CN110728091A - Method and system for predicting grain size based on finite element method of user subprogram - Google Patents
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Abstract
The invention discloses a method and a system for predicting grain size by a finite element method based on a user subprogram, wherein a two-dimensional milling finite element simulation model is established by finite element software; constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software; and performing transient milling simulation by using a two-dimensional milling finite element simulation model to obtain parameters of temperature, stress, strain and strain rate, calling the material model in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milling surface layer material in the cutting process. The invention can carry out real-time prediction and dynamic simulation on the grain size distribution state of the cutting surface layer in the cutting process by developing the user-defined subprogram.
Description
Technical Field
The invention belongs to the technical field of hard state milling, and particularly relates to a method and a system for predicting grain size based on a finite element method of a user subprogram.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Hard cutting generally refers to a process of cutting a material having a work hardness exceeding 45HRC hardness with a superhard material tool. The hard cutting has higher processing flexibility and processing efficiency, less environmental pollution and low energy consumption, can obtain the processing surface quality equivalent to or even exceeding the grinding, and is known as one of the advanced manufacturing technologies with the best application prospect.
Despite the outstanding advantages of hard cutting technology, excessive cutting temperature and severe tool wear remain major challenges for hard cutting. The microstructure of the material of the cutting surface layer is easy to evolve under the combined action of mechanical-thermal load, so that the quality and the mechanical property of the processed surface are reduced. Therefore, real-time prediction and dynamic simulation of the evolution of the microstructure of the cutting surface layer material in the hard cutting process are realized, and the method has great significance for controlling and improving the surface integrity to achieve the purpose of prolonging the service life of the material.
The inventor finds in research that at present, since a material model is generally only suitable for one or a few similar materials, a general finite element simulation software cannot provide a general material microstructure evolution model.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for predicting the grain size by a finite element method based on a user subprogram, which carries out real-time prediction and dynamic simulation on the grain size evolution of the surface layer of hard milling hot work die steel by milling finite element simulation modeling and developing a user self-defined subprogram based on a dislocation density material model, evaluates the influence of cutting parameters on the microstructure of the cutting surface layer, optimizes the cutting parameters and further improves the service performance and service life of a processed part.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
the method for predicting the grain size based on the finite element method of the user subprogram comprises the following steps:
establishing a two-dimensional milling finite element simulation model through finite element software;
constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
and performing transient milling simulation by using a two-dimensional milling finite element simulation model to obtain parameters of temperature, stress, strain and strain rate, calling the material model in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milling surface layer material in the cutting process.
According to the further technical scheme, initial parameters of a dislocation density model equation of the hot die steel are determined;
inputting initial data into a finite element model, calling a file by using a transient cutting finite element simulation method, acquiring a temperature field, a stress field, a strain field and a strain rate at any moment in the milling process, and providing for calling and calculating a self-defined subprogram and outputting total dislocation density and grain size;
and (3) carrying out model parameter calibration by a trial-and-error method, determining the material constant of the hot die steel, and establishing a grain size evolution material model of the hot die steel based on dislocation density.
According to the further technical scheme, in the two-dimensional milling finite element simulation model, the feeding amount is far smaller than the cutting depth, and the milling trajectory trochoid is simplified into a section of circular arc.
According to the further technical scheme, in the two-dimensional milling finite element simulation model, material parameters, cutter parameters and contact parameters are input, boundary conditions are set, a grid unit is of a temperature-displacement coupling type, and a Lagrange algorithm explicit dynamics algorithm is used.
According to a further technical scheme, when the model parameters are calibrated, the grain size experimental value of the milling surface layer material is obtained through a TEM material characterization means through comparative analysis.
A system for predicting grain size based on a finite element method of a user subprogram, comprising:
a finite element simulation model establishing module: establishing a two-dimensional milling finite element simulation model through finite element software;
the user self-defined subprogram establishing module: constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
a real-time simulation module: and (3) performing transient milling simulation by using finite element software to obtain parameters of temperature, stress, strain and strain rate, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
The above one or more technical solutions have the following beneficial effects:
1. the invention solves the problem that the evolution distribution state of the output microstructure can not be calculated by general finite element cutting simulation. By developing a user-defined subprogram, the grain size distribution state of the cutting surface layer in the cutting process can be predicted in real time and dynamically simulated.
