Composite material stress distribution analysis method and system and storage medium

Abstract

The invention discloses a composite material stress distribution analysis method and system and a storage medium. The method comprises the following steps: acquiring a defect detection image obtained by computed tomography or infrared thermal imaging; identifying a defect area in the composite material structure and extracting defect information; according to the defect information and the loading direction, calculating a defect-induced stress enhancement factor and a stress transfer function, and constructing a stress correction factor distribution diagram; establishing a reference finite element model ignoring defects, and calculating a reference stress distribution field; acting the correction factor distribution diagram on the reference stress field to generate a correction stress distribution diagram; combining defect identification errors and material statistical fluctuation to construct an uncertainty propagation model, quantifying the corrected stress diagram, and outputting a stress value interval map; and finally, jointly correcting the stress map and the interval map, and identifying a high-risk area.

Description

Technical Field

[0001] This application relates to the field of materials technology, and in particular to a method, system and storage medium for stress distribution analysis of composite materials. Background Technology

[0002] Composite materials, as engineering materials with excellent specific strength and specific stiffness, are widely used in aerospace, rail transportation, automobile manufacturing, and wind turbine blades. Due to their typically laminated and heterogeneous internal structures, they are prone to various defects during manufacturing or service, such as porosity, inclusions, delamination, and cracks. These defects can affect the stress distribution and mechanical properties of composite structures, thereby impacting their service life and structural safety.

[0003] Currently, the finite element method is widely used in engineering to perform stress analysis on composite material structures to assess their stress state. However, the presence of potential defects in composite materials and the local fluctuations in material properties can pose challenges to the accuracy of stress analysis. Furthermore, in practical engineering applications, defects are typically obtained through methods such as computed tomography, infrared thermography, or ultrasonic testing, and the obtained data requires further processing to extract effective structural information for analysis.

[0004] In some practical applications, it is also necessary to consider the impact of errors in the defect identification process and uncertainties in material parameters on the structural response prediction results. Therefore, how to reasonably handle these uncertainties in the stress analysis process is also a technical problem faced in the evaluation of composite material structures. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, system, and storage medium for analyzing stress distribution in composite materials.

[0006] The technical solution provided in this application is described below: The first aspect of this application provides a method for stress distribution analysis of composite materials, the method comprising: S1. Obtain defect detection images of the composite material structure to be tested, wherein the defect detection images include structural images obtained by computed tomography and infrared thermal imaging. S2. Perform image processing on the defect detection image to identify the defect region in the composite material structure to be tested, and extract the defect information in the defect region; S3. Based on the defect information and the load direction of the composite material structure under test, calculate the defect-induced stress enhancement factor and the stress transfer function used to describe the range of defect influence, and construct a stress correction factor distribution map of the range of defect influence based on the defect-induced stress enhancement factor and the stress transfer function. S4. Based on the geometric data and boundary conditions of the composite material structure to be tested, establish a corresponding reference finite element model, and calculate the corresponding reference stress distribution field under the condition of ignoring defects. S5. Apply the stress correction factor distribution map to the reference stress distribution field to perform local stress correction on the defect influence range, and obtain a corrected stress distribution map that includes the defect influence. S6. Based on the identification error of the defect information and the statistical fluctuation information of the composite material structure to be tested, an uncertainty propagation model is constructed, and the uncertainty of the modified stress distribution map is quantified by the uncertainty model, and a stress value interval map containing confidence information is output. S7. Perform joint analysis on the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level.

[0007] Optionally, constructing a stress correction factor distribution map of the defect influence range based on the defect-induced stress enhancement factor and the stress transfer function includes: The defect-induced stress enhancement factor and the stress transfer function are weighted and fused to form a local perturbation map of the impact of the defect on the adjacent area; Within the defect area, the local disturbance maps of multiple defects are synthesized by spatial superposition to obtain a stress correction factor distribution map that reflects the range of defect influence.

[0008] Optionally, the step of establishing a corresponding reference finite element model based on the geometric data and boundary conditions of the composite material structure under test, and calculating the corresponding reference stress distribution field under the condition of neglecting defects, includes: Based on the geometric data of the composite material structure to be tested, a three-dimensional geometric model is constructed using computer-aided design software. The three-dimensional geometric model includes the laminate structure, thickness distribution, and edge shape characteristics of the composite material structure to be tested. Based on the three-dimensional geometric model, boundary conditions are set and external loads are applied. The boundary conditions include constraint nodes, fixed surfaces, and contact surfaces. The external loads can be one or more of concentrated forces, distributed forces, thermal loads, or vibration loads. The three-dimensional geometric model is divided into finite element meshes to obtain a finite element model containing multiple elements; Each element in the finite element model is assigned corresponding anisotropic material properties, including elastic modulus, Poisson's ratio, shear modulus and lamination angle distribution, and the influence of defect regions on material continuity is ignored during the assignment process. Based on the finite element model, the stress field of the composite material structure under test is calculated using the finite element numerical solution method under the boundary conditions and external loads to obtain the reference stress distribution field without considering the existence of defects.

[0009] Optionally, the stress transfer function is constructed using a defect boundary normal attenuation model, and the construction method includes: The boundary curve of each defect region is extracted into a set of boundary points, and an attenuation path is constructed along the normal direction at each boundary point. The stress transfer coefficient on the attenuation path is calculated according to a preset attenuation function, which includes one or more of an exponential attenuation function or a Gaussian distribution function.

