A method for optimizing the chemical composition of Al-Zn-Mg-Cu aluminum alloy

Abstract

The present invention relates to a method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, and belongs to the technical field of alloys. The present invention performs a full factorial design experiment on multiple alloy elements of an Al-Zn-Mg-Cu aluminum alloy to obtain multiple sets of alloy components; the multiple sets of alloy components use JMatPro thermodynamic calculation software to respectively calculate the mechanical properties of a Mechanical Properties module, calculate the TTT curve of a Phase transformation module, and calculate the solid phase fraction and molar volume of a Solidification module, and respectively export the data and graphs of the calculation results; respectively perform variance analysis and regression modeling processing on the data of the calculation results; comprehensively analyze the different weight factors and regression equations of the yield strength, quenching sensitivity, solid phase fraction, and molar volume established; according to the regression equation and the weight factor, adjust the values ​​of each alloy element, and optimize the chemical composition of the Al-Zn-Mg-Cu aluminum alloy to obtain the chemical composition of the Al-Zn-Mg-Cu aluminum alloy with high strength, low quenching sensitivity, and low solidification shrinkage tendency.

Description

Technical Field

[0001] The invention relates to a method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, belonging to the technical field of alloys. Background Art

[0002] Al-Zn-Mg-Cu alloy is a high-strength and high-toughness aluminum alloy, which is currently widely used in aerospace, rail transportation and civil industry. With the continuous development of aerospace and rail transportation, the load-bearing components are required to be integrated and large-scale, which puts forward higher requirements on the comprehensive performance of Al-Zn-Mg-Cu alloy.

[0003] At present, the traditional method of new material research and development is mainly through continuous melting small-scale experiments and trial and error, which requires researchers to have rich theoretical knowledge and work experience. The traditional method has the disadvantages of long research and development cycle and high cost, such as the research and development of 7085 aluminum alloy. Thermodynamic software is used to optimize the alloy composition design, emphasizing the calculation of the content of MgZn2 phase, S (Al2CuMg) phase, θ (Al2Cu) phase and T (AlZnMgCu) phase, by increasing the content of MgZn2 strengthening phase and reducing the content of harmful impurity phase S (Al2CuMg) phase, θ (Al2Cu) phase and T (AlZnMgCu) phase. This method does not take into account the quenching sensitivity and solidification shrinkage tendency of alloy materials. Summary of the invention

[0004] The present invention aims at the problem that the existing alloy composition optimization method does not comprehensively consider the properties of the alloy such as yield strength, quenching sensitivity and solidification shrinkage tendency, and provides a method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy. The method of the present invention can comprehensively consider the yield strength, quenching sensitivity characteristics, and different weights of solid phase fraction and molar volume thermal properties, and optimize the chemical composition of an Al-Zn-Mg-Cu aluminum alloy with high strength, low quenching sensitivity characteristics and small solidification shrinkage tendency.

[0005] A method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, the specific steps are as follows:

[0006] (1) Full factorial design experiments were conducted on multiple alloying elements of Al-Zn-Mg-Cu aluminum alloy to obtain multiple groups of alloy compositions;

[0007] (2) using JMatPro thermodynamic calculation software to calculate the mechanical properties of the multiple sets of alloy components in step (1) in the Mechanical Properties module, the TTT curve in the Phase transformation module, and the solid phase fraction and molar volume in the Solidification module, and exporting the data and graphs of the calculation results respectively;

[0008] (3) performing variance analysis and regression modeling on the data of the calculation results of step (2);

[0009] (4) comprehensively analyzing the different weighting factors and regression equations of yield strength, quenching sensitivity, solid fraction and molar volume established in step (3);

[0010] (5) The chemical composition of the Al-Zn-Mg-Cu aluminum alloy was optimized by adjusting the values ​​of each alloy element according to the regression equation and the weight factor;

[0011] The multiple alloy elements in the full factorial design experiment in step (1) are 3 to 6 alloy elements;

[0012] The mechanical properties of the Mechanical Properties module in step (2) are the yield strength after T6 aging, wherein the alloy state is forged, the solution treatment temperature is 470-480°C, the aging temperature is 115-125°C, the grain size is 45um-150um, the TTT curve of the Phase transformation module is the TTT curve of the η′ phase, and the molar volume of the Solidification module is the molar volume change between the solid and liquid phase lines;

[0013] The regression modeling process in step (3) adopts Minitab software, and only considers the second-order interaction between alloy elements.

[0014] The beneficial effects of the present invention are:

[0015] (1) The method of the present invention can reliably guide the composition optimization of Al-Zn-Mg-Cu alloys with high yield strength, small solidification shrinkage tendency and low quenching sensitivity;

[0016] (2) The method of the present invention comprehensively considers the yield strength, solidification shrinkage tendency and quenching sensitivity, greatly shortens the material research and development cycle, and is a more reliable composition optimization method for Al-Zn-Mg-Cu alloy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a graph showing the relationship between the yield strength of the alloy Al-xZn-yMg-zCu in Example 1 and the alloy elements after T6 aging;

[0018] Figure 2 It is the TTT curve when the η′ phase in the alloy Al-xZn-yMg-zCu in Example 1 is 0.5%;

[0019] Figure 3This is a graph showing the relationship between the nose tip temperature and alloying elements when the η′ phase in the alloy Al-xZn-yMg-zCu in Example 1 is 0.5%;

