Performance analysis method of aluminum alloy material

Abstract

The invention relates to the technical field of aluminum alloy material performance analysis, in particular to a performance analysis method of an aluminum alloy material. The method comprises the following steps that aluminum alloy material data are collected, and component data of an aluminum alloy material are obtained through component detection; performing initial property analysis on the aluminum alloy material to obtain physical and mechanical property data of the material; carrying out bearing capacity estimation on the aluminum alloy material through load simulation, and carrying out thermal cycle threshold calculation on the material in combination with thermal cycle simulation to obtain thermal cycle performance data of the aluminum alloy material; the comprehensive performance of the aluminum alloy material is analyzed based on the thermal cycle threshold value and the bearing capacity data, the performance of the aluminum alloy material under different working conditions is evaluated, the composition proportion of the aluminum alloy material is optimized according to the performance data of the material, and the optimal material performance is obtained. By optimizing the components of the aluminum alloy material, the performance of the aluminum alloy material is more stable.

Description

Technical Field

[0001] The present invention relates to the technical field of performance analysis of aluminum alloy materials, and particularly to a method for analyzing the performance of aluminum alloy materials. Background Art

[0002] Due to its excellent physical and chemical properties, such as lightweight, high strength, good corrosion resistance and workability, aluminum alloy materials have been widely used in the fields of aerospace, automotive manufacturing, construction and electronics. However, with the continuous improvement of the requirements for material performance in modern engineering applications, it is difficult to comprehensively evaluate the comprehensive performance of aluminum alloy materials based on experience or a single performance index alone. Therefore, it is particularly important to study a systematic and scientific method for analyzing the performance of aluminum alloy materials. The performance analysis of aluminum alloy materials needs to start from the basic composition and microstructure of the materials, and combine their performance manifestations such as load behavior and thermal cycle reaction under actual working conditions to gradually evaluate their mechanical, thermal and chemical properties. For example, the microscopic crystal structure of aluminum alloy materials directly affects their strength and plasticity; the distribution and stability of precipitation phases determine the heat-resistant fatigue performance and corrosion resistance of the materials. In addition, the deformation behavior under load and the thermal expansion and fatigue accumulation under thermal cycle conditions are also the key factors for the performance degradation of aluminum alloys. However, the traditional method for analyzing the performance of an aluminum alloy material has problems of inaccurate performance analysis of the aluminum alloy and inaccurate analysis of the aging trend of the aluminum alloy material. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for analyzing the performance of aluminum alloy materials to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for analyzing the performance of aluminum alloy materials includes the following steps:

[0005] Step S1: Collect aluminum alloy material data; use the aluminum alloy material data to detect the composition of the aluminum alloy material to obtain aluminum alloy material composition data; based on the aluminum alloy material composition data, analyze the initial properties of the aluminum alloy material to obtain aluminum alloy material initial property data;

[0006] Step S2: Perform load simulation on the aluminum alloy material initial property data to obtain aluminum alloy material load simulation data; based on the aluminum alloy material load simulation data, estimate the bearing capacity of the aluminum alloy material to obtain aluminum alloy material bearing capacity data;

[0007] Step S3: Perform thermal cycle simulation on the aluminum alloy material initial property data to obtain aluminum alloy material thermal cycle simulation data; use the aluminum alloy material thermal cycle simulation data to calculate the thermal cycle threshold of the aluminum alloy material to obtain aluminum alloy material thermal cycle threshold data;

[0008] Step S4: Perform performance analysis on the aluminum alloy material based on the thermal cycle threshold and the bearing capacity data of the aluminum alloy material to generate aluminum alloy material performance data; optimize the ingredient ratio of the aluminum alloy material based on the aluminum alloy material performance data for the initial property data of the aluminum alloy material to obtain optimized ingredient ratio data for the aluminum alloy material.

[0009] By collecting aluminum alloy material data and conducting component detection, the present invention can comprehensively grasp the basic chemical composition and component ratio of the material, laying a foundation for subsequent analysis. Based on the component data for initial property analysis, the physical properties, chemical properties, and microstructural characteristics of the material can be effectively evaluated. Further combined with thermal cycle simulation, it can effectively simulate the thermal fatigue behavior and the change law of material properties of aluminum alloy under complex temperature conditions, revealing the influence of the thermal environment on its stability and lifespan. Using the thermal cycle simulation data to calculate the thermal cycle threshold can scientifically predict the bearing limit of the material under temperature fluctuation conditions, avoiding material failure caused by the accumulation of thermal stress. Combining the thermal cycle threshold data with the bearing capacity data for performance analysis can comprehensively evaluate the comprehensive performance of the material under actual working conditions, ensuring the scientificity and rationality of material selection. In addition, through in-depth study of the performance analysis results, optimizing the ingredient ratio of the material can significantly improve the strength, toughness, and fatigue resistance of the aluminum alloy, thus meeting the requirements of higher-standard industrial applications. This method can not only comprehensively improve the accuracy of performance evaluation and optimization efficiency of aluminum alloy materials, but also provide a scientific basis for the design and research and development of new aluminum alloys, with extremely high engineering value and application prospects. Therefore, the present invention is an optimized treatment of the traditional performance analysis method for aluminum alloy materials, solving the problems of inaccurate performance analysis of aluminum alloy and inaccurate analysis of the aging trend of aluminum alloy materials in the traditional performance analysis method for a certain aluminum alloy material. It improves the accuracy of performance analysis of aluminum alloy and the accuracy of analysis of the aging trend of aluminum alloy materials.

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

[0011] Step S11: Collect aluminum alloy material data;

[0012] Step S12: Use the aluminum alloy material data to conduct component detection of the aluminum alloy material, thereby obtaining aluminum alloy material component data;

[0013] Step S13: Based on the aluminum alloy material component data, conduct chemical property detection of the aluminum alloy material to obtain aluminum alloy material chemical property data;

[0014] Step S14: Based on the aluminum alloy material component data, conduct physical property detection of the aluminum alloy material to obtain aluminum alloy material physical property data;

[0015] Step S15: Analyze the initial properties of the aluminum alloy material based on the physical property data and chemical property data of the aluminum alloy material to obtain the initial property data of the aluminum alloy material.

[0016] Through the collection of aluminum alloy material data, the present invention comprehensively obtains the basic information of the sample, providing raw data support for subsequent analysis. Subsequently, through component detection, the main chemical components of the aluminum alloy and their proportion distribution are clarified, thereby revealing the internal structural characteristics and performance potential of the material. The chemical property detection carried out based on the component data can effectively evaluate the corrosion resistance, chemical stability of the material and its performance in various chemical environments, providing a scientific basis for the applicability evaluation of the material. Further physical property detection quantifies key parameters such as the density, hardness, thermal conductivity and electrical conductivity of the material, providing support for its mechanical and thermal properties in actual working conditions. By combining and analyzing the chemical property and physical property data, the overall performance of the aluminum alloy material can be comprehensively evaluated, providing a comprehensive reference for its reliability and applicability in different application scenarios. This method, through systematic initial property analysis, can not only provide a scientific basis for the optimization design of aluminum alloy materials, but also effectively improve the material selection efficiency and application effect, with significant engineering practical value.

[0017] Preferably, step S13 includes the following steps:

[0018] Step S131: Collect the chemical elements of the aluminum alloy material according to the aluminum alloy material component data to obtain the aluminum alloy material chemical element data;

[0019] Step S132: Detect the chemical antioxidant ability of the aluminum alloy material according to the aluminum alloy material chemical element data to obtain the aluminum alloy material antioxidant ability data;

[0020] Step S133: Evaluate the chemical reaction ability of the aluminum alloy material based on the aluminum alloy material antioxidant ability data to obtain the aluminum alloy material chemical reaction ability data;

[0021] Step S134: Analyze the corrosion resistance of the aluminum alloy material according to the aluminum alloy material chemical reaction ability data and the aluminum alloy material antioxidant ability data to obtain the aluminum alloy material corrosion resistance data;

[0022] Step S135: Detect the chemical stability of the aluminum alloy material based on the aluminum alloy material corrosion resistance data and the aluminum alloy material chemical reaction ability data to obtain the aluminum alloy material chemical stability data;

[0023] Step S136: Detect the chemical properties of the aluminum alloy material based on the aluminum alloy material chemical stability data to obtain the aluminum alloy material chemical property data.

[0024] Through the collection of chemical elements, the present invention can accurately obtain the chemical composition information of aluminum alloy materials, providing precise data support for subsequent performance evaluation. The detection of the material's antioxidant ability evaluates its stability in high-temperature or oxidizing environments, thereby providing a reliable reference basis for the application of the material in special environments. On this basis, through the calculation of the chemical reaction ability, the activity of aluminum alloy materials in various chemical media can be comprehensively predicted, clarifying the chemical changes that occur under complex working conditions. The corrosion resistance analysis further deepens the evaluation of the performance of aluminum alloy materials in corrosive environments such as humid heat, acid, and alkali, revealing the service life and environmental adaptability of the materials. In addition, through chemical stability detection, the long-term performance stability of the materials can be systematically quantified, providing a scientific guarantee for the selection of materials in scenarios with high reliability requirements. Through the integrated analysis of the above detection results, the chemical properties of aluminum alloy materials can be comprehensively revealed, providing systematic guidance for material optimization, process improvement, and the design of new aluminum alloys. This method significantly improves the comprehensiveness and accuracy of the chemical performance evaluation of aluminum alloy materials through the coordination of each step, laying a solid foundation for efficient material selection and performance prediction in engineering applications.

[0025] Preferably, step S14 includes the following steps:

[0026] Step S141: Measure the density of the aluminum alloy material based on the aluminum alloy material composition data to generate aluminum alloy material density data;

[0027] Step S142: Scan the element distribution pattern according to the aluminum alloy material composition data to generate aluminum alloy element distribution pattern data;

[0028] Step S143: Analyze the crystal arrangement mode of aluminum alloy elements based on the aluminum alloy element distribution pattern data to obtain aluminum alloy element crystal arrangement mode data;

[0029] Step S144: Collect the crystal microstructure of the aluminum alloy material based on the aluminum alloy element crystal arrangement mode data and the aluminum alloy element distribution pattern data to obtain aluminum alloy material crystal microstructure data;

[0030] Step S145: Calculate the crystal dislocation density according to the aluminum alloy material crystal microstructure data to obtain aluminum alloy material crystal dislocation density data;

[0031] Step S146: Estimate the hardness of the aluminum alloy material based on the aluminum alloy material crystal dislocation density data and the aluminum alloy material crystal microstructure data to obtain aluminum alloy material hardness data;

[0032] Step S147: Detect the physical properties of the aluminum alloy material based on the aluminum alloy material hardness data and the aluminum alloy material density data to obtain aluminum alloy material physical property data.

