CN110197041B - Multi-target surface mesostructure optimization method and system under multi-physical-field working condition - Google Patents
Multi-target surface mesostructure optimization method and system under multi-physical-field working condition Download PDFInfo
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Abstract
The embodiment of the invention discloses a multi-target surface mesostructure optimization method and a system under a multi-physical field working condition, wherein the method comprises the steps of establishing a two-dimensional geometric surface model corresponding to a surface according to the surface geometric shape of a key part to be optimized; creating a corresponding finite element model, setting corresponding material properties and setting corresponding boundary conditions according to actual multi-physical-field working conditions of the surfaces of the key components; performing multi-physical field finite element simulation analysis to obtain performance index parameters met by the surface before optimization; dividing the finite element model into a plurality of areas and confirming corresponding master areas and slave areas; setting an optimization design variable parameter range and establishing a multi-objective optimization model; and carrying out surface morphology optimization based on the multi-objective optimization model to obtain the multi-physical-field working condition surface structure. The invention solves the problems of complex process and high realization difficulty of the coating layer and the mechanical, physical, chemical and other methods for improving the material performance in the modern surface technology.
Description
Technical Field
The invention relates to the technical field of model engineering structure optimization design, in particular to a multi-target surface microstructure optimization method and system under a multi-physical-field working condition.
Background
With the development of advanced equipment manufacturing industry in China, the surface performance requirements of key parts in high-end equipment are gradually improved. Modern surface technology has been used to improve the material's resistance to environmental effects and to impart certain functional properties to the material surface, mainly by applying various coatings or using mechanical, physical, chemical, etc. methods. However, in the prior art (i.e. modern surface technology), the used coating layer and the technology for improving the material performance by using mechanical, physical, chemical and other methods are complex, and the realization difficulty is high.
Disclosure of Invention
Based on the method, in order to solve the defects of complex process for improving material performance by using a coating layer and using mechanical, physical, chemical and other methods and high realization difficulty in the modern surface technology, a multi-target surface microstructure optimization method under the working condition of multiple physical fields is particularly provided.
The multi-target surface mesostructure optimization method under the working condition of multiple physical fields is characterized by comprising the following steps of:
s1, establishing a two-dimensional geometric surface model corresponding to the surface according to the surface geometric shape of the key component to be optimized;
s2, creating a finite element model corresponding to the two-dimensional geometric surface model, setting corresponding material properties, and setting corresponding boundary conditions according to actual multi-physical-field working conditions of the surface of the key component, wherein the finite element model is obtained by carrying out finite element mesh division on the built two-dimensional geometric surface model;
s3, performing multi-physical field finite element simulation analysis on the surface of the key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the performance index parameters are determined by the characteristics of the key component in actual working conditions;
s4, dividing the established finite element model into a plurality of areas and confirming corresponding master areas and slave areas;
s5, setting an optimization design variable parameter range and establishing a multi-objective optimization model;
s6, performing surface morphology optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure.
Optionally, in one embodiment, the step S4 of dividing the established finite element model into a plurality of regions and identifying the corresponding master region and the slave region specifically includes:
s41, judging whether the surface corresponding to the established finite element model is a uniform surface, if so, uniformly dividing the established finite element model into a plurality of areas, randomly selecting one area as a main area, and all the rest areas are auxiliary areas; otherwise, S42 is carried out;
s42, further judging whether the surface corresponding to the established finite element model has a part of uniform surface, if so, uniformly dividing the part of uniform surface into a plurality of areas, randomly selecting one of the areas as a main area, and directly determining the part of the surface without the uniform surface as a main area, wherein the rest of the area is a slave area, and the slave area is zero, and simultaneously re-fusing the divided surface; otherwise, S43 is carried out;
s43, directly determining the surface as a main area, and determining a slave area as zero.
