System for measuring thermal expansion coefficient and stress of composite material

Abstract

The invention relates to the technical field of composite material performance testing, in particular to a composite material thermal expansion coefficient and stress measuring system, which comprises a data acquisition module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data acquisition module moves along the surface of a sample through a local heat source and synchronously performs infrared imaging to obtain a dynamic thermal field image sequence of the sample; the data analysis module is used for extracting delay information of heat wave propagation to a fixed observation point caused by heat source movement from the dynamic thermal field image sequence, and determining the overall thermal expansion coefficient of the sample based on comparison between the delay information and reference information; the time sequence delay information representing the macroscopic thermal expansion coefficient and the space geometric distortion information representing the microscopic stress distribution are synchronously decoupled from the same thermal field image sequence, and integrated synchronous measurement of the thermophysical property and the mechanical state of the composite material is achieved.

Description

Technical Field

[0001] This invention relates to the field of composite material performance testing technology, specifically to a composite material thermal expansion coefficient and stress measurement system. Background Technology

[0002] Composite materials are widely used in high-tech fields such as aerospace and transportation due to their excellent specific strength, specific stiffness and designability. Accurate measurement of their thermal expansion coefficient and internal stress distribution is crucial for evaluating the dimensional stability of components, predicting fatigue life and optimizing manufacturing processes. At present, the measurement of the thermal expansion coefficient and stress of composite materials mostly adopts discrete and different principle techniques. The measurement of the thermal expansion coefficient usually relies on thermomechanical analyzers or special dilatometers equipped with strain gauges. These methods require contact with the sample, which may introduce interference, and generally can only obtain the average expansion characteristics of the whole or a certain direction.

[0003] Existing stress measurement methods mainly include two categories: contact and non-contact. Contact methods, such as strain gauge methods, require attaching sensors, which is cumbersome and may alter the local stress state. Non-contact methods, such as digital image correlation (DIC), photoelasticity, or X-ray irradiation, avoid contact, but the DIC method requires speckle patterns and is sensitive to vibrations in the test environment. Photoelasticity is typically used for transparent or coated models, while X-ray irradiation involves expensive equipment and has limitations in depth resolution. Current technologies lack the means to simultaneously obtain the coefficient of thermal expansion and the overall stress distribution in the same area during the same thermal excitation process. Usually, two independent tests are required, which is not only inefficient but also introduces errors because the test conditions and sample conditions are difficult to completely replicate. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a system for measuring the thermal expansion coefficient and stress of composite materials.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A system for measuring the thermal expansion coefficient and stress of composite materials, comprising: Data acquisition module: A local heat source moves along the sample surface and performs infrared imaging simultaneously to obtain a sequence of dynamic thermal field images of the sample. Data analysis module: Extracts the delay information of heat wave propagation to fixed observation point caused by heat source movement from the dynamic thermal field image sequence, and determines the overall thermal expansion coefficient of the sample based on the comparison of delay information and reference information; Stress distribution determination module: Extracts the steady-state temperature pattern formed by the moving heat source from the dynamic thermal field image sequence, and determines the stress distribution on the sample surface based on the local geometric distortion of the pattern relative to the reference pattern.

[0006] In a preferred embodiment, the data acquisition module controls a local heat source with a spatial size smaller than the sample surface to scan and move along a straight trajectory on the sample surface at a constant speed, thereby exciting a transient heat wave in the sample whose propagation direction is related to the moving trajectory. As the local heat source begins to move, a synchronization trigger signal is sent to the infrared thermal imager to start the infrared thermal imager to continuously acquire images of the sample surface at a fixed acquisition frame rate. The system receives and stores multiple thermal field images output by the infrared thermal imager, arranged in time stamp order. Each thermal field image records the two-dimensional temperature distribution on the sample surface at the corresponding acquisition time. These thermal field images together constitute a dynamic thermal field image sequence characterizing the spatiotemporal evolution process of the transient thermal wave.