[0033] The present invention measures density based on material composition data, which can accurately evaluate the basic quality characteristics of materials and provide a data basis for subsequent performance analysis. The element distribution map scanning further reveals the distribution state of each element in the material, providing support for analyzing the uniformity of crystal structure and performance. By analyzing the crystal arrangement mode of elements, the regularity and directionality of the internal microstructure of the material are deeply understood, which is of great significance for predicting the mechanical properties of the material. The acquisition of crystal microstructure combined with crystal arrangement information provides accurate data support for comprehensively understanding the internal tissue state of the material, thus laying the foundation for the optimal design of the material. The calculation of crystal dislocation density reveals the microscopic deformation behavior of the material under external forces, providing a reliable basis for evaluating the plasticity and strength characteristics of the material. In addition, by combining dislocation density with microstructure data for hardness prediction, the anti-deformation ability of the material is quickly and efficiently deduced, providing a non-destructive method for hardness measurement. The physical property detection by combining hardness data and density data realizes the accurate quantification and comprehensive evaluation of the overall performance of the material. Each step is closely linked. By analyzing various characteristics such as density, microstructure, and mechanical properties, the depth and breadth of the physical property evaluation of aluminum alloy materials are effectively improved, providing important technical support for material performance optimization, use scenario expansion, and the development of high-performance aluminum alloys.

[0034] Preferably, step S2 includes the following steps:

[0035] Step S21: Conduct a load simulation on the initial property data of the aluminum alloy material to obtain load simulation data of the aluminum alloy material;

[0036] Step S22: Use the load simulation data of the aluminum alloy material to conduct a deformation analysis of the aluminum alloy material to obtain deformation data of the aluminum alloy material;

[0037] Step S23: Estimate the aging trend of the aluminum alloy material based on the deformation data of the aluminum alloy material to obtain aging trend data of the aluminum alloy material;

[0038] Step S24: Estimate the bearing capacity of the aluminum alloy material based on the aging trend data and deformation data of the aluminum alloy material to obtain bearing capacity data of the aluminum alloy material.

[0039] The present invention uses initial property data for load simulation, which can accurately reproduce the response behavior of materials under different stress conditions, laying a foundation for the study of the mechanical properties of materials. Deformation analysis further reveals the stress distribution and deformation characteristics of materials under load, facilitating an in-depth understanding of their anti-deformation ability and structural stability. The prediction of the aging trend provides a scientific basis for the risk assessment of the performance degradation of materials during long-term use and can effectively predict the service life of materials in a specific environment. The prediction of the bearing capacity based on deformation data and aging trend data can comprehensively analyze the mechanical property performance of materials under dynamic load and aging conditions, thereby providing crucial guidance for material selection and design optimization. Each step of this method works synergistically. Through systematic analysis from load simulation to bearing capacity, it realizes the comprehensiveness and accuracy of material performance research, providing reliable technical support for the development and application of high-performance aluminum alloy materials.

[0040] Preferably, step S22 includes the following steps:

[0041] Step S221: Measure the tensile state of the aluminum alloy material based on the load simulation data of the aluminum alloy material to obtain the tensile state data of the aluminum alloy material;

[0042] Step S222: Analyze the necking effect of the aluminum alloy material based on the tensile state data of the aluminum alloy material to obtain the necking effect data of the aluminum alloy material;

[0043] Step S223: Calculate the local stress concentration of the aluminum alloy material based on the necking effect data of the aluminum alloy material to obtain the local stress concentration data of the aluminum alloy material;

[0044] Step S224: Estimate the plastic instability of the aluminum alloy material based on the local stress concentration data of the aluminum alloy material to generate the plastic instability data of the aluminum alloy material;

[0045] Step S225: Conduct deformation analysis on the plastic instability data of the aluminum alloy material to obtain the deformation data of the aluminum alloy material.

[0046] Through the measurement of the tensile state of the load simulation data of the aluminum alloy material, the present invention can accurately evaluate the mechanical properties of the material under tensile conditions, providing an important basis for further studying its stress behavior. The necking effect analysis can reveal the local morphological change characteristics of the material in the plastic deformation stage, helping to clarify the non-uniformity of material deformation and potential failure areas. The calculation of local stress concentration further refines the understanding of the internal stress distribution of the material, enabling effective identification of stress concentration risk points, thereby guiding the optimization design of the material structure. The plastic instability estimation based on local stress concentration can predict the instability behavior of the material under extreme stress conditions, providing a scientific basis for material safety assessment. The deformation analysis combined with plastic instability data realizes a comprehensive evaluation of the global and local deformation laws of the material under complex load conditions, providing a high-value reference basis for the selection, processing, and performance optimization of aluminum alloy materials in practical engineering applications.

[0047] Preferably, step S24 includes the following steps:

[0048] Step S241: Detect the crystal slip state of the aluminum alloy material based on the deformation data of the aluminum alloy material to obtain the crystal slip state data of the aluminum alloy material;

[0049] Step S242: Detect the microdamage of the aluminum alloy material according to the crystal slip state data of the aluminum alloy material to obtain the microdamage data of the aluminum alloy material;

[0050] Step S243: Calculate the crack propagation probability of the aluminum alloy material according to the microdamage data of the aluminum alloy material to obtain the crack propagation probability data of the aluminum alloy material;

[0051] Step S245: Estimate the aging trend of the aluminum alloy material based on the microdamage data of the aluminum alloy material and the crack propagation probability data of the aluminum alloy material to obtain the aging trend data of the aluminum alloy material.

[0052] The detection of crystal slip state in the present invention can accurately identify the slip behavior of the crystal structure of the material during the stress process, reveal its deformation mechanism and potential failure paths, and provide a microscopic basis for the optimization of the mechanical properties of aluminum alloy materials. The microscopic damage detection further refines the ability to identify internal defects of the material, can accurately evaluate the local damage characteristics generated by the deformation of the material, and thus enhances the understanding of the material damage accumulation process. The calculation of crack propagation probability provides a scientific basis for evaluating the propagation trend of material cracks under different working conditions, and helps to clarify the potential failure risk points of the material in practical applications. Through the combination of microscopic damage data and crack propagation probability data, the aging trend prediction can comprehensively predict the performance degradation law of the material under long-term service conditions from a microscopic perspective, and provide systematic guidance for the life assessment and design optimization of aluminum alloy materials. At the same time, the organic combination of these steps realizes the multi-scale data integration and analysis from microscopic damage characteristics to macroscopic aging trends, laying a solid technical foundation for the reliability research of the performance of aluminum alloy materials.

[0053] Preferably, step S3 includes the following steps:

[0054] Step S31: Perform a thermal cycle simulation on the initial property data of the aluminum alloy material to obtain the thermal cycle simulation data of the aluminum alloy material;

[0055] Step S32: Conduct a thermal fatigue cumulative analysis on the thermal cycle simulation data of the aluminum alloy material to generate the thermal fatigue cumulative data of the aluminum alloy material;

[0056] Step S33: Estimate the attenuation of the mechanical properties of the aluminum alloy material based on the thermal fatigue cumulative data of the aluminum alloy material to obtain the mechanical property attenuation data of the aluminum alloy material;

[0057] Step S34: Perform a rigidity attenuation detection based on the mechanical property attenuation data of the aluminum alloy material to obtain the rigidity attenuation data of the aluminum alloy material;

[0058] Step S35: Calculate the thermal cycle threshold of the aluminum alloy material by using the rigidity attenuation data of the aluminum alloy material and the mechanical property attenuation data of the aluminum alloy material to obtain the thermal cycle threshold data of the aluminum alloy material.

[0059] Through the implementation of this method, the present invention effectively improves the performance prediction ability of aluminum alloy materials under complex thermal cycling conditions, comprehensively reveals their thermodynamic behavior and failure mechanism, and demonstrates significant technical advantages. Thermal cycling simulation can comprehensively reproduce the actual working conditions of aluminum alloy materials under different thermal cycling conditions, providing basic data support for studying their thermal response characteristics. Thermal fatigue cumulative analysis further clarifies the fatigue evolution law of aluminum alloy materials in high-temperature environments by quantifying the degree of thermal fatigue damage accumulated in the materials during multiple thermal cycles, and can provide a reliable basis for optimizing the service life of the materials. Based on the thermal fatigue cumulative data, the mechanical property attenuation is predicted to accurately evaluate the attenuation trends of the strength and plasticity of the materials caused by thermal fatigue damage, laying a data foundation for the analysis of the mechanical behavior of the materials in a thermal environment. The rigid attenuation detection can reveal the impact of thermal fatigue on the overall structural stability of the materials by further analyzing the law of the rigid decline of the materials during thermal cycling, thereby providing technical support for improving the structural integrity of the materials. The thermal cycling threshold calculation combines the mechanical property attenuation data and the rigid attenuation data to comprehensively evaluate the performance limit of the materials under complex thermal cycling conditions, providing a scientific basis for the application design and service life prediction of aluminum alloy materials.

[0060] Preferably, step S32 includes the following steps:

[0061] Step S321: Calculate the precipitation phase dissolution probability of the aluminum alloy material based on the thermal cycling simulation data of the aluminum alloy material to obtain the precipitation phase dissolution probability data of the aluminum alloy material;

[0062] Step S322: Estimate the deterioration of the microstructure based on the precipitation phase dissolution probability data of the aluminum alloy material to obtain the microstructure deterioration data of the aluminum alloy material;

[0063] Step S323: Analyze the attenuation of the thermal stability of the aluminum alloy material based on the thermal cycling simulation data of the aluminum alloy material to obtain the thermal stability attenuation data of the aluminum alloy material;

[0064] Step S324: Conduct a cumulative analysis of the thermal fatigue of the aluminum alloy material based on the thermal stability attenuation data and the microstructure deterioration data of the aluminum alloy material to generate the cumulative thermal fatigue data of the aluminum alloy material.

[0065] When conducting thermal fatigue analysis on aluminum alloy materials, the present invention calculates the precipitation phase dissolution probability of the materials through thermal cycle simulation data, effectively predicting the change trend of precipitation phases in the materials under different temperatures and stresses, thereby helping to understand the influence of their mechanical properties and thermal stability on temperature changes. This calculation not only provides accurate data support for the subsequent prediction of microstructural deterioration, but also reveals the phase change behavior that occurs during the actual use of the materials, which is of great significance for the performance evaluation and optimal design of aluminum alloys. Combining the precipitation phase dissolution probability data to estimate the microstructural deterioration of aluminum alloy materials accurately predicts the evolution process of grain growth, phase change, or structural defects that occur during long-term thermal cycling of the materials. This estimation provides a basis for the life prediction and thermal fatigue performance evaluation of aluminum alloy materials, helping to detect failure mechanisms in advance, and thus taking appropriate measures to avoid premature fatigue failure of the materials under actual working conditions. Analyzing the thermal stability decay of aluminum alloy materials through thermal cycle simulation data obtains the thermal stability decline of the materials under different working environments and usage conditions. This provides data support for the evaluation of the high-temperature resistance performance of aluminum alloy materials, helping to select suitable aluminum alloy materials and optimize usage conditions in engineering design to improve the reliability of the materials in high-temperature environments. Combining the thermal stability decay data and microstructural deterioration data to conduct a cumulative thermal fatigue analysis of aluminum alloy materials further quantifies the fatigue damage accumulation of the materials under repeated thermal cycling conditions. This analysis can more comprehensively reflect the actual service life and thermal fatigue performance of aluminum alloy materials, and thus provide an important theoretical basis for material selection, process optimization, and application scenarios. Through this series of steps, it is possible to comprehensively evaluate the thermal fatigue performance of aluminum alloy materials in multiple aspects, identify potential failure risks in advance, and provide data support and guiding opinions for material improvement and reliability enhancement.

