shown, the cumulative characteristics of temperature-damage features are shown below. Figure 4 . Figure 3 The relationship exhibits a clear bilinear relationship. The bilinear transition point corresponds to the critical transition stress σ in the predicted composite material's thermal response, which shifts from being dominated by reversible thermoelastic effects to being dominated by irreversible damage dissipation effects. c,R This corresponds to the fatigue limit stress. Figure 4 This is a full-cycle temperature-time curve under typical moderate stress cycling. The initial stage is dominated by matrix damage accumulation, gradually transitioning to interlaminar damage accumulation, and the final stage is dominated by fiber damage accumulation and multi-damage coupling. Combining data from a composite material damage feature library and experimental data, various methods including linear fitting, nonlinear fitting, and machine learning are used to construct the temperature-damage feature-stress relationship, including... and .

[0040] In one embodiment, the process of obtaining the critical transition stress of the composite material to be predicted based on the temperature-damage characteristic-stress relationship, determining the fatigue limit, and obtaining the fatigue life-stress relationship to predict fatigue performance includes: Based on the temperature-damage characteristics-stress relationship, and through various methods such as data fitting or differentiation, the critical transition stress σ at a given stress ratio R, when the thermal response of the composite material to be predicted changes from being dominated by reversible thermoelastic effects to being dominated by irreversible damage dissipation effects, is determined. c,R Determine the fatigue limit; Based on traditional logarithmic and double logarithmic fatigue life relationships or with the aid of machine learning methods, a fatigue life-temperature-damage characteristic relationship for the composite material to be predicted is established, such as N. f (k T ,D) or N f (k T ,ΔT s ,D); The initial temperature rise rate function Or steady-state temperature rise function Substituting the fatigue life-temperature-damage characteristic relationship, we obtain the fatigue life-stress relationship. To predict fatigue performance, where N f Let σ represent fatigue life and σ represent stress.

[0041] The following specific embodiment illustrates the method for predicting the fatigue performance of composite materials based on infrared thermal imaging according to this application. The method involves predicting the compressive-compressive fatigue performance of composite material B after impact, with a stress ratio of 10 and a loading frequency of 0.1 Hz. The specific steps include: 1) Query the basic mechanical properties of composite material B in the composite material performance database, especially the post-impact compressive strength; find the post-impact compressive strength value σ. cmax =360MPa, based on this, the maximum stress corresponding to each cyclic load is determined to be 0.9σ. cmax (correspond Figure 5 (S90%), 0.85σ cmax (correspond Figure 5 S85%), 0.8σ cmax (correspond Figure 5 (S80%), 0.75σ cmax (correspond Figure 5 S75%), 0.7σ cmax (correspond Figure 5 (S70%), 0.65σ cmax (correspond Figure 5 (S65%), 0.6σ cmax (correspond Figure 5 S60%), 0.55σ cmax (correspond Figure 5 (S55%) and 0.5σ cmax (correspond Figure 5 A total of 9 load levels (S50%); 2) Set up an infrared thermal imaging monitoring system and adjust the acquisition frequency to 100Hz; 3) Based on the maximum value of each load level and stress ratio R=10 determined in step 1, measure the specimen size, calculate the cyclic load, and carry out fatigue tests step by step from the lowest load level and collect infrared data. The test should be paused for at least 1 hour between adjacent levels to ensure that the specimen surface temperature is consistent with the standard laboratory temperature at the beginning of each loading level. 4) Extract temperature characteristics under each loading level, see details below. Figure 5 and Figure 6 , Figure 6 The x-coordinate σ in max This represents the maximum stress value. 5) Established temperature characteristics-stress-fatigue life relationship, lgN f =15.39-0.0515σ(k T ), Figure 7 For the prediction results (corresponding to) Figure 7 (predict data) and experimental results (corresponding to) Figure 7 The comparison with the test data shows that the predicted lifetime is close to the lifetime obtained from the experiment, indicating that the prediction method is feasible.

