CN114169225A - Method for optimizing machining sequence of aluminum alloy component based on computer simulation and computer equipment - Google Patents
Method for optimizing machining sequence of aluminum alloy component based on computer simulation and computer equipment Download PDFInfo
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Abstract
The invention discloses a method for optimizing a machining sequence of an aluminum alloy component based on computer simulation and computer equipment, belongs to the technical field of machining, and solves the problems of unreliable simulation results and unreasonable process optimization aiming at the simulation results in the existing machining process. The method of the invention comprises the following steps: acquiring material parameters of the component blank; acquiring a three-dimensional model of the cut component blank according to the engineering drawing of the component; carrying out mesh division on the cut three-dimensional model; performing heat treatment simulation on the three-dimensional model after the grid division to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation; performing machining simulation on the three-dimensional model subjected to the heat treatment simulation to obtain stress field distribution and deformation field distribution of the model subjected to the machining simulation; and acquiring an optimized machining sequence by utilizing a genetic algorithm according to the stress field distribution and the deformation field distribution of the model after the machining simulation. The invention is suitable for machining of the aluminum alloy member.
Description
Technical Field
The application relates to the technical field of machining, in particular to a method and computer equipment for optimizing a machining sequence of an aluminum alloy component based on computer simulation.
Background
The machining is the core sequence section of the component manufacturing process and is the key for determining the component shape and position precision and the service efficiency. In consideration of the existence of residual stress in the blank, the component is deformed during the material removal process in the machining process, so that the machining process of the component and the form and position precision of the component are influenced. The material removal sequence, the removal amount and the like in the machining process are key factors influencing the residual stress release and the deformation, and have decisive influence on the shape and position accuracy of the component. In the manufacturing process of large complex components such as aviation/aerospace cabin wall plates, wind power bearings and the like, because the specification of blank materials is huge and the removal amount of the materials is large, the residual stress release and the deformation response thereof are particularly obvious, and the influence on the form and position of the components is very strong. Therefore, how to control the residual stress/deformation by changing the design of the machining sequence based on the guarantee of the blank performance becomes the basis of the precision manufacturing of large-scale components.
The residual stress/form and position change of the component in the machining process needs to be mastered to make a reasonable machining scheme. In the traditional machining and manufacturing process, because the deformation of parts is random and difficult to predict, the shape and position accuracy of the components is mainly ensured by repeated trial and error and clamping-repositioning. The process completely depends on subjective judgment of an operator, and has no quantitative basis and no consideration of the influence of the characteristics of the blank; in addition, engineers have too single a decision on a goal to plan quantitatively for achieving multiple goals simultaneously. Simulation analysis work is carried out aiming at the problem, but the existing machining simulation method mainly focuses on the machining process and is not sufficient in concern of residual stress of the blank. Meanwhile, in the existing machining simulation process, the genetic chain of stress/deformation of the member is damaged, and therefore reliable prediction of machining deformation cannot be achieved. Therefore, the technology of deformation control by machining sequence optimization is only developed preliminarily, and the unreliability of the simulation result in the machining process and the unreasonable process optimization aiming at the simulation result are two great challenges for scientifically formulating the machining process flow in the current workpiece machining process. The above two challenges become bottlenecks in reducing the manufacturing cost of parts and improving the processing quality of workpieces.
Disclosure of Invention
The invention aims to solve the problems of unreliable simulation results and unreasonable process optimization aiming at the simulation results in the existing machining process, and provides a method and computer equipment for optimizing the machining sequence of an aluminum alloy component based on computer simulation.
The invention is realized by the following technical scheme, and in one aspect, the invention provides a method for optimizing a machining sequence of an aluminum alloy component based on computer simulation, which comprises the following steps:
acquiring material parameters of the component blank;
acquiring a three-dimensional model of the cut component blank according to the engineering drawing of the component;
performing mesh division on the cut three-dimensional model;
performing heat treatment simulation on the three-dimensional model after the grid division according to the material parameters to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation;
performing machining simulation on the three-dimensional model after the heat treatment simulation according to the stress field distribution and the deformation field distribution of the model after the heat treatment simulation to obtain the stress field distribution and the deformation field distribution of the model after the machining simulation;
and acquiring an optimized machining sequence by utilizing a genetic algorithm according to the stress field distribution and the deformation field distribution of the machined simulated model.
