Multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation

Abstract

The invention discloses a multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation. According to the model, mass fraction distribution diagrams of eta phases with different Zn and Mg total amounts and Zn / Mg ratios under the condition that the Cu content is determined in the aluminum-rich angle multi-element aluminum alloy are calculated through a CALPHAD method, then correlation models of M eta, CZn + Mg and RZn / Mg are established for the M eta distribution diagram through a Radial Taylor model, distribution diagrams of M eta and # imgabs0 # under different CZn + Mg and RZn / Mg are drawn, then the model is optimized through a quenching sensitivity experiment, and the aluminum-rich angle multi-element aluminum alloy is obtained. According to the model, the quenching sensitivity of the multi-element aluminum alloy is predicted by constructing a correlation model of M eta, CZn + Mg and RZn / Mg, a predicted value of the model is verified in combination with a TTP curve and a TTT curve, and the model is optimized, so that the accuracy and rapidity of predicting the quenching sensitivity of the multi-element aluminum alloy by the obtained model are ensured.

Description

Technical Field

[0001] The invention relates to a multi-component aluminum alloy quenching sensitivity model, in particular to a multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation, belonging to the technical field of aluminum alloys. Background Art

[0002] Multi-component aluminum alloys are widely used in various industrial fields including the aerospace field due to their many advantages such as high specific strength, good toughness and easy processing. Multi-component aluminum alloys are generally aluminum alloy materials containing three or more elements. In recent years, in order to further optimize the mechanical properties of multi-component alloys, the content of the main elements is often controlled to improve the alloying degree of the material. However, with the improvement of the mechanical properties of multi-component aluminum alloys, the problem of quenching sensitivity of thick plates has become more prominent. For example, for highly alloyed Al-Zn-Mg-Cu alloys, a highly supersaturated solid solution is formed during high-rate quenching, thereby generating large residual stresses, causing deformation, warping and even cracking of the material; during low-rate quenching, a large amount of quenching precipitates will be generated, reducing the hardness and strength of the material after aging. Therefore, when designing a high-strength Al-Zn-Mg-Cu alloy, it is necessary to predict the quenching sensitivity of the designed alloy to obtain the optimal main element content.

[0003] In the prior art, the quenching sensitivity test of multi-component aluminum alloys is mostly reflected through TTP and TTT curves. However, the TTP curve needs to be drawn through isothermal quenching and end quenching experiments, which is labor-intensive and inefficient; the TTT curve cannot intuitively reflect the law of quenching sensitivity changing with the main elements. Although the main strengthening phase and quenching phase in multi-component alloys have a great influence on quenching sensitivity, there is currently no reliable way to intuitively and quickly reflect this relationship. Therefore, the market urgently needs a fast and intuitive method for predicting the quenching sensitivity of multi-component aluminum alloys, so as to significantly reduce the design cost of high-performance aluminum alloys. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention aims to provide a multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation. η , C Zn+Mg and R Zn / Mg The correlation model is used to predict the quenching sensitivity of multi-component aluminum alloys. The predicted value of the model is verified by combining the TTP curve and TTT curve, and the model is optimized to ensure the accuracy and speed of the obtained model in predicting the quenching sensitivity of multi-component aluminum alloys.

[0005] In order to achieve the above technical objectives, the present invention provides a multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation, comprising:

[0006] 1) The mass fraction distribution of η phase in Al-rich multi-component Al alloy with different Zn / Mg ratios under a certain Cu content was calculated by CALPHAD method, where the total amount of Zn and Mg is recorded as C Zn+Mg , the Zn / Mg ratio is denoted as R Zn / Mg , the mass fraction of η phase is recorded as M η ;

[0007] 2) Using the Rational Taylor model to analyze M η Distribution map creation η , C Zn+Mg and R Zn / Mg The correlation model of

[0008] 3) According to the correlation model described in step 2), draw different C Zn+Mg and R Zn / Mg M η and Distribution map of

[0009] 4) According to step 3) η and The distribution diagram of the multi-element aluminum alloy is used to predict the quenching sensitivity, obtain the predicted value, and then the model is optimized through the quenching sensitivity experiment.

[0010] The multi-component aluminum alloy includes Al, Cu, Zn and Mg elements.

[0011] In the technical solution provided by the present invention, for Al-Zn-Mg-Cu alloy, the more Zn and Mg solute atoms there are, the greater the volume fraction of the η' phase obtained after aging, and the greater the volume fraction of the η' phase, the higher the strength of the material. However, as the Zn and Mg content in the alloy increases, the concentration of solute atoms in the solid solution increases and the supersaturation increases after solution treatment. The solid solution is more easily decomposed during the quenching process, and the quenching η phase is quickly formed. The large-scale formation of the quenching η phase consumes the nucleation particles of the η' phase, which significantly reduces the strength of the alloy after aging, thereby improving the quenching sensitivity of the alloy.

[0012] As a preferred solution, the CALPHAD method is calculated by the PanPhaseDiagram module in the PANDAT software.

[0013] As a preferred solution, the mass percentages of the main components in the aluminum-rich multi-component aluminum alloy are: Zn content is 5-11wt.%, Mg content is 1-3.5wt.%, Cu content is 1-3wt.%, and the balance is Al.

[0014] As a preferred solution, the CZn+Mg and R Zn / Mg The calculation process is:

[0015] Formula 1:

[0016] In formula 1: C Zn and C Mg are Zn content and Mg content respectively.

[0017] As a preferred solution, the M η is the theoretical mass fraction of η phase in the equilibrium state at the aging temperature.

[0018] As a preferred solution, the aging temperature ranges from 100 to 160°C.

[0019] As a preferred solution, the M η , C Zn+Mg and R Zn / Mg The correlation model is:

[0020] Formula 2:

[0021] In formula 2: z0, a 01 ~c 01 and a1~c1 are fitting parameters.

[0022] As a preferred solution, the prediction process of the quenching sensitivity of the multi-element aluminum alloy is as follows: according to the different C Zn+Mg and R Zn / Mg M η and The distribution diagram of M η The bigger or The larger the absolute value of the data, the higher its quenching sensitivity.

[0023] As a preferred solution, the quenching sensitivity experiment includes a C curve test and an isothermal transformation curve test, and the results are plotted as a TTP curve and a TTT curve, respectively.

[0024] As a preferred solution, the model optimization method includes: when R Zn / Mg When C Zn+Mg Increase M η The value also increases, and the quenching sensitivity increases; when C Zn+Mg When R Zn / Mg Far from the critical Zn / Mg ratio M η The value decreases accordingly, and the quenching sensitivity decreases; when M η When the value remains unchanged, as R Zn / Mg keep away Value, CZn+Mg As it increases, the quenching sensitivity increases.

[0025] In Al-Zn-Mg-Cu alloy, η phase is a quenching precipitation phase that is easily formed during low-rate quenching. During the quenching process, the more quenching η phase there is in the alloy, the greater the performance loss during subsequent aging, indicating that the alloy has a high quenching sensitivity. The inventors have found through a large number of experiments that the quenching η phase is mainly affected by C Zn+Mg and R Zn / Mg With the influence of C Zn+Mg and R Zn / Mg As the temperature increases, the thermal activation energy of the η phase will decrease, so that the quenched η phase is easily formed during the quenching process. In addition, the inventors established M η , C Zn+Mg and R Zn / Mg The relationship between M η Also affected by C Zn+Mg and R Zn / Mg When R Zn / Mg When C Zn+Mg Increase will inevitably cause M η The value of C Zn+Mg When R Zn / Mg Far from a critical Zn / Mg ratio This will cause M η Based on this rule, M η The size of M is used to predict the quenching sensitivity of the alloy. η The increase in M ​​will lead to an increase in the quenching sensitivity of the alloy. In addition, the inventors found that when M η When the value remains unchanged, as R Zn / Mg keep away value, it will cause C Zn+Mg Increase, thereby improving quenching sensitivity. Under this condition, the chemical composition with the best quenching sensitivity should be R Zn / Mg equal value. The value is M η With R Zn / Mg The point with the smallest change is = 0. Therefore, considering M η and It can effectively predict the quenching sensitivity of Al-Zn-Mg-Cu alloy.

[0026] Compared with the prior art, the beneficial technical effects of the technical solution of the present invention are:

[0027] 1) The model provided by the present invention is constructed by M η , C Zn+Mg and R Zn / MgThe correlation model is used to predict the quenching sensitivity of multi-component aluminum alloys. The predicted value of the model is verified by combining the TTP curve and TTT curve, and the model is optimized to ensure the accuracy and speed of the obtained model in predicting the quenching sensitivity of multi-component aluminum alloys.

[0028] 2) The technical solution provided by the present invention is based on the excellent prediction accuracy of the above model. Compared with the TTP curve, it does not need to be drawn through isothermal quenching and end quenching experiments. Under the premise of reducing the cost of quenching sensitivity detection, it also greatly improves the detection efficiency. η , C Zn+Mg and R Zn / Mg The parameters can directly reflect the changing law between the main alloying elements and quenching sensitivity in multi-component aluminum alloys, effectively overcoming the problem of TTT curve in the prior art, and have important guiding significance for the design of high-performance multi-component alloys. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart for predicting the quenching sensitivity of Al-Zn-Mg-Cu alloy based on CALPHAD calculation provided by an embodiment of the present invention;

[0030] Figure 2 The C content of 1.5wt.% Cu provided in the embodiment of the present invention is Zn+Mg , R Zn / Mg and M η Distribution map of

[0031] Figure 3 The different C in the Rational Taylor model provided by the embodiment of the present invention Zn+Mg and R Zn / Mg M η and Distribution map of

[0032] Among them, the dotted curve is M η The solid curve is value;

[0033] Figure 4 : is the TTP curve of different alloy compositions provided by the embodiment of the present invention;

[0034] Figure 5 1 is the TTT curve of different alloys provided in the embodiments of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] This embodiment provides a multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation. In order to more significantly highlight the beneficial technical effects brought by the technical solution of the present invention, this embodiment takes Al-Zn-Mg-Cu quaternary alloy as an example to model the alloy. The specific process is as follows:

[0038] 1) The total amount of Zn and Mg (C) in the Al-Zn-Mg-Cu alloy with 1.5wt.% Cu content was calculated by CALPHAD method. Zn+Mg ) and Zn / Mg ratio (R Zn / Mg ) of the η phase (M η ) distribution map;

[0039] 2) Using the Rational Taylor model to analyze M η Distribution diagram fitting to establish M η , C Zn+Mg and R Zn / Mg The mathematical model of , the fitted function expression is as follows:

[0040]

[0041] 3) Based on the mathematical model in step 2), draw different C under the Rational Taylor model Zn+Mg and R Zn / Mg M η and Distribution map of

[0042] 4) The alloys to be designed are A1~A4, and their main element compositions are shown in Table 1. The alloys to be designed are judged according to the distribution diagram under the RationalTaylor model in step 3). η The larger the value or The further away from 0, the higher its quenching sensitivity. Therefore, the order of quenching sensitivity of the designed alloy is A4>A3>A2>A1.

[0043] Table 1

[0044]

[0045] In order to verify the design of Example 1, this comparative example verifies the quenching sensitivity of the alloy designed in the experimental example through TTP curve and TTT curve.

[0046] 1) TTP curve verification

[0047] Four alloys A1, A2, A3 and A4 were smelted, and their specific chemical compositions are shown in Table 1. The four alloys were homogenized under the following conditions: temperature at 470°C for 24 h; then they were hot rolled from 15 mm to 4 mm at 400°C, and then cold rolled from 4 mm to 1.3 mm. The four cold-rolled alloys were subjected to T6 heat treatment under the following conditions: temperature at 120°C for 24 h.