[0010] Optionally, applying the stress correction factor distribution map to the reference stress distribution field to perform local stress correction on the defect influence range, thereby obtaining a corrected stress distribution map that includes the defect influence, includes: Based on the numerical results of the reference stress distribution field, and combined with the stress correction factor distribution map within the defect influence range, the corresponding reference stress value and the corresponding correction factor are extracted for each unit or discrete calculation point. The correction factor and the reference stress value are coupled and calculated using a point-by-point multiplication method to obtain the locally corrected correction stress value. The corrected stress values ​​at each calculation point are reassembled to form a complete corrected stress distribution field, which reflects the stress field disturbance induced by defects in the composite material structure under test. Based on the stress disturbance field, a modified stress distribution map with spatial resolution is generated. The modified stress distribution map is used to reflect the spatial influence characteristics of the stress disturbance induced by defects on the overall stress state.

[0011] Optionally, based on the identification error of the defect information and the statistical fluctuation information of the composite material structure under test, an uncertainty propagation model is constructed, and the uncertainty of the modified stress distribution map is quantified through the uncertainty model, outputting a stress value interval spectrum containing confidence information, including: The geometric identification error in the defect information is modeled as a random variable that follows a specific distribution, and the statistical fluctuation of the material parameters of the composite material structure under test is represented as an interval parameter with a mean and a standard deviation. The specific distribution includes either a normal distribution or a triangular distribution. Using the random variables and interval parameters as input variables, an uncertainty propagation model for stress result uncertainty propagation is constructed; The modified stress distribution map is subjected to multiple perturbation simulations using the uncertainty propagation model. Stress response samples are collected at each spatial point, and the upper and lower limits of the stress value under the confidence level are calculated based on the stress response samples to form a stress interval map containing confidence information. The stress interval map is used to reflect the stress response variability under the combined influence of defect information and material uncertainty.

[0012] Optionally, the joint analysis of the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level includes: For each spatial location in the modified stress distribution map, the corresponding modified stress value, the lower limit of the confidence interval in the stress value interval spectrum, and the confidence index are extracted. The stress threshold and confidence threshold for the pre-set structural safety assessment are defined as follows: the stress threshold is the local ultimate bearing standard of the structure under the target load condition, and the confidence threshold is the lowest acceptable level of confidence in a statistical sense. The corrected stress value and the lower limit of the confidence interval are compared with the stress threshold respectively. If either value exceeds the stress threshold and the corresponding confidence index is lower than the confidence threshold, the corresponding spatial location is marked as a high-risk area point. All high-risk areas are clustered into continuous regions to form high-risk areas.

[0013] A second aspect of this application provides a system for stress distribution analysis of composite materials, the system comprising: An image acquisition unit is used to acquire defect detection images of the composite material structure to be tested, the defect detection images including structural images obtained by computed tomography and infrared thermal imaging. The defect identification unit is used to perform image processing on the defect detection image, identify the defect region in the composite material structure under test, and extract the defect information in the defect region. The first processing unit is used to calculate the defect-induced stress enhancement factor and the stress transfer function for describing the range of defect influence based on the defect information and the load direction of the composite material structure under test, and to construct a stress correction factor distribution map of the range of defect influence based on the defect-induced stress enhancement factor and the stress transfer function. The second processing unit is used to establish a corresponding reference finite element model based on the geometric data and boundary conditions of the composite material structure to be tested, and to calculate the corresponding reference stress distribution field under the condition of ignoring defects. The third processing unit is used to apply the stress correction factor distribution map to the reference stress distribution field, perform local stress correction on the defect influence range, and obtain a corrected stress distribution map that includes the defect influence. An uncertainty processing unit is used to construct an uncertainty propagation model based on the identification error of the defect information and the statistical fluctuation information of the composite material structure to be tested, and to perform uncertainty quantification on the modified stress distribution map through the uncertainty model, and output a stress value interval map containing confidence information. The joint analysis unit is used to perform joint analysis on the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level.

[0014] A third aspect of this application provides a system for stress distribution analysis of composite materials, the system comprising: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to execute the first aspect and any one of the optional methods in the first aspect.

[0015] A fourth aspect of this application provides a computer-readable storage medium on which a program is stored, which, when executed on a computer, performs the methods of the first aspect and any one of the first aspects.

[0016] As can be seen from the above technical solutions, this application has the following beneficial effects: 1. By extracting the location, type and size of defects through various non-destructive testing images (including CT scans and infrared thermography), a comprehensive identification of actual defects inside composite materials can be achieved, avoiding the accumulation of errors caused by ignoring defects or making idealized assumptions in traditional modeling. 2. By combining the defect-induced stress enhancement factor with the stress transfer function, a local perturbation map is constructed and a stress correction factor distribution map is synthesized to achieve quantitative correction of the stress distribution in the defect-affected area and improve the local accuracy of stress field calculation. 3. Based on the consideration of defect extraction error and material property fluctuation, an uncertainty propagation model is constructed to output the confidence interval of the modified stress distribution map, so that the stress analysis results have statistical reliability and provide quantitative support for engineering tolerance analysis and life prediction. 4. By combining the analysis of the modified stress diagram and the stress value range spectrum, areas where stress exceeds the limit and confidence is insufficient are identified, and potential failure risk points are accurately screened out, thereby improving the scientificity and efficiency of structural health assessment and operation and maintenance decisions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0018] Figure 1 This is a schematic flowchart of an embodiment of a method for analyzing stress distribution in composite materials provided in this application; Figure 2 This is a schematic flowchart illustrating an implementation method of step S4 in a method for analyzing stress distribution in composite materials provided in this application. Figure 3 This is a schematic flowchart illustrating one implementation of step S5 in a method for analyzing stress distribution in composite materials provided in this application. Figure 4 This is a schematic flowchart illustrating one implementation method of step S7 in a method for analyzing stress distribution in composite materials provided in this application. Figure 5 This is a schematic diagram of an embodiment of a system for analyzing stress distribution in composite materials provided in this application; Figure 6 This is a schematic diagram of an embodiment of another system for analyzing stress distribution in composite materials provided in this application. Detailed Implementation

