A multi-component aluminum alloy quench sensitivity prediction and optimization model based on phase diagram calculation

Abstract

This invention discloses a prediction and optimization model for the quenching sensitivity of multi-component aluminum alloys based on phase diagram calculations. The model calculates the mass fraction distribution of the η phase in aluminum-rich angled multi-component aluminum alloys at different Zn / Mg ratios with a given Cu content using the CALPHAD method. Then, it uses the Rational Taylor model to analyze the quenching sensitivity of the η phase. η Distribution map establishment M η C Zn+Mg and R Zn / Mg The correlation model was constructed and different C values ​​were plotted. Zn+Mg and R Zn / Mg M below η The distribution diagrams of M and are obtained, and the model is optimized through quenching sensitivity experiments. This model is derived by constructing M... η C Zn+Mg and R Zn / Mg The correlation model was used to predict the quenching sensitivity of multi-element aluminum alloys. The predicted values ​​of the model were then verified by combining the TTP curve and TTT curve, and the model was optimized to ensure the accuracy and speed of the obtained model in predicting the quenching sensitivity of multi-element aluminum alloys.

Description

TECHNICAL FIELD

[0001] The present application relates to a multi-element aluminum alloy quench sensitivity model, in particular to a multi-element aluminum alloy quench sensitivity prediction and optimization model based on phase diagram calculation, and belongs to the technical field of aluminum alloys. BACKGROUND

[0002] Multi-element aluminum alloys are widely used in various industrial fields including aerospace due to their high specific strength, good toughness and easy processing. Multi-element aluminum alloys generally refer to aluminum alloy materials containing three or more elements. In recent years, in order to further optimize the mechanical properties of multi-element alloys, the content of main elements is often controlled to increase the alloying degree of the material. However, with the improvement of the mechanical properties of multi-element aluminum alloys, the problem of quench sensitivity of thick plates becomes more prominent. For example, for high-alloyed Al-Zn-Mg-Cu alloys, high supersaturated solid solution is formed during high-speed quenching, resulting in large residual stress, which leads to material deformation, warping and even cracking. During low-speed quenching, a large amount of quenching precipitates are produced, which reduces the hardness and strength of the material after aging. Therefore, when designing high-strength Al-Zn-Mg-Cu alloys, the quench sensitivity of the designed alloy needs to be predicted to obtain the optimal content of main elements.

[0003] In the prior art, the quench sensitivity of multi-element aluminum alloys is tested by TTP and TTT curves. However, TTP curves need to be plotted through isothermal quenching and end quenching experiments, which is time-consuming and inefficient. TTT curves cannot directly reflect the change rule of quench sensitivity with the change of main elements. Although the main strengthening phase and quenching phase in multi-element alloys have a great influence on quench sensitivity, there is no reliable way to directly and quickly reflect this relationship. Therefore, there is an urgent need for a fast and intuitive multi-element aluminum alloy quench sensitivity prediction method to greatly reduce the design cost of high-performance aluminum alloys. SUMMARY

[0004] In view of the problems in the prior art, the present application provides a multi-element aluminum alloy quench sensitivity prediction and optimization model based on phase diagram calculation. η 、C Zn+Mg and R Zn / Mg correlation model to predict the quench sensitivity of multi-element aluminum alloys, and then verify the predicted values of the model by combining TTP curves and TTT curves, and optimize the model, so as to ensure the accuracy and rapidity of the obtained model in predicting the quench sensitivity of multi-element aluminum alloys.

[0005] In order to achieve the above technical purposes, the present application provides a multi-element aluminum alloy quench sensitivity prediction and optimization model based on phase diagram calculation, which comprises:

[0006] 1) Calculate the mass fraction distribution of η phase in Al-rich corner multivariate aluminum alloy with different Zn and Mg total amount Zn / Mg ratio under a certain Cu content by CALPHAD method, wherein the Zn and Mg total amount is denoted as C Zn+Mg , the Zn / Mg ratio is denoted as R Zn / Mg , and the mass fraction of η phase is denoted as M η ;

[0007] 2) Establish the correlation model of M η , C η and R Zn / Mg by Rational Taylor model for M Zn+Mg distribution diagram;

[0008] 3) Draw the distribution diagram of M Zn+Mg and under different C Zn / Mg and R η according to the correlation model in step 2);

[0009] 4) According to the distribution diagram of M η and in step 3), predict the quench sensitivity of the multivariate aluminum alloy, obtain the predicted value, and then optimize the model through quench sensitivity experiment, and obtain the quench sensitivity of the multivariate aluminum alloy.

[0010] The multivariate aluminum alloy comprises Al, Cu, Zn and Mg elements.

[0011] In the technical solution provided by the application, for Al-Zn-Mg-Cu alloy, the more Zn and Mg solute atoms, the greater the volume fraction of η' phase obtained after aging, the greater the volume fraction of η' phase, and the higher the strength of the material. However, with the increase of Zn and Mg content in the alloy, the solute atom concentration in the solid solution increases and the supersaturation increases after solid solution treatment, and the solid solution is more likely to decompose during quenching, and quenching η phase is quickly formed. The large formation of quenching η phase consumes the nucleation sites of η' phase, which significantly reduces the strength of the alloy after aging, and further improves the quenching sensitivity of the alloy.