Optionally, in one embodiment, the step S5 of setting an optimization design variable parameter range and establishing a multi-objective optimization model specifically includes:
the objective function corresponding to the multi-objective optimization model and the constraint condition s.t. are as follows:
where ω is the displacement minimizing sub-target weight, u max j (h) For the maximum displacement of the jth master/slave region under the multi-physical-field working condition, Λ (h) is the natural frequency of the optimized surface mesostructure under the constraint state, Λ max Sum lambda min The results obtained by maximizing and minimizing the natural frequency under the constraint condition are respectively that F is an external force vector born by the structure, K is a rigidity matrix, U is a displacement vector of the structure, h is a design variable, h min For the minimum value of the design variable h, i.e. the depth of work required for the optimized mesostructure, the negative sign indicates the material removed, α * And w min Buffer angle and minimum width of optimized surface mesostructure respectively;
at the same time make use of trade-offsThe programming theory synthesizes the flexibility under each working condition, and the variation range of the lambda (h) is limited in [ lambda ] min ，Λ max ]Within the closed interval.
Optionally, in one embodiment, the step S6 of performing surface morphology optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure specifically includes: and (3) carrying out surface morphology optimization based on the multi-objective optimization model and combining a finite element analysis method, and adding a surface mesostructure with a specified depth range h in the normal direction of an optimized plane to obtain a multi-physical-field working condition surface structure.
Optionally, in one embodiment, the surface topography optimization further comprises directly copying the optimization results of the master region to the slave region such that the finally obtained surface microstructure optimization results are repeatable.
In addition, in order to solve the defects of complex process for improving material performance by using a coating layer and using mechanical, physical, chemical and other methods and high realization difficulty in the modern surface technology, a multi-target surface microstructure optimization system under the working condition of multiple physical fields is particularly provided.
A multi-objective surface mesostructure optimization system under multi-physical field conditions, comprising:
the first data acquisition unit is used for establishing a two-dimensional geometric surface model corresponding to the surface according to the surface geometric shape of the key component to be optimized;
the second data acquisition unit is used for creating a finite element model corresponding to the two-dimensional geometric surface model, setting corresponding material properties and giving corresponding boundary conditions according to actual multi-physical-field working conditions of the surface of the key component, wherein the finite element model is obtained by carrying out finite element mesh division on the established two-dimensional geometric surface model;
the third data acquisition unit is used for carrying out multi-physical field finite element simulation analysis on the surface of the key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the performance index parameters are determined by the characteristics of the key component in actual working conditions;
a first data processing unit for dividing the established finite element model into a plurality of regions and confirming corresponding master and slave regions;
the second data processing unit is used for setting the parameter range of the optimal design variable and establishing a multi-objective optimization model;
and the first data output unit is used for carrying out surface morphology optimization based on the multi-objective optimization model so as to obtain a multi-physical-field working condition surface structure.
Optionally, in one embodiment, the first data processing unit divides the established finite element model into a plurality of areas and identifies the corresponding master area and the slave area specifically includes the following procedures:
t1, judging whether the surface corresponding to the established finite element model is a uniform surface, if so, uniformly dividing the established finite element model into a plurality of areas, randomly selecting one area as a main area, and all the rest areas are auxiliary areas; otherwise, T2 is carried out;
t2, further judging whether the surface corresponding to the established finite element model has a part of uniform surface, if so, uniformly dividing the part of uniform surface into a plurality of areas, randomly selecting one of the areas as a main area, and directly determining the part of the surface without the uniform surface as a main area, wherein the rest of the area is a slave area, and the slave area is zero, and simultaneously re-fusing the divided surface; otherwise, T3 is carried out;
and T3, directly determining the surface as a main area, and enabling a slave area to be zero.
