CN112877628A - Coordination optimization method and system for low-energy grain boundary density and grain size - Google Patents
Coordination optimization method and system for low-energy grain boundary density and grain size Download PDFInfo
- Publication number
- CN112877628A CN112877628A CN202110043372.2A CN202110043372A CN112877628A CN 112877628 A CN112877628 A CN 112877628A CN 202110043372 A CN202110043372 A CN 202110043372A CN 112877628 A CN112877628 A CN 112877628A
- Authority
- CN
- China
- Prior art keywords
- energy
- low
- grain boundary
- boundary density
- grain size
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Thermal Sciences (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Crystallography & Structural Chemistry (AREA)
- Materials Engineering (AREA)
- Mechanical Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The invention discloses a coordination optimization method and a coordination optimization system for low-energy grain boundary density and grain size, which are used for coordinating and optimizing thermoplastic deformation process parameters by introducing key judgment index energy storage to obtain a structure with uniform refinement and high low-energy grain boundary density; obtaining isothermal thermal compression experimental data of a material to be detected, and establishing a response relation and a model of energy storage and average grain size; establishing a low-energy grain boundary density response relation with energy storage and average grain size as variables, and further establishing a low-energy grain boundary density evolution model; developing a low-energy grain boundary density prediction and analysis system, obtaining a core subprogram of the grain size and the low-energy grain boundary density, coupling the core subprogram into finite element software, and iteratively correcting main process parameters by monitoring the average grain size, the stored energy and the low-energy grain boundary density in real time to realize dynamic coordination optimization between the grain size and the low-energy grain boundary density. The invention can reveal the evolution of low-energy grain boundary density in the thermoplastic deformation process and realize the coordination optimization of the low-energy grain boundary density and the grain size.
Description
Technical Field
The invention belongs to the field of metal plastic forming in material processing engineering.
Background
The nickel-based superalloy is a low-fault-energy high-temperature alloy and a low-energy crystal boundary sigma 3n(n-1, 2,3) low grain boundary content aggravates grain boundary crack propagation, while high content, uniformly refined ∑ 3nThe grain boundary can lead the free grain boundary network to be fully cracked and effectively prevent the generation and the expansion of cracks, thereby obviously improving the fatigue resistance and the creep resistance of the alloy and having important significance for solving the problems of power reduction and even sudden damage of a prime motor caused by low cracking degree of the grain boundary network of the nickel-based superalloy gas valve blank. However, at present, there is no effective method for monitoring the content and distribution of low-energy grain boundaries in real time except for the conventional trial and error method. In order to meet the strategic scale requirements of China, aviation and ship internal combustion engines develop to higher power and more reliable depths, the section size and specification of a gas valve are continuously upgraded, the performance of a nickel-based superalloy material is continuously strengthened, the processing and manufacturing deformation difficulty is improved, the electric upsetting specification is continuously upgraded, the coordination difficulty of parameters, grains, grain boundaries and other structures is continuously increased, and the formed grain morphology defects and mixed grain boundaries become prominent and more difficult-to-solve problems. Thus. The method has the advantages that the content and the distribution of the nickel-based superalloy low-energy grain boundary are monitored in real time in the electric upsetting process, and the formation and the distribution of the low-energy grain boundary can be accurately and efficiently predicted and regulated by coordinately controlling deformation process parameters.
In the relatively low temperature range, the grain size is small. According to the conventional research conclusion, the low-energy grain boundary density is higher when the grain size is small. However, the inventors have found that the density of low-energy grain boundaries is not necessarily high when the grain size is small due to the difference in dislocation density and stored energy under different process parameters during the thermoplastic deformation. Therefore, the coordination optimization of the low-energy grain boundary density regulation and the grain size is needed, and the situation that the increase of the low-energy grain boundary density cannot be completely ensured due to the fact that the small grain size is sought on the surface is avoided.
Disclosure of Invention
Aiming at the technical defects, the invention provides a coordination optimization method of low-energy grain boundary density and grain size, and solves the technical problem of how to realize the coordination optimization of low-energy grain boundary density regulation and grain size by searching out proper thermoplastic deformation parameters in the thermoplastic processing process of a metal material.
In order to solve the technical problems, the invention provides a coordination optimization method of low-energy grain boundary density and grain size, which comprises the following steps:
introducing key evaluation index energy storage to coordinate and optimize thermoplastic deformation process parameters to obtain a structure with uniform refinement and high low-energy grain boundary density; in order to parametrically measure the evolution law of the energy storage and the grain size under different thermoplastic deformation parameters, the response relation between the energy storage and the average grain size is established:
in the formula, EsRepresents the average strain energy of the material during the thermoplastic deformation, i.e. the stored energy; d represents the average grain size; n represents a correlation index of the grain size, and the range is 0.4-0.8; c. C3、c4Are all constants;
acquiring experimental data:
selecting a sample of a material to be tested, completing an isothermal hot compression experiment, and then rapidly quenching to retain a high-temperature microstructure of the sample; and then, performing microstructure characterization on the sample, and observing the microstructure morphology, the grain orientation and the grain boundary distribution characteristics after the thermoplastic deformation by adopting back scattering diffraction (EBSD): obtaining a grain boundary distribution diagram containing low-energy grain boundaries under different thermoplastic deformation parameters and KAM diagrams under different thermoplastic deformation parameters;
calculating the average grain size under different thermoplastic deformation parameters and the low-energy grain boundary density under different thermoplastic deformation parameters according to the grain boundary distribution map; calculating the stored energy under different thermoplastic deformation parameters according to the KAM diagram;
and (3) excavating a response relation, and constructing a mapping map of different thermoplastic deformation parameters and physical quantities:
obtaining energy storage values based on the KAM diagram, establishing contour diagrams of the energy storage values under different thermoplastic deformation parameters, simultaneously superposing the contour diagrams of the average grain sizes under the corresponding thermoplastic deformation parameters, establishing contour line superposition diagrams of the energy storage values and the average grain sizes, and further disclosing the relation between the energy storage values and the average grain sizes under different thermoplastic deformation parameters; obtaining a low-energy grain boundary density value based on the grain boundary distribution diagram, establishing a contour diagram of the low-energy grain boundary density under different thermoplastic deformation parameters, simultaneously superposing a contour diagram of energy storage, establishing a contour diagram of the low-energy grain boundary density and the energy storage, and further disclosing the relation between the low-energy grain boundary density and the energy storage under the same thermoplastic deformation parameters; based on the contour maps of the low-energy grain boundary density under different thermoplastic deformation parameters, superposing the contour map of the average grain size, establishing a contour line superposition map of the low-energy grain boundary density and the average grain size, and further disclosing the relation between the low-energy grain boundary density and the average grain size under different thermoplastic deformation parameters;