Further, the material parameters include, but are not limited to: density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio, and yield strength.
Further, the obtaining of the material parameters of the component blank specifically includes:
measuring density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio and yield strength;
obtaining a creep constitutive relation of the blank through a creep test;
obtaining the constitutive relation of the blank through a thermal compression experiment;
and determining the boundary conditions of the heat treatment process through multipoint temperature measurement of the structural element.
Further, the obtaining of the three-dimensional model after the cutting of the component blank according to the engineering drawing of the component specifically includes:
constructing a three-dimensional model of the component blank through CATIA software;
numbering the machining sequence of the three-dimensional model;
and cutting the three-dimensional model according to a proposed machining sequence to obtain the cut three-dimensional model of the component blank.
Further, the mesh division of the cut three-dimensional model specifically includes:
importing the three-dimensional model series cut in the CATIA software into Hyper Mesh software for grid division;
the components and the blank allowance required to be removed by different machining sequences are respectively set into different components.
Further, the performing thermal treatment simulation on the three-dimensional model after the grid division according to the material parameters to obtain stress field distribution and deformation field distribution of the model after the thermal treatment simulation specifically includes:
carrying out heat treatment simulation on the three-dimensional model after meshing by ABAQUS finite element analysis software, and specifically comprising the following steps:
setting, by ABAQUS, the density, the coefficient of thermal expansion, the specific heat capacity, the thermal conductivity, the elastic modulus, the poisson's ratio, the yield strength parameter, the initial temperature field of the component, the geometrical constraints of the lattice and the time of the heat treatment according to the heat treatment process and according to the material parameters;
and carrying out heat conduction calculation and thermal coupling calculation on the three-dimensional model after the grid division to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation.
Further, the performing machining simulation on the three-dimensional model after the thermal treatment simulation according to the stress field distribution and the deformation field distribution of the model after the thermal treatment simulation to obtain the stress field distribution and the deformation field distribution of the model after the machining simulation specifically includes:
and simulating the machining process of the component by a living and dead unit technology according to the stress field distribution and the deformation field distribution of the heat treatment simulated model to obtain the stress field distribution and the deformation field distribution of the machined component.
Further, the obtaining of the optimized machining sequence by using the genetic algorithm specifically includes:
determining a sequence to be optimized in a processing sequence, and numbering the sequence;
and taking the sequence of the machining sequence as a genetic variable, setting an objective function as the deformation of the member in machining, and acquiring the optimized machining sequence by utilizing the variation and intersection in the genetic algorithm process.
In a second aspect, the present invention provides a computer apparatus comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, performs the steps of a method of optimizing a machining sequence of an aluminium alloy component based on computer simulation as hereinbefore described.
In a third aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of a method of optimizing a machining sequence of an aluminium alloy component based on computer simulation as described above.
The invention has the beneficial effects that:
firstly, the method can continuously simulate the 'heat treatment-machining' process of the component from the blank in the zero stress state as a starting point, and completely cover the stress state of the blank and the key manufacturing process, so that the reliable prediction of the residual stress/shape position of the finished component can be supported.
Secondly, the method of the invention carries out global trial calculation and screening on the machining sequence based on the genetic algorithm, thereby realizing the intelligent design of the machining sequence, avoiding the limitation that the traditional process design depends on personal experience and single element optimization, and greatly reducing the trial-and-error period and trial-and-error cost.
The invention is suitable for machining of the aluminum alloy member.
Drawings
In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a graph of the aged creep curve at 160 ℃ of a 7055 aluminum alloy;
FIG. 2 is a schematic view of a three-dimensional model of a component;
FIG. 3 is a schematic view of processing features;
FIG. 4 is a genetic algorithm framework diagram;
FIG. 5 is a parent selection flow diagram;
FIG. 6 is a schematic diagram of a parent cross;
FIG. 7 is a schematic diagram of progeny variation;
FIG. 8 is a fitness function trend graph in 50 iterations of the genetic algorithm;
FIG. 9 is a schematic view of a three-dimensional model after the component is machined.
Detailed Description
The invention relates to a method for optimizing an aluminum alloy component machining sequence based on computer simulation, which is based on trial calculation of process simulation in a heat treatment-machining process and intelligent global optimization of a machining sequence-key position residual stress/deformation database based on a genetic algorithm.