[0007] In a preferred embodiment, the data analysis module predefines a fixed observation point in the field of view of the sample surface corresponding to the dynamic thermal field image sequence. This observation point is located outside the movement trajectory of the local heat source. From the dynamic thermal field image sequence, the temperature change curve of the fixed observation point area over time is extracted. Based on the temperature change curve over time, the first moment when the thermal wave front propagates to the fixed observation point and causes its temperature to start to rise significantly is identified and determined. Based on the preset moving speed and starting position information of the local heat source, calculate the second moment corresponding to the position on the moving path that is closest to the fixed observation point in terms of spatial vertical distance; The difference between the first time point and the second time point is calculated to obtain the thermal wave delay time, which characterizes the time required for the thermal wave to propagate from the heat source location to the fixed observation point; Obtain the reference thermal wave delay time by measuring a standard sample under identical test conditions, wherein the coefficient of thermal expansion of the standard sample is known. Compare the thermal wave delay time with the reference thermal wave delay time and calculate the proportionality coefficient k between the two. The specific calculation formula is as follows: ; in, This indicates the thermal wave delay time of the test sample. Indicates the reference thermal wave delay time; Based on the proportionality coefficient k and the known thermal expansion coefficient of the standard sample, the overall thermal expansion coefficient of the sample is calculated using a conversion relationship. The specific calculation formula is as follows:

[0008] in, The known coefficient of thermal expansion of the standard sample is represented by the conversion relationship established based on the following principle: under the exact same test conditions, for materials with similar structure and composition, the thermal wave delay time is proportional to the coefficient of thermal expansion of the material according to a preset principle.

[0009] In a preferred embodiment, the stress distribution determination module selects one or more characteristic moments from a sequence of dynamic thermal field images pre-acquired by excitation by a moving heat source and synchronous infrared imaging, at which the spatial morphology of the temperature field in the region behind the moving heat source reaches a relatively stable state. Extract the thermal field image corresponding to each of the aforementioned feature moments as an analytical temperature distribution pattern characterizing the current thermal conduction state of the material; for each analytical temperature distribution pattern, identify and extract the set of isotherms in the pattern. Simultaneously, a reference temperature distribution pattern corresponding to each of the aforementioned characteristic moments is obtained by measuring a reference sample of the same material in a stress-free state under identical test conditions, and the same geometric feature extraction operation is performed on the reference temperature distribution pattern. Subsequently, for each characteristic time, the isotherms in the temperature distribution pattern used for analysis are spatially registered with the isotherms or temperature gradient fields in the reference temperature distribution pattern at the corresponding time, and the local geometric distortion degree is quantitatively calculated. The quantitative calculation of the degree of local geometric distortion includes the following steps: For each registered isotherm, a series of sampling points are selected along it, and the difference between the local curvature of the isotherm to be analyzed and the local curvature of the reference isotherm is calculated at each sampling point as the curvature deviation at that point. The curvature deviation of all sampling points is recorded according to their spatial location to form a quantitative dataset of the local geometric distortion of the entire field; Stress transformation is performed based on a quantitative mapping relationship model established in advance through calibration experiments. The model establishment process is as follows: for multiple calibration specimens with known stress states whose surface stress distribution has been accurately measured by independent methods, the aforementioned complete steps from selecting characteristic time to forming a quantitative