2. The invention is applicable to different cutting parameters and tool geometry ranges. The invention can be suitable for the change of parameters such as different cutting speeds, feed quantity, radial cutting depth, front angles, back angles and the like of the cutter.
3. The method for predicting the grain size evolution has good flexibility. By replacing the material model in the user-defined subroutine VUSFLD file, the cutting surface layer microstructure evolution prediction of other materials can be realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the cutting simulation based on dislocation density material model according to the present invention;
FIGS. 2(a) -2 (f) are milling surface layer dislocation density and grain size distribution clouds of an embodiment of the invention at cutting speeds of 200m/min, 400m/min, feed rate of 0.2mm/tooth, and radial depth of cut of 2.0mm at different cutting speeds (SDV15 for dislocation density, SDV22 for grain size, the same applies below);
in the figure, the 200m/min milling surface layer dislocation density simulation result of figure 2 (a);
FIG. 2(b) the simulation result of the grain size of the 200m/min milling surface layer;
FIG. 2(c) distribution of 200m/min grain size along the depth direction of the milling surface;
FIG. 2(d) simulation results of dislocation density of 400m/min milled surface layer;
FIG. 2(e) the grain size simulation result of 400m/min milling surface layer;
FIG. 2(f) distribution of 400m/min grain size along the depth direction of the milling surface;
FIGS. 3(a) to 3(f) are the dislocation density and grain size distribution cloud charts of the milling surface layer under different feeding amounts of the embodiment of the invention with the feeding amount of 0.1mm/tooth, the feeding amount of 0.3mm/tooth, the cutting speed of 300m/min and the radial cutting depth of 2.0 mm;
in the figure, FIG. 3(a) results of simulation of dislocation density of a 0.1mm/tooth milling surface layer;
FIG. 3(b)0.1mm/tooth milling surface layer grain size simulation results;
FIG. 3(c) distribution of 0.1mm/tooth grain size along the depth direction of the milling surface;
FIG. 3(d) results of a 0.3mm/tooth milling surface layer dislocation density simulation;
FIG. 3(e)0.3mm/tooth milling surface layer grain size simulation results;
FIG. 3(f) distribution of 0.3mm/tooth grain size along the depth direction of the milling surface;
FIGS. 4(a) to 4(f) are graphs of dislocation density and grain size distribution clouds of the milled surface layer under the conditions of radial cutting depth of 1.0mm and 3.0mm, cutting speed of 300m/min and feeding amount of 0.2mm/tooth according to the embodiment of the invention under different radial cutting depths;
in the figure, FIG. 4(a) simulation results of dislocation density of a 1.0mm milled surface layer;
FIG. 4(b) simulation results of grain size of 1.0mm milled surface layer;
FIG. 4(c) distribution of 1.0mm grain size along the depth direction of the milling surface;
FIG. 4(d)3.0mm milled surface layer dislocation density simulation results;
FIG. 4(e) simulation results of grain size of 3.0mm milled surface layer;
FIG. 4(f) distribution of 3.0mm grain size along the depth direction of the milling surface;
FIGS. 5(a) -5 (d) are experimental measurements of grain sizes of the milled surface layer of the original substrate and three cutting conditions according to an embodiment of the present invention;
in the figure, FIG. 5(a) is a raw substrate; FIG. 5(b) vc＝200m/min，fz＝0.2mm/tooth， ae2.0 mm; FIG. 5(c) vc＝300m/min，fz＝0.3mm/tooth，ae2.0 mm; FIG. 5(d) vc＝300m/min，fz＝0.2mm/tooth，ae＝3.0mm。
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and further it is to be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the stated features, steps, operations, devices, components, and/or combinations thereof.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
The general idea provided by the invention is as follows:
firstly, establishing a two-dimensional milling finite element simulation model through finite element software Abaqus/Explicit; constructing a dislocation density-based grain size evolution prediction material model, and assembling the dislocation density-based material model into a user-defined subroutine VUSFLD (for format) file which can be identified and called by finite element software in a Visual Studio environment by means of Fortran programming language; transient milling simulation is carried out by using finite element software Abaqus/Explicit to obtain parameters such as temperature, stress, strain rate and the like, a material model in a user-defined subprogram is called to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, and finally the size of an updated crystal grain is calculated; the accuracy and the applicability of the material model are verified by analyzing the grain sizes of the surface layer under different milling parameters and comparing experimental measurement data. The method is time-saving, labor-saving and cost-saving, provides reference evidence and visual data support for the microstructure evolution of the hard milling surface layer material, and is beneficial to regulating and controlling the processing quality and realizing the anti-fatigue manufacture of parts.
