Method for predicting characteristics of aluminum member

Abstract

This aluminum member characteristic prediction method comprises: a mechanical characteristic measurement step for measuring the mechanical characteristics of an unforged first aluminum member and a plurality of forged second aluminum members after a T6 treatment; a Z-factor calculation step in which the temperature and strain rate in each forging step are obtained by numerical analysis for the second aluminum member being forged, and a Z-factor is calculated using the obtained temperature and strain rate; a regression expression derivation step for deriving a regression expression indicating the relationship between the mechanical characteristics and the Z factor by using a combination of a plurality of measured values of the mechanical characteristics corresponding to the strengths and the calculated values of the Z factor; and a mechanical characteristic prediction step for predicting the mechanical characteristics of the third aluminum member after the T6 treatment after the arbitrary forging step using a regression equation.

Description

Technical Field

[0001] This invention relates to a method for predicting the properties of aluminum components.

[0002] This application claims priority based on Japanese patent application 2023-171242 filed on October 2, 2023, the contents of which are incorporated herein by reference. Background Technology

[0003] In recent years, the increase in vehicle weight due to battery installation has become unavoidable in EV and BEV (battery electric vehicle) sales. However, from the perspective of improving fuel economy, vehicle weight reduction is essential, leading to an increase in the use of aluminum in forged components such as suspension systems. In forged components like suspension systems, JIS 6000 series (Al-Mg-Si series) aluminum alloys are frequently used to improve strength and corrosion resistance. In forged components made of JIS 6000 series aluminum alloys, specific mechanical properties (conditional yield strength σ) are required. 0.2 (Tensile strength, elongation, etc.) require forging and subsequent heat treatment processes such as solution treatment and artificial aging.

[0004] Reducing GHG (greenhouse gas) emissions has become essential during the manufacturing and development of automobiles and automotive components, but this trend continues even in the design phase. Typically, forging and heat treatment processes are performed multiple times during the design phase until a manufacturing process that yields the desired mechanical properties is determined. However, this means that energy usage in each process is also repeated. To achieve GHG emission reductions during the design phase, it is important to reduce the number of processes up to the determination of the manufacturing process. Therefore, techniques for predicting the mechanical properties of aluminum components manufactured through each process are indispensable. Patent documents 1-3 disclose techniques for determining the properties of aluminum alloys before forging or heat treatment processes.

[0005] In Patent Document 1, for hot-forged products of JIS 6000 series aluminum alloys, sufficient hardness and strength are obtained by specifying the alloy composition and the length of small-angle grain boundaries. As a target for forging processes containing a length of small-angle grain boundaries exceeding the specified value, the Z-factor (Zener-Hollomon parameter) is envisioned to be 1×10⁻⁶. 11 Processing speeds of 1 / s or more.

[0006] In Patent Document 2, the characteristics of hot-forged products of the same JIS 6000 series aluminum alloy are shown, demonstrating that the increase in hardness during subsequent heat treatment can be predicted after hot forging, expressed by a certain Z-factor. The Z-factor used to predict the increase in hardness is calculated based on arbitrary time-averaged temperature and time-averaged strain rate.

[0007] Patent document 3 discloses that, in order to provide aluminum alloy forged components with excellent mechanical properties and fully ensure the overall strength and reliability of the components, by specifying the composition range of JIS 6000 series aluminum alloys and specifying the range of Z factor in the forging process, the microstructure of the forged components can be controlled into subgrain or fine recrystallized grains.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent Document 1: Japanese Patent Application Publication No. 2021-066900

[0011] Patent Document 2: Japanese Patent Application Publication No. 2021-090998

[0012] Patent Document 3: Japanese Patent Application Publication No. 2022-161591 Summary of the Invention

[0013] In Patent Documents 1-3, the Z-factor representing the forging process is calculated based on the average material temperature and strain rate at the start and end of processing, which does not fully represent the complex temperature and strain rate variations during forging. In Patent Document 2, similarly, time-averaged values ​​are used for both temperature and strain rate. Patent Document 3 specifies a range for the Z-factor used to control the microstructure of the forged component to subcrystalline or fine recrystallized state, but does not predict the characteristics of the forged component itself.

[0014] When a billet is forged after heating the billet and die to a specified temperature, the temperature and strain rate of the forged component always vary in different parts, resulting in a non-uniform overall structure. This is one of the main factors making it difficult to predict the mechanical properties of forged components. The Z-factor calculated based on time-averaged temperature and time-averaged strain rate sometimes fails to reflect the actual forging process, making it difficult to accurately predict the mechanical properties of forged components.