dataset of local geometric distortion are performed respectively to obtain the quantitative dataset of each calibration specimen and its corresponding known stress distribution dataset. Then, a quantitative mathematical relationship between the quantitative value of local geometric distortion and the difference of local principal stress is established through regression analysis to form a model; The quantitative dataset of the local geometric distortion of the test sample is input into the quantitative mapping relationship model to calculate the preliminary principal stress difference at each sampling point on the sample surface. The specific calculation logic is as follows: The quantized value of each sampling point recorded in the local geometric distortion quantification dataset is read sequentially and input into the quantitative mapping relationship model. The model then completes a data query according to its internally defined fixed rules, and directly outputs a value corresponding to the input quantized value. This value is the preliminary principal stress difference at the sampling point. By traversing all sampling points and repeating this input and output process, a set of preliminary principal stress differences covering the entire sample analysis area and the spatial location of the sampling points can be obtained. The quantitative dataset of the local geometric distortion of the test sample is input into the quantitative mapping relationship model to calculate the preliminary principal stress difference at each sampling point on the sample surface. Then, based on the theory of elasticity, a set of mathematical equations describing the stress distribution on the sample surface, including stress balance equations and stress compatibility equations, is constructed. The stress balance equations can be expressed as: ; in, ), Let x and y represent the normal stresses along the x and y directions at the sampling points (x and y), respectively. The stress compatibility equation, representing the shear stress at the sampling point (x, y) along the xy plane, can be expressed as: ; in, Indicates to Find the second partial derivative with respect to the y-coordinate. Indicates to Find the second partial derivative with respect to the x-coordinate; The preliminary principal stress difference is used as part of the input conditions of the equation system, and a complete solution constraint system is constructed by combining the known boundary conditions and the accurate stress measurement values ​​obtained at specific locations on the sample surface through auxiliary measurement methods. Finally, the system of equations is numerically solved using an iterative optimization algorithm, specifically including: Based on the preliminary principal stress difference distribution calculated through the quantitative mapping relationship model, an initial hypothesis on the stress distribution on the sample surface is constructed. The known geometric boundary conditions of the specimen and the discrete, precise stress measurements obtained through auxiliary measurements are applied as constraints to the initial stress distribution assumption. Enter the iterative optimization loop and perform the following operations: calculate the degree to which the current stress distribution assumption satisfies the preset set of elasticity equations, and at the same time calculate the degree of difference between it and the preliminary principal stress difference distribution; Based on the calculation results of the degree of satisfaction and degree of difference, a new stress distribution hypothesis is generated through a numerical optimization algorithm. The new stress distribution hypothesis reduces the deviation from the mechanical equations while reducing the difference from the initial principal stress difference distribution, and strictly satisfies all the constraints. Determine whether the current iteration result meets the preset convergence criteria: including the change in stress distribution between the two iterations being less than the set threshold, and the degree of deviation of the current stress distribution from the mechanical equation set and the degree of difference from the initial principal stress difference distribution both meeting the preset accuracy requirements; If the convergence criterion is not met, the current assumption is replaced with the new stress distribution assumption, and the calculation step is returned to continue the next iteration. If the convergence criterion is met, the iteration is terminated, and the current stress distribution assumption is output as the final converged two-dimensional stress distribution map of the sample surface.