In one embodiment, a method for optimizing a machining sequence of an aluminum alloy component based on computer simulation, the method comprises:
acquiring material parameters of the component blank;
acquiring a three-dimensional model of the cut component blank according to the engineering drawing of the component;
performing mesh division on the cut three-dimensional model;
performing heat treatment simulation on the three-dimensional model after the grid division according to the material parameters to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation;
performing machining simulation on the three-dimensional model after the heat treatment simulation according to the stress field distribution and the deformation field distribution of the model after the heat treatment simulation to obtain the stress field distribution and the deformation field distribution of the model after the machining simulation;
and acquiring an optimized machining sequence by utilizing a genetic algorithm according to the stress field distribution and the deformation field distribution of the machined simulated model.
The present embodiment mainly includes: firstly, acquiring material parameters of a component blank; constructing a three-dimensional model of the component blank and dividing a grid; thirdly, simulating a blank heat treatment process and calculating residual stress (stress redistribution and deformation field distribution); fourthly, simulating a machining process; and fifthly, optimizing the machining sequence automatically based on the genetic algorithm.
The present embodiment is preferred for the real-time tracking of residual stress/deformation and the machining sequence for deformation control in the component machining process. Wherein, tracking residual stress/deformation in real time is performed based on machining process simulation, and the machining sequence is preferably realized based on genetic algorithm.
In a second embodiment, the method for optimizing a machining sequence of an aluminum alloy component based on computer simulation is further defined, and in the first embodiment, the material parameters are further defined, and include but are not limited to: density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio, and yield strength.
The following physical parameters of the material are required in the simulation of the member: density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio, and yield strength. According to the embodiment, the accuracy of the simulation result can be ensured by testing the physical property parameters of the material at different temperatures.
In a third embodiment, the method for optimizing a machining sequence of an aluminum alloy component based on computer simulation according to the first embodiment is further defined, and the step of obtaining material parameters of a component blank in the first embodiment is further defined, specifically including:
measuring density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio and yield strength;
obtaining a creep constitutive relation of the blank through a creep test;
obtaining the constitutive relation of the blank through a thermal compression experiment;
and determining the boundary conditions of the heat treatment process through multipoint temperature measurement of the structural element.
The embodiment provides a method for acquiring material parameters of a component blank, and it should be noted that the method for acquiring material parameters may select different methods according to actual situations.
In a fourth embodiment, the present invention is further limited to the method for optimizing a machining sequence of an aluminum alloy member based on computer simulation according to the first embodiment, wherein the step of obtaining a three-dimensional model of the member blank after cutting according to a project drawing of the member is further limited to specifically include:
constructing a three-dimensional model of the component blank through CATIA software;
numbering the machining sequence of the three-dimensional model;
and cutting the three-dimensional model according to a proposed machining sequence to obtain the cut three-dimensional model of the component blank.
In this embodiment, a three-dimensional model of the machined blank is constructed by CATIA software according to the engineering drawing of the component, and a geometric basis is provided for next meshing of the component.
In a fifth embodiment, the method for optimizing a machining sequence of an aluminum alloy component based on computer simulation in the first embodiment is further defined, and the step of meshing the cut three-dimensional model is further defined, specifically including:
importing the three-dimensional model series cut in the CATIA software into Hyper Mesh software for grid division;
the components and the blank allowance required to be removed by different machining sequences are respectively set into different components.
In the embodiment, the constructed three-dimensional model is subjected to grid division through the Hyper Mesh according to the processing sequence, and a relatively complex geometric structure adopts a small-size grid to perform local encryption processing. Under the condition that the model is relatively simple, an eight-node hexahedron reduction integral unit grid is adopted, and a tetrahedron four-node reduction integral unit grid can be adopted when the model is relatively complex.
In a sixth embodiment, the method for optimizing a machining sequence of an aluminum alloy component based on computer simulation in the first embodiment is further defined, and the step of performing heat treatment simulation on the three-dimensional model after grid division according to the material parameters to obtain stress field distribution and deformation field distribution of the model after heat treatment simulation is further defined, which specifically includes:
carrying out heat treatment simulation on the three-dimensional model after meshing by ABAQUS finite element analysis software, and specifically comprising the following steps:
setting, by ABAQUS, the density, the coefficient of thermal expansion, the specific heat capacity, the thermal conductivity, the elastic modulus, the poisson's ratio, the yield strength parameter, the initial temperature field of the component, the geometrical constraints of the lattice and the time of the heat treatment according to the heat treatment process and according to the material parameters;
and carrying out heat conduction calculation and thermal coupling calculation on the three-dimensional model after the grid division to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation.