[0019] Please see Figure 1 This application first provides an embodiment of a method for analyzing stress distribution in composite materials, the embodiment including: S1. Obtain defect detection images of the composite material structure to be tested, wherein the defect detection images include structural images obtained by computed tomography and infrared thermal imaging. The following detailed description, in conjunction with specific embodiments, illustrates a method for analyzing the stress distribution of composite materials according to the present invention. It should be understood that these embodiments are merely illustrative of the technical solutions of the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0020] In this embodiment, the method is mainly applied to accurately analyze the stress distribution of a carbon fiber reinforced composite structural component (such as an aerospace skin segment or a wind turbine blade segment) under service conditions, and specifically includes the following steps: First, defect detection images of the composite material structure under test are acquired. Industrial CT scanning equipment is used to perform layered imaging of the structural components, obtaining their three-dimensional internal structural data. Simultaneously, infrared thermal imaging technology is employed to record thermal response images of the component's surface and near-surface areas under heating or loading conditions. The acquired structural images are then registered and denoised to form a unified format defect detection image set, which serves as input data for subsequent processing.

[0021] S2. Perform image processing on the defect detection image to identify the defect region in the composite material structure to be tested, and extract the defect information in the defect region; Image processing was performed on the aforementioned defect detection images to identify various defect regions in the composite material structure. The image processing included histogram equalization, adaptive threshold segmentation, wavelet edge detection, and connected component labeling. For each identified defect region, its spatial location, geometric dimensions (e.g., length, width, depth), and defect type (e.g., pores, delamination, cracks, inclusions) were further extracted. This defect information was recorded in a structured form for subsequent calculation of the stress correction factor.

[0022] S3. Based on the defect information and the load direction of the composite material structure under test, calculate the defect-induced stress enhancement factor and the stress transfer function used to describe the range of defect influence, and construct a stress correction factor distribution map of the range of defect influence based on the defect-induced stress enhancement factor and the stress transfer function. Based on the extracted defect information and the main load-bearing directions of the composite material structure under test, the defect-induced stress enhancement factor is calculated. This enhancement factor is determined according to the defect type and its orientation relative to the load direction. For example, when crack-like defects are arranged along the principal stress direction, their stress enhancement factor is significantly higher than that of perpendicularly arranged defects. Simultaneously, a stress transfer function is constructed based on the geometric parameters of the defects. This function characterizes the spatial diffusion effect of defects on the stress state of surrounding material elements. Based on this, local stress perturbation maps are generated for all defects, and these maps are combined into a stress correction factor distribution map covering the entire structural region through spatial coordinate mapping and weighted superposition. The perturbation quantities within the overlapping regions are fused using a Gaussian weighted average to ensure the continuity and physical consistency of the perturbation field.

[0023] The stress transfer function is constructed using a defect boundary normal attenuation model, and the construction method includes: The boundary curve of each defect region is extracted into a set of boundary points, and an attenuation path is constructed along the normal direction at each boundary point. The stress transfer coefficient on the attenuation path is calculated according to a preset attenuation function, which includes one or more of an exponential attenuation function or a Gaussian distribution function.

[0024] In one specific implementation, the stress correction factor distribution map of the defect influence range constructed in step S3 based on the defect-induced stress enhancement factor and the stress transfer function can be achieved in the following manner: The defect-induced stress enhancement factor and the stress transfer function are weighted and fused to form a local perturbation map of the impact of the defect on the adjacent area; Within the defect area, the local disturbance maps of multiple defects are synthesized by spatial superposition to obtain a stress correction factor distribution map that reflects the range of defect influence.

[0025] For each identified defect, based on its defect type (such as cracks, pores, inclusions, debonding, etc.), spatial location, geometric dimensions, and orientation relative to the principal load direction, a corresponding defect-induced stress enhancement factor is determined. This factor is used to characterize the degree of local stress concentration at the defect point and can be a real number greater than 1.

[0026] Based on defect type and material response characteristics, a stress transfer function is constructed to characterize the spatial attenuation law of the stress field in the vicinity of a defect. In its implementation, this transfer function can be constructed using any of the following models: exponential decay model (representing that the defect influence decreases exponentially with distance); Gaussian kernel function model (suitable for smooth fusion of influences among multiple defects); and empirical fitting model (fitting different defect types using material experimental data). Defect-induced stress enhancement factor With stress transfer function Weighted fusion is performed to generate a local perturbation map with the defect center as the core and the spatial neighborhood as the support. The fusion method can take the following forms: .

[0027] Where R(x,y,z) represents the stress correction factor value at the structural coordinate point (x,y,z). A value greater than 1 indicates stress enhancement, and a value less than 1 indicates stress reduction.

[0028] For cases involving multiple defects, local perturbation maps for each defect are generated sequentially and then spatially superimposed within their respective areas of influence. Specifically, the superposition method can take any of the following forms: Weighted average method: For spatially overlapping regions, a weighted average is performed using the Gaussian kernel weights of each defect perturbation map to ensure perturbation continuity; Maximum disturbance criterion: For overlapping areas, take the maximum disturbance factor value from all disturbance maps to cover the most unfavorable operating conditions; Decreasing combination method: Introduce a disturbance attenuation coefficient and iteratively merge the defect disturbance maps one by one to avoid distortion accumulation.

[0029] Finally, the local perturbation maps of all the aforementioned defects are mapped and synthesized in three dimensions within the structural coordinate system to obtain a stress correction factor distribution map covering the entire analysis region. This distribution map is structured in units of mesh nodes, with each node associated with a corresponding correction factor value, forming a structured data layer that can be directly input into subsequent finite element solutions or image analysis systems.

