Virtual prediction method for mechanical behavior of composite material

Abstract

The invention relates to the technical field of data prediction, in particular to a virtual prediction method for mechanical behaviors of a composite material. The method comprises the following steps: acquiring a material level data set; performing data preprocessing on the material level data set to generate a multi-level material processing data set; random field defect analysis is carried out on the multi-level material processing data set, and material microdefect distribution data is generated; carrying out nonlinear behavior factor extraction on the material microdefect distribution data to generate a composite material microcosmic nonlinear behavior factor; therefore, through the multi-level data set processing and interface mechanical modeling technology, the problems that traditional material design depends on experimental verification and is long in period and high in cost are solved, and the precision and efficiency of performance optimization of the composite material are improved.

Description

Technical Field

[0001] The invention relates to the technical field of data prediction, and in particular to a virtual prediction method for mechanical behavior of composite materials. Background Art

[0002] In the prior art, many mechanical behavior prediction methods still rely mainly on empirical formulas or simplified models, which often ignore the complex behavior of composite materials at different scales. At the macroscopic scale, although there are some mature mechanical models, these models are usually unable to effectively capture the microscopic and mesoscopic behaviors of composite materials, especially in terms of the influence of microscopic properties such as fiber-matrix interface and pore distribution on mechanical properties. Most of the existing models assume that the material is homogeneous and do not fully consider the complexity of factors such as microscopic defects, interface slip, and debonding behavior, which leads to limitations and insufficient accuracy of the prediction results. The prior art often breaks between different scales and lacks effective multi-scale data fusion methods, which affects the continuity and consistency of the model and makes it difficult to achieve comprehensive mechanical behavior prediction. The prior art also has shortcomings in material defect analysis. At present, many prediction methods have a relatively simple consideration of material defects, usually relying only on simple defect descriptions such as porosity and crack length, and lack a comprehensive analysis of the distribution of microscopic defects in the material. For example, the research on interfacial bonding strength, fiber arrangement and defect distribution in composite materials has not been fully combined with data-driven nonlinear behavior analysis. This deficiency limits the deep understanding and accurate prediction of the real mechanical behavior of composite materials. Summary of the invention

[0003] Based on this, it is necessary to provide a virtual prediction method for the mechanical behavior of composite materials to solve at least one of the above technical problems.

[0004] To achieve the above object, a virtual prediction method for the mechanical behavior of composite materials is provided, the method comprising the following steps:

[0005] Step S1: obtaining a material level data set; performing data preprocessing on the material level data set to generate a multi-level material processing data set;

[0006] Step S2: performing random field defect analysis on the multi-level material processing data set to generate material microscopic defect distribution data; performing nonlinear behavior factor extraction on the material microscopic defect distribution data to generate microscopic nonlinear behavior factors of the composite material;

[0007] Step S3: using the composite material microscopic nonlinear behavior factor to model the interface mechanical behavior of the multi-level material processing data set to generate a composite material mechanical behavior prediction model;

[0008] Step S4: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data; constructing a prediction report based on the composite material mechanical behavior prediction data, thereby completing the virtual prediction operation of the composite material mechanical behavior.

[0009] The beneficial effect of the present invention is that the material level data set is obtained and processed, and the integrity and consistency of the data are ensured through multi-level data preprocessing, which lays the foundation for subsequent analysis. Then, through random field defect analysis, the material micro defect distribution data is generated. This process can effectively reveal the defect characteristics of the composite material at the micro scale and provide data support for further nonlinear behavior factor extraction. The nonlinear behavior factor extraction ensures that the nonlinear response of the material under complex loading conditions is accurately described by quantifying the micromechanical behavior characteristics of the material, which is crucial for the practical application of composite materials, especially under high stress or dynamic load. Next, the interface mechanical behavior modeling is carried out in combination with the micro nonlinear behavior factor, and the interaction between the fiber and the matrix, the interface damage and its influence on the overall mechanical properties are fully considered. The mechanical behavior of the composite material under different use conditions is predicted by the model, which provides accurate data support for design and optimization. On this basis, the prediction results of the model can generate detailed mechanical behavior prediction data, and the prediction results are effectively displayed in the form of report construction, so that designers can have a deeper understanding and judgment of the material performance before actual production. The advantage of this process is that through virtual prediction and simulation, the demand for actual experiments is reduced, the cycle of material design and testing is shortened, and the design efficiency and material reliability are improved. Therefore, the present invention solves the problem of reliance on experimental verification with long cycles and high costs in traditional material design through multi-level data set processing and interface mechanics modeling technology, and improves the accuracy and efficiency of composite material performance optimization.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire a material hierarchical data set, divide the material hierarchical data set into hierarchical levels, and obtain material hierarchical type data, wherein the material hierarchical type data includes micro-level data, meso-level data, and macro-level data, the micro-level data includes fiber arrangement data, pore distribution data, and material matrix property data, the meso-level data includes stress data, strain data, and crack data, and the macro-level data includes tensile performance data and bending performance data;

[0012] Step S12: performing micro-scale material property analysis on the micro-level data to generate micro-level processing data; performing characteristic behavior analysis on the meso-level data to generate meso-level processing data; performing mechanical property analysis on the macro-level data to generate macro-level processing data;

[0013] Step S13: fusing the meso-level processed data and the macro-level processed data to generate meso-level-macro-level fused data; using multi-scale fusion to process the meso-level-macro-level fused data and the micro-level processed data into a mechanical properties data set to generate a multi-level material processing data set.

[0014] The present invention provides rich material characterization information for subsequent analysis by acquiring data sets including micro, meso and macro levels. Micro-level data include key factors such as fiber arrangement, pore distribution and matrix characteristics. These microstructural features directly affect the basic mechanical properties of the material; meso-level data describe the local deformation and damage of the material during loading through physical parameters such as stress, strain and crack; macro-level data provide the tensile and bending properties of the material under the overall load, providing designers with the global mechanical response of the material. Through the careful analysis and processing of data at each level, a more accurate quantitative description is provided for the characteristics of each level, so that the characteristics of each scale of the material are fully understood. Using data fusion technology, the meso-level processed data is combined with the macro-level processed data to generate meso-level-macro-level fusion data, realizing the organic combination of cross-scale information. Multi-scale fusion further optimizes data processing, and effectively enhances the integrity and accuracy of the mechanical performance data set by combining the details of the micro-level with the overall performance information of the meso- and macro-levels. This process enables the relationship between the levels to be accurately captured, and through multi-scale modeling, the accuracy and reliability of the prediction of the mechanical behavior of the material are improved. Ultimately, the generated multi-level material processing dataset not only provides comprehensive data support for the mechanical behavior of composite materials, but also provides deeper insights into material design and optimization, significantly reducing the need for experimental verification and improving design efficiency.

[0015] Preferably, step S12 comprises the following steps:

[0016] Step S121: perform finite element analysis on the fiber arrangement data to generate fiber finite element arrangement data; perform pore morphology analysis on the pore distribution data to generate pore distribution morphology data; perform Raman spectroscopy processing on the material matrix characteristic data to generate material matrix spectral data; perform spectral imaging processing on the material matrix spectral data to generate material matrix imaging data; perform material mechanical contact strength analysis based on the fiber finite element arrangement data and the material matrix imaging data to generate fiber-matrix contact strength data; perform interface bonding strength analysis based on the pore distribution morphology data and the fiber finite element arrangement data to generate pore distribution-fiber interface bonding strength data;

[0017] Step S122: performing cumulative pressure test on stress data and strain data to generate stress-strain cumulative pressure test data; performing load curve analysis on the stress-strain cumulative pressure test data to generate stress-strain load curve data; performing crack step-by-step extension analysis on crack data to generate crack extension data; performing material propagation path drawing on crack extension data to generate crack extension path data;

[0018] Step S123: performing a strain hardening test on the stress-strain load curve data and the tensile performance data to generate composite material strain hardening data; performing a three-point bending test on the stress-strain load curve data and the bending performance data to generate composite material bending performance data;

[0019] Step S124: Pack the fiber-matrix contact strength data and the pore distribution-fiber interface bonding strength data to generate micro-level processing data; merge the stress-strain load curve data and the crack propagation path data to generate meso-level processing data; fuse the composite material strain hardening data and the composite material bending performance data to generate macro-level processing data.

