An aluminum alloy macro-microscopic rule analysis method, device, equipment and storage medium

Abstract

The present application relates to the technical field of aluminum alloy analysis, and particularly relates to an aluminum alloy macro-microscopic rule analysis method, device, equipment and storage medium; stress analysis is carried out based on hot compression experimental data and material parameters to obtain yield stress estimation, dislocation density stress, grain size evolution related stress and precipitate related stress; the pre-trained sparrow optimization algorithm, the preset numerical simulation method, the yield stress estimation, the dislocation density stress, the grain size evolution related stress and the precipitate related stress are used for rule analysis on the material parameters and the hot compression experimental data to obtain the aluminum alloy high-temperature flow stress and the microstructure evolution rule; the stress component analysis is used for comprehensively characterizing the high-temperature deformation mechanism of the material, the sparrow optimization algorithm is combined to optimize the model, the prediction deviation is reduced, the flow stress and the organization evolution are deduced, and support is provided for the aluminum alloy hot deformation research and process optimization.

Description

Technical Field

[0001] This invention relates to the field of aluminum alloy analysis technology, and in particular to a method, apparatus, equipment, and storage medium for analyzing the macroscopic and microscopic properties of aluminum alloys. Background Technology

[0002] The high-temperature hot deformation process of aluminum alloys involves complex plastic rheological properties and microstructure evolution. The changes in rheological stress, dynamic recrystallization behavior, grain size evolution, and precipitate strengthening mechanism are coupled with each other and are factors affecting the quality of hot working. At present, hot deformation analysis technology cannot fully take into account the multi-dimensional micro-mechanisms such as dislocation evolution, grain evolution, and precipitate effects, resulting in poor matching between plasticity calculation and microstructure prediction. At the same time, conventional model parameters rely on empirical values, which have problems such as weak parameter adaptability and low simulation prediction accuracy. It is difficult to reproduce the true hot deformation law of aluminum alloys under different temperature and strain rate conditions, and cannot meet the practical application requirements of high-precision hot deformation mechanism analysis and hot working process optimization. Summary of the Invention

[0003] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method, apparatus, equipment and storage medium for analyzing the macro- and micro-scale laws of aluminum alloys.

[0004] A method for analyzing the macro- and micro-structure of aluminum alloys includes: acquiring hot compression experimental data during a hot compression experiment; acquiring material parameters of the sample under test; performing stress analysis based on the hot compression experimental data and material parameters to obtain predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress; and performing regularity analysis on the material parameters and hot compression experimental data according to a pre-trained sparrow optimization algorithm, a preset numerical simulation method, predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress to obtain the high-temperature rheological stress and microstructure evolution law of the aluminum alloy.

[0005] Furthermore, a method for analyzing the macro- and micro-scale properties of aluminum alloys includes: acquiring hot compression experimental data during the hot compression experiment; acquiring material parameters of the sample under test; performing stress analysis based on the hot compression experimental data and material parameters to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress; and performing regularity analysis on the material parameters and hot compression experimental data according to a pre-trained sparrow optimization algorithm, a preset numerical simulation method, the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress to obtain the high-temperature rheological stress and microstructure evolution law of the aluminum alloy.

[0006] Furthermore, the stress analysis based on hot compression experimental data and material parameters to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation phase-related stress includes: calculating the predicted yield stress based on the hot compression experimental data and material parameters according to a preset yield stress prediction formula; calculating the dislocation density stress based on a preset first stress function relationship; calculating the grain size evolution-related stress based on a preset second stress function relationship; and calculating the precipitation phase-related stress based on a preset third stress function relationship.

[0007] Furthermore, the step of performing regularity analysis on material parameters and hot compression experimental data based on a pre-trained sparrow optimization algorithm, a preset numerical simulation method, yield stress prediction, dislocation density stress, grain size evolution-related stress, and precipitation phase-related stress to obtain the high-temperature rheological stress and microstructure evolution law of aluminum alloy includes: performing numerical simulation calculations on the yield stress prediction, dislocation density stress, grain size evolution-related stress, and precipitation phase-related stress based on preset hot compression test conditions, preset real strain increments, and numerical simulation methods to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size; and performing regularity analysis on material parameters and hot compression experimental data based on the sparrow optimization algorithm, numerical simulation method, predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size to obtain the high-temperature rheological stress and microstructure evolution law of aluminum alloy.

[0008] Furthermore, the step of performing numerical simulation calculations on the yield stress prediction value, dislocation density stress, grain size evolution-related stress, and precipitation-related stress according to preset hot compression test conditions, preset real strain increments, and numerical simulation methods to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size includes: constructing a flow stress prediction model based on the yield stress prediction value, dislocation density stress, grain size evolution-related stress, and precipitation-related stress; and performing numerical simulation calculations on the flow stress prediction model according to the hot compression test conditions, real strain increments, and numerical simulation methods to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size.

[0009] Furthermore, the step of performing regularity analysis on material parameters and hot compression experimental data based on the sparrow optimization algorithm, numerical simulation method, predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy includes: obtaining the experimental dynamic recrystallization volume fraction and experimental average grain size from the hot compression experimental data; obtaining the true stress-strain curve during the hot compression experiment; performing error analysis on the predicted step rheological stress, predicted dynamic recrystallization volume fraction, predicted average grain size, experimental dynamic recrystallization volume fraction, experimental average grain size, and true stress-strain curve to obtain the first mean square error, second mean square error, and third mean square error; and performing regularity analysis on material parameters and hot compression experimental data based on the sparrow optimization algorithm, numerical simulation method, first mean square error, second mean square error, and third mean square error to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy.

[0010] Further, the step of performing error analysis on the predicted step rheological stress, predicted dynamic recrystallization volume fraction, predicted average grain size, experimental dynamic recrystallization volume fraction, experimental average grain size, and true stress-strain curve to obtain a first mean square error, a second mean square error, and a third mean square error includes: calculating the predicted step rheological stress and true stress-strain curve according to a preset first mean square error function to obtain a first mean square error; calculating the predicted dynamic recrystallization volume fraction and experimental dynamic recrystallization volume fraction according to a preset second mean square error function to obtain a second mean square error; and calculating the predicted average grain size and experimental average grain size according to a preset third mean square error function to obtain a third mean square error.