[0015] The present invention was made in view of the above circumstances, and its object is to provide a method for predicting the properties of aluminum components that can accurately predict the mechanical properties of aluminum components that have undergone arbitrary forging processes.

[0016] To address the aforementioned issues, the present invention employs the following methods.

[0017] (1) A method for predicting the characteristics of aluminum components according to one aspect of the present invention has the following features:

[0018] The mechanical property testing process involves measuring the mechanical properties of the unforged first aluminum component and multiple second aluminum components forged with different strengths after T6 treatment.

[0019] The Z-factor calculation process involves determining the temperature and strain rate at each forging step for any part of the second aluminum component during forging, using numerical analysis, and then calculating the Z-factor using the determined temperature and strain rate.

[0020] The regression derivation process uses multiple combinations of measured values ​​of the mechanical property corresponding to the strength and calculated values ​​of the Z-factor to derive a regression expression representing the relationship between the mechanical property and the Z-factor; and

[0021] The mechanical property prediction process uses the regression equation to predict the mechanical properties of the third aluminum component after T6 treatment following any forging step.

[0022] (2) In the method for predicting the characteristics of aluminum components described in (1) above, it is preferable that: in the Z-factor calculation process, at a predetermined interval t step The temperature and strain rate are measured at multiple consecutive different times i, and the Z-factor Z is calculated using the measured values ​​of the temperature and strain rate at each of the various times i. i The Z factor Z will be calculated according to the following equation (1). i The sum of these two factors is called the Z factor.

[0023]

[0024] (3) In the method for predicting the characteristics of aluminum components described in any one of (1) and (2) above, it is preferable to have:

[0025] In the process of calculating the rate of change of angle, for the second aluminum component, the mechanical properties in the main axis direction and the mechanical properties in the direction inclined from the main axis direction are measured, and the rate of change of the mechanical properties relative to the angle inclined from the main axis direction is calculated using the results of the measurements.

[0026] The angle detection process detects the angle formed by the stretching direction of the third aluminum component relative to the main axis direction of the second aluminum component in the mechanical property prediction process; and

[0027] The mechanical properties of the third aluminum component in the main axis direction are predicted by taking into account the detected angle and the rate of change of the mechanical properties.

[0028] (4) In the Z factor calculation step of the method for predicting the characteristics of aluminum components described in any of (1) to (3) above, it is preferable to: investigate the relationship between the presence or absence of recrystallization in the second aluminum component after the T6 treatment and the Z factor, and remove aluminum components from the plurality of second aluminum components that correspond to the situation where recrystallization has occurred.

[0029] According to the present invention, a method for predicting the properties of aluminum components that have undergone arbitrary forging processes can be provided. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the process flow of a method for predicting the characteristics of aluminum components according to one embodiment of the present invention.

[0031] Figure 2 It is a diagram showing the process flow for measuring mechanical properties.

[0032] Figure 3A This diagram illustrates the state of an aluminum component before forging.

[0033] Figure 3B This diagram illustrates the state of an aluminum component after forging.

[0034] Figure 4 This is a diagram showing the process flow for calculating the Z-factor.

[0035] Figure 5 This is a diagram illustrating the method for calculating the cumulative Z-factor.

[0036] Figure 6 This is a graph showing the relationship between the mechanical properties of aluminum components and the Z-factor.

[0037] Figure 7A This is a diagram showing the portion of the aluminum component being evaluated after T6 treatment, viewed from the expansion direction orthogonal to the upsetting direction.

[0038] Figure 7B This is a top-down view from the upsetting direction of the actual evaluation portion of the aluminum component after T6 treatment.

[0039] Figure 8 It represents the conditional yield strength σ of the aluminum component. 0.2 A graph showing the relationship with the Z-factor. Detailed Implementation

[0040] The following detailed description uses the accompanying drawings to illustrate the method for predicting the properties of aluminum components according to embodiments of the present invention. Furthermore, in the drawings used in the following description, for ease of understanding, some features are sometimes shown enlarged for convenience, and the dimensional ratios of the constituent elements may not be the same as actual dimensions. Additionally, the materials, dimensions, etc., illustrated in the following description are examples, and the present invention is not limited thereto; it can be implemented with appropriate modifications without altering its essence.