[0030] The stress correction factor distribution map generated by this method can accurately and precisely reflect the disturbance effects of various defects on the local and global stress fields, providing high-quality input for subsequent construction of corrected stress maps and uncertainty analysis.

[0031] S4. Based on the geometric data and boundary conditions of the composite material structure to be tested, establish a corresponding reference finite element model, and calculate the corresponding reference stress distribution field under the condition of neglecting defects. A baseline finite element model of the composite material structure under defect-free conditions was established. Based on the actual geometry of the structure and known boundary constraints (e.g., fixed boundaries, concentrated loads, uniformly distributed surface forces), the model was constructed in finite element analysis software, and material property parameters and laminated structure information were set. By applying loads to the model and performing static solutions, the stress distribution field of the structure under ideal conditions was obtained, serving as the baseline stress spectrum.

[0032] See Figure 2 In a specific implementation, step S4 can be implemented as follows: S41. Based on the geometric data of the composite material structure to be tested, a three-dimensional geometric model is constructed using computer-aided design software. The three-dimensional geometric model includes the laminated structure, thickness distribution, and edge shape characteristics of the composite material structure to be tested. In this step, a three-dimensional geometric model is constructed in a computer-aided design (CAD) platform based on the geometric data of the composite material structure under test. The geometric data includes structural outline dimensions, thickness distribution, laminate configuration, and edge boundary shapes. For structures composed of multiple layers of composite materials, such as honeycomb sandwich panels or carbon fiber laminates, a layer-by-layer modeling approach is used to ensure that the thickness, material orientation, and layup angle of each layer are accurately represented in the geometric model. High-precision feature extraction algorithms are employed for areas with geometric features such as bends, cavities, and chamfers to preserve key geometric details.

[0033] S42. Based on the three-dimensional geometric model, set boundary conditions and apply external loads. The boundary conditions include constraint nodes, fixed surfaces, and contact surfaces. The external loads can be one or more of concentrated forces, distributed forces, thermal loads, or vibration loads. After completing the 3D geometric model, boundary conditions and loading methods are set based on actual application conditions. These boundary conditions include fixed boundaries (e.g., constraining all degrees of freedom on a single surface), sliding boundaries (e.g., constraining normal displacement at nodes), and contact boundaries (e.g., defining contact surfaces between multiple components). External loads can be selected according to the structural application scenario, including concentrated forces, surface-distributed loads, thermal loads, gravity loads, vibration excitation, etc., and multiple loads can be applied simultaneously if necessary. Under complex loads, multi-condition simulations of load path changes over time can be considered to improve model realism.

[0034] S43. Divide the three-dimensional geometric model into a finite element mesh to obtain a finite element model containing multiple elements; In this step, the 3D geometric model is meshed using finite element preprocessing tools. Specifically, volumetric elements (such as hexahedral and tetrahedral elements) are used to mesh the structural entity. For areas with significant thickness variations or local structural details, local mesh refinement is employed to improve simulation accuracy. The mesh quality must meet engineering standards such as element twist rate, surface orthogonality, and element aspect ratio to avoid introducing errors into the calculation. The entire mesh model forms multiple structural elements and nodes, constituting the solution carrier.

[0035] S44. Assign corresponding anisotropic material properties to each element in the finite element model. The anisotropic material properties include elastic modulus, Poisson's ratio, shear modulus and lamination angle distribution. The influence of defect regions on material continuity is ignored during the assignment process. For each element in the mesh model, corresponding anisotropic material properties are assigned based on its location and material lamination relationship. These properties include parameters such as the elastic modulus, shear modulus, Poisson's ratio, and lamination angle of each layer. During the assignment process, potential defect areas in the structure are ignored; that is, each material property is allocated according to an ideal continuous medium, serving as a reference state under "defect-free" conditions. For different layup levels, local coordinate system mapping is used to align the material orientation with the structural orientation, ensuring accurate transmission of mechanical properties.

[0036] S44. Based on the finite element model, the stress field of the composite material structure under test is calculated using the finite element numerical solution method under the boundary conditions and external loads to obtain the reference stress distribution field without considering the existence of defects.

[0037] After material assignment, a finite element method (FEM) solution (such as ANSYS, ABAQUS, MSC.Nastran, etc.) is used to perform static analysis on the model. Under the set boundary conditions and external loads, the stress distribution of each element or node within the composite material structure is calculated. Linear or nonlinear static analysis modules are used during the solution process, and solution parameters are set according to material properties and loading paths. The calculation output includes equivalent stress distribution diagrams, principal stress direction diagrams, and nodal displacement fields. The stress distribution diagrams described above serve as the baseline stress field diagrams under the defect-free assumption, providing fundamental reference data for subsequent corrected stress calculations.

[0038] This approach enables the creation of composite material finite element models with accurate physical properties, realistic stress boundaries, and excellent mesh quality, providing a stable and reliable benchmark calculation platform for subsequent defect disturbance analysis and uncertainty quantification.

[0039] S5. Apply the stress correction factor distribution map to the reference stress distribution field to perform local stress correction on the defect influence range, and obtain a corrected stress distribution map that includes the defect influence. Based on this, the stress correction factor distribution map is applied to the reference stress distribution field. For each mesh node or element within the defect influence range, its reference stress value is adjusted according to the correction factor at the corresponding location, specifically as follows:

[0040] Where x, y, and z represent the coordinates of a grid node. Indicates corrected stress, Indicates the reference stress. This represents the stress correction factor.

[0041] After the above operations are completed, a corrected stress distribution map containing the effects of defect disturbances is obtained, which reflects the stress state of the structure under the action of actual defects.