In this embodiment, according to the heat treatment process and the obtained material parameters, the density, the thermal expansion coefficient, the specific heat capacity, the thermal conductivity, the elastic modulus, the poisson's ratio, the yield strength parameters, the initial temperature field of the component, the geometric constraint of the mesh, and the heat treatment time of the material are set by ABAQUS.
And after the parameters are set, performing heat conduction calculation and thermal coupling calculation on the established grid model to obtain the stress field distribution and the deformation field distribution of the model after the heat treatment process.
In a seventh embodiment, the method for optimizing a machining sequence of an aluminum alloy component based on computer simulation according to the first embodiment is further defined, and the step of performing machining simulation on the three-dimensional model after the thermal treatment simulation according to the stress field distribution and the deformation field distribution of the model after the thermal treatment simulation to obtain the stress field distribution and the deformation field distribution of the model after the machining simulation is further defined, and specifically includes:
and simulating the machining process of the component by a living and dead unit technology according to the stress field distribution and the deformation field distribution of the heat treatment simulated model to obtain the stress field distribution and the deformation field distribution of the machined component.
In the embodiment, the thermal treatment simulation result is used as a starting point, the machining process of the component is simulated through the living and dead unit technology, and the stress field distribution and the deformation field distribution of the machined component are obtained and used as the optimization target (objective function) of the machining sequence.
In an eighth embodiment, the method for optimizing a machining sequence of an aluminum alloy component based on computer simulation in the first embodiment is further limited, and the step of obtaining the optimized machining sequence by using a genetic algorithm in the first embodiment is further limited, and specifically includes:
determining a sequence to be optimized in a processing sequence, and numbering the sequence;
and taking the sequence of the machining sequence as a genetic variable, setting an objective function as the deformation of the member in machining, and acquiring the optimized machining sequence by utilizing the variation and intersection in the genetic algorithm process.
In this embodiment, a sequence to be optimized in the processing sequence is determined and the sequence is numbered. The sequence of the processing sequence is used as a genetic variable, the objective function is set as the deformation of a member in machining, the initial population number of the heredity can be set as 100 individuals, the optimization algebra is set as 50 generations, and the population number of each generation is 100. And obtaining the processing path with smaller deformation in the processing process through variation and intersection in the genetic algorithm process.
In the ninth embodiment, the specific process of the method for optimizing the machining sequence of the aluminum alloy component based on computer simulation is described by taking machining of a certain complex 7055 aluminum alloy frame beam as an example:
step one, acquisition of materials and process parameters
(1) The density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio and yield strength were measured. The parameters except the Poisson ratio are all temperature-related functions, and specific numerical values of the parameters are shown in tables 1-4;
table 17055 aluminium alloy density with temperature change table
| Temperature (. degree.C.) | 0 | 100 | 200 | 300 | 400 | 500 |
| Density (Kg/m)3) | 2800 | 2770 | 2770 | 2740 | 2710 | 2690 |
TABLE 27055 aluminum alloy thermal conductivity and thermal conductivity vs. temperature
| Temperature (. degree.C.) | 0 | 100 | 200 | 300 | 400 | 500 |
| Thermal conductivity lambda (W/(m. degree. C.)) | 110 | 120 | 140 | 150 | 160 | 170 |
| Specific heat capacity c (J/(kg. degree. C.)) | 837 | 896 | 963 | 1043 | 1130 | 1193 |
TABLE 37055 aluminum alloy thermal expansion coefficient with temperature change chart
| Temperature (. degree.C.) | 20 | 100 | 200 | 300 | 400 |
| Coefficient of thermal expansion alpha (10)-6) | 21.8 | 23.6 | 24.5 | 25.4 | 26.3 |
TABLE 47055 aluminum alloy modulus of elasticity and yield strength as a function of temperature
| Temperature (. degree.C.) | 20 | 100 | 200 | 300 | 400 |
| Modulus of elasticity E (GPa) | 72.5 | 69.7 | 64.9 | 58.7 | 53.8 |
| Yield strength (MPa) | 425 | 392 | 360 | 159 | 60 |
(2) Obtaining the creep constitutive relation of the blank through a creep test: in this example, the component is subjected to a single-stage ageing heat treatment at 160 ℃ for 40 h. The 7055 aluminum alloy test piece was subjected to creep tests by applying loads of 275MPa, 300MPa, and 325MPa, respectively, at 160 ℃. The creep curve of the 7055 aluminum alloy at 160 ℃ was obtained as shown in fig. 1.