[0011] Furthermore, the step of performing regularity analysis on material parameters and hot compression experimental data based on the sparrow optimization algorithm, numerical simulation method, first mean square error, second mean square error, and third mean square error to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy includes: summing the first mean square error, second mean square error, and third mean square error to obtain the minimum sum of errors; performing optimization analysis on material parameters based on the sparrow optimization algorithm, the preset maximum number of iteration steps, and the minimum sum of errors to obtain the optimal model parameters; and performing iterative incremental calculation on the optimal model parameters based on the numerical simulation method, the preset isothermal and constant strain rate conditions, and hot compression experimental data to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy.

[0012] Furthermore, an aluminum alloy macro-microstructure analysis device includes: a data acquisition module for acquiring hot compression experimental data during a hot compression experiment; a parameter acquisition module for acquiring material parameters of the sample under test; a stress analysis module for performing stress analysis based on the hot compression experimental data and material parameters to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress; and a pattern analysis module for performing pattern analysis on the material parameters and hot compression experimental data according to a pre-trained sparrow optimization algorithm, a preset numerical simulation method, the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress to obtain the high-temperature rheological stress and microstructure evolution patterns of the aluminum alloy.

[0013] Furthermore, an aluminum alloy macro-microscopic law analysis device is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the aluminum alloy macro-microscopic law analysis device to perform the various steps of the aluminum alloy macro-microscopic law analysis method as described above.

[0014] Furthermore, a computer-readable storage medium stores instructions that, when executed by a processor, implement the steps of the macro- and micro-scale law analysis method for aluminum alloys as described above.

[0015] In the technical solution of this invention, hot compression experimental data and material parameters are obtained through standardized multi-condition hot compression experiments, providing a reliable experimental benchmark for model calculations. By standardizing material parameters, the input parameters are standardized, ensuring the accuracy and stability of subsequent calculations. The various characteristic stress components obtained from stress analysis are comprehensively quantified to quantify the mechanical contributions of yielding behavior, dislocation evolution, grain evolution, and precipitate strengthening, covering the microscopic mechanism of high-temperature deformation of materials. At the same time, by combining pre-trained sparrow optimization algorithm and numerical simulation technology, the model parameters are iteratively optimized, effectively reducing the deviation between predicted and experimental values, deducing the evolution law of high-temperature rheological stress and microstructure of aluminum alloys, and improving the model prediction accuracy. This can provide reliable technical support for the study of aluminum alloy hot deformation mechanism, numerical model optimization, and hot working process parameter optimization. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a first flowchart of a method for analyzing the macro- and micro-scale properties of aluminum alloys provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an aluminum alloy macro-microscopic law analysis device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an aluminum alloy macro-microscopic law analysis device provided in an embodiment of the present invention; Figure 4 This is a comparison chart of experimental and predicted values ​​of rheological stress at a deformation temperature of 470℃ in an embodiment of the present invention. Figure 5 shows the correlation between physical experiments on rheological stress and the predicted data from the established model. Figure 6 This is a comparison chart of the experimental and predicted values ​​of the dynamic recrystallization volume fraction at a deformation temperature of 470°C in an embodiment of the present invention. Figure 7 This is a comparison chart of the experimental and predicted average grain size values ​​of the present invention at a deformation temperature of 470°C. Detailed Implementation

[0017] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the macro- and micro-scale law analysis method for aluminum alloys in this invention includes: 101. Obtain thermal compression experimental data during the thermal compression experiment; In this embodiment, the deformation temperature is obtained from the hot compression test data, and an alloy cylindrical sample is prepared using wire cutting technology to set the deformation temperature. strain rate High-temperature hot compression experiments were conducted under a compression rate of 60%, and true stress-strain curves were collected. Immediately after the experiment, the samples were water-quenched to preserve the instantaneous microstructure of the deformation. Electron backscatter diffraction was then used to examine the samples and obtain microstructure data such as the volume fraction and size of precipitates, the volume fraction of dynamic recrystallization, and the average grain size. The above mechanical curves and micro parameters together constitute complete hot compression experimental data, which serve as the measured reference data for subsequent stress analysis and model calculation. 102. Obtain the material parameters of the sample being tested; In this embodiment, by organizing the information recorded throughout the hot compression experiment, the actual deformation temperature corresponding to each group of samples is extracted, and the inherent material parameters of the tested aluminum alloy samples are collected. The deformation temperature and material parameters are uniformly organized and classified as the basic input variables for subsequent stress calculation and model operation, ensuring the completeness and accuracy of parameters in subsequent analysis steps, and providing preliminary data support for multi-dimensional stress solution. 103. Based on hot compression experimental data and material parameters, stress analysis is performed to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress. In this embodiment, hot compression experimental data and material parameters are used as inputs for computation, and a systematic stress analysis is carried out based on the corresponding dedicated calculation model. The solution is performed sequentially according to the established calculation logic and formulas to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress. The four stress components correspond to the initial yield characteristics of the material, dislocation evolution behavior, grain structure evolution, and precipitation strengthening effect, respectively. This completes the stress quantification and decomposition under different microscopic mechanisms, providing mechanical calculation results for subsequent algorithm analysis, numerical simulation, and deformation law research. 104. Based on the pre-trained sparrow optimization algorithm, the preset numerical simulation method, the yield stress prediction, dislocation density stress, grain size evolution related stress and precipitation phase related stress, the material parameters and hot compression experimental data are analyzed to obtain the high-temperature rheological stress and microstructure evolution law of aluminum alloy. In this embodiment, simulation prediction is carried out in numerical simulation software by setting a standard working condition of constant temperature and constant strain rate. The pre-trained sparrow optimization algorithm is called, and four types of stress components are used as the calculation basis. Global optimization, iterative verification and correlation analysis are carried out on material parameters and hot compression experimental data. The model calculation results are continuously compared with experimental measured data, the parameter combination is continuously optimized, the intrinsic correlation of each parameter under different working conditions is sorted out, and the high-temperature rheological stress change trend of aluminum alloy is gradually deduced by relying on the entire set of computing system. At the same time, the evolution law of microstructure such as dynamic recrystallization and grain size is obtained simultaneously, and the entire process of hot deformation of aluminum alloy under the target working condition is completely reproduced, realizing the prediction of rheological behavior and microstructure evolution law. In this embodiment, hot compression experimental data and material parameters are obtained through standardized multi-condition hot compression experiments, providing a reliable experimental benchmark for model calculations. By standardizing material parameters, the input parameters are standardized, ensuring the accuracy and stability of subsequent calculations. The various characteristic stress components obtained from stress analysis are comprehensively quantified to quantify the mechanical contributions of yield behavior, dislocation evolution, grain evolution, and precipitate strengthening, covering the microscopic mechanism of high-temperature deformation of materials. At the same time, by combining pre-trained sparrow optimization algorithm and numerical simulation technology, the model parameters are iteratively optimized, effectively reducing the deviation between predicted and experimental values, deducing the evolution law of high-temperature rheological stress and microstructure of aluminum alloys, and improving the model prediction accuracy. This can provide reliable technical support for the study of aluminum alloy hot deformation mechanism, numerical model optimization, and hot working process parameter optimization.

