Aging temperature-time-performance prediction method of Mg-Gd alloy

Abstract

The invention discloses an aging temperature-time-performance prediction method of an Mg-Gd alloy. The aging temperature-time-performance prediction method comprises the following steps: 1, determining preliminary thermodynamic parameters; 2, optimizing parameters of the precipitation kinetic model; 3, microstructure simulation and parameter iterative correction; 4, establishment of an aging temperature-time-transformation curve TTT; 5, establishing a hardness prediction model; 6, predicting the aging hardness of the alloy; 7, establishing an aging temperature-time-performance atlas; and 8, designing an alloy aging process. A two-stage aging process method is obtained based on the prediction method, the technical effect that the target hardness increment is larger than 40 HV is achieved, namely precise determination of beta'precipitated phase thermodynamic parameters and kinetic model parameters is achieved, and a TTP graph is constructed without large-scale experiments.

Description

Technical Field

[0001] The present invention relates to the fields of materials and metallurgy, and in particular to a method for predicting the aging temperature-time-performance of a Mg-Gd alloy. Background Art

[0002] Isothermal aging precipitation strengthening is a common method for improving the mechanical properties of Mg-Gd alloys. Its core lies in the nucleation and growth kinetics of the precipitate phase, which is jointly influenced by the alloy composition, aging temperature, and aging time. Differences in the microstructure of the precipitate phase under different conditions directly lead to changes in material properties. The time-temperature-transformation (TTT) curve is an important tool for describing phase transformation kinetics. It can intuitively display the quantitative relationship between temperature, time, and phase volume fraction and has been widely used in the steel industry. However, the TTT curve only reflects the evolution of volume fraction and cannot predict the impact of microscopic characteristics such as precipitate size and number density on alloy properties, making it unable to directly guide performance optimization.

[0003] In order to solve the problem that the TTT curve cannot predict alloy properties and conduct aging process design, a time-temperature-performance TTP diagram can be established for prediction. For example, existing document 1 ("TTP and TTT diagrams for quench sensitivity and aging of 1424 alloy" Materials Science and Engineering: A, 2000, 280 (1): 76-82.) measured the yield strength of 1424 aluminum alloy at different aging temperatures and aging times. The aging temperature was 100-450℃, the step size was 25℃, and 8-10 aging times were selected for each aging temperature, totaling 120-150 alloy samples, and the TTP spectrum of 1424 aluminum alloy was constructed. This technical solution shows that by experimentally measuring the yield strength of the alloy at different aging temperatures and aging times, the TTP diagram of the alloy can be successfully constructed and the aging process design of the alloy can be carried out. However, this technical solution requires measuring the yield strength of 120 alloys to achieve the above effect. That is, such experimental-based technical solutions all have technical problems such as high cost and long cycle due to reliance on a large amount of experimental data.

[0004] In order to solve the technical problems of establishing TTP maps based on experimental methods, a TTP map can be established using a machine learning method. For example, existing document 2 (《Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning》Journal of Materials Science & Technology, 2022, 107, 52-63.)The quantitative relationship between the mechanical properties of the Mg-Al-Zn alloy system and the microstructure and aging process was trained by the ANN model in machine learning, and the TTP map of the Mg-Al-Zn alloy system was predicted. This technical solution shows that machine learning can effectively reduce experimental costs and predict the mechanical properties of the alloy during the aging process, and establish a TTP map. This technical solution also has the problem of requiring a large amount of experimental data, because the accuracy of the prediction of such technical solutions decreases sharply with the reduction of training data. Specifically, in order to achieve the predicted accuracy of the record, existing document 2 still needs to collect and process 288 data sets in the early model training to achieve the technical effect of achieving the prediction, that is, there is still a long technical problem.

[0005] In order to solve the technical problem that the existing technical solutions rely on a large amount of experimental data, precipitation kinetics calculations based on physical mechanisms can be used. For example, the existing document 3 ("Precipitation ofγ" in Inconel 718alloy frommicrostructure to mechanical properties" Materialsia, 2021, 20, 101187.) couples the precipitation kinetics calculation with the yield strength model, realizes the calculation of the γ" phase size and volume fraction of the Inconel 718 alloy with aging, and successfully predicts the aging strength, and establishes the TTP spectrum of the Inconel 718 alloy. This technical solution significantly reduces the dependence on experimental data because it adopts the physical mechanism of precipitate nucleation and growth. However, the prediction model and parameters established by this technical solution are only applicable to the TTP diagram prediction of Inconel718 alloy, that is, the existing technical solution cannot be applied to the Mg-Gd system by simple parameter modification. The reason is that the precipitate phase in the Inconel 718 alloy is a stable precipitate phase, while the precipitate phase in the Mg-Gd system is a metastable precipitate phase. Due to the above reasons, this technical solution is directly unable to determine the thermodynamic parameters, kinetic model parameters and mechanical property model parameters in the Mg-Gd system, as well as to predict the TTP diagram and design the aging process.