[0042] Secondly, embodiments of this application also provide a composite material fatigue performance prediction system based on infrared thermal imaging, used to execute the composite material fatigue performance prediction method based on infrared thermal imaging as described in any embodiment of the first aspect, the system comprising: The parameter determination module is used to determine the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted. The test system construction module is used to build a fatigue performance test system for composite materials, including a loading system, an infrared thermal imaging detection system, and a synchronous control and data acquisition system. The temperature field sequence acquisition module is used to determine the stress and load of progressive fatigue loading based on the determined fatigue performance characterization parameters and corresponding static strength values. The fatigue performance testing system is used to apply fatigue cyclic loads with progressively increasing stress levels to the composite material to be predicted for a given cycle, and the temperature field sequence is obtained. The temperature field sequence includes the location and time of the temperature acquisition point, the stress and stress ratio of the cyclic loading level. The temperature feature parameter extraction module is used to extract temperature feature parameters that characterize the energy dissipation state of materials based on temperature field sequences. The correspondence establishment module is used to obtain the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level based on temperature characteristic parameters and composite material damage infrared characteristic library; The fatigue limit determination and fatigue performance prediction module is used to obtain the critical transition stress of the composite material to be predicted based on the temperature-damage characteristics-stress relationship, determine the fatigue limit, obtain the fatigue life-stress relationship, and predict the fatigue performance.

[0043] Furthermore, the loading system adopts a servo hydraulic fatigue testing machine or an electric servo fatigue testing machine, and the loading system operates stably from 0.1Hz to 20Hz.

[0044] Furthermore, the infrared thermal imaging detection system uses a cooled infrared thermal imager, which has a noise equivalent temperature difference better than 20mK and a frame rate not less than 800 times the loading frequency of the loading system.

[0045] Furthermore, the synchronous control and data acquisition system is equipped with a synchronous trigger box or uses a data acquisition system with synchronous trigger function. The controller of the loading system outputs a TTL trigger pulse synchronized with the load signal directly through the external trigger port of the infrared thermal imaging detection system.

[0046] The embodiments provided by this invention have the following significant advantages compared to existing grouping and elevation methods for assessing the fatigue properties of composite materials: 1. Fast and efficient: The entire prediction process takes only a few days, which is significantly more efficient than the traditional testing cycle of several months, greatly accelerating the material research and development and selection process; 2. Non-destructive and accurate: Based on the physical nature of energy dissipation mechanisms, a fatigue performance prediction model for composite materials is constructed by identifying critical transformation stresses. The physical meaning is clear and the prediction accuracy is high. 3. Rich in information: It comprehensively utilizes multiple temperature characteristics, avoiding misjudgments that may be caused by a single parameter, resulting in more reliable and robust results; 4. Data / Model Utilization and Accumulation: In the process of using this method to determine the fatigue performance of composite materials, existing historical data can be fully utilized, and the database related to composite materials can be continuously enriched and expanded. This also accumulates rich and detailed data and models for the construction of composite material fatigue performance prediction methods and models.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the fatigue properties of composite materials based on infrared thermal imaging, characterized in that, The method includes: Determine the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted; A fatigue performance testing system for composite materials was established, including a loading system, an infrared thermal imaging detection system, and a synchronous control and data acquisition system. Based on the determined fatigue performance characterization parameters and corresponding static strength values, the stress and load of progressive fatigue loading are determined. The fatigue performance testing system is used to apply progressively increasing stress levels of fatigue cyclic loads to the composite material to be predicted for a given cycle, and the temperature field sequence is obtained. The temperature field sequence includes the location and time of the temperature acquisition point, the stress and stress ratio of the cyclic loading level. Based on the temperature field sequence, temperature characteristic parameters characterizing the energy dissipation state of the material are extracted; Based on temperature characteristic parameters and infrared feature library of composite material damage, the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level is obtained. Based on the temperature-damage characteristics-stress relationship, the critical transition stress of the composite material to be predicted is obtained, the fatigue limit is determined, and the fatigue life-stress relationship is obtained to predict fatigue performance. The extraction of temperature characteristic parameters characterizing the energy dissipation state of materials based on temperature field sequences includes: Based on the temperature field sequence, the slope of temperature increase over time in the initial stage of loading is obtained, and this slope is used as the initial temperature rise rate. Based on the temperature field sequence, the average temperature rise value of the sample surface temperature to reach a steady state is obtained, and this average temperature rise value is taken as the steady-state temperature rise. The process of obtaining the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level includes: Obtain the initial temperature rise rate function of the composite material to be predicted under each cyclic loading level with respect to stress level, stress ratio, and damage type. ; Obtain the steady-state temperature rise function of the composite material to be predicted under each cyclic loading level with respect to stress level, stress ratio, and damage type. ; in, Let be the initial temperature rise rate function. Let σ be the steady-state temperature rise function. i R represents the stress under cyclic loading, R is the stress ratio, and D is the damage type. The process of obtaining the critical transition stress of the composite material to be predicted based on the temperature-damage characteristics-stress relationship, determining the fatigue limit, and obtaining the fatigue life-stress relationship to predict fatigue performance includes: Based on the temperature-damage characteristics-stress relationship, the critical transition stress σ at a given stress ratio R, where the thermal response of the composite material to be predicted changes from being dominated by reversible thermoelastic effects to being dominated by irreversible damage dissipation effects, is determined. c,R Determine the fatigue limit; Based on the traditional logarithmic and double logarithmic fatigue life relationships, a fatigue life-temperature-damage characteristic relationship is established for the composite material to be predicted. The initial temperature rise rate function Or steady-state temperature rise function Substituting the fatigue life-temperature-damage characteristic relationship, we obtain the fatigue life-stress relationship. To predict fatigue performance, where N f Let σ represent fatigue life and σ represent stress.

