A prediction model for anisotropy of pure aluminum alloy based on deformation conditions

Abstract

The invention discloses a pure aluminum alloy anisotropy prediction model based on deformation conditions, comprising the steps of sampling a pure aluminum alloy for hot compression deformation, performing EBSD testing, and drawing the average size of inclusions, impurity content, and orientation distribution density peak value f after deformation. max (g) Relationship diagram. Regression processing is performed based on the relationship diagram. The univariate regression equation and correlation coefficient r obtained by regression are used to predict the performance of the material. By constructing a prediction model, the plastic deformation conditions (deformation temperature and strain rate) and the orientation distribution density peak f are constructed and analyzed after plastic processing of pure aluminum alloy. max (g) The prediction model obtained by regression shows that the deformation conditions have a significant effect on the preferred orientation of the hot deformation structure of pure aluminum alloys; the Z parameter has an important influence on the orientation distribution density level of the material after hot deformation. The regression result is highly significant, and the regression equation can be used to predict the structure of the material.

Description

Technical Field

[0001] The invention relates to the technical field of nonferrous metal material processing and application, and in particular to a pure aluminum alloy anisotropy prediction model based on deformation conditions. Background Art

[0002] Pure aluminum alloys can be used in press working to produce aluminum and aluminum alloy sheets and strips with thicknesses less than 0.2mm. Due to its excellent properties, aluminum foil is widely used as packaging for food, beverages, cigarettes, medicines, photographic plates, and household items; as a material for electrolytic capacitors; and as thermal insulation for buildings, vehicles, ships, and houses. It can also be used as decorative gold and silver thread, wallpaper, and decorative labels for various stationery, printed materials, and light industrial products. Severe deformation conditions (deformation temperature and strain rate) can cause aluminum foil to break or rollers to damage. These different deformation conditions affect the material's ultimate properties (anisotropy level) after plastic deformation.

[0003] The orientation distribution function (ODF) is a preferred representation of the three-dimensional spatial distribution of elements in a crystal (or sample). It provides quantitative information about the orientation distribution. The volume fraction of a crystal within a certain range of Euler space can also be calculated from the ODF. Using the ODF, it is easier to link texture with anisotropy in physical and mechanical properties. Crystal anisotropy manifests itself in differences in elastic modulus, hardness, fracture resistance, yield strength, thermal expansion coefficient, thermal conductivity, resistivity, electric displacement vector, electric polarization, magnetic susceptibility, and refractive index along different crystal directions. As a key property of crystals, anisotropy holds considerable research value. However, research in this area, both domestically and internationally, still focuses primarily on the modification and introduction of hot-rolling equipment, or solely on using it as a forming method focused solely on the final macroscopic performance. Few studies have exploited it as a means to control microstructure and properties. Deformation conditions and forming effects lack systematic and reliable theoretical guidance and experimental evidence, resulting in insufficient persuasiveness. Therefore, a pure aluminum alloy anisotropy prediction model based on deformation conditions is constructed to judge the state of plastic deformation of the alloy (expressed by the orientation distribution density level), determine the relationship between metallurgical factors and plastic forming processing effects, provide a theoretical basis for the hot working of pure aluminum alloys, and guide actual extrusion, drawing or rolling production.

[0004] In order to overcome the inability of existing technologies to accurately judge deformation conditions, deformation difficulty and anisotropy theoretically, and the lack of a theoretical basis for judgment, the present invention proposes an anisotropy prediction model for pure aluminum alloys based on deformation conditions, including the correlation function of deformation temperature and strain rate and the orientation distribution density level generated by thermal deformation of pure aluminum alloys, thereby judging the anisotropy level of aluminum alloy plastic processing. Summary of the Invention

[0005] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained through the structures particularly pointed out in the description, claims, and other drawings of the description.

[0006] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a pure aluminum alloy anisotropy prediction model based on deformation conditions.

[0007] To achieve the above objectives, the technical solution of the present invention is:

[0008] A pure aluminum alloy anisotropy prediction model based on deformation conditions includes the following steps:

[0009] Step 1: Pure aluminum alloy samples were cut from pure aluminum billets and processed into hot compression specimens. The hot compression test was carried out on a dynamic thermal / mechanical thermal simulator. The deformation temperatures were designed to be 300°C, 400°C, 450°C, and 500°C. The strain rate was 0.01s at each temperature. -1 , 0.1s -1 , 1.0s -1 、10.0s -1 , 50.0s -1 In the test, the compression deformation is 50% (ie true strain 0.7).