[0006] Furthermore, the prior art lacks a database suitable for predicting the thermodynamic parameters of metastable precipitation phases, i.e., the Mg-Gd alloy system. Furthermore, there is a lack of a quantitative mapping relationship between microstructural characteristics and hardness. Consequently, it is impossible to accurately draw the TTP diagram of the Mg-Gd alloy system, ultimately leading to an inability to resolve the aforementioned prior art problems. Summary of the Invention

[0007] The present invention aims to provide a method for predicting the aging time-temperature-performance of Mg-Gd alloys. The present invention addresses the technical problems of existing technical solutions, which rely on a large amount of experimental data and are not applicable to metastable precipitates. The basic principles of the present invention are as follows:

[0008] 1. Conduct dynamic simulations of Mg-Gd alloys based on physical mechanisms, establish a quantitative mapping relationship between microstructural characteristics and hardness, and efficiently construct TTT and TTP diagrams for Mg-Gd alloys, thereby overcoming the technical problem of existing technical solutions that rely on large amounts of experimental data;

[0009] 2. Through iterative parameter correction through kinetic simulation, the thermodynamic parameters, kinetic model parameters and hardness prediction model of the β′ precipitation phase were established, overcoming the technical problem that the existing technical solutions are not suitable for metastable precipitation phases, and ultimately realizing the alloy aging process design.

[0010] To achieve the above objectives, the technical solutions of the present invention are as follows:

[0011] Step 1, determination of preliminary thermodynamic parameters, determining preliminary thermodynamic parameters of β′ precipitation phase in Mg-Gd alloy based on experimental data;

[0012] The experimental data are alloy composition, aging temperature and volume fraction of β′ precipitate phase at peak aging state;

[0013] The preliminary thermodynamic parameters are: formation enthalpy Δ f H and formation entropy Δ f S, the specific method of determining the preliminary thermodynamic parameters is, first, Δ f H and Δ f S is substituted into the Gibbs free energy expression of the precipitate phase, and the Gibbs free energy expression is written into the stable thermodynamic database of the Mg-Gd system. Then, the PanPhaseDiagram module of the Pandat software is used to input the alloy composition and temperature for point calculation. The volume fraction of the β′ precipitate phase is calculated and compared with the experimental data. Finally, by manually adjusting Δ f H and Δ f S makes the error between the calculated volume fraction and the experimental volume fraction less than 0.02.

[0014] Step 2, optimization of precipitation kinetic model parameters, optimizing the precipitation kinetic model parameters based on kinetic experimental data at an aging temperature of 200°C;

[0015] The kinetic experimental data are the size, volume fraction and number density of β′ precipitates in alloys with different aging times;

[0016] The parameters of the precipitation kinetics model are the interfacial energy and the number of nucleation sites of the β′ precipitation phase;

[0017] The specific method for optimizing the precipitation kinetics model parameters is as follows: first, using the PanPrecipitation module in the Pandat software to calculate and obtain the evolution of the β′ precipitation phase with aging time when the aging temperature is 200°C, that is, the calculation result; then, comparing the calculation result with the experimental result to determine whether the error between the calculation result and the experimental result meets the requirements; if not, manually adjusting the interface energy and the number of nucleation sites so that the error between the calculation result and the experimental result meets the requirements; the conditions for determining whether the error between the calculation result and the experimental result meets the requirements are that the size calculation error is less than 3 nm, the volume fraction error is less than 0.02, and the number density error is less than 1 order of magnitude; these conditions are also applicable to the error judgment between the calculation result and the experimental result in the subsequent step 3.

[0018] Step 3, microstructure simulation and parameter iterative correction;

[0019] First, based on the preliminary thermodynamic parameters obtained in step 1 and the precipitation kinetic model parameters obtained in step 2, the evolution of the β' precipitation phase with aging time at an aging temperature of 250°C was calculated. The calculation process was consistent with the specific method for optimizing the precipitation kinetic model parameters described in step 2. Then, it was determined whether the error between the calculated results and the experimental results at an aging temperature of 250°C met the requirements. If not, the thermodynamic parameters described in step 1 were manually adjusted to ensure that the error between the calculated results and the experimental results met the requirements.

[0020] Step 4, establishment of TTT curve;

[0021] The TTT curve includes a transition start line and a transition end line;

[0022] The transition start line is composed of the time points at which the relative volume fraction of the transition is 5% at different temperatures;

[0023] The transformation termination line is formed by the time point at which 90% of the relative volume fraction is transformed at different temperatures;

[0024] The relative volume fraction is the ratio of the volume fraction calculated at the current moment to the equilibrium volume fraction calculated by the thermodynamic database;

[0025] The conditions for the high-throughput calculation are: a temperature range of 100-350°C, a temperature step of 5°C, and an aging time of 5000h.

[0026] Step 5: Establishment of a hardness prediction model, which includes the strengthening effect of β′ precipitation phase and the solid solution strengthening effect of matrix solute;

[0027] The input values of the hardness prediction model are the size and volume fraction of the β′ precipitate phase and the solute concentration of the matrix;

[0028] The output value of the hardness prediction model is the hardness increment during the precipitation process, specifically the difference between the strengthening effect of the β′ precipitation phase and the solid solution strengthening effect of the matrix solute.

[0029] Step 6, prediction of alloy aging hardness, inputting the microstructure information of the β′ precipitated phase obtained in step 3 into the hardness prediction model obtained in step 5, and calculating the predicted value of the hardness increment of the aged Mg-Gd alloy;

[0030] Step 7, establishing a TTP map, inputting the high-throughput calculation results of the β′ precipitation phase microstructure obtained in step 4 into the hardness prediction model obtained in step 5, calculating the hardness increment of the Mg-Gd alloy at different aging temperatures and aging times, and drawing a TTH map of aging temperature-time-hardness increment;

[0031] Step 8: Design of alloy aging process. Based on the TTH map and target hardness requirements, the process design scheme that meets the hardness target and has the shortest aging time is output. This completes the prediction method and obtains the shortest aging process that meets the hardness target.