establishing a model:
utilizing the average grain size under the different thermoplastic deformation parameters and the stored energy under the different thermoplastic deformation parameters, and fitting according to the response relation between the stored energy and the average grain size to obtain a constant c3And c4So as to obtain a response model of the energy storage and the average grain size of the material to be detected;
establishing a low-energy grain boundary density response relation of a material to be tested in a thermoplastic deformation process by using average grain sizes under different thermoplastic deformation parameters, low-energy grain boundary densities under different thermoplastic deformation parameters and energy storage under different thermoplastic deformation parameters based on a growth accident model theory and on the basis of a classic Pande model, and establishing a low-energy grain boundary density evolution model of the material to be tested by combining the energy storage of the material to be tested and a response model of the average grain size;
repeating the following steps until suitable thermoplastic deformation parameters are found to meet the requirements:
by establishing a thermoplastic deformation finite element model, simulating, analyzing and dynamically acquiring deformation basic parameter field quantities such as a temperature field and a strain rate field generated under the loading of an external complex condition; calculating the grain size according to a dynamic recrystallization model or a grain growth model based on a core subprogram of the coupled grain size and low-energy grain boundary density, calculating the energy storage and low-energy grain boundary density according to the average grain size by using a low-energy grain boundary density evolution model, and obtaining a field distribution diagram including the average grain size, the energy storage and the low-energy grain boundary density in finite element simulation software so as to predict and monitor the low-energy grain boundary density and the average grain size in the thermoplastic deformation process;
observing whether the average grain size and the low-energy grain boundary density meet the requirements or not through a field distribution diagram of the average grain size and the low-energy grain boundary density, if so, continuing to perform thermoplastic deformation simulation on the blank according to the external loading condition under the current thermoplastic deformation parameters; if not, the current basic deformation parameter is compared with the mapping map of the physical quantity under different thermoplastic deformation parameters in real time, the mapping map comprises a contour line superposition map of energy storage and average grain size, a contour line superposition map of low-energy grain boundary density and energy storage, and a contour line superposition map of low-energy grain boundary density and average grain size, the variation trends of the low-energy grain boundary density and the average grain size along with the energy storage are obtained, the energy storage which enables the low-energy grain boundary density and the average grain size to be coordinated and optimized is searched, corresponding thermoplastic deformation parameters are obtained according to the searched energy storage, the optimized thermoplastic deformation parameters are re-modified, finite element simulation is continuously executed, and the dynamic iteration process is continued until the forming is finished.
The invention also provides a coordination optimization system of the low-energy grain boundary density and the grain size, which comprises a low-energy grain boundary density evolution model, a low-energy grain boundary density prediction and analysis system, a finite element model and a parameter regulation and control module;
the low-energy grain boundary density evolution model is used for calculating the low-energy grain boundary density according to the average grain size and the stored energy;
the low-energy grain boundary density prediction and analysis system is used for inputting thermoplastic deformation parameters including temperature, strain rate and strain so as to analyze and calculate the dynamic recrystallization volume fraction, the average grain size, the energy storage and the low-energy grain boundary density;
the low-energy grain boundary density prediction and analysis system is also used for coupling the grain size calculation model with the low-energy grain boundary density evolution model, calculating the grain size according to the combined thermoplastic deformation parameter, calculating the average grain size based on the grain size to calculate the low-energy grain boundary density, and generating a subroutine file of the low-energy grain boundary density and the grain size; the grain size calculation model comprises a dynamic recrystallization model and a grain growth model;
the finite element model is used for carrying out finite element simulation on the thermoplastic forming process, and monitoring the low-energy grain boundary density and the average grain size in the thermoplastic deformation process by implanting the subprogram file into the finite element model; observing whether the average grain size and the low-energy grain boundary density meet the requirements or not through a field distribution diagram of the average grain size and the low-energy grain boundary density, if so, continuing to perform thermoplastic deformation simulation on the blank according to the external loading condition under the current thermoplastic deformation parameters; if not, the current basic deformation parameter is compared with the mapping map of the physical quantity under different thermoplastic deformation parameters in real time, the mapping map comprises a contour line superposition map of energy storage and average grain size, a contour line superposition map of low-energy grain boundary density and energy storage, and a contour line superposition map of low-energy grain boundary density and average grain size, the variation trends of the low-energy grain boundary density and the average grain size along with the energy storage are obtained, the energy storage which enables the low-energy grain boundary density and the average grain size to be coordinated and optimized is searched, corresponding thermoplastic deformation parameters are obtained according to the searched energy storage, the optimized thermoplastic deformation parameters are re-modified, finite element simulation is continuously executed, and the dynamic iteration process is continued until the forming is finished.
The parameter regulation and control module is used for regulating and controlling the thermoplastic forming loading condition to realize the regulation and control of basic deformation parameters, and based on the established mapping maps of different thermoplastic deformation parameters and physical quantities, the parameter intervals of the thermoplastic deformation process with high energy storage, small crystal grains and high low-energy grain boundary density are identified, the coordination optimization of the crystal grain size and the low-energy grain boundary density is realized, and finally, the structure with uniform refinement and high low-energy grain boundary density is obtained.
Compared with the prior art, the invention has the advantages that:
1. the invention breaks through the conclusion that the low-energy grain boundary density is certain high when the grain size is small in the traditional research, and seeks a proper physical quantity to coordinate the process parameters to obtain a structure with uniform refinement and high low-energy grain boundary density so as to improve the performance of the gas valve. For the complex thermoplastic forming process such as electric upsetting forming, the basic parameters of plastic deformation are dynamically changed at any moment due to the change of external loading conditions (such as upsetting force, loading current and the like in the electric upsetting process). Therefore, the dynamic coordination optimization of the process parameters in the complex forming process such as electric upsetting needs to optimize the external loading condition in real time. The complex coordination relationship is that finite element modeling analysis is carried out on the forming process, the grain size and the low-energy grain boundary density and the distribution condition of the low-energy grain boundary density in the electric upsetting process are monitored in real time, the low-energy grain boundary density is accurately and efficiently predicted and regulated by coordinating and controlling forming process parameters, dynamic coordination optimization among the process parameters, the grain size and the low-energy grain boundary density is realized, and finally, a structure with uniform refinement and high low-energy grain boundary density is obtained.