2. The method for predicting the fatigue properties of composite materials based on infrared thermal imaging according to claim 1, characterized in that, The determination of the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted includes: Step 1.1: Query the composite material performance database to determine the basic mechanical properties, preparation process, component materials and content data of the composite material to be predicted. If found, determine the corresponding static strength value and proceed to step 1.2; if not found, conduct a static test based on the most relevant basic mechanical property test standard in the composite material fatigue method database to determine the corresponding static strength value, and then proceed to step 1.

2. Step 1.2: Query the composite material fatigue method database, select the specimen configuration and load application fixture, and perform specimen processing and fixture processing for the composite material to be predicted. Spray matte black heat-resistant paint on the test surface of the specimen working section to ensure that the emissivity is not less than 0.

93.

3. The method for predicting the fatigue performance of composite materials based on infrared thermal imaging according to claim 1, characterized in that, The damage types include matrix damage, interlaminar damage, fiber damage, and coupling damage.

4. A composite material fatigue performance prediction system based on infrared thermal imaging, used to execute the composite material fatigue performance prediction method based on infrared thermal imaging as described in any one of claims 1-3, characterized in that, The system includes: The parameter determination module is used to determine the fatigue performance characterization parameters and corresponding static strength values ​​of the composite material to be predicted. The test system construction module is used to build a fatigue performance test system for composite materials, including a loading system, an infrared thermal imaging detection system, and a synchronous control and data acquisition system. The temperature field sequence acquisition module is used to determine the stress and load of progressive fatigue loading based on the determined fatigue performance characterization parameters and corresponding static strength values. The fatigue performance testing system is used to apply fatigue cyclic loads with progressively increasing stress levels to the composite material to be predicted for a given cycle, and the temperature field sequence is obtained. The temperature field sequence includes the location and time of the temperature acquisition point, the stress and stress ratio of the cyclic loading level. The temperature feature parameter extraction module is used to extract temperature feature parameters that characterize the energy dissipation state of materials based on temperature field sequences. The correspondence establishment module is used to obtain the temperature-damage characteristics-stress relationship of the composite material to be predicted under each cyclic loading level based on temperature characteristic parameters and composite material damage infrared characteristic library; The fatigue limit determination and fatigue performance prediction module is used to obtain the critical transition stress of the composite material to be predicted based on the temperature-damage characteristics-stress relationship, determine the fatigue limit, obtain the fatigue life-stress relationship, and predict the fatigue performance.

5. The composite material fatigue performance prediction system based on infrared thermal imaging according to claim 4, characterized in that, The loading system uses a servo-hydraulic fatigue testing machine or an electric servo fatigue testing machine, and the loading system operates stably from 0.1Hz to 20Hz.

6. The composite material fatigue performance prediction system based on infrared thermal imaging according to claim 4, characterized in that, The infrared thermal imaging detection system uses a cooled infrared thermal imager. The noise equivalent temperature difference of the cooled infrared thermal imager is better than 20mK, and the frame rate of the cooled infrared thermal imager is not less than 800 times the loading frequency of the loading system.

7. The composite material fatigue performance prediction system based on infrared thermal imaging according to claim 4, characterized in that, The synchronous control and data acquisition system is equipped with a synchronous trigger box or uses a data acquisition system with synchronous trigger function. The controller of the loading system outputs a TTL trigger pulse that is synchronized with the load signal and directly passes through the external trigger port of the infrared thermal imaging detection system.

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