[0032] The target hardness requirement is that the hardness increment is greater than 40HV; the aging process of the obtained alloy is a two-stage aging process. Specifically, the two-stage aging process is based on the hardness increment reaching 40HV as the basic requirement. The conditions for the first stage aging are: aging temperature of 210°C, aging time of 7h, and the conditions for the second stage aging are: aging temperature of 235°C, aging time of 9h, and the total aging time is 16h.

[0033] The beneficial effects of the present invention are reflected in that the present invention performs kinetic simulation of Mg-Gd alloy based on physical mechanisms, and achieves accurate determination of thermodynamic parameters and kinetic model parameters of β′ precipitation phase through iterative optimization of experimental data and kinetic calculations, so that the calculated results of microstructure are consistent with the measured results, and a quantitative mapping relationship between microstructure characteristics and hardness is established. Without conducting large-scale experiments, the TTT and TTP diagrams of Mg-Gd alloy can be efficiently constructed, and ultimately the alloy aging process design can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flow chart of the aging temperature-time-performance TTP prediction method of Mg-Gd alloy of the present invention;

[0035] Figure 2 HAADF-STEM image of Mg-14Gd alloy at aging temperature of 200℃ and aging time of 128h;

[0036] Figure 3 HAADF-STEM images of Mg-14Gd alloy at aging temperature of 200℃ and aging time of (a) 16h and (b) 128h.

[0037] Figure 4 HAADF-STEM images of Mg-14Gd alloy at aging temperature of 250℃ and aging time of (a) 2h, (b) 8h, and (c) 32h.

[0038] Figure 5 Predicted microstructure of β′ precipitates in Mg-14Gd alloy and compared with experimental values: (a) size, (b) volume fraction, (c) number density, and (d) matrix solute content.

[0039] Figure 6 The predicted TTT curve of β′ precipitation phase in Mg-14Gd alloy is compared with the experimental value;

[0040] Figure 7 is the predicted TTT curve of β′ precipitation phase in Mg-(8-16)Gd alloy;

[0041] Figure 8 The predicted aging hardness increment curves of Mg-14Gd alloy at 200 and 250℃ are compared with the experimental values;

[0042] Figure 9 is the predicted TTH diagram of Mg-14Gd alloy;

[0043] Figure 10 This is a diagram showing the evolution of hardness increment with aging time during the aging process for different aging processes. DETAILED DESCRIPTION

[0044] The present invention is further described in detail through embodiments and in conjunction with the accompanying drawings, but the present invention is not limited thereto.

[0045] A method for predicting the aging temperature-time-performance TTP of Mg-Gd alloy, the flow chart is as follows Figure 1 As shown, the following steps are included:

[0046] Step 1, determination of preliminary thermodynamic parameters;

[0047] Step 2, optimization of precipitation kinetic model parameters;

[0048] Step 3, microstructure simulation and parameter iterative correction;

[0049] Step 4, establishment of TTT curve;

[0050] Step 5, establishment of hardness prediction model;

[0051] Step 6, prediction of alloy aging hardness;

[0052] Step 7, establishment of TTP map;

[0053] Step 8: Design of alloy aging process.

[0054] The specific steps are as follows;

[0055] Step 1, determination of preliminary thermodynamic parameters, determining preliminary thermodynamic parameters of β′ precipitation phase in Mg-Gd alloy based on experimental data;

[0056] The experimental data are alloy composition, aging temperature and volume fraction of β′ precipitate phase at peak aging state;

[0057] The preliminary thermodynamic parameters are: formation enthalpy Δ f H and formation entropy Δ f S;

[0058] The specific method of determining the preliminary thermodynamic parameters is as follows: first, Δ f H and Δ f S is substituted into the Gibbs free energy expression of the precipitate phase, and the Gibbs free energy expression is written into the stable thermodynamic database of the Mg-Gd system. Then, the PanPhaseDiagram module of the Pandat software is used to input the alloy composition and temperature for point calculation. The volume fraction of the β′ precipitate phase is calculated and compared with the experimental data. Finally, by manually adjusting Δ f H and Δ f S makes the error between the calculated volume fraction and the experimental volume fraction less than 0.02;

[0059] The expression of the Gibbs free energy of the precipitated phase is shown in formula (1),

[0060]

[0061] Among them, G i 0 is the Gibbs free energy of element i in its stable structure, derived from the Scientific Group Thermodata Europe (SGTE) thermochemical database, x i is the atomic percentage of element i, T is the absolute temperature;

[0062] The calculation formula of the volume fraction of the β′ precipitated phase is shown in formula (2):

[0063]

[0064] Among them, A A is (0001) α The area fraction of β′ precipitated phase on the surface, l is the area fraction of β′ precipitated phase along

[0001] α The length in the direction, h is the sample thickness, and the above parameters are extracted from the TEM test results;

[0065] Step 2, optimization of precipitation kinetic model parameters, optimizing the precipitation kinetic model parameters based on kinetic experimental data at an aging temperature of 200°C;

[0066] The kinetic experimental data are the size, volume fraction and number density of β′ precipitates in alloys with different aging times;

[0067] The parameters of the precipitation kinetics model are the interfacial energy and the number of nucleation sites of the β′ precipitation phase;

[0068] The specific method for optimizing the precipitation kinetics model parameters is as follows: first, using the PanPrecipitation module in the Pandat software to calculate the evolution of the β′ precipitation phase with aging time when the aging temperature is 200°C, i.e., the calculation result; then, comparing the calculation result with the experimental result to determine whether the error between the calculation result and the experimental result meets the requirement; if not, manually adjusting the interface energy and the number of nucleation sites to make the error between the calculation result and the experimental result meet the requirement;

[0069] The conditions for judging whether the error between the calculated results and the experimental results meets the requirements are that the size calculation error is less than 3 nm, the volume fraction error is less than 0.02, and the number density error is less than 1 order of magnitude. These conditions also apply to the error judgment between the calculated results and the experimental results in the subsequent step 3.

[0070] The specific method for adjusting the interface energy and the number of nucleation sites is as follows: the adjustment range of the interface energy is determined by the interface energy of different orientations of the β′ precipitate phase, and the adjustment range of the number of nucleation sites is determined by the number of solute atoms in the alloy;

[0071] The calculation formula for the size of the β′ precipitated phase is shown in formula (3),

[0072]

[0073] Where w is the β′ precipitation phase [10-10] α The length of the direction, t is the length of the β′ precipitation phase along [11-20] α The lengths of the directions, w and t, were obtained by extracting from the TEM test results;

[0074] The volume fraction of the β′ precipitated phase is obtained by formula (2);

[0075] The number density of the β′ precipitated phase is the number of β′ precipitated phases per unit volume;

[0076] Step 3: Microstructure simulation and parameter iterative correction. In order to simulate the microstructure at different aging temperatures, first, based on the preliminary thermodynamic parameters obtained in step 1 and the precipitation kinetics model parameters obtained in step 2, the evolution of the β' precipitation phase with aging time at an aging temperature of 250°C is calculated. The calculation process is consistent with the specific method for optimizing the precipitation kinetics model parameters described in step 2. Then, it is determined whether the error between the calculated results and the experimental results at an aging temperature of 250°C meets the requirements. If not, the thermodynamic parameters described in step 1 are manually adjusted to ensure that the error between the calculated results and the experimental results meets the requirements.

[0077] Step 4: establishing a TTT curve, wherein the TTT curve includes a transition start line and a transition end line;

[0078] The transition start line is composed of the time points at which the relative volume fraction of the transition is 5% at different temperatures;

[0079] The transformation termination line is formed by the time point at which 90% of the relative volume fraction is transformed at different temperatures;

[0080] The relative volume fraction is the ratio of the volume fraction calculated at the current moment to the equilibrium volume fraction calculated by the thermodynamic database;

[0081] The conditions for the high-throughput calculation are: a temperature range of 100-350°C, a temperature step of 5°C, and an aging time of 5000h;

[0082] Step 5: Establishment of a hardness prediction model, which includes the strengthening effect of β′ precipitation phase and the solid solution strengthening effect of matrix solute;

[0083] The input values of the hardness prediction model are the size and volume fraction of the β′ precipitate phase and the solute concentration of the matrix;

[0084] The output value of the hardness prediction model is the hardness increment during the precipitation process, specifically the difference between the strengthening effect of the β′ precipitation phase and the solid solution strengthening effect of the matrix solute, that is, HV = HV p -HV s ;

[0085] The strengthening effect of the β′ precipitation phase is calculated as shown in formula (4):

[0086]

[0087] The matrix solute concentration solid solution strengthening effect is calculated as shown in formula (5):

[0088]

[0089] Where γ is the antiphase boundary energy of β′ phase, which is 40mJ / m 2 , b is the Burgers vector, which is taken as 0.32; G is the shear modulus, which is taken as 16.6 GPa; ω is the dislocation repulsion, which is taken as 0.24; M is the Taylor factor, which is taken as 4.2; k is the proportional coefficient, which is taken as 0.52, k ss is the solid solution strengthening coefficient, which is 523MPa (at.%) -2 / 3 , n ss Take 2 / 3, d and f are the size and volume fraction of β′ phase respectively, c i is the atomic percentage content of the matrix solute;

[0090] Step 6, prediction of alloy aging hardness, inputting the microstructure information of the β′ precipitated phase obtained in step 3 into the hardness prediction model obtained in step 5, and calculating the predicted value of the hardness increment of the aged Mg-Gd alloy;

[0091] Step 7, establishing a TTP map, inputting the high-throughput calculation results of the β′ precipitation phase microstructure obtained in step 4 into the hardness prediction model obtained in step 5, calculating the hardness increment of the Mg-Gd alloy at different aging temperatures and aging times, and drawing a TTH map of aging temperature-time-hardness increment;

[0092] Step 8: Design of alloy aging process. Based on the TTH map and target hardness requirements, the process design scheme that meets the hardness target and has the shortest aging time is output. This completes the prediction method and obtains the shortest aging process that meets the hardness target.

[0093] The target hardness requirement is that the hardness increment is greater than 40HV;

[0094] The design process of the alloy aging process is as follows: select a range with a hardness increment greater than 40HV in the TTH diagram, select the aging temperature with the shortest aging time required to achieve the selected range, and output the aging temperature and aging time;

[0095] The design process of the alloy aging process also includes calculating the hardness increment of graded aging through a model, and outputting graded aging temperature and aging time that meet target performance.

[0096] In order to demonstrate the effectiveness of the aging temperature-time-performance (TTP) prediction method for Mg-Gd alloys, Mg-14Gd alloy was used as an example to determine experimental data, optimize thermodynamic and kinetic parameters, and establish TTT curves and TTP maps. The specific steps are as follows:

[0097] Step 1, determination of preliminary thermodynamic parameters: determining preliminary thermodynamic parameters of β′ precipitation phase in Mg-14Gd alloy based on experimental data;

[0098] The experimental data are alloy composition, aging temperature and volume fraction of β′ precipitate phase in peak aging state;

[0099] In order to obtain the volume fraction of β′ precipitates in Mg-14Gd alloy at peak aging state, Mg-14Gd alloy was aged and then the volume fraction of β′ precipitates was obtained by TEM experiment.

[0100] The aging treatment process of the alloy is as follows: first, under the condition of aging temperature of 200℃, the aging hardness of the alloy is measured at different aging times. Then, the hardness of the unaged alloy is subtracted from the aging hardness to obtain the experimental hardness increment at different aging times. The experimental hardness increment of the Mg-14Gd alloy at different aging times under the condition of aging temperature of 200℃ is shown in Table 1. Finally, the peak aging time of the Mg-14Gd alloy under the condition of aging temperature of 200℃ is 128h.

[0101] Table 1 Experimental values of hardness increment of Mg-Gd alloy at different aging temperatures and aging times

[0102]

[0103]

[0104] In order to obtain the volume fraction of β′ precipitate phase in Mg-14Gd alloy at the peak aging state (i.e., the peak aging time of 128h) under the condition of aging temperature of 200℃, TEM test was carried out. Figure 2 As shown in the figure, the precipitation phase of Mg-14Gd alloy includes Mg matrix phase, β′ precipitation phase, β F ′ precipitate phase and tail structure, the volume fraction of Mg matrix phase is 87%, the volume fraction of β′ precipitate phase is 13%, and the volume fraction of β F The volume fractions of precipitated phase and tail structure are negligible;

[0105] In order to determine the preliminary thermodynamic parameters, the PanPhaseDiagram module of Pandat software was used for point calculation. The input alloy composition was Mg-14Gd, and the input temperature was 200℃, which was the aging temperature. The calculated volume fraction of the β′ precipitate phase was 6.8%. The calculated volume fraction was compared with the experimental result of 13%. The error between the calculated and experimental results was 0.062. It was determined that the calculated results did not meet the requirements. Then, by reducing Δ f H and increase Δ f After S is adjusted, the calculated volume fraction is 12.9% with a calculated error of 0.001. It is determined that the error between the calculated result and the experimental result meets the requirements.

[0106] Step 2, optimization of precipitation kinetic model parameters, optimizing the precipitation kinetic model parameters based on kinetic experimental data at an aging temperature of 200°C;

[0107] The specific method for obtaining the kinetic experimental data is to perform TEM tests on Mg-14Gd alloys at aging temperatures of 200°C and aging times of 16h, 128h, and 2000h, respectively, to obtain the size, volume fraction, and number density of the β′ precipitate phase in the alloys at different aging times at 200°C. The test results are shown in FIG. Figure 3 As shown in the figure, the precipitation phase of Mg-14Gd alloy includes Mg matrix phase, β′ precipitation phase, β F The size, volume fraction and number density of the β′ precipitate phase and tail structure are statistically obtained as shown in Table 2.

[0108] The volume fraction of Mg matrix phase in the alloy with aging time of 16 h is 99.8%, the volume fraction of β′ precipitate phase is 0.2%, the size of β′ precipitate phase is 2.2 nm, and the number density of β′ precipitate phase is 1.4×10 23 m -3 ;

[0109] The volume fraction of Mg matrix phase in the alloy with aging time of 128h is 87%, the volume fraction of β′ precipitate phase is 13%, the size of β′ precipitate phase is 6.9nm, and the number density of β′ precipitate phase is 5.1×10 23 m -3 ;

[0110] The volume fraction of Mg matrix phase in the alloy with aging time of 2000 h is 89.3%, the volume fraction of β′ precipitate phase is 10.7%, the size of β′ precipitate phase is 13 nm, and the number density of β′ precipitate phase is 2.1×10 23 m -3 , β F The volume fraction of the precipitated phase and tail structure is negligible.

[0111] Table 2 Summary of experimentally measured microstructures of Mg-14Gd alloys aged at 200℃ and 250℃ for different times

[0112]

[0113] In order to optimize the precipitation kinetics model parameters, the PanPrecipitation module in the Pandat software was used to calculate the precipitation kinetics. The input alloy composition was Mg-14Gd, the input temperature was 200℃, that is, the aging temperature, and the input time was 2000h. The evolution of the size, volume fraction, and number density of the β′ precipitate phase with aging time was obtained, that is, the calculation results. The size calculation error was 9nm, the volume fraction calculation error was 0.05, and the number density calculation error was 2 orders of magnitude. It was determined that the calculation results did not meet the requirements. Then, adjustments were made by reducing the interface energy and increasing the number of nucleation sites. After adjustment, the size calculation error was 3nm, the volume fraction calculation error was 0.02, and the number density calculation error was less than 1 order of magnitude. It was determined that the errors between the calculation results and the experimental results met the requirements.

[0114] Step 3, microstructure simulation and parameter iterative correction. First, in order to simulate the microstructure at different aging temperatures, the PanPrecipitation module in the Pandat software is used for calculation based on the preliminary thermodynamic parameters obtained in step 1 and the precipitation kinetic model parameters obtained in step 2. The calculation method is consistent with the calculation method for optimizing the precipitation kinetic model parameters in step 2. The difference is that the input temperature is 250 ° C, and the evolution of the size, volume fraction and number density of the β' precipitate phase with aging time is obtained. The calculation results show that the size calculation error is 6nm, the volume fraction calculation error is 0.06, and the number density calculation error is 2 orders of magnitude. It is determined that the calculation results do not meet the requirements. Then, by adjusting the thermodynamic parameters, specifically, by increasing Δ f H and reduce Δ f S was adjusted, and after adjustment, the error in size calculation was 3nm, the error in volume fraction calculation was 0.02, and the error in number density calculation was less than 1 order of magnitude. It was determined that the error between the calculated results and the experimental results met the requirements;

[0115] The method for obtaining the experimental results is consistent with the specific method for obtaining the kinetic experimental data in step 1, except that the aging temperature is 250°C and the aging times are 2h, 8h and 32h respectively. Figure 4 As shown in the figure, the precipitation phase of Mg-14Gd alloy includes Mg matrix phase, β′ precipitation phase, β F The size, volume fraction and number density of the β′ precipitate phase and tail structure are statistically obtained as shown in Table 2.

[0116] The volume fraction of Mg matrix phase in the alloy with aging time of 2 h is 93.8%, the volume fraction of β′ precipitate phase is 6.2%, the size of β′ precipitate phase is 8.9 nm, and the number density of β′ precipitate phase is 1.6×10 23 m -3 ;

[0117] The volume fraction of Mg matrix phase in the alloy with aging time of 8 h is 89.6%, the volume fraction of β′ precipitate phase is 10.4%, the size of β′ precipitate phase is 12.1 nm, and the number density of β′ precipitate phase is 8.8×10 22 m -3 ;

[0118] The volume fraction of Mg matrix phase in the alloy with aging time of 32h is 88.7%, the volume fraction of β′ precipitate phase is 11.3%, the size of β′ precipitate phase is 16.9nm, and the number density of β′ precipitate phase is 4.4×10 22 m -3 , β F The volume fraction of the precipitated phase and tail structure is negligible.

[0119] The final thermodynamic parameters and kinetic model parameters were input into the PanPrecipitation module in the Pandat software for precipitation kinetics calculation. The input alloy composition was Mg-14Gd, the input temperatures were 200℃ and 250℃, i.e., the aging temperatures, and the input time was 5000h. The evolution curves of the size, volume fraction, and number density of the β′ precipitate phase with aging time were obtained, as shown in Figure 2. Figure 5 As shown, the error in size calculation is 3nm, the error in volume fraction calculation is 0.02, and the error in number density calculation is less than 1 order of magnitude. It is determined that the error between the calculated results and the experimental results meets the requirements;

[0120] Step 4: establishing a TTT curve, wherein the TTT curve includes a transition start line and a transition end line;

[0121] The transition start line is composed of the time points at which the relative volume fraction of the transition is 5% at different temperatures;

[0122] The transformation termination line is formed by the time point at which 90% of the relative volume fraction is transformed at different temperatures;

[0123] The relative volume fraction is the ratio of the volume fraction calculated at the current moment to the equilibrium volume fraction calculated from the thermodynamic database. When the aging temperature is 200°C, the equilibrium volume fraction is 0.136, and when the aging temperature is 250°C, the equilibrium volume fraction is 0.113.

[0124] The conditions for the high-throughput calculation are: a temperature range of 100-350°C, a temperature step of 5°C, and an aging time of 5000h;

[0125] The calculated TTT curve of Mg-14Gd alloy is as follows: Figure 6As shown in Figure 2, the predicted transformation start line is consistent with the experimentally measured appearance time of the β′ precipitation phase. In addition, in order to prove that the prediction method of the present invention can also be used to predict the TTT curves of Mg-Gd alloys with other Gd contents, the established thermodynamic parameters and kinetic parameters are further used to predict the TTT curve of Mg-(8-16)Gd composition. The predicted transformation start line of the β′ precipitation phase during the aging process of Mg-(8-16)Gd alloy is shown in Figure 2. Figure 5 As shown, the transformation starting line of the β′ precipitation phase changes towards low temperature and long aging time as the Gd content decreases;

[0126] Step 5: Establishment of a hardness prediction model, which includes the strengthening effect of β′ precipitation phase and the solid solution strengthening effect of matrix solute;

[0127] Step 6, prediction of alloy aging hardness, the microstructure calculation results obtained in step 3 at aging temperatures of 200℃ and 250℃ are input into the hardness model obtained in step 5, and the predicted values of hardness increment at aging temperatures of 200℃ and 250℃ are obtained as shown in Table 3. The comparison between the predicted aging hardness increment curves of Mg-14Gd alloy at 200℃ and 250℃ and the experimental values is shown in Table 3. Figure 6 As shown in the figure, the comparison results show that the error between the calculated results and the experimental results is within a reasonable range, that is, it is accurate;

[0128] To further demonstrate the accuracy of the present prediction method, the present prediction method was used to predict the hardness increment of Mg-15Gd alloy and Mg-16Gd alloy during the aging process. The calculated results were compared and analyzed with the data in the reference literature. The calculated results and the experimental results in the reference literature, i.e., the hardness increment data at different aging times, are summarized in Table 3. The comparative results show that the predicted hardness increment of Mg-15Gd alloy and Mg-16Gd alloy during aging, i.e., the calculated results, predicted by the present prediction method, is within a reasonable range compared with the experimental results, demonstrating that the present prediction method is accurate in predicting the hardness increment of Mg-(14-16)Gd alloy.