There are a number of constitutive models that describe material creep, where a Time hardening model (Time hardening form) combines the effects of stress on the material creep behavior in the Norton stress equation and the effects of Time on the material behavior in the Bailey equation, and is applicable to creep processes with nearly constant static loading of stress. The experiment can use a time hardening model to describe the steady state creep process in the experiment.
The specific expression form of the time hardening model is shown in the following formula (3-16):
in the formula
Which is indicative of the rate of strain,
a represents a constant associated with the material,
n represents an index of stress,
m represents an index of time,
a represents the equivalent stress of the steel sheet,
t represents the time of the creep,
integration over time t for the left and right sides of equation (3-16):
the following can be obtained:
if epsilon is 0, and t is 0, then C is 0, so formula (3-18) is:
the creep data at different stresses at 160 ℃ in FIG. 1 were fitted to the equations (3-19):
275MPa：m＝-0.961
300MPa：m＝-0.940
325MPa：m＝-0.956
taking the average to obtain: m is-0.952
Substituting m-0.952 into (3-16) to obtain:
A＝8.31×10-7；n＝1.363
the creep constitutive equation of the 7055 aluminum alloy material at 160 ℃ is obtained as follows:
step two, three-dimensional model of frame beam piece blank
(1) Constructing a three-dimensional model of the frame beam blank through CATIA software; the constructed three-dimensional model map is shown in fig. 2.
(2) And after the three-dimensional model of the blank is built, numbering the machining sequence of the model. The machining sequence number comparison table is shown in table 5, and the machining characteristic diagram is shown in fig. 3.
TABLE 5 comparison table of processing characteristic numbers and processing techniques
(3) After the construction of the three-dimensional model of the blank is completed, cutting the three-dimensional model according to a proposed machining sequence (table 6); the part after cutting is shown in figure 9;
TABLE 6 machining sequence Listing
And step three, dividing the hexahedral Mesh of the three-dimensional model through Hyper Mesh by referring to a machining sequence.
(1) Importing the three-dimensional model series cut in CATIA software into Hyper Mesh for grid division; if the workpiece is a symmetrical model, cutting the workpiece model according to a symmetrical plane through a geometric cleaning module in the Hyper Mesh, and deleting symmetrical model characteristics to reduce the calculated amount;
(2) geometrically dividing the model according to the geometric characteristics of the workpiece model, and dividing the complex workpiece into a plurality of simple geometric shapes;
(3) after the meshes of all the geometric models are divided, the parts and blank allowance needing to be removed in different machining sequences are respectively set into different components;
(4) and finishing grid division.
And fourthly, carrying out thermal coupling loading on the divided grid model through ABAQUS to simulate the heat treatment process of the grid model.
(1) The method adopts a thermal power sequence coupling mode to simulate the heat treatment process of the workpiece. The heat treatment process of the frame beam sequentially comprises the following steps: solution treatment at 472 ℃, water quenching at 20 ℃ to room temperature, single-stage aging at 160 ℃ for 40 hours, and air cooling.
(2) Introducing the divided meshes into ABAQUS, setting material parameters, and setting the steps, wherein in the first step, a Heat transfer process (Heat transfer Procedure) is selected, and the time is set to 600 s; secondly, selecting a heat conduction process, and setting the time to be 43200 s; and thirdly, selecting a heat conduction process, and setting the time to 10000 s.
(3) In a contact setting module (Interaction), the Surface of a workpiece is set to be a Surface film layer contact condition (Surface film contact), and the heat Sink temperature (Sink temperature) is set to be 20 ℃ to simulate the water quenching process of the workpiece.
(4) The 472 ℃ solution treatment process was simulated at 472 ℃ with the workpiece initially set to a Predefined field (Predefined field) at the Load application module (Load) to a temperature field of 472 ℃.