Optionally, in one embodiment, the objective function corresponding to the multi-objective optimization model in the second data processing unit and the constraint condition s.t. thereof are:
where ω is the displacement minimizing sub-target weight, u max j (h) For the maximum displacement of the jth master/slave region under the multi-physical field condition, Λ (h) is the natural frequency of the optimized surface mesostructure under the constraint state,Λ max sum lambda min The results obtained by maximizing and minimizing the natural frequency under the constraint condition are respectively that F is an external force vector born by the structure, K is a rigidity matrix, U is a displacement vector of the structure, h is a design variable, h min For the minimum value of the design variable h, i.e. the depth of work required for the optimized mesostructure, the negative sign indicates the material removed, α * And w min Buffer angle and minimum width of optimized surface mesostructure respectively; meanwhile, the flexibility under each working condition is synthesized by utilizing a compromise programming theory, and the variation range of Λ (h) is limited in [ Λ ] min ，Λ max ]Within the closed interval.
Optionally, in one embodiment, the surface topography optimization performed by the first data output unit based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure specifically includes the following processes: and (3) carrying out surface morphology optimization based on the multi-objective optimization model and combining a finite element analysis method, and adding a surface mesostructure with a specified depth range h in the normal direction of an optimized plane to obtain a multi-physical-field working condition surface structure.
Optionally, in one embodiment, the surface topography optimization further comprises directly copying the optimization results of the master region to the slave region such that the finally obtained surface microstructure optimization results are repeatable.
In addition, to solve the deficiencies of the prior art in the face, a computer readable storage medium is also proposed, comprising computer instructions which, when run on a computer, cause the computer to perform the method.
The implementation of the embodiment of the invention has the following beneficial effects:
the invention provides a method mainly aiming at the optimization design of a surface microstructure of a key component with high-performance surface requirements under the action of a multi-physical-field working condition. The invention realizes various performances of the surface of the key component by combining a multi-physical field coupling analysis technology and utilizing a multi-objective optimization method of the surface microstructure, and by the technology, the surface microstructure with a certain processing depth can be designed on the surface of the key component, and meanwhile, the surface performance of the key component can be effectively reserved or improved, such as improving the surface rigidity, optimizing the surface equivalent stress distribution, improving the surface natural frequency and the like.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
FIG. 1 is a flow chart of the core steps of an implementation technique in one embodiment;
FIG. 2 is a schematic diagram showing the effect of a multi-physical-field-condition multi-objective-surface mesostructure optimization method in one embodiment
FIG. 3 is a schematic diagram of a multi-objective optimization result of a microstructure of a surface of a high-speed heavy-duty brake disc under consideration of thermal structure coupling in one embodiment;
FIG. 4 is a schematic view of a microstructure reconstruction of a brake rotor according to the result of FIG. 3 to obtain a high-speed heavy-duty brake rotor surface microstructure reconstruction in one embodiment.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
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. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. It will be understood that the terms first, second, etc. as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present application. Both the first element and the second element are elements, but they are not the same element.
In order to solve the defects that in the modern surface technology, the used coating layer and the technology for improving the material performance by using mechanical, physical, chemical and other methods are complex and the realization difficulty is high, in the embodiment, a multi-target surface microstructure optimization method under the working condition of multiple physical fields is specially provided, the surface performance of the microstructure processed on the surface is considered in the design stage of a key component, and the method can be used as the basis of the prior art, can realize the substitution of the prior art, simplify the design and manufacture process flow of the key component or realize superposition with the prior art, and further improve the surface performance of the key component; specifically, as shown in fig. 1, the method comprises the following steps:
s1, establishing a two-dimensional geometric surface model corresponding to the surface according to the surface geometric shape of the key component to be optimized;
s2, creating a finite element model corresponding to the two-dimensional geometric surface model, setting corresponding material properties, and setting corresponding boundary conditions according to actual multi-physical-field working conditions of the surface of the key component, wherein the finite element model is obtained by carrying out finite element mesh division on the built two-dimensional geometric surface model; in some embodiments, the multiple physical fields referred to in the present invention need to be given in conjunction with actual multiple physical field conditions, i.e., consider the effects of coupling of two or more of force, heat, electromagnetic, vibration, etc. simultaneously.
