CN113127973B - CAE simulation technology-based multi-material intelligent material selection method and system and electronic equipment - Google Patents
CAE simulation technology-based multi-material intelligent material selection method and system and electronic equipment Download PDFInfo
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Abstract
The invention discloses a CAE simulation technology-based multi-material intelligent material selection method, a system and electronic equipment, wherein the method comprises the following steps: embedding an initial CAE model of a material selection object, acquiring alternative materials matched with the performance parameters of the target material from a material database, and constructing an initial material set; constructing a topological optimization model according to preset optimization parameters, an initial material set and an initial CAE model, and solving the model to obtain an optimization result; constructing a CAE model set according to the optimization result, and constructing an intermediate material set according to the material attribute in each CAE model contained in the CAE model set; performing multi-dimensional index evaluation according to the CAE model set and the intermediate material set to obtain a target material set containing a mark; and constructing a single-target optimization design model according to the target material set, solving the model, and determining a material selection scheme according to an optimal solution result obtained by solving. The invention realizes intelligent material selection and intelligent generation of material selection schemes, improves material selection efficiency and reduces material selection cost.
Description
Technical Field
The invention belongs to the technical field of auxiliary engineering simulation, and particularly relates to a multi-material intelligent material selection method and system for a CAE simulation technology and electronic equipment.
Background
Taking an engineering product as an automobile as an example, the raw material cost for producing one automobile accounts for 53% of the production cost, so that the reasonable selection, scientific treatment and accurate design of the materials are directly related to the cost and quality of the automobile product. With the continuous innovation and development of technologies and related products, the automobile products are updated more frequently, the structures of the automobile products are more complex, and the material selection is more frequent and difficult. The traditional material selection method is continuously repeated, verified and modified through manual experience and experimental means, so that higher time, energy consumption and material cost are caused, and the requirements of technical development are far from being met.
Disclosure of Invention
The invention aims to provide a CAE simulation technology-based multi-material intelligent material selection method, a system and electronic equipment, so as to solve the technical problems of the traditional material selection method.
Based on the above purpose, in a first aspect, the present invention provides a multi-material intelligent material selection method based on CAE simulation technology, including:
embedding an initial CAE model of a material selection object, acquiring an alternative material matched with the performance parameters of the target material from a preset material database, and constructing an initial material set;
constructing a topological optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solving the topological optimization model to obtain an optimization result;
constructing a CAE model set according to the optimization result, and constructing an intermediate material set according to the material attribute in each CAE model contained in the CAE model set;
performing multi-dimensional index evaluation according to the CAE model set and the intermediate material set to obtain a target material set containing a mark;
and constructing a single-target optimization design model according to the target material set, solving the model, and determining a material selection scheme according to an optimal solution result obtained by solving.
Preferably, the preset optimization parameters include optimization constraints and optimization objectives; the constructing a topology optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solving the topology optimization model to obtain an optimization result comprises the following steps:
setting optimization constraints; the optimization constraints comprise rigidity constraints and strength constraints of the material selection object;
setting an optimization target; the optimization target comprises the maximum volume, the lightest weight and the optimal material distribution of the material selection object;
modifying the material attributes in the initial CAE model according to the initial material set, and constructing M topological optimization models according to the optimization constraints and the optimization targets;
and solving each topological optimization model to obtain a corresponding optimization result.
Preferably, the performing multidimensional index evaluation according to the CAE model set and the intermediate material set to obtain a target material set containing a marker includes:
constructing a multi-dimensional index system; the multi-dimensional index system comprises a performance index, a forming index, a lightweight index, a cost index, a technical maturity index and other indexes;
performing multi-dimensional index evaluation on each alternative material in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system to obtain a corresponding weight factor;
preprocessing the weight factors of the dimension indexes;
marking the preprocessed weight factors into the intermediate material set to generate a marked target material set.
Preferably, the constructing a single-target optimization design model according to the target material set and solving the model, and determining a material selection scheme according to an optimal solution result obtained by solving, includes:
constructing a single-target optimization design model according to the target material set and the weight factors of the dimension indexes;
and solving the single-target optimization design model through a heuristic optimization algorithm to obtain the optimal solution result.
Preferably, the single-objective optimization design model is:
where ρ (x)1,x2,Λ,xn) Design model for single-target optimization, β is smoothing factor, g (y)i) (i ═ 1,2, Λ, m) is the absolute value of the difference between the target value and the current value of the design target, g (y)i) Comprises the following steps:
g(yi)＝|Vi-Ri|，
wherein, ViTarget value for design purposes, RiIs the current value of the design objective.