[0019] A second embodiment of the method for analyzing the macro- and micro-scale properties of aluminum alloys in this invention includes: 201. Calculate the hot compression test data and material parameters according to the preset yield stress prediction formula to obtain the yield stress prediction value; In this embodiment, calculations are performed based on a preset yield stress prediction formula to obtain a yield stress prediction value. This yield stress prediction value is then used as an initial reference value and imported into the subsequent calculation model to provide a basic initial value for the sparrow optimization algorithm to perform iterative solutions, thus completing the preliminary quantitative calculation of the yield stress. The expression of the yield stress prediction formula is as follows: , In the formula, The first material parameter, This is the second material parameter. This is the third material parameter, which includes the first material parameter, the second material parameter, and the third material parameter. The gas constant is... The deformation temperature. For yield stress, Strain rate; 202. Calculate the hot compression test data and material parameters according to the preset first stress function relationship to obtain the dislocation density stress; In this embodiment, the first stress function is a dedicated calculation formula for dislocation density-related stress. This function comprehensively considers physical quantities such as dislocation density, dislocation density evolution rate, Taylor factor, dislocation interaction coefficient, Burgers vector, and shear modulus. The formula is subdivided into three types of action mechanisms: work hardening, dynamic recovery, and dynamic recrystallization, which correspond to different dislocation density change rates. Sub-calculation formulas are constructed by combining variables such as substructure size, grain size, average spacing of precipitates, and mean free path of dislocations. Each sub-formula is configured with corresponding material parameters. The deformation temperature, hot compression experimental data, and some material parameters are substituted into the function in sequence to solve each sub-variable layer by layer. Finally, the dislocation density stress is calculated, realizing a quantitative characterization from microscopic dislocation evolution to macroscopic stress. The first stress function relationship is as follows: , , , , , In the formula, For dislocation density stress, This represents the current dislocation density. The dislocation density evolution rate, Taylor factor, For dislocation interaction coefficients, Burgers vector, Shear modulus; The rate of change of dislocation density caused by work hardening mechanism. The mean free path of dislocation migration, , Substructure dimensions, The grain size is from thermal compression experimental data. The average spacing of the precipitated phases in the slip surface; along with Changeable, usually expressed as , The work hardening coefficient, , This is the fourth material parameter. This is the fifth material parameter. This is the sixth material parameter. This represents the actual strain (from thermal compression experimental data). The average spacing represents the true strain rate (from hot compression experimental data). It can be represented as , The average volume fraction of the precipitated phase (from hot compression experimental data), spacing factor Represented as , For the seventh material parameter, This is the eighth material parameter. The ninth material parameter; the mean free path of dislocations is , The value is the equivalent radius of the precipitated phase (from thermal compression experimental data). The atomic diffusion parameter is given by the following formula: , The dynamic response coefficient. , This is the tenth material parameter. This is the eleventh material parameter. This is the twelfth material parameter. This refers to the dislocation consumption rate caused by dynamic recrystallization. This is the initial value of the dislocation density. It is the volume fraction of dynamic recrystallization, in which It is the dynamic recrystallization coefficient. , This is the thirteenth material parameter. This is the fourteenth material parameter. This is the fifteenth material parameter. The rate of change of dynamic recrystallization can be expressed as: , For the sixteenth material parameter, This is the seventeenth material parameter. This is the eighteenth material parameter. This is the nineteenth material parameter. For incubation score, To represent the critical strain. , It is the peak strain. , For the twentieth material parameter, This is the twenty-first material parameter. Twenty-second material parameter, rate of change of incubation fraction , This is the twenty-third material parameter. This is the twenty-fourth material parameter. This is the twenty-fifth material parameter; 203. Calculate the hot compression experimental data and material parameters according to the preset second stress function relationship to obtain the stress related to grain size evolution; In this embodiment, a preset second stress function relationship is used to calculate the stress components corresponding to grain size evolution (i.e., grain size evolution-related stress). The average grain size is used as a variable, and the main calculation logic is constructed by combining the grain size coefficient and the grain size evolution rate. The grain size coefficient is obtained by converting the corresponding material parameters. The grain size evolution rate is further divided into two parts: the grain coarsening change rate and the dynamic recrystallization change rate. Multiple sets of special material parameters are configured, and the hot compression experimental data and the corresponding material parameters are uniformly substituted into the function to complete the simultaneous calculation. The stress related to grain size evolution is obtained by solving the problem, quantifying the contribution of the microstructure evolution behaviors such as grain growth and dynamic recrystallization to the overall rheological stress, supplementing the stress calculation content of the grain dimension, and establishing the stress related to grain size evolution. The expression for the second stress function is as follows: In the formula, Stress related to grain size evolution, The average grain size, The grain size factor can be expressed as: , This is the twenty-sixth material parameter. This is the twenty-seventh material parameter. This is the twenty-eighth material parameter. The rate of change of grain size. The rate of grain size change caused by grain coarsening. This refers to the rate of grain size change caused by dynamic recrystallization. This is the twenty-ninth material parameter. This is the thirtieth material parameter. This is the thirty-first material parameter. This is the thirty-second material parameter. This is the thirty-third material parameter. This is the thirty-fourth material parameter. This is the thirty-fifth material parameter; 204. Calculate the thermal compression test data and material parameters according to the preset third stress function relationship to obtain the stress related to the precipitation phase; In this embodiment, the third stress function relationship is the basis for calculating the precipitate strengthening stress. The function includes a specific material coefficient, which can be converted and solved by combining the precipitate characteristics and the corresponding material parameters. All the hot compression experimental data and the corresponding material parameters are substituted into the function expression to carry out the calculation, and finally the precipitate-related stress is obtained, which characterizes the strengthening effect of the precipitate on the aluminum alloy matrix, completes the stress solution of the precipitate dimension, and completes the stress calculation system corresponding to the microstructure. Establish stresses associated with precipitates Functional relationship: The expression for the third stress function is as follows: In the formula, the correlation coefficient of precipitation phase is... Represented as , To release phase-related stress, For material coefficients, This is the thirty-sixth material parameter. This is the thirty-seventh material parameter. This is the thirty-eighth material parameter; In this embodiment, a layered, multi-mechanism quantitative calculation system for high-temperature rheological stress in aluminum alloys was constructed, effectively improving the logic of material thermal deformation mechanical analysis. First, the yield stress prediction value was quantified using the yield stress prediction formula, providing initial values ​​for subsequent algorithm iteration and optimization, and ensuring the stability of parameter optimization. Simultaneously, relying on multiple sets of dedicated stress function relationships, the stress composition was decomposed from three microscopic dimensions: dislocation evolution, grain size evolution, and precipitation phase strengthening. Combined with hot compression experimental data and multiple sets of material parameters, the contribution of different microstructure behaviors to rheological stress was quantified. The entire calculation process is progressive and fully covers multiple mechanisms, breaking the limitations of single stress calculation. It realizes the coupled quantitative analysis of macroscopic mechanical properties and microscopic evolution mechanisms, effectively improving the completeness of high-temperature rheological stress calculation for aluminum alloys. It can provide rigorous and reliable theoretical calculation support for subsequent model parameter calibration, thermal deformation law analysis, and material hot processing technology optimization.