[0041] Figure 1 This is a flowchart illustrating the main steps S of a method for predicting the properties of aluminum components according to one embodiment of the present invention. The method for predicting the properties of aluminum components mainly includes a mechanical property measurement step S1, a Z-factor calculation step S2, a regression derivation step S3, and a mechanical property prediction step S4. In this embodiment, components including aluminum and aluminum alloys (JIS 6000 series (Al-Mg-Si series), etc.) are referred to as aluminum components.

[0042] (Mechanical property measurement procedure)

[0043] Figure 2 This is a flowchart showing the process flow included in the mechanical characteristic measurement process S1. The mechanical characteristic measurement process S1 is further subdivided into processes S11 and S12.

[0044] In process S11, for the specified parts of the first aluminum component (aluminum forging billet) that has not undergone upsetting or other forging processes, the mechanical properties (conditional yield strength σ) after T6 treatment (artificial aging treatment) are measured. 0.2 (e.g., tensile strength).

[0045] In process S12, the mechanical properties of specified parts of multiple second aluminum components (aluminum forging components) forged with different strengths (compression ratios) are measured after T6 treatment. The following example illustrates the case of upsetting forging as a forging process.

[0046] T6 treatment is a process in which aluminum components (first aluminum component and second aluminum component) are solution-treated, cooled (quenched), and then age-hardened. The conditions for T6 treatment can be determined during the treatment process, or they can be determined by considering data that is relevant to the obtained mechanical properties.

[0047] Figure 3A , 3B This is a diagram showing the state of the second aluminum component 100 before and after forging (upsetting) in process S12. For example... Figure 3AAs shown, an unprocessed aluminum forging billet 100A is placed on a female die (lower die) 101, and a punch (upper die) 102 is positioned on the opposite side (upper side in this case) of the female die 101. The punch 102 is moved towards the female die 101 (lower side in this case) to press the aluminum forging billet 100A, thereby... Figure 3B As shown, an aluminum forging component 100B compressed in the pressing direction L is obtained. The compression ratio generated by upsetting is expressed as (h0-h1) / h0 using the length h0 of the aluminum forging blank 100A in the pressing direction L and the length h1 of the aluminum forging component 100B.

[0048] (Z-factor calculation process)

[0049] Figure 4 This is a flowchart representing the process flow included in process S2 calculated by the Z factor. Process S2 calculated by the Z factor is further subdivided into processes S21, S22, S23, S24, and S25.

[0050] In process S21, a model of the forging process (the relationship between temperature, strain rate, and compressibility) is reproduced through numerical simulation. Analytical software for this numerical simulation can be, for example, DEFORM manufactured by SFTC. The coefficient of friction μ and the heat transfer coefficient h of the contact surfaces are set for the aluminum component, punch (upper die), and die (lower die) as the workpiece. The aluminum component is treated as a rigid-plastic body, and the punch and die as rigid bodies. Material data for the aluminum component can be obtained from the software's built-in aluminum database or material data specifically acquired for the aluminum component. Material data for the punch and die can also be obtained from the software's built-in die steel (SKD61). Next, a mesh is created for each aluminum component model.

[0051] In process S22, the boundary conditions for the numerical simulation are set. Alignment of each component is performed, the speed of the die and aluminum component is set to 0, and the processing speed information is input to the punch. Alternatively, the motion curve can be set according to the actual forging process.

[0052] In process S23, a thermal coupling analysis is performed to model the internal heat transfer of the aluminum component, namely, the plastic deformation of the aluminum component and the internal heat transfer (or conduction), and the numerical analysis of the heat transfer between the aluminum component and the mold.

[0053] In step S24, the temperature history and strain rate history of the evaluation part are extracted from the part that has undergone tensile testing after upsetting based on a specified compression ratio (e.g., 0.3 to 0.7).

[0054] In step S25, the Z-factor is calculated based on the temperature history and strain rate history extracted in step S24. When calculating the Z-factor, the following equation (1) is generally used. Z, ε (ε˙), Q, and R in equation (1) are defined as follows.

[0055] Z: Z factor (s) -1 )

[0056] ε: Strain rate

[0057] Q: Activation energy of aluminum's self-diffusion (144 kJ / mol)

[0058] R: Gas constant (8.314 J / mol / K)

[0059] T: Temperature (K)

[0060]

[0061] In equation (1) above, temperature T and strain rate ε are constant values. However, in typical forging processes, strictly speaking, temperature T and strain rate ε are not constant values. Therefore, from the viewpoint of accurately calculating the Z factor Z, it is preferable to reflect the effects of changes in temperature T and strain rate ε during forging processes in equation (1).

