CN110684938A - Method for predicting dynamic recrystallization grain size of metal or alloy material under variable-strain-rate working condition - Google Patents
Method for predicting dynamic recrystallization grain size of metal or alloy material under variable-strain-rate working condition Download PDFInfo
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- CN110684938A CN110684938A CN201910798906.5A CN201910798906A CN110684938A CN 110684938 A CN110684938 A CN 110684938A CN 201910798906 A CN201910798906 A CN 201910798906A CN 110684938 A CN110684938 A CN 110684938A
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- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
- C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
- C22F1/10—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of nickel or cobalt or alloys based thereon
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Abstract
The invention discloses a method for predicting the size of a metal or alloy dynamic recrystallization grain under the working condition of an allergic rate. The method comprises the following steps: (1) carrying out a thermal compression simulation experiment on the material to obtain a deformed tissue; (2) obtaining a metallographic picture of the deformation sample by means of a metallographic experiment; (3) counting the size of the dynamic recrystallization grains of the sample; (with what software does not hinder the results!) (4) a traditional dynamic recrystallization grain size prediction model is obtained by regression processing; (5) the traditional crystal grain size prediction model is improved into a new model capable of predicting the dynamic recrystallization crystal grain size of the deformation process of the two-stage allergic rate. The method can accurately predict the size of the dynamic recrystallization grains of the forge piece under the deformation working condition of the variable strain rate, and provides an important technical support for reasonably establishing the hot working process of the nickel-based alloy.
Description
Technical Field
The invention belongs to the technical field of forging, and relates to a method for predicting the size of a dynamic recrystallization grain of a metal or alloy material under the working condition of an allergic rate.
Background
The nickel-based alloy is a typical aging strengthening type alloy, and is widely applied to the preparation of core parts of aerospace engines and gas turbines, such as turbine disks, blades, casings and the like, due to excellent high-temperature mechanical properties, corrosion resistance, fatigue resistance and welding performance. Meanwhile, the method is widely applied to the fields of nuclear industry, energy, electric power and the like. The nickel-based alloy is a high-alloying and low-layer fault energy material, and a metallurgical mechanism of dynamic recrystallization grain nucleation, growth and the like exists in the thermal deformation process, so that the grain structure evolution of the nickel-based alloy is very complex. The evolution of the grain structure influences the high-temperature rheological behavior of the material and also obviously influences the performance of the material structural member. Generally, a material structure possessing a uniform fine grain structure has high strength, plasticity, toughness, fatigue properties, and corrosion resistance. The dynamic recrystallization process is one of the important ways to refine grains during the thermal deformation of materials. The quantitative representation of the evolution law of the dynamic recrystallization grain structure in the material structural part in the thermal deformation process has important significance for optimizing the thermal forming behavior and performance of the material. Therefore, it is necessary to establish a prediction model that can accurately and quantitatively describe the size of the dynamically recrystallized grains of the material.
At present, the existing traditional dynamic recrystallization grain size model can only predict the evolution law of the size of the dynamic recrystallization grain of the material under the working condition of constant temperature and constant strain rate, and the evolution law of the structure of the dynamic recrystallization grain of the material under the working condition of time variation is difficult to predict. Therefore, a method for accurately and quantitatively predicting the dynamic recrystallization grain size evolution law of the material under the working condition of the strain rate is urgently needed.
Disclosure of Invention
The invention aims to provide a method for predicting the size of a dynamic recrystallization grain of a metal or alloy material under the working condition of variable strain rate, which can establish an improved dynamic recrystallization grain size prediction model through a thermal compression simulation experiment, a metallographic experiment and a regression method, so that the dynamic recrystallization grain size prediction model can accurately describe the size of the dynamic recrystallization grain of the metal or alloy material in the thermal deformation process of the variable strain rate, and the problem that the traditional model is difficult to predict the size of the dynamic recrystallization grain of the material under the working condition of variable strain rate is solved.
The scheme for solving the problems is as follows:
step 1: obtaining true stress-true strain experimental data of the sample under different deformation working conditions through a thermal compression simulation experiment, and retaining a microstructure after thermal deformation through water quenching;
step 2: obtaining metallographic pictures of sample grain structures under different deformation working conditions by using a metallographic microscope;
and step 3: using Image-Pro Plus software to statistically analyze the grain structure in the metallographic picture to obtain the dynamic recrystallization grain size of the deformation sample under different deformation working conditions;
and 4, step 4: according to the true stress-true strain experimental data, the material constant in the traditional dynamic recrystallization grain size prediction model is determined by fitting the experimental data through a regression method, and the traditional dynamic recrystallization grain size prediction model is established by:
in the formula: ddrexAnd XdrexRespectively, dynamic recrystallization grain size and fraction, and epsilon is true strain0.5True strain, ε, at 50% for dynamic recrystallizationcA, n true strain at which dynamic recrystallization occurs1、m1、Q、n、a1、l1、Q1、a2、l2、Q2Is a material constant, and R is an ideal gas constant (8.314J. mol)-1·K-1)。
And 5: according to the traditional dynamic recrystallization grain size prediction model established in the step 4, the improved dynamic recrystallization grain size prediction model is established as follows:
in the formula: material constant A, n1、m1、Q、n、a1、l1、Q1、a2、l2、Q2R is the ideal gas constant (8.314J. mol.) as in step 4-1·K-1)。
Drawings
FIG. 1 is a gold phase diagram of an initial grain structure of a GH4169 alloy sample;
fig. 2 obtains a true stress-true strain curve (T1010 ℃) at a constant temperature and constant strain rate through a thermal simulation compression experiment;
FIG. 3 is a gold phase diagram of a grain structure of GH4169 alloy under constant temperature and constant strain rate working condition: (a) t is 980-0.001 s-1；(b) T＝1010℃-0.01s-1；
FIG. 4 shows dynamic recrystallization fraction and grain size under different thermal deformation conditions;
FIG. 5 is a graph comparing the predicted dynamic recrystallization grain size with the experimental results under the variable rate conditions;
Detailed Description
The invention is described in detail below with reference to the drawings and the specific embodiments.
The invention relates to a method for predicting the size of a dynamic recrystallization grain of a metal or alloy material under the working condition of an allergic rate. Details of the practice of the present invention are described below, taking as an example the prediction of the dynamic recrystallization grain size of a typical nickel-based alloy (GH4169 alloy), the method comprising:
step 1: and (3) obtaining true stress-true strain experimental data of the sample under different deformation working conditions by adopting a constant-temperature constant-strain-rate thermal compression simulation experiment, and keeping a microstructure after thermal deformation through water quenching. The specific thermal compression simulation experiment constants are: the deformation temperature is 950-1010 ℃, and the strain rate is 0.001-1 s-1The deformation degree is 30-70%.
Step 2: obtaining metallographic pictures of sample grain structures under different deformation working conditions by using a metallographic microscope; and (3) cutting the thermally deformed sample in the step (1) from the center along the thermal compression direction by a wire cutting device, and mechanically polishing and polishing the section. The polished surface of the sample was then immersed in a polishing solution (2.5g of CuCl)2+50ml CH3CH2OH +50ml HCl) for 2-5 minutes, then washing with alcohol, and then drying; and finally, shooting metallographic pictures of the sample grain structure under different deformation working conditions through a metallographic microscope. A metallographic picture of the center position of a typical thermo-compressed sample is shown in fig. 3.
And step 3: the grain structure in the metallographic picture is statistically analyzed through Image-Pro Plus software, the size of dynamic recrystallization grains is quantitatively determined by using a line cutting method, 5-8 pictures are taken for determination in different areas of the center position of the sample under each thermal deformation condition, then the average value of the sizes of the dynamic recrystallization grains is taken, and the dynamic recrystallization volume fraction and the sizes of the grains of the deformation sample under different deformation conditions are obtained according to the same method.