2. The invention coordinates and optimizes the thermoplastic deformation process parameters by introducing key evaluation index energy storage to obtain a uniformly refined and low-energy high-grain-boundary-density tissue; the low-energy grain boundary density evolution model simultaneously considers the influence of energy storage and average grain size on the low-energy grain boundary density, has physical significance, and can well reveal the low-energy grain boundary density evolution in the thermoplastic deformation process.
3. The invention further discloses the relation between the stored energy and the average grain size, the relation between the low-energy grain boundary density and the stored energy and the relation between the low-energy grain boundary density and the average grain size under different thermoplastic deformation parameters through the mapping map of different thermoplastic deformation parameters and physical quantities in the modeling process, thereby providing reference for the low-energy grain boundary density regulation and the coordination optimization with the grain size.
4. The low-energy grain boundary density prediction and analysis system can analyze and calculate the dynamic recrystallization volume fraction, the average grain size, the energy storage and the low-energy grain boundary density according to the input thermoplastic deformation parameters, can also intelligently output a subprogram of the grain size and the low-energy grain boundary density in a one-key mode and is used for finite element simulation in the thermoplastic forming process, dynamically collects the deformation basic parameter field quantity (a temperature field and a strain rate field), the average grain size, the energy storage and the low-energy grain boundary density field quantity distribution generated under the external complex condition loading through simulation analysis, and monitors the grain size and the low-energy grain boundary density in real time; and comparing the current basic deformation parameters with the mapping maps of the physical quantities under different thermoplastic deformation parameters in real time to realize the coordination optimization of the grain size and the low-energy grain boundary density.
Drawings
FIG. 1 shows a graph including Sigma 3 under different deformation parametersnThe grain boundary distribution map of (a);
FIG. 2 is a diagram of KAM under different deformation parameters;
FIG. 3 is a graph of stored energy versus grain size at different deformation temperatures and strain rates;
FIG. 4 is a BLD Σ 3 for different deformation temperatures and strain ratesnA graph of relationship to stored energy;
FIG. 5 is a BLD Σ 3 for different deformation temperatures and strain ratesnThe relationship to the grain size;
FIG. 6 is a regulatory system architecture diagram of Ni80A superalloy low energy grain boundary density;
FIG. 7 is a Ni80A superalloy thermoplastic deformation low energy grain boundary density prediction and analysis system interface;
FIG. 8 is a flow chart of monitoring and controlling low energy grain boundary density during electric upsetting;
FIG. 9 is a flow chart of a calculation of low energy grain boundary density;
FIG. 10 is a graph showing a field distribution of electric upsetting blank garlic bulbs at 1000 seconds;
fig. 11 is a field distribution diagram of the electric upsetting blank garlic bulb at 2000 seconds.
Detailed Description
One), establishing the response relation between the stored energy and the grain size
During the deformation process, the stored energy of the deformed material can be estimated by the dislocation density.
Wherein G is the shear modulus, b is the absolute value of the Berger vector, ρ is the dislocation density, K is the arithmetic mean of 1 and (1- ν), and ν is the Poisson's ratio.
The dislocation density ρ is related to the steady state flow stress σ, as in equation (2), where c1Is a constant.
During steady state thermal deformation of a material, the average grain size is in a power law relationship with the steady state flow stress, as in equation (3).
Wherein n is a correlation index with the grain size, and is in the range of 0.4-0.8; c. C2Is a constant.
Thus, in combination with equations (1), (2) and (3), the formula for storing energy can be expressed as equation (4), where c3And c4Is a constant.
The formula (4) is a general formula of the relationship between the energy storage and the grain size response of the metal material. The present embodiment is illustrated with an electrical upset Ni80A superalloy as an example, but the present invention is not limited to an electrical upset Ni80A superalloy.
II), acquiring experimental data
Isothermal compression experiments were conducted on wrought Ni80A superalloys using sample sizes of
Standard cylindrical sample of (2). The 20 samples are respectively subjected to experiment temperature of 1273K, 1323K, 1323K and 1723K by adopting a Gleeble-3500 thermophysical simulation experiment machine; strain rate of 0.01s-1，0.1s-1，1s-1，10s-1Thermal compression test with a compression amount of 60% (true strain of 0.9). After the isothermal thermo-compression test, a quenching process was rapidly performed to retain the high temperature microstructure of the specimen. And then, performing microstructure characterization on the sample, observing the deformed microstructure morphology, the grain orientation and the grain boundary distribution characteristics by using back scattering diffraction (EBSD), and processing the experimental result by using Channel 5 software. FIG. 1 shows a graph including Sigma 3 under different deformation parametersnDistribution of grain boundaries. Exclusion of Σ 3nThe average grain size of the grain boundary under different deformation parameters is counted by Channel 5 software, the statistical result is shown in table 1, and the low-energy grain boundary density (BLD Σ 3) is counted by an image statistical methodn) The calculation was performed, and the calculation results are shown in table 2.
TABLE 1 average grain size (. mu.m) at different deformation parameters
TABLE 2 BLD Σ 3 under different deformation parametersn(μm-1)
By utilizing EBSD data, the stored energy E can be calculated by the orientation angle distribution obtained through experiments and considering the contribution of crystal boundaries of different orientation angles to stored energys. FIG. 2 is a KAM (kernel average missimulation) diagram under different deformation parameters. The energy storage calculated from fig. 2 is shown in table 3.
TABLE 3 energy storage E under different deformation parameterss(106J/m3)
Third), establishing a model
Fitting the relation between the average grain size and the stored energy at different strain rates by adopting a formula (4), and fitting a constant c at different strain rates by adopting a polynomial function3And c4Fitting is carried out to obtain the response relation between the stored energy and the average grain size as shown in the formula (5).