Preferably, the heuristic optimization algorithm is a particle swarm optimization algorithm; solving the single-target optimization design model through a heuristic optimization algorithm to obtain the optimal solution result, wherein the solving comprises the following steps:
obtaining the optimal current value in the single-target optimization design model through a particle swarm optimization algorithm;
detecting whether a material selection scheme corresponding to the optimal current value is a satisfiable scheme;
if the scheme is not satisfied, acquiring all design constraints and design targets in the scheme which is not satisfied, and acquiring an optimal target value;
judging whether the current iteration times are less than or equal to a preset iteration threshold value or not;
if yes, returning to the step: obtaining the optimal current value in the single-target optimization design model through a particle swarm optimization algorithm;
if not, determining that no optimal solution result exists.
In a second aspect, the present invention provides a multi-material intelligent material selection system based on CAE simulation technology, including:
the material set generation module is used for embedding an initial CAE model of the material selection object, acquiring an alternative material matched with the performance parameters of the target material from a preset material database, and constructing an initial material set;
the topological optimization module is used for constructing a topological optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solving the topological optimization model to obtain an optimization result;
the material set processing module is used for constructing a CAE model set according to the optimization result and constructing an intermediate material set according to the material attribute in each CAE model contained in the CAE model set;
the multi-dimensional evaluation module is used for carrying out multi-dimensional index evaluation through the CAE model according to the CAE model set and the intermediate material set to obtain a target material set containing a mark;
and the scheme generation module is used for constructing a single-target optimization design model according to the target material set, solving the model and determining a material selection scheme according to an optimal solution result obtained by solving.
Preferably, the topology optimization module comprises:
a constraint unit for setting an optimization constraint; the optimization constraints comprise rigidity constraints and strength constraints of the material selection object;
a target unit for setting an optimization target; the optimization target comprises the maximum volume, the lightest weight and the optimal material distribution of the material selection object;
the topology optimization unit modifies the material attributes in the initial CAE model according to the initial material set and constructs M topology optimization models according to the optimization constraints and the optimization targets;
and the topological optimization solving unit is used for solving each topological optimization model to obtain a corresponding optimization result.
Preferably, the multidimensional evaluation module comprises:
the system construction unit is used for constructing a multi-dimensional index system; the multi-dimensional index system comprises a performance index, a forming index, a lightweight index, a cost index, a technical maturity index and other indexes;
the evaluation unit is used for carrying out multi-dimensional index evaluation on each alternative material in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system to obtain a corresponding weight factor;
the preprocessing unit is used for preprocessing the weight factors of the dimension indexes;
and the marking unit is used for marking the preprocessed weighting factors into the intermediate material set to generate a marked target material set.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements any one of the above-mentioned multi-material intelligent material selection methods based on CAE simulation technology when executing the program.
According to the multi-material intelligent material selection method, system and electronic equipment based on the CAE simulation technology, firstly, a CAE model is embedded, a material construction material set is intelligently selected from a material database, then, a CAE solver and a topology optimization model are combined to obtain an optimized CAE model, multi-dimensional index evaluation is further carried out on the basis of the optimized CAE model, a material set containing a mark is constructed, and finally satisfiability solution is carried out through a single-target optimization design model to obtain a material selection scheme. According to the invention, a material database technology and a CAE simulation technology are combined, a multi-dimensional index evaluation system is constructed, the programming, quantification, datamation and intellectualization of a material selection process can be realized, a single-target optimization design model is constructed, the intelligent generation of a material selection scheme of an engineering product can be realized, the material selection efficiency is improved, the material selection cost is reduced, and the early-stage design requirements can be met.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of an intelligent multi-material selection method based on CAE simulation technology in an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the step S20 of the intelligent multi-material selection method based on the CAE simulation technique according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating the step S40 of the intelligent multi-material selection method based on the CAE simulation technique according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating the step S50 of the intelligent multi-material selection method based on the CAE simulation technique according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a multi-material intelligent material selection system based on the CAE simulation technology in an embodiment of the present invention;
FIG. 6 is a schematic block diagram of a topology optimization module of a multi-material intelligent material selection system based on the CAE simulation technology in an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a multidimensional evaluation module of a multi-material intelligent material selection system based on the CAE simulation technology in an embodiment of the present invention;
fig. 8 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In an embodiment, as shown in fig. 1, a multi-material intelligent material selection method based on CAE simulation technology is provided, which includes the following steps:
and step S10, embedding an initial CAE model of the material selection object, acquiring the alternative material matched with the performance parameters of the target material from a preset material database, and constructing an initial material set A.