[0010] The relationship between the flow stress and deformation conditions during thermal deformation of a material can be described by the temperature-compensated strain rate Z parameter:

[0011]

[0012] Where A, n, α and ΔH are all material constants, R is the gas constant, and A(s -1 ) is the structure factor, n is the stress exponent, α(MPa -1 ) is the stress level parameter, ΔH is the free enthalpy, and σ is the mean steady-state flow stress.

[0013] Draw the stress-strain curve.

[0014] Step 2: Obtain the flow stress value and material constants (structure factor A, stress level parameter α, and stress exponent n) of the pure aluminum alloy according to the stress-strain curve, and calculate the Z parameter and its natural logarithm lnZ from the material constants using Equation 1 of the Z parameter.

[0015] Step 3: The sample was prepared as a thin slice, and was double-sprayed with HClO3 + anhydrous ethanol solution on a magnetically driven double-spray electrolytic thinning instrument to reduce the sample without penetrating the sample. The sample was then subjected to electron backscatter diffraction (EBSD) emission scanning electron microscope (FE-SEM). The Kikuchi band was automatically calibrated using the integrated analysis system of electron backscatter diffraction (EBSD) to obtain an electron backscatter diffraction pattern (EBSP). The microscopic crystal orientation and microtexture were inspected and analyzed using the TSLOIM system. The ODF map was obtained to obtain the peak value of the distribution density of different orientations f. max (g).

[0016] Step 4: According to the lnZ and orientation distribution density peak f in the hot deformation of pure aluminum alloy max (g) The figure plots the average size of inclusions, impurity content, and peak orientation distribution density f after deformation. max (g) Correlation diagram, from the orientation distribution density peak f max (g) The relationship curve with lnZ was subjected to regression processing. The regression equation and correlation coefficient r obtained by regression are as follows:

[0017] f max (g) = -0.0142 (lnZ) 3 +1.2665(lnZ) 2 -36.821lnZ+363.45r=0.9312 (Formula 2)

[0018] Orientation distribution density peak f max (g) and Z parameters satisfy the univariate regression relationship, and the peak value of the orientation distribution density f is compared. max (g) and the one-variable regression correlation coefficient r of lnZ. According to the regression results, it is judged that the Z parameter of the pure aluminum alloy has an important influence on the level of hot deformation orientation distribution density, and then the performance of the material is predicted.

[0019] Preferably, the material constants of pure aluminum alloy are structure factor A=8.5983×10 11 S -1 , stress level parameter α=0.0300mm 2 / N, stress index n=6.4228, strain rate sensitivity coefficient m=0.1460.

[0020] Preferably, the size of the hot compression sample is Φ10×12 mm, and the hot compression sample is cylindrical.

[0021] Preferably, the dynamic thermal / mechanical thermal simulator is Gleeble-1500, the emission scanning electron microscope (FE-SEM) is JEOL JSM6500F, and the integrated analysis system for electron backscatter diffraction (EBSD) is EDAX CENESIS7000EDS-PEGASIS 7000OIM.

[0022] Preferably, the sample slice is prepared to a thickness of 0.1 mm to 0.2 mm.

[0023] Preferably, the magnetically driven double-jet electrolytic thinning instrument is of model MTP-1A.

[0024] Preferably, the concentration combination of HClO 3 and anhydrous ethanol solution is 5% HClO 3 + 95% anhydrous ethanol solution.

[0025] Preferably, the operating voltage of the magnetically driven double-jet electrolytic thinning instrument is 50-60V, the current is 10-20mA, and the temperature is -25°C; the acceleration voltage of the electron backscatter diffraction (EBSD) is 15.0-20.0KV, the scanning step is 0.5-1.5μm, the magnification is 200-2000 times, and the scanning area is 350μm×350μm~400μm×400μm.

[0026] Working principle of the present invention:

[0027] When constructing the anisotropy prediction model of pure aluminum alloy based on deformation conditions, pure aluminum alloy samples were cut from pure aluminum billets and processed into hot compression specimens. The hot compression experiment was carried out on a dynamic thermal / mechanical thermal simulator. The deformation temperatures were designed to be 300°C, 400°C, 450°C, and 500°C. The strain rate was 0.01s at each temperature. -1 , 0.1s -1 , 1.0s -1 、10.0s -1 , 50.0s -1 In the test, the compression deformation is 50% (ie true strain 0.7).