Based on KAM diagram, energy storage value is obtained, contour diagrams of energy storage under different thermoplastic deformation parameters are established, contour diagrams of average grain size under corresponding thermoplastic deformation parameters are simultaneously superposed, contour line superposition diagrams of energy storage and average grain size are established, and further the relation between energy storage and average grain size under different deformation temperatures and strain rates is disclosed, as shown in FIG. 3.
Obtaining a low energy grain boundary density value (BLD Σ 3) based on a grain boundary profilen) And establishing contour maps of low-energy grain boundary density under different thermoplastic deformation parameters, simultaneously superposing the contour maps of stored energy, establishing a contour map of low-energy grain boundary density and stored energy, and further revealing BLD sigma 3 under different deformation temperatures and strain ratesnThe relationship with stored energy is shown in fig. 4.
Based on the contour maps of the low-energy grain boundary density under different thermoplastic deformation parameters, the contour map of the average grain size is superposed, a contour line superposition map of the low-energy grain boundary density and the average grain size is established, and further the BLD sigma 3 under different deformation temperatures and strain rates is disclosednThe relationship with the average grain size is shown in FIG. 5.
The average grain size under different thermoplastic deformation parameters, the low-energy grain boundary density under different thermoplastic deformation parameters and the energy storage under different thermoplastic deformation parameters, namely the data in tables 1,2 and 3, are utilized to establish a low-energy grain boundary density response relation of the material to be tested in the thermoplastic deformation process by taking the energy storage and the average grain size as variables based on a growth accudent model theory and a classic Pande model, and the low-energy grain boundary density evolution model of the material to be tested is established by combining the energy storage and the average grain size response model of the material to be tested as shown in the formula (6).
Fourthly), development of coordination optimization system of low-energy grain boundary density and grain size
Firstly, a low-energy grain boundary density prediction and analysis system is developed:
aiming at basic parameters of plastic deformation, the low-energy grain boundary density prediction and analysis system can realize temperature, strain rate and strain parameters through input, and realize the analytical calculation of the volume fraction of dynamic recrystallization, the average grain size, energy storage and the low-energy grain boundary density; based on the established mapping maps of different thermoplastic deformation parameters and physical quantities, the process parameters can be further coordinated and optimized, wherein the mapping maps comprise a contour line overlay map of energy storage and average grain size, a contour line overlay map of low-energy grain boundary density and energy storage, and a contour line overlay map of low-energy grain boundary density and average grain size.
Referring to fig. 6, a coordination optimization system for low-energy grain boundary density and grain size includes a low-energy grain boundary density evolution model, a low-energy grain boundary density prediction and analysis system, a finite element model and a parameter regulation module;
the low-energy grain boundary density evolution model is used for calculating the low-energy grain boundary density according to the average grain size and the stored energy;
the low-energy grain boundary density prediction and analysis system is used for inputting thermoplastic deformation parameters including temperature, strain rate and strain so as to analyze and calculate the dynamic recrystallization volume fraction, the average grain size, the energy storage and the low-energy grain boundary density;
the low-energy grain boundary density prediction and analysis system is also used for coupling the grain size calculation model with the low-energy grain boundary density evolution model, calculating the grain size according to the combined thermoplastic deformation parameter, calculating the average grain size based on the grain size to calculate the low-energy grain boundary density, and generating a subroutine file of the low-energy grain boundary density and the grain size; the grain size calculation model comprises a dynamic recrystallization model and a grain growth model;
the finite element model is used for carrying out finite element simulation on the thermoplastic forming process, and monitoring the low-energy grain boundary density and the average grain size in the thermoplastic deformation process by implanting the subprogram file into the finite element model; observing whether the average grain size and the low-energy grain boundary density meet the requirements or not through a field distribution diagram of the average grain size and the low-energy grain boundary density, if so, continuing to perform thermoplastic deformation simulation on the blank according to the external loading condition under the current thermoplastic deformation parameters; if not, the current basic deformation parameter is compared with the mapping map of the physical quantity under different thermoplastic deformation parameters in real time, the mapping map comprises a contour line superposition map of energy storage and average grain size, a contour line superposition map of low-energy grain boundary density and energy storage, and a contour line superposition map of low-energy grain boundary density and average grain size, the variation trends of the low-energy grain boundary density and the average grain size along with the energy storage are obtained, the energy storage which enables the low-energy grain boundary density and the average grain size to be coordinated and optimized is searched, corresponding thermoplastic deformation parameters are obtained according to the searched energy storage, the optimized thermoplastic deformation parameters are re-modified, finite element simulation is continuously executed, and the dynamic iteration process is continued until the forming is finished.
The parameter regulation and control module is used for regulating and controlling the thermoplastic forming loading condition to realize the regulation and control of basic deformation parameters, and based on the established mapping maps of different thermoplastic deformation parameters and physical quantities, the parameter intervals of the thermoplastic deformation process with high energy storage, small crystal grains and high low-energy grain boundary density are identified, the coordination optimization of the crystal grain size and the low-energy grain boundary density is realized, and finally, the structure with uniform refinement and high low-energy grain boundary density is obtained.
The regulation process of the regulation system is shown in fig. 8:
in a relatively complicated forming process such as electric upsetting forming, forging forming and the like, basic parameters of plastic deformation are dynamically changed at any moment in the thermoplastic forming process due to the change of external loading conditions (such as upsetting force, loading current and the like in the electric upsetting process). Therefore, the external loading conditions need to be optimized in real time aiming at the dynamic coordination optimization of the process parameters in the complex forming process. This complex coordination necessitates finite element modeling analysis of the forming process. Based on a low-energy grain boundary density prediction and analysis system, obtaining a core subprogram of the grain size and the low-energy grain boundary density, coupling the core subprogram into finite element software, monitoring the average grain size, the stored energy and the low-energy grain boundary density in the thermoplastic deformation process in real time, and realizing dynamic coordination optimization among process parameters, the grain size and the low-energy grain boundary density.
