Nickel-based superalloy yield strength prediction model, modeling method, device and system

Abstract

The embodiment of the present application provides a yield strength prediction model, a modeling method, a device and a system for a nickel-based superalloy. The modeling method comprises the following steps: step 1, simplifying a gamma prime precipitation strengthening model formula to establish a simplified gamma prime precipitation strengthening model related to probability; step 2, establishing a yield strength model for composition design of the nickel-based superalloy according to a grain boundary strengthening contribution, a solid solution strengthening contribution and the simplified gamma prime precipitation strengthening model; step 3, introducing a temperature variable and a parameter, and establishing a new yield strength model applicable to a full temperature range according to the yield strength model; step 4, obtaining a coefficient of the new yield strength model according to yield strength data in composition design of the nickel-based superalloy; and step 5, obtaining a yield strength model formula of the nickel-based superalloy according to the coefficient and the new yield strength model. The embodiment is suitable for yield strength prediction of the nickel-based superalloy in a full temperature range.

Description

Technical Field

[0001] The present invention relates to the field of mechanical technology of alloy composition design, and in particular to a nickel-based high-temperature alloy yield strength prediction model, a nickel-based high-temperature alloy yield strength modeling method, a nickel-based high-temperature alloy yield strength modeling device, and a nickel-based high-temperature alloy yield strength modeling system. Background Art

[0002] Nickel-based superalloys are widely used in hot-end components such as gas turbines and aircraft engines because they maintain excellent strength, creep resistance, fatigue resistance, oxidation resistance, and corrosion resistance even at high temperatures. These unique properties stem from their distinctive microstructure, which includes a multimodal size distribution of L12 precipitates (γ') and a face-centered cubic (FCC) matrix (γ). The superior yield strength of nickel-based superalloys is primarily derived from grain boundary strengthening, solid solution strengthening, and γ' precipitation strengthening. These three strengthening methods have been extensively studied and well-established models have been established.

[0003] Due to the extremely complex operating environment of engines, the geometric morphology of components made from nickel-based superalloys is also more complex. While investment casting, a traditional processing method, can achieve precision casting, it is relatively complex, has relatively low processing efficiency, and has certain limitations in producing complex-shaped components. Laser powder bed fusion, as a form of additive manufacturing, can produce complex geometries, offering fewer process steps, greater processing freedom, and more efficient material utilization. However, given the high temperature gradients, high cooling rates, and cyclic heating-cooling characteristics of laser powder bed fusion, the microstructure of nickel-based superalloys produced by additive manufacturing (AM) can differ from those produced by traditional processes. Therefore, for the design of nickel-based superalloy compositions for AM, it is necessary to model the yield strength of nickel-based superalloys for AM processes. Furthermore, traditional yield strength models for nickel-based superalloys do not consider the inclusion of temperature variables to predict the yield strength of nickel-based superalloys at different temperatures. However, nickel-based superalloys are used in different scenarios, and the reference temperature for designing compositions for these environments often differs.

[0004] Therefore, there is an urgent need for a new yield strength model that can be applied to the composition design of nickel-based high-temperature alloys in the entire temperature range to improve the efficiency of nickel-based high-temperature alloy composition design. Summary of the Invention

[0005] Therefore, in order to overcome at least some of the defects and shortcomings in the prior art, embodiments of the present invention provide a nickel-based high-temperature alloy yield strength prediction model, a nickel-based high-temperature alloy yield strength modeling method, a nickel-based high-temperature alloy yield strength modeling device and a nickel-based high-temperature alloy yield strength modeling system.

[0006] Specifically, on one hand, an embodiment of the present invention provides a nickel-based high-temperature alloy yield strength prediction model, characterized in that: the calculation expression of the prediction model is:

[0007]

[0008] Where T is temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0009] On the other hand, an embodiment of the present invention provides a yield strength modeling method for a nickel-based high-temperature alloy, comprising: step 1: simplifying the γ' precipitation strengthening model formula to establish a probability-related simplified γ' precipitation strengthening model; step 2: establishing a yield strength model for the composition design of the nickel-based high-temperature alloy based on the grain boundary strengthening contribution, the solid solution strengthening contribution and the simplified γ' precipitation strengthening model; step 3: introducing temperature variables and parameters, and establishing a new yield strength model applicable to the entire temperature range based on the yield strength model; step 4: obtaining the coefficient of the new yield strength model based on the yield strength data in the composition design of the nickel-based high-temperature alloy; step 5: obtaining the yield strength model formula of the nickel-based high-temperature alloy based on the coefficient and the new yield strength model.