Example one
The embodiment discloses a method for predicting grain size based on a finite element method of a user subprogram, and as shown in fig. 1, a prediction model is used for calculating an equation in a material model, calculating process intermediate quantities, finally calculating grain size, outputting dislocation density and grain size, and predicting the influence of different parameters on the two.
The method for predicting the grain size based on the finite element method of the user subprogram comprises the following steps:
analysis of the end milling Process, feed fzIs usually much smaller than the cutting depth aeGenerally, the cutting depth is more than 5 times of the cutting depth, so that the method can be simplified without causing great influence on the precision, and the milling trajectory trochoid can be simplified into a section of circular arc, thereby facilitating the construction of a geometric model. Establishing a simplified two-dimensional milling workpiece and tool geometric model by using the CAE function of an Abaqus/Explicit software;
developing a user-defined subroutine VUSFLD (for format) file based on dislocation density material models (in formulas (2) to (11)) by using Fortran language, and determining initial parameters of an AISI H13 hot-work die steel dislocation density model equation through experimental tests and reference documents, wherein the initial parameters are shown in a table 4;
inputting initial data into an Abaqus/Explicit two-dimensional milling finite element simulation model, inputting material parameters, cutter parameters and contact parameters, setting boundary conditions such as Displacement constraint, initial Temperature field and speed field, selecting a Dynamic, Temperature-Displacement analysis step, and using a grid unit in a Temperature-Displacement coupling type for the milling process of a mechanical-thermal coupling process. And (3) using a transient cutting finite element simulation method, calling a for file, acquiring a temperature field, a stress field, a strain rate and the like at any time t in the milling process, performing iterative calculation of an equation in a calling material model of a self-defined subroutine VUSFLD, and outputting the total dislocation density and the grain size.
Comparing the experimental test results, and performing model parameter calibration by a trial-and-error method to enable the relative errors of the simulated chip morphology, the cutting force, the temperature field and the like and the experimental results to be within an allowable range, so as to determine the material constant of the AISI H13 steel. And obtaining a grain size experimental value of the milled surface layer material by a TEM material characterization means, comparing the grain size experimental value with a grain size simulation result, and verifying the reliability of the grain size prediction by a finite element method based on the dislocation density material model.
The method specifically comprises the following steps:
step 1: a workpiece material is selected as AISI H13 hot work die steel, and a determined Johnson-Cook constitutive equation is shown as a formula (1). The mechanical and thermophysical parameters are shown in tables 1 and 2, respectively. The cutter material is Ti (C, N) -Al2O3The physical parameters of the coated tungsten carbide cemented carbide, without taking the deformation into account, are given in table 3.
In the formula, σyIs the equivalent flow stress; ε is the equivalent plastic strain;
is the plastic strain rate; t is the instantaneous temperature in units; parameter 1469 is the initial yield strength, in MPa; 321.39 is the strain hardening coefficient, in MPa; 0.278 is the strain hardening index; 0.028 is a strain rate sensitive coefficient(ii) a 1.18 is the coefficient of thermal softening; 1490 is the melting point unit of the workpiece material; and 25 is the reference temperature in degrees celsius.
TABLE 1 thermophysical parameters of AISI H13 Hot work die steels
TABLE 2 thermophysical parameters of AISI H13 Hot work die steels
TABLE 3 physical parameters of the tool Material
Step 2: summary of grain size prediction material models based on dislocation theory.
Theoretical equations based on the dislocation density material model are shown in formulas (2) to (11), and model constants are shown in table 4.
Rate of increase of intracellular dislocation
Statistical dislocation growth rate
Geometric dislocation growth rate
Rate of growth of cell wall dislocations
In the expressions (2) to (5), ξ represents the proportion of geometrically essential dislocations in the cell wall dislocations. Alpha is alpha*、β*、 k0Is a parameter describing the rate of change of dislocations during plastic deformation of a material, d represents the size of the grain size, n is a temperature sensitive parameter, f is the volume fraction of cell wall dislocations, b is the value of the burgers vector,
and
the cell wall shear strain rate and the intracellular shear strain rate are expressed respectively, and the formula (6) is satisfied, so that the strain uniformity of the material is satisfied.