[0042] See Figure 3 In one optional embodiment, step S5 may be implemented as follows: S51. Based on the numerical results of the reference stress distribution field, and combined with the stress correction factor distribution map within the defect influence range, extract the corresponding reference stress value and the corresponding correction factor for each unit or discrete calculation point. Based on the numerical output of the reference stress distribution field obtained in step S4, a traversal operation is performed on all finite element elements or discrete mesh calculation points in the model. At each element or calculation node, its corresponding reference stress value is extracted. This value can be equivalent stress (von Mises), principal stress, shear stress, or other user-defined stress parameters. Simultaneously, based on the geometric position of the calculation point in the structural space, the correction factor value corresponding to it in the stress correction factor distribution map generated in step S3 is queried, forming a one-to-one mapping relationship.

[0043] S52. Using a point-by-point multiplication method, the correction factor and the reference stress value are coupled and calculated to obtain the locally corrected correction stress value. The obtained reference stress value and correction factor are coupled and calculated, and local stress correction is performed using a point-by-point multiplication method. Specifically, for each calculation point, let its reference stress be σ0 and its correction factor be α. Then, its corrected stress value σ′ satisfies the following relationship: ; This calculation method enables the quantitative superposition of defect effects, allowing different defects to exert stress disturbances of varying magnitudes on the surrounding area. For regions with overlapping defects, the effects of each source disturbance have been comprehensively considered through a previously synthesized superposition correction factor α, thus eliminating the need for further adjustment.

[0044] S53. Reassemble the corrected stress values ​​of each calculation point to form a complete corrected stress distribution field, wherein the corrected stress distribution field reflects the stress field disturbance induced by defects in the composite material structure under test. The corrected stress values ​​of all calculation points are reassembled according to their spatial distribution order in the finite element model to construct a complete corrected stress distribution field. This corrected stress field retains the structural topology and boundary conditions of the original reference model, while introducing local stress perturbation information based on defect interference, thus more realistically reflecting the stress state of the composite material structure under defect conditions.

[0045] S54. Based on the stress disturbance field, a modified stress distribution map with spatial resolution is generated. The modified stress distribution map is used to reflect the spatial influence characteristics of the stress disturbance induced by the defect on the overall stress state.

[0046] Based on the aforementioned corrected stress distribution field, a spatially resolved graphical representation is generated, forming a corrected stress distribution map. This map supports various formats, including node coloring, contour plots, and heatmaps, visually representing the spatial variation trend of stress values ​​within the structural range, particularly highlighting the increase in stress levels or the redistribution of stress distribution within the defect's influence area. This map can be used for subsequent uncertainty propagation analysis as well as to assist in manual inspection and structural risk identification.

[0047] Through the above steps, it is possible to effectively map the stress state from the ideal stress distribution to the stress state under the interference of actual structural defects, providing quantitative support for the fatigue assessment, strength analysis and life prediction of composite material structures.

[0048] S6. Based on the identification error of the defect information and the statistical fluctuation information of the composite material structure to be tested, an uncertainty propagation model is constructed, and the uncertainty of the modified stress distribution map is quantified by the uncertainty model, and a stress value interval map containing confidence information is output. To account for measurement errors in the defect identification process and the statistical fluctuations of composite material constitutive parameters, an uncertainty propagation model is constructed. Specifically, parameters such as defect size and location are set as Gaussian distributed variables, and properties such as material elastic modulus are set as confidence interval parameters. Multiple sample inputs are generated through Monte Carlo sampling or the Latin hypersolution method, and their corresponding corrected stress maps are calculated. Finally, the confidence interval of the stress output at each spatial location is statistically analyzed to form a stress value interval map containing confidence information.

[0049] In one optional embodiment, step S6 can be implemented as follows: The geometric identification error in the defect information is modeled as a random variable that follows a specific distribution, and the statistical fluctuation of the material parameters of the composite material structure under test is represented as an interval parameter with a mean and a standard deviation. The specific distribution includes either a normal distribution or a triangular distribution. Using the random variables and interval parameters as input variables, an uncertainty propagation model for stress result uncertainty propagation is constructed; The modified stress distribution map is subjected to multiple perturbation simulations using the uncertainty propagation model. Stress response samples are collected at each spatial point, and the upper and lower limits of the stress value under the confidence level are calculated based on the stress response samples to form a stress interval map containing confidence information. The stress interval map is used to reflect the stress response variability under the combined influence of defect information and material uncertainty.

[0050] In this embodiment, the defect information identified in step S2, including the defect's location, size (such as crack length, hole diameter), and orientation angle, is modeled as a random variable following a specific probability distribution, taking into account detection accuracy and imaging noise. In a preferred implementation, this distribution can be set as a Gaussian distribution or a triangular distribution. The Gaussian distribution is suitable for cases where the measurement error is a random disturbance, while the triangular distribution is suitable for cases where the detection error has a maximum limit but lacks sufficient statistical samples.

[0051] Meanwhile, for the material property parameters of composite materials, such as elastic modulus, shear modulus, and Poisson's ratio, we consider the statistical fluctuations caused by factors such as batch differences in the process and uneven fiber distribution. We model them as interval variables with expected value (mean) and standard deviation, which can be further sampled as random variables for simulation.

[0052] Using the random variables (defect error) and interval parameters (material fluctuations) obtained from the above modeling as input variables, an uncertainty propagation model is constructed. This model is used to characterize the sensitivity propagation path of input parameter disturbances to the corrected stress results.

[0053] Using the uncertainty propagation model described above, multiple random perturbation simulations are performed on the corrected stress distribution map obtained in step S5. Each perturbation simulation corresponds to a set of possible combinations of defects and material properties, resulting in a set of stress response results for the entire structural region. By accumulating multiple perturbation samples, the stress response distribution corresponding to each spatial calculation point in the structure is formed.