By establishing a thermoplastic deformation finite element model, simulating, analyzing and dynamically acquiring deformation basic parameter field quantities such as a temperature field and a strain rate field generated under the loading of an external complex condition; calculating the grain size according to a dynamic recrystallization model or a grain growth model based on a core subprogram of the coupled grain size and low-energy grain boundary density, calculating the energy storage and low-energy grain boundary density according to the average grain size by using a low-energy grain boundary density evolution model, and obtaining a field distribution diagram including the average grain size, the energy storage and the low-energy grain boundary density in finite element simulation software so as to predict and monitor the low-energy grain boundary density and the average grain size in the thermoplastic deformation process;
observing whether the average grain size and the low-energy grain boundary density meet the requirements or not through a field distribution diagram of the average grain size and the low-energy grain boundary density, if so, continuing to perform thermoplastic deformation simulation on the blank according to the external loading condition under the current thermoplastic deformation parameters; if not, the current basic deformation parameter is compared with the mapping map of the physical quantity under different thermoplastic deformation parameters in real time, the mapping map comprises a contour line superposition map (figure 3) of energy storage and average grain size, a contour line superposition map (figure 4) of low-energy grain boundary density and energy storage, and a contour line superposition map (figure 5) of low-energy grain boundary density and average grain size, the variation trends of the low-energy grain boundary density and the average grain size along with the energy storage are obtained, the energy storage which can lead the low-energy grain boundary density and the average grain size to be coordinated and optimized is searched, corresponding thermoplastic deformation parameters are obtained according to the searched energy storage, the thermoplastic deformation parameters after external loading conditions are modified to meet the optimization are re-modified, finite element simulation is continuously executed, and the dynamic iteration process is continued until the forming is finished.
The above steps are repeated until suitable thermoplastic deformation parameters are found to meet the requirements.
Fifth), monitoring and regulating method of low-energy grain boundary density and average grain size in Ni80A superalloy electric heading process
In the process of thermoplastic deformation of the Ni80A superalloy, the grain size is a combined effect of dynamic recrystallization refinement and grain growth and coarsening, and the evolution of low-energy grain boundary density is directly determined. Therefore, in order to realize the prediction of the grain size and the low-energy grain boundary density in the thermoplastic deformation process of the Ni80A superalloy, a dynamic recrystallization model, a grain growth model and a low-energy grain boundary density evolution model are compiled to generate a core subprogram, and theoretical guidance and technical reference are provided for tissue regulation and control in the thermoplastic deformation process of the Ni80A superalloy.
Based on a grain size and low-energy grain boundary density evolution model, on a Visual Studio2013 window application program development platform, a C # language is used for carrying out Ni80A superalloy thermoplastic deformation low-energy grain boundary density prediction and analysis system application design. The analysis system mainly comprises a dynamic recrystallization model, a grain growth model and a low-energy grain boundary density evolution model. Aiming at different thermal deformation parameters, the analysis system can realize the prediction of average grain size and low-energy grain boundary density in the process of nickel-base superalloy thermoplastic deformation. The interface of the Ni80A superalloy thermoplastic deformation low energy grain boundary density prediction and analysis system is shown in FIG. 7. The interface can be used for carrying out microstructure evolution models and parameter query in the process of Ni80A superalloy thermoplastic deformation, wherein the microstructure evolution models comprise a recrystallization model, a grain growth model and a low-energy grain boundary density model. And clicking the 'generation of grain size and low-energy grain boundary density subprogram', generating a core subprogram file of the grain size and low-energy grain boundary density evolved by the Ni80A superalloy microstructure, and realizing prediction and analysis of the average grain size and the low-energy grain boundary density in the thermoplastic deformation process.
By the analysis system, a subprogram for predicting and analyzing the grain size and the low-energy grain boundary density can be established intelligently in a one-key mode, and the problem of secondary development based on software such as Fortran is solved.
The dynamic recrystallization model, grain growth model, and low-energy grain boundary density evolution model of the Ni80A superalloy are shown in table 4.
TABLE 4 dynamic recrystallization model, grain growth model, and low-energy grain boundary density evolution model of Ni80A superalloy
Wherein epsiloncIs the critical strain; epsilonpIs the peak strain; epsilon0.5Strain at which the integrated number of recrystallized bodies reaches 50%; q, Q1Is the deformation activation energy under different conditions; xdrxIs the dynamic recrystallization volume fraction; ddrxIs the dynamic recrystallization grain size; d is the grain diameter after growth; BLDΣ3 nIs a low energy grain boundary density; d is the average grain size.
Grain growth model:
in the formula, Q1Is the deformation activation energy under the condition of crystal grain growth; d is the grain diameter after growth; m is1And a4Are all coefficients.
When the dynamic recrystallization volume fraction reached 95%, grains grew, and the flow of calculation of low energy grain boundary density based on grain size is shown in fig. 9. When the integral number of the dynamic recrystallization reaches 95%, calculating the grain size by adopting a grain growth model; and when the integral number of the dynamic recrystallization is less than 95%, calculating the grain size by using a dynamic recrystallization model.
And (3) obtaining a sub-program of grain size and low-energy grain boundary density evolution based on a Ni80A superalloy thermoplastic deformation low-energy grain boundary density prediction and analysis system, wherein the file type is required to be fortran (. f).
And coupling the obtained subprogram into an electric upsetting finite element model to realize the monitoring of the grain size and the low-energy grain boundary density. The material is Ni80A super alloy, the diameter of the blank is 105mm, the length is 3700mm, and the total stroke of the blank is 2420 mm. In order to ensure that the end surface of the garlic bulb does not have the defect of a pit, the end surface of the blank is rounded, and the size of the round angle is R25 mm; the initial force of the electric upsetting is 900KN, and the initial value of the current is 29 KN; the temperature in the electric upsetting process is 1000-1150 ℃.
And then, on the basis of monitoring the grain size and the low-energy grain boundary density, regulating and controlling electric upsetting parameters by monitoring data (temperature, grain size, energy storage and low-energy grain boundary density in the electric upsetting process) in real time to obtain a uniformly refined and high low-energy grain boundary density structure. As can be seen from fig. 3, at relatively low temperatures, the grain size is small, whether the strain rate is high or low; in fig. 5, however, the grain size is small and the low energy grain boundary density must be high in a relatively high strain rate and relatively low temperature region. As can be seen from fig. 4, the energy storage is high and the low-energy grain boundary density is high in a relatively high strain rate and a relatively low temperature region. Therefore, the low-energy grain boundary density can be ensured to be high only by conforming to the characteristics of high energy storage and small grain size when the plastic deformation process parameters are coordinated and optimized, such as marking a DOM interval in FIG. 5. Using the garlic core as a reference point, based on the established relation between the stored energy and the grain size (figure 3), BLD sigma 3nRelation to stored energy (FIG. 4) and BLD Σ 3nThe relationship with the grain size (fig. 5) provides guidance and reference for low-energy grain boundary density control and coordinated optimization with the grain size. For example, at high temperatures and low strain rates, the grain size is large, the energy storage is small, and the low energy grain boundary density is low. As can be seen from fig. 4 and 5, the temperature during the electric upsetting process should be reduced to obtain a structure with small grains, high energy storage and high low-energy grain boundary density, so as to improve the performance of the gas valve.