Analytic shear strain rate
In formula (7): m is a Taylor constant,is the equivalent plastic strain rate.
Temperature sensitivity coefficient n:
m＝A/T (8)
volume fraction of cell wall dislocation f:
total dislocation density ρtot：
ρtot＝fρw+(1-f)ρc(10)
Grain size d:
in formulae (8) to (11): t is temperature, f0The proportion of initial wall dislocations to total dislocations, f∞In order to saturate the proportion of cell wall dislocations to total dislocations, gammarIn order to be able to apply a shear strain,
for reference shear strain, K is the material constant.
TABLE 4 material model parameters of AISI H13 die steels based on dislocation theory
And step 3: referring to the reference manual of the Abaqus user subprogram, selecting the VUSDFLD subprogram, compiling the data in the step 2 based on the dislocation density theory equation in a Visual Studio development environment by using a FORTRAN program language, and generating an ([ x ] for format) file which can be identified and called by Abaqus/Explicit finite element software; in the subroutine, SDV15 is defined to indicate the total dislocation density and SDV22 indicates the grain size.
Simulation example 1
Choosing f according to the experimental feed and radial depth of cutz＝0.2mm/tooth，aeWhen the cutting speed is 2.0mm, v is selected respectivelyc200m/min and 400 m-min, the simulation simulates the total dislocation density and grain size of the milled surface at two cutting speeds (see FIG. 2 (a); FIG. 2 (b); FIG. 2 (c); FIG. 2 (d); FIG. 2 (e); FIG. 2(f)), and it can be seen from FIGS. 2(a) and 2(d) that the propagation and concentration of dislocation density is mainly concentrated in three deformation zones: a first deformation zone (shear band), a second deformation zone (chip contact zone) and a third deformation zone (milling surface). As can be seen from FIGS. 2(b) and 2(e), the grain size reduction also occurs mainly in the three deformation regions, and the grain size of the milled surface is reduced from 1.2 μm to 300-400 nm. The reduction in grain size in the depth direction of the milled surface to the substrate shows a tendency to increase in gradient until the original value is reached. FIGS. 2(c) and 2(f) each represent vcThe grain size of the milled surface is about 300nm, and the influence of the cutting speed on the grain size of the milled surface is small. At higher cutting speeds, the grain size tends to grow more slowly than at lower cutting speeds.
Simulation example 2
V is selected according to experimental cutting speed and radial depth of cutc＝300m/min，aeWhen the thickness is 2.0mm, the feed f is selected respectivelyzSimulations simulate the total dislocation density and grain size of the milled surfaces at both feeds (see fig. 3 (a); fig. 3 (b); fig. 3 (c); fig. 3 (d); fig. 3 (e); fig. 3 (f)). Similarly to example 1, it can be seen from fig. 3(a) and 3(d) that the propagation and accumulation of dislocation density are still concentrated in the three deformation regions. It can be seen from fig. 3(b) and 3(e) that the grain size of the milled surface is reduced from the original 1.2 μm to about 500 nm. It can be understood from fig. 3(c) and 3(f) that the grain size growth in the depth direction of the milled surface is relatively slow when the feed is large.
Simulation example 3
V is chosen according to experimental cutting speed and feedc＝300m/min，fzWhen the thickness is 2.0mm, the feed a is selected respectivelyeThe simulation simulates the total dislocation density and grain size of the milled surface at two radial cutting depths (see fig. 4 (a); fig. 4 (b); fig. 4 (c); fig. 4 (d); fig. 4 (e));fig. 4 (f)). Similarly to examples 1 and 2, it can be seen from fig. 4(a) and 4(d) that the proliferation of dislocation density is concentrated in three deformed regions. It can be seen from fig. 4(b) and 4(e) that the grain size of the milled surface is reduced from the original 1.2 μm to about 300 nm. It can be seen in fig. 4(c) and 4(f) that as the radial depth of cut is greater, the gradient of the grain size along the depth of the milled surface is smaller, and the depth of influence is greater.
And 4, step 4: inputting the initial parameters into Abaqus/Explicit software, calling a developed user-defined subroutine VUSDFLD file by using a transient cutting finite element simulation method before submitting files for operation, and outputting the distribution condition of the grain size of a cut surface layer at any time in the milling process. And verifying the accuracy and the applicability of the model by comparing and analyzing the experimental values of the grain size of the surface layer and the simulation results under the conditions of different cutting speeds, different feed amounts and different radial cutting depths.
The grain size experiment of the milling surface layer is verified; in order to verify the finite element milling simulation model which is established by the invention and is based on the dislocation density theory to predict the grain size, the invention carries out the verification by developing the method of observing the grain size of the milled surface layer material by means of a high-resolution transmission electron microscope (HR-TEM). For the preparation of TEM observation samples, the following procedures are adopted in sequence: cutting a milling surface layer with the thickness of about 0.5mm by linear cutting and slow wire feeding → mechanically grinding and polishing to be less than or equal to 100 mu m → thinning a sample to be less than or equal to 50nm by an ion thinning instrument. The original substrate was chosen and three experimental conditions: FIG. 5(a) original sample, FIG. 5(b) vc＝200m/min，fz＝0.2mm/tooth,ae2.0mm, FIG. 5(c) vc＝300m/min，fz＝0.3 mm/tooth,ae2.0mm, FIG. 5(d) vc＝300m/min，fz＝0.2mm/tooth,aeThe grain size of the milled surface obtained by means of the HR-TEM test is shown in fig. 5(a), fig. 5(b), fig. 5(c) and fig. 5d, 3.0 mm. As can be seen from fig. 5(a), the microstructure of the raw H13 steel is lath martensite, with a dimension in the length direction of about micrometer scale and a width direction of about several hundred nanometers. As can be seen from FIGS. 5(a), 5(b) and 5(c), the microstructure of the milled surface material is approximately equiaxed grains with a size of 200-400And nm, compared with the experimental conditions of FIG. 5(b), FIG. 5(c) and FIG. 5(d), the simulation results are respectively about 330nm, 550nm and 320nm, the values are closer, and the consistency is better. Meanwhile, the appearance of high-density dislocation is seen in the HR-TEM experimental test result, which is the same as the increase trend of the total dislocation density of the milling surface simulated by simulation. However, the experimental results and simulation results of fig. 5(b) still have a certain error, the predicted value is about 550nm, and the experimental results are about 300nm, and the error is mainly caused by: (1) the blade is considered to be absolutely sharp in the simulation model, and the radius of the blade is not considered; (2) the simulation process does not consider the problems of cutter abrasion, cutter coating, heat dissipation and the like, and the accuracy of the simulation result is also influenced.
The invention is based on dislocation density theoretical model, finite element simulation and hard state milling experiment method, carries out prediction and experimental research on AISI H13 hot work die steel milling surface layer grain size, and the main contents comprise:
analyzing dislocation density increase and grain size evolution of a cutting surface layer material in a hard milling process based on a dislocation density theoretical model, and constructing a material model of grain size evolution of the cutting surface layer material of the AISI H13 hot work die steel;
compiling the constructed dislocation density material model into a user self-defined subprogram in a Visual Studio environment by utilizing a FORTRAN language, and calling the user self-defined subprogram for Abaqus/Explicit identification;
a mechanical-thermal-microstructure coupled cutting surface layer grain size prediction finite element model is constructed, and the grain size evolution of a milling surface layer material in the cutting process is predicted in real time and simulated dynamically.
The method realizes real-time prediction and dynamic simulation of the microstructure evolution of the cutting surface layer, and develops a specific user self-defined subprogram by means of a user secondary interface reserved by finite element software. Along with the research and application of the hard cutting technology in the aspect of die manufacturing, AISI H13 hot work die steel hard milling surface layer microstructure evolution simulation prediction is developed, and the method has important significance for completing system upgrading from shape control manufacturing to shape synergetic manufacturing.