[0010] The beneficial effects of this invention are as follows: This invention achieves integrated synchronous measurement of the thermal properties and mechanical state of composite materials by simultaneously decoupling the temporal delay information characterizing the macroscopic thermal expansion coefficient and the spatial geometric distortion information characterizing the microscopic stress distribution from the same thermal field image sequence. Based on the mechanism of the thermal conduction process being jointly modulated by thermal expansion characteristics and stress state, material characteristics are extracted from two dimensions, resulting in high information utilization. It achieves full-field, non-contact measurement, avoids sample interference, and can obtain the thermal expansion coefficient and full-field stress distribution simultaneously in one test, significantly improving efficiency. At the same time, the dynamic thermal wave method is sensitive to changes in internal state and can detect changes in thermal conduction characteristics caused by weak stress. Attached Figure Description

[0011] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0014] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0015] like Figure 1 This embodiment provides: a system for measuring the thermal expansion coefficient and stress of composite materials, comprising: Data acquisition module: A local heat source moves along the sample surface and performs infrared imaging simultaneously to obtain a sequence of dynamic thermal field images of the sample. In this embodiment of the invention, the data acquisition module needs to be specifically described. The data acquisition module uses a local heat source with a spatial size smaller than the sample surface to control the local heat source to scan and move along a straight trajectory on the sample surface at a constant speed, thereby exciting a transient heat wave in the sample whose propagation direction is related to the moving trajectory. It should be noted that movement refers to the continuous or regular change of the spatial position of the heat source relative to the sample surface according to a preset trajectory and speed. Compared with static uniform heating, this moving local heat source can generate a temperature field inside the material. The process of this heat wave spreading from the current position of the heat source to the surrounding material will be modulated by the material's own thermophysical properties (such as thermal conductivity and thermal diffusivity) and internal state (such as stress). Therefore, moving the heat source creates a detectable physical probe (thermal disturbance) that interacts with the internal properties of the material and encodes this property information in the generated dynamic temperature field. The moving speed needs to be matched with the material's thermal diffusion time and the infrared thermal imager's acquisition frame rate. If it is too fast, the heat wave will not have enough time to diffuse, and if it is too slow, it may lead to thermal saturation. The ideal speed should make the propagation and diffusion process of the heat wave clearly distinguishable in the image sequence.

[0016] As the local heat source begins to move, a synchronous trigger signal is sent to the infrared thermal imager to start the infrared thermal imager to continuously acquire images of the sample surface at a fixed acquisition frame rate, wherein the set value of the acquisition frame rate ensures that the propagation details of the transient heat wave can be captured. Receive and store multiple thermal field images output by the infrared thermal imager, arranged in time stamp order, wherein each thermal field image records the two-dimensional temperature distribution on the sample surface at the corresponding acquisition time, and the thermal field images together constitute a dynamic thermal field image sequence characterizing the spatiotemporal evolution process of the transient thermal wave. Each frame of the sequence records the absolute or relative temperature value of every point (pixel) on the sample surface at the corresponding acquisition time, thus depicting the temperature distribution at that moment. The dynamic thermal field image sequence reproduces the complete dynamic process of the temperature field being excited, diffused, evolved, and decayed as the heat source moves after the start of self-heating excitation by playing each frame of the image in the time dimension.

[0017] During implementation, the sample needs to be placed in an environmental isolation chamber to eliminate interference from external airflow and environmental radiation on the thermal field image sequence.

[0018] Data analysis module: Extracts the delay information of heat wave propagation to fixed observation point caused by heat source movement from the dynamic thermal field image sequence, and determines the overall thermal expansion coefficient of the sample based on the comparison of delay information and reference information; In this embodiment of the invention, the data analysis module needs to be specifically described. In the field of view of the sample surface corresponding to the dynamic thermal field image sequence, the data analysis module predefines an observation point with a fixed position, which is located outside the movement trajectory of the local heat source. From the dynamic thermal field image sequence, the temperature change curve of the fixed observation point area over time is extracted. Based on the temperature change curve over time, the first moment when the thermal wave front propagates to the fixed observation point and causes its temperature to start to rise significantly is identified and determined. Based on the preset moving speed and starting position information of the local heat source, calculate the second moment corresponding to the position on the moving path that is closest to the fixed observation point in terms of spatial vertical distance; The difference between the first time point and the second time point is calculated to obtain the thermal wave delay time, which characterizes the time required for the thermal wave to propagate from the heat source location to the fixed observation point; In a dynamic thermal field induced by a moving local heat source, thermal disturbances diffuse from the heat source location to the surrounding materials. The propagation speed is not determined by the speed of the heat source movement, but is dominated by the thermal diffusivity of the material itself. The thermal diffusivity of the material is directly related to its density, specific heat capacity, and thermal conductivity. For composite materials, there is an intrinsic correlation between the overall thermal expansion coefficient and the material's heat capacity (the product of density and specific heat capacity). This is because thermal expansion behavior essentially reflects the amplitude of molecular motion under the action of thermal energy, which directly affects its ability to store thermal energy. Therefore, the speed at which heat waves propagate in the material and the delay time required to reach a certain point in space become physical quantities that indirectly characterize the overall thermal expansion of the material. Extracting this delay information means converting the macroscopic thermophysical properties of the material into a time-series parameter. A fixed observation point is predefined on the sample surface. The selection of this point must meet the following conditions: First, it is not on the direct scanning trajectory of the local heat source to avoid being directly and intensely heated by the heat source. Second, it maintains a fixed spatial distance (usually a vertical distance) from the scanning trajectory. It is specifically used to detect the heat wave signal diffused from the moving heat source. Then, based on the aforementioned dynamic thermal field image sequence, two key moments are determined: The first moment is when the physical position of the heat source passes through the point with the closest spatial distance to the fixed observation point. This can be obtained based on the preset moving speed and starting position of the heat source. The second moment is when the heat wave front reaches the fixed observation point. It is usually defined as the turning point where the temperature at this point starts to rise significantly from the ambient background. The difference between these two moments is calculated. This time difference is defined as the delay information, which directly quantifies the time required for the heat wave to propagate from the heat source location to the observation point.