At 1000 seconds, the temperature, dynamic recrystallization volume fraction, grain size, energy storage, and low energy grain boundary density profile of the electro-upset blank are shown in fig. 10.
At 2000 seconds, the temperature, dynamic recrystallization volume fraction, grain size, energy storage, and low energy grain boundary density profile of the electro-upset blank are shown in fig. 11.
The result shows that the established low-energy grain boundary density evolution model and the low-energy grain boundary density prediction and analysis system can realize the monitoring of the grain size and the low-energy grain boundary density in the electric upsetting process of the Ni80A superalloy. BLD Σ 3 based on established energy storage vs. die size (FIG. 3)nRelation to stored energy (FIG. 4) and BLD Σ 3nAnd the relation with the grain size (figure 5) can further regulate and control the electric upsetting parameters to obtain a structure with uniform refinement and high low-energy grain boundary density so as to improve the performance of the gas valve. By the method and the analysis system, theoretical guidance and reference can be provided for realizing the regulation and control of low-energy grain boundary density and the coordinated optimization of grain size in the process of thermoplastic deformation of the Ni80A superalloy.
Claims (7)
1. A coordinated optimization method of low-energy grain boundary density and grain size is characterized by comprising the following steps:
introducing key evaluation index energy storage to coordinate and optimize thermoplastic deformation process parameters to obtain a structure with uniform refinement and high low-energy grain boundary density; in order to parametrically measure the evolution law of the energy storage and the grain size under different thermoplastic deformation parameters, the response relation between the energy storage and the average grain size is established:
in the formula, EsRepresents the average strain energy of the material during the thermoplastic deformation, i.e. the stored energy; d represents the average grain size; n represents a correlation index of the grain size, and the range is 0.4-0.8; c. C3、c4Are all constants;
acquiring experimental data:
selecting a sample of a material to be tested, completing an isothermal hot compression experiment, and then rapidly quenching to retain a high-temperature microstructure of the sample; and then, performing microstructure characterization on the sample, and observing the microstructure morphology, the grain orientation and the grain boundary distribution characteristics after the thermoplastic deformation by adopting back scattering diffraction (EBSD): obtaining a grain boundary distribution diagram containing low-energy grain boundaries under different thermoplastic deformation parameters and KAM diagrams under different thermoplastic deformation parameters;
calculating the average grain size under different thermoplastic deformation parameters and the low-energy grain boundary density under different thermoplastic deformation parameters according to the grain boundary distribution map; calculating the stored energy under different thermoplastic deformation parameters according to the KAM diagram;
and (3) excavating a response relation, and constructing a mapping map of different thermoplastic deformation parameters and physical quantities:
obtaining energy storage values based on the KAM diagram, establishing contour diagrams of the energy storage values under different thermoplastic deformation parameters, simultaneously superposing the contour diagrams of the average grain sizes under the corresponding thermoplastic deformation parameters, establishing contour line superposition diagrams of the energy storage values and the average grain sizes, and further disclosing the relation between the energy storage values and the average grain sizes under different thermoplastic deformation parameters; obtaining a low-energy grain boundary density value based on the grain boundary distribution diagram, establishing a contour diagram of the low-energy grain boundary density under different thermoplastic deformation parameters, simultaneously superposing a contour diagram of energy storage, establishing a contour diagram of the low-energy grain boundary density and the energy storage, and further disclosing the relation between the low-energy grain boundary density and the energy storage under different thermoplastic deformation parameters; based on the contour maps of the low-energy grain boundary density under different thermoplastic deformation parameters, superposing the contour map of the average grain size, establishing a contour line superposition map of the low-energy grain boundary density and the average grain size, and further disclosing the relation between the low-energy grain boundary density and the average grain size under different thermoplastic deformation parameters;
establishing a model:
utilizing the average grain size under the different thermoplastic deformation parameters and the stored energy under the different thermoplastic deformation parameters, and fitting according to the response relation between the stored energy and the average grain size to obtain a constant c3And c4So as to obtain a response model of the energy storage and the average grain size of the material to be detected;
establishing a low-energy grain boundary density response relation of a material to be tested in a thermoplastic deformation process by using average grain sizes under different thermoplastic deformation parameters, low-energy grain boundary densities under different thermoplastic deformation parameters and energy storage under different thermoplastic deformation parameters based on a growth accident model theory and on the basis of a classic Pande model, and establishing a low-energy grain boundary density evolution model of the material to be tested by combining the energy storage of the material to be tested and a response model of the average grain size;
repeating the following steps until suitable thermoplastic deformation parameters are found to meet the requirements:
by establishing a thermoplastic deformation finite element model, simulating, analyzing and dynamically acquiring deformation basic parameter field quantities such as a temperature field and a strain rate field generated under the loading of an external complex condition; calculating the grain size according to a dynamic recrystallization model or a grain growth model based on a core subprogram of the coupled grain size and low-energy grain boundary density, calculating the energy storage and low-energy grain boundary density according to the average grain size by using a low-energy grain boundary density evolution model, and obtaining a field distribution diagram including the average grain size, the energy storage and the low-energy grain boundary density in finite element simulation software so as to predict and monitor the low-energy grain boundary density and the average grain size in the thermoplastic deformation process;
observing whether the average grain size and the low-energy grain boundary density meet the requirements or not through a field distribution diagram of the average grain size and the low-energy grain boundary density, if so, continuing to perform thermoplastic deformation simulation on the blank according to the external loading condition under the current thermoplastic deformation parameters; if not, the current basic deformation parameter is compared with the mapping map of the physical quantity under different thermoplastic deformation parameters in real time, the mapping map comprises a contour line superposition map of energy storage and average grain size, a contour line superposition map of low-energy grain boundary density and energy storage, and a contour line superposition map of low-energy grain boundary density and average grain size, the variation trends of the low-energy grain boundary density and the average grain size along with the energy storage are obtained, the energy storage which enables the low-energy grain boundary density and the average grain size to be coordinated and optimized is searched, corresponding thermoplastic deformation parameters are obtained according to the searched energy storage, the optimized thermoplastic deformation parameters are re-modified, finite element simulation is continuously executed, and the dynamic iteration process is continued until the forming is finished.