S3, performing multi-physical field finite element simulation analysis on the surface of the key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the performance index parameters are determined by the characteristics of the key component in actual working conditions; in some specific embodiments, the performance index parameters should be combined with the characteristics of the key component in actual working conditions, for example, the friction surface needs to have performance requirements of wear resistance, noise reduction, vibration reduction, and the like, parameters such as displacement, stress, natural frequency, and the like need to be considered, if the key component is a heat dissipation structure surface, temperature, and the like need to be considered, and if the key component is applied in an electromagnetic field, the fine structure optimization of the surface needs to provide performance indexes according to the physical field where the key component is located and the self requirements.
S4, dividing the established finite element model into a plurality of areas and confirming corresponding master areas and slave areas; in some specific embodiments, the surface model is set up to be a fine structure optimization model, in order to facilitate the processing of the fine structure optimized, the finite element model is divided into a plurality of areas and corresponding main areas and auxiliary areas are confirmed, the basic principle is determined according to the surface characteristics of the model, and if the surface is similar to the surface of the brake disc in the embodiment, the surface has certain symmetry, the surface is evenly divided; if the surface has no symmetry, this step can be skipped, or it can be understood that only one region is divided and this region is determined as the master region and no slave region; meanwhile, the determination of the master/slave areas has randomness, namely if the established finite element surface model is equally divided, one of the finite element surface model is randomly selected as the master area, and the rest of the finite element surface model is all the slave areas; if the finite element surface model is not symmetrical, directly selecting the finite element surface model as a main area, and not selecting a secondary area; if the built finite element surface model part has a part of uniform surface and a part of non-uniform surface, respectively carrying out corresponding treatment and then directly fusing the treated surfaces; the method specifically comprises the following steps: s41, judging whether the surface corresponding to the established finite element model is a uniform surface, if so, uniformly dividing the established finite element model into a plurality of areas, randomly selecting one area as a main area, and all the rest areas are auxiliary areas; otherwise, S42 is carried out; s42, further judging whether the surface corresponding to the established finite element model has a part of uniform surface, if so, uniformly dividing the part of uniform surface into a plurality of areas, randomly selecting one of the areas as a main area, and directly determining the part of the surface without the uniform surface as a main area, wherein the rest of the area is a slave area, and the slave area is zero, and simultaneously re-fusing the divided surface; otherwise, S43 is carried out; s43, directly determining the surface as a main area, and determining a slave area as zero. The structural characteristics of the optimized slave region are consistent with those of the master region, so that the surface of the key component to be optimized has repeatability, and the processing of the surface microstructure after the optimization is easy.
S5, setting an optimization design variable parameter range and establishing a multi-objective optimization model; in some specific embodiments, the step S5 of setting the optimization design variable parameter range and establishing the multi-objective optimization model specifically includes:
the objective function corresponding to the multi-objective optimization model and the constraint condition s.t. are as follows:
where ω is the displacement minimizing sub-target weight, u max j (h) For the maximum displacement of the jth master/slave region under the multi-physical-field working condition, Λ (h) is the natural frequency of the optimized surface mesostructure under the constraint state, Λ max Sum lambda min The results obtained by maximizing and minimizing the natural frequency under the constraint condition are respectively that F is an external force vector born by the structure, K is a rigidity matrix, U is a displacement vector of the structure, h is a design variable, h min For the minimum value of the design variable h, i.e. the depth of work required for the optimized mesostructure, the negative sign indicates the material removed, α * And w min Buffer angle and minimum width of optimized surface mesostructure respectively; meanwhile, the flexibility under each working condition is synthesized by utilizing a compromise programming theory, and the variation range of Λ (h) is limited in [ Λ ] min ，Λ max ]Within the closed interval.