[0066] Preferably, step S4 includes the following steps:

[0067] Step S41: Conduct stability detection of the aluminum alloy material according to the thermal cycle threshold of the aluminum alloy material and the load-bearing capacity data of the aluminum alloy material to obtain aluminum alloy material stability data;

[0068] Step S42: Estimate the service life of the aluminum alloy material based on the aluminum alloy material stability data to obtain aluminum alloy material service life data;

[0069] Step S43: Conduct performance analysis of the aluminum alloy material based on the aluminum alloy material service life data and the aluminum alloy material stability data to generate aluminum alloy material performance data;

[0070] Step S44: Optimize the component ratio of the aluminum alloy material initial property data according to the aluminum alloy material performance data to obtain aluminum alloy material component ratio optimization data.

[0071] Through the comprehensive analysis of the thermal cycle threshold and load-bearing capacity data of aluminum alloy materials, the stability detection of aluminum alloy materials is carried out to comprehensively evaluate the stability of the materials under different working conditions. This detection helps to confirm the load-bearing capacity and structural stability of aluminum alloy materials under long-term load and high-temperature environment, providing data support for further performance optimization and service condition design. Based on the stability data, the service life prediction can quantify the long-term performance of the materials in actual use, predict in advance the fatigue damage, aging or degradation processes that the materials will encounter, and thus effectively predict the service life of aluminum alloy materials. This process provides a basis for material selection in the design and production processes, ensuring the selection of aluminum alloy materials with the best service life in specific applications, thereby avoiding potential safety hazards caused by material failure. Based on the comprehensive performance analysis of service life data and stability data, a comprehensive performance evaluation of aluminum alloy materials can be provided. This analysis not only considers the service life of the materials, but also comprehensively takes into account multiple aspects such as the load-bearing capacity, thermal stability, and fatigue performance of the materials, helping to form a comprehensive performance data map. This is of great significance for optimizing material performance, ensuring its reliability under actual working conditions, and improving the utilization efficiency of the materials. By optimizing the composition ratio of aluminum alloy materials based on performance data, the composition ratio of the materials can be effectively adjusted, thereby improving key performance indicators such as its mechanical properties, high-temperature resistance, and corrosion resistance.

[0072] The present invention lies in that by collecting data of aluminum alloy materials and conducting component detection, the basic chemical composition and component ratio of the materials can be comprehensively grasped, laying a foundation for subsequent analysis. Based on the component data, initial property analysis can be carried out to effectively evaluate the physical properties, chemical properties and microstructural characteristics of the materials. Further combined with thermal cycle simulation, the thermal fatigue behavior and the change law of material properties of aluminum alloy under complex temperature conditions can be effectively simulated, revealing the influence of the thermal environment on its stability and life. Using the thermal cycle simulation data to calculate the thermal cycle threshold can scientifically predict the bearing limit of the material under temperature fluctuation conditions, avoiding material failure caused by the accumulation of thermal stress. Combining the thermal cycle threshold data with the bearing capacity data for performance analysis can comprehensively evaluate the comprehensive performance of the material under actual working conditions, ensuring the scientificity and rationality of material selection. In addition, through in-depth research on the performance analysis results, optimizing the material composition ratio can significantly improve the strength, toughness and fatigue resistance of aluminum alloy, thus meeting the requirements of higher-standard industrial applications. This method can not only comprehensively improve the accuracy of performance evaluation and optimization efficiency of aluminum alloy materials, but also provide a scientific basis for the design and research and development of new aluminum alloys, with extremely high engineering value and application prospects. Therefore, the present invention is an optimization treatment of the traditional performance analysis method of aluminum alloy materials, solving the problems that the traditional performance analysis method of an aluminum alloy material has inaccurate performance analysis of aluminum alloy and inaccurate analysis of the aging trend of aluminum alloy materials. It improves the accuracy of performance analysis of aluminum alloy and the accuracy of analysis of the aging trend of aluminum alloy materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic flow chart of the steps of a performance analysis method for an aluminum alloy material;

[0074] Figure 2 For Figure 1 It is a schematic detailed implementation step flow chart of step S2 in

[0075] Figure 3 For Figure 1 It is a schematic detailed implementation step flow chart of step S3 in

[0076] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the accompanying drawings in combination with embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0078] 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 drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0079] 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 only used to distinguish one unit from another. 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.

[0080] To achieve the above object, please refer to Figures 1 to 3 , a method for analyzing the performance of an aluminum alloy material, comprising the following steps:

[0081] Step S1: Collect aluminum alloy material data; use the aluminum alloy material data to detect the composition of the aluminum alloy material to obtain aluminum alloy material composition data; based on the aluminum alloy material composition data, analyze the initial properties of the aluminum alloy material to obtain aluminum alloy material initial property data;

[0082] Step S2: Perform aluminum alloy material load simulation on the aluminum alloy material initial property data to obtain aluminum alloy material load simulation data; based on the aluminum alloy material load simulation data, estimate the bearing capacity of the aluminum alloy material to obtain aluminum alloy material bearing capacity data;

[0083] Step S3: Perform thermal cycle simulation on the aluminum alloy material initial property data to obtain aluminum alloy material thermal cycle simulation data; use the aluminum alloy material thermal cycle simulation data to calculate the thermal cycle threshold of the aluminum alloy material to obtain aluminum alloy material thermal cycle threshold data;

[0084] Step S4: Analyze the performance of the aluminum alloy material according to the aluminum alloy material thermal cycle threshold and the aluminum alloy material bearing capacity data to generate aluminum alloy material performance data; optimize the component ratio of the aluminum alloy material according to the aluminum alloy material performance data for the aluminum alloy material initial property data to obtain aluminum alloy material component ratio optimization data.

[0085] In the embodiments of the present invention, with reference to Figure 1As shown, in this example, the method for analyzing the properties of an aluminum alloy material includes the following steps:

[0086] Step S1: Collect aluminum alloy material data; use the aluminum alloy material data to perform composition detection on the aluminum alloy material to obtain aluminum alloy material composition data; based on the aluminum alloy material composition data, perform initial property analysis on the aluminum alloy material to obtain aluminum alloy material initial property data.

[0087] In an embodiment of the present invention, data of the aluminum alloy material is collected. These data are obtained through actual sampling, measurement, or using existing databases, covering the chemical composition, physical properties, and necessary performance indicators of the aluminum alloy material. Then, composition detection is performed by using these collected aluminum alloy material data. Composition detection usually uses advanced analytical instruments, such as X-ray fluorescence spectrometry (XRF), laser-induced breakdown spectroscopy (LIBS), etc., to accurately determine the content of each element in the aluminum alloy, including important alloy elements such as aluminum, magnesium, silicon, copper, etc. Through these techniques, the composition data of the aluminum alloy material is generated to ensure the accuracy and reliability of the composition. Based on this composition data, initial property analysis of the aluminum alloy material is performed. Initial property analysis includes detecting the basic physical properties (such as density, melting point, hardness) and mechanical properties (such as yield strength, tensile strength, etc.) of the aluminum alloy material. Commonly used test methods include Vickers hardness test, tensile test, impact test, etc., and the initial property data of the aluminum alloy material is obtained according to standard test methods.

[0088] Step S2: Perform load simulation on the aluminum alloy material initial property data to obtain aluminum alloy material load simulation data; based on the aluminum alloy material load simulation data, perform bearing capacity prediction on the aluminum alloy material to obtain aluminum alloy material bearing capacity data.

[0089] In an embodiment of the present invention, load simulation is performed on the initial property data of the aluminum alloy material. Load simulation is carried out by applying finite element analysis software (such as ANSYS, ABAQUS) to simulate the stress and strain of the aluminum alloy material in the actual use environment. By setting appropriate boundary conditions and load scenarios, phenomena such as deformation and crack propagation of the aluminum alloy material under long-term stress are simulated. The load simulation data includes information such as stress distribution, deformation amount, and plastic zone. Based on these simulation data, further bearing capacity prediction of the aluminum alloy material is performed. Bearing capacity prediction uses a material mechanics model to calculate the maximum bearing capacity of the aluminum alloy by simulating its performance under different load conditions. These data are usually obtained through methods such as load-displacement curves and yield points, so as to provide the bearing capacity of the material in actual applications.

[0090] Step S3: Perform thermal cycle simulation on the initial property data of the aluminum alloy material to obtain the thermal cycle simulation data of the aluminum alloy material; use the thermal cycle simulation data of the aluminum alloy material to calculate the thermal cycle threshold of the aluminum alloy material, so as to obtain the thermal cycle threshold data of the aluminum alloy material;

[0091] In the embodiment of the present invention, thermal cycle simulation is performed on the initial property data of the aluminum alloy material. The thermal cycle simulation models the thermal behavior of the aluminum alloy material under different temperature conditions by using thermodynamic simulation software (such as Thermo-Calc, JMatPro). This simulation requires inputting parameters such as the composition data of the material, the temperature change range, and the thermal cycle frequency, and simulates phenomena such as phase transformation, stress accumulation, and material degradation that occur during the repeated heating and cooling of the aluminum alloy material. Through the thermal cycle simulation, thermal cycle simulation data of the aluminum alloy material are generated, and these data include detailed information on the temperature, stress, and phase transformation experienced by the material during the thermal cycle. Then, the thermal cycle threshold of the aluminum alloy material is calculated by using the thermal cycle simulation data. This step calculates the thermal cycle threshold of the aluminum alloy material based on the fatigue life and phase transformation behavior of the material under different cycle numbers and temperature conditions. The thermal cycle threshold data helps to evaluate the durability of the material during long-term thermal cycles and determine the limit conditions for the material to withstand thermal cycles.

[0092] Step S4: Perform performance analysis on the aluminum alloy material according to the thermal cycle threshold of the aluminum alloy material and the load-bearing capacity data of the aluminum alloy material to generate performance data of the aluminum alloy material; optimize the component ratio of the aluminum alloy material according to the performance data of the aluminum alloy material for the initial property data of the aluminum alloy material to obtain optimized component ratio data of the aluminum alloy material.

[0093] In the embodiment of the present invention, performance analysis of the aluminum alloy material is performed according to the thermal cycle threshold and load-bearing capacity data of the aluminum alloy material. This analysis evaluates the performance of the material under actual working conditions by combining the thermal cycle threshold with the load-bearing capacity data. During the analysis process, mathematical tools such as multivariate regression analysis and response surface method are used to comprehensively evaluate these data to generate performance data of the aluminum alloy material. The performance data reflects the comprehensive performance of the material in actual applications, such as heat resistance, strength, fatigue life and other indicators. Based on these performance data, the component ratio of the aluminum alloy material is optimized for the initial property data. During the optimization process, by adjusting the component ratio of the aluminum alloy and adopting optimization methods such as design of experiments (DOE), the contents of alloying elements such as aluminum, magnesium, and silicon are adjusted to optimize its mechanical properties and thermal stability. The optimized component ratio data generates a new aluminum alloy material formula.

[0094] Preferably, step S1 includes the following steps:

[0095] Step S11: Collect aluminum alloy material data;

[0096] Step S12: Detect the composition of the aluminum alloy material using the aluminum alloy material data, so as to obtain the aluminum alloy material composition data;

[0097] Step S13: Detect the chemical properties of the aluminum alloy material based on the aluminum alloy material composition data, and obtain the aluminum alloy material chemical property data;

[0098] Step S14: Detect the physical properties of the aluminum alloy material based on the aluminum alloy material composition data, and obtain the aluminum alloy material physical property data;

[0099] Step S15: Analyze the initial properties of the aluminum alloy material with respect to the aluminum alloy material physical property data and the aluminum alloy material chemical property data, and obtain the aluminum alloy material initial property data.

[0100] In the embodiments of the present invention, the acquisition of aluminum alloy material data is the first step in the entire analysis process. The purpose of this step is to obtain the basic information of the aluminum alloy material, including chemical composition, physical properties, and related performance data. The acquisition of these data usually relies on the actual testing of samples and existing data records. In actual operation, representative aluminum alloy samples need to be selected, and these samples can be collected from aluminum alloy manufacturers, materials research institutions, or industrial sites. The collected data not only includes the external morphological characteristics of the aluminum alloy, but also should contain the main components of the alloy (such as the proportions of elements like aluminum, magnesium, copper, zinc, etc.) and its conventional mechanical properties. These data can be obtained through different testing equipment and means. For example, surface element analysis can be carried out using a scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS), or more precise detection of the aluminum alloy composition can be performed using inductively coupled plasma mass spectrometry (ICP-MS). Using the collected aluminum alloy material data for composition detection is the key step to obtain aluminum alloy composition data. In this process, techniques such as X-ray fluorescence spectroscopy (XRF) are used to detect the composition of aluminum alloy samples. Through the XRF technique, the content of each element in the sample is accurately measured, and the mass percentage of each component element is obtained through spectral analysis, especially the quantitative analysis of the main elements such as aluminum, magnesium, silicon, copper, etc. At the same time, laser-induced breakdown spectroscopy (LIBS) can also be used for further confirmation. Especially in the case of small samples, it can efficiently and non-destructively obtain the composition information of the aluminum alloy. Based on the aluminum alloy composition data, the chemical properties of the aluminum alloy material are detected. The chemical property detection aims to evaluate the chemical stability, corrosion resistance, and chemical characteristics affecting performance of the aluminum alloy material. The corrosion resistance of the aluminum alloy is detected by using a corrosion test. By immersing the aluminum alloy sample in acid and alkali solutions with different concentrations, the corrosion rate and surface changes of the aluminum alloy material are recorded, and the corrosion resistance performance of the aluminum alloy is calculated. At the same time, dynamic thermal analysis (DTA) or differential scanning calorimetry (DSC) is used to test the thermal stability of the aluminum alloy, and its chemical reaction characteristics under high-temperature environments are obtained. Based on the aluminum alloy material composition data, physical property detection is carried out. The goal of physical property detection is to evaluate the basic physical properties of the aluminum alloy material, such as density, thermal conductivity, electrical conductivity, etc. A high-precision electronic densitometer is used for density measurement to ensure the accuracy of the aluminum alloy material density. This data is crucial for subsequent material stability and structure analysis. In addition, the thermal conductivity of the aluminum alloy is measured by a thermal conductivity meter (such as a linear thermal diffusivity meter) to evaluate its thermal conductivity performance under high-temperature environments. The electrical conductivity is measured by the four-probe method to judge the electrical conductivity performance of the aluminum alloy material. The physical property data and chemical property data of the aluminum alloy material are comprehensively analyzed to carry out the initial property analysis of the aluminum alloy material. This analysis combines the physical and chemical property data and forms the initial property data of the aluminum alloy material through calculation and modeling.In this process, based on the existing physical and chemical property data, statistical analysis methods such as principal component analysis (PCA) or multiple regression analysis are applied to integrate the data and identify the factors that have the greatest impact on the properties of aluminum alloy materials. Through these data analyses, the overall properties of aluminum alloy materials can be better understood, and whether they meet the usage conditions can be evaluated. Based on the results of the initial property analysis, reference data can be provided for the next load simulation and bearing capacity prediction.

[0101] Preferably, step S13 includes the following steps:

[0102] Step S131: Collect the chemical elements of the aluminum alloy material according to the aluminum alloy material composition data to obtain the aluminum alloy material chemical element data;

[0103] Step S132: Detect the chemical antioxidant ability of the aluminum alloy material according to the aluminum alloy material chemical element data to obtain the aluminum alloy material antioxidant ability data;

[0104] Step S133: Evaluate the chemical reaction ability of the aluminum alloy material based on the aluminum alloy material antioxidant ability data to obtain the aluminum alloy material chemical reaction ability data;

[0105] Step S134: Analyze the corrosion resistance of the aluminum alloy material according to the aluminum alloy material chemical reaction ability data and the aluminum alloy material antioxidant ability data to obtain the aluminum alloy material corrosion resistance data;

[0106] Step S135: Detect the chemical stability of the aluminum alloy material based on the aluminum alloy material corrosion resistance data and the aluminum alloy material chemical reaction ability data to obtain the aluminum alloy material chemical stability data;

[0107] Step S136: Detect the chemical properties of the aluminum alloy material for the aluminum alloy material chemical stability data to obtain the aluminum alloy material chemical property data.

[0108] In the embodiments of the present invention, chemical element collection is carried out based on the composition data of aluminum alloy materials, aiming to extract the main chemical elements from aluminum alloy samples and obtain detailed chemical element data. This process requires quantitative analysis of the chemical elements of aluminum alloy materials through high-precision instruments such as inductively coupled plasma mass spectrometry (ICP-MS) or laser-induced breakdown spectroscopy (LIBS). The ICP-MS technology excites the aluminum alloy sample into a plasma and analyzes the mass spectrum of the elements in the sample, thereby accurately obtaining the specific content of the elements, especially elements such as aluminum, copper, magnesium, silicon, and zinc. In the LIBS technology, a laser pulse is used to excite the surface of the aluminum alloy sample into a plasma, and the elemental composition is obtained by analyzing the emission spectrum. These technologies can provide the specific composition of all elements in aluminum alloy materials, including trace elements, ensuring comprehensive and accurate chemical element data. To detect the antioxidant ability of aluminum alloy materials based on the chemical element data of aluminum alloy materials, it is necessary to understand the proportion of oxides and easily oxidized metal elements contained in the aluminum alloy materials, such as elements like copper and magnesium, which will directly affect the antioxidant ability of the aluminum alloy. The antioxidant test is usually carried out through a high-temperature oxidation test. The aluminum alloy sample is exposed to an oxygen environment using a high-temperature furnace and heated at a set temperature for a certain period of time. Subsequently, the antioxidant ability is evaluated by observing the formation of the oxide layer on the sample surface and the oxidation rate. At the same time, technologies such as an atmosphere furnace or plasma-enhanced chemical vapor deposition (PECVD) can also be used to simulate the performance of aluminum alloy in different oxidation environments, combined with thermogravimetric analysis (TGA) to record the mass change during the oxidation process, and further quantitatively analyze the antioxidant ability of the aluminum alloy. Based on the antioxidant ability data of aluminum alloy materials, the chemical reaction ability of aluminum alloy materials is calculated. The calculation of the chemical reaction ability needs to consider the reactivity of each element in the aluminum alloy with the external environment, especially the reactivity of aluminum alloy materials in different chemical environments, such as the reaction with acid, alkali, and salt solutions. Usually, by using a chemical reaction kinetics model and combining the composition data of the aluminum alloy, the chemical activity of each element is calculated. In actual operation, a database or existing literature data is used to establish the relationship between the aluminum alloy composition and reactivity. Further, through the combination of experimental data and theoretical calculations, the reactivity score of aluminum alloy materials under different conditions is obtained, especially their performance at different temperatures and in different solution environments. Based on the chemical reaction ability data and antioxidant ability data of aluminum alloy materials, the corrosion resistance analysis of aluminum alloy materials is carried out. The corrosion resistance analysis evaluates the long-term stability of the material in a corrosive environment by comprehensively considering the chemical reaction ability and antioxidant ability of the aluminum alloy. Through a salt spray test, the environment in which the aluminum alloy material is exposed to a salt-containing solution is simulated, and the surface corrosion condition and corrosion rate are observed. By conducting an accelerated corrosion test on the aluminum alloy sample, the physical and chemical changes of the aluminum alloy during the corrosion process are analyzed to further evaluate the corrosion resistance of the material.During this process, by combining the chemical element data of aluminum alloy with its reactivity, the process of the corrosion reaction is simulated using chemical kinetics to obtain the corrosion resistance data of the material in a specific corrosion environment. Based on the corrosion resistance data and chemical reaction ability data of the aluminum alloy material, the chemical stability of the aluminum alloy material is detected. The chemical stability test mainly evaluates the durability of aluminum alloy under different environmental conditions, especially its performance in harsh environments. An accelerated aging test is adopted to simulate the scenario of long-term exposure of aluminum alloy materials to a chemical corrosion environment, such as soaking in a humid, high-temperature, acidic or alkaline solution for a period of time. Through periodic chemical analysis, the degree of surface corrosion of aluminum alloy and the changes in chemical reactions are monitored. In addition, electrochemical tests (such as polarization curves) are used to evaluate the corrosion potential of aluminum alloy in different environments to further judge its chemical stability. These experimental data will be combined with the chemical reaction ability data to evaluate the durability of aluminum alloy under long-term exposure. The chemical stability data of the aluminum alloy material is subjected to chemical property detection. Chemical property detection involves a comprehensive analysis of the chemical composition of the aluminum alloy material and its performance in a specific environment. The changes in aluminum alloy under high temperature, high pressure or chemical environment are detected by high performance liquid chromatography (HPLC) or gas chromatography (GC), especially the reaction products of aluminum alloy with different media. Secondly, infrared spectroscopy analysis (FTIR) or Raman spectroscopy analysis (Raman) is used to analyze the surface chemical substances of aluminum alloy to detect the changes in its surface structure and the generated oxides. By combining these methods, the chemical property data of aluminum alloy is obtained, reflecting its stability, reactivity and durability under different conditions. By summarizing these data, a comprehensive chemical property file of aluminum alloy is generated.

[0109] Preferably, step S14 includes the following steps:

[0110] Step S141: Measure the density of the aluminum alloy material according to the aluminum alloy material composition data to generate aluminum alloy material density data;

[0111] Step S142: Scan the element distribution map according to the aluminum alloy material composition data to generate aluminum alloy element distribution map data;

[0112] Step S143: Analyze the crystal arrangement mode of aluminum alloy elements according to the aluminum alloy element distribution map data to obtain aluminum alloy element crystal arrangement mode data;

[0113] Step S144: Collect the crystal microstructure of the aluminum alloy material based on the aluminum alloy element crystal arrangement mode data and the aluminum alloy element distribution map data to obtain aluminum alloy material crystal microstructure data;

[0114] Step S145: Calculate the crystal dislocation density according to the aluminum alloy material crystal microstructure data to obtain aluminum alloy material crystal dislocation density data;

[0115] Step S146: Estimate the hardness of the aluminum alloy material based on the crystal dislocation density data and crystal microstructure data of the aluminum alloy material to obtain the hardness data of the aluminum alloy material;

[0116] Step S147: Detect the physical properties of the aluminum alloy material based on the hardness data and density data of the aluminum alloy material to obtain the physical property data of the aluminum alloy material.

[0117] In the embodiments of the present invention, the density of the aluminum alloy material is measured based on the composition data of the aluminum alloy material to generate aluminum alloy material density data. The volume and mass of the aluminum alloy sample are measured using the standard Archimedes' principle. By placing the aluminum alloy material in a liquid with a known density and observing the volume of the liquid displaced by the sample, its volume is then calculated. Subsequently, a precision electronic balance is used to measure the mass of the aluminum alloy sample. According to the measured mass and volume data, the density of the aluminum alloy material is calculated through the formula ρ = m / V (density = mass / volume). This process varies for aluminum alloys with different compositions. Therefore, the accuracy of the density data is directly affected by the composition of the aluminum alloy. During the density measurement process, the experimental environment is ensured to be at a constant temperature and air interference is avoided to improve the accuracy of the data. An elemental distribution map of the aluminum alloy is scanned based on the composition data of the aluminum alloy material to generate aluminum alloy elemental distribution map data. A scanning electron microscope (SEM) or an electron probe X-ray microanalyzer (EPMA) is used to perform a high-precision scan of the aluminum alloy material to obtain the elemental distribution map on its surface or inside. During the operation, the sample will be first processed into a flat surface suitable for microscopic scanning, and then the surface of the material is scanned by an electron beam, and the spatial distribution of elements is analyzed by combining X-ray or secondary electron signals. These instruments generate an elemental distribution map by analyzing the elemental composition of different regions, thereby showing the distribution of different elements in the aluminum alloy material. This map will show the local distribution of each element in the aluminum alloy material. Based on the aluminum alloy elemental distribution map data, the crystal arrangement mode of the aluminum alloy elements is analyzed to obtain aluminum alloy elemental crystal arrangement mode data. The microstructure of the aluminum alloy is analyzed in detail by using a transmission electron microscope (TEM) or X-ray diffraction (XRD) technology. In TEM, the atomic-level arrangement of the aluminum alloy is directly observed through high-magnification imaging, and then the arrangement mode of the aluminum alloy elements in the crystal is obtained; while in XRD, the crystal structure type and its relative arrangement of different elements in the aluminum alloy are determined through the analysis of the diffraction pattern. These methods, combined with the data in the elemental distribution map, accurately obtain the crystal arrangement mode of each element in the aluminum alloy, such as the crystal lattice point spacing, crystal defect distribution, etc. Based on the aluminum alloy elemental crystal arrangement mode data and the aluminum alloy elemental distribution map data, the crystal microstructure of the aluminum alloy material is collected to obtain aluminum alloy material crystal microstructure data. In this step, a high-resolution scanning electron microscope (SEM) or a transmission electron microscope (TEM) is used to obtain the crystal microstructure image of the aluminum alloy material. In the SEM operation, the surface of the aluminum alloy material is coated, and then the surface of the material is scanned by an electron beam, and an image is formed by analyzing its secondary electron signal to obtain a high-resolution surface microstructure image. In TEM, by placing a thin sheet of aluminum alloy material under the microscope and using an electron beam to scan the inside of the crystal, a detailed internal microstructure image is obtained.This data reveals information such as the grain size, grain boundary distribution, defects, and dislocations of aluminum alloy crystals. Based on the crystal microstructure data of aluminum alloy materials, the crystal dislocation density is calculated to obtain the crystal dislocation density data of aluminum alloy materials. The calculation of dislocation density usually relies on high-resolution transmission electron microscope (TEM) images. During the experiment, the microstructure images of aluminum alloy materials are collected, and then image analysis software is used to calibrate and count the dislocations in the images. The specific method is as follows: analyze the crystal defects or dislocation lines in the TEM images and calculate the number of dislocations per unit volume or unit area. Combining data such as the crystal structure and element distribution of aluminum alloy, the dislocation density is further calculated through a mathematical model. A high dislocation density usually means higher hardness of the material. Based on the crystal dislocation density data and crystal microstructure data of aluminum alloy materials, the hardness of aluminum alloy materials is predicted to obtain the hardness data of aluminum alloy materials. Hardness prediction is usually achieved by combining the relationship between dislocation density and crystal microstructure. A high dislocation density usually leads to a decrease in the plasticity of the crystal, thereby increasing the hardness of the material. In this step, the hardness value is estimated by combining experimental formulas or existing material hardness models with microstructural characteristics such as dislocation density and grain size. In specific operations, combined with the microstructural characteristics of aluminum alloy materials, standard hardness measurement methods such as Vickers hardness, Rockwell hardness, or Brinell hardness are used for verification. The hardness of aluminum alloy samples is measured by these methods and compared with the calculated values to ensure the accuracy of the hardness prediction data. Based on the hardness data and density data of aluminum alloy materials, the physical properties of aluminum alloy materials are detected to obtain the physical property data of aluminum alloy materials. In this process, combining the hardness data and density data, the physical properties of aluminum alloy, such as elastic modulus and tensile strength, are further determined using standard physical test methods, such as tensile tests, compression tests, and bending tests. By measuring the deformation of aluminum alloy materials under different forces, the mechanical properties of the materials are obtained. By measuring the stress-strain curve of the sample and combining the known density and hardness data, physical property indicators such as the elastic modulus and tensile strength of aluminum alloy materials are deduced, and comprehensive physical property data of aluminum alloy materials are obtained through comprehensive test data.

[0118] Preferably, step S2 includes the following steps:

[0119] Step S21: Perform a load simulation on the initial property data of the aluminum alloy material to obtain the load simulation data of the aluminum alloy material;

[0120] Step S22: Use the load simulation data of the aluminum alloy material to perform a deformation analysis of the aluminum alloy material to obtain the deformation data of the aluminum alloy material;

[0121] Step S23: Estimate the aging trend of the aluminum alloy material based on the deformation data of the aluminum alloy material to obtain the aging trend data of the aluminum alloy material;

[0122] Step S24: Estimate the bearing capacity of the aluminum alloy material based on the aging trend data of the aluminum alloy material and the deformation data of the aluminum alloy material to obtain the bearing capacity data of the aluminum alloy material.

[0123] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0124] Step S21: Perform a load simulation on the initial property data of the aluminum alloy material to obtain the load simulation data of the aluminum alloy material;

[0125] In the embodiment of the present invention, a load simulation is performed on the initial property data of the aluminum alloy material to obtain the load simulation data of the aluminum alloy material. Based on the initial property data of the aluminum alloy material, a three-dimensional finite element model of the aluminum alloy material is established. In this process, data such as the composition, density, hardness, crystal structure, and physical properties of the aluminum alloy are used as input parameters and simulated by numerical calculation methods. Using finite element software, such as ANSYS or ABAQUS, an external load is applied to the aluminum alloy material and stress analysis is performed. The mechanical behavior of the material under different loading conditions, including different types of loads such as tension, compression, and shear, should be considered during the simulation. The output data of the simulation will provide a basis for the stress distribution, deformation conditions, etc. that the aluminum alloy material experiences during actual use, and obtain the load simulation data of the aluminum alloy material.

[0126] Step S22: Use the load simulation data of the aluminum alloy material to perform deformation analysis on the aluminum alloy material to obtain the deformation data of the aluminum alloy material;

[0127] In the embodiment of the present invention, the load simulation data of the aluminum alloy material is used to perform deformation analysis on the aluminum alloy material to obtain the deformation data of the aluminum alloy material. According to the load simulation data generated in step S21, the deformation characteristics of the aluminum alloy material after being stressed are further analyzed. This step further processes the deformation data of the aluminum alloy by finite element analysis methods, focusing on analyzing the stress-strain response of the aluminum alloy material under different loads. Combining the plastic deformation characteristics of the aluminum alloy material, the deformation modes that occur during the stress process of the material, such as elastic deformation and plastic deformation, are simulated. During this analysis process, by combining the microscopic structure data of the aluminum alloy, especially parameters such as dislocation density and grain size, the deformation behavior of the material under large loads can be described more accurately. Through deformation analysis, the deformation data of the aluminum alloy material is obtained, including stress distribution, deformation amount, strain concentration area, etc.

[0128] Step S23: Estimate the aging trend of the aluminum alloy material based on the deformation data of the aluminum alloy material to obtain the aging trend data of the aluminum alloy material;

[0129] In the embodiment of the present invention, the aging trend of the aluminum alloy material is estimated based on the deformation data of the aluminum alloy material to obtain the aging trend data of the aluminum alloy material. During long-term use, the aluminum alloy material will age due to repeated load effects and external environmental influences. The estimation of the aging trend is crucial for the long-term reliability assessment of the material. Based on the strain data obtained from the deformation analysis, the aging situation of the aluminum alloy during long-term use is speculated. This process utilizes the fatigue life theory of the aluminum alloy material, combines the deformation data, and calculates the aging trend of the material under different environmental and load conditions through a life prediction model. The fatigue strength and aging rate of the aluminum alloy material are estimated using fatigue test data and empirical formulas. By gradually applying loads with different cycles, the performance degradation of the material during use is simulated to obtain the aging trend data of the aluminum alloy material, describing the performance changes of the material at different time periods.

[0130] Step S24: Estimate the load-bearing capacity of the aluminum alloy material based on the aging trend data of the aluminum alloy material and the deformation data of the aluminum alloy material to obtain the load-bearing capacity data of the aluminum alloy material.

[0131] In the embodiment of the present invention, the load-bearing capacity of the aluminum alloy material is estimated based on the aging trend data of the aluminum alloy material and the deformation data of the aluminum alloy material to obtain the load-bearing capacity data of the aluminum alloy material. In this step, the deformation data and aging trend data of the aluminum alloy material are combined for a comprehensive assessment of the load-bearing capacity. Through fatigue life prediction and aging models, the change in the load-bearing capacity of the aluminum alloy after long-term use is simulated. Based on the stress-strain curve and aging trend in the deformation analysis, the load-bearing capacity of the aluminum alloy at different usage stages is further calculated. In specific operations, by simulating the application of loads with different amplitudes and frequencies multiple times, observing the deformation, fracture, and fatigue phenomena of the material at different usage stages, the maximum load-bearing capacity of the aluminum alloy material in actual applications and its trend of change over time can be obtained.

[0132] Preferably, step S22 includes the following steps:

[0133] Step S221: Measure the tensile state of the aluminum alloy material according to the load simulation data of the aluminum alloy material to obtain the tensile state data of the aluminum alloy material;

[0134] Step S222: Analyze the necking effect of the aluminum alloy material based on the tensile state data of the aluminum alloy material to obtain the necking effect data of the aluminum alloy material;

[0135] Step S223: Calculate the local stress concentration of the aluminum alloy material based on the necking effect data of the aluminum alloy material to obtain the local stress concentration data of the aluminum alloy material;

[0136] Step S224: Estimate the plastic instability of the aluminum alloy material based on the local stress concentration data of the aluminum alloy material to generate plastic instability data of the aluminum alloy material;

[0137] Step S225: Analyze the deformation of the aluminum alloy material for the plastic instability data of the aluminum alloy material to obtain deformation data of the aluminum alloy material.

[0138] In the embodiments of the present invention, the tensile state of the aluminum alloy material is measured based on the load simulation data of the aluminum alloy material to obtain the tensile state data of the aluminum alloy material. In this step, it is necessary to use the load simulation data, combined with the initial properties of the aluminum alloy material, to evaluate the performance of the aluminum alloy material under different tensile conditions through numerical simulation methods. By applying different tensile forces during the simulation process, the stress-strain change curves of the aluminum alloy material in the tensile state are calculated and recorded. When measuring the tensile state, special attention is paid to whether necking occurs during the tensile process of the material and the plastic deformation of the material near the yield point. The data obtained in this process include stress-strain curves, elastic modulus, yield strength, tensile strength, and fracture strain, etc. The tensile state data of the aluminum alloy material are obtained through these data. The necking effect analysis of the aluminum alloy material is carried out based on the tensile state data of the aluminum alloy material to obtain the necking effect data of the aluminum alloy material. At this stage, through the analysis of the tensile state of the aluminum alloy material, it is mainly evaluated whether the material will produce local plastic deformation during the tensile process and form a necking phenomenon in the local area. The finite element analysis software (such as ANSYS, ABAQUS) is used to perform tensile simulation on the aluminum alloy material to observe the strain distribution of the material during the tensile process, especially in the areas with large deformation. During the simulation process, by identifying the drastic changes in the local strain on the material surface, the generation conditions and positions of the necking effect are analyzed, and the strain and stress distributions in the necking area are quantitatively analyzed. The necking effect data include information such as the local strain concentration area and the local necking morphology of the material. The local stress concentration calculation of the aluminum alloy material is carried out based on the necking effect data of the aluminum alloy material to obtain the local stress concentration data of the aluminum alloy material. In this step, based on the necking effect data generated during the tensile process of the aluminum alloy material, the stress concentration phenomenon in the local area of the material is further calculated. The numerical analysis method is used to simulate the stress state of the aluminum alloy material under different load conditions, especially the stress distribution in the local necking area of the material. The stress concentration calculation should cover the geometric shape of the material, the loading method, and the influence of the necking effect, and through the finite element analysis software, a stress distribution map is generated, with special attention paid to the stress concentration situation around the necking area. At this time, the calculated local stress concentration data include the maximum stress value and stress gradient in the necking area. The plastic instability estimation of the aluminum alloy material is carried out based on the local stress concentration data of the aluminum alloy material to generate the plastic instability data of the aluminum alloy material. According to the stress distribution in the local stress concentration area of the aluminum alloy material, combined with the plastic characteristics of the material, the plastic instability is estimated. The occurrence of plastic instability is usually closely related to the stress concentration and the accumulation of plastic deformation of the material. In this step, by combining the constitutive model of the material, it is analyzed whether the material in the local stress concentration area reaches the conditions of plastic instability. The numerical calculation method is used to predict the local instability phenomena that occur in the aluminum alloy material, such as the yield, crack initiation, or fracture of the material, based on the stress concentration data.Plastic instability data includes the instability critical points of materials under different load conditions, the deformation characteristics in the instability region, etc. Deformation analysis of aluminum alloy materials is carried out on the plastic instability data of aluminum alloy materials to obtain the deformation data of aluminum alloy materials. In this step, based on the plastic instability data in the previous step, deformation analysis of aluminum alloy materials is carried out. Deformation analysis mainly focuses on the macroscopic and microscopic deformation conditions of materials under the action of load, especially the deformation behavior in the instability region. Through the finite element analysis method, the deformation process of materials is simulated, and combined with the plastic instability estimation results, the stress-strain relationship of materials under loading states such as tension and compression is analyzed, and further the deformation amount, deformation distribution and strain concentration region of materials are calculated. The output data of deformation analysis includes stress-strain curves, material deformation modes, changes in strain concentration regions, etc.

[0139] Preferably, step S24 includes the following steps:

[0140] Step S241: Detect the crystal slip state of the aluminum alloy material based on the deformation data of the aluminum alloy material to obtain the crystal slip state data of the aluminum alloy material;

[0141] Step S242: Detect the microdamage of the aluminum alloy material according to the crystal slip state data of the aluminum alloy material to obtain the microdamage data of the aluminum alloy material;

[0142] Step S243: Calculate the crack propagation probability of the aluminum alloy material according to the microdamage data of the aluminum alloy material to obtain the crack propagation probability data of the aluminum alloy material;

[0143] Step S245: Estimate the aging trend of the aluminum alloy material based on the microdamage data of the aluminum alloy material and the crack propagation probability data of the aluminum alloy material to obtain the aging trend data of the aluminum alloy material.

[0144] In the embodiments of the present invention, based on the deformation data of the aluminum alloy material, the crystal slip state of the aluminum alloy material is detected to obtain the crystal slip state data of the aluminum alloy material. In this step, the deformation data of the aluminum alloy material under load is utilized, and by observing the crystal slip inside the material, the deformation mechanism of the material under external force is detected. A high-resolution scanning electron microscope (SEM) or atomic force microscope (AFM) is used, combined with X-ray diffraction technology, to analyze the crystal structure and crystal slip of the aluminum alloy material. Information such as the formation of slip bands, the distribution of slip lines, and the relative movement of grains will be used to identify the state of crystal slip. Through image analysis technology, geometric features of the crystal slip bands are extracted, such as the length, width, and distribution of the slip bands, to form the crystal slip state data of the aluminum alloy material. According to the crystal slip state data of the aluminum alloy material, the microscopic damage of the aluminum alloy material is detected to obtain the microscopic damage data of the aluminum alloy material. In this step, based on the data of the crystal slip state, the microscopic damage inside the material is further analyzed, especially the interaction between dislocation slip bands and grain boundaries and the initiation of microcracks inside the material. A scanning electron microscope (SEM) or transmission electron microscope (TEM) is used to carefully observe the cross-section of the aluminum alloy material to identify microscopic damage phenomena such as microcracks, holes, and pores. Based on high-resolution images, combined with image processing technology, quantitative analysis of microcracks is carried out, including the length, depth, and crack propagation direction of the cracks. By means of fracture surface analysis, tensile tests, and fatigue experiments, combined with the results of crystal slip, the microscopic damage state of the aluminum alloy material is evaluated to obtain the microscopic damage data of the aluminum alloy material. According to the microscopic damage data of the aluminum alloy material, the crack propagation probability of the aluminum alloy material is calculated to obtain the crack propagation probability data of the aluminum alloy material. In this step, combined with the microscopic damage data of the aluminum alloy material, the crack propagation behavior of the material is calculated through a crack propagation model. Specifically, the linear elastic fracture mechanics (LEFM) or elastic-plastic fracture mechanics (EPFM) model is used, and based on the characteristics of crack size, distribution, and stress concentration in the microscopic damage data, the crack propagation rate is calculated. By combining parameters such as the stress-strain curve, stress intensity factor, and fracture toughness of the material, the propagation trend of cracks under different loads is determined. At the same time, by calculating the crack propagation probability of the material, the risk of crack propagation under different working conditions is evaluated. This step uses a stochastic process or Monte Carlo simulation method to statistically analyze the crack propagation to obtain the crack propagation probability data of the aluminum alloy material. Based on the microscopic damage data of the aluminum alloy material and the crack propagation probability data of the aluminum alloy material, the aging trend of the aluminum alloy material is predicted to obtain the aging trend data of the aluminum alloy material. In this step, by combining the aforementioned microscopic damage data and crack propagation probability data, the performance degradation of the aluminum alloy material is predicted through an aging model.The specific operation is to calculate the aging trend of the material in long-term use based on the accumulated damage of the material, taking into account the influence of environmental factors (such as temperature, humidity, corrosion, etc.) on the material, and combining the crack growth rate and the fatigue characteristics of the material under different loads. The fatigue damage model, damage mechanics model and life prediction model are used to quantitatively analyze the aging process of the material. These models simulate the aging speed and degree of degradation of the material through the damage accumulation of the material under different working conditions, and estimate the aging trend of aluminum alloy materials under long-term load conditions to obtain the aging trend data of aluminum alloy materials.

[0145] Preferably, step S3 comprises the following steps:

[0146] Step S31: performing thermal cycle simulation on the initial property data of the aluminum alloy material, thereby obtaining thermal cycle simulation data of the aluminum alloy material;

[0147] Step S32: performing a thermal fatigue cumulative analysis of the aluminum alloy material according to the thermal cycle simulation data of the aluminum alloy material to generate thermal fatigue cumulative data of the aluminum alloy material;

[0148] Step S33: estimating the attenuation of the mechanical properties of the aluminum alloy material based on the accumulated data of thermal fatigue of the aluminum alloy material to obtain the attenuation data of the mechanical properties of the aluminum alloy material;

[0149] Step S34: performing rigidity attenuation detection according to the mechanical property attenuation data of the aluminum alloy material to obtain the rigidity attenuation data of the aluminum alloy material;

[0150] Step S35: Calculate the thermal cycle threshold value of the aluminum alloy material using the aluminum alloy material rigidity attenuation data and the aluminum alloy material mechanical property attenuation data, thereby obtaining the aluminum alloy material thermal cycle threshold value data.

[0151] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0152] Step S31: performing thermal cycle simulation on the initial property data of the aluminum alloy material, thereby obtaining thermal cycle simulation data of the aluminum alloy material;

[0153] In the embodiments of the present invention, the initial property data of the aluminum alloy material is subjected to thermal cycle simulation to obtain the thermal cycle simulation data of the aluminum alloy material. In this step, by establishing a thermodynamic model of the aluminum alloy material, the initial physical and chemical property data of the aluminum alloy is used as input to simulate its response during repeated thermal cycles. Using thermo-mechanical coupling simulation software such as ANSYS or ABAQUS, the initial properties of the aluminum alloy material (such as thermal conductivity, specific heat capacity, coefficient of expansion, etc.) and the load conditions (including temperature change range, loading frequency, etc.) are input. The thermal cycle process of the aluminum alloy is set in the model, and the cycle conditions of material heating, cooling, and constant load are defined. By simulating the stress, strain, and temperature distribution of the material during multiple thermal cycles, the behavior data of the aluminum alloy material during the thermal cycle process is generated. The thermal cycle simulation results will include the thermal stress distribution of the material, the thermal expansion response, and the deformation conditions under different temperature conditions.

[0154] Step S32: Perform thermal fatigue cumulative analysis on the aluminum alloy material based on the thermal cycle simulation data of the aluminum alloy material to generate thermal fatigue cumulative data of the aluminum alloy material;

[0155] In the embodiments of the present invention, thermal fatigue cumulative analysis is performed on the aluminum alloy material based on the thermal cycle simulation data of the aluminum alloy material to generate thermal fatigue cumulative data of the aluminum alloy material. In this step, based on the thermal cycle simulation data obtained in step S31, through thermal fatigue analysis, the damage accumulation situation of the aluminum alloy material under multiple thermal cycles is evaluated. Using thermal fatigue models (such as Miner's rule, thermal fatigue life model, etc.), according to the thermal cycle simulation data of the material, the fatigue damage contribution of each thermal cycle stage to the material is calculated. By analyzing variables such as stress, strain, and temperature of the aluminum alloy material, a thermal fatigue damage evolution equation is established. Combining the fatigue life data of the material and the thermal fatigue damage criterion, the damage effects of multiple thermal cycles are accumulated to obtain the thermal fatigue cumulative data, including the damage accumulation amount and damage rate of the material under different thermal cycle conditions.

[0156] Step S33: Estimate the attenuation of the mechanical properties of the aluminum alloy material based on the thermal fatigue cumulative data of the aluminum alloy material to obtain the mechanical property attenuation data of the aluminum alloy material;

[0157] In the embodiment of the present invention, based on the thermal fatigue cumulative data of the aluminum alloy material, the mechanical property attenuation of the aluminum alloy material is predicted to obtain the mechanical property attenuation data of the aluminum alloy material. In this step, according to the fatigue cumulative data of the aluminum alloy material during the thermal cycle, the mechanical property attenuation analysis of the material is further carried out. The fatigue life model is used in combination with thermal fatigue damage accumulation to predict the change in the mechanical properties of the aluminum alloy material after different numbers of thermal cycles. By calculating the attenuation values of mechanical properties such as the yield strength, tensile strength, ductility, and hardness of the material, the influence of fatigue damage on these mechanical properties is analyzed. Usually, the mechanical test data of the material is used as calibration, and the change trend of the material's performance after multiple thermal cycles is obtained through fatigue tests (such as tensile tests, bending tests, etc.). Combining with the actual application situation of the material, the attenuation rate of its mechanical properties is deduced, and the mechanical property attenuation data is obtained.

[0158] Step S34: Perform a rigidity attenuation detection based on the mechanical property attenuation data of the aluminum alloy material to obtain the rigidity attenuation data of the aluminum alloy material;

[0159] In the embodiment of the present invention, a rigidity attenuation detection is performed according to the mechanical property attenuation data of the aluminum alloy material to obtain the rigidity attenuation data of the aluminum alloy material. In this step, the rigidity of the aluminum alloy material is detected by a method combining experimental testing and numerical simulation. The stiffness change of the aluminum alloy material is tested through mechanical tests (such as three-point bending tests, compression tests, etc.) at different numbers of thermal cycles. According to the thermal cycle simulation and the mechanical property attenuation data, the law of rigidity attenuation of the material under repeated thermal loads is analyzed. The stiffness change is an important indicator reflecting the degradation of the material. By recording the loading-unloading curve of the aluminum alloy material, the stiffness data is extracted, and the stiffness attenuation curve under different thermal cycle conditions is plotted. The rigidity attenuation data of the aluminum alloy material under different conditions is obtained by comparing the experimental results.

[0160] Step S35: Calculate the thermal cycle threshold of the aluminum alloy material by using the rigidity attenuation data of the aluminum alloy material and the mechanical property attenuation data of the aluminum alloy material, so as to obtain the thermal cycle threshold data of the aluminum alloy material.

[0161] In the embodiments of the present invention, the thermal cycle threshold of the aluminum alloy material is calculated by using the rigid attenuation data and the mechanical property attenuation data of the aluminum alloy material, so as to obtain the thermal cycle threshold data of the aluminum alloy material. In this step, the rigid attenuation data and the mechanical property attenuation data obtained in step S34 are used to determine the fatigue failure threshold of the aluminum alloy material during the thermal cycle process. According to the changes in the stiffness and mechanical properties of the material at different thermal cycle times, the thermal cycle threshold standard is set. When the attenuation of the stiffness or mechanical properties of the material reaches a certain critical value, it is considered that the material enters the failure region. The numerical analysis method is adopted, combined with mechanical theories (such as the thermal fatigue failure criterion), to calculate the thermal cycle threshold of the aluminum alloy material. This threshold usually represents the maximum number of thermal cycles that the material can withstand. Beyond this threshold, obvious structural failure or performance degradation of the material occurs, and the obtained thermal cycle threshold data.

[0162] Preferably, step S32 includes the following steps:

[0163] Step S321: Calculate the precipitation phase dissolution probability of the aluminum alloy material according to the thermal cycle simulation data of the aluminum alloy material to obtain the precipitation phase dissolution probability data of the aluminum alloy material;

[0164] Step S322: Estimate the deterioration of the microstructure based on the precipitation phase dissolution probability data of the aluminum alloy material to obtain the microstructure deterioration data of the aluminum alloy material;

[0165] Step S323: Analyze the attenuation of the thermal stability of the aluminum alloy material according to the thermal cycle simulation data of the aluminum alloy material to obtain the thermal stability attenuation data of the aluminum alloy material;

[0166] Step S324: Perform cumulative thermal fatigue analysis of the aluminum alloy material based on the thermal stability attenuation data and the microstructure deterioration data of the aluminum alloy material to generate the cumulative thermal fatigue data of the aluminum alloy material.

[0167] In the embodiments of the present invention, based on the thermal cycle simulation data of aluminum alloy materials, the dissolution behavior of precipitation phases in aluminum alloys under different temperature conditions is evaluated by numerical calculation methods. A thermodynamic model of the aluminum alloy material is established using thermodynamic calculation software (such as Thermo-Calc or DICTRA), and information such as the composition of the aluminum alloy, temperature, and heating and cooling rates during the thermal cycle process is input. Through thermodynamic simulation, the dissolution and re-precipitation probabilities of various precipitation phases (such as Al3Mg2, Mg2Si, etc.) in the aluminum alloy material during the thermal cycle are calculated. By analyzing the proportion of dissolved phases at different temperatures and their effects on the properties of the aluminum alloy, the probability data of precipitation phase dissolution are obtained. This data is used to evaluate the phase transformation behavior of the aluminum alloy material during thermal cycling and its impact on the mechanical properties of the material. Based on the precipitation phase dissolution probability data of the aluminum alloy material, a prediction of microstructural deterioration is carried out to obtain the microstructural deterioration data of the aluminum alloy material. In this step, the probability data of precipitation phase dissolution obtained in step S321 is used, combined with the microstructural characteristics of the material, to quantitatively predict microstructural deterioration. By combining scanning electron microscope (SEM) observations and X-ray diffraction (XRD) analyses, the microstructural characteristics of the aluminum alloy at different temperatures during the thermal cycle are obtained. Using the thermal fatigue simulation results, the influence of the periodic changes in precipitation phase dissolution and re-precipitation on the microstructure is considered. Based on information such as the precipitation phase distribution, grain structure, and porosity of the aluminum alloy material, a microstructure evolution model is constructed to analyze the damage and aging process of the microstructure during thermal cycling. Combining computer simulation and experimental data, the trend of microstructural deterioration of the aluminum alloy material during thermal fatigue cycling is predicted to obtain the microstructural deterioration data of the aluminum alloy material. According to the thermal cycle simulation data of the aluminum alloy material, an analysis of the thermal stability attenuation of the aluminum alloy material is carried out to obtain the thermal stability attenuation data of the aluminum alloy material. In this step, the data obtained in steps S321 and S322 are used to analyze the thermal stability attenuation of the aluminum alloy material after multiple thermal cycles. Through thermal cycle simulation, combined with the composition and thermodynamic behavior of the aluminum alloy, the thermal stability of the material at high temperatures is analyzed, including grain growth, phase transformation behavior, and the dissolution process of precipitation phases. Using models of the relationship between physical properties (such as thermal expansion coefficient, thermal conductivity, melting point, etc.) and temperature, the attenuation of the physical and chemical stability of the aluminum alloy material during the thermal cycle is calculated. Further, through mechanical tests (such as high-temperature tensile tests, thermal fatigue tests, etc.), the attenuation of the mechanical properties of the aluminum alloy material after multiple thermal cycles is verified to obtain the thermal stability attenuation data of the aluminum alloy material. Based on the thermal stability attenuation data of the aluminum alloy material and the microstructural deterioration data of the aluminum alloy material, a cumulative analysis of the thermal fatigue of the aluminum alloy material is carried out to generate the cumulative thermal fatigue data of the aluminum alloy material.In this step, by combining the thermal stability attenuation data obtained in step S323 with the microstructure deterioration data obtained in step S322, through thermal fatigue analysis, the cumulative damage situation that occurs to the aluminum alloy material during the thermal cycling process is predicted. Based on the thermal stability attenuation data of the aluminum alloy material, the influence of thermal cycling on the attenuation of the mechanical properties of the material is analyzed; then, based on the microstructure deterioration data, the further influence of behaviors such as grain growth, dissolution and reprecipitation of precipitates in the material on the mechanical properties of the material is evaluated. By accumulating the damage and fatigue effects at different thermal cycle numbers, the thermal fatigue life is estimated using a thermal fatigue damage model (such as the Miner's rule), and the thermal fatigue cumulative data of the aluminum alloy material is generated.

[0168] Preferably, step S4 includes the following steps:

[0169] Step S41: Detect the stability of the aluminum alloy material according to the thermal cycle threshold of the aluminum alloy material and the bearing capacity data of the aluminum alloy material to obtain the aluminum alloy material stability data;

[0170] Step S42: Estimate the service life of the aluminum alloy material based on the aluminum alloy material stability data to obtain the aluminum alloy material service life data;

[0171] Step S43: Analyze the properties of the aluminum alloy material based on the aluminum alloy material service life data and the aluminum alloy material stability data to generate the aluminum alloy material property data;

[0172] Step S44: Optimize the component ratio of the aluminum alloy material for the initial property data of the aluminum alloy material according to the aluminum alloy material property data to obtain the optimized aluminum alloy material component ratio data.

[0173] In the embodiments of the present invention, the stability of the aluminum alloy material is detected according to the thermal cycle threshold of the aluminum alloy material and the bearing capacity data of the aluminum alloy material, and the stability data of the aluminum alloy material is obtained. In this step, a comprehensive stability evaluation model is established through the aforementioned thermal cycle threshold and bearing capacity data. A mechanical property test device (such as a universal material testing machine) is used to conduct stress-strain tests on the aluminum alloy material to obtain stress response data of the material under different thermal cycle loads. At the same time, in combination with the thermal cycle threshold of the aluminum alloy material, the cyclic load encountered by the material during actual use is simulated, and the stability of the material is analyzed. For this purpose, a thermal cycle simulation system is used to simulate the stress state of the aluminum alloy material under different working conditions, including the influence of parameters such as low temperature, high temperature, and temperature change rate on the material stability. According to the stress response and the thermal cycle model, the stability data of the aluminum alloy material during the thermal cycle is obtained, including the plastic deformation of the material, the strength change, and its correlation with the thermal cycle. The service life of the aluminum alloy material is estimated based on the stability data of the aluminum alloy material to obtain the service life data of the aluminum alloy material. In this step, the obtained stability data of the aluminum alloy material is used in combination with the fatigue characteristics of the material to estimate the service life. Using a known life prediction model, such as a fatigue life prediction model based on the Miners' rule, in combination with the stability data of the aluminum alloy, the fatigue life of the material under multiple thermal cycles and loadings is calculated. Through multiple loading tests (for example, using a fatigue testing machine to conduct alternating high and low temperature loading on the aluminum alloy material), the stress-strain reaction during actual use is simulated. During the loading process, the crack propagation, plastic deformation, and microstructural changes of the aluminum alloy material are monitored. By analyzing the damage accumulation, the fatigue life of the material within a certain service period is predicted. These data are summarized by statistical methods to obtain the expected service life of the aluminum alloy material. The performance of the aluminum alloy material is analyzed based on the service life data of the aluminum alloy material and the stability data of the aluminum alloy material to generate the performance data of the aluminum alloy material. In this step, a comprehensive performance analysis is carried out by combining the service life data and the stability data. Using the fatigue life and stability evaluation model, a quantitative analysis of the comprehensive performance of the aluminum alloy material under actual use conditions is carried out. Comprehensive analysis methods, such as multivariate analysis method, regression analysis method, etc., are used in combination with multiple factors such as temperature, load, and environmental conditions to evaluate the mechanical property changes of the aluminum alloy during long-term operation. Instrument equipment (such as a scanning electron microscope, X-ray diffractometer, etc.) is used to regularly inspect the microstructure of the aluminum alloy material, and monitor the relationship between its tissue changes and performance degradation. Through tests on the aluminum alloy under various environments such as thermal fatigue and corrosion, the performance degradation of the aluminum alloy is comprehensively analyzed to generate the performance data of the aluminum alloy material, covering aspects such as hardness, strength, ductility, and corrosion resistance. The initial property data of the aluminum alloy material is optimized for the component ratio of the aluminum alloy material according to the performance data of the aluminum alloy material to obtain the optimized data of the component ratio of the aluminum alloy material.In this step, the obtained performance data of the aluminum alloy material is utilized to optimize the composition ratio of the aluminum alloy. By comprehensively analyzing the influence of different components on the material performance, a mathematical model of the material performance and the composition ratio is constructed. Using optimization algorithms such as genetic algorithms or particle swarm optimization algorithms, based on considering multiple performance requirements of the aluminum alloy material, such as strength, ductility, and corrosion resistance, the content of each element of the aluminum alloy is adjusted. During the optimization process, the objective function is set according to the performance data to ensure that the aluminum alloy has the best physical and chemical properties while meeting the usage requirements. Through computer simulation or experimental testing, the influence of different composition ratios on the material performance is evaluated, and repeated adjustments are made to obtain the optimal composition ratio scheme, generating the optimized data of the aluminum alloy material composition ratio.

[0174] The above are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can 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 these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A performance analysis method for aluminum alloy materials, characterized in that: The following steps are involved: Step S1: collecting aluminum alloy material data; Using the aluminum alloy material data to detect the composition of the aluminum alloy material, to obtain the composition data of the aluminum alloy material; Perform initial property analysis of the aluminum alloy material based on the composition data of the aluminum alloy material to obtain initial property data of the aluminum alloy material; Step S2: performing aluminum alloy material load simulation on the initial property data of the aluminum alloy material to obtain aluminum alloy material load simulation data; Based on the load simulation data of aluminum alloy materials, the bearing capacity of aluminum alloy materials is estimated to obtain the bearing capacity data of aluminum alloy materials; Step S3: performing thermal cycle simulation on the initial property data of the aluminum alloy material, thereby obtaining thermal cycle simulation data of the aluminum alloy material; The thermal cycle threshold value of the aluminum alloy material is calculated by using the thermal cycle simulation data of the aluminum alloy material, so as to obtain the thermal cycle threshold value data of the aluminum alloy material; Step S4: Analyze the performance of the aluminum alloy material according to the thermal cycle threshold value of the aluminum alloy material and the bearing capacity data of the aluminum alloy material to generate the performance data of the aluminum alloy material; optimize the composition ratio of the aluminum alloy material according to the initial property data of the aluminum alloy material according to the performance data of the aluminum alloy material to obtain the optimized composition ratio data of the aluminum alloy material.

2. The performance analysis method of aluminum alloy material according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting aluminum alloy material data; Step S12: using the aluminum alloy material data to perform aluminum alloy material composition detection, thereby obtaining the aluminum alloy material composition data; Step S13: performing chemical property detection of the aluminum alloy material based on the aluminum alloy material composition data to obtain chemical property data of the aluminum alloy material; Step S14: performing a physical property test on the aluminum alloy material based on the aluminum alloy material composition data to obtain physical property data of the aluminum alloy material; Step S15: Performing initial property analysis on the physical property data of the aluminum alloy material and the chemical property data of the aluminum alloy material to obtain the initial property data of the aluminum alloy material.

3. The performance analysis method of aluminum alloy material according to claim 2, characterized in that: Step S1 includes the following steps: Step S131: collecting chemical elements of the aluminum alloy material according to the composition data of the aluminum alloy material to obtain chemical element data of the aluminum alloy material; Step S132: performing a chemical oxidation resistance test on the aluminum alloy material according to the chemical element data of the aluminum alloy material to obtain oxidation resistance data of the aluminum alloy material; Step S133: evaluating the chemical reaction ability of the aluminum alloy material based on the oxidation resistance data of the aluminum alloy material to obtain the chemical reaction ability data of the aluminum alloy material; Step S134: performing corrosion resistance analysis on the aluminum alloy material according to the chemical reaction ability data and the oxidation resistance ability data of the aluminum alloy material to obtain corrosion resistance data of the aluminum alloy material; Step S135: performing a chemical stability test on the aluminum alloy material based on the corrosion resistance data of the aluminum alloy material and the chemical reaction ability data of the aluminum alloy material to obtain the chemical stability data of the aluminum alloy material; Step S136: Performing a chemical property test on the chemical stability data of the aluminum alloy material to obtain the chemical property data of the aluminum alloy material.

4. The performance analysis method of aluminum alloy material according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: measuring the density of the aluminum alloy material according to the aluminum alloy material composition data to generate the aluminum alloy material density data; Step S142: scanning the element distribution spectrum according to the aluminum alloy material composition data to generate aluminum alloy element distribution spectrum data; Step S143: analyzing the aluminum alloy element crystal arrangement mode according to the aluminum alloy element distribution spectrum data to obtain the aluminum alloy element crystal arrangement mode data; Step S144: collecting the crystal microstructure of the aluminum alloy material based on the aluminum alloy element crystal arrangement data and the aluminum alloy element distribution spectrum data to obtain the crystal microstructure data of the aluminum alloy material; Step S145: calculating the crystal dislocation density according to the crystal microstructure data of the aluminum alloy material to obtain the crystal dislocation density data of the aluminum alloy material; Step S146: estimating the hardness of the aluminum alloy material according to the dislocation density data of the aluminum alloy material crystal and the crystal microstructure data of the aluminum alloy material to obtain the hardness data of the aluminum alloy material; Step S147: Performing a physical property test on the aluminum alloy material based on the hardness data and density data of the aluminum alloy material to obtain the physical property data of the aluminum alloy material.

5. The performance analysis method of aluminum alloy material according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing aluminum alloy material load simulation on the initial property data of the aluminum alloy material to obtain aluminum alloy material load simulation data; Step S22: performing deformation analysis of the aluminum alloy material using the load simulation data of the aluminum alloy material to obtain deformation data of the aluminum alloy material; Step S23: estimating the aging trend of the aluminum alloy material according to the deformation data of the aluminum alloy material to obtain aging trend data of the aluminum alloy material; Step S24: estimating the bearing capacity of the aluminum alloy material based on the aluminum alloy material aging trend data and the aluminum alloy material deformation data to obtain the aluminum alloy material bearing capacity data.

6. The performance analysis method of aluminum alloy material according to claim 5, characterized in that: Step S22 includes the following steps: Step S221: measuring the tensile state of the aluminum alloy according to the load simulation data of the aluminum alloy material to obtain the tensile state data of the aluminum alloy material; Step S222: performing necking effect analysis on the aluminum alloy material according to the tensile state data of the aluminum alloy material to obtain necking effect data of the aluminum alloy material; Step S223: performing local stress concentration calculation of the aluminum alloy material according to the necking effect data of the aluminum alloy material to obtain local stress concentration data of the aluminum alloy material; Step S224: estimating the plastic instability of the aluminum alloy material based on the local stress concentration data of the aluminum alloy material to generate plastic instability data of the aluminum alloy material; Step S225: performing deformation analysis on the aluminum alloy material plastic instability data to obtain deformation data of the aluminum alloy material.

7. The performance analysis method of aluminum alloy material according to claim 5, characterized in that: Step S24 includes the following steps: Step S241: performing a crystal slip state detection of the aluminum alloy material based on the deformation data of the aluminum alloy material to obtain crystal slip state data of the aluminum alloy material; Step S242: performing microscopic damage detection on the aluminum alloy material according to the crystal slip state data of the aluminum alloy material to obtain microscopic damage data of the aluminum alloy material; Step S243: calculating the crack extension probability of the aluminum alloy material according to the microscopic damage data of the aluminum alloy material to obtain the crack extension probability data of the aluminum alloy material; Step S245: based on the micro damage data of the aluminum alloy material and the crack extension probability data of the aluminum alloy material, the aging trend of the aluminum alloy material is estimated to obtain the aging trend data of the aluminum alloy material.

8. The performance analysis method of aluminum alloy material according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing thermal cycle simulation on the initial property data of the aluminum alloy material, thereby obtaining thermal cycle simulation data of the aluminum alloy material; Step S32: performing a thermal fatigue cumulative analysis of the aluminum alloy material according to the thermal cycle simulation data of the aluminum alloy material to generate thermal fatigue cumulative data of the aluminum alloy material; Step S33: estimating the attenuation of the mechanical properties of the aluminum alloy material based on the accumulated data of thermal fatigue of the aluminum alloy material to obtain the attenuation data of the mechanical properties of the aluminum alloy material; Step S34: performing rigidity attenuation detection according to the mechanical property attenuation data of the aluminum alloy material to obtain the rigidity attenuation data of the aluminum alloy material; Step S35: Calculate the thermal cycle threshold value of the aluminum alloy material using the aluminum alloy material rigidity attenuation data and the aluminum alloy material mechanical property attenuation data, thereby obtaining the aluminum alloy material thermal cycle threshold value data.

9. The performance analysis method of aluminum alloy material according to claim 8, characterized in that: Step S32 includes the following steps: Step S321: Calculating the probability of dissolution of the precipitated phase of the aluminum alloy material according to the thermal cycle simulation data of the aluminum alloy material to obtain the probability of dissolution of the precipitated phase of the aluminum alloy material; Step S322: performing microstructure deterioration prediction based on the dissolution probability data of the precipitated phase of the aluminum alloy material to obtain microstructure deterioration data of the aluminum alloy material; Step S323: performing thermal stability attenuation analysis on the aluminum alloy material according to the thermal cycle simulation data of the aluminum alloy material to obtain thermal stability attenuation data of the aluminum alloy material; Step S324: performing a thermal fatigue cumulative analysis of the aluminum alloy material based on the thermal stability decay data of the aluminum alloy material and the microstructure deterioration data of the aluminum alloy material to generate thermal fatigue cumulative data of the aluminum alloy material.

10. The performance analysis method of aluminum alloy material according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing stability testing of the aluminum alloy material according to the thermal cycle threshold of the aluminum alloy material and the bearing capacity data of the aluminum alloy material to obtain stability data of the aluminum alloy material; Step S42: estimating the service life of the aluminum alloy material based on the stability data of the aluminum alloy material to obtain service life data of the aluminum alloy material; Step S43: performing aluminum alloy material performance analysis based on the aluminum alloy material service life data and the aluminum alloy material stability data to generate aluminum alloy material performance data; Step S44: optimizing the composition ratio of the aluminum alloy material based on the initial property data of the aluminum alloy material according to the performance data of the aluminum alloy material, and obtaining optimized composition ratio data of the aluminum alloy material.

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