In this embodiment, the target material performance parameters include, but are not limited to, material general performance, service performance, and the like.
In the process of generating a material set, an initial CAE (Computer Aided Engineering) model of a material selection object is embedded, the CAE model comprises material attributes, after target material performance parameters which are randomly set by a specified user according to experience and competitive product characteristics are obtained, alternative materials meeting performance parameters such as conventional performance, service performance and the like are automatically screened out through a retrieval function of a preset material database, and therefore an initial material set A to be evaluated is formed by all the alternative materials in a combined mode. Wherein the designated user may be a product design engineer.
Preferably, different types of materials may be used for comparison, and the material set generation process of step S10 is repeatedly performed, for example: if the competitive product 1 is an aluminum alloy, an aluminum alloy evaluation set A can be generated1(ii) a If the competitive product 2 is high-strength steel, a high-strength steel evaluation set A can be generated again2(ii) a If the competitive product 3 is a composite material, a composite material evaluation set A can be generated again3The initial material set a to be evaluated finally generated at this time may be represented as:
A＝A1+A2+A3 (1)
and step S20, constructing a topological optimization model according to the preset optimization parameters, the initial material set A and the initial CAE model, and solving the topological optimization model to obtain an optimization result.
In this embodiment, the preset optimization parameters include optimization constraints and optimization objectives, and in this case, as shown in fig. 2, the step S20 includes the following steps:
step S201, setting optimization constraints, wherein the optimization constraints comprise performance constraints such as rigidity constraint and strength constraint of a material selection object.
Step S202, an optimization target is set, wherein the optimization target comprises the targets of maximum volume, minimum weight, optimal material distribution and the like of the material selection object.
And S203, modifying the material attributes in the initial CAE model according to the initial material set A, and constructing M topological optimization models according to optimization constraints and optimization targets.
And step S204, solving each topological optimization model to obtain a corresponding optimization result.
In this embodiment, the number M of topology optimization models is the same as the number N of candidate materials in the initial material set a.
In the structural topology optimization process, material attributes in the initial CAE model are modified according to the initial material set A, topology optimization models matched with the quantity of alternative materials in the initial material set A are derived by combining optimization constraints, optimization targets and the like, optimization solution is carried out on each topology optimization model, optimization results corresponding to each topology optimization model are recorded, and the optimized CAE model can be obtained through analysis of the optimization results.
And step S30, constructing a CAE model set M according to the optimization result, and constructing an intermediate material set B according to the material attribute in each CAE model contained in the CAE model set M.
In this embodiment, based on the optimization result obtained in step S20, k (k is greater than or equal to 1) initial CAE models to be modified are determined, the initial CAE models are modified according to the corresponding optimized CAE models, then k modified initial CAE models and N-k (N is the number of candidate materials in the initial material set a) unmodified initial CAE models are combined to form a CAE model set M, and meanwhile, the material attributes in each CAE model included in the CAE model set M are recorded, so as to obtain an intermediate material set B.
And step S40, performing multi-dimensional index evaluation according to the CAE model set M and the intermediate material set B to obtain a target material set A' containing the mark.
In the multidimensional evaluation process, based on the CAE model set M and the intermediate material set B in step S30, the CAE model of the CAE model set M is respectively invoked to perform multidimensional index evaluation on each candidate material in the intermediate material set B, so as to obtain a weight factor of each dimensional index, and the weight factor corresponding to each dimensional index is marked in the intermediate material set B, so as to finally generate a target material set a' containing a mark.
Preferably, as shown in fig. 3, step S40 specifically includes the following steps:
step S401, a multi-dimensional index system is constructed, and the multi-dimensional index system comprises a performance index, a forming index, a lightweight index, a cost index, a technical maturity index and other indexes.
Step S402, based on the CAE model and the multi-dimensional index system, multi-dimensional index evaluation is carried out on each alternative material in the intermediate material set B through a preset index analysis interface to obtain a corresponding weight factor Ri。
Step S403, for each dimension indexWeight factor RiAnd (4) carrying out pretreatment. Wherein the preprocessing comprises forward processing and dimensionless processing, and is used for weighting factors R of diversity and multiple dimensionsiThe treatment is a consistent, dimensionless standard weight factor.
Step S404, weighting factor R after pretreatmentiAnd marking the target material into the intermediate material set B to generate a marked target material set A'.
In this embodiment, the preset index analysis interfaces include a performance CAE analysis interface, a profiling CAE analysis interface, a topology optimization analysis interface, a cost analysis interface, a technical maturity analysis interface, and other extensible index analysis interfaces.