[0020] The present invention generates more refined and physically meaningful fiber finite element arrangement data, pore distribution morphological data and material matrix spectrum data through finite element analysis of fiber arrangement data, morphological analysis of pore distribution data and Raman spectroscopy processing of matrix characteristic data. These data provide a basis for the prediction of the microscopic mechanical behavior of composite materials, especially when analyzing the contact strength between fiber and matrix, the bonding strength of pore and fiber interface, and can more accurately characterize its microscopic interaction and mechanical properties. Then, by performing cumulative pressure test on stress and strain data, stress-strain cumulative pressure test data is obtained, and further stress-strain load curve data is generated by load curve analysis. This process effectively describes the elastic and plastic deformation behavior of the material during loading. At the same time, by crack extension analysis and path drawing, the mechanism and mode of crack extension are revealed, which provides key information for material damage prediction. By performing strain hardening test and three-point bending experiment on stress-strain load curve data and tensile properties and bending properties data, the strain hardening behavior and bending properties of the composite material are obtained from a macroscopic level, which is crucial for predicting the mechanical response of the material under complex loads. Finally, the data at the micro, meso and macro levels are integrated through data packaging, data merging and data fusion technology to generate a comprehensive multi-level processing data set, which not only improves the integrity and consistency of the data, but also enables the effective combination of multi-scale information. Through this series of data processing and analysis, the mechanical behavior of composite materials at different scales can be accurately reflected, thereby providing more comprehensive prediction capabilities for material design, optimization and application.

[0021] Preferably, step S13 includes the following steps:

[0022] Step S131: performing material yield evaluation on the stress-strain load curve data and the composite material strain hardening data to generate composite material hardware yield data; performing fracture toughness simulation on the crack propagation path data and the composite material bending performance data to generate composite material fracture toughness simulation data;

[0023] Step S132: fusing the composite material hardware yield data and the composite material fracture toughness simulation data to generate meso-level-macro-level fusion data;

[0024] Step S133: Use multi-scale fusion to process the meso-level-macro-level fusion data and the micro-level processing data into a mechanical properties data set to generate a multi-level material processing data set.

[0025] The present invention combines stress-strain load curve data with composite material strain hardening data to evaluate material yield and generate composite material hardware yield data. This process can quantitatively analyze the yield behavior of composite materials under different stress conditions, revealing its plastic deformation ability under high load, thereby providing accurate quantitative indicators for the material's anti-yield performance. At the same time, the crack propagation path data is combined with the composite material bending performance data, and the fracture toughness simulation data of the composite material is generated through fracture toughness simulation, which can effectively predict the material's anti-fracture ability during crack propagation, providing a theoretical basis for the composite material's anti-crack performance. The composite material hardware yield data is combined with the composite material fracture toughness simulation data through data fusion, generating fusion data of the meso-level and macro-level. This process not only integrates the mechanical behavior of the material at different scales, but also optimizes the correlation and consistency of the data, and enhances the reliability of composite material performance prediction. Finally, multi-scale fusion technology was used to combine the meso-level-macro-level fusion data with the micro-level processing data, and a multi-level material processing data set was generated through mechanical properties data set processing. This integration process enables data at each level to be mapped and optimized at multiple scales, greatly improving the accuracy and stability of the prediction of mechanical properties of multi-level composite materials.

[0026] Preferably, step S2 comprises the following steps:

[0027] Step S21: performing random field defect analysis on the multi-level material processing data set to generate material microscopic defect distribution data;

[0028] Step S22: Analyze the mechanical energy sensitivity of the material microscopic defect distribution data to generate composite material sensitivity index data;

[0029] Step S23: extracting nonlinear behavior factors according to the composite material sensitivity index data to generate microscopic nonlinear behavior factors of the composite material.

[0030] The present invention generates material microscopic defect distribution data by performing random field defect analysis on multi-level material processing data sets. This process can reveal the defects and their distribution characteristics existing at the microscopic scale of the material, and provide basic data for subsequent mechanical property prediction. Random field analysis can effectively simulate and quantify the randomness and inhomogeneity inside the material, which is crucial for the prediction of the mechanical behavior of composite materials, especially when considering the influence of material defects on mechanical properties. By performing mechanical energy sensitivity analysis on the material microscopic defect distribution data, composite material sensitivity index data is generated. This step deeply explores the sensitivity of defects to the mechanical properties of the material and quantifies the mechanical response changes of the material under different defect conditions. This analysis can provide decision support for material design and optimization, so that designers can accurately predict the performance fluctuations of the material in practical applications, especially under complex working conditions. Nonlinear behavior factors are extracted according to the composite material sensitivity index data to generate composite material microscopic nonlinear behavior factors. This process further refines the microscopic mechanical behavior of the composite material by extracting nonlinear behavior factors, especially the performance under high stress or extreme loading conditions. These nonlinear factors can accurately reflect the complex nonlinear responses encountered by the material in practical applications, making the prediction of mechanical properties more comprehensive and accurate.

[0031] Preferably, step S21 includes the following steps:

[0032] Step S211: Perform Poisson distribution on the pore distribution-fiber interface bonding strength data to generate composite material pore-fiber defect distribution data; perform hardware arrangement changes according to the composite material hardware yield data to generate composite material hardware arrangement defect data;

[0033] Step S212: searching for crack initiation points on composite material fracture toughness simulation data to generate composite material crack initiation point data;

[0034] Step S213: performing random spatial field analysis on the composite material pore-fiber defect distribution data, the composite material hardware arrangement defect data and the composite material crack starting point data to generate material microscopic defect distribution data.

[0035] The present invention generates composite material pore-fiber defect distribution data by performing Poisson distribution analysis on pore distribution-fiber interface bonding strength data, which can effectively quantify the defect distribution characteristics of pores and fiber interfaces in materials, and reveal the structural inhomogeneity of materials on a microscopic scale and their potential impact on mechanical properties. Poisson distribution analysis provides a mathematical description for the randomness of defects in materials, so that in practical applications, the defect distribution of materials can be effectively predicted, and lays the foundation for subsequent mechanical performance simulation. At the same time, hardware arrangement changes are performed based on the hardware yield data of composite materials to generate composite material hardware arrangement defect data. This process provides a new dimension for the yield behavior of materials by quantifying the impact of hardware arrangement on mechanical properties, and can further reveal the microscopic impact of different arrangement modes on material properties. By searching the crack starting point of composite material fracture toughness simulation data, composite material crack starting point data is generated. Using this data, the starting mechanism and expansion law of cracks can be deeply understood, which provides an important basis for the evaluation of crack resistance of materials. Finally, random spatial field analysis is performed on the composite material pore-fiber defect distribution data, hardware arrangement defect data and crack starting point data to generate material microscopic defect distribution data. Random spatial field analysis can combine multiple defect types for collaborative analysis and comprehensively evaluate the defect evolution characteristics of materials at the microscopic scale. This analysis not only improves the understanding of the defect evolution process of materials, but also enhances the prediction accuracy of the mechanical behavior of materials under complex loads and usage conditions. Through this series of data processing, the defect distribution and mechanical response of composite materials at the microscopic level can be fully and meticulously revealed, providing more scientific and accurate data support for the design, optimization and application of composite materials.

[0036] Preferably, step S23 includes the following steps:

[0037] Step S231: performing power law nonlinear fitting on stress-strain load curve data to generate stress-strain load nonlinear fitting data; performing crack interface analysis on composite material fracture toughness simulation data using energy release rate criterion to generate composite crack extension-fracture toughness interface analysis data;

[0038] Step S232: performing a yield strength nonlinear factor analysis on the stress-strain load nonlinear fitting data to generate a composite material yield strength nonlinear factor; performing a plastic deformation modulus nonlinear factor analysis on the stress-strain load nonlinear fitting data to generate a composite material plastic deformation modulus nonlinear factor;

[0039] Step S233: using the continuum damage mechanics method, a nonlinear degradation analysis is performed on the nonlinear factor of the yield strength of the composite material and the nonlinear factor of the plastic deformation modulus of the composite material to generate a factor that conforms to the microscopic nonlinear behavior of the material.