[0020] A third embodiment of the method for analyzing the macro- and micro-scale properties of aluminum alloys in this invention includes: 301. Based on the preset hot compression test conditions, preset real strain increments and numerical simulation methods, numerical simulation calculations are performed on the predicted yield stress, dislocation density stress, grain size evolution related stress and precipitate related stress to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction and predicted average grain size. In this embodiment, predetermined hot compression test conditions are used as the simulation boundary benchmark. External working condition parameters such as deformation temperature, strain rate, and total compression deformation are clearly defined. At the same time, the actual strain increment is set as a uniform iteration step size to discretize the deformation process and control the calculation. Relying on numerical simulation methods, four types of components are used as calculation objects: predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress. In each iteration step, the current working condition, strain data, and microstructure parameters of the previous step are combined, and each sub-model is called in sequence to complete the calculation of mechanical effects and microstructure parameters. Various stress components are superimposed to obtain the current rheological stress, and the microstructure data is updated simultaneously. Through stepwise iterative calculation, the predicted rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size are finally output, providing complete predictive data support for subsequent parameter analysis and law research. 302. Based on the sparrow optimization algorithm, numerical simulation method, prediction step rheological stress, prediction dynamic recrystallization volume fraction and prediction average grain size, the material parameters and hot compression experimental data are analyzed to obtain the evolution law of high temperature rheological stress and microstructure of aluminum alloy. In this embodiment, the predicted rheological stress, dynamic recrystallization volume fraction, and average grain size are used as evaluation criteria. Parameter analysis is conducted by combining the sparrow optimization algorithm and numerical simulation methods. The model prediction results are compared with the measured data from hot compression experiments. With minimizing the deviation as the optimization objective, the sparrow optimization algorithm is used to perform global search and local iterative optimization of material parameters and hot compression experimental data, continuously correcting parameter values ​​and repeatedly conducting numerical simulation verification. The matching degree between the calculation results and experimental data under different parameter combinations is continuously compared. After completing multiple rounds of iterative optimization, the intrinsic correlation between parameters under different working conditions is analyzed. The evolution trend of high-temperature rheological stress, dynamic recrystallization volume fraction, and average grain size of aluminum alloy with deformation conditions is comprehensively analyzed, ultimately clarifying the overall change law (i.e., the microstructure evolution law) of the high-temperature rheological behavior of aluminum alloy and various microscopic parameters. In this embodiment, an integrated analysis system combining numerical simulation and intelligent algorithm optimization was constructed, effectively improving the prediction accuracy of the high-temperature hot deformation behavior and microstructure evolution of aluminum alloys. Using real hot compression test conditions as the simulation benchmark, multi-stress component coupling calculations were achieved based on a fixed strain iteration step size, outputting predicted data for predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size, ensuring a high degree of consistency between the simulation process and the actual deformation process. Simultaneously, a sparrow optimization algorithm was introduced, aiming to minimize the deviation between predicted and experimental values, and performing global iterative optimization of material and experimental parameters, effectively correcting model parameter errors. This enabled the analysis of macroscopic mechanical response and microstructure evolution, uncovering the variation patterns of various parameters with deformation conditions, and providing reliable technical support and basis for the study of aluminum alloy hot deformation mechanisms, model optimization, and the design of hot processing parameters.