Example two
A system for predicting grain size based on a finite element method of a user subprogram, comprising:
a finite element simulation model establishing module: establishing a two-dimensional milling finite element simulation model through finite element software;
the user self-defined subprogram establishing module: constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
a real-time simulation module: and (3) performing transient milling simulation by using finite element software to obtain parameters of temperature, stress, strain and strain rate, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
EXAMPLE III
An object of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program, including:
establishing a two-dimensional milling finite element simulation model through finite element software;
constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
and (3) performing transient milling simulation by using finite element software to obtain temperature, stress, strain and strain rate parameters, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
Example four
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, performs the steps of:
establishing a two-dimensional milling finite element simulation model through finite element software;
constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
and (3) performing transient milling simulation by using finite element software to obtain temperature, stress, strain and strain rate parameters, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented using general purpose computing apparatus, or alternatively, they may be implemented using program code executable by computing apparatus, whereby the modules or steps may be stored in a memory device and executed by computing apparatus, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. The method for predicting the grain size based on the finite element method of the user subprogram is characterized by comprising the following steps:
establishing a two-dimensional milling finite element simulation model through finite element software;
constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
and performing transient milling simulation by using a two-dimensional milling finite element simulation model to obtain parameters of temperature, stress, strain and strain rate, calling the material model in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milling surface layer material in the cutting process.
2. The finite element method for predicting grain size based on user subprogram as claimed in claim 1, wherein, determining the initial parameters of hot working die steel dislocation density model equation;
inputting initial data into a finite element model, calling a file by using a transient cutting finite element simulation method, acquiring a temperature field, a stress field, a strain field and a strain rate at any moment in the milling process, and providing for calling and calculating a self-defined subprogram and outputting total dislocation density and grain size;
and (3) carrying out model parameter calibration by a trial-and-error method, determining the material constant of the steel, and establishing a dislocation density-based grain size evolution material model of the hot-work die steel.
3. The method of claim 1, wherein the feeding amount is much smaller than the cutting depth in the two-dimensional milling finite element simulation model, and the milling trajectory trochoid is simplified into a circular arc.
4. The method of claim 1, wherein the two-dimensional milling finite element simulation model is inputted with material parameters, tool parameters, contact parameters, and boundary conditions, the grid elements are of temperature-displacement coupled type, and the lagrangian algorithm is used for explicit dynamics algorithm.
5. The method for predicting grain size by the finite element method based on the user subprogram as claimed in claim 1, wherein the calibration of the model parameters requires the comparative analysis to obtain the experimental grain size values of the milled surface layer material by the TEM material characterization means.
6. The system for predicting the grain size based on the finite element method of the user subprogram is characterized by comprising the following steps:
a finite element simulation model establishing module: establishing a two-dimensional milling finite element simulation model through finite element software;
the user self-defined subprogram establishing module: constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
a real-time simulation module: and (3) performing transient milling simulation by using finite element software to obtain parameters of temperature, stress, strain and strain rate, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
7. The system for finite element method based on user subprogram of claim 6, wherein the initial parameters of hot work die steel dislocation density model equation are determined;
inputting initial data into a finite element model, calling a file by using a transient cutting finite element simulation method, acquiring a temperature field, a stress field, a strain field and a strain rate at any moment in the milling process, and providing for calling and calculating a self-defined subprogram and outputting total dislocation density and grain size;
and (3) calibrating the model parameters by a trial-and-error method, determining the material constant of the hot die steel, and establishing a dislocation density-based grain size evolution material model of the hot die steel.
8. The system for predicting grain size by the finite element method based on the user subprogram as claimed in claim 6, wherein in the two-dimensional milling finite element simulation model, the feeding amount is far less than the cutting depth, and the milling trajectory trochoid is simplified into a section of circular arc.
9. A computing device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program, the computing device comprising:
establishing a two-dimensional milling finite element simulation model through finite element software;
constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
and (3) performing transient milling simulation by using finite element software to obtain parameters of temperature, stress, strain and strain rate, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
10. A computer-readable storage medium, having a computer program stored thereon, the program, when executed by a processor, performing the steps of:
establishing a two-dimensional milling finite element simulation model through finite element software;
constructing a dislocation density-based grain size evolution prediction material model, and compiling the dislocation density-based material model into a user-defined subroutine file which can be identified and called by finite element software;
and (3) performing transient milling simulation by using finite element software to obtain parameters of temperature, stress, strain and strain rate, calling material models in a user-defined subprogram to calculate the density of intracellular dislocation, cell wall dislocation and total dislocation, finally calculating and updating the grain size, and performing real-time prediction and dynamic simulation on the grain size evolution of the milled surface layer material in the cutting process.
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