[0019] Obtain the reference thermal wave delay time by measuring a standard sample under identical test conditions, wherein the coefficient of thermal expansion of the standard sample is known. Compare the thermal wave delay time with the reference thermal wave delay time and calculate the proportionality coefficient k between the two. The specific calculation formula is as follows: ; in, The thermal wave delay time of the test sample refers to the time difference between the thermal wave propagating in the test sample and the detected response signal. It represents the reference thermal wave delay time, which is the thermal wave delay time measured by a standard sample under the exact same test conditions as the sample to be tested. Based on the proportionality coefficient k and the known thermal expansion coefficient of the standard sample, the overall thermal expansion coefficient of the sample is calculated using a conversion relationship. The specific calculation formula is as follows: ; in, The known coefficient of thermal expansion of the standard sample is represented by the conversion relationship established based on the following principle: under the exact same test conditions, for materials with similar structure and composition, the thermal wave delay time is proportional to the coefficient of thermal expansion of the material according to a preset principle.

[0020] Stress distribution determination module: Extracts the steady-state temperature pattern formed by the moving heat source from the dynamic thermal field image sequence, and determines the stress distribution on the sample surface based on the local geometric distortion of the pattern relative to the reference pattern.

[0021] In this embodiment of the invention, the stress distribution determination module is specifically described. The stress distribution determination module selects one or more characteristic moments from the dynamic thermal field image sequence obtained in advance through excitation by a moving heat source and synchronous infrared imaging, at which the spatial morphology of the temperature field in the region behind the moving heat source reaches a relatively stable state. Extract the thermal field image corresponding to each of the aforementioned feature moments as an analytical temperature distribution pattern characterizing the current thermal conduction state of the material; for each analytical temperature distribution pattern, identify and extract the set of isotherms in the pattern. Simultaneously, a reference temperature distribution pattern corresponding to each of the aforementioned characteristic moments is obtained by measuring a reference sample of the same material in a stress-free state under identical test conditions, and the same geometric feature extraction operation is performed on the reference temperature distribution pattern. Subsequently, for each characteristic time, the isotherms in the temperature distribution pattern used for analysis are spatially registered with the isotherms or temperature gradient fields in the reference temperature distribution pattern at the corresponding time, and the local geometric distortion degree is quantitatively calculated. The quantitative calculation of the degree of local geometric distortion includes the following steps: For each registered isotherm, a series of sampling points are selected along it, and the difference between the local curvature of the isotherm to be analyzed and the local curvature of the reference isotherm is calculated at each sampling point as the curvature deviation at that point. The curvature deviation of all sampling points is recorded according to their spatial location to form a quantitative dataset of the local geometric distortion of the entire field; Stress transformation is performed based on a quantitative mapping relationship model established in advance through calibration experiments. The model establishment process is as follows: for multiple calibration specimens with known stress states whose surface stress distribution has been accurately measured by independent methods, the aforementioned complete steps from selecting characteristic time to forming a quantitative dataset of local geometric distortion are performed respectively to obtain the quantitative dataset of each calibration specimen and its corresponding known stress distribution dataset. Then, a quantitative mathematical relationship between the quantitative value of local geometric distortion and the difference of local principal stress is established through regression analysis to form a model; The quantitative dataset of the local geometric distortion of the test sample is input into the quantitative mapping relationship model to calculate the preliminary principal stress difference at each sampling point on the sample surface. The specific calculation logic is as follows: The quantized value of each sampling point recorded in the local geometric distortion quantification dataset is read sequentially and input into the quantitative mapping relationship model. The model then completes a data query according to its internally defined fixed rules, and directly outputs a value corresponding to the input quantized value. This value is the preliminary principal stress difference at the sampling point. By traversing all sampling points and repeating this input and output process, a set of preliminary principal stress differences covering the entire sample analysis area