2. The method for the coordinated optimization of low energy grain boundary density and grain size as claimed in claim 1, wherein the grain size is calculated using a grain growth model when the dynamic recrystallization fraction reaches 95%; when the integral number of the dynamic recrystallization is less than 95%, calculating the grain size by adopting a dynamic recrystallization model; carrying out statistical calculation on the average grain size under different thermoplastic deformation parameters by adopting Channel 5 software; and calculating the density of the low-energy grain boundary by adopting an image statistical method.
3. The method of claim 1, wherein the method is used for Ni80A superalloy, and the low energy grain boundary density evolution model is as follows:
in the formula (I), the compound is shown in the specification,
which represents the density of low-energy grain boundaries,
representing strain rate, constant c3And c4Fitting by using a polynomial function.
4. The method for the coordinated optimization of low-energy grain boundary density and grain size as claimed in claim 1, wherein when the low-energy grain boundary density condition is displayed, the temperature distribution condition, the dynamic recrystallization volume fraction distribution condition, the average grain size distribution condition and the energy storage distribution condition are displayed through the corresponding field distribution diagram; the temperature, the grain size, the energy storage and the low-energy grain boundary density in the thermoplastic deformation process are monitored in real time, and the thermoplastic deformation parameters are regulated and controlled to obtain a uniformly refined and high-low energy grain boundary density structure.
5. The method for the coordinated optimization of the low-energy grain boundary density and the grain size according to claim 1, wherein the established mapping maps of different thermoplastic deformation parameters and physical quantities comprise a contour line superposition map of stored energy and average grain size, a contour line superposition map of low-energy grain boundary density and stored energy, and a contour line superposition map of low-energy grain boundary density and average grain size; at relatively low temperatures, whether the strain rate is high or low, the grain size is small, but in the relatively high strain rate and relatively low temperature regime the grain size is small and the low energy grain boundary density is somewhat high; the energy storage is high and the low energy grain boundary density is high in a relatively high strain rate and a relatively low temperature range. The characteristics of high energy storage and small grain size are met when the plastic deformation process parameters are coordinated and optimized, so that the low-energy grain boundary density is high; at high temperatures and low strain rates, if the grain size is large, the energy storage is small, and the low energy grain boundary density is low, the temperature of the thermoplastic deformation is reduced to obtain a structure with small grains, high energy storage, and high low energy grain boundary density.
6. A coordinated optimization system of low-energy grain boundary density and grain size is characterized in that: the system comprises a low-energy grain boundary density evolution model, a low-energy grain boundary density prediction and analysis system, a finite element model and a parameter regulation and control module;
the low-energy grain boundary density evolution model is used for calculating the low-energy grain boundary density according to the average grain size and the stored energy;
the low-energy grain boundary density prediction and analysis system is used for inputting thermoplastic deformation parameters including temperature, strain rate and strain so as to analyze and calculate the dynamic recrystallization volume fraction, the average grain size, the energy storage and the low-energy grain boundary density;
the low-energy grain boundary density prediction and analysis system is also used for coupling the grain size calculation model with the low-energy grain boundary density evolution model, calculating the grain size according to the combined thermoplastic deformation parameter, calculating the average grain size based on the grain size to calculate the low-energy grain boundary density, and generating a subroutine file of the low-energy grain boundary density and the grain size; the grain size calculation model comprises a dynamic recrystallization model and a grain growth model;
the finite element model is used for carrying out finite element simulation on the thermoplastic forming process, and monitoring the low-energy grain boundary density and the average grain size in the thermoplastic deformation process by implanting the subprogram file into the finite element model;
the parameter regulation and control module is used for regulating and controlling the thermoplastic forming loading condition to realize the regulation and control of basic deformation parameters, and based on the established mapping maps of different thermoplastic deformation parameters and physical quantities, the parameter intervals of the thermoplastic deformation process with high energy storage, small crystal grains and high low-energy grain boundary density are identified, the coordination optimization of the crystal grain size and the low-energy grain boundary density is realized, and finally, the structure with uniform refinement and high low-energy grain boundary density is obtained.