[0054] After completing all perturbation simulations, a set of stress response samples is extracted for each spatial calculation point. Based on a set statistical confidence level (e.g., 90%, 95%), the upper and lower limits of the stress value at that point are calculated, resulting in a set of stress confidence interval maps with interval constraints. This map not only provides the stress estimate for each point but also reflects its corresponding fluctuation range or uncertainty, thereby achieving a quantitative assessment of the uncertainty characteristics of the stress field of the entire structure.

[0055] S7. Perform joint analysis on the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level.

[0056] The obtained modified stress distribution map and stress value interval map are jointly analyzed. An upper stress threshold (e.g., 80% of yield strength) and a lower confidence level (e.g., 90%) are set, and spatial regions within the entire structure that simultaneously meet the criteria of "stress exceeding the threshold" and "confidence level below the lower limit" are identified. These regions are potential high-risk areas and can be used as a basis for subsequent structural maintenance, local reinforcement design, or service life assessment decisions.

[0057] Through the above steps, this embodiment not only achieves a precise modeling of the stress distribution of composite material structures under actual defect conditions, but also introduces an uncertainty quantification and risk identification mechanism, which significantly improves the accuracy of structural safety assessment and its engineering practical value.

[0058] See Figure 4 In one optional embodiment, step S7 can be implemented as follows: S71. For each spatial location in the modified stress distribution map, extract the corresponding modified stress value and the lower limit of the confidence interval and the confidence index in the stress value interval spectrum. For each discrete spatial location point in the corrected stress distribution map obtained in step S5, the corrected stress value at that point is extracted. Simultaneously, the lower limit of the confidence interval corresponding to that point and its associated confidence index are extracted from the stress value interval spectrum generated in step S6. The lower limit of the confidence interval is used to characterize the most conservative estimate of stress at a set confidence level, and the confidence index is used to quantify the reliability of this estimate.

[0059] S72. Preset stress threshold and confidence threshold for structural safety assessment, wherein the stress threshold is the local ultimate bearing standard of the structure under the target load condition, and the confidence threshold is the lowest acceptable level of credible judgment in a statistical sense; Two types of structural safety assessment standards are pre-defined: Stress threshold: This refers to the ultimate stress value that a local area of ​​a composite material can withstand under the target working condition. It can be determined based on experimental data, material handbooks, or engineering design standards. Confidence threshold: This represents the lowest level of confidence that is statistically acceptable, such as 0.90 or 0.95. It means that when the confidence of the stress results in a certain area is lower than this value, it is considered an unacceptable uncertainty.

[0060] S73. Compare the corrected stress value and the lower limit of the confidence interval with the stress threshold respectively. If either value exceeds the stress threshold and the corresponding confidence index is lower than the confidence threshold, then mark the corresponding spatial location as a high-risk area point. Based on the above data, the following decision logic is executed point by point: If the corrected stress value at a spatial point is greater than the stress threshold, or if the lower limit of its confidence interval is greater than the stress threshold, it indicates that there is a risk of overload at that point. Meanwhile, if the confidence index at this point is less than the preset confidence threshold, it indicates that the stress result at this point is not reliable enough. If both of the above conditions are met, the point is marked as a high-risk area, indicating that it has a potential structural failure risk with low reliability.

[0061] S74. Cluster all high-risk areas into continuous regions to form high-risk areas.

[0062] All spatial locations that meet the high-risk criteria are used as input data, and spatial clustering algorithms (such as density clustering DBSCAN, connected component labeling, eight-neighbor merging, etc.) are executed to identify whether they constitute a continuous distribution area in space.

[0063] One or more groups of spatially continuous regions formed after clustering are defined as high-risk areas, which can be used for downstream engineering decisions such as structural health diagnosis, remaining life prediction, or maintenance priority ranking.

[0064] This method not only enables the identification of stress extreme value regions, but also comprehensively judges the reliability of the results by combining confidence index, effectively avoiding misjudgments caused by "artificial high stress" or "false negative regions", and enhancing the ability to identify risks of composite material structures under complex defects and uncertain conditions.

[0065] Please see Figure 5 This application also provides a system for analyzing stress distribution in composite materials, the system comprising: Image acquisition unit 501 is used to acquire defect detection images of the composite material structure to be tested, the defect detection images including structural images obtained by computed tomography and infrared thermal imaging. The defect identification unit 502 is used to perform image processing on the defect detection image, identify the defect region in the composite material structure to be tested, and extract the defect information in the defect region. The first processing unit 503 is used to calculate the defect-induced stress enhancement factor and the stress transfer function for describing the range of defect influence based on the defect information and the load direction of the composite material structure to be tested, and to construct a stress correction factor distribution map of the range of defect influence based on the defect-induced stress enhancement factor and the stress transfer function. The second processing unit 504 is used to establish a corresponding reference finite element model based on the geometric data and boundary conditions of the composite material structure to be tested, and to calculate the corresponding reference stress distribution field under the condition of ignoring defects. The third processing unit 505 is used to apply the stress correction factor distribution map to the reference stress distribution field, perform local stress correction on the defect influence range, and obtain a corrected stress distribution map that includes the defect influence. Uncertainty processing unit 506 is used to construct an uncertainty propagation model based on the identification error of the defect information and the statistical fluctuation information of the composite material structure to be tested, and to perform uncertainty quantification on the modified stress distribution map through the uncertainty model, and output a stress value interval map containing confidence information. The joint analysis unit 507 is used to perform joint analysis on the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level.

[0066] Optionally, the first processing unit 503 is specifically used for: The defect-induced stress enhancement factor and the stress transfer function are weighted and fused to form a local perturbation map of the impact of the defect on the adjacent area; Within the defect area, the local disturbance maps of multiple defects are synthesized by spatial superposition to obtain a stress correction factor distribution map that reflects the range of defect influence.