[0048] Isothermal quenching experiments were performed on the four T6 alloys to obtain the TTP curves of the four alloys. Figure 4 As shown, through Figure 4 It can be seen that the quenching sensitivity of the four alloys is: A4>A3>A2>A1, which is consistent with the conclusion obtained by the above model, proving that the model provided by the present invention is real and feasible for predicting the quenching sensitivity of multi-element aluminum alloys.

[0049] 2) TTT curve verification

[0050] In order to more comprehensively illustrate the credibility and compatibility of the above model, the present invention also carried out TTT curve verification, the process of which is: using JMatPro7.0 software to simulate the TTT curves of alloys A1 to A4 in the embodiment, the results are as follows Figure 5 shown; through Figure 5 It can be seen that the quenching sensitivity of the four alloys is: A4>A3>A2>A1, which is still consistent with the conclusion of the model obtained in the present invention, further proving that the model provided by the present invention is real and feasible for predicting the quenching sensitivity of multi-element alloys.

[0051] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. For those skilled in the art, improvements and changes obtained without departing from the technical concept of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation, characterized in that: include: 1) The mass fraction distribution of η phase in Al-rich multi-component Al alloy with different Zn / Mg ratios under a certain Cu content was calculated by CALPHAD method, where the total amount of Zn and Mg is recorded as C Zn+Mg , the Zn / Mg ratio is denoted as R Zn / Mg , the mass fraction of η phase is recorded as M η ; 2) Using the Rational Taylor model to analyze M η Distribution map creation η , C Zn+Mg and R Zn / Mg The correlation model of 3) According to the correlation model described in step 2), draw different C Zn+Mg and R Zn / Mg M η and Distribution map of 4) According to step 3) η and The distribution diagram of the multi-element aluminum alloy is used to predict the quenching sensitivity, obtain the predicted value, and then the model is optimized through the quenching sensitivity experiment. The multi-component aluminum alloy includes Al, Cu, Zn and Mg elements.

2. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The CALPHAD method is calculated by the PanPhaseDiagram module in the PANDAT software.

3. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The mass percentages of the main components in the aluminum-rich multi-component aluminum alloy are: Zn content is 5-11wt.%, Mg content is 1-3.5wt.%, Cu content is 1-3wt.%, and the balance is Al.

4. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The C Zn+Mg and R Zn / Mg The calculation process is: Formula 1: In formula 1: C Zn and C Mg are the Zn content and Mg content respectively.

5. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The M η is the theoretical mass fraction of the η phase in the equilibrium state at the aging temperature; the aging temperature ranges from 100 to 160°C.

6. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The M η , C Zn+Mg and R Zn / Mg The correlation model is: Formula 2: In formula 2: z0, a 01 ~c 01 and a1~c1 are fitting parameters.

7. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The prediction process of the quenching sensitivity of the multi-element aluminum alloy is as follows: according to the different C Zn+Mg and R Zn / Mg M η and The distribution diagram of M η The bigger or The larger the absolute value of the data, the higher its quenching sensitivity.

8. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1 is characterized in that: The quenching sensitivity experiment includes a C curve test and an isothermal transformation curve test, and the results are plotted as a TTP curve and a TTT curve, respectively.

9. The multi-component aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation according to claim 1, characterized in that: The model optimization method includes: when R Zn / Mg When C Zn+Mg Increase M η The value also increases, and the quenching sensitivity increases; when C Zn+Mg When R Zn / Mg Far from the critical Zn / Mg ratio (R Z C n / Mg ), M η The value decreases accordingly, and the quenching sensitivity decreases; when M η When the value remains unchanged, as R Zn / Mg Stay away from R Z C n / Mg Value, C Zn+Mg As it increases, the quenching sensitivity increases.

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