7. The system of claim 6, wherein the low energy grain boundary density and grain size are optimized in concert: the dynamic recrystallization model, the grain growth model and the low-energy grain boundary density evolution model are respectively as follows;
dynamic recrystallization model:
in the formula, XdrxIs the dynamic recrystallization volume fraction; epsiloncIs the critical strain; epsilonpIs the peak strain; epsilon0.5Is the strain at which the dynamic recrystallization integral number reaches 50%; q is the deformation activation energy under dynamic recrystallization; ddrxIs the dynamic recrystallization grain size; d0Indicates the initial grain size; r is an Avogastron constant; t is the temperature; a. beta is ad、kdH, n and m are coefficients;
grain growth model:
in the formula, Q1Is the deformation activation energy under the condition of crystal grain growth; d is the grain diameter after growth; m is1And a4Are all coefficients;
the average grain size D is calculated as follows:
the low-energy grain boundary density evolution model comprises the following steps:
in the formula (I), the compound is shown in the specification,
represents a low-energy grain boundary density; esRepresents the average strain energy, namely the stored energy, of the material in the thermoplastic deformation process; d represents the average grain size; n represents a correlation index of the grain size, and the range is 0.4-0.8; c. C3、c4Are all constants;
indicating the strain rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110043372.2A CN112877628B (en) | 2021-01-13 | 2021-01-13 | Coordination optimization method and system for low-energy grain boundary density and grain size |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110043372.2A CN112877628B (en) | 2021-01-13 | 2021-01-13 | Coordination optimization method and system for low-energy grain boundary density and grain size |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112877628A true CN112877628A (en) | 2021-06-01 |
| CN112877628B CN112877628B (en) | 2021-09-21 |
Family
ID=76045545
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110043372.2A Active CN112877628B (en) | 2021-01-13 | 2021-01-13 | Coordination optimization method and system for low-energy grain boundary density and grain size |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112877628B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115198214A (en) * | 2022-05-13 | 2022-10-18 | 重庆大学 | Manufacturing method of marine diesel engine air valve capable of regulating and controlling mixed crystal boundary |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2241358A (en) * | 1989-12-21 | 1991-08-28 | Nat Res Inst Metals | Calculating alloy composition |
| US5584947A (en) * | 1994-08-18 | 1996-12-17 | General Electric Company | Method for forming a nickel-base superalloy having improved resistance to abnormal grain growth |
| CN104928605A (en) * | 2015-07-20 | 2015-09-23 | 中南大学 | Method for predicting nickel base alloy high temperature flow stress and dynamic recrystallization behavior |
| CN108660380A (en) * | 2018-08-03 | 2018-10-16 | 中国科学院金属研究所 | Low energy crystal boundary ratio method in iron nickel base alloy is improved by single step thermomechanical treatment |
| CN110684938A (en) * | 2019-08-28 | 2020-01-14 | 中南大学 | Method for predicting dynamic recrystallization grain size of metal or alloy material under variable-strain-rate working condition |
| CN110964994A (en) * | 2020-01-19 | 2020-04-07 | 中南大学 | Method for making hot working process of nickel-based alloy |
| CN111767665A (en) * | 2020-06-10 | 2020-10-13 | 中国航发北京航空材料研究院 | Cavity design method of die for blank making of high-temperature alloy disc forging |
-
2021
- 2021-01-13 CN CN202110043372.2A patent/CN112877628B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2241358A (en) * | 1989-12-21 | 1991-08-28 | Nat Res Inst Metals | Calculating alloy composition |
| US5584947A (en) * | 1994-08-18 | 1996-12-17 | General Electric Company | Method for forming a nickel-base superalloy having improved resistance to abnormal grain growth |
| CN104928605A (en) * | 2015-07-20 | 2015-09-23 | 中南大学 | Method for predicting nickel base alloy high temperature flow stress and dynamic recrystallization behavior |
| CN108660380A (en) * | 2018-08-03 | 2018-10-16 | 中国科学院金属研究所 | Low energy crystal boundary ratio method in iron nickel base alloy is improved by single step thermomechanical treatment |
| CN110684938A (en) * | 2019-08-28 | 2020-01-14 | 中南大学 | Method for predicting dynamic recrystallization grain size of metal or alloy material under variable-strain-rate working condition |
| CN110964994A (en) * | 2020-01-19 | 2020-04-07 | 中南大学 | Method for making hot working process of nickel-based alloy |
| CN111767665A (en) * | 2020-06-10 | 2020-10-13 | 中国航发北京航空材料研究院 | Cavity design method of die for blank making of high-temperature alloy disc forging |
Non-Patent Citations (1)
| Title |
|---|
| GUO-ZHENG QUAN ET AL.: "Determination of dynamic recrystallization parameter domains of Ni80A superalloy by enhanced processing maps", 《TRANS. NONFERROUS MET. SOC. CHINA》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115198214A (en) * | 2022-05-13 | 2022-10-18 | 重庆大学 | Manufacturing method of marine diesel engine air valve capable of regulating and controlling mixed crystal boundary |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112877628B (en) | 2021-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | Microstructure and damage based constitutive modelling of hot deformation of titanium alloys | |
| Chen et al. | High-temperature deformation mechanisms and physical-based constitutive modeling of ultra-supercritical rotor steel | |
| US20160034614A1 (en) | Materials property predictor for cast aluminum alloys | |
| CN105373683A (en) | Prediction method for microstructure evolution law of 20CrMnTiH steel in thermal deformation process | |
| Cai et al. | Constitutive equation and model validation for 33Cr23Ni8Mn3N heat-resistant steel during hot compression | |
| Tang et al. | Behavior and modeling of microstructure evolution during metadynamic recrystallization of a Ni-based superalloy | |
| CN105631183A (en) | Method for establishing microstructure prediction model of TC6 titanium alloy forging forming | |
| Wu et al. | A unified internal state variable material model for Ti2AlNb-alloy and its applications in hot gas forming | |
| CN112877628B (en) | Coordination optimization method and system for low-energy grain boundary density and grain size | |
| Ji et al. | Damage evolution of 7075 aluminum alloy basing the Gurson Tvergaard Needleman model under high temperature conditions | |
| CN108897960A (en) | A kind of turbo blade thermal mechanical fatigue Probabilistic Life Prediction method based on uncertainty quantization | |
| EP3611672A1 (en) | Material selection and optimization process for component manufacturing | |
| Fu et al. | A modified θ projection model for constant load creep curves-II. Application of creep life prediction | |
| Al-Shammari et al. | FE simulation of multi-stage cold forging process for metal shell of spark plug manufacturing | |
| CN111678823A (en) | Method for measuring microhardness of surface layer of titanium alloy milled | |
| Owolabi et al. | Modeling and simulation of microstructurally small crack formation and growth in notched nickel-base superalloy component | |
| Tianshuai et al. | Detail fatigue rating method based on bimodal weibull distribution for DED Ti-6.5 Al-2Zr-1Mo-1V titanium alloy | |
| CN115015318A (en) | Large-scale member hot forging full-process macro-micro analysis method and platform | |
| McElfresh et al. | Initial grain orientation controls static recrystallization outcomes in cold-worked iron: Insight from coupled crystal plasticity/vertex dynamics modeling | |
| Longuet et al. | Advanced modeling tools for processing and lifing of aeroengine components | |
| Tarasov et al. | Application of artificial neural networks for the replenishment of nickel-based superalloys catalogues | |
| Boeff et al. | On the numerical modeling of nucleation and growth of microstructurally short cracks in polycrystals under cyclic loading | |
| JP6032105B2 (en) | Forging structure prediction method | |
| CN113486553A (en) | Complex equipment reliability analysis method based on Thiessen polygon area division | |
| Stewart et al. | Analytical method to determine the tertiary creep damage constants of the Kachanov-Rabotnov constitutive model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |