Cutting nickel-based high-temperature alloy microstructure evolution prediction method based on dislocation gradient model

Abstract

The invention provides a nickel-based high-temperature alloy cutting microstructure prediction method based on a dislocation gradient model, and aims to realize optimization of process parameters through multi-scale modeling. The method comprises the following steps: firstly, calculating dislocation mobility under different temperature and pressure conditions by utilizing molecular dynamics simulation; and a dislocation generation and annihilation rate model is established in combination with a dislocation dynamics theory, so that a dynamic relationship between cutting strain and dislocation evolution is disclosed. And further through data fitting, a constitutive equation between the dislocation density and the material hardening behavior is constructed, and the evolution law of the dislocation cell structure is described emphatically. On an ABAQUS platform, a two-dimensional cutting model containing a thermal coupling effect is established, a dislocation density constitutive equation is embedded into a user-defined material module, and closed-loop correlation of macroscopic cutting parameters and microstructure evolution is achieved. According to the method, the dislocation density is creatively used as a cross-scale bridge, the understanding of a dynamic recrystallization and dislocation motion coupling mechanism in the cutting process is deepened, a prediction model is provided for technological parameter optimization, and the method has important engineering guiding significance for improving the integrity of the machined surface of the high-temperature alloy part.

Description

Technical Field

[0001] The present invention relates to the technical field of material processing and simulation, and particularly relates to a method for predicting the microstructure evolution of nickel-based superalloys during cutting based on a dislocation gradient model. Background Art

[0002] Nickel-based superalloys are widely used in key fields such as aerospace and energy power due to their excellent high-temperature properties. During the manufacturing process of components, the influence of cutting on the material microstructure is crucial. Unreasonable processing parameters can lead to microstructure defects such as grain coarsening and increased residual stress, reducing the performance and service life of parts. Therefore, accurately predicting the evolution law of the microstructure of nickel-based superalloys during cutting is of great significance for optimizing the processing technology and improving the quality and performance of parts.

[0003] Currently, the commonly used microstructure prediction methods have limitations. Although the experimental observation method is intuitive, it has high costs and a long cycle, and it is difficult to obtain dynamic change information in real time; the traditional numerical simulation method does not accurately depict the microstructure evolution mechanism and cannot effectively correlate the macroscopic stress-strain field with the microstructure changes. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for predicting the microstructure evolution of nickel-based superalloys during cutting based on a dislocation gradient model, so as to solve the deficiencies in the prior art, achieve accurate prediction of the microstructure evolution during cutting, and provide a theoretical basis for process optimization.

[0005] The technical solution of the present invention is as follows: A method for predicting the microstructure evolution of nickel-based superalloys during cutting based on a dislocation gradient model, comprising the following steps: Step 1: Use the molecular dynamics method to simulate the movement behavior of atoms in nickel-based superalloys, and calculate the mobility of dislocations under different temperature and stress conditions, providing basic data for the subsequent microstructure evolution model.

[0006] Step 2: Apply the dislocation dynamics method, combine the strain rate and stress state of the material during cutting, establish a dislocation dislocation gradient model, and calculate the generation rate and annihilation rate of dislocations inside the dislocation cell to describe the change in the number of dislocations during cutting.

[0007] Step 3: Use MATLAB software to combine experimental data and theoretical models, and determine the relationships between variables such as dislocation density and temperature between the inside and the wall of the dislocation cell through a non-linear fitting algorithm, thereby establishing a mathematical model for the evolution of the dislocation cell structure.

[0008] Step 4: Based on the ABAQUS platform, establish a two-dimensional orthogonal cutting model, set cutting parameters, tool geometry, workpiece dimensions, material properties, etc., and consider factors such as contact, friction, and thermal effects during the cutting process to ensure that the model can truly reflect the actual cutting situation.

[0009] Step 5: Embed the constitutive equation based on dislocation density into the cutting model. This constitutive equation fully considers the influence of dislocation density on material flow stress, hardening behavior, and microstructural evolution, and realizes the organic combination of the constitutive equation and the cutting model through user-defined material models.

[0010] Step 6: Use the ABAQUS / Explicit solver to perform numerical simulations on the cutting process, calculate the spatial distribution of dislocation density and grain size in the cutting area, so as to realize the prediction of the microstructural evolution of nickel-based superalloys during cutting.

[0011] The technical effects of the present invention are Under the condition of considering the connection between the macroscopic stress-strain field and the material microstructure, obtain data on the influence of different parameters on the material microstructure evolution under cutting conditions, deepen the understanding of the mechanism of material microstructure evolution during the cutting process, realize the parameter optimization of the cutting process, and provide guidance for improving the surface quality of the cutting process. By using dislocation density as a bridging variable, a mapping relationship from macroscopic cutting parameters to microstructure evolution is established, which can accurately predict the change law of the microstructure of nickel-based superalloys during the cutting process, provide strong technical support for optimizing cutting process parameters, improving processing efficiency and quality, and has significant technical effects and application values. Description of the Drawings Figure 1 It is a schematic flow chart of a method for predicting the microstructure evolution of cutting nickel-based superalloys based on a dislocation gradient model provided in an embodiment of the present invention; Figure 2 It is a model diagram of cutting nickel-based superalloys; Figure 3 It is an atomic model diagram of an edge dislocation inserted into the material and under the action of a shear force; Figure 4 It is a dislocation dynamics model diagram of the material; Figure 5 It is a diagram of the evolution of the micro-mechanical behavior of the material under high strain rate conditions; Figure 6 It is a dislocation density distribution diagram of the material during the cutting process; Figure 7 It is a grain size distribution diagram of the material during the cutting process; Figure 8 It is an electron microscope image of the grain refinement on the surface of the material after cutting; Figure 9It is an electron microscopy image of the dislocation tangles formed in the material under high strain rate conditions. Specific implementation method First, a molecular dynamics atomic model of nickel-based superalloy is established. According to the face-centered cubic structure of the target alloy, a cell system containing a sufficient number of atoms is constructed. The selection of the interatomic interaction potential function directly affects the simulation accuracy. The embedded atom potential (EAM) that has been experimentally verified is selected. This potential function can accurately describe the bonding and energy states between alloy elements through the embedded atomic electron density distribution.

[0013] During the simulation process, the evolution of dislocation lines is monitored in real time (such as Figure 2 ), and the dislocation extraction algorithm (DXA) is used to identify the dislocation types and movement trajectories. The dislocation slip velocity is inverted through the atomic displacement vector field, and the velocity-stress response curves under different stress levels are recorded.

[0014] Dislocation mobility Calculated through the velocity-stress relationship: In the formula, v is the average dislocation slip velocity, and is the local shear stress. Nonlinear fitting is performed on multiple groups of temperature / stress condition data to establish an empirical equation for the mobility and temperature.

[0015] Based on the ParaDis discrete dislocation dynamics platform, a three-dimensional dislocation evolution model of nickel-based superalloy is established. The initial dislocation network adopts the gradient dislocation model, and the mobility coefficient is obtained by calibration through molecular dynamics simulation, such as Figure 3 .

[0016] During the dynamic loading process, a uniaxial compressive load with a strain rate is applied along the

[001] crystal direction, and the self-consistent calculation of the long-range stress field is realized through the mixed boundary conditions (periodic in the X / Y directions and free in the Z direction). Generation rate calculation: Based on the dislocation source activation mechanism and dislocation multiplication law in the dislocation dynamics model, combined with the data of the change of dislocation density with time during the simulation process, the generation rate of dislocations is calculated, such as Figure 4 . The following formula can be used to calculate the dislocation generation rate: Among them, is the dislocation generation rate, is the dislocation density at time t0, is the dislocation density at time t1, and t is the time.

[0017] Parameter , , and are control parameters that can adjust the evolution rate of the dislocation density in the material, thereby affecting the activation and operation of Frank-Read dislocation sources. Specifically, regulates the transfer process between the inside of the dislocation cell and the dislocation cell wall, while and control the annihilation process of mobile dislocations. Here, represents the Burgers vector length, represents the grain size, represents the volume fraction of the dislocation cell wall, is the reference shear strain rate, usually set to

[0018] Considering the proliferation and annihilation of immobile dislocations, the increase in the density of immobile dislocations caused by the capture of mobile dislocations can be expressed mathematically as: The increase rate of the dislocation density in the cell wall caused by dislocation dipoles is: The following cross-slip annihilation equation is obtained: . represents the Burgers vector length, represents the grain size, represents the volume fraction inside the cell, represents the volume fraction of the cell wall, represents the average free path of dislocation motion inside the cell, represents the average free path of dislocation motion in the cell wall, represents the Debye frequency, represents the shear strain rate, represents the immobile dislocation proliferation rate inside the cell, represents the immobile dislocation proliferation rate in the cell wall, represents the total dislocation density, represents the dislocation density inside the cell, represents the dislocation density inside the cell, represents the dislocation proliferation rate caused by immobile dislocation dipoles in the cell wall, represents the immobile dislocation annihilation rate inside the cell, represents the immobile dislocation annihilation rate in the cell wall, represents the elastic shear modulus, represents the Poisson's ratio, represents the immobile dislocation annihilation probability inside the cell, represents the immobile dislocation annihilation probability in the cell wall, represents the fitting material constant.

[0019] The grain size, the volume fraction of the dislocation cell wall, and the total dislocation density can be obtained through the following formula:

[0020] The parameters of the dislocation density and flow stress relationship model are fitted using the non - linear least - squares method. By minimizing the error between the experimental data and the model prediction values, the parameters in the model are determined.

[0021] A two - dimensional orthogonal cutting model is established in ABAQUS. The workpiece mesh element type is CPE4RT (four - node temperature - displacement coupled reduced - integration element) to ensure the mesh accuracy and calculation efficiency. The tool rake angle is set to 3°, the clearance angle is set to 6°, and the corner radius is set to 0.04 mm. The tool mesh element type is CPE3T (three - node temperature - displacement coupled linear element) to simplify the tool mesh generation process. The contact between the tool and the workpiece is set as node - to - node contact, and the friction coefficient is set to 0.3. The contact setting is realized through the contact pair function of ABAQUS to ensure that the interaction forces between the tool and the workpiece can be accurately transmitted as Figure 5 . The bottom of the workpiece is fixed, and the tool moves along the cutting direction at the set cutting speed. The cutting speed is set to 100 m / min, and the feed rate is set to .1 mm / r. In this paper, the initial values of dislocations in the element and dislocations in the dislocation wall are set to 1e7 and 1e7 mm⁻² respectively.

[0022] In ABAQUS, the above - mentioned constitutive equation based on dislocation density is embedded into the cutting model by writing a user - subroutine. The vusdfld subroutine is written in Fortran language to define the stress - strain relationship, hardening law, and microstructure evolution equation of the material.

[0023] The written vusdfld subroutine is compiled and linked to ABAQUS. Through simple mechanical simulation tests such as tension or compression, the correctness and stability of the custom constitutive equation are verified. By comparing the simulation results with the theoretical expectations or experimental data, check whether the stress - strain curve, dislocation density change, etc. meet the expectations. If there are deviations, the equations and parameters in the vusdfld subroutine need to be debugged and corrected until satisfactory results are obtained.

[0024] According to the actual cutting situation, reasonable boundary conditions and loads are applied. And parameters such as analysis time, time step size, output variables, etc. are set. Since the cutting process involves large deformations and high - rate effects, the time step size needs to be reasonably set to ensure the stability and accuracy of the calculation.

[0025] Start the ABAQUS / Explicit solver to begin the numerical simulation of the cutting process. During the calculation, the solver will gradually solve the mechanical response and microstructure evolution in the cutting process according to the set model, material properties, constitutive equations, boundary conditions, and loads. Since the cutting simulation has a large amount of calculation and may take a long time to complete, the calculation tasks can be reasonably arranged according to the computing resources.

[0026] After the calculation is completed, use the post-processing module of ABAQUS or other data processing tools to extract the spatial distribution data of the dislocation density and grain size during the cutting process. By visualizing methods such as drawing contour maps and nephograms, intuitively display the variation laws of the dislocation density and grain size in the cutting area. At the same time, other relevant results can be further analyzed, such as cutting force, cutting temperature, stress-strain distribution, etc., to comprehensively understand the mechanical and physical behaviors of the cutting process Figures 6 - 9 .

Claims

1. When calculating the generation rate and annihilation rate of dislocations inside dislocation cells by the dislocation dynamics method, it is characterized in that: A model based on dislocation gradient is used, which describes the distribution and evolution of dislocations through the dislocation density gradient. Its formula: where is the dislocation distribution coefficient, is the basal dislocation, is the dislocation density of the layer where it is located, is the average distance of the layer where it is located.

2. Embed the constitutive equation based on dislocation density into the cutting model, characterized in that: The multiplication and annihilation of immobile dislocations are considered. The increase in the density of immobile dislocations caused by the capture of mobile dislocations can be mathematically expressed as: The rate of increase in the dislocation density in the cell wall caused by dislocation dipoles is: The following cross-slip annihilation equation is obtained: . represents the length of the Burgers vector, represents the grain size, represents the internal volume fraction of the cell, represents the volume fraction of the cell wall, represents the average mean free path of dislocation motion inside the cell, represents the average mean free path of dislocation motion in the cell wall, represents the Debye frequency, represents the shear strain rate, represents the immobile dislocation multiplication rate inside the cell, represents the immobile dislocation multiplication rate in the cell wall, represents the total dislocation density, represents the dislocation density inside the cell, represents the dislocation density inside the cell, represents the dislocation multiplication rate caused by immobile dislocation dipoles in the cell wall, represents the immobile dislocation annihilation rate inside the cell, represents the immobile dislocation annihilation rate in the cell wall, represents the elastic shear modulus, represents the Poisson's ratio, represents the immobile dislocation annihilation probability inside the cell, represents the immobile dislocation annihilation probability in the cell wall, represents the fitting material constant.

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