[0021] The fourth embodiment of the method for analyzing the macro- and micro-scale properties of aluminum alloys in this invention includes: 401. A flow stress prediction model is constructed based on the yield stress prediction value, dislocation density stress, grain size evolution related stress, and precipitate related stress. In this embodiment, the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress obtained by the solution are integrated to build a flow stress prediction model. The model expression defines the flow stress as the superposition result of yield stress, dislocation density-related stress, grain size-related stress, and precipitation-related stress. Each component in the formula corresponds to the initial yield characteristics of the material, the effect of dislocation evolution, the effect of grain structure evolution, and the strengthening effect of precipitation, respectively. The mechanical contributions corresponding to different microscopic mechanisms are unified and integrated to form a complete flow stress calculation model, providing a calculation framework for subsequent numerical simulation calculations. The expression for the flow stress prediction model is as follows: In the formula, For flow stress; 402. Based on the hot compression test conditions, the actual strain increment, and the numerical simulation method, the flow stress prediction model is numerically simulated to obtain the predicted step flow stress, the predicted dynamic recrystallization volume fraction, and the predicted average grain size. In this embodiment, calculations based on a MATLAB model are performed using a numerical simulation platform. The hot compression test conditions refer to the boundary parameters determined by actual high-temperature uniaxial compression tests, including... Deformation temperature, Strain rate and 60% of total compressive deformation are the external working condition benchmarks followed by the simulation calculation to ensure that the simulation environment is consistent with the real test. The true strain increment is the unified step size of the numerical iteration, which is the single-step deformation obtained by discretizing the total deformation. It is used to control the calculation step size and calculation accuracy. First, according to the hot compression test conditions, basic variables such as deformation temperature, strain rate, initial grain size, initial dislocation density, precipitate volume fraction and equivalent radius are configured, and the true strain increment is set as the iteration step size. In a single iteration step, the current working condition parameters and the micro-state data output by the previous iteration step are combined, and the sub-models of stress, dislocation density, dynamic recrystallization, grain size and precipitate strengthening are called in sequence to calculate the work hardening, dynamic recovery, dynamic recrystallization, grain coarsening and precipitate strengthening effects. The microstructure parameters are updated and each stress component is superimposed to obtain the current flow stress. At the same time, the current micro-parameters are passed to the next iteration step to realize the coupled calculation of macroscopic mechanical parameters and microstructure variables. Finally, the predicted flow stress, predicted dynamic recrystallization volume fraction and predicted average grain size are output. In this embodiment, a flow stress prediction model is constructed by integrating multiple stress components, and a complete computational framework is formed by incorporating multiple mechanisms such as yield characteristics, dislocation evolution, grain evolution, and precipitate strengthening. The simulation boundary is set in conjunction with actual hot compression test conditions, and discrete calculations are carried out using the actual strain increment as the iteration step size to ensure a high degree of consistency between the simulation environment and the real test. Through multi-sub-model linkage iteration, the coupled solution of macroscopic flow stress and microscopic parameters such as dislocation density, dynamic recrystallization volume fraction, and grain size is achieved, completely reproducing the entire process of high-temperature hot deformation of aluminum alloys. This model fully combines the multi-scale mechanical behavior of materials, with rigorous calculation logic and realistic working conditions, effectively improving the prediction accuracy of flow stress and microstructure parameters. It can provide reliable model support and data basis for the study of aluminum alloy hot deformation mechanism, numerical simulation analysis, and hot working process design and optimization.

[0022] The fifth embodiment of a method for analyzing the macro- and micro-scale properties of aluminum alloys according to the present invention includes: 501. The experimental dynamic recrystallization volume fraction and the experimental average grain size were obtained from the hot compression experimental data; In this embodiment, an alloy cylindrical specimen is prepared, and the experimental conditions are set within the specified range: deformation temperature. strain rate A high-temperature uniaxial hot compression test was conducted with a compression deformation of 60% and multiple combinations of deformation temperature and strain rate. After the test, the sample was tested. The complete true stress-true strain curve was obtained by collecting and converting data through experimental equipment, which provides a real and reliable experimental basis for error analysis and verification of subsequent model prediction results. 502. Obtain the true stress-strain curve during the hot compression experiment; In this embodiment, the experimentally measured true stress-strain curve, dynamic recrystallization volume fraction, and average grain size are used as reference standards. The predicted rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size output by the model are compared with these standards one by one. Based on the three preset mean square error functions, the data deviation is calculated for each sampling point and statistical operations are completed. The first mean square error, the second mean square error, and the third mean square error are obtained in sequence. The three types of errors quantify the prediction deviation of the model in terms of mechanical properties, recrystallization behavior, and grain size evolution, respectively, forming a multi-dimensional evaluation index, which provides a quantitative evaluation basis for subsequent algorithm optimization and model optimization. 503. Error analysis was performed on the predicted step rheological stress, predicted dynamic recrystallization volume fraction, predicted average grain size, experimental dynamic recrystallization volume fraction, experimental average grain size, and true stress-strain curve to obtain the first mean square error, the second mean square error, and the third mean square error. In this embodiment, the experimentally measured true stress-strain curve, dynamic recrystallization volume fraction, and average grain size are used as reference standards. The predicted rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size output by the model are compared with these standards one by one. Based on the three preset mean square error functions, the data deviation is calculated for each sampling point and statistical calculation is completed. The first mean square error, the second mean square error, and the third mean square error are obtained in sequence. The three types of errors quantify the prediction deviation of the model in terms of mechanical properties, recrystallization behavior, and grain size evolution, respectively, forming a multi-dimensional evaluation index, which provides a quantitative evaluation basis for subsequent algorithm optimization and model optimization. 504. Based on the sparrow optimization algorithm, numerical simulation method, first mean square error, second mean square error and third mean square error, the material parameters and hot compression experimental data are analyzed to obtain the evolution law of high temperature rheological stress and microstructure of aluminum alloy. In this embodiment, a comprehensive objective function is constructed by integrating three types of mean square errors. The optimization direction is to minimize the total error. The sparrow optimization algorithm is used to perform global optimization and local iteration on the model material parameters and hot compression related experimental parameters to obtain the optimal parameter combination. The optimal parameters are substituted into the numerical simulation platform, and incremental iterative calculation is carried out in combination with constant temperature and constant strain rate conditions. Each sub-model is called to calculate and update data synchronously. Finally, the high-temperature rheological stress of aluminum alloy, as well as the changing trend of parameters such as dynamic recrystallization volume fraction and average grain size with strain are calculated. Based on the entire set of calculation results, the whole process law analysis of aluminum alloy hot deformation is completed. In this embodiment, true stress-strain curves of aluminum alloy mechanics are simultaneously obtained through multi-condition high-temperature hot compression experiments, laying a true benchmark for model verification. Using three types of mean square error functions, the predicted deviations are quantified from three dimensions: rheological stress, dynamic recrystallization, and grain size, forming a comprehensive model evaluation system. With the goal of minimizing the total error, intelligent parameter calibration is completed using a sparrow optimization algorithm, effectively improving parameter matching accuracy. Then, iterative calculations are performed based on numerical simulation to solve the variation laws of high-temperature rheological stress and microscopic parameters. The entire process organically combines experimental data, error evaluation, intelligent algorithms, and simulation calculations, improving the accuracy of predicting the hot deformation behavior and microstructure evolution of aluminum alloys. This provides solid data support and technical reference for the study of material hot deformation mechanisms, hot processing technology design, and parameter optimization.