[0028] The relationship between the flow stress and deformation conditions during thermal deformation of a material can be described by the temperature-compensated strain rate Z parameter:

[0029]

[0030] The stress-strain curve is drawn according to Formula 1.

[0031] The flow stress value and material constants (structure factor A, stress level parameter α and stress exponent n) of the pure aluminum alloy are obtained according to the stress-strain curve, and the Z parameter and its natural logarithm lnZ are calculated from the material constants using Formula 1 of the Z parameter.

[0032] The sample was prepared as a thin slice and thinned without penetration by double spraying HClO3 + anhydrous ethanol solution on a magnetically driven double-spray electrolytic thinning instrument. Electron backscattered diffraction (EBSD) was performed on an emission scanning electron microscope (FE-SEM). The Kikuchi band was automatically calibrated using an integrated analysis system for electron backscattered diffraction (EBSD) to obtain an electron backscattered diffraction pattern (EBSP). The microscopic crystal orientation and microtexture were inspected and analyzed using the TSLOIM system. The obtained ODF map obtained the peak value of the distribution density f of different orientations. max (g).

[0033] According to the lnZ and orientation distribution density peak f in the hot deformation of pure aluminum alloy max (g) The figure plots the average size of inclusions, impurity content, and peak orientation distribution density f after deformation. max (g) Correlation diagram, from the orientation distribution density peak f max (g) The relationship curve with lnZ was subjected to regression processing. The regression equation and correlation coefficient r obtained by regression are as follows:

[0034] f max (g) = -0.0142 (lnZ) 3 +1.2665(lnZ) 2 -36.821lnZ+363.45r=0.9312 (Formula 2)

[0035] Orientation distribution density peak f max (g) and Z parameters satisfy the univariate regression relationship, and the peak value of the orientation distribution density f is compared. max (g) and the one-variable regression correlation coefficient r of lnZ. According to the regression results, it is judged that the Z parameter of the pure aluminum alloy has an important influence on the level of hot deformation orientation distribution density, and then the performance of the material is predicted.

[0036] By adopting the above technical solution, the beneficial effects of the present invention are:

[0037] The present invention constructs a prediction model, and after the pure aluminum alloy is plastically processed, the plastic deformation conditions (deformation temperature and strain rate) and the orientation distribution density peak value f are obtained. max(g) The prediction model obtained by regression shows that the deformation conditions have a significant effect on the preferred orientation of the hot deformation structure of pure aluminum alloys; the Z parameter has an important influence on the orientation distribution density level of the material after hot deformation, and the regression result is highly significant. The regression equation can be used to predict the structure of the material; the prediction model formula 2 can predict the orientation distribution density level of the hot deformation of the pure aluminum alloy, and the level of the orientation distribution density can be used to determine the anisotropy level of the material (different metallurgical quality) after the same deformation conditions; the anisotropy of the crystal is specifically manifested in the different elastic modulus, hardness, fracture resistance, yield strength, thermal expansion coefficient, thermal conductivity, resistivity, electric displacement vector, electric polarization intensity, magnetic susceptibility and refractive index in different directions of the crystal. An analysis model is constructed to determine the anisotropy level of plastic processing of aluminum alloys. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention but do not constitute a limitation of the present invention.

[0039] In the drawings, like components are given like reference numerals, and the drawings are schematic and not necessarily drawn to scale.

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only one or several embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on such drawings without paying any creative work.

[0041] Figure 1 It is an ODF cross-sectional diagram of pure aluminum alloy at different strain rates in the pure aluminum alloy anisotropy prediction model based on deformation conditions of the present invention;

[0042] Figure 2 Schematic diagram of the structure of the ODF cross-sectional diagram of pure aluminum alloy at different deformation temperatures in the pure aluminum alloy anisotropy prediction model based on deformation conditions of the present invention;

[0043] Figure 3 The peak value f of the orientation distribution density in a pure aluminum alloy in the pure aluminum alloy anisotropy prediction model based on deformation conditions of the present invention is max (g) Schematic diagram of the structure in relationship with lnZ. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but are not intended to limit the present invention.

[0045] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. Throughout this specification, the illustrative expressions of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features and characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0046] Reference Figure 1-Figure 3 , Figure 1 It is an ODF cross-sectional diagram of pure aluminum alloy at different strain rates in the pure aluminum alloy anisotropy prediction model based on deformation conditions of the present invention; Figure 2 Schematic diagram of the structure of the ODF cross-sectional diagram of pure aluminum alloy at different deformation temperatures in the pure aluminum alloy anisotropy prediction model based on deformation conditions of the present invention; Figure 3 The peak value f of the orientation distribution density in a pure aluminum alloy in the pure aluminum alloy anisotropy prediction model based on deformation conditions of the present invention is max (g) Schematic diagram of the structure in relationship with lnZ.

[0047] This embodiment provides a pure aluminum alloy anisotropy prediction model based on deformation conditions. The model construction specifically includes the following parts:

[0048] 1. Thermal compression deformation treatment of samples

[0049] Samples were cut from pure aluminum billets and processed into cylindrical hot compression specimens of Φ10×12mm. The hot compression experiments were conducted on a Gleeble-1500 dynamic thermal / mechanical thermal simulator. The deformation temperatures were designed to be 300℃, 400℃, 450℃, and 500℃, and the strain rate was 0.01s at each temperature. -1 , 0.1s -1 , 1.0s -1 、10.0s -1 , 50.0s -1 In the test, the compression deformation is 50% (ie true strain 0.7).

[0050] The relationship between the flow stress and deformation conditions during thermal deformation of a material can be described by the temperature-compensated strain rate Z parameter:

[0051]

[0052] Where A, n, α and ΔH are all material constants, R is the gas constant, and A(s -1 ) is the structure factor, n is the stress exponent, α(MPa -1 ) is the stress level parameter, ΔH is the free enthalpy, and σ is the mean steady-state flow stress. The material constants of this pure aluminum alloy are shown in Table 1.

[0053] Table 1 Material constants of pure aluminum alloys

[0054]

[0055] The flow stress value and material constants (structure factor A, stress level parameter α, and stress exponent n) of the pure aluminum alloy are obtained according to the stress-strain curve, and the Z parameter and its natural logarithm lnZ are calculated from the material constants using Equation 1 of the Z parameter, as shown in Table 2.

[0056] Table 2 lnZ of pure aluminum alloys under different deformation conditions

[0057]

[0058] 2. Conduct EBSD testing

[0059] Samples were prepared as 0.1-0.2 mm thick slices and thinned without penetration using a 5% HClO₃ + 95% anhydrous ethanol solution on an MTP-1A magnetically driven double-jet electrolytic thinning instrument at a voltage of 50-60 V, a current of 10-20 mA, and a temperature of -25°C. Electron backscatter diffraction (EBSD) was performed on a JEOL JSM6500F field emission scanning electron microscope (FE-SEM). Automatic calibration of the Kikuchi band was performed using the EDAX CENESIS 7000EDS-PEGASIS 7000OIM electron backscatter diffraction (EBSD) integrated analysis system. The acceleration voltage was 15.0-20.0 kV, the scanning step was 0.5-1.5 μm, the magnification was 200-2000 times, and the scanning area was 350 μm × 350 μm ~ 400 μm × 400 μm. Electron backscatter diffraction patterns (EBSP) were obtained. The microscopic crystal orientation and microtexture were inspected and analyzed using the TSLOIM system. The ODF images obtained are shown in Figure 1. Figure 1 、 Figure 2 shown. Figure 1 The ODF cross-sectional diagrams of medium-purity aluminum alloys at different strain rates (400℃) are divided into five groups, as follows: (a) 0.01s -1 (b) 0.1s -1 、(c)1.0s -1 (d) 10.0s -1 、(e)50.0s -1, Figure 2 ODF cross-sectional diagram (0.1s-1) of medium-purity aluminum alloy at different deformation temperatures, the temperature is divided into (a) 300℃; (b) 400℃; (c) 450℃; (d) 500℃, and then Figure 1 、 Figure 2 Obtain the peak value f of the distribution density of different orientations max (g), see Table 3.