(5) And after the grid type is set, submitting a task, completing the calculation of the heat conduction process, and obtaining the temperature field distribution of the component in the quenching process.
(6) After the simulation of the heat conduction process is completed, the first step in the step of setting the model to be opened again is set as a Static step (Static General) to simulate the evolution of stress deformation in the quenching process, and the second step is set as Vi sco to simulate the creep process of single-stage aging at 160 ℃ for 40 hours, so that the stress field distribution of the aged component is obtained.
And fifthly, continuously simulating the machining process of the workpiece by using the heat treatment simulation result as a starting point through a life and death unit technology.
(1) Taking the heat treatment simulation result obtained in the fourth step as a starting point of the machining simulation;
(2) the machining sequence was numbered and the grid of specific components (components already set to completion in step three) was killed each time using the live-dead cell method to simulate the process of material removal during machining. The numbering of the processing sequences is shown in Table 7.
TABLE 7 processing sequence coding Table
| Processed noodles | Inclined plane | Side surface | Inner circular surface | Outer circular surface |
| |
1 | 2 | 3 | 4 |
| Front outer side groove | Back outer side groove | Front inner side groove | Inner side groove of back | 10 are provided |
| 5 | 6 | 7 | 8 | 9-18 |
Wherein, the number of the 10 lightening holes is counted from the lightening holes at the bottom surface of the component ring, namely the number of the lightening holes at the bottom of the ring is 9, and the number of the lightening holes at the top is 18.
And step six, automatically optimizing a machining sequence through a genetic algorithm. Aiming at the problem of optimization of a machining sequence, the step is realized by adopting a Python software editing script. The general framework flow diagram of the genetic algorithm is shown in fig. 4.
(1) Parent selection
The parent selection operator in the genetic algorithm adopts an elite selection strategy: sorting according to the magnitude of the parent fitness function, leaving a parent with the fitness function ranked at the top 50, and setting the individual with the maximum current fitness function as GBest(ii) a Selecting a pair of parents by adopting a roulette mode according to the fitness function: p1、P2(ii) a ③ generating a random number R between (0, 1), if R>0.5 then P2Replacement by elite individual GBest(ii) a And fourthly, iterating the steps to select 50 pairs of crossed parents. A parent selection process flow diagram is shown in figure 5.
(2) Crossover operation
After the parent is selected, entering a parent crossing process, wherein the parent crossing process comprises the following steps:
in the parent P1Randomly selecting a position j, and setting the nth position after j as k;
according to P2Adjusting P in sequence1The order between j-k;
generation of a New offspring C1；
Finding parent P2Middle P1The corresponding j, k sequence segment;
generation of offspring C by the same method2. Wherein j is less than or equal to 18-n;
the parent cross flow diagram is shown in fig. 6.
(3) Variation of progeny
The mutation process adopts the following procedures:
generating a random sequence NrGenerating a random number R between (0, 1);
number of sequence variations:
taking a random sequence NrThe first W numbers;
transforming original chromosome D1In which the order of the same number is converted into NrThe order of (1);
the mutation process is schematically shown in FIG. 7.
The results of the deformation of 100 processing sequences randomly generated in the initial generation of the genetic algorithm and the corresponding target surface are shown in table 8.
Finally, the simulation of the workpiece manufacturing process under the optimized machining sequence shows the following experimental effects:
after 50 iterations, the overall deformation is reduced by about 20%, and the iteration trend of the iteration process is shown in fig. 8.
TABLE 8 Primary Generation of random sequences and target surface deformation data
The optimization results according to the genetic algorithm show that the genetic algorithm is as follows:
2,3,7,12,15,6,11,4,10,8,17,5,18,14,16,13,1,9, the distortion of the target surface is minimal when machining the component.
The invention relates to a computer simulation-based optimized aluminum alloy component machining sequence. The invention aims to realize the whole process tracking of stress/deformation in the machining process of a component based on simulation and realize the intelligent selection of a machining sequence based on a genetic algorithm. The method is used for carrying out machining process simulation on the blank material model containing residual stress, and the machining sequence is optimized according to the machining process simulation. Therefore, the integrity of the residual stress evolution chain can be ensured, and the reliable prediction of the manufacturing deformation is supported; in addition, the method automatically optimizes the machining sequence based on the genetic algorithm, thereby greatly shortening the trial-and-manufacture period and the trial-and-error cost.