[0020] Figure 4 This is a graph showing the relationship between the nose tip temperature and the alloying elements when 0.5% of the η′ phase is precipitated in the Al-xZn-yMg-zCu alloy in Example 1;

[0021] Figure 5 This is a graph showing the relationship between the solidification solid phase fraction curve and temperature in the alloy Al-xZn-yMg-zCu in Example 1;

[0022] Figure 6 This is a graph showing the relationship between the solid-liquid phase temperature range and alloying elements in the alloy Al-xZn-yMg-zCu in Example 1;

[0023] Figure 7 The graph is a graph showing the relationship between the molar volume curve and the temperature in the solid-liquid phase temperature range of the alloy Al-xZn-yMg-zCu in Example 1;

[0024] Figure 8 This is a graph showing the relationship between the molar volume difference and the change of elements within the solid-liquid phase temperature range in the alloy Al-xZn-yMg-zCu in Example 1.

[0025] Fig. 9 This is a graph showing the relationship between the yield strength of the alloy Al-xZr-yZn-zMg-lCu in Example 2 after T6 aging and the change in alloy elements;

[0026] Fig.10 This is a graph showing the relationship between the nose tip temperature and alloying elements when the η′ phase in the alloy Al-xZr-yZn-zMg-lCu in Example 2 is precipitated at 0.5%;

[0027] Fig.11 This is a graph showing the relationship between the nose tip temperature and the alloying elements when 0.5% η′ phase is precipitated in the alloy Al-xZr-yZn-zMg-lCu in Example 2;

[0028] Fig.12 This is a graph showing the relationship between the solid-liquid phase temperature range and alloying elements in the alloy Al-xZr-yZn-zMg-lCu in Example 2;

[0029] Fig.13 This is a graph showing the relationship between the molar volume difference and the change of elements within the solid-liquid phase temperature range in the alloy Al-xZr-yZn-zMg-lCu in Example 2.

[0030] Fig.14This is a graph showing the relationship between the yield strength of the alloy Al-xZr-yZn-zMg-lCu-mFe-nSi in Example 3 after T6 aging and the change in alloy elements;

[0031] Fig.15 This is a graph showing the relationship between the nose tip temperature and alloying elements when the η′ phase in the alloy Al-xZr-yZn-zMg-lCu-mFe-nSi in Example 3 is precipitated at 0.5%;

[0032] Fig.16 This is a graph showing the relationship between the nose tip temperature and the alloying elements when 0.5% of the η′ phase is precipitated in the alloy Al-xZr-yZn-zMg-lCu-mFe-nSi in Example 3;

[0033] Fig.17 This is a graph showing the relationship between the solid-liquid phase temperature range and alloying elements in the alloy Al-xZr-yZn-zMg-lCu-mFe-nSi in Example 3;

[0034] Fig.18 This is a graph showing the relationship between the molar volume difference and the change of elements within the solid-liquid phase temperature range in the alloy Al-xZr-yZn-zMg-lCu-mFe-nSi in Example 3. DETAILED DESCRIPTION

[0035] The present invention is further described in detail below in conjunction with specific implementation modes, but the protection scope of the present invention is not limited to the described contents.

[0036] Embodiment 1: A method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, the specific steps are as follows:

[0037] (1) A full factorial design experiment was conducted on the three alloying elements Zn, Mg and Cu of Al-Zn-Mg-Cu aluminum alloy, and 27 groups of alloy compositions were obtained (see Table 1);

[0038] Table 1 Alloy composition

[0039]

[0040] (2) The 27 groups of alloy components obtained in step (1) are firstly used to calculate the yield strength after T6 aging in the Mechanical Properties module using the JMatPro thermodynamic software, wherein the alloy state is forged, the solution treatment temperature is 475°C, the aging temperature is 120°C, and the grain size is 80 μm; secondly, the JMatPro thermodynamic software is used to calculate the TTT curve in the Phase transformation module, wherein only the TTT curve of the η′ phase is calculated, and the starting temperature is 475°C; finally, the JMatPro thermodynamic software is used to calculate the solid phase fraction and molar volume in the Solidification module, with a starting temperature of 700°C and a step size of 1°C. After the calculation is completed, the calculation result data and graphs are exported respectively;

[0041] (3) The yield strength data calculated in step (2) are subjected to variance analysis to obtain a relationship diagram of yield strength versus alloy element content as shown in Figure 1 shown; from Figure 1 We can see that the yield strength increases with the increase of alloy element content; within the set alloy element content range, the influence of Mg element on the yield strength is the most significant. In order to accurately describe the relationship between elements and yield strength, the relationship between elements and yield strength is processed by regression modeling. During regression, only the second-order interaction between alloy elements is considered, and the items with no significant effect on the target value are removed. Finally, the regression equation between yield strength and each alloy element is established:

[0042] σ b =361-9.3Zn+51.6Mg+55Cu+5.16Zn*Mg+1.83Zn*Cu-22.81Mg*Cu (1)

[0043] The model summary of the regression analysis shows that the prediction accuracy of formula (1) for yield strength can reach 90.1%. Within the set range of alloy elements, according to regression equation (1), it can be concluded that the range of yield strength is 506.3MPa to 543.5MPa, and the yield strength increases by 35.3MPa for every 1% increase in Mg content, 22MPa for every 1% increase in Cu content, and 4.1MPa for every 1% increase in Zn content. Therefore, if the yield strength of the alloy needs to be increased, the content of Zn, Mg, and Cu elements should be increased as much as possible.

[0044] (4) The TTT curve data calculated in step (2) when the η′ phase precipitates 0.5% is processed to obtain a graph as shown in FIG. Figure 2 As shown, from Figure 2It can be seen from the figure that the TTT curve when the η′ phase precipitates 0.5% is a C-shaped curve, and the nose tip temperature and nose tip time of the TTT curve when the η′ phase precipitates 0.5% will also change with the change of alloy composition; in order to further understand the law between the nose tip temperature and nose tip time of the TTT curve when the η′ phase precipitates 0.5%, the variance analysis of the nose tip temperature data of the TTT curve when the η′ phase precipitates 0.5% is performed, and the relationship between the nose tip temperature of the TTT curve and the alloy element content is obtained as shown in the figure below: Figure 3 As shown, within the set alloy element content range, Figure 3 It can be seen that the nose tip temperature increases with the increase of alloy element content. The increase of Mg element content contributes the most to the increase of nose tip temperature, followed by the increase of Zn and Cu element content. In order to accurately describe the relationship between elements and nose tip temperature, regression modeling is performed on elements and nose tip temperature. Only the second-order interaction between alloy elements is considered during regression, and the items with no significant effect on the target value are removed. Finally, the regression equation between nose tip temperature T and each alloy element is established:

[0045] T=-40+37.1Zn+109.1Mg+69.7Cu-8.96Zn*Mg-4.90Zn*Cu-14.93Mg*Cu (2)

[0046] The model summary of the regression analysis shows that the prediction accuracy of equation (2) for the nose tip temperature T can reach 72.68%. Within the set alloy element range, according to regression equation (2), it can be concluded that the range of the nose tip temperature T is 308.6℃~332℃;

[0047] The variance analysis of the TTT curve nose time data when the η′ phase precipitates 0.5% is performed to obtain the relationship between the TTT curve nose time and the alloy element content. Figure 4 As shown, within the set alloy element content range, Figure 4 It can be seen that the nose time decreases with the increase of the content of alloying elements Mg, Cu, and Zn. In order to accurately describe the relationship between elements and nose time, regression modeling is performed on them. During regression, only the second-order interactions between alloying elements are considered, and the items with no significant effect on the target value are removed. Finally, the regression equation between nose time t and each alloying element is established:

[0048] t=104.24-5.328Zn-14.540Mg-5.719Cu (3)

[0049] The model summary of the regression analysis shows that the prediction accuracy of equation (3) for nose time t can reach 96.95%. Within the set range of alloy elements, according to regression equation (3), it can be concluded that the range of nose time t is 34.8s to 15.9s. For every 1% decrease in Mg content, the nose time increases by 37.1s, for every 1% decrease in Cu content, the nose time increases by 32.1s, and for every 1% decrease in Zn content, the nose time increases by 21.2s; therefore, to reduce the quenching sensitivity of the alloy, it is necessary to reduce the Mg, Cu, and Zn contents as much as possible;

[0050] (5) The solid fraction data calculated in step (2) is processed to obtain a graph such as Figure 5 As shown in the figure, the temperature point with a solid fraction of 1% is set as the liquid phase point, and the temperature point with a solid fraction of 99% is set as the solid phase point. Figure 5 It can be seen that the change in alloy element content has a greater impact on the temperature of the solid phase point, resulting in different temperature ranges between the solid and liquid phases. Figure 6 The figure shows the relationship between the solid-liquid phase temperature range and the alloy elements. Figure 6 It can be seen that the solid-liquid phase temperature range in the alloy decreases with the increase of Mg content, and increases with the increase of Zn and Cu content; in order to study the effect of temperature range on the solidification shrinkage performance of the alloy, the molar volume of the solid-liquid phase range of each alloy component is found and the data is sorted out to draw a graph as shown in Figure 7 As shown, from Figure 7 It can be seen that the change of alloying element content has a great influence on the molar volume of the solid phase point. In order to further study the influence of alloying element content change on the molar volume change of the solid-liquid phase interval, the calculated data of the molar volume change of the solid-liquid phase interval are subjected to variance analysis. Figure 8 As shown, from Figure 8 It can be seen that as the Mg content increases, the Zn and Cu content decreases, the smaller the molar volume change in the solid-liquid phase interval, the smaller the tendency of the alloy to form shrinkage and shrinkage cavities during solidification. In order to accurately describe the relationship between the elements and the molar volume change in the solid-liquid phase interval, they are processed by regression modeling. During the regression, only the second-order interaction between the alloy elements is considered, and the terms that have no significant effect on the target value are removed. Finally, the molar volume change V in the solid-liquid phase interval is established. m The regression equation between each alloy element:

[0051] Vm=0.9271-0.0019Zn+0.0085Mg-0.0166Cu-0.00208Zn*Mg+0.00425Zn*Cu-0.004Mg*Cu (4)

[0052] The model summary of the regression analysis shows that equation (4) is effective for the molar volume change V between the solid and liquid phases. mThe prediction accuracy can reach 66.48%. Within the set alloy element range, the molar volume change V in the solid-liquid phase interval can be obtained according to regression equation (4): m The range is 0.9006mol / ml~0.9154mol / ml. The molar volume change V of the solid-liquid phase interval for each 1% increase in Mg content m The molar volume change between solid and liquid phases is V when the Zn content decreases by 1% m The molar volume change V of the solid-liquid phase interval is reduced by 0.00521 mol / ml, and the Cu content decreases by 1% m Decrease by 0.00433 mol / ml, so the molar volume change V between the solid and liquid phases should be reduced m , it is necessary to increase the Mg content and reduce the Zn and Cu content;

[0053] (6) Combining steps (3)(4)(5), it can be concluded that the yield strength increases with the increase of alloy elements. The yield strength increases by 35.3MPa for every 1% increase in Mg content, 22MPa for every 1% increase in Cu content, and 4.1MPa for every 1% increase in Zn content. Therefore, in order to improve the yield strength of the alloy, the content of Zn, Mg, and Cu elements should be increased as much as possible; the nose time t increases with the decrease of Mg, Cu, and Zn content. The nose time increases by 37.1s for every 1% decrease in Mg content, 32.1s for every 1% decrease in Cu content, and 21.2s for every 1% decrease in Zn content. Therefore, in order to reduce the quenching sensitivity of the alloy, it is necessary to reduce the content of Mg, Cu, and Zn as much as possible; the molar volume change V between the solid and liquid phases m It decreases with the increase of Mg content, and increases with the increase of Zn and Cu content. The molar volume change ΔV of the solid-liquid phase interval for each 1% increase of Mg content m The molar volume change ΔV of the solid-liquid phase interval is reduced by 0.01586 mol / ml, and the Zn content decreases by 1% m The molar volume change ΔV of the solid-liquid phase interval is reduced by 0.00521 mol / ml, and the Cu content decreases by 1% m Decrease by 0.00433 mol / ml, so the molar volume change ΔV between the solid and liquid phases should be reduced m , it is necessary to increase the Mg content and reduce the Zn and Cu content;

[0054] In this example, the weight of yield strength is 0.4, the weight of quenching sensitivity is 0.3, and the weight of solidification shrinkage is 0.3. The weight of Zn element reduction is 0.6, the weight of Mg element increase is 0.7, and the weight of Cu element reduction is 0.6. The Zn content is 6.1%, the Mg element is 2.39%, and the Cu element is 2.24%. At this time, the yield strength can reach 529MPa, the nose time of the TTT curve when the η′ phase precipitates 0.5% is 24.2s, and the molar volume change ΔV between the solid and liquid phases is m 0.0102mol / ml;

[0055] (7) Finally, the chemical composition of the Al-Zn-Mg-Cu aluminum alloy with high strength, low quenching sensitivity and small solidification shrinkage tendency was optimized, Al-6.1Zn-2.39Mg-2.24Cu.

[0056] Embodiment 2: A method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, the specific steps are as follows:

[0057] (1) A full factorial design experiment was conducted on the four alloying elements Zr, Zn, Mg and Cu of Al-Zn-Mg-Cu aluminum alloy, and 81 sets of alloy compositions were obtained (see Table 2);

[0058] Table 2 Alloy composition

[0059]

[0060] (2) The 81 alloy components obtained in step (1) are first used in the Mechanical Properties module to calculate the yield strength after T6 aging using the JMatPro thermodynamic software, wherein the alloy state is forged, the solution treatment temperature is 470°C, the aging temperature is 115°C, and the grain size is 45 μm; secondly, the JMatPro thermodynamic software is used to calculate the TTT curve in the Phase transformation module, wherein only the TTT curve of the η′ phase is calculated, and the starting temperature is 500°C; finally, the JMatPro thermodynamic software is used to calculate the solid phase fraction and molar volume in the Solidification module, with a starting temperature of 700°C and a step size of 1°C. After the calculation is completed, the calculation result data and graphs are exported respectively;

[0061] (3) The yield strength data calculated in step (2) are subjected to variance analysis to obtain a relationship diagram of yield strength versus alloy element content as shown in Fig. 9 shown; from Fig. 9We can see that the yield strength increases with the increase of Zn and Mg alloying element contents, the yield strength remains basically unchanged with the increase of Zr element content, and the yield strength first increases and then remains unchanged with the increase of Cu element content; within the set alloying element content range, the influence of Mg element on yield strength is the most significant. In order to accurately describe the relationship between elements and yield strength, the relationship between elements and yield strength is modeled by regression. During regression, only the second-order interaction between alloying elements is considered, and the items with insignificant influence on the target value are removed. Finally, the regression equation between yield strength and each alloying element is established:

[0062] σ b =271.2+9.51Zn+103.89Mg+44.07Cu-3.654Zn*Mg+2.103Zn*Cu-22.43Mg*Cu (5)

[0063] The model summary of the regression analysis shows that the prediction accuracy of formula (5) for yield strength can reach 94.46%. Within the set range of alloy elements, according to regression equation (5), the yield strength range can be obtained to be 478.7MPa to 582.6MPa. For every 1% increase in Mg content, the yield strength increases by 51.95MPa, for every 1% increase in Cu content, the yield strength increases by 10.75MPa, and for every 1% increase in Zn content, the yield strength increases by 13.9MPa. Therefore, if the yield strength of the alloy needs to be increased, the content of Zn, Mg, and Cu elements should be increased as much as possible.

[0064] (4) Process the TTT curve data when the η′ phase precipitation is 0.5% calculated in step (2) to obtain a graph and Figure 2 Similarly, in order to further understand the law between the nose tip temperature and nose tip time of the TTT curve when the η′ phase precipitates 0.5%, the variance analysis of the TTT curve nose tip temperature data when the η′ phase precipitates 0.5% is performed to obtain the relationship between the nose tip temperature of the TTT curve and the alloy element content. Fig.10 As shown, within the set alloy element content range, Fig.10 It can be seen that the nose tip temperature increases with the increase of Zn, Mg and Cu alloying element contents, and remains basically unchanged with the increase of Zr alloying element content. The increase of Mg element content contributes the most to the increase of nose tip temperature, followed by the increase of Zn and Cu element contents. In order to accurately describe the relationship between elements and nose tip temperature, regression modeling is performed on elements and nose tip temperature. Only the second-order interaction between alloying elements is considered during regression, and the items with no significant effect on the target value are removed. Finally, the regression equation between nose tip temperature T and each alloying element is established:

[0065] T=164.5+13.80Zn+42.92Mg+22.08Cu-2.778Zn*Mg-1.111Zn*Cu-4.167Mg*Cu (6)

[0066] The model summary of the regression analysis shows that the prediction accuracy of equation (6) for the nose tip temperature T can reach 86.6%. Within the set alloy element range, according to regression equation (6), it can be concluded that the range of the nose tip temperature T is 292.4℃~345.2℃;

[0067] The variance analysis of the TTT curve nose time data when the η′ phase precipitates 0.5% is performed to obtain the relationship between the TTT curve nose time and the alloy element content. Fig.11 As shown, within the set alloy element content range, Fig.11 It can be seen that the nose time decreases with the increase of the content of alloying elements Mg, Cu, and Zn, and the nose time remains unchanged with the increase of the content of alloying element Zr. In order to accurately describe the relationship between elements and nose time, regression modeling is performed on them. During regression, only the second-order interaction between alloying elements is considered, and the items with no significant effect on the target value are removed. Finally, the regression equation between nose time t and each alloying element is established:

[0068] t=145.45-12.33Zn-28.09Mg-21.48Cu+2.001Zn*Mg+1.663Zn*Cu+3.107Mg*Cu (7)

[0069] The model summary of the regression analysis shows that the prediction accuracy of equation (7) for nose time t can reach 84.81%. Within the set range of alloy elements, according to regression equation (7), it can be concluded that the range of nose time t is 8.6s to 43.9s; the nose time increases by 13.41s for every 1% decrease in Mg content, 8.5s for every 1% decrease in Cu content, and 6.82s for every 1% decrease in Zn content. Therefore, to reduce the quenching sensitivity of the alloy, it is necessary to reduce the Mg, Cu, and Zn contents as much as possible;

[0070] (5) Process the solid fraction data calculated in step (2) to obtain a graph and Figure 5 Similarly, the temperature point with a solid fraction of 1% is set as the liquidus point, and the temperature point with a solid fraction of 99% is set as the solidus point. Fig.12 The figure shows the relationship between the solid-liquid phase temperature range and the alloy elements. Fig.12It can be seen that the solid-liquid phase temperature range in the alloy decreases with the increase of Mg content, and increases with the increase of Zn and Cu content; in order to study the effect of temperature range on the solidification shrinkage performance of the alloy, the molar volume of the solid-liquid phase range of each alloy component is found and the data is sorted out to draw a graph and Figure 7 Similarly, it can be seen from the figure that the change in alloying element content has a great influence on the molar volume of the solid phase point. In order to further study the influence of the change in alloying element content on the change in molar volume between the solid and liquid phases, the calculated data of the change in molar volume between the solid and liquid phases are subjected to variance analysis. Fig.13 As shown, from Fig.13 It can be seen that as the Mg content increases, the Zn and Cu content decreases, the smaller the molar volume change in the solid-liquid phase interval, the smaller the tendency of the alloy to form shrinkage and shrinkage cavities during solidification. In order to accurately describe the relationship between the elements and the molar volume change in the solid-liquid phase interval, they are processed by regression modeling. During the regression, only the second-order interaction between the alloy elements is considered, and the terms that have no significant effect on the target value are removed. Finally, the molar volume change V in the solid-liquid phase interval is established. m The regression equation between each alloy element:

[0071] Vm=0.89335+0.00325Zn-0.01529Mg-0.000072Zn*Cu+0.00282Mg*Cu (8)

[0072] The model summary of the regression analysis shows that equation (8) gives the molar volume change V between the solid and liquid phases. m The prediction accuracy can reach 52.25%. Within the set alloy element range, the molar volume change V in the solid-liquid phase interval can be obtained according to regression equation (8): m The range is 0.8703mol / ml~0.9092mol / ml. The molar volume change V of the solid-liquid phase interval for each 1% increase in Mg content m The molar volume change between solid and liquid phases is V when the Zn content decreases by 1% m Decrease by 0.003 mol / ml, the molar volume change V between solid and liquid phases for every 1% decrease in Cu content m Decrease by 0.00365 mol / ml, so the molar volume change V between the solid and liquid phases should be reduced m , it is necessary to increase the Mg content and reduce the Zn and Cu content;

[0073] (6) Combining steps (3)(4)(5), it can be concluded that the yield strength increases with the increase of alloying elements. For every 1% increase in Mg content, the yield strength increases by 51.95MPa, for every 1% increase in Cu content, the yield strength increases by 10.75MPa, and for every 1% increase in Zn content, the yield strength increases by 13.9MPa. Therefore, in order to improve the yield strength of the alloy, the content of Zn, Mg, and Cu elements should be increased as much as possible. The nose time t increases with the decrease in the content of Mg, Cu, and Zn elements. For every 1% decrease in Mg content, the nose time increases by 13.41s, for every 1% decrease in Cu content, the nose time increases by 8.5s, and for every 1% decrease in Zn content, the nose time increases by 6.82s. Therefore, in order to reduce the quenching sensitivity of the alloy, the content of Mg, Cu, and Zn should be reduced as much as possible. The molar volume change V between the solid and liquid phases m It decreases with the increase of Mg content, and increases with the increase of Zn and Cu content. The molar volume change ΔV of the solid-liquid phase interval for each 1% increase of Mg content m The molar volume change ΔV of the solid-liquid phase interval is reduced by 0.0054 mol / ml, and the Zn content decreases by 1% m The molar volume change ΔV of the solid-liquid phase interval is reduced by 0.003 mol / ml, and the Cu content decreases by 1% m Reduce by 0.00365 mol / ml, so the molar volume change ΔV between the solid and liquid phases should be reduced m , it is necessary to increase the Mg content and reduce the Zn and Cu content;

[0074] In this example, the weight of yield strength is 0.4, the weight of quenching sensitivity is 0.3, and the weight of solidification shrinkage is 0.3. The weight of Zn element reduction is 0.6, the weight of Mg element increase is 0.7, and the weight of Cu element reduction is 0.6. The Zn content is 6.2%, the Mg element is 2.9%, and the Cu element is 2.3%. At this time, the yield strength can reach 547.5MPa, the nose time of the TTT curve when the η′ phase precipitates 0.5% is 18.57s, and the molar volume change ΔV between the solid and liquid phases is m 0.8866mol / ml;

[0075] (7) Finally, the chemical composition of the Al-Zn-Mg-Cu aluminum alloy with high strength, low quenching sensitivity and small solidification shrinkage tendency was optimized, Al-6.2Zn-2.9Mg-2.3Cu.

[0076] Embodiment 3: A method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, the specific steps are as follows:

[0077] (1) A full factorial design experiment was conducted on the six alloying elements of Al-Zn-Mg-Cu aluminum alloy, namely Zr, Zn, Mg, Cu, Fe and Si, to obtain 729 sets of alloy compositions (see Table 3);

[0078] Table 3 Alloy composition

[0079]

[0080] (2) The 729 alloy components obtained in step (1) are first used to calculate the yield strength after T6 aging in the Mechanical Properties module using the JMatPro thermodynamic software, wherein the alloy state is forged, the solution treatment temperature is 475°C, the aging temperature is 125°C, and the grain size is 150 μm; secondly, the JMatPro thermodynamic software is used to calculate the TTT curve in the Phase transformation module, wherein only the TTT curve of the η′ phase is calculated, and the starting temperature is 500°C; finally, the JMatPro thermodynamic software is used to calculate the solid phase fraction and molar volume in the Solidification module, with a starting temperature of 700°C and a step size of 1°C. After the calculation is completed, the calculation result data and graphs are exported respectively;

[0081] (3) The yield strength data calculated in step (2) are subjected to variance analysis to obtain a relationship diagram of yield strength versus alloy element content as shown in Fig.14 shown; from Fig.14 We can see that the yield strength increases with the increase of Zn, Mg, and Cu alloying element contents, and decreases with the increase of Fe and Si alloying element contents. The change of Zr alloying element content has little effect on the yield strength. Within the set alloying element content range, the influence of Mg element on the yield strength is the most significant. In order to accurately describe the relationship between elements and yield strength, the relationship between elements and yield strength is modeled by regression. During regression, only the second-order interaction between alloying elements is considered, and the items with insignificant influence on the target value are removed. Finally, the regression equation between yield strength and each alloying element is established:

[0082] σ b =387.6-8.09Zn+51.55Mg+43.13Cu-179.6Si+3.696Zn*Mg+2.605Zn (9)

[0083] *Cu-19.44Mg*Cu+14.58Mg*Fe-31.19Cu*Fe+54.60Cu*Si

[0084] The model summary of the regression analysis shows that the prediction accuracy of formula (9) for yield strength can reach 94.89%; within the set range of alloy elements, according to regression equation (9), it can be concluded that the range of yield strength is 506.8MPa to 555.3MPa, and the yield strength increases by 35.8MPa for every 1% increase in Mg element content, 22.7MPa for every 1% increase in Cu element content, 4.1MPa for every 1% increase in Zn element content, 34.2MPa for every 1% decrease in Fe element content, and 70MPa for every 1% decrease in Si element content. The change in Zr element content has basically no effect on the yield strength. Therefore, if the yield strength of the alloy needs to be improved, the content of Zn, Mg, and Cu elements should be increased as much as possible, and the content of Fe and Si elements should be reduced;

[0085] (4) Process the TTT curve data when the η′ phase precipitation is 0.5% calculated in step (2) to obtain a graph and Figure 2 Similarly, it can be seen from the figure that the TTT curve when the η′ phase precipitates 0.5% is a C-shaped curve, and the nose tip temperature and nose tip time of the TTT curve when the η′ phase precipitates 0.5% will also change with the change of alloy composition; in order to further understand the law between the nose tip temperature and nose tip time of the TTT curve when the η′ phase precipitates 0.5%, the variance analysis of the nose tip temperature data of the TTT curve when the η′ phase precipitates 0.5% is performed, and the relationship between the nose tip temperature of the TTT curve and the alloy element content is obtained as shown in the figure Fig.15 As shown, within the set alloy element content range, Fig.15 It can be seen that the nose tip temperature increases with the increase of Zn, Mg, and Cu alloying element content, decreases with the increase of Si alloying element content, and remains basically unchanged with the change of Zr and Fe alloying element content. The increase of Mg element content contributes the most to the increase of nose tip temperature, followed by the increase of Zn and Cu element content. In order to accurately describe the relationship between elements and nose tip temperature, regression modeling is performed on elements and nose tip temperature. Only the second-order interaction between alloying elements is considered during regression, and the items with no significant effect on the target value are removed. Finally, the regression equation between nose tip temperature T and each alloying element is established:

[0086] T=111.3+19.26Zn+37.27Mg+61.89Cu+122.7Si-6.17Zn*Cu-13.58Zn*Si-7.94Mg*Cu-26.46Mg*Si (10)

[0087] The model summary of the regression analysis shows that the prediction accuracy of equation (10) for the nose tip temperature T can reach 75.70%. Within the set alloy element range, according to the regression equation (10), it can be concluded that the range of the nose tip temperature T is 314.5℃~336.3℃;

[0088] The variance analysis of the TTT curve nose time data when the η′ phase precipitates 0.5% is performed to obtain the relationship between the TTT curve nose time and the alloy element content. Fig.16 As shown, within the set alloy element content range, Fig.16 It can be seen that the nose tip time decreases with the increase of the content of alloying elements Mg, Cu, and Zn, increases with the increase of the content of alloying element Si, and remains unchanged with the increase of the content of Zr and Fe. In order to accurately describe the relationship between elements and nose tip time, regression modeling is performed on them. During regression, only the second-order interaction between alloying elements is considered, and the items with no significant effect on the target value are removed. Finally, the regression equation between nose tip time t and each alloying element is established:

[0089] t=167.60-15.751Zn-36.67Mg-30.71Cu+25.88Si+2.883Zn*Mg

[0090] +2.677Zn*Cu+4.450Mg*Cu-6.25Mg*Si+0.818Cu*Fe

[0091] -31.1Fe*Si (11)

[0092] The model summary of the regression analysis shows that the prediction accuracy of equation (11) for the nose time t can reach 96.95%. Within the set range of alloy elements, according to the regression equation (11), it can be concluded that the range of the nose time t is 26.45s to 13.18s. For every 1% decrease in Mg content, the nose time increases by 6.24s; for every 1% decrease in Cu content, the nose time increases by 1.2s; for every 1% decrease in Zn content, the nose time increases by 1.43s; for every 1% increase in Fe content, the nose time increases by 1.5s; and for every 1% increase in Si content, the nose time increases by 8.8s. Therefore, in order to reduce the quenching sensitivity of the alloy, it is necessary to reduce the Mg, Cu, and Zn contents as much as possible and increase the Fe and Si content;

[0093] (5) Process the solid fraction data calculated in step (2) to obtain a graph and Figure 5 Similarly, the temperature point with a solid phase fraction of 1% is set as the liquidus point, and the temperature point with a solid phase fraction of 99% is set as the solidus point. It can be seen from the figure that the change in alloy element content has a greater impact on the temperature of the solidus point, resulting in different temperature ranges between the solid and liquid phases. Fig.17 The figure shows the relationship between the solid-liquid phase temperature range and the alloy elements. Fig.17It can be seen that the solid-liquid phase temperature range in the alloy decreases with the increase of Mg content, increases with the increase of Zn, Cu, and Si content, and remains basically unchanged with the increase of Zr and Fe content; in order to study the effect of temperature range on the solidification shrinkage performance of the alloy, the molar volume of the solid-liquid phase range of each alloy component is found and the data is sorted out to draw a graph and Figure 7 Similarly, it can be seen from the figure that the change in alloying element content has a great influence on the molar volume of the solid phase point. In order to further study the influence of the change in alloying element content on the change in molar volume between the solid and liquid phases, the calculated data of the change in molar volume between the solid and liquid phases are subjected to variance analysis. Fig.18 As shown, from Fig.18 It can be seen that as the Mg content increases, the Zn and Cu content decreases, and the Zr, Fe, and Si content take the middle value, the smaller the molar volume change in the solid-liquid phase interval is, the smaller the tendency of the alloy to form shrinkage and shrinkage cavities during solidification is; in order to accurately describe the relationship between the elements and the molar volume change in the solid-liquid phase interval, they are processed by regression modeling. During the regression, only the second-order interaction between the alloy elements is considered, and the terms that have no significant effect on the target value are removed. Finally, the molar volume change V in the solid-liquid phase interval is established. m The regression equation between each alloy element:

[0094] Vm=0.93509+0.316Zr-0.05261Mg-0.0594Zr*Zn+0.00591Zn*Mg+0.00105Zn*Cu+0.0847Fe*Si (12)

[0095] The model summary of the regression analysis shows that equation (12) is effective for the molar volume change V between the solid and liquid phases. m The prediction accuracy can reach 45.05%. Within the set alloy element range, the molar volume change V in the solid-liquid phase interval can be obtained according to regression equation (12): m The range is 0.8945mol / ml~0.92mol / ml; the molar volume change V of the solid-liquid phase interval for each 1% increase in Mg content m The molar volume change between solid and liquid phases is V when the Zn content decreases by 1% m The molar volume change V of the solid-liquid phase range is reduced by 0.0092 mol / ml, and the Cu content decreases by 1% m The molar volume change V of the solid-liquid phase range is reduced by 0.0072 mol / ml, and the Zr content increases by 1% m Decrease by 0.083 mol / ml, so the molar volume change V between the solid and liquid phases should be reduced m , it is necessary to increase the content of Mg and Zr elements and reduce the content of Zn and Cu elements;

[0096] (6) Combining steps (3)(4)(5), it can be concluded that the yield strength increases with the increase of Zn, Mg, and Cu alloying elements, decreases with the increase of Fe and Si alloying elements, and remains basically unchanged with the increase of Zr alloying elements. The yield strength increases by 35.8 MPa for every 1% increase in Mg content, the yield strength increases by 22.7 MPa for every 1% increase in Cu content, the yield strength increases by 4.1 MPa for every 1% increase in Zn content, the yield strength increases by 34.2 MPa for every 1% decrease in Fe content, and the yield strength increases by 70 MPa for every 1% decrease in Si content. Therefore, to improve the yield strength of the alloy, the content of Zn, Mg, and Cu elements should be increased as much as possible, and the content of Fe and Si elements should be reduced; the nose time t increases with the decrease of Mg, Cu, and Zn content, and increases with the increase of Fe and Si content. The nose time increases by 6.24s for every 1% decrease in Mg content, 1.2s for every 1% decrease in Cu content, 1.43s for every 1% decrease in Zn content, 1.5s for every 1% increase in Fe content, and 8.8s for every 1% increase in Si content. Therefore, to reduce the quenching sensitivity of the alloy, it is necessary to reduce the content of Mg, Cu, and Zn as much as possible, and increase the content of Fe and Si elements; the molar volume change V in the solid-liquid phase interval m The molar volume change of solid-liquid phase interval V with each 1% increase in Mg content m The molar volume change between solid and liquid phases is V when the Zn content decreases by 1% m The molar volume change V of the solid-liquid phase range is reduced by 0.0092 mol / ml, and the Cu content decreases by 1% m The molar volume change V of the solid-liquid phase range is reduced by 0.0072 mol / ml, and the Zr content increases by 1% m Decrease by 0.083 mol / ml, so the molar volume change V between the solid and liquid phases should be reduced m , it is necessary to increase the content of Mg and Zr elements and reduce the content of Zn and Cu elements;

[0097] In this example, the weight of yield strength is 0.4, the weight of quenching sensitivity is 0.3, and the weight of solidification shrinkage is 0.3. The weight of Zr element increase is 1, the weight of Zn element decrease is 0.6, the weight of Mg element increase is 0.7, the weight of Cu element decrease is 0.6, the weight of Fe element decrease is 1.0, and the weight of Si element decrease is 1.0. The Zr content is 0.15, the Zn content is 6.1%, the Mg element is 2.39%, the Cu element is 2.24%, the Fe element is 0.03, and the Si element is 0.02. At this time, the yield strength can reach 540.89MPa, the nose time of TTT curve when η′ phase precipitation is 0.5% is 17.04s, and the molar volume change ΔV between solid and liquid phases is m is 0.01004;

[0098] (7) Finally, the chemical composition of the Al-Zn-Mg-Cu aluminum alloy with high strength, low quenching sensitivity and small solidification shrinkage tendency was optimized, Al-0.15Zr-6.1Zn-2.39Mg-2.24Cu-0.03Fe-0.02Si.

[0099] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A method for optimizing the chemical composition of an Al-Zn-Mg-Cu aluminum alloy, characterized in that: The specific steps are as follows: (1) Full factorial design experiments were conducted on multiple alloying elements of Al-Zn-Mg-Cu aluminum alloy to obtain multiple groups of alloy compositions; (2) using JMatPro thermodynamic calculation software to calculate the mechanical properties of the multiple groups of alloy components in step (1) in the Mechanical Properties module, the TTT curve in the Phase transformation module, and the solid phase fraction and molar volume in the Solidification module, and exporting the data and graphs of the calculation results respectively; the mechanical properties in the Mechanical Properties module are the yield strength after T6 aging, wherein the alloy state is forged, the solution treatment temperature is 470-480°C, the aging temperature is 115°C-125°C, and the grain size is 45um-150um; the TTT curve in the Phase transformation module is the TTT curve of the η′ phase; and the molar volume in the Solidification module is the molar volume change between the solid and liquid phase lines; (3) performing variance analysis and regression modeling on the data of the calculation results of step (2); the regression modeling is performed using Minitab software, and only the second-order interaction between the alloy elements is considered; (4) comprehensively analyzing the different weighting factors and regression equations of yield strength, quenching sensitivity, solid fraction and molar volume established in step (3); (5) The chemical composition of Al-Zn-Mg-Cu aluminum alloy was optimized by adjusting the value of each alloy element according to the regression equation and weight factor.

2. The method for optimizing the chemical composition of Al-Zn-Mg-Cu aluminum alloy according to claim 1, characterized in that: The multiple alloy elements of the full factorial design experiment in step (1) are 3 to 6 alloy elements.

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