[0060] Table 3 lnZ and orientation distribution density peak f during hot deformation of pure aluminum alloys max (g)

[0061]

[0062] 3. Model Building

[0063] (1) According to Table 3, the average size of inclusions, impurity content and peak value of orientation distribution density after deformation f are plotted. max (g) Correlation diagram, see Figure 3 ;

[0064] (2) By Figure 3 The relationship curve is subjected to regression processing, and the regression equation and correlation coefficient r obtained are as follows:

[0065] f max (g) = -0.0142 (lnZ) 3 +1.2665(lnZ) 2 -36.821lnZ+363.45 r=0.9312 (Equation 2)

[0066] Orientation distribution density peak f max (g) and Z parameters satisfy the univariate regression relationship, and the orientation distribution density peak f max The univariate regression correlation coefficient r of (g) and lnZ is higher than r 0.001 (6)(r 0.001 (6) = 0.9249), the regression result is highly significant. Therefore, the Z parameter of the aluminum alloy has an important influence on the level of hot deformation orientation distribution density and can be used to predict the performance of the material.

[0067] The main chemical composition of pure aluminum alloys:

[0068]

[0069] The working principle of this embodiment is as follows:

[0070] When constructing the anisotropy prediction model of pure aluminum alloy based on deformation conditions, samples were cut from pure aluminum billets and processed into hot compression specimens. The hot compression experiment was carried out on a dynamic thermal / mechanical thermal simulator. The deformation temperatures were designed to be 300°C, 400°C, 450°C, and 500°C. The strain rate was 0.01s at each temperature. -1 , 0.1s -1 , 1.0s -1 、10.0s -1 , 50.0s -1 In the test, the compression deformation is 50% (ie true strain 0.7).

[0071] The relationship between the flow stress and deformation conditions during thermal deformation of a material can be described by the temperature-compensated strain rate Z parameter:

[0072]

[0073] The stress-strain curve is drawn according to Formula 1.

[0074] The flow stress value and material constants (structure factor A, stress level parameter α and stress exponent n) of the pure aluminum alloy are obtained according to the stress-strain curve, and the Z parameter and its natural logarithm lnZ are calculated from the material constants using Formula 1 of the Z parameter.

[0075] The sample was prepared as a thin slice and thinned without penetration by double spraying HClO3+anhydrous ethanol solution on a magnetically driven double-spray electrolytic thinning instrument. Electron backscattered diffraction (EBSD) emission scanning electron microscope (FE-SEM) was used. The Kikuchi band was automatically calibrated using the integrated analysis system of electron backscattered diffraction (EBSD) to obtain electron backscattered diffraction pattern (EBSP). The microscopic crystal orientation and microtexture were inspected and analyzed using the TSLOIM system. The obtained ODF map obtained the peak value of the distribution density f of different orientations. max (g).

[0076] According to the lnZ and orientation distribution density peak f in the hot deformation of pure aluminum alloy max (g) The figure plots the average size of inclusions, impurity content, and peak orientation distribution density f after deformation. max (g) Correlation diagram, from the orientation distribution density peak f max (g) The relationship curve with lnZ was subjected to regression processing. The regression equation and correlation coefficient r obtained by regression are as follows:

[0077] f max (g) = -0.0142 (lnZ) 3 +1.2665(lnZ) 2 -36.821lnZ+363.45r=0.9312 (Formula 2)

[0078] Orientation distribution density peak f max (g) and Z parameters satisfy the univariate regression relationship, and the peak value of the orientation distribution density f is compared. max (g) and the one-variable regression correlation coefficient r of lnZ. According to the regression results, it is judged that the Z parameter of the pure aluminum alloy has an important influence on the level of hot deformation orientation distribution density, and then the performance of the material is predicted.

[0079] It should be understood that the embodiments disclosed herein are not limited to the specific structures disclosed herein, but should be extended to equivalent replacements of such features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is only used to describe specific embodiments and is not intended to be limiting.

[0080] The "embodiment" mentioned in the specification means that a particular feature or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase or "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0081] Furthermore, the described features or characteristics may be combined in any other suitable manner into one or more embodiments. In the above description, some specific details, such as thickness, quantity, etc., are provided to provide a comprehensive understanding of the embodiments of the present invention. However, those skilled in the relevant art will appreciate that the present invention may be implemented without one or more of the above specific details or may also be implemented using other structures, components, etc.