[0062] As a method to reflect the time-varying changes of temperature T and strain rate ε in forging, one example is the method of calculating the cumulative Z-factor by adding the time-varying Z-factors. Figure 5 This is a diagram illustrating the calculation method. The horizontal axis of the diagram represents the forging time divided into multiple calculation steps (unit time) t. step Number of computation steps S in the case of partitioning i The vertical axis of the graph represents the number of calculation steps S. i The corresponding Z-factor size.

[0063] In this calculation method, the calculation steps are S. i Calculate the Z factor Z i As shown in equation (2) below, the Z factor Z of all steps is calculated. i Multiply by the calculation step t step The results are then summed. The resulting cumulative Z-factor reflects the influence of changes in temperature T and strain rate ε during forging, thus allowing for accurate calculation of the Z-factor Z. Furthermore, if the Z-factor Z can be expressed as a function of time Z(t), it can also be calculated by integrating this function over the processing time.

[0064]

[0065] (Regression-based derivation process)

[0066] In the regression derivation process S3, the relationship between the mechanical properties measured in the mechanical property measurement process S1 and the Z-factor calculated in the Z-factor calculation process S2 is determined. That is, the combination (A, B) of multiple measured values ​​A of mechanical properties corresponding to forging strengths such as compression ratio and calculated values ​​B of Z-factor is obtained and plotted on the AB plane. Figure 6 This is a graph illustrating an example of plotting. The horizontal axis represents the Z-factor, and the vertical axis represents the conditional yield strength σ of the first aluminum component (forged billet) after T6 treatment. 0.2 The conditional yield strength σ of the second aluminum component (forged component) after T6 treatment, based on (F0). 0.2 The increment of (F), i.e., F-F0. The plotted points of (A, B) are distributed along a specific curve (e.g., a parabola). Therefore, a regression equation representing the relationship between mechanical properties and the Z factor can be derived from the distribution of the plotted points of (A, B).

[0067] (Mechanical property prediction process)

[0068] The regression formula derived in the regression formula derivation process has a unique shape based on the material composition of the second aluminum component being forged. Therefore, it is possible to use this regression formula to predict the mechanical properties of the third aluminum component after T6 treatment, regardless of the forging step.

[0069] <Determination of the Recrystallization Range>

[0070] In hot forging processes that increase the Z-factor, a large number of small-angle grain boundaries are introduced along with the grain refinement of the processed microstructure. By introducing a large number of small-angle grain boundaries, for example in 6000 series alloys, high strength can be obtained by promoting the precipitation of Mg2Si during the artificial aging process.

[0071] On the other hand, if a certain amount of small-angle grain boundaries are introduced, recrystallization will occur during the subsequent solution treatment. Due to the disappearance of the introduced small-angle grain boundaries and the coarsening of the grains, the mechanical properties will decrease or the property values ​​will fluctuate more significantly, becoming a major factor in the deterioration of the accuracy of the mechanical properties. Therefore, in order to predict the mechanical properties with high accuracy, it is preferable to investigate the relationship between the presence or absence of recrystallization in the second aluminum component after T6 treatment and the Z factor.

[0072] The correlation between recrystallization and the Z-factor can be confirmed using the same method as the relationship between mechanical properties and the Z-factor. Specifically, the magnitude of the Z-factor at which recrystallization occurs can be determined by observing the microstructure after hot working and solution treatment under different Z-factor conditions. Based on this result, at least the Z-factor at which recrystallization occurs can be identified. By selecting hot working with a Z-factor smaller than this Z-factor, aluminum components corresponding to the recrystallization situation can be removed from multiple second aluminum components, and the mechanical properties of the second aluminum components that suppress recrystallization can be predicted.

[0073] Understanding Relationships Between Opposites

[0074] Generally, aluminum forged components exhibit anisotropy caused by the metal flow during forging. The mechanical properties of the aluminum forged component change depending on the direction in which the tensile specimen is prepared and tested relative to the metal flow during tensile testing. This anisotropy affects the accuracy of mechanical property predictions; therefore, it is preferable to consider the effects of anisotropy based on numerical simulation results.

[0075] For example, numerical simulations can be used to obtain data on the direction of the maximum principal strain and the changes in mechanical properties when the angle relative to that direction is changed. Based on this data, the mechanical properties predicted through the mechanical property prediction process can be revised. Specifically, in addition to the above processes, an angle change rate calculation process and an angle detection process are added.