[0040] The present invention generates stress-strain load nonlinear fitting data by performing power law nonlinear fitting on stress-strain load curve data. This process accurately fits the nonlinear response of the material under stress through the power law model, can better reflect the plastic deformation characteristics of the composite material under high stress state, and solves the problem that the traditional linear analysis method cannot accurately describe the nonlinear behavior. Then, the crack interface property analysis of the composite fracture toughness simulation data is performed using the energy release rate criterion, and the composite crack extension-fracture toughness interface analysis data is generated. This analysis provides a quantitative basis for the mechanical response of the material during the crack extension process by considering the relationship between crack extension and fracture toughness, and further reveals the influence of the crack interface on the overall mechanical properties of the material, thereby optimizing the evaluation method of crack resistance. For the analysis of the nonlinear behavior of the material, the yield strength nonlinear factor analysis is performed on the stress-strain load nonlinear fitting data to generate the composite yield strength nonlinear factor, and the plastic deformation modulus nonlinear factor analysis is performed on the same data to generate the composite plastic deformation modulus nonlinear factor. Through this process, the yield strength and plastic deformation capacity of the material under different stress states can be quantified, providing a more detailed description for the mechanical performance of the composite material in actual use. These nonlinear factors provide important parameters and basis for further material design and optimization. Finally, by using the continuum damage mechanics method to perform nonlinear degradation analysis on the nonlinear factors of the yield strength of composite materials and the plastic deformation modulus of composite materials, factors that conform to the microscopic nonlinear behavior of the material are generated. This analysis reveals the microscopic damage mechanism and performance degradation law of the material by considering the degradation process of the material under different stresses, greatly improving the prediction accuracy of the mechanical properties of composite materials under complex loading conditions.

[0041] Preferably, step S3 comprises the following steps:

[0042] Step S31: performing material interface mechanical behavior analysis on the pore distribution-fiber interface bonding strength data to generate composite material interface mechanical behavior data;

[0043] Step S32: performing interface slip-debonding behavior processing on the composite material interface mechanical behavior data to generate composite material interface slip-debonding behavior data;

[0044] Step S33: using the composite material microscopic nonlinear behavior factors and the composite material interface slip-debonding behavior data to perform interface mechanical behavior modeling on the multi-level material processing data set, and generating a composite material mechanical behavior prediction model.

[0045] The present invention generates composite material interface mechanical behavior data by analyzing the material interface mechanical behavior of the pore distribution-fiber interface bonding strength data. This process deeply explores the mechanical properties between the fiber and matrix interface, and reveals how the mechanical response of the interface layer affects the overall mechanical properties of the composite material, especially the change of the interface bonding strength under high stress. Through this analysis, the contribution of the interface mechanical behavior to the mechanical response of the composite material such as tension, compression and bending can be accurately described, providing reliable data support for subsequent material performance prediction. In the study of interface mechanical behavior, the composite material interface slip-debonding behavior data is processed by interface slip-debonding behavior of the composite material interface mechanical behavior data. Interface slip and debonding are one of the common failure modes of composite materials in practical applications. They can effectively capture and quantify the slip and debonding phenomena of the interface layer under different loading conditions, which has an important influence on the crack resistance, fatigue life and other characteristics of the composite material. By processing and quantifying this behavior, the failure mechanism of the composite material under extreme working conditions can be deeply analyzed, and data support can be provided for material optimization. Finally, by using the microscopic nonlinear behavior factors of composite materials and the slip-debonding behavior data of composite interfaces, the multi-level material processing data set was modeled for interface mechanical behavior, and a composite mechanical behavior prediction model was generated. This modeling process combines the microscopic nonlinear behavior of the material and the slip-debonding behavior of the interface layer, providing a more accurate prediction of the overall mechanical properties of the composite material, especially under the synergistic effect of the interface and the material body under complex working conditions, which can better simulate the actual performance of the composite material.

[0046] Preferably, step S33 includes the following steps:

[0047] Step S331: Establishing a fiber-matrix three-dimensional material model using the fiber-matrix contact strength data to generate a preliminary three-dimensional model of the fiber-matrix composite material;

[0048] Step S332: optimizing the defect nonlinear factor of the preliminary three-dimensional model of the fiber-matrix composite material by using the composite material yield strength nonlinear factor and the composite material plastic deformation modulus nonlinear factor to generate a composite material driving factor optimized three-dimensional model;

[0049] Step S332: Use the composite material interface slip-debonding behavior data to simulate the physical properties of the composite material driving factor optimization three-dimensional model, and use the composite material fracture toughness simulation data to simulate the material mechanical behavior properties to generate a composite material mechanical behavior prediction model.

[0050] The present invention establishes a fiber-matrix three-dimensional material model by using fiber-matrix contact strength data, and generates a preliminary three-dimensional model of the composite material. This modeling process captures the interaction force between the fiber and the matrix, especially the mechanical response of the fiber-matrix interface under different contact strength conditions. The three-dimensional model provides a structural basis for subsequent optimization and simulation, and can accurately reflect the microscopic mechanical behavior of the material, especially the stress distribution of the interface layer under the action of external force. By optimizing the defect nonlinear factor of the model using the nonlinear factor of the yield strength of the composite material and the nonlinear factor of the plastic deformation modulus of the composite material, a three-dimensional model of the composite material driving factor optimization is generated. This process introduces nonlinear factors such as yield strength and plastic deformation modulus into the model optimization, takes into account the nonlinear response and defect evolution of the material under different stress conditions, and deeply explores the failure mechanism, deformation capacity and durability of the material, thereby improving the prediction accuracy of the three-dimensional model under complex working conditions. Finally, by using the composite interface slip-debonding behavior data to simulate the physical properties of the driving factor optimization three-dimensional model, and using the composite fracture toughness simulation data to simulate the material mechanical behavior properties, a composite material mechanical behavior prediction model is generated. This simulation process further incorporates key characteristics such as the slip and debonding behavior of the interface layer, the fracture toughness and mechanical properties of the material into the overall prediction model, thereby more realistically reproducing the mechanical performance of composite materials in actual applications.

[0051] Preferably, step S4 comprises the following steps:

[0052] Step S41: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data;

[0053] Step S42: performing a prediction behavior integrity assessment based on the composite material mechanical behavior prediction data to generate composite material mechanical behavior integrity assessment data;

[0054] Step S43: constructing a prediction report based on the composite material mechanical behavior integrity assessment data, thereby completing the virtual prediction operation of the composite material mechanical behavior.

[0055] The present invention generates composite material mechanical behavior prediction data by predicting the composite material mechanical behavior prediction model. The data covers the mechanical response of the composite material under different working conditions, including changes in properties such as tension, compression, and bending. By accurately simulating the mechanical behavior of the material, detailed prediction information can be provided for material design, and data support can be provided for subsequent optimization and application. Composite material mechanical behavior integrity evaluation data is generated by performing a prediction behavior integrity evaluation on the composite material mechanical behavior prediction data. This evaluation process not only verifies the reliability and comprehensiveness of the prediction data, but also analyzes the accuracy and consistency of the material behavior prediction under different working conditions. By conducting an in-depth evaluation of the integrity of the data, the scope of application and actual performance of the prediction model are ensured, helping designers identify potential prediction deviations and risks, thereby providing a more reliable basis for the actual use of the material. Finally, a prediction report is constructed based on the composite material mechanical behavior integrity evaluation data to complete the virtual prediction operation of the composite material mechanical behavior. This step presents all the prediction data and evaluation results in the form of a report, intuitively summarizes the performance and performance of the composite material under various working conditions, and provides systematic and operational guidance for engineering applications. Through this prediction report, designers can accurately grasp the mechanical behavior characteristics of composite materials, thereby optimizing the design of materials, selecting appropriate application scenarios, and effectively monitoring their long-term performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the steps of a virtual prediction method for the mechanical behavior of composite materials;

[0057] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0058] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0059] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 4 , a virtual prediction method for mechanical behavior of composite materials, the method comprising the following steps:

[0065] Step S1: obtaining a material level data set; performing data preprocessing on the material level data set to generate a multi-level material processing data set;

[0066] Step S2: performing random field defect analysis on the multi-level material processing data set to generate material microscopic defect distribution data; performing nonlinear behavior factor extraction on the material microscopic defect distribution data to generate microscopic nonlinear behavior factors of the composite material;

[0067] Step S3: using the composite material microscopic nonlinear behavior factor to model the interface mechanical behavior of the multi-level material processing data set to generate a composite material mechanical behavior prediction model;

[0068] Step S4: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data; constructing a prediction report based on the composite material mechanical behavior prediction data, thereby completing the virtual prediction operation of the composite material mechanical behavior.

