Method for predicting mechanical properties of material at different temperatures based on dynamic strain aging of high-temperature alloy

Abstract

The invention belongs to the technical field of metal material mechanical property prediction, and relates to a method for predicting mechanical properties of a material at different temperatures based on dynamic strain aging of a high-temperature alloy. The method comprises the following steps: obtaining a true stress-strain curve of the high-temperature alloy through a tensile test; fitting original data to establish a true stress-strain curve equation; respectively establishing prediction equations of the strain hardening index n, the internal defect saturation strength sigma y and the second-stage hardening rate theta II with the temperature; obtaining a prediction equation of the mechanical properties of the material at different temperatures based on the dynamic strain aging of the high-temperature alloy through the associated equation; and finishing material mechanical property prediction at the specified temperature by utilizing the prediction equation. The prediction method quantifies the influence of the temperature and the dynamic strain aging effect on the high-temperature alloy strength and plasticity matching for the first time, is suitable for the high-temperature alloy and other metal materials with the DSA phenomenon, and has important significance on establishment of the tensile constitutive relation of the high-temperature alloy.

Description

Technical Field

[0001] This invention belongs to the field of mechanical property prediction technology of metallic materials, and relates to a method for predicting the mechanical properties of materials at different temperatures based on dynamic strain aging of high-temperature alloys. Specifically, it relates to a method for predicting the matching of tensile curves and strength-plasticity of materials at different temperatures based on dynamic strain aging of high-temperature alloys. Background Technology

[0002] High-temperature alloys are a class of key metallic materials that maintain good and stable mechanical and chemical properties under high temperature and complex stress environments. Their superior performance stems from excellent high-temperature strength, outstanding creep and fatigue resistance, and excellent oxidation and corrosion resistance. This makes them irreplaceable core materials in aerospace, energy, and high-end industrial fields.

[0003] In the high-temperature field, the application of high-temperature alloys is particularly noteworthy. Hot-end components such as turbine disks and blades in modern aero-engines and heavy-duty gas turbines operate for extended periods in extreme environments far exceeding the melting point of their parent alloys, enduring enormous centrifugal stress and thermal shock. Their superior high-temperature performance ensures the efficiency and reliability of aerospace engines, greatly propelling the leapfrog development of air transport and space exploration. Meanwhile, in the low-temperature field, such as superconductivity, deep space exploration, and liquid fuel storage, some high-temperature alloys also exhibit good low-temperature strength and toughness, serving as a bridge between extreme high and low temperature conditions and demonstrating their wide applicability as advanced structural materials.

[0004] However, as scientific research and applications deepen, cutting-edge equipment places increasingly stringent demands on the prediction and reliability of high-temperature alloy components under complex operating conditions. Currently, the research and development of high-temperature alloys and component design still heavily rely on time-consuming and costly trial-and-error experiments, which significantly hinders innovation efficiency. Therefore, advancing quantitative prediction research on the properties of high-temperature alloys at different temperatures has significant scientific and engineering implications.

[0005] Tensile properties, as a fundamental indicator of a material's mechanical behavior, play a crucial guiding role in predicting the performance of high-temperature alloys at different service temperatures. Currently, numerous studies have demonstrated the prevalence of dynamic strain aging (DSA) in the high-temperature tensile process of high-temperature alloys, specifically manifested as a sawtooth-shaped flow pattern in the stress-strain curve during the tensile work hardening stage. However, our understanding of the DSA effect is still at the stage of analyzing the microscopic mechanisms underlying its formation. The impact of the DSA effect on tensile properties and the exploration of its microscopic mechanisms remain lacking, making quantitative analysis difficult. High-temperature alloys such as GH4169 not only require stable operation of engines and fuel injectors at high temperatures but also the safe storage of liquid fuel at low temperatures. Therefore, quantitative predictions of tensile properties at different temperatures are of significant reference value for the practical application of high-temperature alloys.

[0006] Therefore, there is an urgent need to develop a method for predicting the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging. Summary of the Invention

[0007] To address the challenge of lacking an effective quantitative relationship between tensile curves and strength-ductility matching in existing high-temperature alloys at different temperatures, this invention aims to provide a method for predicting the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging (DSA), particularly a method for predicting the matching between tensile curves and strength-ductility at different temperatures. This invention combines the exponential hardening relation generally satisfied by face-centered cubic metals during tensile testing with the phenomenon of dynamic strain aging (DSA) during the tensile process of high-temperature alloys, proposing a DSA effect influence coefficient n. DSA This invention quantifies the effect of dynamic strain aging (DSA), a common phenomenon during high-temperature tensile testing, on the value of n in the work-hardening stage. By segmenting the influence of temperature and DSA on the n value, it quantifies the impact of temperature and DSA on the tensile work-hardening stage for the first time. It provides quantitative equations for predicting tensile curves of high-temperature alloys at different temperatures, leading to a predictive method for strength-plasticity matching based on dynamic strain aging of high-temperature alloys. Using this invention, a small number of tensile tests within the service temperature range can yield predicted tensile curves and strength-plasticity matching equations for that range. Only the temperature value needs to be input to predict the tensile curve and strength-plasticity matching results.

[0008] To achieve the above objectives, this invention proposes a method for predicting the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging, comprising the following steps: (1) Obtain the true stress-strain curve of high-temperature alloy materials through tensile testing; (2) Fit the original data of the true stress-strain curves mentioned above, and combine them with the saturation strength σ of the material. s Residual strength σ r With the saturation strength σ of internal defects in the materialy The relationship is established to create a saturation strength σ containing internal defects. y Second stage hardening rate Θ Ⅱ The equation for the true stress-strain curve; (3) The strain hardening exponent n and the saturation strength σ of the internal defect in the true stress-strain curve equation. y Second stage hardening rate Θ Ⅱ Prediction equations between strain hardening exponent n and temperature were established, with the prediction equation between strain hardening exponent n and temperature based on dynamic strain aging of high-temperature alloys. (4) Substitute the three prediction equations from step (3) into the true stress-strain curve equation and the elastoplastic matching equation to obtain the prediction equations for the mechanical properties of materials at different temperatures based on the dynamic strain aging of high-temperature alloys. (5) Use the above prediction equation to predict the mechanical properties of the material at the specified temperature.

[0009] Step (2) is as follows: After fitting the original data of the true stress-strain curves, the equations of the true stress-strain curves at different temperatures are obtained, as well as the σ in the equations of the true stress-strain curves. s σ r The numerical values ​​of the parameters n; Then, based on the saturation strength σ of the material s Residual strength σ r With the saturation strength σ of internal defects in the material y The relationship between the saturation strength σ of the material s Residual strength σ r Substituting these values ​​into the true stress-strain curve equations at different temperatures yields the saturation strength σ containing internal defects. y Second stage hardening rate Θ Ⅱ The equation for the true stress-strain curve.

[0010] After fitting the original data of the true stress-strain curves, the equations of the true stress-strain curves at different temperatures are obtained, as follows: ; Where, σ s σ r ε and n correspond to the saturation strength, residual strength and strain hardening index of the material, respectively, and ε represents the true strain.

[0011] saturation strength σ of the material s Residual strength σ r With the saturation strength σ of internal defects in the material y The relationship between them is as follows: ; ; ; Where, σ y Indicates the saturation intensity of internal defects, η and Θ Ⅱ These represent the non-dislocation contribution ratio coefficient of the material and the second-stage hardening rate, respectively.

[0012] In step (3), the strain hardening exponent n and the internal defect saturation strength σ in the true stress-strain curve equation are... y Second stage hardening rate Θ Ⅱ Establish prediction equations between temperature and temperature, including the following steps: The following prediction equation is established based on the dynamic strain aging of high-temperature alloys and the relationship between strain hardening exponent n and temperature: ; in, ; ; T DSA The temperature at which dynamic strain aging begins; for a given tensile rate, n0, , Both T0 and k1 are constants, and λ1 and T0 are constants. All are constant values; Based on the saturation intensity σ of internal defects y The relationship between temperature and σ is exponential, establishing σ y The prediction equation between temperature and temperature is as follows: ; Based on the second-stage hardening rate Θ Ⅱ The relationship between Θ and temperature is exponential, establishing Θ Ⅱ The prediction equation between temperature and temperature is as follows: ; Where k2, k3, λ2, λ3, and All are constant values.

[0013] In step (4), the prediction equations for the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging are as follows: ; ; ; Where, σ t ε represents the true stress during the tensile process at temperature T. t Represents the true strain during the tensile process at temperature T. When the composition remains constant, η is a constant. Indicates the uniform elongation in plasticity. This refers to the tensile strength in the strength properties.

[0014] When the temperature changes, the material composition remains unchanged, and η is a constant. When η is 1, the prediction equations for the mechanical properties of the material at different temperatures based on the dynamic strain aging of high-temperature alloys are as follows: ; ; ; In step (5), the above prediction equation is used to predict the mechanical properties of the material at a specific temperature, as follows: The temperature T is input into the strain hardening exponent n and the internal defect saturation strength σ in the true stress-strain curve equation. y Second stage hardening rate Θ Ⅱ In the prediction equations established with respect to temperature, the strain hardening exponent n and the internal defect saturation intensity σ are then used. y Second stage hardening rate Θ Ⅱ The data is input into the prediction equation for the mechanical properties of the material, and the true stress-strain curves and strength-plasticity matching results at different temperatures are obtained, thus completing the prediction of the mechanical properties of the material at the specified temperature.

[0015] The tensile test in step (1) is conducted within the service temperature range of the high-temperature alloy material. The temperature is measured on an absolute temperature scale in Kelvin, and the tensile rate is 2 × 10⁻⁶. -2 / s and below.

[0016] The fitting in step (2) takes the original data of the true stress-strain curve after 2% strain.

[0017] The theoretical derivation and technical route of this invention are as follows: Theoretical Derivation: For face-centered cubic metals, the tensile stress-strain constitutive relation (tensile true stress-strain curve) generally satisfies the Voce exponential form, which can be simplified to: (1) Where, σ s σ r and n correspond to the saturation strength, residual strength, and strain hardening exponent of the material, respectively, while ε represents the true strain. Based on this understanding, Zhenjun Zhang's team defined the saturation strength σ of internal defects. y And proposed σ s σ r The microscopic form, namely: (2) (3) (4) Where η and Θ Ⅱ These respectively reflect the proportion of non-dislocation contribution of the material and the hardening rate of the second stage.

[0018] Based on this, the tensile true stress under temperature T -strain The curve can be used with four parameters (Θ). Ⅱ , n, σ y Quantitatively describing η, the specific form can be simplified to: (5) In reality, the non-dislocation contribution ratio η is sensitive to the material composition. When the composition remains constant, η is a constant. Therefore, it is only necessary to determine n and σ. y and Θ Ⅱ By establishing a quantitative predictive relationship with temperature T, we can quantitatively predict the tensile true stress-strain curves of high-temperature alloys under different temperature conditions.

[0019] The work hardening rate curve is obtained from the tensile true stress-strain curve. The intersection of the work hardening rate curve and the tensile true stress-strain curve represents the strength-plasticity matching relationship under true stress-strain. , where ε n With e n These represent the uniform elongation and tensile strength of the material, respectively, and can be simplified to the following forms: (6) (7) Therefore, with the composition remaining unchanged, it is only necessary to determine n and σ. y and Θ Ⅱ By quantitatively predicting the relationship between temperature T and the strength-plasticity matching of high-temperature alloys at different temperatures, we can make a quantitative prediction.

[0020] The design mechanism and beneficial effects of this invention are as follows: 1. This invention addresses the dynamic strain aging (DSA) effect observed during the high-temperature tensile process of high-temperature alloys, analyzing its influence on the tensile work hardening stage and its strengthening mechanism from a microscopic perspective. In fact, the DSA effect promotes improved strength and ductility: from a microscopic perspective, the DSA effect influences dislocation motion through pinning and unpinning of dislocations. On one hand, pinning makes dislocation movement difficult, limiting their planar slip and cross-slip capabilities; on the other hand, unpinning increases the local dislocation movement rate, making it difficult for high-speed dislocations to change their slip direction, thus increasing the difficulty of cross-slip. Both effects limit the range of dislocation movement, reducing the probability of dislocations encountering and annihilating with antidislocations, making them less prone to annihilation. During material tensile processing, changes in work hardening capability are the result of the combined effects of dislocation multiplication and annihilation. When the number of annihilated dislocations decreases, it contributes to the accumulation of dislocations within the material, macroscopically manifesting as an increase in work hardening capability and microscopically as a decrease in the strain hardening exponent n. Therefore, it is necessary to determine the effects of temperature and DSA effect on the value of n in segments.

[0021] 2. This invention proposes an influence coefficient n for the DSA effect. DSA This study quantifies the effect of DSA on the n-value during the work hardening stage and determines the influence of temperature and DSA on the n-value by segmentation. For the first time, it quantifies the influence of temperature and DSA on the work hardening stage and provides a method for quantitatively predicting the tensile curves and strength-plasticity matching of high-temperature alloys at different temperatures. This has important guiding significance for the application of high-temperature alloys at different service temperatures and is of great significance for understanding and establishing the tensile constitutive relationship of high-temperature alloys.

[0022] 3. This invention enables the quantitative prediction of the relationship between the tensile curve and the strength-plasticity of high-temperature alloys through tensile tests at several typical temperatures within the service temperature range. This significantly reduces the experimental time and cost of tensile testing of high-temperature alloys, while also saving energy consumption caused by creating low-temperature and high-temperature experimental environments. It provides a fast, reliable, and environmentally friendly prediction method, which has important practical significance for accelerating the innovative research and development of high-temperature alloys.

[0023] 4. This invention facilitates the establishment of tensile curves and quantitative relationships between strength-plasticity matching and strain and temperature over a wide temperature range through a small number of tensile tests. This significantly reduces the experimental time and cost associated with tensile testing, while also saving energy consumption associated with creating low-temperature and high-temperature experimental environments. It provides a rapid, reliable, and environmentally friendly prediction method, offering important guidance for the application of high-temperature alloys at different service temperatures. This prediction method also quantifies for the first time the influence of temperature and dynamic strain aging (DSA) effect on the strength-plasticity matching of high-temperature alloys. It is applicable to high-temperature alloys and other metallic materials exhibiting DSA phenomena, and is of great significance for establishing the tensile constitutive relations of high-temperature alloys. Attached Figure Description

[0024] Figure 1 Typical temperature tensile stress-strain curves (a) and true stress-strain curves (b) of high-temperature alloy GH4169 within its service temperature range. Figure 2 The material saturation strength σ when η=1 s Residual strength σ r With σ y Relationship curve; Figure 3 The intrinsic hardening index n T The influence coefficient of DSA effect n DSA And the quantitative prediction relationship curve between the coupling n value and temperature T; Figure 4 For σ y Θ Ⅱ The quantitative predictive relationship curve between temperature T; Figure 5 A comparison of the predicted true stress-strain curves and the actual curves of the high-temperature alloy GH4169 at typical temperatures; Figure 6 The quantitative prediction curve for the three-dimensional relationship of uniform elongation-temperature-tensile strength (a) and strength-plasticity matching (b) is shown. Figure 7 Comparison of the predicted true stress-strain curve and the actual curve of high-temperature alloy GH4169 at 300℃; Figure 8 This is a graph showing the error between predicted and actual values ​​of typical temperature tensile strength (UTS) and uniform elongation (UE). Detailed Implementation

[0025] The present invention will be described in more detail below with reference to examples. These examples are merely descriptions of the best embodiments of the present invention and do not limit the scope of the invention in any way.

[0026] This invention provides a method for predicting the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging, comprising the following steps.

[0027] (1) The engineering stress-strain curves at several typical temperatures within the service temperature range of the high-temperature alloy were obtained by tensile testing, and the true stress-strain curves were obtained by the conversion formula between engineering and true stress-strain curves.

[0028] (2) Using Origin software, the original data of the above true stress-strain curves are imported, and after fitting according to formula (1), the equations of tensile true stress-strain curves at different temperatures are obtained, that is, σ is obtained. s , σ rThe actual values ​​of n; further, the three relevant parameters (i.e., strain hardening exponent n, internal defect saturation strength σ) are determined by formulas (2), (3), and (4). y Second stage hardening rate Θ Ⅱ The actual value of η (where η is a constant).

[0029] (1) (2) (3) (4) (3) Establish n, σ y Θ Ⅱ The quantitative relationship between temperature T and intrinsic hardening index n is determined by the coupling effect of temperature and DSA effect, specifically by the intrinsic hardening index n in the low-temperature region without DSA effect. T The actual value of n is equal to n. T Influence coefficient n of DSA effect with high temperature DSA The product of n, respectively establish n T n DSA The quantitative predictive relationship between temperature and the coupled n-value is obtained, leading to a quantitative predictive relationship between temperature T; simultaneously, σ is established. y Θ Ⅱ Quantitative prediction relationship with temperature.

[0030] (4) Let n and σ y Θ Ⅱ The quantitative relationship between temperature T and the material mechanical properties based on dynamic strain aging of high-temperature alloys can be obtained by formulas (5), (6), and (7).

[0031] (5) Input the temperature T to obtain the tensile curve and strength-plasticity matching results at that temperature.

[0032] In step (1), the stretching rate must be the same at all temperatures, and the stretching rate is 2 × 10⁻⁶. -2 / s and below are feasible; at the same time, within the service temperature range, the tensile fracture mechanism does not change significantly and does not exhibit obvious brittle fracture characteristics.

[0033] In step (2), the initial stage quantitatively described by formula (1) needs to start from 2% true strain to eliminate the influence of micro-yield phenomenon on the fitting of the work hardening stage. When the temperature is changed, the material composition does not change, and η is a constant value. Since no new dislocations are introduced into the material, the change in strength is contributed by non-dislocation factors. Therefore, in the example, η=1 is taken.

[0034] In step (3), for cases where DSA occurs during the stretching process, the actual value of n is the coupling effect of temperature and DSA. Therefore, it is necessary to determine the influence of temperature and DSA on the value of n in segments and establish a quantitative relationship between the two effects and temperature T: In the first segment, there is no DSA effect at low temperatures. In this stage, the intrinsic hardening index n, which varies with temperature, can be obtained. T Kocks proposed n T A quantitative relationship exists between temperature and rate, specifically in the form of: (8) in, This represents the actual stretching rate, for a given stretching rate. n0、 Both T0 and T0 are constant values.

[0035] In the second paragraph, as the temperature increases, the DSA effect gradually intensifies from its initial appearance. This invention proposes a DSA effect influence coefficient n. DSA To quantify the effect of DSA on the value of n in the work hardening stage, n DSA The relationship between temperature and temperature is exponential, specifically in the form of: (9) Where, k1, λ1 and All are constant values, and the influence coefficient n of the DSA effect is... DSA It only changes with temperature T.

[0036] Finally, by multiplying the two effects, a quantitative relationship between the coupled n value and temperature can be established.