[0010] In a specific embodiment of the present invention, the step 4 specifically includes: step 4.1: obtaining yield strength data and alloy composition data of nickel-based high-temperature alloys with different compositions at different temperatures; step 4.2: calculating the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase and the volume fraction of the γ' strengthening phase in the nickel-based high-temperature alloy at different temperatures based on the alloy composition data; step 4.3: obtaining the coefficient based on the temperature, the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, the volume fraction of the γ' strengthening phase and the yield strength data.

[0011] In a specific embodiment of the present invention, the yield strength model formula of the nickel-based high-temperature alloy is:

[0012]

[0013] Where T is temperature, f γis the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0014] In a specific embodiment of the present invention, step 1 specifically includes: introducing dislocations through the probability and average intensity of the γ' precipitation phase in a weak pair coupling mechanism, a strong pair coupling mechanism and an Orowan mechanism, simplifying the γ' precipitation strengthening model formula to obtain a simplified γ' precipitation strengthening model.

[0015] In a specific embodiment of the present invention, the simplified γ' precipitation strengthening model is:

[0016]

[0017] Where M is the Taylor factor, G is the shear model, b is the Burgers vector length, P w 、P s and P o are the probabilities of dislocation passing through the γ' precipitate phase via weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, r w 、r s and r o are the radius of the precipitate phase through which dislocations pass by the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, APB is the reverse boundary energy, f γ′ is the volume fraction of γ' phase.

[0018] In a specific embodiment of the present invention, the yield strength model formula in step 2 is:

[0019]

[0020] Among them, σ0 is the increase in basic yield strength, σ GB The increase in yield strength due to grain boundary strengthening, σ ss The increase in yield strength due to solid solution strengthening, σ γ′ To simplify the γ' precipitation strengthening model, k is the Hall-Petch constant, d is the average grain size, and f is the average grain size. γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, M is the Taylor factor, G is the shear model, b is the Burgers vector length, r w 、r s and r o are the radius of the precipitate phase through which dislocations pass by the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, APBis the reverse boundary energy, f γ′ is the volume fraction of γ' phase.

[0021] In a specific embodiment of the present invention, the new yield strength model applicable to the full temperature range in step 3 is:

[0022]

[0023] Among them, a, o, b, p, c, q, k, g, j and h are the coefficients to be fitted, T is the temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0024] On the other hand, an embodiment of the present invention also provides a nickel-based high-temperature alloy yield strength modeling device, including: a data acquisition module, used to obtain yield strength data and alloy composition data of nickel-based high-temperature alloys with different compositions at different temperatures; a thermodynamic calculation module, used to calculate the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase and the volume fraction of the γ' strengthening phase in the nickel-based high-temperature alloy at different temperatures based on the alloy composition data; a model optimization module, used to simplify the γ' precipitation strengthening model formula to obtain a probability-related simplified γ' precipitation strengthening model, and establish a yield strength model for the composition design of nickel-based high-temperature alloys based on the grain boundary strengthening contribution, the solid solution strengthening contribution and the simplified γ' precipitation strengthening model, introduce temperature variables and parameters, establish a new yield strength model applicable to the entire temperature range based on the yield strength model formula, and obtain the yield strength model formula of the nickel-based high-temperature alloy.

[0025] On the other hand, an embodiment of the present invention further provides a nickel-based high-temperature alloy yield strength modeling system, comprising: a processor and a memory connected to the processor, wherein the memory stores a computer program, and when the processor executes the computer program, it executes the nickel-based high-temperature alloy yield strength modeling method described above.

[0026] From the above, it can be seen that the nickel-based high-temperature alloy yield strength modeling method provided by the embodiment of the present invention establishes a yield strength model in the composition design of the nickel-based high-temperature alloy based on the grain boundary strengthening contribution, the solid solution strengthening contribution and the simplified γ' precipitation strengthening model by simplifying the γ' precipitation strengthening model, introduces temperature variables and parameters, establishes a new yield strength model applicable to the entire temperature range, and obtains the correlation coefficient based on the yield strength data, thereby establishing a yield strength model formula for the nickel-based high-temperature alloy in the entire temperature range, which is applicable to the prediction of the yield strength of the nickel-based high-temperature alloy in the entire temperature range, so as to improve the efficiency of the composition design of the nickel-based high-temperature alloy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A schematic flow chart of a method for modeling the yield strength of a nickel-based high-temperature alloy provided in accordance with the first embodiment of the present invention.