[0069] The beneficial effect of the present invention is that the material level data set is obtained and processed, and the integrity and consistency of the data are ensured through multi-level data preprocessing, which lays the foundation for subsequent analysis. Then, through random field defect analysis, the material micro defect distribution data is generated. This process can effectively reveal the defect characteristics of the composite material at the micro scale and provide data support for further nonlinear behavior factor extraction. The nonlinear behavior factor extraction ensures that the nonlinear response of the material under complex loading conditions is accurately described by quantifying the micromechanical behavior characteristics of the material, which is crucial for the practical application of composite materials, especially under high stress or dynamic load. Next, the interface mechanical behavior modeling is carried out in combination with the micro nonlinear behavior factor, and the interaction between the fiber and the matrix, the interface damage and its influence on the overall mechanical properties are fully considered. The mechanical behavior of the composite material under different use conditions is predicted by the model, which provides accurate data support for design and optimization. On this basis, the prediction results of the model can generate detailed mechanical behavior prediction data, and the prediction results are effectively displayed in the form of report construction, so that designers can have a deeper understanding and judgment of the material performance before actual production. The advantage of this process is that through virtual prediction and simulation, the demand for actual experiments is reduced, the cycle of material design and testing is shortened, and the design efficiency and material reliability are improved. Therefore, the present invention solves the problem of reliance on experimental verification with long cycles and high costs in traditional material design through multi-level data set processing and interface mechanics modeling technology, and improves the accuracy and efficiency of composite material performance optimization.

[0070] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a virtual prediction method for the mechanical behavior of a composite material according to the present invention. In this example, the virtual prediction method for the mechanical behavior of a composite material includes the following steps:

[0071] Step S1: obtaining a material level data set; performing data preprocessing on the material level data set to generate a multi-level material processing data set;

[0072] In an embodiment of the present invention, a material level data set is obtained, which generally includes material property data at different scales, such as fiber arrangement, pore distribution, matrix property data at the micro level, stress-strain data, crack data at the meso level, and tensile properties, bending properties and other data at the macro level. These data are related to various physical phenomena and mechanical behaviors, with different scales and complex interactions. At this stage, the core purpose of data preprocessing is to clean, standardize, and normalize the original data to ensure its consistency and accuracy in subsequent analysis. Specifically, it is first necessary to identify and remove noise and outliers in the data, and ensure the quality and validity of the data through data filtering and outlier detection technology. Secondly, for material data of different scales, appropriate interpolation methods are used to complete the data, such as using Kriging interpolation or polynomial interpolation to fill in missing data, thereby ensuring the integrity of the data. At the same time, for physical quantities with different dimensions, it is necessary to unify the dimensions, such as converting the data into a unified standard range through normalization or standardization methods, so as to facilitate subsequent multi-level analysis. Data standardization can also be achieved through methods such as Z-score standardization or Min-Max normalization to ensure that data at each level are comparable. In addition, when processing data, different processing strategies need to be adopted for different types of data. For example, for image data, noise reduction and feature extraction are required, while for structural data, analysis and meshing are required to extract effective mechanical information. Through the above data preprocessing methods, a multi-level material processing data set is finally generated, which contains the integrated data of material properties at each level and lays a solid data foundation for subsequent analysis and modeling.

[0073] Step S2: performing random field defect analysis on the multi-level material processing data set to generate material microscopic defect distribution data; performing nonlinear behavior factor extraction on the material microscopic defect distribution data to generate microscopic nonlinear behavior factors of the composite material;

[0074] In the embodiment of the present invention, random field analysis is a mathematical tool for describing irregularly distributed defects in materials, and is applicable to situations where there is spatial variability inside the material. The analysis is usually based on the microstructure data of the material, such as fiber arrangement, pore distribution, etc., and reveals its influence on the overall performance of the material by simulating the spatial distribution and variability of defects (such as pores, cracks, etc.) in the material. At the data level, it is first necessary to extract the microscopic level data in the multi-level data set, and perform spatial modeling based on known material properties (such as porosity, fiber density, etc.). Then, numerical methods such as Monte Carlo method or finite element method are used to generate random defect fields in the microstructure of the material, and these methods are used to perform statistical analysis on the distribution of defects, thereby obtaining the spatial distribution data of the material's microscopic defects. The key to this process is to consider the non-uniformity and complexity of defects in a randomized manner to ensure that the simulation results can reflect the various defect distributions encountered by the material in practical applications. Based on the material microscopic defect distribution data, nonlinear behavior factors are extracted to generate microscopic nonlinear behavior factors of composite materials. At this stage, the focus is on extracting key factors describing the nonlinear mechanical behavior of materials through the microstructural characteristics and defect distribution of the material. These factors usually include the yield strength, plastic deformation capacity, crack propagation behavior, etc. of the material, which play a decisive role in the prediction of material properties. Technical means of data processing include using nonlinear regression models, principal component analysis (PCA) or support vector machines (SVM) and other methods to conduct in-depth analysis of micro defect data and extract key nonlinear behavior factors. In specific operations, finite element analysis (FEA) can be used for mechanical simulation, combined with the material micro defect distribution data, to calculate the stress-strain response of the material under different loading conditions, and then identify the nonlinear behavior at the micro level. For example, the nonlinear characteristics of the material at the micro scale can be obtained by calculating stress integration or local energy release. These characteristics reflect the nonlinear response of the material in terms of crack initiation, propagation, and interface slip.

[0075] Step S3: using the composite material microscopic nonlinear behavior factor to model the interface mechanical behavior of the multi-level material processing data set to generate a composite material mechanical behavior prediction model;

[0076] In the embodiment of the present invention, the microscopic nonlinear behavior factors of the composite material have been extracted through the above analysis, and these factors include the behavior characteristics of the material at the microscopic level, such as yield, plasticity, crack extension, etc. Therefore, these factors can be used to provide basic data input for the mechanical behavior of multi-level materials. At the data level, the microscopic nonlinear behavior factors usually include nonlinear terms in the stress-strain relationship, such as yield strength, strain hardening and plastic deformation, etc. These characteristics reflect the mechanical response of the material under different loads, especially the influence on the mechanical behavior at the microscopic scale. In order to realize the modeling of interface mechanical behavior, it is first necessary to refine the multi-level material processing data set by finite element analysis (FEA) or computational mechanics methods. The microscopic nonlinear behavior factors of the composite material are used as material parameters and input into the interface mechanical model, and the contact, interface debonding, slip and other behaviors of the fiber and matrix at the interface are considered to establish a mechanical model of the interface layer. In this process, the microstructural characteristics of the material (such as fiber arrangement, pore distribution, etc.) and the mechanical properties of the material (such as nonlinear behavior factors) are combined, and the contact mechanical relationship between the fiber and the matrix is ​​constructed to simulate the influence of the interface behavior on the overall composite material performance. Generally, the modeling of interface mechanical behavior needs to consider factors such as interface bonding strength, interface friction, and interface debonding. Through nonlinear contact algorithms and mechanical simulation methods, the behavior of the interface area under different loading conditions is evaluated. Through the coupling analysis of microstructure data and macroscopic mechanical behavior in the multi-level material processing data set, a prediction model of the mechanical behavior of the composite material is finally generated. At the data level, the model usually adopts a multiscale coupling method to model the interaction between micromechanical behavior and macroscopic performance. Specifically, the mechanical behavior simulation can be performed at the microscale and macroscale respectively through the finite element method or multiscale simulation technology, and then the calculation results of these scales are fused. Through data interpolation, dimensionality reduction technology or machine learning methods, the influence of microscopic nonlinear behavior is effectively transferred to the macroscale, thereby realizing the prediction of the overall mechanical behavior of the composite material. This process not only considers the mechanical response inside the material, but also combines the interaction between the interface and the fiber matrix, so that the final mechanical behavior prediction model can more accurately reflect the mechanical properties of the actual material, especially its mechanical response under complex loading conditions.

[0077] Step S4: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data; constructing a prediction report based on the composite material mechanical behavior prediction data, thereby completing the virtual prediction operation of the composite material mechanical behavior.

[0078] In an embodiment of the present invention, according to the aforementioned composite material mechanical behavior prediction model, this process relies on numerical calculation methods, especially based on finite element analysis (FEA) or other computational mechanics tools to simulate mechanical behavior. After inputting the geometry, mechanical properties, boundary conditions and load conditions of the composite material, the model performs numerical solution to obtain the response data of the material under different working conditions. These data include mechanical indicators such as stress, strain, and deformation, as well as multi-dimensional information such as temperature and time dependence. Through this process, the mechanical behavior of the composite material in the actual use environment, including yielding, destruction, fatigue and other phenomena, can be accurately predicted in a virtual environment. Based on the composite material mechanical behavior prediction data, a prediction report is constructed. This process usually includes a variety of data processing and analysis techniques. First, the mechanical behavior prediction data needs to be screened, extracted and sorted to ensure that it can reflect the key mechanical properties of the composite material. Specifically, data dimensionality reduction, cluster analysis and other methods can be used to extract important features related to material properties, failure modes, strength prediction, etc. Through the analysis of these data, actionable report content can be generated. The construction of the report is usually carried out through automated tools, data visualization technology, report template generation, etc., so that complex numerical results can be presented to engineers or researchers in a clear and easy-to-understand form. In the report, in addition to showing the predicted results, it is also necessary to give the performance of the material under different working conditions, as well as the existing failure modes and recommended optimization measures. In addition, risk analysis technology is combined to conduct uncertainty assessment of the performance of the material in long-term use, and provide data support for subsequent design and material selection. In the end, the virtual prediction of the mechanical behavior of composite materials was completed through this series of technical means, integrating the complete process from modeling to prediction, from data processing to report generation.

[0079] Preferably, step S1 comprises the following steps:

[0080] Step S11: Acquire a material hierarchical data set, divide the material hierarchical data set into hierarchical levels, and obtain material hierarchical type data, wherein the material hierarchical type data includes micro-level data, meso-level data, and macro-level data, the micro-level data includes fiber arrangement data, pore distribution data, and material matrix property data, the meso-level data includes stress data, strain data, and crack data, and the macro-level data includes tensile performance data and bending performance data;

[0081] Step S12: performing micro-scale material property analysis on the micro-level data to generate micro-level processing data; performing characteristic behavior analysis on the meso-level data to generate meso-level processing data; performing mechanical property analysis on the macro-level data to generate macro-level processing data;

[0082] Step S13: fusing the meso-level processed data and the macro-level processed data to generate meso-level-macro-level fused data; using multi-scale fusion to process the meso-level-macro-level fused data and the micro-level processed data into a mechanical properties data set to generate a multi-level material processing data set.

[0083] In the embodiment of the present invention, the microscopic level data generally includes fiber arrangement data, pore distribution data and material matrix property data, which are obtained by high-resolution experimental means such as scanning electron microscopy (SEM), X-ray tomography (CT), Raman spectroscopy, etc., and accurate fiber and pore distribution are obtained by image processing and segmentation algorithm; the matrix properties are obtained by chemical composition analysis and mechanical property testing of materials. The mesoscopic level data includes stress, strain and crack data, which are usually collected by mechanical tests (such as tensile, compression, fatigue testing) and non-destructive detection technologies (such as acoustic emission, infrared thermal imaging, etc.) of materials under different loading conditions; the macroscopic level data includes tensile performance and bending performance data, which are usually obtained by standardized material testing methods, such as tensile tests and three-point bending tests of universal material testing machines. Analyze and process data at different levels. The analysis of microscopic level data mainly focuses on the structural analysis of materials, and often models the interaction between fibers and matrices, pore effects, etc. through finite element analysis (FEA), molecular dynamics simulation or multi-scale modeling methods, thereby generating microscopic material property data. These data are helpful to describe the influence of the microstructural characteristics of the material on the overall mechanical properties. For meso-level data, characteristic behavior analysis mainly includes modeling of stress-strain relationship and analysis of crack extension behavior. Common analysis methods include constitutive model fitting based on stress-strain curve and numerical simulation of crack extension. Through these data, the mechanical response characteristics of the material at the meso-scale can be obtained. The mechanical property analysis of macro-level data depends on large-scale mechanical experimental data and structural mechanics models. It is usually carried out using classical linear or nonlinear finite element analysis methods, aiming to obtain the mechanical properties required for materials in practical applications, such as tensile strength, yield point, bending modulus, etc. The main task is to fuse multi-level data. The fusion of meso-level processed data and macro-level processed data first needs to unify the data at different scales through technical means such as data mapping and interpolation to ensure that their corresponding relationships at different scales can accurately reflect the global behavior of the material. Then, multi-scale fusion technology is used to process the meso-level and macro-level data with the micro-level processed data into a mechanical property data set. Common techniques include multi-scale modeling methods, improved multi-scale algorithms, principal component analysis (PCA) and other dimensionality reduction techniques. These methods can effectively integrate data at different levels, transfer microscopic detail information to macroscopic overall behavior simulation through adaptive algorithms, and ultimately generate a multi-level material performance data set.

[0084] Preferably, step S12 comprises the following steps:

[0085] Step S121: perform finite element analysis on the fiber arrangement data to generate fiber finite element arrangement data; perform pore morphology analysis on the pore distribution data to generate pore distribution morphology data; perform Raman spectroscopy processing on the material matrix characteristic data to generate material matrix spectral data; perform spectral imaging processing on the material matrix spectral data to generate material matrix imaging data; perform material mechanical contact strength analysis based on the fiber finite element arrangement data and the material matrix imaging data to generate fiber-matrix contact strength data; perform interface bonding strength analysis based on the pore distribution morphology data and the fiber finite element arrangement data to generate pore distribution-fiber interface bonding strength data;

[0086] Step S122: performing cumulative pressure test on stress data and strain data to generate stress-strain cumulative pressure test data; performing load curve analysis on the stress-strain cumulative pressure test data to generate stress-strain load curve data; performing crack step-by-step extension analysis on crack data to generate crack extension data; performing material propagation path drawing on crack extension data to generate crack extension path data;

[0087] Step S123: performing a strain hardening test on the stress-strain load curve data and the tensile performance data to generate composite material strain hardening data; performing a three-point bending test on the stress-strain load curve data and the bending performance data to generate composite material bending performance data;

[0088] Step S124: Pack the fiber-matrix contact strength data and the pore distribution-fiber interface bonding strength data to generate micro-level processing data; merge the stress-strain load curve data and the crack propagation path data to generate meso-level processing data; fuse the composite material strain hardening data and the composite material bending performance data to generate macro-level processing data.

[0089] In an embodiment of the present invention, finite element analysis (FEA) is performed on the fiber arrangement data. This method obtains fiber finite element arrangement data by establishing a fiber arrangement model in the material and simulating based on the geometric and mechanical properties of the material. Through this technology, the distribution characteristics of the fiber in the composite material and its influence on the mechanical behavior can be accurately captured. For the pore distribution data, pore morphology analysis methods such as image processing technology, porosity calculation and distribution feature extraction are used to generate pore distribution morphology data. These data reveal the morphological structure of the pores inside the material and its potential influence on the mechanical properties of the material, especially in terms of strength and durability. The material matrix characteristic data is processed by Raman spectroscopy technology to obtain the molecular structure information of the matrix material, and then generate the material matrix spectral data; then, the Raman spectral data is further analyzed using spectral imaging technology, and the spectral data is converted into spatial distribution information to form material matrix imaging data, thereby providing detailed chemical and physical structure data for the analysis of mechanical behavior. Based on these data, the material mechanical contact strength analysis is carried out by combining the fiber finite element arrangement data and the material matrix imaging data. The fiber-matrix contact strength data is obtained through multi-scale mechanical modeling, and the interface bonding strength analysis is further carried out through the pore distribution morphology data and the fiber finite element arrangement data to generate the pore distribution-fiber interface bonding strength data, aiming to reveal the mechanical properties of the interface interaction. The cumulative pressure test is carried out by combining the stress data and the strain data to generate the stress-strain cumulative pressure test data. Through the analysis of the cumulative data, the load curve is further fitted to obtain the stress-strain load curve data, which helps to characterize the nonlinear behavior of the material under different loading conditions. The crack data is analyzed by step-by-step extension, and the crack extension path is calculated using the principle of fracture mechanics to obtain the crack extension data. The crack propagation path is drawn using these data to generate the crack extension path data. This type of analysis can reveal the crack extension trend in the material and its influence on the mechanical properties, especially in terms of damage prediction and life assessment. The strain hardening test is carried out using the stress-strain load curve data and the tensile performance data. Combined with the classic stress-strain model, the hardening characteristics of the composite material during the loading process are obtained, and the strain hardening data of the composite material is generated. At the same time, combined with stress-strain load curve data and bending performance data, the mechanical performance data of the composite material in the bending state is obtained through a three-point bending test to generate composite material bending performance data. These tests and analyses can fully reflect the mechanical response of the material under different loading modes. Combined with the fiber-matrix contact strength data and the pore distribution-fiber interface bonding strength data, data are packaged to generate micro-level processing data. Through data packaging technology, relevant data are merged to form a comprehensive data set at the micro level of the material.At the same time, the stress-strain load curve data and crack propagation path data are merged to generate meso-level processing data, aiming to reveal the meso-level behavior of materials in the process of stress, strain and crack propagation. For macro-level data, the composite material strain hardening data and bending performance data are fused to generate macro-level processing data, which comprehensively describes the mechanical properties of materials at a larger scale.

[0090] Preferably, step S13 includes the following steps:

[0091] Step S131: performing material yield evaluation on the stress-strain load curve data and the composite material strain hardening data to generate composite material hardware yield data; performing fracture toughness simulation on the crack propagation path data and the composite material bending performance data to generate composite material fracture toughness simulation data;

[0092] Step S132: fusing the composite material hardware yield data and the composite material fracture toughness simulation data to generate meso-level-macro-level fusion data;

[0093] Step S133: Use multi-scale fusion to process the meso-level-macro-level fusion data and the micro-level processing data into a mechanical properties data set to generate a multi-level material processing data set.

[0094] In an embodiment of the present invention, the material yield is evaluated by stress-strain load curve data and composite material strain hardening data. This process is usually based on the fitting and analysis of the stress-strain curve, combined with the strain hardening model, and the yield strength of the material is evaluated by fitting the curve of the hardened area. The strain hardening data reflects the strengthening behavior of the material after yielding, which can help evaluate the toughness and deformation resistance of the material. The crack propagation path data is combined with the composite material bending performance data to simulate the fracture toughness, and the fracture mechanics principle is used to analyze the expansion behavior of the crack under the bending load, and then evaluate the fracture toughness of the material. Such simulations are usually based on crack propagation models, such as stress intensity factor (K value) or J integral method, combined with the bending performance of the material, to generate fracture toughness simulation data, reflecting the material's resistance to crack propagation. The composite material hardware yield data and fracture toughness simulation data are combined by data fusion to generate meso-level-macro-level fusion data. Data fusion technology usually includes multi-source data integration and optimization algorithms, such as weighted average method, principal component analysis (PCA), Kalman filtering, etc., through which data from different levels are integrated into a unified data set. This process can effectively combine information at different levels (such as micro, meso and macro), so that the multi-level mechanical properties of the material can be fully reflected. Multi-scale fusion technology is used to process the meso-level-macro fusion data and micro-level processing data into a mechanical properties data set to generate a multi-level material processing data set. Multi-scale fusion technology plays an important role in this step. It can effectively integrate data at different scales (such as micro, macro and meso levels) to generate a unified and operational mechanical properties data set. Through the multi-scale modeling method, the physical behavior of materials at different scales and their mutual influence are considered, so that all mechanical behaviors from micro to macro scales can be consistently described and analyzed.

[0095] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0096] Step S21: performing random field defect analysis on the multi-level material processing data set to generate material microscopic defect distribution data;

[0097] Step S22: Analyze the mechanical energy sensitivity of the material microscopic defect distribution data to generate composite material sensitivity index data;

[0098] Step S23: extracting nonlinear behavior factors according to the composite material sensitivity index data to generate microscopic nonlinear behavior factors of the composite material.

[0099] In an embodiment of the present invention, random field defect analysis is performed on a multi-level material processing data set. This process usually models defects in the material (such as pores, cracks, uneven fiber arrangement, etc.) through random field theory, and generates spatial distribution data of defects based on statistical mechanics methods. Random field analysis can effectively capture the randomness and inhomogeneity in the material, reveal the spatial correlation of microscopic defects, and thus obtain more realistic material defect distribution data. These data are the basis for subsequent analysis, can reveal the variability and complexity of the microstructure in the material, and have strong practical significance. Mechanical energy sensitivity analysis is performed on the material microscopic defect distribution data. By analyzing the mechanical response of the defect data, it is possible to evaluate how the defects affect the mechanical behavior of the material under different loads and stress states. Mechanical energy sensitivity analysis can be performed based on methods such as finite element analysis (FEA) or molecular dynamics (MD), calculate the impact of defects on material properties under different stress conditions, and extract defect sensitivity index data by comparing energy changes. These sensitivity indicators provide important references for performance prediction and optimization of composite materials, revealing which microscopic defects have the greatest impact on overall material behavior, thereby guiding subsequent material design and optimization. Based on the composite material sensitivity index data, nonlinear behavior factors are extracted. The nonlinear behavior factor is a key parameter that describes the nonlinear response of a material under external forces, such as the nonlinear region in the stress-strain curve, the hardening behavior of the material after yielding, etc. The nonlinear behavior factor of the material under complex loading conditions can be extracted through nonlinear analysis methods such as power law fitting, stress-strain relationship model, or the more complex continuum damage mechanics (CDM) model.

[0100] Preferably, step S21 includes the following steps:

[0101] Step S211: Perform Poisson distribution on the pore distribution-fiber interface bonding strength data to generate composite material pore-fiber defect distribution data; perform hardware arrangement changes according to the composite material hardware yield data to generate composite material hardware arrangement defect data;

[0102] Step S212: searching for crack initiation points on composite material fracture toughness simulation data to generate composite material crack initiation point data;

[0103] Step S213: performing random spatial field analysis on the composite material pore-fiber defect distribution data, the composite material hardware arrangement defect data and the composite material crack starting point data to generate material microscopic defect distribution data.

[0104] In the embodiment of the present invention, the pore distribution-fiber interface bonding strength data is subjected to Poisson distribution analysis. As a classic probability distribution model, Poisson distribution is suitable for describing the number of events that occur randomly in a fixed area. In composite materials, the bonding strength between the pores and the fiber interface usually presents a certain randomness, and Poisson distribution analysis can effectively simulate the position and change law of these pores and bonding interfaces, thereby generating composite pore-fiber defect distribution data. In addition, the hardware arrangement is changed according to the hardware yield data of the composite material, and the hardware arrangement defect data can be obtained by mathematically modeling the fluctuation and distribution characteristics of the yield strength data, which provides a key basis for subsequent mechanical property prediction. By searching the crack starting point for the composite fracture toughness simulation data, the crack propagation theory or fracture mechanics model is used to find the initial generation position of the crack. The determination of the crack starting point is the basis for evaluating the fracture behavior of the composite material. This analysis usually relies on fracture toughness simulation, and uses numerical simulation methods such as finite element method (FEA) to conduct in-depth research on the crack propagation path, stress distribution and energy release rate of the material, thereby generating crack starting point data. Random spatial field analysis was performed on the pore-fiber defect distribution data of composite materials, the hardware arrangement defect data of composite materials, and the crack initiation point data of composite materials. Random spatial field analysis is a mathematical method used to process the random distribution characteristics in materials. By constructing a random field model, it can perform spatial correlation analysis on defects in materials, thereby revealing how these defects are distributed in the material and their impact on the overall performance of the material.

[0105] Preferably, step S23 includes the following steps:

[0106] Step S231: performing power law nonlinear fitting on stress-strain load curve data to generate stress-strain load nonlinear fitting data; performing crack interface analysis on composite material fracture toughness simulation data using energy release rate criterion to generate composite crack extension-fracture toughness interface analysis data;

[0107] Step S232: performing a yield strength nonlinear factor analysis on the stress-strain load nonlinear fitting data to generate a composite material yield strength nonlinear factor; performing a plastic deformation modulus nonlinear factor analysis on the stress-strain load nonlinear fitting data to generate a composite material plastic deformation modulus nonlinear factor;

[0108] Step S233: using the continuum damage mechanics method, a nonlinear degradation analysis is performed on the nonlinear factor of the yield strength of the composite material and the nonlinear factor of the plastic deformation modulus of the composite material to generate a factor that conforms to the microscopic nonlinear behavior of the material.

[0109] In an embodiment of the present invention, a power law nonlinear fitting is performed on the stress-strain load curve data. The power law fitting method is a mathematical tool commonly used to describe the behavior of nonlinear materials, and is particularly suitable for the plastic deformation characteristics of materials during stress. Through power law fitting, the strain behavior of composite materials at different stress levels can be revealed, and stress-strain load nonlinear fitting data can be generated, further providing a theoretical basis for material performance analysis. In addition, the energy release rate criterion is used to perform crack interface analysis on the fracture toughness simulation data of the composite material. The energy release rate criterion is an important method in fracture mechanics. By calculating the energy change during crack propagation, the crack propagation behavior of the composite material under load can be accurately predicted, and composite crack propagation-fracture toughness interface analysis data can be generated. Using the stress-strain load nonlinear fitting data, the yield strength and plastic deformation modulus of the composite material are respectively subjected to nonlinear factor analysis. The yield strength is a key indicator of the material yield stage, reflecting the resistance of the material before plastic deformation; while the plastic deformation modulus characterizes the deformation ability of the material. The nonlinear factor analysis of these two indicators can reveal the performance change law of materials under different stress conditions, and then generate the nonlinear factor of the yield strength and plastic deformation modulus of the composite material, providing an in-depth quantitative description of the strength and deformation behavior of the material. The continuous damage mechanics method is used to perform a nonlinear degradation analysis on the nonlinear factor of the yield strength and the nonlinear factor of the plastic deformation modulus of the composite material. Continuous damage mechanics is a theoretical framework for studying the gradual damage and degradation of materials under stress, which can describe the evolution of damage to the material during loading and its impact on the mechanical behavior.

[0110] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0111] Step S31: performing material interface mechanical behavior analysis on the pore distribution-fiber interface bonding strength data to generate composite material interface mechanical behavior data;

[0112] Step S32: performing interface slip-debonding behavior processing on the composite material interface mechanical behavior data to generate composite material interface slip-debonding behavior data;

[0113] Step S33: using the composite material microscopic nonlinear behavior factors and the composite material interface slip-debonding behavior data to perform interface mechanical behavior modeling on the multi-level material processing data set, and generating a composite material mechanical behavior prediction model.

[0114] In the embodiment of the present invention, the pore distribution-fiber interface bonding strength data is subjected to material interface mechanical behavior analysis. Material interface mechanical behavior analysis reveals the mechanical response characteristics of the interface layer by calculating and simulating factors such as the contact strength, bonding characteristics and interface friction between the fiber and the matrix in the composite material. The pore distribution data can be used to further analyze the influence of the structural heterogeneity of the interface layer on the mechanical behavior, and the fiber interface bonding strength determines the stability of the interface bonding and the transfer efficiency during the loading process. Through these analyses, the interface mechanical behavior data of the composite material is generated, laying the foundation for subsequent mechanical analysis and simulation. Interface slip-debonding behavior processing is performed using the composite material interface mechanical behavior data. Interface slip and debonding behavior processing is one of the main failure modes of the interface layer of the composite material, and the slip, crack extension and debonding phenomenon of the interface layer under loading conditions. Through the careful analysis of the interface mechanical data, the plastic deformation, microcrack generation and extension process of the interface layer are captured, thereby obtaining the interface slip-debonding behavior data. The key to this step is to identify the failure mode of the interface material under different loading conditions, and accurately describe its mechanical behavior by combining experimental data with theoretical models. The interface mechanical behavior modeling of the multi-level material processing data set is carried out using the micro-nonlinear behavior factors of the composite materials and the interface slip-debonding behavior data. The micro-nonlinear behavior factors are key parameters obtained through the previous analysis, which reflect the nonlinear mechanical properties of the material at the micro scale, including the initiation and extension of microcracks and local plastic deformation. Combined with the interface slip-debonding behavior data, a more comprehensive and accurate interface mechanical behavior model can be established within the framework of the multi-level material processing data set. The multi-level modeling not only considers the interaction between micro- and macro-mechanical properties, but also integrates the failure mechanisms at different scales, thereby forming a predictive model for the mechanical behavior of composite materials.

[0115] Preferably, step S33 includes the following steps:

[0116] Step S331: Establishing a fiber-matrix three-dimensional material model using the fiber-matrix contact strength data to generate a preliminary three-dimensional model of the fiber-matrix composite material;

[0117] Step S332: optimizing the defect nonlinear factor of the preliminary three-dimensional model of the fiber-matrix composite material by using the composite material yield strength nonlinear factor and the composite material plastic deformation modulus nonlinear factor to generate a composite material driving factor optimized three-dimensional model;

[0118] Step S333: Use the composite material interface slip-debonding behavior data to simulate the physical properties of the composite material driving factor optimization three-dimensional model, and use the composite material fracture toughness simulation data to simulate the material mechanical behavior properties to generate a composite material mechanical behavior prediction model.

[0119] In the embodiment of the present invention, the fiber-matrix contact strength data is used to establish a fiber-matrix three-dimensional material model. The contact strength between the fiber and the matrix is ​​a key factor in the mechanical properties of the composite material, which directly determines the stress distribution and failure mode of the material under the external load. By processing the fiber-matrix contact strength data, the mechanical interaction between the fiber and the matrix interface layer can be revealed. This data-driven model can accurately describe the microstructure and mechanical properties of the material, and then generate a preliminary three-dimensional material model. This process uses computer modeling technology to combine the mechanical properties of the fiber and the matrix, the interface contact situation and the geometric morphology of the material to form a preliminary three-dimensional structural model of the composite material. The defect nonlinear factor of the preliminary three-dimensional model of the fiber-matrix composite material is optimized using the composite material yield strength nonlinear factor and the composite material plastic deformation modulus nonlinear factor. The yield strength nonlinear factor and the plastic deformation modulus nonlinear factor are key parameters reflecting the yield and plastic behavior of the material. By optimizing these factors, the small defects, stress concentration and plastic deformation areas in the composite material can be corrected, thereby generating a more accurate composite material driving factor optimized three-dimensional model. This optimization process is based on a data-driven algorithm, taking into account the nonlinear response characteristics of composite materials under different working environments, and further adjusting the preliminary model to be more consistent with the actual mechanical behavior. The physical properties of the optimized three-dimensional model of the composite driving factor are simulated using the composite interface slip-debonding behavior data, and the mechanical behavior properties of the material are simulated using the composite fracture toughness simulation data. Interface slip-debonding behavior is an important form of failure of the interface layer of composite materials. Especially under high loads and extreme environments, the slip and debonding of the interface layer will significantly affect the mechanical properties of the material. Therefore, simulation using interface slip-debonding behavior data can effectively simulate the damage and crack propagation of the material during loading, thereby better predicting the failure mode of the material. In addition, by simulating the fracture toughness data of the composite material, the anti-fracture performance of the material under extreme conditions can be further revealed, thereby providing accurate data support for the prediction of mechanical behavior.

[0120] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0121] Step S41: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data;

[0122] Step S42: performing a prediction behavior integrity assessment based on the composite material mechanical behavior prediction data to generate composite material mechanical behavior integrity assessment data;

[0123] Step S43: constructing a prediction report based on the composite material mechanical behavior integrity assessment data, thereby completing the virtual prediction operation of the composite material mechanical behavior.

[0124] In an embodiment of the present invention, the prediction data of mechanical behavior of composite materials are generated by predicting the prediction model of mechanical behavior of composite materials. This step uses the existing prediction model of mechanical behavior of composite materials to simulate and predict the stress, strain, deformation and other behaviors of composite materials under different loading conditions. By inputting the microscopic and macroscopic mechanical property data of the material, combined with the external load conditions, advanced numerical methods such as finite element analysis (FEA) or computational fluid dynamics (CFD) are used to perform model calculations to obtain prediction data. These prediction data include key information such as yield strength, elastic modulus, crack propagation behavior, deformation mode, etc. of the material, providing a data basis for subsequent material performance evaluation. According to the prediction data of mechanical behavior of composite materials, the prediction behavior integrity assessment is performed to generate the mechanical behavior integrity assessment data of composite materials. This step aims to analyze the integrity and reliability of the material prediction results and evaluate the applicability of the prediction model under different working conditions. The specific method includes quantitative analysis of the behavioral integrity of the prediction data, and the use of techniques such as sensitivity analysis, error analysis and uncertainty analysis to evaluate the accuracy and stability of various prediction indicators (such as yield strength, failure mode, etc.). By establishing behavioral integrity evaluation indicators, it is possible to identify potential errors, data uncertainties or overfitting problems in the prediction model, and then further optimize the model to ensure its accuracy and reliability in practical applications. A prediction report is constructed based on the integrity evaluation data of the mechanical behavior of composite materials to complete the virtual prediction of the mechanical behavior of composite materials. This step generates a detailed prediction report by integrating the integrity evaluation data with the prediction results. The report includes not only the prediction data of the mechanical properties of the material, but also the evaluation of the prediction results and the reliability analysis of the model. The report content usually covers key information such as the performance of the material under different conditions of use, potential failure modes, and directions for improvement. Through this report, designers and engineers can gain an in-depth understanding of the performance of composite materials and predict their behavior during actual use, thereby providing a basis for material selection, processing technology design, and structural optimization.