[0129] Reference 1: XIE Z, YANG Q, LV S, et al. Adjustment of a novelβ′-typeprecipitate in acreep-aged Mg-15wt.%Gd alloy[J].Mater.Sci.Eng.,A,2024,889:145982.

[0130] Reference 2: ZHANG Y, RONG W, WU YJ, et al.Acomparative study of the role of Ag in microstructures and mechanical properties of Mg-Gd and Mg-Y alloys[J].Mater.Sci.Eng.,A,2018,731:609-22.

[0131] Table 3 Experimental and calculated values of hardness increment of Mg-Gd alloy at different aging temperatures and aging times

[0132]

[0133]

[0134] Step 7, the establishment of TTP map, the high-throughput calculation results of the β′ precipitation phase microstructure obtained in step 4 are input into the hardness prediction model obtained in step 5, the hardness increment of the Mg-Gd alloy at different aging temperatures and aging times is calculated, and the aging temperature-time-hardness increment TTH diagram is drawn, as shown in the following example: Figure 9 As shown in the figure, when the aging temperature is fixed, the hardness increment of the alloy first increases and then decreases with the increase of aging time. When the aging time is fixed, the hardness increment of the alloy first increases and then decreases with the increase of aging temperature. When the aging temperature is low and the aging time is long, the alloy can obtain a high hardness increment.

[0135] Step 8: Design of alloy aging process, designing an aging process for Mg-14Gd alloy with a hardness increment greater than 40 HV;

[0136] The aging process of the alloy obtained in Example 1 is a two-stage aging process, named process 1. The specific two-stage aging process is to take the hardness increment of 40HV as the basic requirement.

[0137] The first stage is a low aging temperature, which is used to obtain a high number density of β' precipitates, the number density of which is greater than 10 23 m -3 ;

[0138] The second stage is a high aging temperature, which is used to quickly make the alloy reach the target hardness increment value;

[0139] In specific embodiment 1, the conditions for the first stage of aging are: aging temperature of 210° C., aging time of 7 h, and the conditions for the second stage of aging are: aging temperature of 235° C., aging time of 9 h, and the total aging time is 16 h.

[0140] At the same time, in order to compare the effectiveness of the alloy aging process design and the corresponding technical effects, Process 2, Process 3 and Process 4 are provided, all of which use the conventional aging process, that is, the one-stage aging process, as a reference; the basic requirements of the process design are also based on the TTH diagram obtained in the present invention, that is, Figure 9 Determine the hardness increment to 40HV, where

[0141] The aging process of process 2 is that at an aging temperature of 200°C, the aging time for the hardness increment to reach 40HV is 63h;

[0142] The process 3 aging process is that when the aging temperature is 250°C, the hardness increment can reach up to 35.5HV, and the aging time is 16h;

[0143] Explanation: According to the prediction method, when the aging temperature is 250℃, the hardness increment cannot reach 40HV, which means it cannot meet the requirements.

[0144] The process 4 aging process is, according to the prediction results, the condition for the hardness increment to reach 40HV the fastest, specifically, the aging temperature is 225°C and the aging time is 19 hours.

[0145] In order to verify the aging process design, that is, the accuracy of the predicted results, the Mg-14Gd alloy was aged under the above four processes, and the hardness increment of the alloy was measured. The hardness increment of the Mg-14Gd alloy under different aging processes is shown in Table 4 and Figure 10 As shown,

[0146] The measured aging hardness increment of process 1 is 39.3±3.7HV, which is consistent with the predicted aging hardness increment of 40HV, indicating that the prediction method and process of the present invention are effective;

[0147] The measured aging hardness increment of process 2 is 39.9±3.8HV, which is consistent with the predicted aging hardness increment of 40HV, indicating that the prediction method of the present invention is effective. However, since process 2 is a conventional aging process, the aging time is as long as 63h.

[0148] The measured aging hardness increment of process 3 is 33.0±5.8HV, which is consistent with the predicted aging hardness increment of 35.5HV, but cannot reach the aging hardness increment of 40HV;

[0149] The measured aging hardness increment of process 4 is 43.8±4.1HV, which is consistent with the predicted aging hardness increment of 40HV, indicating that the prediction method of the present invention is effective. However, the problem is the same as that of process 2. Since process 4 is a conventional aging process, the aging time still requires 19 hours.

[0150] Table 4 Design and verification of aging process for Mg-14Gd alloy with hardness increment greater than 40HV

[0151]

[0152]

[0153] Through the above experiments and comparative analysis, we can draw the following two conclusions:

[0154] 1. The prediction method of the present invention is effective;

[0155] 2. The two-stage aging process designed according to the prediction method of the present invention has a shorter aging time than the conventional one-stage aging process when achieving the same aging hardness increment.