[0067] Optionally, the second processing unit 504 is specifically used for: Based on the geometric data of the composite material structure to be tested, a three-dimensional geometric model is constructed using computer-aided design software. The three-dimensional geometric model includes the laminate structure, thickness distribution, and edge shape characteristics of the composite material structure to be tested. Based on the three-dimensional geometric model, boundary conditions are set and external loads are applied. The boundary conditions include constraint nodes, fixed surfaces, and contact surfaces. The external loads can be one or more of concentrated forces, distributed forces, thermal loads, or vibration loads. The three-dimensional geometric model is divided into finite element meshes to obtain a finite element model containing multiple elements; Each element in the finite element model is assigned corresponding anisotropic material properties, including elastic modulus, Poisson's ratio, shear modulus and lamination angle distribution, and the influence of defect regions on material continuity is ignored during the assignment process. Based on the finite element model, the stress field of the composite material structure under test is calculated using the finite element numerical solution method under the boundary conditions and external loads to obtain the reference stress distribution field without considering the existence of defects.

[0068] Optionally, the third processing unit 505 is specifically used for: Based on the numerical results of the reference stress distribution field, and combined with the stress correction factor distribution map within the defect influence range, the corresponding reference stress value and the corresponding correction factor are extracted for each unit or discrete calculation point. The correction factor and the reference stress value are coupled and calculated using a point-by-point multiplication method to obtain the locally corrected correction stress value. The corrected stress values ​​at each calculation point are reassembled to form a complete corrected stress distribution field, which reflects the stress field disturbance induced by defects in the composite material structure under test. Based on the stress disturbance field, a modified stress distribution map with spatial resolution is generated. The modified stress distribution map is used to reflect the spatial influence characteristics of the stress disturbance induced by defects on the overall stress state.

[0069] Optionally, the third processing unit 506 is specifically used for: The geometric identification error in the defect information is modeled as a random variable that follows a specific distribution, and the statistical fluctuation of the material parameters of the composite material structure under test is represented as an interval parameter with a mean and a standard deviation. The specific distribution includes either a normal distribution or a triangular distribution. Using the random variables and interval parameters as input variables, an uncertainty propagation model for stress result uncertainty propagation is constructed; The modified stress distribution map is subjected to multiple perturbation simulations using the uncertainty propagation model. Stress response samples are collected at each spatial point, and the upper and lower limits of the stress value under the confidence level are calculated based on the stress response samples to form a stress interval map containing confidence information. The stress interval map is used to reflect the stress response variability under the combined influence of defect information and material uncertainty.

[0070] Optionally, the uncertainty processing unit 507 is specifically used for: For each spatial location in the modified stress distribution map, the corresponding modified stress value, the lower limit of the confidence interval in the stress value interval spectrum, and the confidence index are extracted. The stress threshold and confidence threshold for the pre-set structural safety assessment are defined as follows: the stress threshold is the local ultimate bearing standard of the structure under the target load condition, and the confidence threshold is the lowest acceptable level of confidence in a statistical sense. The corrected stress value and the lower limit of the confidence interval are compared with the stress threshold respectively. If either value exceeds the stress threshold and the corresponding confidence index is lower than the confidence threshold, the corresponding spatial location is marked as a high-risk area point. All high-risk areas are clustered into continuous regions to form high-risk areas.

[0071] Please see Figure 6 This application also provides a system for analyzing stress distribution in composite materials, comprising: Processor 601, memory 602, input / output unit 603, bus 604; The processor 601 is connected to the memory 602, the input / output unit 603, and the bus 604; The memory 602 stores a program, and the processor 601 calls the program to execute any of the methods described above.

[0072] This application also relates to a computer-readable storage medium on which a program is stored, which, when run on a computer, causes the computer to perform any of the methods described above.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for stress distribution analysis of composite materials, characterized in that, The method includes: S1. Obtain defect detection images of the composite material structure to be tested, wherein the defect detection images include structural images obtained by computed tomography and infrared thermal imaging. S2. Perform image processing on the defect detection image to identify the defect region in the composite material structure to be tested, and extract the defect information in the defect region; S3. Based on the defect information and the load direction of the composite material structure under test, calculate the defect-induced stress enhancement factor and the stress transfer function used to describe the range of defect influence, and construct a stress correction factor distribution map of the range of defect influence based on the defect-induced stress enhancement factor and the stress transfer function. S4. Based on the geometric data and boundary conditions of the composite material structure to be tested, establish a corresponding reference finite element model, and calculate the corresponding reference stress distribution field under the condition of ignoring defects. S5. Apply the stress correction factor distribution map to the reference stress distribution field to perform local stress correction on the defect influence range, and obtain a corrected stress distribution map that includes the defect influence. S6. Based on the identification error of the defect information and the statistical fluctuation information of the composite material structure to be tested, an uncertainty propagation model is constructed, and the uncertainty of the modified stress distribution map is quantified by the uncertainty model, and a stress value interval map containing confidence information is output. S7. Perform joint analysis on the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level.

2. The method for analyzing stress distribution in composite materials according to claim 1, characterized in that, The stress correction factor distribution map of the defect influence range is constructed based on the defect-induced stress enhancement factor and the stress transfer function, including: The defect-induced stress enhancement factor and the stress transfer function are weighted and fused to form a local perturbation map of the impact of the defect on the adjacent area; Within the defect area, the local disturbance maps of multiple defects are synthesized by spatial superposition to obtain a stress correction factor distribution map that reflects the range of defect influence.