[0125] The beneficial effect of the present invention is that the material level data set is obtained and processed, and the integrity and consistency of the data are ensured through multi-level data preprocessing, which lays the foundation for subsequent analysis. Then, through random field defect analysis, the material micro defect distribution data is generated. This process can effectively reveal the defect characteristics of the composite material at the micro scale and provide data support for further nonlinear behavior factor extraction. The nonlinear behavior factor extraction ensures that the nonlinear response of the material under complex loading conditions is accurately described by quantifying the micromechanical behavior characteristics of the material, which is crucial for the practical application of composite materials, especially under high stress or dynamic load. Next, the interface mechanical behavior modeling is carried out in combination with the micro nonlinear behavior factor, and the interaction between the fiber and the matrix, the interface damage and its influence on the overall mechanical properties are fully considered. The mechanical behavior of the composite material under different use conditions is predicted by the model, which provides accurate data support for design and optimization. On this basis, the prediction results of the model can generate detailed mechanical behavior prediction data, and the prediction results are effectively displayed in the form of report construction, so that designers can have a deeper understanding and judgment of the material performance before actual production. The advantage of this process is that through virtual prediction and simulation, the demand for actual experiments is reduced, the cycle of material design and testing is shortened, and the design efficiency and material reliability are improved. Therefore, the present invention solves the problem of reliance on experimental verification with long cycles and high costs in traditional material design through multi-level data set processing and interface mechanics modeling technology, and improves the accuracy and efficiency of composite material performance optimization.

[0126] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0127] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A virtual prediction method for mechanical behavior of composite materials, characterized in that: The following steps are involved: Step S1: obtaining a material level data set; performing data preprocessing on the material level data set to generate a multi-level material processing data set; Step S2: Perform random field defect analysis on the multi-level material processing data set to generate material micro defect distribution data; Extract nonlinear behavior factors from material microscopic defect distribution data to generate microscopic nonlinear behavior factors of composite materials; Step S3: using the composite material microscopic nonlinear behavior factor to model the interface mechanical behavior of the multi-level material processing data set to generate a composite material mechanical behavior prediction model; Step S4: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data; A prediction report is constructed based on the prediction data of the mechanical behavior of composite materials, thereby completing the virtual prediction task of the mechanical behavior of composite materials.

2. The virtual prediction method of mechanical behavior of composite materials according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a material hierarchical data set, divide the material hierarchical data set into hierarchical levels, and obtain material hierarchical type data, wherein the material hierarchical type data includes micro-level data, meso-level data, and macro-level data, the micro-level data includes fiber arrangement data, pore distribution data, and material matrix property data, the meso-level data includes stress data, strain data, and crack data, and the macro-level data includes tensile performance data and bending performance data; Step S12: performing micro-scale material property analysis on the micro-level data to generate micro-level processing data; performing characteristic behavior analysis on the meso-level data to generate meso-level processing data; performing mechanical property analysis on the macro-level data to generate macro-level processing data; Step S13: fusing the meso-level processed data and the macro-level processed data to generate meso-level-macro-level fused data; using multi-scale fusion to process the meso-level-macro-level fused data and the micro-level processed data into a mechanical properties data set to generate a multi-level material processing data set.

3. The virtual prediction method of mechanical behavior of composite materials according to claim 2, characterized in that: Step S12 includes the following steps: Step S121: perform finite element analysis on the fiber arrangement data to generate fiber finite element arrangement data; perform pore morphology analysis on the pore distribution data to generate pore distribution morphology data; perform Raman spectroscopy processing on the material matrix characteristic data to generate material matrix spectral data; perform spectral imaging processing on the material matrix spectral data to generate material matrix imaging data; perform material mechanical contact strength analysis based on the fiber finite element arrangement data and the material matrix imaging data to generate fiber-matrix contact strength data; perform interface bonding strength analysis based on the pore distribution morphology data and the fiber finite element arrangement data to generate pore distribution-fiber interface bonding strength data; Step S122: performing cumulative pressure test on stress data and strain data to generate stress-strain cumulative pressure test data; performing load curve analysis on the stress-strain cumulative pressure test data to generate stress-strain load curve data; performing crack step-by-step extension analysis on crack data to generate crack extension data; performing material propagation path drawing on crack extension data to generate crack extension path data; Step S123: performing a strain hardening test on the stress-strain load curve data and the tensile performance data to generate composite material strain hardening data; performing a three-point bending test on the stress-strain load curve data and the bending performance data to generate composite material bending performance data; Step S124: Pack the fiber-matrix contact strength data and the pore distribution-fiber interface bonding strength data to generate micro-level processing data; merge the stress-strain load curve data and the crack propagation path data to generate meso-level processing data; fuse the composite material strain hardening data and the composite material bending performance data to generate macro-level processing data.

4. The virtual prediction method of mechanical behavior of composite materials according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: performing material yield evaluation on the stress-strain load curve data and the composite material strain hardening data to generate composite material hardware yield data; performing fracture toughness simulation on the crack propagation path data and the composite material bending performance data to generate composite material fracture toughness simulation data; Step S132: fusing the composite material hardware yield data and the composite material fracture toughness simulation data to generate meso-level-macro-level fusion data; Step S133: Use multi-scale fusion to process the meso-level-macro-level fusion data and the micro-level processing data into a mechanical properties data set to generate a multi-level material processing data set.

5. The virtual prediction method of mechanical behavior of composite materials according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing random field defect analysis on the multi-level material processing data set to generate material microscopic defect distribution data; Step S22: Analyze the mechanical energy sensitivity of the material microscopic defect distribution data to generate composite material sensitivity index data; Step S23: extracting nonlinear behavior factors according to the composite material sensitivity index data to generate microscopic nonlinear behavior factors of the composite material.

6. The virtual prediction method of mechanical behavior of composite materials according to claim 5, characterized in that: Step S21 includes the following steps: Step S211: Perform Poisson distribution on the pore distribution-fiber interface bonding strength data to generate composite material pore-fiber defect distribution data; perform hardware arrangement changes according to the composite material hardware yield data to generate composite material hardware arrangement defect data; Step S212: searching for crack initiation points on composite material fracture toughness simulation data to generate composite material crack initiation point data; Step S213: performing random spatial field analysis on the composite material pore-fiber defect distribution data, the composite material hardware arrangement defect data and the composite material crack starting point data to generate material microscopic defect distribution data.

7. The virtual prediction method of mechanical behavior of composite materials according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: performing power law nonlinear fitting on stress-strain load curve data to generate stress-strain load nonlinear fitting data; performing crack interface analysis on composite material fracture toughness simulation data using energy release rate criterion to generate composite crack extension-fracture toughness interface analysis data; Step S232: performing a yield strength nonlinear factor analysis on the stress-strain load nonlinear fitting data to generate a composite material yield strength nonlinear factor; performing a plastic deformation modulus nonlinear factor analysis on the stress-strain load nonlinear fitting data to generate a composite material plastic deformation modulus nonlinear factor; Step S233: using the continuum damage mechanics method, a nonlinear degradation analysis is performed on the nonlinear factor of the yield strength of the composite material and the nonlinear factor of the plastic deformation modulus of the composite material to generate a factor that conforms to the microscopic nonlinear behavior of the material.

8. The virtual prediction method of mechanical behavior of composite materials according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing material interface mechanical behavior analysis on the pore distribution-fiber interface bonding strength data to generate composite material interface mechanical behavior data; Step S32: performing interface slip-debonding behavior processing on the composite material interface mechanical behavior data to generate composite material interface slip-debonding behavior data; Step S33: using the composite material microscopic nonlinear behavior factors and the composite material interface slip-debonding behavior data to perform interface mechanical behavior modeling on the multi-level material processing data set, and generating a composite material mechanical behavior prediction model.

9. The virtual prediction method of mechanical behavior of composite materials according to claim 8, characterized in that: Step S33 includes the following steps: Step S331: Establishing a fiber-matrix three-dimensional material model using the fiber-matrix contact strength data to generate a preliminary three-dimensional model of the fiber-matrix composite material; Step S332: optimizing the defect nonlinear factor of the preliminary three-dimensional model of the fiber-matrix composite material by using the composite material yield strength nonlinear factor and the composite material plastic deformation modulus nonlinear factor to generate a composite material driving factor optimized three-dimensional model; Step S333: Use the composite material interface slip-debonding behavior data to simulate the physical properties of the composite material driving factor optimization three-dimensional model, and use the composite material fracture toughness simulation data to simulate the material mechanical behavior properties to generate a composite material mechanical behavior prediction model.

10. The virtual prediction method of mechanical behavior of composite materials according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: predicting the composite material mechanical behavior prediction model to generate composite material mechanical behavior prediction data; Step S42: performing a prediction behavior integrity assessment based on the composite material mechanical behavior prediction data to generate composite material mechanical behavior integrity assessment data; Step S43: constructing a prediction report based on the composite material mechanical behavior integrity assessment data, thereby completing the virtual prediction operation of the composite material mechanical behavior.

Images