Claims

1. A pure aluminum alloy anisotropy prediction model based on deformation conditions, characterized in that: The steps include: Step 1: Pure aluminum alloy samples were cut from pure aluminum billets and processed into hot compression specimens. The hot compression test was carried out on a dynamic thermal / mechanical thermal simulator. The deformation temperatures were designed to be 300°C, 400°C, 450°C, and 500°C. The strain rate was 0.01s at each temperature. -1 , 0.1s -1 , 1.0s -1 , 10.0s -1 , 50.0s -1 For the test, the compression deformation is 50%; The relationship between the flow stress and deformation conditions during thermal deformation of the material can be described by the temperature-compensated strain rate Z parameter: In the formula, A, n, α and ΔH are all material constants, R is the gas constant, and A(s -1 ) is the structural factor, n is the stress exponent, α(MPa -1 ) is the stress level parameter, ΔH is the free enthalpy, and σ is the mean value of steady-state flow stress; Draw the stress-strain curve; Step 2: Obtain the flow stress value and material constant of the pure aluminum alloy according to the stress-strain curve, the material constant includes the structure factor A, the stress level parameter α and the stress exponent n, and calculate the Z parameter and its natural logarithm lnZ from the material constant through the Z parameter formula 1; Step 3: The sample was prepared as a thin slice, and was double-sprayed with HClO3+anhydrous ethanol solution on a magnetically driven double-spray electrolytic thinning instrument for thinning without penetration. It was performed on an electron backscatter diffraction (EBSD) emission scanning electron microscope (FE-SEM), and the integrated analysis system of electron backscatter diffraction (EBSD) was used to automatically calibrate the Kikuchi band to obtain the electron backscatter diffraction pattern (EBSP). The microscopic crystal orientation and microtexture were inspected and analyzed by the TSLOIM system, and the ODF map was obtained to obtain the peak value of the distribution density f of different orientations. max (g); Step 4: According to the lnZ and orientation distribution density peak value f in the hot deformation of pure aluminum alloy max (g) Figure plots the average size of inclusions, impurity content, and peak value of orientation distribution density after deformation f max (g) Correlation diagram, based on the orientation distribution density peak f max (g) The relationship curve with lnZ is subjected to regression processing, and the univariate regression equation and correlation coefficient r obtained by regression are as follows: f max (g) = -0.0142(lnZ) 3 + 1.2665(lnZ) 2 - 36.821lnZ + 363.45 r = 0.9312 (Equation 2) Orientation distribution density peak value f max (g) and Z parameters satisfy the univariate regression relationship, comparing the orientation distribution density peak f max (g) and the univariate regression correlation coefficient r of lnZ. According to the regression results, it is judged that the Z parameter of the pure aluminum alloy has an important influence on the level of hot deformation orientation distribution density, and then the performance of the material is predicted.

2. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1 is characterized in that: The material constants of the pure aluminum alloy are structure factor A = 8.5983 × 10 11 S -1 , stress level parameter α=0.0300mm 2 / N, stress exponent n=6.4228, strain rate sensitivity coefficient m=0.1460.

3. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1 is characterized in that: The size of the hot compression specimen is Φ10×12 mm, and the hot compression specimen is cylindrical.

4. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1 is characterized in that: The model of the dynamic thermal / mechanical thermal simulator is Gleeble-1500, the model of the emission scanning electron microscope (FE-SEM) is JEOL JSM6500F, and the integrated analysis system of electron backscatter diffraction (EBSD) is EDAX CENESIS 7000EDS-PEGASIS7000OIM.

5. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1, characterized in that: The sample slices were prepared with a thickness of 0.1 mm to 0.2 mm.

6. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1, characterized in that: The model of the magnetically driven double-jet electrolytic thinning instrument is MTP-1A.

7. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1 is characterized in that: The concentration combination of HClO3 and anhydrous ethanol solution is 5% HClO3 + 95% anhydrous ethanol solution.

8. The pure aluminum alloy anisotropy prediction model based on deformation conditions according to claim 1 is characterized in that: The working voltage of the magnetically driven double-jet electrolytic thinning instrument is 50-60V, the current is 10-20mA, and the temperature is -25°C; the acceleration voltage of the electron backscatter diffraction (EBSD) is 15.0-20.0KV, the scanning step is 0.5-1.5μm, the magnification is 200-2000 times, and the scanning area is 350μm×350μm~400μm×400μm.

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