[0076] In the process of calculating the rate of change of angle, for the second aluminum component, the mechanical properties in the main axis direction and the mechanical properties in the direction inclined from the main axis direction are measured, and the rate of change of mechanical properties relative to the angle inclined from the main axis direction is calculated using the measurement results.

[0077] In the angle detection process, the angle formed by the stretching direction of the second aluminum component relative to the main axis direction of the second aluminum component in the mechanical property prediction process is detected. The mechanical properties of the third aluminum component in the main axis direction are predicted by considering the detected angle and the rate of change of mechanical properties calculated in the angle change rate calculation process, and the predicted value is corrected as needed.

[0078] As described above, the aluminum component characteristic prediction method of this embodiment utilizes the inherent material correlation between the mechanical properties of the forged second aluminum component and the Z-factor obtained through numerical simulation. According to this embodiment, the mechanical properties of any part of the forged second aluminum component can be predicted without T6 treatment, based on this correlation. Therefore, repeated trial production is unnecessary to investigate the conditions under which the specified mechanical properties can be obtained, thus reducing the energy consumption associated with trial production. For example, by applying this embodiment to predict mechanical properties in advance for hot-forged aluminum alloy components used in automotive suspension arms, the number of trial productions and mold making can be reduced, thereby reducing GHG emissions during the development phase.

[0079] Example

[0080] The effects of the present invention will be further illustrated by the following embodiments. Furthermore, the present invention is not limited to the following embodiments and can be implemented with appropriate modifications without altering its spirit.

[0081] Following the above implementation method, the properties of aluminum components were predicted. As samples (tests) of aluminum components before forging, three JIS 6000 series aluminum alloys with compositions shown in Table 1 were used. The (average) grain sizes of the three components were 56 μm, 70 μm, and 96 μm, respectively. The only difference between the three components was the grain size.

[0082]

[0083] To obtain the relationship between mechanical properties and the Z-factor, the following experiments were conducted. Upsetting was performed by heating the 6000 series aluminum alloy casting bar (diameter Φ57 × height (h0) 75 mm) to an initial temperature of 480°C. The temperatures of the die and punch were set to 180°C. Upsetting was carried out under various conditions, with the upsetting ratio (h0-h1) / h0, expressed as the initial height h0 and the height h1 after upsetting, set to 0.3, 0.4, 0.5, 0.6, and 0.7. A crankshaft connecting rod type press with a driving stroke distance of 180 mm at 25 SPM and a pressing capacity of 630 t was used, and the position of the bottom dead center was adjusted to process the 6000 series aluminum alloy casting bar at each upsetting ratio.

[0084] The samples after upsetting (aluminum forging components) and the samples with an upsetting rate of 0 (the state of continuously cast material) (aluminum forging billets) were subjected to a specified T6 treatment. Here, the holding temperature of the solution treatment was set to 530°C and the holding time was set to 3 hours, and the holding temperature of the subsequent artificial aging treatment was set to 180°C and the holding time was set to 6 hours.

[0085] The conditional yield strength σ of aluminum forging billet samples and aluminum forging components forged at various upsetting rates was determined. 0.2 Determine the conditional yield strength σ of each aluminum forging component sample relative to the aluminum forging billet sample. 0.2 The increase.

[0086] Figure 7A This is a top view of the T6-treated aluminum forging component from an extension direction orthogonal to the upsetting direction. Figure 7B This is a top view of the forged component from the upsetting direction. The sample of the aluminum forged component is taken from the midpoint between the center and the outermost periphery; that is, from a position at a thickness h1 / 2 relative to the center C (where the wall thickness h1 is the center) and at a position at a radius r1 / 2 relative to the maximum radius r1 measured from the center C. The sample of the aluminum forging billet... Figure 3A The sample shown is taken from a region extending along the height direction L, at a position where the maximum radius r0 is 2 relative to the center.

[0087] For the numerical simulation used to calculate the Z-factor, models and meshes of the aforementioned upsetting forging billet and die were created. A ring compression test was conducted to determine the friction coefficient based on the ring's deformation state, and this friction coefficient was used as a boundary condition in the numerical simulation. Material property values ​​were used by adding the deformation resistance values ​​obtained from the high-temperature compression test to the material database of the numerical simulation software.

[0088] Figure 8 It represents the conditional yield strength σ of a sample of an aluminum forged component. 0.2 A graph showing the relationship between Z and the Z-factor. The horizontal axis of the graph represents the Z-factor, and the vertical axis represents the conditional yield strength σ of the aluminum forging billet after T6 treatment. 0.2 (F0) is the conditional yield strength σ of the T6-treated aluminum forged component. 0.2 The increment of (F), i.e., F-F0. Plotting the conditional yield strength σ corresponding to the upsetting rate. 0.2 Multiple combinations (A, B) of the measured value A and the calculated value B of the Z factor.