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

[0013] As a preferred scheme, the main components in the Al-rich corner multivariate aluminum alloy are as follows: 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 scheme, the CZn+Mg and R Zn / Mg The calculation process is as follows:

[0015] Formula 1:

[0016] In Equation 1: C Zn and C Mg These represent the Zn content and the Mg content, respectively.

[0017] As a preferred option, the M η This represents the theoretical mass fraction of the η phase at equilibrium under aging temperature.

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

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

[0020] Formula 2:

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

[0022] As a preferred embodiment, the prediction process for the quenching sensitivity of the multi-element aluminum alloy is as follows: based on the different C values ​​in step 3), Zn+Mg and R Zn / Mg M below η and The distribution map of M, where M η The larger or The larger the absolute value of the data, the higher its quenching sensitivity.

[0023] As a preferred embodiment, the quenching sensitivity experiment includes C-curve testing and isothermal transformation curve testing, the results of which are plotted as TTP curves and TTT curves, respectively.

[0024] As a preferred approach, the model optimization method includes: when R Zn / Mg When C remains unchanged, as C Zn+Mg Increase M η The value also increases accordingly, and the quenching sensitivity increases; when C Zn+Mg When R remains constant, 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 With the increase, the quench sensitivity is improved.

[0025] In Al-Zn-Mg-Cu alloy, the η phase is a quenching precipitated phase that is easy to form during the low-rate quenching process. The more the number of quenching η phase in the alloy during the quenching process, the greater the performance loss of subsequent aging, which indicates that the alloy has high quench sensitivity. The inventors 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 increase of C Zn+Mg and R Zn / Mg , the transformation heat activation energy of the η phase will decrease, so that the quenching η phase is easy to form during the quenching process. In addition, the inventors established the relationship between M η , C Zn+Mg and R Zn / Mg through CALPHAD calculation. Specifically, M η is also affected by C Zn+Mg and R Zn / Mg . When R Zn / Mg is constant, the increase of C Zn+Mg must cause the increase of M η value; when C Zn+Mg is constant, the R Zn / Mg moving away from a certain critical Zn / Mg ratio will cause the decrease of M η value. Based on this rule, the quench sensitivity of the alloy can be predicted by the size of M η , that is, the increase of M η will cause the increase of quench sensitivity of the alloy. In addition, the inventors found that when M η value is constant, the R Zn / Mg moving away from value will cause the increase of C Zn+Mg , thereby improving the quench sensitivity. Under this condition, the chemical composition with the best quench sensitivity should be R Zn / Mg equal to value. And value is the point at which M η changes the least with R Zn / Mg , that is, is equal to 0. Therefore, the quench sensitivity of the Al-Zn-Mg-Cu alloy can be effectively predicted by comprehensively considering M η and .

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

[0027] 1) The model provided by the present application can effectively predict the quench sensitivity of the Al-Zn-Mg-Cu alloy by constructing M η , C Zn+Mg and R Zn / MgThe correlation model was used to predict the quenching sensitivity of multi-element aluminum alloys. The predicted values ​​of the model were then verified by combining the TTP curve and TTT curve, and the model was optimized to ensure the accuracy and speed of the obtained model in predicting the quenching sensitivity of multi-element aluminum alloys.

[0028] 2) The technical solution provided by this invention is based on the excellent prediction accuracy of the above model. Compared with the TTP curve, it does not require plotting through isothermal quenching and end-quenching experiments. While reducing the cost of quenching sensitivity detection, it also significantly improves detection efficiency. Furthermore, this model is based on M... η C Zn+Mg and R Zn / Mg The parameters can directly reflect the variation law between the main alloying elements and quenching sensitivity in multi-element aluminum alloys, effectively overcoming the problem of TTT curves in existing technologies, and have important guiding significance for the design of high-performance multi-element alloys. Attached Figure Description

[0029] Figure 1 This is a flowchart of the prediction of the quenching sensitivity of Al-Zn-Mg-Cu alloy based on CALPHAD calculation provided in the embodiments of the present invention;

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

[0031] Figure 3 The different C values ​​under the Rational Taylor model provided in the embodiments of the present invention are Zn+Mg and R Zn / Mg M below η and Distribution map;

[0032] Wherein, the dashed curve is M η Value, the real curve is value;

[0033] Figure 4 These are TTP curves for different alloy compositions provided in the embodiments of the present invention;

[0034] Figure 5 These are the TTT curves of different alloys provided in the embodiments of the present invention. Detailed Implementation

[0035] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts are within the scope of the present application.

[0036] Embodiment 1

[0037] The embodiment provides a quench sensitivity prediction and optimization model of a multivariate aluminum alloy based on a phase diagram calculation. In order to more obviously highlight the beneficial technical effects brought by the technical solutions of the present application, the embodiment takes Al-Zn-Mg-Cu quaternary alloy as an example for modeling, and the specific process is as follows:

[0038] 1) The mass fraction (M η ) distribution diagram of the η phase of the Al-Zn-Mg-Cu alloy at 1.5wt.%Cu content is calculated by the CALPHAD method, wherein C Zn+Mg is the total amount of Zn and Mg (C Zn+Mg ), and R Zn / Mg is the Zn / Mg ratio (R Zn / Mg );

[0039] 2) The mathematical model of M η , C Zn+Mg and R Zn / Mg is established by fitting the M η distribution diagram by the Rational Taylor model, and the function expression after fitting is as follows:

[0040]

[0041] 3) According to the mathematical model in step 2), the distribution diagram of M η and under different C Zn+Mg and R Zn / Mg in the Rational Taylor model is drawn;

[0042] 4) The designed alloys A1-A4 are designed, the main element compositions of which are shown in Table 1, and the designed alloys are judged according to the distribution diagram in the Rational Taylor model in step 3); the greater the M η value of the alloy or the farther the is from 0, the higher the quench sensitivity of the alloy is, and therefore, the order of the quench sensitivity of the designed alloys is A4>A3>A2>A1;

[0043] Table 1

[0044]

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

[0046] 1) TTP curve verification

[0047] Four alloys A1, A2, A3 and A4 are smelted, and the specific chemical components are shown in Table 1. The four alloys are homogenized, and the homogenization conditions are: temperature 470℃, time 24h; then hot-rolled from 15mm to 4mm thick at 400℃, and then cold-rolled from 4mm thick to 1.3mm. The four cold-rolled alloys are subjected to T6 heat treatment, and the T6 heat treatment conditions are: temperature 120℃, time 24h.

[0048] The isothermal quenching experiment is carried out on the four T6 state alloys, and the TTP curves of the four alloys can be obtained, and the results are shown in Figure 4 , and it can be seen from Figure 4 that the quenching sensitivity of the four alloys is: A4>A3>A2>A1, which is consistent with the conclusion of the above model, proving that the model provided by the present application is real and feasible for predicting the quenching sensitivity of multi-element aluminum alloy.

[0049] 2) TTT curve verification

[0050] In order to more comprehensively illustrate the credibility and compatibility of the above-mentioned model, the present application also carries out TTT curve verification, and the process is: the TTT curves of the alloys A1-A4 in the examples are simulated by using JMatPro7.0 software, and the results are shown in Figure 5 , and it can be seen from Figure 5 that the quenching sensitivity of the four alloys is: A4>A3>A2>A1, which is still consistent with the conclusion of the model of the present application, further proving that the model provided by the present application is real and feasible for predicting the quenching sensitivity of multi-element alloy.

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

Claims

1. A prediction and optimization model for the quenching sensitivity of multi-element aluminum alloys based on phase diagram calculations, characterized in that, include: 1) Calculate the mass fraction distribution of the η phase in aluminum-rich angle multi-element aluminum alloys with different total Zn and Mg amounts and Zn / Mg ratios at a given Cu content using the CALPHAD method. The total Zn and Mg amounts are denoted as... The Zn / Mg ratio is denoted as The mass fraction of the η phase is denoted as ; 2) Using the Rational Taylor model to analyze Distribution Map Establishment , and The correlation model; 3) Based on the correlation model described in step 2), draw different... and Below and Distribution map; 4) As described in step 3) and The distribution map is used to predict the quenching sensitivity of multi-element aluminum alloys, and the predicted value is obtained. Then, the model is optimized through quenching sensitivity experiments. The multi-element aluminum alloy includes Al, Cu, Zn, and Mg elements; The and The calculation process is as follows: Formula 1: ; In Equation 1: C Zn and C Mg These are the Zn content and Mg content, respectively. The M η C Zn+Mg and R Zn / Mg The correlation model is as follows: Formula 2: ; In Equation 2: z0, a 01 ~c 01 a1~c1 are the fitting parameters; The prediction process for the quenching sensitivity of the multi-element aluminum alloy is as follows: based on the different C values ​​in step 3), Zn+Mg and R Zn / Mg M below η and The distribution map of M, where M η The larger or The larger the absolute value of the data, the higher its quenching sensitivity; The model optimization methods include: when When unchanged, as Increase M η The value also increases accordingly, and the quenching sensitivity increases; when When R remains constant, 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, C Zn+Mg As a result, the quenching sensitivity increases.

2. The multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation as described in claim 1, characterized in that: The CALPHAD method is calculated using the PanPhaseDiagram module in the PANDAT software.

3. The multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation as described in claim 1, characterized in that: The main components of the aluminum-rich angle multi-element aluminum alloy are as follows by mass percentage: Zn content is 5~11 wt.%, Mg content is 1~3.5 wt.%, Cu content is 1~3 wt.%, and the balance is Al.

4. The multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation as described in claim 1, characterized in that: The M η The theoretical mass fraction of phase η is given at equilibrium under the aging temperature; the aging temperature ranges from 100 to 160°C.

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

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