3. The method for analyzing stress distribution in composite materials according to claim 1, characterized in that, The process of establishing a corresponding reference finite element model based on the geometric data and boundary conditions of the composite material structure under test, and calculating the corresponding reference stress distribution field while neglecting defects, includes: Based on the geometric data of the composite material structure to be tested, a three-dimensional geometric model is constructed using computer-aided design software. The three-dimensional geometric model includes the laminate structure, thickness distribution, and edge shape characteristics of the composite material structure to be tested. Based on the three-dimensional geometric model, boundary conditions are set and external loads are applied. The boundary conditions include constraint nodes, fixed surfaces, and contact surfaces. The external loads can be one or more of concentrated forces, distributed forces, thermal loads, or vibration loads. The three-dimensional geometric model is divided into finite element meshes to obtain a finite element model containing multiple elements; Each element in the finite element model is assigned corresponding anisotropic material properties, including elastic modulus, Poisson's ratio, shear modulus and lamination angle distribution, and the influence of defect regions on material continuity is ignored during the assignment process. Based on the finite element model, the stress field of the composite material structure under test is calculated using the finite element numerical solution method under the boundary conditions and external loads to obtain the reference stress distribution field without considering the existence of defects.

4. The method for analyzing stress distribution in composite materials according to claim 1, characterized in that, The step of applying the stress correction factor distribution map to the reference stress distribution field to perform local stress correction on the defect influence range, thereby obtaining a corrected stress distribution map that includes the defect influence, includes: Based on the numerical results of the reference stress distribution field, and combined with the stress correction factor distribution map within the defect influence range, the corresponding reference stress value and the corresponding correction factor are extracted for each unit or discrete calculation point. The correction factor and the reference stress value are coupled and calculated using a point-by-point multiplication method to obtain the locally corrected correction stress value. The corrected stress values ​​at each calculation point are reassembled to form a complete corrected stress distribution field, which reflects the stress field disturbance induced by defects in the composite material structure under test. Based on the stress disturbance field, a modified stress distribution map with spatial resolution is generated. The modified stress distribution map is used to reflect the spatial influence characteristics of the stress disturbance induced by defects on the overall stress state.

5. The method for analyzing stress distribution in composite materials according to claim 1, characterized in that, Based on the identification error of the defect information and the statistical fluctuation information of the composite material structure under test, an uncertainty propagation model is constructed. The uncertainty model is then used to quantify the uncertainty of the modified stress distribution map, outputting a stress value interval spectrum containing confidence information, including: The geometric identification error in the defect information is modeled as a random variable that follows a specific distribution, and the statistical fluctuation of the material parameters of the composite material structure under test is represented as an interval parameter with a mean and a standard deviation. The specific distribution includes either a normal distribution or a triangular distribution. Using the random variables and interval parameters as input variables, an uncertainty propagation model for stress result uncertainty propagation is constructed; The modified stress distribution map is subjected to multiple perturbation simulations using the uncertainty propagation model. Stress response samples are collected at each spatial point, and the upper and lower limits of the stress value under the confidence level are calculated based on the stress response samples to form a stress interval map containing confidence information. The stress interval map is used to reflect the stress response variability under the combined influence of defect information and material uncertainty.

6. The method for analyzing stress distribution in composite materials according to claim 1, characterized in that, The joint analysis of the modified stress distribution map and the stress value interval map to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level includes: For each spatial location in the modified stress distribution map, the corresponding modified stress value, the lower limit of the confidence interval in the stress value interval spectrum, and the confidence index are extracted. The stress threshold and confidence threshold for the pre-set structural safety assessment are defined as follows: the stress threshold is the local ultimate bearing standard of the structure under the target load condition, and the confidence threshold is the lowest acceptable level of confidence in a statistical sense. The corrected stress value and the lower limit of the confidence interval are compared with the stress threshold respectively. If either value exceeds the stress threshold and the corresponding confidence index is lower than the confidence threshold, the corresponding spatial location is marked as a high-risk area point. All high-risk areas are clustered into continuous regions to form high-risk areas.

7. The method for analyzing stress distribution in composite materials according to claim 1, characterized in that, The stress transfer function is constructed using a defect boundary normal attenuation model, and the construction method includes: The boundary curve of each defect region is extracted into a set of boundary points, and an attenuation path is constructed along the normal direction at each boundary point. The stress transfer coefficient on the attenuation path is calculated according to a preset attenuation function, which includes one or more of an exponential attenuation function or a Gaussian distribution function.

8. A system for analyzing stress distribution in composite materials, characterized in that, The system includes: An image acquisition unit is used to acquire defect detection images of the composite material structure to be tested, the defect detection images including structural images obtained by computed tomography and infrared thermal imaging. The defect identification unit is used to perform image processing on the defect detection image, identify the defect region in the composite material structure under test, and extract the defect information in the defect region. The first processing unit is used to calculate the defect-induced stress enhancement factor and the stress transfer function for describing the range of defect influence based on the defect information and the load direction of the composite material structure under test, and to construct a stress correction factor distribution map of the range of defect influence based on the defect-induced stress enhancement factor and the stress transfer function. The second processing unit is used to establish a corresponding reference finite element model based on the geometric data and boundary conditions of the composite material structure to be tested, and to calculate the corresponding reference stress distribution field under the condition of ignoring defects. The third processing unit is used to apply the stress correction factor distribution map to the reference stress distribution field, perform local stress correction on the defect influence range, and obtain a corrected stress distribution map that includes the defect influence. An uncertainty processing unit is used to construct an uncertainty propagation model based on the identification error of the defect information and the statistical fluctuation information of the composite material structure to be tested, and to perform uncertainty quantification on the modified stress distribution map through the uncertainty model, and output a stress value interval map containing confidence information. The joint analysis unit is used to perform joint analysis on the modified stress distribution map and the stress value interval spectrum to identify high-risk areas where the stress exceeds the safety threshold and the confidence level is lower than the set level.

9. A system for analyzing stress distribution in composite materials, characterized in that, include: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the method as described in any one of claims 1 to 7.

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