[0089] The plotted points are distributed along a common curve. Samples of all crystal grain sizes exhibit the same tendency. These results indicate an inherent material correlation between mechanical properties and the Z-factor, which can be expressed by a regression equation. Therefore, if this regression equation is known in advance, it is possible to predict the mechanical properties of any part of any forged aluminum component without T6 treatment, based on this regression equation.

[0090] For multiple samples that underwent upsetting with different Z-factors, the magnitude of the Z-factor, the presence or absence of recrystallization, and the conditional yield strength σ were investigated.0.2 The relationship between the prediction accuracy and the prediction accuracy is shown in Table 2.

[0091]

[0092] With a Z-factor of 3.0 × 10 11 Under the above conditions, recrystallization occurs in the internal structure of the aluminum forging component, and the conditional yield strength σ 0.2 The error in the predicted values ​​increases. Conversely, when the Z-factor is less than 3.0 × 10⁻⁶, the error increases. 11 In the case where no recrystallization occurs, the conditional yield strength σ 0.2 The error in the predicted values ​​was suppressed to below 10%. These results indicate that the presence or absence of recrystallization changes based on a specific value of the Z-factor. Furthermore, it is found that if recrystallization occurs within the internal structure of the aluminum forging component, the accuracy of the mechanical property predictions decreases significantly. Therefore, it is believed that by pre-investigating the relationship between the Z-factor and recrystallization, and limiting the Z-factor during forging to a range where recrystallization does not occur, the mechanical properties of aluminum components can be predicted with high accuracy.

[0093] Explanation of reference numerals in the attached figures

[0094] 100···Second aluminum component

[0095] 100A Forged Billet

[0096] 100B Forged Components

[0097] 101···Dice

[0098] 102··· Punch

Claims

1. A method for predicting the characteristics of aluminum components, characterized in that, have: The mechanical property testing process involves measuring the mechanical properties of the unforged first aluminum component and multiple second aluminum components forged with different strengths after T6 treatment. The Z-factor is calculated by using numerical analysis to determine the temperature and strain rate at each forging step for any part of the second aluminum component during forging. The Z-factor is then calculated using the determined temperature and strain rate. The regression derivation process uses a combination of multiple measured values ​​of the mechanical property corresponding to the strength and calculated values ​​of the Z factor to derive a regression expression representing the relationship between the mechanical property and the Z factor. and The mechanical property prediction process uses the regression equation to predict the mechanical properties of the third aluminum component after T6 treatment following any forging step.

2. The method for predicting the characteristics of aluminum components according to claim 1, characterized in that, In the Z-factor calculation process, At a specified interval t step The temperature and strain rate are measured at multiple consecutive different times i, and the Z-factor Z is calculated using the measured values ​​of the temperature and strain rate at each of the various times i. i The Z factor Z will be calculated according to the following equation (1). i The Z obtained by adding them together is the Z factor. 。 3. The method for predicting the properties of aluminum components according to any one of claims 1 and 2, characterized in that, have: In the process of calculating the rate of change of angle, for the second aluminum component, the mechanical properties in the main axis direction and the mechanical properties in the direction inclined from the main axis direction are measured, and the rate of change of the mechanical properties relative to the angle inclined from the main axis direction is calculated using the results of the measurements. The angle detection process detects the angle formed by the stretching direction of the third aluminum component relative to the main axis direction of the second aluminum component in the mechanical property prediction process. and The mechanical properties of the third aluminum component in the main axis direction are predicted by taking into account the detected angle and the rate of change of the mechanical properties.

4. The method for predicting the properties of aluminum components according to any one of claims 1 and 2, characterized in that, In the Z-factor calculation process, Investigate the relationship between the presence or absence of recrystallization in the second aluminum component after the T6 treatment and the Z factor, and remove aluminum components from the plurality of second aluminum components that correspond to the case where recrystallization has occurred.

5. The method for predicting the characteristics of aluminum components according to claim 3, characterized in that, In the Z-factor calculation process, Investigate the relationship between the presence or absence of recrystallization in the second aluminum component after the T6 treatment and the Z factor, and remove aluminum components from the plurality of second aluminum components that correspond to the case where recrystallization has occurred.

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