and the spatial location of the sampling points can be obtained. The quantitative dataset of the local geometric distortion of the test sample is input into the quantitative mapping relationship model to calculate the preliminary principal stress difference at each sampling point on the sample surface. Then, based on the theory of elasticity, a set of mathematical equations describing the stress distribution on the sample surface, including stress balance equations and stress compatibility equations, is constructed. The stress balance equations can be expressed as: ; in, ), Let x and y represent the normal stresses along the x and y directions at the sampling points (x and y), respectively. The stress compatibility equation, representing the shear stress at the sampling point (x, y) along the xy plane, can be expressed as: ; in, Indicates to Find the second partial derivative with respect to the y-coordinate. Indicates to Find the second partial derivative with respect to the x-coordinate; The preliminary principal stress difference is used as part of the input conditions of the equation system, and a complete solution constraint system is constructed by combining the known boundary conditions and the accurate stress measurement values ​​obtained at specific locations on the sample surface through auxiliary measurement methods. Finally, the system of equations is numerically solved using an iterative optimization algorithm, specifically including: Based on the preliminary principal stress difference distribution calculated through the quantitative mapping relationship model, an initial hypothesis on the stress distribution on the sample surface is constructed. The known geometric boundary conditions of the specimen and the discrete, precise stress measurements obtained through auxiliary measurements are applied as constraints to the initial stress distribution assumption. Enter the iterative optimization loop and perform the following operations: calculate the degree to which the current stress distribution assumption satisfies the preset set of elasticity equations, and at the same time calculate the degree of difference between it and the preliminary principal stress difference distribution; Based on the calculation results of the degree of satisfaction and degree of difference, a new stress distribution hypothesis is generated through a numerical optimization algorithm. The new stress distribution hypothesis reduces the deviation from the mechanical equations while reducing the difference from the initial principal stress difference distribution, and strictly satisfies all the constraints. Determine whether the current iteration result meets the preset convergence criteria: including the change in stress distribution between the two iterations being less than the set threshold, and the degree of deviation of the current stress distribution from the mechanical equation set and the degree of difference from the initial principal stress difference distribution both meeting the preset accuracy requirements; If the convergence criterion is not met, the current assumption is replaced with the new stress distribution assumption, and the calculation step is returned to continue the next iteration. If the convergence criterion is met, the iteration is terminated, and the current stress distribution assumption is output as the final converged two-dimensional stress distribution map of the sample surface.

[0022] It should be noted that the threshold setting refers to the numerical criterion set in advance to determine whether the iterative calculation has reached stable convergence. Specifically, it is the maximum allowable value of the difference between the stress distributions obtained in two iterations. The preset accuracy requirements include two aspects: mechanical consistency accuracy and data matching accuracy. The former requires that the iterative stress distribution meets the residual limit of the elasticity equation system, and the latter requires that it meet the global error limit of the difference distribution between the iterative stress and the initial principal stress.

[0023] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0024] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0025] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0026] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0027] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0028] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0029] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A system for measuring the thermal expansion coefficient and stress of composite materials, characterized in that, include: Data acquisition module: A local heat source moves along the sample surface and performs infrared imaging simultaneously to obtain a sequence of dynamic thermal field images of the sample. Data analysis module: Extracts the delay information of heat wave propagation to fixed observation point caused by heat source movement from the dynamic thermal field image sequence, and determines the overall thermal expansion coefficient of the sample based on the comparison of delay information and reference information; Stress distribution determination module: Extracts the steady-state temperature pattern formed by the moving heat source from the dynamic thermal field image sequence, and determines the stress distribution on the sample surface based on the local geometric distortion of the pattern relative to the reference pattern.