And 4, step 4: according to the real stress-real strain experimental data and the dynamic recrystallization fraction and the grain size under different thermal deformation conditions (as shown in figure 4) measured by statistics, a least square method is adopted, and the experimental data is fitted through a regression method to determine the material constants A and n in the traditional dynamic recrystallization grain size prediction model1，m1，Q，n，a1，l1，Q1，a2，l2And Q2Substituting the fitted material constant into the conventional dynamic recrystallization grain size prediction model as follows:
in the formula: ddrexAnd XdrexRespectively, dynamic recrystallization grain size and fraction, and epsilon is true strain0.5True strain, ε, at 50% for dynamic recrystallizationcR is an ideal gas constant (8.314J. mol.) for true strain at the time of dynamic recrystallization-1·K-1)。
And 5: according to the traditional dynamic recrystallization grain size prediction model established in the step 4, the improved dynamic recrystallization grain size prediction model is established as follows:
in the formula: material constant A, n1、m1、Q、n、a1、l1、Q1、a2、l2、Q2R is an ideal gas constant (8.314J. mol.) as in step 4-1·K-1)。
To validate the established improved dynamic recrystallization grain size prediction model, a typical two-stage allergy rate hot compression protocol was designed as shown in table 1. Wherein the deformation temperature is 950 deg.C, 980 deg.C and 1010 deg.C, the strain rate is set to two stages, and the strain rate in the first stage is 0.01s-1、0.1s-1And 1s-1The strain rates in the second stage were all 0.001s-1The true strain at which the strain rate transition occurs is 0.36 and the total strain is 1.2, as shown in table 1. The specific thermal compression experimental steps are as follows: the sample was heated to the deformation temperature at a heating rate of 10 ℃/s, and the strain rates in the first stage (0.01 s) were measured-1、0.1s-1And 1s-1) Compression to true strain of 0.36 under operating conditions, followed by a reduction in strain rate to 0.001s-1The hot compression was continued to a total strain of 1.2 and then immediately the hot deformed texture was retained by cold water quenching.
TABLE 1 protocol for thermo-compression experiments with constant temperature allergic rate
FIG. 5 is a comparison graph of experimental values of the sizes of dynamic recrystallization grains at the core position of a thermally deformed sample under different strain rates and predicted values of a model of the sizes of the dynamic recrystallization grains at the strain rate established by the method provided by the invention. The result shows that the dynamic recrystallization grain size model established by the method provided by the invention can accurately predict the grain structure evolution law of the material in the thermal deformation process under the time-varying working condition.
The present invention is described above with reference to the accompanying drawings, but the present invention is not limited to the above specific embodiments, and the above specific embodiments are only illustrative and not restrictive, and any invention not exceeding the claims of the present invention is within the protection of the present invention.
Claims (1)
1. A method for predicting the size of a dynamic recrystallization grain of a metal or alloy material under an allergic rate condition is characterized by comprising the following steps: the method fully considers the influence of the strain rate process on the grain structure of the nickel-base alloy forging, establishes a prediction model capable of predicting the size of the metal or alloy dynamic recrystallization grain under the condition of the strain rate based on a traditional dynamic recrystallization grain size prediction model in the thermal deformation process of metal or alloy materials, and comprises the following steps:
step 1: obtaining true stress-true strain data of the material under different deformation working conditions through a thermal simulation experiment, and immediately performing water quenching to keep a high-temperature tissue after the thermal simulation experiment is finished;
step 2: obtaining pictures of sample grain structures under different deformation conditions through experimental observation, and calculating the average grain size of dynamic recrystallization;
and step 3: obtaining true stress-true strain experimental data under different working conditions according to the step 1 and obtaining dynamic recrystallization average grain size data according to the step 2, and determining a material constant in a traditional dynamic recrystallization grain size prediction model through a regression method, wherein the traditional dynamic recrystallization grain size prediction model is as follows:
in formula (1): ddrexAnd XdrexRespectively, dynamic recrystallization grain size and fraction, and epsilon is true strain0.5True strain, ε, at 50% for dynamic recrystallizationcA, n true strain at which dynamic recrystallization occurs1、m1、Q、n、a1、l1、Q1、a2、l2And Q2Is a material constant, and R is an ideal gas constant (8.314J. mol)-1·K-1)。
And 4, step 4: an improved dynamic recrystallization grain size prediction model is adopted to predict the dynamic recrystallization grain size of the metal or alloy material under the working condition of the metal or alloy material allergic rate, and the improved dynamic recrystallization grain size prediction model is as follows:
material constant A, n in formula (2)1、m1、Q、n、a1、l1、Q1、a2、l2、Q2In step 3, the material constants of the traditional dynamic recrystallization grain size prediction model obtained by the regression method are consistent, and R is an ideal gas constant (8.314J. mol)-1·K-1)。
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| CN111879627A (en) * | 2020-06-18 | 2020-11-03 | 闽南理工学院 | Method for constructing thermal deformation microstructure and performance prediction model of aluminum alloy |
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