Claims (10)
1. A method for optimizing a machining sequence of an aluminum alloy component based on computer simulation, the method comprising:
acquiring material parameters of the component blank;
acquiring a three-dimensional model of the cut component blank according to the engineering drawing of the component;
performing mesh division on the cut three-dimensional model;
performing heat treatment simulation on the three-dimensional model after the grid division according to the material parameters to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation;
performing machining simulation on the three-dimensional model after the heat treatment simulation according to the stress field distribution and the deformation field distribution of the model after the heat treatment simulation to obtain the stress field distribution and the deformation field distribution of the model after the machining simulation;
and acquiring an optimized machining sequence by utilizing a genetic algorithm according to the stress field distribution and the deformation field distribution of the machined simulated model.
2. The method for optimizing a machining sequence of an aluminum alloy component based on computer simulation of claim 1, wherein the material parameters include but are not limited to: density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio, and yield strength.
3. The method for optimizing the machining sequence of the aluminum alloy component based on the computer simulation as claimed in claim 1, wherein the obtaining of the material parameters of the component blank specifically comprises:
measuring density, coefficient of thermal expansion, specific heat capacity, thermal conductivity, elastic modulus, poisson's ratio and yield strength;
obtaining a creep constitutive relation of the blank through a creep test;
obtaining the constitutive relation of the blank through a thermal compression experiment;
and determining the boundary conditions of the heat treatment process through multipoint temperature measurement of the structural element.
4. The method for optimizing the machining sequence of the aluminum alloy component based on the computer simulation as claimed in claim 1, wherein the step of obtaining the cut three-dimensional model of the component blank according to the engineering drawing of the component specifically comprises:
constructing a three-dimensional model of the component blank through CATIA software;
numbering the machining sequence of the three-dimensional model;
and cutting the three-dimensional model according to a proposed machining sequence to obtain the cut three-dimensional model of the component blank.
5. The method for optimizing a machining sequence of an aluminum alloy component based on computer simulation of claim 1, wherein the gridding the cut three-dimensional model specifically comprises:
importing the three-dimensional model series cut in the CATIA software into Hyper Mesh software for grid division;
the components and the blank allowance required to be removed by different machining sequences are respectively set into different components.
6. The method for optimizing the machining sequence of the aluminum alloy component based on the computer simulation as recited in claim 1, wherein the step of performing the thermal treatment simulation on the gridded three-dimensional model according to the material parameters to obtain the stress field distribution and the deformation field distribution of the model after the thermal treatment simulation comprises:
carrying out heat treatment simulation on the three-dimensional model after meshing by ABAQUS finite element analysis software, and specifically comprising the following steps:
setting, by ABAQUS, the density, the coefficient of thermal expansion, the specific heat capacity, the thermal conductivity, the elastic modulus, the poisson's ratio, the yield strength parameter, the initial temperature field of the component, the geometrical constraints of the lattice and the time of the heat treatment according to the heat treatment process and according to the material parameters;
and carrying out heat conduction calculation and thermal coupling calculation on the three-dimensional model after the grid division to obtain stress field distribution and deformation field distribution of the model after the heat treatment simulation.
7. The method for optimizing the machining sequence of the aluminum alloy component based on the computer simulation as recited in claim 1, wherein the step of performing the machining simulation on the three-dimensional model after the heat treatment simulation according to the stress field distribution and the deformation field distribution of the model after the heat treatment simulation to obtain the stress field distribution and the deformation field distribution of the model after the machining simulation comprises:
and simulating the machining process of the component by a living and dead unit technology according to the stress field distribution and the deformation field distribution of the heat treatment simulated model to obtain the stress field distribution and the deformation field distribution of the machined component.
8. The method for optimizing the machining sequence of the aluminum alloy component based on the computer simulation as recited in claim 1, wherein the obtaining the optimized machining sequence by using the genetic algorithm specifically comprises:
determining a sequence to be optimized in a processing sequence, and numbering the sequence;
and taking the sequence of the machining sequence as a genetic variable, setting an objective function as the deformation of the member in machining, and acquiring the optimized machining sequence by utilizing the variation and intersection in the genetic algorithm process.
9. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the steps of the method of any one of claims 1 to 8 are performed when the processor runs the computer program stored by the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.
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