[0029] Figure 2 Another schematic flow chart of the method for modeling the yield strength of nickel-based high-temperature alloys provided in the first embodiment of the present invention.

[0030] Figure 3 for Figure 1 Flowchart of step 4 in [1].

[0031] Figure 4 It is a model of grain boundary strengthening, solid solution strengthening and γ' precipitation strengthening mechanisms.

[0032] Figure 5 The yield strength modeling method of the nickel-based high-temperature alloy provided in an embodiment of the present invention predicts the yield strength.

[0033] Figure 6 A schematic structural diagram of a nickel-based high-temperature alloy yield strength modeling device provided in the second embodiment of the present invention.

[0034] Figure 7 A schematic structural diagram of a nickel-based high-temperature alloy yield strength modeling system provided in the third embodiment of the present invention.

[0035] Figure 8 A schematic structural diagram of a storage medium provided in a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments described in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work shall fall within the scope of protection of the present invention.

[0037] In the embodiments of the present invention, references to "first," "second," and the like are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of these features.

[0038] [First embodiment]

[0039] The first embodiment of the present invention provides a nickel-based high-temperature alloy yield strength prediction model, the calculation expression of the prediction model is:

[0040]

[0041] Where T is temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0042] See also Figure 1 A first embodiment of the present invention provides a method for modeling the yield strength of a nickel-based high-temperature alloy, which may specifically include the following steps:

[0043] S10, step 1: simplifying the γ' precipitation strengthening model formula and establishing a probability-related simplified γ' precipitation strengthening model;

[0044] S20, step 2: establishing a yield strength model for composition design of nickel-based high-temperature alloys based on the grain boundary strengthening contribution, the solid solution strengthening contribution, and the simplified γ' precipitation strengthening model;

[0045] S30, step 3: introducing temperature variables and parameters, and establishing a new yield strength model applicable to the entire temperature range based on the yield strength model;

[0046] S40, step 4: obtaining coefficients of the novel yield strength model according to yield strength data in the nickel-based high-temperature alloy composition design;

[0047] S50, step 5: obtaining a yield strength model formula of the nickel-based high-temperature alloy according to the coefficient and the new yield strength model.

[0048] Specifically, the yield strength model formula of the nickel-based high-temperature alloy described in this embodiment is:

[0049]

[0050] Where T is temperature, f γ is the volume fraction of γ phase, β iis the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0051] Step 1: Simplify the formula of the γ' precipitation strengthening model and establish a probability-related simplified γ' precipitation strengthening model. Specifically, for example, the probability and average intensity of dislocations passing through the γ' precipitation phase through the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism are introduced to simplify the formula of the γ' precipitation strengthening model to obtain a simplified γ' precipitation strengthening model.

[0052] Specifically, the original γ' precipitation strengthening model formula is modified. A probability-related precipitation strengthening mechanism is proposed to simplify the model. Figure 2 and Figure 3 As shown, the interaction mechanism between dislocation and precipitate depends on the radius of the common circular plane between the dislocation slip plane and the γ' precipitate. s The dislocation may pass through the precipitate in the form of weak-pair coupling mechanism, strong-pair coupling mechanism and Orowan mechanism. The Orowan mechanism is a form of precipitation strengthening in metal alloys. When the moving dislocation encounters an unshearable precipitate phase, its movement is hindered. By increasing the applied stress, the dislocation can bypass or shear the precipitate phase, thereby continuing to slide, resulting in Orowan strengthening behavior. In this regard, the present invention introduces P w 、P s and (1-P w -P s ) represent the probability of dislocation passing through the γ' precipitate phase by weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism respectively. and They represent the average strengths of the weak pair coupling mechanism, strong pair coupling mechanism, and Orowan mechanism, respectively. The formulas are as follows:

[0053]

[0054] Among them, P w 、P s and P o are the probabilities of dislocations passing through the γ' strengthened phase via the weak pair coupling mechanism, the strong pair coupling mechanism, and the Orowan mechanism, respectively. and are the average intensities provided by the weak pair coupling mechanism, strong pair coupling mechanism, and Orowan mechanism respectively. and The simplified γ' precipitation strengthening model can be obtained as follows:

[0055]

[0056] Where M is the Taylor factor, G is the shear model, b is the Burgers vector length, P w 、P s and P o are the probabilities of dislocation passing through the γ' precipitate phase via weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, r w 、r s and r o are the radius of the precipitate phase through which dislocations pass by the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, APB is the reverse boundary energy, f γ′ is the volume fraction of γ' phase.

[0057] Step 2: Based on the grain boundary strengthening contribution, solid solution strengthening contribution and the simplified γ' precipitation strengthening model, a yield strength model formula for the composition design of nickel-based high-temperature alloys is established. Specifically, the grain boundary strengthening and solid solution strengthening formulas are first listed as follows:

[0058]

[0059] where k is the Hall-Page constant, d is the average grain size, and f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the ith atom.

[0060] The grain boundary strengthening, solid solution strengthening and γ' precipitation strengthening models are combined, that is, formulas (2), (3) and (4) are combined to establish the yield strength model formula for the composition design of nickel-based high-temperature alloys. The specific formula is:

[0061]

[0062] Among them, σ0 is the increase in basic yield strength, σ GB The increase in yield strength due to grain boundary strengthening, σ ss The increase in yield strength due to solid solution strengthening, σ γ′ To simplify the γ' precipitation strengthening model, k is the Hall-Petch constant, d is the average grain size, and f is the average grain size. γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, M is the Taylor factor, G is the shear model, b is the Burgers vector length, r w 、r s and r o are the radius of the precipitate phase through which dislocations pass by the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively,APB is the reverse boundary energy, f γ′ is the volume fraction of γ' phase.

[0063] In this embodiment, the parameters that are insensitive to the composition can be replaced by constants, and then formula (5) can be expressed as follows:

[0064]

[0065] Where a, b and c are the coefficients to be fitted.

[0066] Furthermore, step 3: introducing temperature variables and parameters, and establishing a new yield strength model applicable to the full temperature range according to the yield strength model formula, thereby expanding the applicable temperature range of the model formula. The new yield strength model applicable to the full temperature range is:

[0067]

[0068] Among them, a, o, b, p, c, q, k, g, j and h are the coefficients to be fitted, T is the temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0069] See also Figure 3 Step 4: Obtaining the coefficients of the new yield strength model based on the yield strength data in the nickel-based high-temperature alloy composition design, specifically comprising the following steps:

[0070] S41, step 4.1: obtaining yield strength data and alloy composition data of nickel-based high-temperature alloys with different compositions at different temperatures;

[0071] S42, step 4.2: calculating the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, and the volume fraction of the γ' strengthening phase in the nickel-based high-temperature alloy at different temperatures based on the alloy composition data;

[0072] S43, step 4.3: obtaining the coefficient according to the temperature, the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, the volume fraction of the γ' strengthening phase and the yield strength data.

[0073] The coefficients are fitted by applying the above formula (7) to the composition design of a nickel-based superalloy as an example. The nickel-based superalloy in this embodiment can be, for example, an additively manufactured nickel-based superalloy, but this embodiment is not limited to this. Specifically, yield strength data of nickel-based superalloys with different compositions at different temperatures are obtained, and the alloy composition data, temperature, and yield strength data are recorded in detail. For example, the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, and the volume fraction of the γ' strengthening phase at the corresponding temperature can be obtained based on the alloy composition data at different temperatures. For example, the corresponding thermodynamic database and alloy composition data can be imported in sequence through the high-throughput module in the thermodynamic software, and then the volume fraction of each phase and the concentration of each solute atom in each phase at different temperatures are calculated. The calculation results are batch processed using the Result Analysis module in the thermodynamic software to obtain the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, and the volume fraction of the γ' strengthening phase at the corresponding temperature. Based on the above-obtained temperature, the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, the volume fraction of the γ' strengthening phase, and the yield strength data, the coefficients are fitted by combining the global optimization algorithm and the local optimization algorithm. Specifically, for example, each optimization can first be performed using the differential evolution algorithm, and then further optimized using the L-BFGS-B algorithm, and the best result is selected in multiple runs. In this way, better results can be provided. The yield strength model formula of the nickel-based high-temperature alloy finally obtained is:

[0074]

[0075] Where T is temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

[0076] See also Figure 4 , Figure 4 The following is a comparison chart of the predicted alloy yield strength and the actual alloy yield strength. Figure 4 It is readily apparent that the nickel-based superalloy yield strength modeling method provided in this embodiment can effectively predict the yield strength of nickel-based superalloys at different temperatures. In summary, the present invention can predict the yield strength of nickel-based superalloys from room temperature to 1300K based on the alloy composition, providing valuable support for the compositional design of nickel-based superalloys for additive manufacturing.

[0077] [Second embodiment]

[0078] See also Figure 6A second embodiment of the present invention provides a nickel-based high-temperature alloy yield strength modeling device 10 , which may specifically include: a data acquisition module 100 , a thermodynamic module 200 and a model optimization module 300 .

[0079] The data acquisition module 100 is used to obtain the yield strength data and alloy composition data of nickel-based high-temperature alloys with different compositions at different temperatures;

[0080] Thermodynamic calculation module 200 is used to calculate the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase and the volume fraction of the γ' strengthening phase in the nickel-based high-temperature alloy at different temperatures according to the alloy composition data;

[0081] The model optimization module 300 is used to simplify the γ' precipitation strengthening model formula to obtain a probability-related simplified γ' precipitation strengthening model, establish a yield strength model for the composition design of nickel-based high-temperature alloys based on the grain boundary strengthening contribution, the solid solution strengthening contribution and the simplified γ' precipitation strengthening model, introduce temperature variables and parameters, establish a new yield strength model applicable to the entire temperature range based on the yield strength model formula, and obtain the yield strength model formula of the nickel-based high-temperature alloy.

[0082] The specific working process and technical effects of the various modules in the nickel-based high-temperature alloy yield strength modeling device 10 of this embodiment refer to the description of the first embodiment above.

[0083] [Third embodiment]

[0084] See also Figure 7 A third embodiment of the present invention provides a nickel-based superalloy yield strength modeling system 20. The nickel-based superalloy yield strength modeling system 20 includes a processor 400 and a memory 500 connected to the processor 400. The memory 500 may be, for example, a non-volatile storage cavity, and a computer program 510 is stored in the memory 500. The processor 400 may be, for example, an embedded processor. When the processor 400 executes the computer program 510, the nickel-based superalloy yield strength modeling method described in the first embodiment is performed.

[0085] The specific working process and technical effects of the nickel-based high-temperature alloy yield strength modeling system 20 in this embodiment refer to the description of the first embodiment above.

[0086] [Fourth embodiment]

[0087] See also Figure 8 The fourth embodiment of the present invention provides a storage medium 30 storing a computer program 510 .

[0088] The storage medium 30 is, for example, a non-volatile memory, such as a magnetic medium (e.g., a hard disk, a floppy disk, or a magnetic tape), an optical medium (e.g., a CDROM or a DVD), a magneto-optical medium (e.g., an optical disk), or a hardware device specifically configured to store and execute the computer program 510 (e.g., a read-only memory (ROM), a random access memory (RAM), a flash memory, etc.). The storage medium 30 stores the computer program 510, which can be executed by a processor of the device on which the storage medium 30 resides. The storage medium 30 can be used by one or more processors or processing devices to execute the computer program 510 to implement the nickel-based superalloy yield strength modeling method described in the first embodiment.

[0089] In addition, it can be understood that the aforementioned embodiments are merely exemplary descriptions of the present invention. Under the premise that the technical features do not conflict, the structures do not contradict, and the purpose of the present invention is not violated, the technical solutions of the various embodiments can be arbitrarily combined and used in combination.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A modeling method for predicting the yield strength of a nickel-based high-temperature alloy, characterized in that: include: Step 1: Simplify the γ' precipitation strengthening model formula and establish a probability-related simplified γ' precipitation strengthening model; Step 2: Based on the grain boundary strengthening contribution, the solid solution strengthening contribution and the simplified γ' precipitation strengthening model, a yield strength prediction model for the composition design of nickel-based high-temperature alloys is established; Step 3: Introducing temperature variables and parameters, and establishing a new yield strength prediction model applicable to the entire temperature range based on the yield strength prediction model; Step 4: Obtaining coefficients of the novel yield strength prediction model based on yield strength data in nickel-based superalloy composition design; Step 5: Obtaining a yield strength prediction model formula of the nickel-based high-temperature alloy according to the coefficient and the new yield strength prediction model; The yield strength prediction model formula of the nickel-based high-temperature alloy is: Where T is temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

2. The method for modeling the yield strength prediction model of nickel-based high-temperature alloy according to claim 1, wherein: The step 4 specifically includes: Step 4.1: Obtain yield strength data and alloy composition data of nickel-based superalloys with different compositions at different temperatures; Step 4.2: Calculating the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, and the volume fraction of the γ' strengthening phase in the nickel-based high-temperature alloy at different temperatures based on the alloy composition data; Step 4.3: Obtain the coefficient according to the temperature, the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, the volume fraction of the γ' reinforcement phase and the yield strength data.

3. The modeling method of the nickel-based high-temperature alloy yield strength prediction model according to claim 1, characterized in that: The step 1 specifically includes: The probability and average strength of dislocations passing through the γ' precipitation phase by weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism are introduced to simplify the formula of γ' precipitation strengthening model and obtain a simplified γ' precipitation strengthening model.

4. The modeling method of the nickel-based high-temperature alloy yield strength prediction model according to claim 3, characterized in that: The simplified γ' precipitation strengthening model is: Where M is the Taylor factor, G is the shear model, b is the Burgers vector length, P w 、P s and P o are the probabilities of dislocation passing through the γ' precipitate phase via weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, r w 、r s and r o are the radius of the precipitate phase through which the dislocation passes by the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, APB is the reverse boundary energy, f γ ′ is the volume fraction of γ′ phase.

5. The modeling method of the nickel-based high-temperature alloy yield strength prediction model according to claim 4, characterized in that: The yield strength prediction model formula in step 2 is: Among them, σ0 is the increase in basic yield strength, σ GB The increase in yield strength due to grain boundary strengthening, σ ss The increase in yield strength due to solid solution strengthening, σ γ′ To simplify the γ' precipitation strengthening model, k is the Hall-Petch constant, d is the average grain size, and f is the average grain size. γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, M is the Taylor factor, G is the shear model, b is the Burgers vector length, r w 、r s and r o are the radius of the precipitate phase through which dislocations pass by the weak pair coupling mechanism, strong pair coupling mechanism and Orowan mechanism, respectively, APB is the reverse boundary energy, f γ′ is the volume fraction of γ' phase.

6. The modeling method according to claim 5, characterized in that: The new yield strength prediction model applicable to the full temperature range in step 3 is: Among them, a, o, b, p, c, q, k, g, j and h are the coefficients to be fitted, T is the temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

7. A modeling device for a nickel-based high-temperature alloy yield strength prediction model, characterized in that: include: A data acquisition module is used to obtain yield strength data and alloy composition data of nickel-based high-temperature alloys with different compositions at different temperatures; a thermodynamic calculation module for calculating the volume fraction of the γ matrix phase, the concentration of each solute atom in the γ matrix phase, and the volume fraction of the γ' strengthening phase in the nickel-based high-temperature alloy at different temperatures based on the alloy composition data; a model optimization module for simplifying the γ' precipitation strengthening model formula to obtain a probability-dependent simplified γ' precipitation strengthening model, establishing a yield strength prediction model for the composition design of nickel-based superalloys based on the grain boundary strengthening contribution, the solid solution strengthening contribution, and the simplified γ' precipitation strengthening model, introducing temperature variables and parameters, and establishing a new yield strength prediction model applicable to the entire temperature range based on the yield strength prediction model formula, thereby obtaining the yield strength prediction model formula for the nickel-based superalloy; The yield strength prediction model formula of the nickel-based high-temperature alloy is: Where T is temperature, f γ is the volume fraction of γ phase, β i is the solid solution strengthening coefficient of the i-th atom, x i is the concentration of the i-th atom, f γ′ is the volume fraction of γ' phase.

8. A modeling system for a nickel-based high-temperature alloy yield strength prediction model, characterized in that: include: A processor and a memory connected to the processor, wherein the memory stores a computer program, and when the processor executes the computer program, the modeling method of the nickel-based high-temperature alloy yield strength prediction model according to any one of claims 1 to 6 is executed.

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