Claims

1. A method for predicting the aging temperature-time-performance of Mg-Gd alloys, characterized in that The following steps are involved: Step 1, determination of preliminary thermodynamic parameters, determining preliminary thermodynamic parameters of β′ precipitation phase in Mg-Gd alloy based on experimental data; Step 2, optimization of precipitation kinetic model parameters, optimizing the precipitation kinetic model parameters based on kinetic experimental data at an aging temperature of 200°C; Step 3, microstructure simulation and parameter iterative correction; Step 4, establishing the aging temperature-time-transformation TTT curve; Step 5: Establishment of a hardness prediction model, which includes the strengthening effect of β′ precipitation phase and the solid solution strengthening effect of matrix solute; Step 6, prediction of alloy aging hardness, inputting the microstructure information of the β′ precipitated phase obtained in step 3 into the hardness prediction model obtained in step 5, and calculating the predicted value of the hardness increment of the aged Mg-Gd alloy; Step 7, establishing an aging temperature-time-performance (TTP) diagram, inputting the high-throughput calculation results of the β′ precipitation phase microstructure obtained in step 4 into the hardness prediction model obtained in step 5, calculating the hardness increment of the Mg-Gd alloy at different aging temperatures and aging times, and drawing an aging temperature-time-hardness increment (TTH) diagram; Step 8, design of the alloy aging process. According to the TTH map and the target hardness requirement, the process design scheme that meets the hardness target and has the shortest aging time is output. This completes the prediction method and obtains the shortest aging process that meets the hardness target.

2. The method according to claim 1, wherein: In step 1, the experimental data are the alloy composition, aging temperature and volume fraction of β′ precipitate phase at peak aging state; The preliminary thermodynamic parameters are: formation enthalpy Δ f H and formation entropy Δ f S; The specific method of determining the preliminary thermodynamic parameters is as follows: first, Δ f H and Δ f S is substituted into the Gibbs free energy expression of the precipitation phase, and the Gibbs free energy expression is written into the thermodynamic database of the Mg-Gd system. Then, the PanPhaseDiagram module of the Pandat software is used to input the alloy composition and temperature for point calculation. The volume fraction of the β′ precipitation phase is calculated and compared with the experimental data. Finally, by manually adjusting Δ f H and Δ f S makes the error between the calculated volume fraction and the experimental volume fraction less than 0.

02.

3. The method according to claim 1, wherein: In step 2, the kinetic experimental data are the size, volume fraction and number density of the β′ precipitate phase in the alloy at different aging times; The parameters of the precipitation kinetics model are the interfacial energy and the number of nucleation sites of the β′ precipitation phase; The specific method for optimizing the precipitation kinetics model parameters is as follows: first, using the PanPrecipitation module in the Pandat software to calculate and obtain the evolution of the β′ precipitation phase with aging time when the aging temperature is 200°C, that is, the calculation result; then, comparing the calculation result with the experimental result to determine whether the error between the calculation result and the experimental result meets the requirements; if not, manually adjusting the interface energy and the number of nucleation sites so that the error between the calculation result and the experimental result meets the requirements; the conditions for determining whether the error between the calculation result and the experimental result meets the requirements are that the size calculation error is less than 3 nm, the volume fraction error is less than 0.02, and the number density error is less than 1 order of magnitude; these conditions are also applicable to the error judgment between the calculation result and the experimental result in the subsequent step 3.

4. The method according to claim 1, wherein: In step 3, microstructure simulation and parameter iterative correction are performed. First, based on the preliminary thermodynamic parameters obtained in step 1 and the precipitation kinetics model parameters obtained in step 2, the evolution of the β' precipitation phase with aging time when the aging temperature is 250°C is calculated. The calculation process is consistent with the specific method for optimizing the precipitation kinetics model parameters described in step 2. Then, it is determined whether the error between the calculated result and the experimental result meets the requirements when the aging temperature is 250°C. If not, the thermodynamic parameters described in step 1 are manually adjusted to ensure that the error between the calculated result and the experimental result meets the requirements.

5. The method according to claim 1, wherein: In step 4, the TTT curve includes a transition start line and a transition end line; The transition start line is composed of the time points at which the relative volume fraction of the transition is 5% at different temperatures; The transformation termination line is formed by the time point at which 90% of the relative volume fraction is transformed at different temperatures; The relative volume fraction is the ratio of the volume fraction calculated at the current moment to the equilibrium volume fraction calculated by the thermodynamic database; The conditions for the high-throughput calculation are: a temperature range of 100-350°C, a temperature step of 5°C, and an aging time of 5000h.

6. The method according to claim 1, wherein: In step 5, the input values of the hardness prediction model are the size and volume fraction of the β′ precipitate phase and the solute concentration of the matrix; The output value of the hardness prediction model is the hardness increment during the precipitation process, specifically the difference between the strengthening effect of the β′ precipitation phase and the solid solution strengthening effect of the matrix solute.

7. The method according to claim 1, wherein: In step 8, the target hardness requirement is that the hardness increment is greater than 40 HV; the aging process of the obtained alloy is a two-stage aging process. Specifically, the two-stage aging process is based on the hardness increment reaching 40 HV. The conditions for the first stage aging are an aging temperature of 210° C. and an aging time of 7 hours. The conditions for the second stage aging are an aging temperature of 235° C. and an aging time of 9 hours. The total aging time is 16 hours.

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