[0037] Internal defect saturation strength σ y Second stage hardening rate Θ Ⅱ It is not sensitive to the DSA effect, and only changes with temperature T, showing an exponential relationship with temperature, specifically in the form of: (10) (11) Where k2, k3, λ2, λ3, and Both are constants, σ y and Θ Ⅱ It only changes with temperature T.

[0038] In step (4), the predicted temperature T needs to be within the service temperature range, and all tensile prediction curves and strength-plasticity matching conditions reflect the results under the same strain rate. This quantitative prediction method is applicable to high-temperature alloys and other metallic materials exhibiting the DSA phenomenon.

[0039] In step (5), inputting temperature T will yield the tensile curve and strength-plasticity matching result at that temperature: inputting temperature T into the strain hardening exponent n and the internal defect saturation strength σ in the true stress-strain curve equation. y Second stage hardening rate Θ Ⅱ In the prediction equations established with respect to temperature, the strain hardening exponent n and the internal defect saturation intensity σ are then used. y Second stage hardening rate Θ Ⅱ The data is input into the prediction equation for the mechanical properties of the material, and the true stress-strain curves and strength-plasticity matching results at different temperatures are obtained, thus completing the prediction of the mechanical properties of the material at the specified temperature.

[0040] In constructing the quantitative relationship in this method, the temperature is always measured using an absolute temperature scale, with the unit being Kelvin (K).

[0041] Example 1: This example uses a stretching rate of 2×10. -3 Taking the high-temperature alloy GH4169 with a sample gauge length of 25 mm and an actual tensile speed of 3 mm / min as an example, the predicted tensile true stress-strain curve and the matching of strength and plasticity within its service temperature range (-180℃-600℃) are given, and the material mechanical properties at 300℃ are predicted.

[0042] Step 1: Obtain a tensile rate of 2×10⁻⁶ through a tensile test. -3 The engineering stress-strain curves of GH4169 at five temperatures within its service temperature range at / s (3mm / min) are shown below. Figure 1 As shown in (a), the temperatures are -180℃, 25℃, 200℃, 450℃, and 600℃, respectively. The corresponding true stress-strain curves are obtained using the engineering-to-true stress-strain curve conversion formula, as shown below. Figure 1 As shown in (b), the curves in the figure correspond to the sample numbers in Table 1, namely LS-2-30, LS-2-8, LS-2-6, LS-2-16, and LS-2-20.

[0043] Step 2: Import the original data of the above true stress-strain curves using Origin software. Fit the tensile curve after 2% true strain according to formula (1) to obtain the tensile true stress-strain curves at different temperatures, that is, obtain the σ at different temperatures. s , σ r The actual values ​​of n are shown in Table 1. The results in the table are rounded to two decimal places. Three samples were made at each temperature.

[0044] Table 1 σ at different temperatures s , σ r The actual value of n

[0045] When only the temperature is changed, the non-dislocation contribution ratio parameter η=1, and the saturation strength σ of the material is... s Residual strength σ r With σ y The relationship curves are as follows: Figure 2 (a) and Figure 2 As shown in (b), σ is further determined at different temperatures according to formulas (2), (3), and (4). y and Θ Ⅱ The actual values ​​are shown in Table 2, with the results rounded to two decimal places.

[0046] Table 2 σ at different temperatures when η=1 y and Θ Ⅱ actual value

[0047] Step 3: Determine the intrinsic hardening index n through the low-temperature DSA-free section. T Value, actual value of n and n T Dividing the two yields the influence coefficient n of the DSA effect at high temperature. DSA n is established using formulas (8) and (9) respectively. T n DSA The quantitative predictive relationship between temperature and the coupled n-value is further multiplied to obtain the quantitative predictive relationship between temperature T and the coupled n-value. DSA In this embodiment, T represents the temperature at which dynamic strain aging begins (the temperature at which the tensile curve begins to fluctuate). DSA The temperature is 25℃, or 298.15K. The quantitative prediction curve is as follows: Figure 3 As shown, the corresponding expression is as follows: (12) (13) (14) Step 4: Establish σ using experimental values ​​and formulas (10) and (11) respectively. y Θ Ⅱ The quantitative relationship with temperature, and the quantitative prediction curve are as follows: Figure 4 As shown, the corresponding expression is as follows. (15) (16) Step 5: With η=1, formulas (5), (6), and (7) are simplified to obtain the true stress-strain curve and strong plastic coordinates, which have the following forms: (17) (18) (19) By substituting the quantitative relationships between the three parameters and temperature determined by formulas (14), (15), and (16) into formula (17), the quantitative predictive relationship between true stress and strain and temperature at a specific temperature can be obtained, such as... Figure 5 As shown, the predicted true stress-strain curves and actual curves are compared at temperatures of -180℃, 25℃, 200℃, 450℃, and 600℃, showing good agreement. The corresponding expressions are as follows: (20) (twenty one) (twenty two) (twenty three) (twenty four) Similarly, substituting the quantitative relationship between the three parameters determined in (14), (15), and (16) and temperature into formulas (18) and (19), and then converting them to engineering coordinates, we can obtain the quantitative prediction curve of the three-dimensional relationship between uniform elongation, temperature, and tensile strength, as shown in the figure. Figure 6 As shown in (a), its projection onto the UE (uniform elongation)-UTS (tensile strength) plane is the quantitative prediction curve of GH4169 strength-plasticity matching at different temperatures, as follows. Figure 6 As shown in (b).

[0048] Step 6: Prediction of tensile curve at 300℃: The three parameters are determined by formulas (14), (15), and (16). Substituting them into formula (17) yields the true stress-strain prediction curve at 300℃, and the expression for σ(ε) is as follows: (25) Prediction of strong-plasticity matching relationship at 300℃: Substitute the three parameters determined by formulas (14), (15) and (16) into formulas (18) and (19) respectively to obtain the predicted coordinates of strong-plasticity matching at 300℃, which are (0.169, 1171.4).

[0049] To verify the accuracy of the prediction results, a tensile verification experiment was conducted at 300℃. The predicted true stress-strain curve was compared with the actual curve. Figure 7 As shown, the agreement is good. Furthermore, error analysis was performed on the predicted tensile strength (UTS) and uniform elongation (UE) at temperatures of -180℃, 25℃, 200℃, 300℃, 450℃, and 600℃, as shown below. Figure 8As shown, the error between the prediction and the actual result is within 2%, proving the reliability of the prediction method of this invention.

[0050] Through the above six steps, this invention completes the quantitative prediction of the tensile curve and strength-plasticity matching of high-temperature alloy GH4169 at 300℃, and proves the reliability of the prediction through tensile verification experiments.

Claims

1. A method for predicting the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging, characterized in that, Includes the following steps: (1) Obtain the true stress-strain curve of high-temperature alloy materials through tensile testing; (2) Fit the original data of the true stress-strain curves mentioned above, and combine them with the saturation strength σ of the material. s Residual strength σ r With the saturation strength σ of internal defects in the material y The relationship is established to create a saturation strength σ containing internal defects. y Second stage hardening rate Θ Ⅱ The equation for the true stress-strain curve; (3) The strain hardening exponent n and the saturation strength σ of the internal defect in the true stress-strain curve equation. y Second stage hardening rate Θ Ⅱ Prediction equations between strain hardening exponent n and temperature were established, with the prediction equation between strain hardening exponent n and temperature based on dynamic strain aging of high-temperature alloys. (4) Substitute the three prediction equations from step (3) into the true stress-strain curve equation and the elastoplastic matching equation to obtain the prediction equations for the mechanical properties of materials at different temperatures based on the dynamic strain aging of high-temperature alloys. (5) Use the above prediction equation to predict the mechanical properties of the material at the specified temperature.