[0023] The sixth embodiment of a method for analyzing the macro- and micro-scale properties of aluminum alloys according to the present invention includes: 601. Calculate the predicted step hydrodynamic stress and true stress-strain curves according to the preset first mean square error function to obtain the first mean square error; In this embodiment, the first mean square error function serves as the quantification standard for the deviation of rheological stress data. It performs error calculations on the predicted rheological stress output by the model and the experimentally measured true stress-strain curve. The expression for the first mean square error function is as follows: , In the formula, These are the predicted stress values ​​(derived from the predicted step rheological stress data). These are experimental stress values ​​(from true stress-strain curves). The number of data points to calculate stress, The first mean square error is calculated by comparing the data of the corresponding sampling points point by point, combining the number of sampling points to complete the mathematical statistics, and finally calculating the first mean square error between the experimental value and the model prediction value. This directly reflects the degree of deviation between the model prediction result and the actual experimental data under the rheological stress dimension, and provides the first quantitative reference index for parameter optimization. 602. The predicted dynamic recrystallization volume fraction and the experimental dynamic recrystallization volume fraction are calculated according to the preset second mean square error function to obtain the second mean square error; In this embodiment, the second mean square error function is used to quantify the prediction deviation of the dynamic recrystallization volume fraction. The predicted dynamic recrystallization volume fraction calculated by the model and the measured dynamic recrystallization volume fraction obtained by experimental detection are used as the calculation objects. The expression of the second mean square error function is as follows: , In the formula, This is the second mean square error. This is the predicted volume fraction of dynamic recrystallization (from the predicted volume fraction of dynamic recrystallization data). These are experimental values ​​for the volume fraction of dynamic recrystallization (derived from experimental dynamic recrystallization volume fraction data). To calculate the number of data points for the average grain volume fraction, the deviation is calculated point by point according to the function rule and the number of sampling points, and the second mean square error is obtained. This error value can characterize the accuracy of the model in predicting the dynamic recrystallization evolution law and become the second basis for evaluating the model's ability to predict microstructure. 603. Calculate the predicted average grain size and the experimental average grain size according to the preset third mean square error function to obtain the third mean square error; In this embodiment, the third mean square error function serves as the basis for calculating the error of the average grain size data. The predicted average grain size output by the model and the actual average grain size measured experimentally are substituted into the function for calculation. Based on the number of sampling points, full data deviation statistics are performed to obtain the third mean square error, which is used to measure the model's prediction error regarding the grain size evolution law and supplement the evaluation data for the microstructure dimension. The expression for the third mean square error function is as follows: , In the formula, This is the predicted volume fraction of dynamic recrystallization (derived from the predicted average grain size). This represents the experimental average grain size (derived from experimental average grain size data). To calculate the number of data points for the average grain size, The third mean square error; In this embodiment, a three-level mean square error quantification system is constructed to achieve a multi-dimensional and accurate evaluation of the prediction accuracy of the aluminum alloy hot deformation model. First, the predicted flow stress and the experimental true stress-strain curve are compared point by point using the first mean square error function to quantify the prediction deviation of mechanical behavior. Second, the deviation between the predicted and experimental values ​​of the dynamic recrystallization volume fraction is calculated using the second mean square error function to characterize the prediction accuracy of the microstructure evolution law. Finally, the error statistics of the predicted and experimental average grain size are performed using the third mean square error function to supplement the evaluation index of the grain evolution dimension. This three-level error system comprehensively and objectively measures the fit between the model prediction results and experimental data from three key aspects: macroscopic mechanical properties, recrystallization behavior, and grain size evolution. It provides quantifiable optimization targets for subsequent intelligent algorithm parameter optimization and effectively improves the overall prediction accuracy and reliability of the model.