2. The composite material thermal expansion coefficient and stress measurement system according to claim 1, characterized in that, The data acquisition module uses a local heat source with a spatial size smaller than the sample surface to control the local heat source to scan and move along a straight trajectory on the sample surface at a constant speed, thereby exciting a transient heat wave in the sample whose propagation direction is related to the moving trajectory. As the local heat source begins to move, a synchronization trigger signal is sent to the infrared thermal imager to start the infrared thermal imager to continuously acquire images of the sample surface at a fixed acquisition frame rate. The system receives and stores multiple thermal field images output by the infrared thermal imager, arranged in time stamp order. Each thermal field image records the two-dimensional temperature distribution on the sample surface at the corresponding acquisition time. These thermal field images together constitute a dynamic thermal field image sequence characterizing the spatiotemporal evolution process of the transient thermal wave.

3. The composite material thermal expansion coefficient and stress measurement system according to claim 1, characterized in that, The data analysis module predefines a fixed observation point in the field of view of the sample surface corresponding to the dynamic thermal field image sequence. This observation point is located outside the movement trajectory of the local heat source. From the dynamic thermal field image sequence, the temperature change curve of the fixed observation point area over time is extracted. Based on the temperature change curve over time, the first moment when the thermal wave front propagates to the fixed observation point and causes its temperature to start to rise significantly is identified and determined. Based on the preset moving speed and starting position information of the local heat source, the second moment corresponding to the position on the moving path that is closest to the fixed observation point in terms of spatial vertical distance is calculated.

4. The composite material thermal expansion coefficient and stress measurement system according to claim 3, characterized in that, The difference between the first time point and the second time point is calculated to obtain the thermal wave delay time, which characterizes the time required for the thermal wave to propagate from the heat source location to the fixed observation point; Obtain the reference thermal wave delay time by measuring a standard sample under identical test conditions, wherein the coefficient of thermal expansion of the standard sample is known. Compare the thermal wave delay time with the reference thermal wave delay time and calculate the proportionality coefficient k between the two. The specific calculation formula is as follows: ； in, This indicates the thermal wave delay time of the test sample. Indicates the reference thermal wave delay time; Based on the proportionality coefficient k and the known thermal expansion coefficient of the standard sample, the overall thermal expansion coefficient of the sample is calculated using a conversion relationship. The specific calculation formula is as follows: ； in, The known coefficient of thermal expansion of the standard sample is represented by the conversion relationship established based on the following principle: under the exact same test conditions, for materials with similar structure and composition, the thermal wave delay time is proportional to the coefficient of thermal expansion of the material according to a preset principle.

5. The composite material thermal expansion coefficient and stress measurement system according to claim 1, characterized in that, The stress distribution determination module selects one or more characteristic moments from a sequence of dynamic thermal field images pre-acquired by excitation from a moving heat source and synchronous infrared imaging, at which the spatial morphology of the temperature field in the region behind the moving heat source reaches a relatively stable state. Extract the thermal field image corresponding to each of the aforementioned feature moments as an analytical temperature distribution pattern characterizing the current thermal conduction state of the material; for each analytical temperature distribution pattern, identify and extract the set of isotherms in the pattern. Simultaneously, a reference temperature distribution pattern corresponding to each of the aforementioned characteristic moments is obtained by measuring a reference sample of the same material in a stress-free state under identical test conditions, and the same geometric feature extraction operation is performed on the reference temperature distribution pattern. Subsequently, for each characteristic time, the isotherms in the temperature distribution pattern used for analysis are spatially registered with the isotherms or temperature gradient fields in the reference temperature distribution pattern at the corresponding time, and the local geometric distortion degree is quantitatively calculated.