S6, performing surface morphology optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure. In some specific embodiments, the step S6 of performing surface topography optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure specifically includes: and (3) carrying out surface morphology optimization based on the multi-objective optimization model and combining a finite element analysis method, and adding a surface mesostructure with a specified depth range h in the normal direction of an optimized plane to obtain a multi-physical-field working condition surface structure. In some specific embodiments, the surface topography optimization further comprises directly copying the optimization results of the master region to the slave region such that the resulting surface microstructure optimization results are repeatable.
In addition, in order to solve the defects of complex process for improving material performance by using a coating layer and using mechanical, physical, chemical and other methods and high realization difficulty in the modern surface technology, a multi-target surface microstructure optimization system under the working condition of multiple physical fields is particularly provided.
A multi-objective surface mesostructure optimization system under multi-physical field conditions, comprising:
the first data acquisition unit is used for establishing a two-dimensional geometric surface model corresponding to the surface according to the surface geometric shape of the key component to be optimized;
the second data acquisition unit is used for creating a finite element model corresponding to the two-dimensional geometric surface model, setting corresponding material properties and giving corresponding boundary conditions according to actual multi-physical-field working conditions of the surface of the key component, wherein the finite element model is obtained by carrying out finite element mesh division on the established two-dimensional geometric surface model;
the third data acquisition unit is used for carrying out multi-physical field finite element simulation analysis on the surface of the key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the performance index parameters are determined by the characteristics of the key component in actual working conditions;
a first data processing unit for dividing the established finite element model into a plurality of regions and confirming corresponding master and slave regions; in some specific embodiments, the first data processing unit divides the established finite element model into a plurality of areas and identifies corresponding master areas and slave areas, and specifically includes the following procedures: t1, judging whether the surface corresponding to the established finite element model is a uniform surface, if so, uniformly dividing the established finite element model into a plurality of areas, randomly selecting one area as a main area, and all the rest areas are auxiliary areas; otherwise, T2 is carried out; t2, further judging whether the surface corresponding to the established finite element model has a part of uniform surface, if so, uniformly dividing the part of uniform surface into a plurality of areas, randomly selecting one of the areas as a main area, and directly determining the part of the surface without the uniform surface as a main area, wherein the rest of the area is a slave area, and the slave area is zero, and simultaneously re-fusing the divided surface; otherwise, T3 is carried out; and T3, directly determining the surface as a main area, and enabling a slave area to be zero. The structural characteristics of the optimized slave region are consistent with those of the master region, so that the surface of the key component to be optimized has repeatability, and the processing of the surface microstructure after the optimization is easy;
the second data processing unit is used for setting the parameter range of the optimal design variable and establishing a multi-objective optimization model; in some specific embodiments, the objective function corresponding to the multi-objective optimization model in the second data processing unit and the constraint condition s.t. thereof are:
where ω is the displacement minimizing sub-target weight, u max j (h) For the maximum displacement of the jth master/slave region under the multi-physical-field working condition, Λ (h) is the natural frequency of the optimized surface mesostructure under the constraint state, Λ max Sum lambda min The results obtained by maximizing and minimizing the natural frequency under the constraint condition are respectively that F is an external force vector born by the structure, K is a rigidity matrix, U is a displacement vector of the structure, h is a design variable, h min For the minimum value of the design variable h, i.e. the depth of work required for the optimized mesostructure, the negative sign indicates the material removed, α * And w min Buffer angle and minimum width of optimized surface mesostructure respectively; meanwhile, the flexibility under each working condition is synthesized by utilizing a compromise programming theory, and the variation range of Λ (h) is limited in [ Λ ] min ，Λ max ]Within the closed interval.
And the first data output unit is used for carrying out surface morphology optimization based on the multi-objective optimization model so as to obtain a multi-physical-field working condition surface structure. In some specific embodiments, the surface topography optimization performed by the first data output unit based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure specifically includes the following processes: and (3) carrying out surface morphology optimization based on the multi-objective optimization model and combining a finite element analysis method, and adding a surface mesostructure with a specified depth range h in the normal direction of an optimized plane to obtain a multi-physical-field working condition surface structure. In some specific embodiments, the surface topography optimization further comprises directly copying the optimization results of the master region to the slave region such that the resulting surface microstructure optimization results are repeatable.
Furthermore, a computer readable storage medium is proposed, comprising computer instructions which, when run on a computer, cause the computer to perform the method.
The invention is further described in detail below by way of an example of a high-speed heavy-duty brake disc surface microstructure multi-objective optimization design under the action of the thermal structure coupling fields of figures 2-4.
Firstly, a two-dimensional geometric surface model corresponding to the surface is established according to the geometric shape of the surface of a key component to be optimized, the outline size and structural material parameters of a design area are specifically established, the size of the design area is the actual size of a high-speed heavy-load brake disc, the diameter of the outer circumference is 0.8 meter, the diameter of the inner circumference is 0.2 meter, the material is Q345B, the elastic modulus is 2.08X101 Pa, the Poisson ratio is 0.31, the density is 7850 kg/cubic meter, the thermal conductivity is 48W/(meter DEG C), the specific heat capacity is 462J/(kg DEG C), the linear expansion coefficient is 12.8X10-6/DEG C, and the sliding friction coefficient between the brake pad and the brake pad is 0.3.
Creating a finite element model corresponding to a two-dimensional geometric surface model, setting corresponding material properties, giving corresponding boundary conditions, simultaneously carrying out multi-physical-field finite element simulation analysis on the surface of a key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the model is subjected to finite element dispersion by using quadrilateral grids, the grid size is 0.002 m multiplied by 0.002 m, and according to the actual working condition of a high-speed heavy-load brake disc, applying constraint of the degree of freedom in space on the inner circumference of the brake disc, and applying brake torque and temperature load on the surface of the brake disc to determine the boundary conditions of a design area.
Dividing the region, confirming the main region corresponding to the model, setting the parameter range of the optimized design variable after the main region and the auxiliary region are identified, establishing a multi-target optimized model, and establishing a multi-target structure optimized target model of the microstructure on the surface of the high-speed heavy-duty brake disc under the action of the thermal structure coupling field. In this embodiment, the single objectives are respectively the minimization of the maximum displacement of each master/slave area of the surface of the brake disc and the maximization of the natural frequency of the brake disc itself; respectively taking the maximum value lambda of frequency optimization under constraint state max 30.9289 Hz, minimum value Λ min 5.2453 Hz the maximum depth of finish h for the mesostructured surface min At-0.004 m, upper limit of displacement constraint5X 10-4 m, minimum width w of surface microstructure min Buffer angle alpha of surface mesostructure of 0.004 m * 60 degrees;
finally, carrying out surface morphology optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure, namely carrying out multi-objective surface microstructure optimization calculation to obtain a multi-objective optimization result schematic diagram of the high-speed heavy-duty brake disc surface microstructure under the consideration of the thermal structure coupling effect shown in fig. 3; and according to the result of fig. 3, performing mesostructure reconstruction on the brake disc to obtain a mesostructure reconstruction schematic diagram of the surface of the high-speed heavy-duty brake disc shown in fig. 4.
In addition, in fig. 2: 1 is the surface to be optimized; 2 is an optimized designed microstructure to be processed; h is a design variable, namely the processing depth of the optimized mesostructure; alpha is a buffer angle for preventing stress concentration, and is generally 45-60 degrees; wmin is the minimum width of the mesostructure. In fig. 3: the light color is the original surface, namely the processing depth is 0 m; the dark color is the processing surface and the deepest processing depth is 0.004 m.
In conclusion, by adopting the multi-objective topological optimization design method of the multi-physical-field working condition structure, the mesoscopic structural distribution of the high-speed heavy-load brake disc under the complex working condition can be obtained, and meanwhile, the requirements of minimizing the surface displacement of the brake disc and maximizing the silicone oil frequency are met. The embodiment illustrates the effectiveness of the multi-physical-field working condition multi-target surface mesostructure optimization design method.
The implementation of the embodiment of the invention has the following beneficial effects:
the invention provides a method mainly aiming at the optimization design of a surface microstructure of a key component with high-performance surface requirements under the action of a multi-physical-field working condition. The invention realizes various performances of the surface of the key component by combining a multi-physical field coupling analysis technology and utilizing a multi-objective optimization method of the surface microstructure, and by the technology, the surface microstructure with a certain processing depth can be designed on the surface of the key component, and meanwhile, the surface performance of the key component can be effectively reserved or improved, such as improving the surface rigidity, optimizing the surface equivalent stress distribution, improving the surface natural frequency and the like.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. The multi-target surface mesostructure optimization method under the working condition of multiple physical fields is characterized by comprising the following steps of:
s1, establishing a two-dimensional geometric surface model corresponding to the surface according to the surface geometric shape of the key component to be optimized;
s2, creating a finite element model corresponding to the two-dimensional geometric surface model, setting corresponding material properties, and setting corresponding boundary conditions according to actual multi-physical-field working conditions of the surface of the key component, wherein the finite element model is obtained by carrying out finite element mesh division on the built two-dimensional geometric surface model;
s3, performing multi-physical field finite element simulation analysis on the surface of the key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the performance index parameters are determined by the characteristics of the key component in actual working conditions;
s4, dividing the established finite element model into a plurality of areas and confirming corresponding master areas and slave areas;
s5, setting an optimization design variable parameter range and establishing a multi-objective optimization model;
s6, performing surface morphology optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure.
2. The method according to claim 1, wherein the step S4 of dividing the established finite element model into a plurality of regions and identifying the corresponding master region and slave region specifically comprises:
s41, judging whether the surface corresponding to the established finite element model is a uniform surface, if so, uniformly dividing the established finite element model into a plurality of areas, randomly selecting one area as a main area, and all the rest areas are auxiliary areas; otherwise, S42 is carried out;
s42, further judging whether the surface corresponding to the established finite element model has a part of uniform surface, if so, uniformly dividing the part of uniform surface into a plurality of areas, randomly selecting one of the areas as a main area, and directly determining the part of the surface without the uniform surface as a main area, wherein the rest of the area is a slave area, and the slave area is zero, and simultaneously re-fusing the divided surface; otherwise, S43 is carried out;
s43, directly determining the surface as a main area, and determining a slave area as zero.
3. The method according to claim 1, wherein the step S5 of setting the optimization design variable parameter range and establishing the multi-objective optimization model specifically includes:
the objective function corresponding to the multi-objective optimization model and the constraint condition s.t. are as follows:
where ω is the displacement minimizing sub-target weight, u maxj (h) For the maximum displacement of the jth master/slave region under the multi-physical-field working condition, Λ (h) is the natural frequency of the optimized surface mesostructure under the constraint state, Λ max Sum lambda min The results obtained by maximizing and minimizing the natural frequency under the constraint condition are respectively that F is an external force vector born by the structure, K is a rigidity matrix, U is a displacement vector of the structure, h is a design variable, namely a designated depth range, h min For the minimum value of the design variable h, i.e. the depth of work required for the optimized mesostructure, the negative sign indicates the material removed, α * And w min Buffer angle and minimum width of optimized surface mesostructure respectively;
meanwhile, the flexibility under each working condition is synthesized by utilizing a compromise programming theory, and the variation range of Λ (h) is limited in [ Λ ] min ,Λ max ]Within the closed interval.
4. The method according to claim 1, wherein optionally, the step S6 of performing surface topography optimization based on the multi-objective optimization model to obtain a multi-physical-field working condition surface structure specifically includes: and (3) carrying out surface morphology optimization based on the multi-objective optimization model and combining a finite element analysis method, and adding a surface mesostructure with a specified depth range h in the normal direction of an optimized plane to obtain a multi-physical-field working condition surface structure.
5. The method of claim 1, wherein the surface topography optimization further comprises directly copying the optimization results of the master region to the slave region such that the resulting surface microstructure optimization results are repeatable.
6. A multi-objective surface mesostructure optimization system under multi-physical field conditions, comprising:
the first data acquisition unit is used for establishing a two-dimensional geometric surface model corresponding to the surface according to the surface geometric shape of the key component to be optimized;
the second data acquisition unit is used for creating a finite element model corresponding to the two-dimensional geometric surface model, setting corresponding material properties and giving corresponding boundary conditions according to actual multi-physical-field working conditions of the surface of the key component, wherein the finite element model is obtained by carrying out finite element mesh division on the established two-dimensional geometric surface model;
the third data acquisition unit is used for carrying out multi-physical field finite element simulation analysis on the surface of the key component to be optimized to obtain performance index parameters met by the surface before optimization, wherein the performance index parameters are determined by the characteristics of the key component in actual working conditions;
a first data processing unit for dividing the established finite element model into a plurality of regions and confirming corresponding master and slave regions;
the second data processing unit is used for setting the parameter range of the optimal design variable and establishing a multi-objective optimization model;
and the first data output unit is used for carrying out surface morphology optimization based on the multi-objective optimization model so as to obtain a multi-physical-field working condition surface structure.
7. The system according to claim 6, wherein the first data processing unit divides the established finite element model into a plurality of regions and identifies the corresponding master region and slave region, specifically comprising the following procedures:
t1, judging whether the surface corresponding to the established finite element model is a uniform surface, if so, uniformly dividing the established finite element model into a plurality of areas, randomly selecting one area as a main area, and all the rest areas are auxiliary areas; otherwise, T2 is carried out;
t2, further judging whether the surface corresponding to the established finite element model has a part of uniform surface, if so, uniformly dividing the part of uniform surface into a plurality of areas, randomly selecting one of the areas as a main area, and directly determining the part of the surface without the uniform surface as a main area, wherein the rest of the area is a slave area, and the slave area is zero, and simultaneously re-fusing the divided surface; otherwise, T3 is carried out;
and T3, directly determining the surface as a main area, and enabling a slave area to be zero.
8. The system according to claim 6, wherein the objective function corresponding to the multi-objective optimization model in the second data processing unit and the constraint s.t. thereof are:
where ω is the displacement minimizing sub-target weight, u maxj (h) For the maximum displacement of the jth master/slave region under the multi-physical-field working condition, Λ (h) is the natural frequency of the optimized surface mesostructure under the constraint state, Λ max Sum lambda min The results obtained by maximizing and minimizing the natural frequency under the constraint condition are respectively that F is an external force vector born by the structure, K is a rigidity matrix, U is a displacement vector of the structure, h is a design variable, namely a designated depth range, h min For the minimum value of the design variable h, i.e. the depth of work required for the optimized mesostructure, the negative sign indicates the material removed, α * And w min Buffer angle and minimum width of optimized surface mesostructure respectively; meanwhile, the flexibility under each working condition is synthesized by utilizing a compromise programming theory, and the variation range of Λ (h) is limited in [ Λ ] min ,Λ max ]Within the closed interval.
9. The system of claim 6, wherein the first data output unit performs surface topography optimization based on the multi-objective optimization model to obtain a multi-physical field operating condition surface structure specifically comprises the following processes: and (3) carrying out surface morphology optimization based on the multi-objective optimization model and combining a finite element analysis method, and adding a surface mesostructure with a specified depth range h in the normal direction of an optimized plane to obtain a multi-physical-field working condition surface structure.
10. The system of claim 6, wherein the surface topography optimization further comprises directly copying the optimization results of the master region to the slave region such that the resulting surface microstructure optimization results are repeatable.
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