[0024] The seventh embodiment of the method for analyzing the macro- and micro-scale properties of aluminum alloys in this invention includes: 701. Sum the first mean square error, the second mean square error, and the third mean square error to obtain the minimum sum of errors; In this embodiment, the objective function is established as follows: In the formula, To minimize the sum of all mean square errors, the first, second, and third mean square errors are integrated to construct a comprehensive objective function and perform a summation operation. The optimization objective is to minimize the sum of the three errors. This objective function unifies and integrates the deviations between macroscopic mechanical indicators and microscopic organizational indicators, transforms multidimensional dispersed errors into a single quantitative evaluation index, and comprehensively reflects the degree of matching between the overall prediction results of the model and the experimental data. It provides a unified and clear judgment standard for subsequent intelligent algorithm optimization and serves as the calculation basis for optimizing model parameters. 702. Based on the sparrow optimization algorithm, the preset maximum number of iterations and the minimum sum of errors, the material parameters are optimized to obtain the best model parameters; In this embodiment, a sparrow optimization algorithm is embedded in MATLAB software to perform parameter calibration. Reasonable ranges are set for each parameter in the material parameters, and upper and lower bounds are defined for each parameter. The basic algorithm configuration is completed: maximum iteration count is set to 1000, sparrow population size is set to 500, discoverer percentage is 20%, watcher percentage is 10%, and safety threshold is set to 0.8. This scheme constructs the objective function using the sum of three types of mean square errors, and uses the sum of squared errors between experimental values ​​and model predictions as the fitness evaluation index to determine the fitness corresponding to different parameter combinations. Based on the position update rules corresponding to discoverers, followers, and watchers in the sparrow search algorithm, global search and local optimization are performed on the material parameters. The matching effect between the model calculation results and experimental data under each set of parameters is continuously compared. After each round of parameter optimization, the model program is updated synchronously and recalculated, forming a cyclic iterative mechanism. When the sum of squared errors converges to the ideal range, or the algorithm reaches the preset maximum number of iterations, the optimization process is terminated. Finally, all material parameters in the physical model are extracted, the physical model parameters are fitted, and the optimal model parameter combination is determined. 703. Based on numerical simulation methods, preset isothermal and constant strain rate conditions, and hot compression experimental data, iterative incremental calculations are performed on the optimal model parameters to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy. In this embodiment, the determined optimal model parameters are input into numerical simulation software. Combined with hot compression experimental data and the established working conditions of constant temperature and constant strain rate, incremental iterative calculations are performed on the hot deformation process of aluminum alloy. The calculation is advanced step by step with the actual strain increment as the step size. In each calculation step, the current temperature, strain rate, actual strain, and variables such as dislocation density, dynamic recrystallization volume fraction, average grain size, and precipitate state retained from the previous iteration step are combined to simultaneously calculate the effects of work hardening, dynamic recovery, dynamic recrystallization, grain coarsening, and precipitate strengthening. Based on this, the high-temperature rheological stress, dynamic recrystallization volume fraction, and average grain size of the current step are updated. The software stores the calculation results of each iteration step throughout the process, which is convenient for technicians to carry out process monitoring, data comparison, and subsequent analysis. After multiple rounds of continuous iteration, the changing trends of each parameter under different strain stages are finally obtained, generating true stress-true strain curves, dynamic recrystallization volume fraction change curves, and average grain size change curves with strain, which fully characterize the high-temperature rheological behavior and microstructure evolution characteristics of aluminum alloy during hot deformation. In this embodiment, a comprehensive objective function is constructed by integrating three types of mean square errors: rheological stress, dynamic recrystallization volume fraction, and average grain size. This unified quantification of multi-dimensional prediction biases establishes a clear objective for parameter optimization. The model parameters are intelligently calibrated using a sparrow optimization algorithm. By combining global search and local iteration, the optimal model parameters are obtained through optimization analysis, effectively improving parameter fitting accuracy. Subsequently, incremental iterative calculations are performed using numerical simulation to achieve coupled solution of macroscopic rheological stress and microstructure parameters, fully reconstructing the entire hot deformation process of aluminum alloys. The entire system considers both mechanical properties and microstructure evaluation dimensions, ensuring the overall prediction accuracy of the model. It can also output multiple types of variation curves for data analysis and process traceability, providing reliable technical support for the study of aluminum alloy hot deformation mechanisms and the design and optimization of hot processing techniques.

[0025] The curves comparing rheological stress are shown in the figure below. Figure 4 As shown, the correlation coefficient between the experimental rheological stress and the model-predicted rheological stress reached 0.997, with an average absolute relative error of 5.42%, proving that this invention is an accurate and reliable numerical method. Furthermore, the evolution law of the dynamic recrystallization volume fraction is as follows... Figure 6 As shown, the evolution of the average grain size is as follows: Figure 7 As shown in the results, the numerical values ​​predicted by the physical model are in good agreement with the experimental values, indicating that the method of the present invention can accurately predict the high-temperature rheological stress and microstructure evolution of forged Al-Zn-Mg-Cu alloys. The above describes a method for analyzing the macro- and micro-scale properties of aluminum alloys according to an embodiment of the present invention. The following describes a device for analyzing the macro- and micro-scale properties of aluminum alloys according to an embodiment of the present invention. Please refer to [link / reference]. Figure 2One embodiment of the macro- and micro-scale law analysis device for aluminum alloys according to the present invention includes: Data acquisition module 1 is used to acquire hot compression experimental data during the hot compression experiment. Parameter acquisition module 2 is used to acquire the material parameters of the sample being tested; Stress analysis module 3 is used to perform stress analysis based on hot compression experimental data and material parameters to obtain the yield stress prediction, dislocation density stress, grain size evolution related stress and precipitation phase related stress; The regularity analysis module 4 is used to perform regularity analysis on material parameters and hot compression experimental data based on the pre-trained sparrow optimization algorithm, preset numerical simulation method, yield stress prediction, dislocation density stress, grain size evolution related stress and precipitation phase related stress, so as to obtain the high-temperature rheological stress and microstructure evolution law of aluminum alloy. In this embodiment, hot compression experimental data and material parameters are obtained through standardized multi-condition hot compression experiments, providing a reliable experimental benchmark for model calculations. By standardizing material parameters, the input parameters are standardized, ensuring the accuracy and stability of subsequent calculations. The various characteristic stress components obtained from stress analysis are comprehensively quantified to quantify the mechanical contributions of yield behavior, dislocation evolution, grain evolution, and precipitate strengthening, covering the microscopic mechanism of high-temperature deformation of materials. At the same time, by combining pre-trained sparrow optimization algorithm and numerical simulation technology, the model parameters are iteratively optimized, effectively reducing the deviation between predicted and experimental values, deducing the evolution law of high-temperature rheological stress and microstructure of aluminum alloys, and improving the model prediction accuracy. This can provide reliable technical support for the study of aluminum alloy hot deformation mechanism, numerical model optimization, and hot working process parameter optimization.

[0026] Figure 3 This is a schematic diagram of the structure of an aluminum alloy macro-microscopic law analysis device 900 provided in an embodiment of the present invention. This aluminum alloy macro-microscopic law analysis device 900 can vary considerably due to different configurations or performance. It may include one or more central processing units (CPUs) 910 (e.g., one or more processors) and a memory 920, and one or more storage media 930 (e.g., one or more mass storage devices) storing application programs 933 or data 932. The memory 920 and storage media 930 can be temporary or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the aluminum alloy macro-microscopic law analysis device 900. Furthermore, the processor 910 may be configured to communicate with the storage media 930 and execute a series of instruction operations in the storage media 930 on the aluminum alloy macro-microscopic law analysis device 900 to implement the steps of the aluminum alloy macro-microscopic law analysis method provided in the above-described method embodiments.

[0027] An aluminum alloy macro-microscopic law analysis device 900 may further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The illustrated structure of an aluminum alloy macro-microscopic law analysis device does not constitute a limitation on an aluminum alloy macro-microscopic law analysis device. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0028] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a method for analyzing the macro- and micro-scale properties of aluminum alloys.

[0029] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0030] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the macro- and micro-scale characteristics of aluminum alloys, characterized in that, include: Acquire thermal compression experimental data during the thermal compression experiment; Obtain the material parameters of the sample being tested; Stress analysis was performed based on hot compression experimental data and material parameters to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitation-related stress. Based on the pre-trained sparrow optimization algorithm, the preset numerical simulation method, the yield stress prediction, dislocation density stress, grain size evolution related stress, and precipitation phase related stress, the material parameters and hot compression experimental data are analyzed to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy.

2. The method for analyzing the macro- and micro-scale characteristics of aluminum alloys as described in claim 1, characterized in that, The stress analysis based on hot compression experimental data and material parameters to obtain the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitate-related stress includes: The yield stress is estimated by calculating the hot compression test data and material parameters according to the preset yield stress estimation formula. The hot compression test data and material parameters are calculated based on the preset first stress function relationship to obtain the dislocation density stress; The hot compression experimental data and material parameters are calculated based on the preset second stress function relationship to obtain the stress related to grain size evolution; The thermal compression test data and material parameters are calculated based on the preset third stress function relationship to obtain the precipitation phase related stress.

3. The method for analyzing the macro- and micro-scale characteristics of aluminum alloys as described in claim 1, characterized in that, The method involves analyzing the material parameters and hot compression experimental data based on a pre-trained sparrow optimization algorithm, a preset numerical simulation method, yield stress prediction, dislocation density stress, grain size evolution-related stress, and precipitation phase-related stress to obtain the high-temperature rheological stress and microstructure evolution laws of aluminum alloys, including: Numerical simulations were performed on the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitate-related stress based on the preset hot compression test conditions, preset real strain increments, and numerical simulation methods to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size. Based on the sparrow optimization algorithm, numerical simulation method, prediction step rheological stress, prediction dynamic recrystallization volume fraction, and prediction average grain size, the material parameters and hot compression experimental data are analyzed to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy.

4. The method for analyzing the macro- and micro-scale characteristics of aluminum alloys as described in claim 3, characterized in that, The step involves performing numerical simulations on the predicted yield stress, dislocation density stress, grain size evolution-related stress, and precipitate-related stress based on preset hot compression test conditions, preset true strain increments, and numerical simulation methods to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size, including: A flow stress prediction model was constructed based on the yield stress prediction value, dislocation density stress, grain size evolution related stress, and precipitation phase related stress. Numerical simulations were performed on the flow stress prediction model based on hot compression test conditions, actual strain increments, and numerical simulation methods to obtain the predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size.

5. The method for analyzing the macro- and micro-scale characteristics of aluminum alloys as described in claim 3, characterized in that, The method involves analyzing the material parameters and hot compression experimental data using the sparrow optimization algorithm, numerical simulation method, predicted step rheological stress, predicted dynamic recrystallization volume fraction, and predicted average grain size to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloys, including: The experimental dynamic recrystallization volume fraction and average grain size were obtained from the hot compression experimental data; Obtain the true stress-strain curve during the hot compression experiment; Error analysis was performed on the predicted step rheological stress, predicted dynamic recrystallization volume fraction, predicted average grain size, experimental dynamic recrystallization volume fraction, experimental average grain size, and true stress-strain curve to obtain the first mean square error, the second mean square error, and the third mean square error. Based on the sparrow optimization algorithm, numerical simulation method, first mean square error, second mean square error and third mean square error, the material parameters and hot compression experimental data are analyzed to obtain the evolution law of high temperature rheological stress and microstructure of aluminum alloy.

6. The method for analyzing the macro- and micro-scale characteristics of aluminum alloys as described in claim 5, characterized in that, The error analysis performed on the predicted step rheological stress, predicted dynamic recrystallization volume fraction, predicted average grain size, experimental dynamic recrystallization volume fraction, experimental average grain size, and true stress-strain curve to obtain the first mean square error, the second mean square error, and the third mean square error includes: The predicted step hydrodynamic stress and true stress-strain curves are calculated based on the preset first mean square error function to obtain the first mean square error; The predicted dynamic recrystallization volume fraction and the experimental dynamic recrystallization volume fraction are calculated based on the preset second mean square error function to obtain the second mean square error. The predicted average grain size and the experimental average grain size are calculated based on the preset third mean square error function to obtain the third mean square error.

7. The method for analyzing the macro- and micro-scale characteristics of aluminum alloys as described in claim 5, characterized in that, The method involves analyzing the material parameters and hot compression experimental data using the sparrow optimization algorithm, numerical simulation method, first mean square error, second mean square error, and third mean square error to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy, including: The first mean square error, the second mean square error, and the third mean square error are summed to obtain the minimum sum of errors; The material parameters are optimized based on the sparrow optimization algorithm, the preset maximum number of iterations, and the minimum sum of errors to obtain the optimal model parameters. Based on numerical simulation methods, preset isothermal and constant strain rate conditions, and hot compression experimental data, iterative incremental calculations were performed on the optimal model parameters to obtain the evolution law of high-temperature rheological stress and microstructure of aluminum alloy.

8. A device for analyzing the macro- and micro-scale properties of aluminum alloys, characterized in that, include: The data acquisition module is used to acquire the hot compression experimental data during the hot compression experiment. The parameter acquisition module is used to acquire the material parameters of the sample being tested. The stress analysis module is used to perform stress analysis based on hot compression experimental data and material parameters to obtain the yield stress estimate, dislocation density stress, grain size evolution related stress, and precipitation phase related stress. The pattern analysis module is used to perform pattern analysis on material parameters and hot compression experimental data based on the pre-trained sparrow optimization algorithm, preset numerical simulation methods, yield stress prediction, dislocation density stress, grain size evolution related stress, and precipitation phase related stress, so as to obtain the high-temperature rheological stress and microstructure evolution law of aluminum alloy.

9. A device for analyzing the macro- and micro-scale properties of aluminum alloys, characterized in that, The aluminum alloy macro-microscopic law analysis device includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the aluminum alloy macro-microstructure analysis device to perform the steps of the aluminum alloy macro-microstructure analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement each step of the macro-microscopic law analysis method for aluminum alloys as described in any one of claims 1-7.

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