2. The prediction method according to claim 1, characterized in that, Step (2) involves fitting the original data of the true stress-strain curves described above, combined with the saturation strength σ of the material. s Residual strength σ r With the saturation strength σ of internal defects in the material y The relationship is established to create a saturation strength σ containing internal defects. y Second stage hardening rate Θ Ⅱ The equation for the true stress-strain curve includes the following steps: After fitting the original true stress-strain curve data, the equations for the true stress-strain curves at different temperatures are obtained, along with the σ value in the true stress-strain curve equations. s σ r The numerical values ​​of the parameters n; Then, based on the saturation strength σ of the material s Residual strength σ r With the saturation strength σ of internal defects in the material y The relationship between the saturation strength σ of the material s Residual strength σ r Substituting these values ​​into the true stress-strain curve equations at different temperatures yields the saturation strength σ containing internal defects. y Second stage hardening rate Θ Ⅱ The equation for the true stress-strain curve.

3. The prediction method according to claim 2, characterized in that, After fitting the original data based on the true stress-strain curve, the equations for the true stress-strain curve at different temperatures are obtained, as follows: ； Where, σ s σ r ε and n correspond to the saturation strength, residual strength and strain hardening index of the material, respectively, and ε represents the true strain.

4. The prediction method according to claim 2, characterized in that, The saturation strength σ of the material s Residual strength σ r With the saturation strength σ of internal defects in the material y The relationship between them is as follows: ； ； ； Where, σ y Representing the saturation intensity of internal defects, η and Θ Ⅱ These represent the non-dislocation contribution ratio coefficient of the material and the second-stage hardening rate, respectively.

5. The prediction method according to claim 1, characterized in that: Step (3) involves the strain hardening exponent n and the internal defect saturation strength σ in the true stress-strain curve equation. y Second stage hardening rate Θ Ⅱ Establish prediction equations between temperature and temperature, including the following steps: The following prediction equation is established based on the dynamic strain aging of high-temperature alloys and the relationship between strain hardening exponent n and temperature: ； in, ; ； T DSA The temperature at which dynamic strain aging begins; for a given tensile rate, n0, , Both T0 and k1 are constants, and λ1 and T0 are constants. All are constant values; Based on the saturation intensity σ of internal defects y The relationship between temperature and σ is exponential, establishing σ y The prediction equation between temperature and temperature is as follows: ； Based on the second-stage hardening rate Θ Ⅱ The relationship between Θ and temperature is exponential, establishing Θ Ⅱ The prediction equation between temperature and temperature is as follows: ； Where k2, k3, λ2, λ3, and All are constant values.

6. The prediction method according to claim 1, characterized in that: In step (4), the prediction equations for the mechanical properties of high-temperature alloys at different temperatures based on dynamic strain aging are as follows: ； ； ； Where, σ t ε represents the true stress during the tensile process at temperature T. t Represents the true strain during the tensile process at temperature T. When the composition remains constant, η is a constant. Indicates the uniform elongation in plasticity. This refers to the tensile strength in the strength properties.

7. The prediction method according to claim 6, characterized in that: When the temperature changes, the material composition remains unchanged, and η is a constant. When η is 1, the prediction equations for the mechanical properties of the material at different temperatures based on the dynamic strain aging of high-temperature alloys are as follows: ； ； 。 8. The prediction method according to claim 1, characterized in that: Step (5) involves using the above prediction equation to predict the mechanical properties of the material at a specific temperature, specifically as follows: Input the temperature T into the strain hardening exponent n and the internal defect saturation strength σ in the true stress-strain curve equation. y Second stage hardening rate Θ Ⅱ In the prediction equations established with temperature, the strain hardening exponent n and the internal defect saturation intensity σ are then used. y Second stage hardening rate Θ Ⅱ The data is input into the prediction equation for the mechanical properties of the material, and the true stress-strain curves and strength-plasticity matching results at different temperatures are obtained, thus completing the prediction of the mechanical properties of the material at the specified temperature.

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