6. The composite material thermal expansion coefficient and stress measurement system according to claim 5, characterized in that, The quantitative calculation of the degree of local geometric distortion includes the following steps: For each registered isotherm, a series of sampling points are selected along it, and the difference between the local curvature of the isotherm to be analyzed and the local curvature of the reference isotherm is calculated at each sampling point as the curvature deviation at that point. The curvature deviation of all sampling points is recorded according to their spatial location to form a quantitative dataset of the local geometric distortion of the entire field; Stress transformation is performed based on a quantitative mapping relationship model established in advance through calibration experiments. The model establishment process is as follows: for multiple calibration specimens with known stress states whose surface stress distribution has been accurately measured by independent methods, the aforementioned complete steps from selecting characteristic time to forming a quantitative dataset of local geometric distortion are performed respectively to obtain the quantitative dataset of each calibration specimen and its corresponding known stress distribution dataset. Then, a quantitative mathematical relationship between the quantified value of the local geometric distortion degree and the difference of the local principal stress is established by regression analysis to form a model; The quantitative dataset of the local geometric distortion of the test sample is input into the quantitative mapping relationship model to calculate the preliminary principal stress difference at each sampling point on the sample surface. The specific calculation logic is as follows: The quantized values ​​of each sampling point recorded in the local geometric distortion quantification dataset are read sequentially and input into the quantitative mapping relationship model. The model then completes a data query according to its internally defined fixed rules, and directly outputs a value corresponding to the input quantized value. This value is the preliminary principal stress difference at the sampling point. By traversing all sampling points and repeating this input and output process, a set of preliminary principal stress differences covering the entire sample analysis area and corresponding to the spatial positions of the sampling points can be obtained.

7. The composite material thermal expansion coefficient and stress measurement system according to claim 6, characterized in that, The quantitative dataset of the local geometric distortion of the test sample is input into the quantitative mapping relationship model to calculate the preliminary principal stress difference at each sampling point on the sample surface. Then, based on the theory of elasticity, a set of mathematical equations describing the stress distribution on the sample surface, including stress balance equations and stress compatibility equations, is constructed. The stress balance equations can be expressed as: ； in, ), Let x and y represent the normal stresses along the x and y directions at the sampling points (x and y), respectively. The stress compatibility equation, representing the shear stress at the sampling point (x, y) along the xy plane, can be expressed as: ； in, Indicates to Find the second partial derivative with respect to the y-coordinate. Indicates to Find the second partial derivative with respect to the x-coordinate; The initial principal stress difference is used as part of the input conditions of the equation system, and a complete solution constraint system is constructed by combining the known boundary conditions and the accurate stress measurement values ​​obtained at specific locations on the sample surface through auxiliary measurement methods.

8. The composite material thermal expansion coefficient and stress measurement system according to claim 7, characterized in that, Finally, the system of equations is numerically solved using an iterative optimization algorithm, specifically including: Based on the preliminary principal stress difference distribution calculated through the quantitative mapping relationship model, an initial hypothesis on the stress distribution on the sample surface is constructed. The known geometric boundary conditions of the specimen and the discrete, precise stress measurements obtained through auxiliary measurements are applied as constraints to the initial stress distribution assumption. Enter the iterative optimization loop and perform the following operations: calculate the degree to which the current stress distribution assumption satisfies the preset set of elasticity equations, and at the same time calculate the degree of difference between it and the preliminary principal stress difference distribution; Based on the calculation results of the degree of satisfaction and degree of difference, a new stress distribution hypothesis is generated through a numerical optimization algorithm. The new stress distribution hypothesis reduces the deviation from the mechanical equations while reducing the difference from the initial principal stress difference distribution, and strictly satisfies all the constraints. Determine whether the current iteration result meets the preset convergence criteria: including the change in stress distribution between the two iterations being less than the set threshold, and the degree of deviation of the current stress distribution from the mechanical equation set and the degree of difference from the initial principal stress difference distribution both meeting the preset accuracy requirements; If the convergence criterion is not met, the current assumption is replaced with the new stress distribution assumption, and the calculation step is returned to continue the next iteration. If the convergence criterion is met, the iteration is terminated, and the current stress distribution assumption is output as the final converged two-dimensional stress distribution map of the sample surface.

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