That is, for each alternative material in the intermediate material set B, the corresponding CAE model is called, and each dimension index is evaluated by using each index analysis interface to obtain the corresponding weight factor RiFurther, each weight factor R is determinediCorresponding to each candidate material and marking the candidate materials into the intermediate material set B, thereby obtaining a target material set A'. Each index analysis interface corresponds to one dimension index.
Further, in an embodiment, in order to primarily screen the most suitable material, after step S404, the following steps may be included:
firstly, inputting each weight factor associated with each alternative material in a target material set A' containing a mark into a preset material selection evaluation model, and acquiring a comprehensive index weight output by the preset material selection evaluation model; the preset material selection evaluation model can be as follows:
F＝∑Ri*fj (2)
in the formula (2), F is the weight of the comprehensive index, Ri(I ═ 1,2, Λ, I) is a weighting factor of candidate materials in each dimension index, fj(J ═ 1,2, Λ, J) is an index value of the specific properties of the candidate material.
Then, the candidate materials in the target material set A' are sorted according to the comprehensive index weight F, and the candidate material with the comprehensive index weight F equal to or larger than the weight threshold value is determined as the most appropriate material.
And S50, constructing a single-target optimization design model according to the target material set A', solving the model, and determining a material selection scheme according to the optimal solution result obtained by the solution.
Preferably, as shown in fig. 4, step S50 includes the steps of:
step S501, a single-target optimization design model is constructed according to the target material set A' and the weight factors of the dimension indexes. Wherein, the single-target optimization model can be:
in formula (3), ρ (x)1,x2,Λ,xn) Design model for single-target optimization, β is smoothing factor, g (y)i) (i ═ 1,2, Λ, m) is the absolute value of the difference between the target value and the current value of the design target, g (y)i) Can be expressed as:
g(yi)＝|Vi-Ri| (4)
in the formula (4), ViTarget value for design purposes, RiIs the current value of the design objective.
Understandably, a single-objective optimization design model ρ (x)1,x2,Λ,xn) Design variable x in (1)iDesign target y for candidate materials in target Material set AiCurrent value of RiAnd selecting a weight factor of the candidate material in the selected evaluation dimension.
And step S502, solving the single-target optimization design model through a heuristic optimization algorithm to obtain an optimal solution result.
In this embodiment, the heuristic optimization algorithm is a particle swarm optimization algorithm, and step S502 may include the following steps:
step one, obtaining the optimal current value in the single-target optimization design model through a particle swarm optimization algorithm.
And step two, detecting whether the material selection scheme corresponding to the optimal current value is a satisfiable scheme.
If the scheme is not satisfied, acquiring all design constraints and design targets in the scheme which is not satisfied, and acquiring an optimal target value; otherwise, outputting the satisfied scheme as an optimal solution result.
And step four, judging whether the current iteration times is less than or equal to a preset iteration threshold value.
And step five, if yes, returning to the step S5021, and otherwise, determining that no optimal solving result exists.
In the optimization solving process, firstly, the optimal current value R in the single-target optimization design model is found through the particle swarm optimization algorithmbestDetecting the best current value RbestThe satisfiability of the corresponding material selection scheme, and when the material selection scheme is a satisfiable scheme, outputting the satisfiable scheme as a solution result; when the material selection scheme is an unsatisfied scheme, recording all design constraints and design targets in the unsatisfied scheme, and finding an optimal target value Vbest。
Then judging whether the current iteration time T is less than or equal to a preset iteration threshold value TmaxAt T ≦ TmaxThen, returning to the step one, and updating the optimal current value RbestAnd an optimum target value Vbest(ii) a And at t>TmaxAnd if so, ending the iteration process.
Understandably, each dimension index corresponds to a single-target optimization design model, each single-target optimization design model is solved by adopting a particle swarm optimization algorithm, a series of satisfiable schemes can be obtained, and finally, a final material selection scheme can be determined from the series of satisfiable schemes.
Further, after step S502, the following steps may be further included: and when the optimal solution result does not exist, obtaining the satisfied solution result, and determining a material selection scheme according to the satisfied solution result.
That is, when the evaluation dimension is too large or the weight factor is set improperly, there may be no optimal solution result or the optimal solution time is too long, and at this time, any satisfiable solution result may be found, and creative generation of the material selection scheme is performed based on the satisfiable solution result.
The CAE simulation technology-based multi-material intelligent material selection method of the embodiment includes the steps of firstly embedding a CAE model, intelligently selecting a material construction material set from a material database, then combining a CAE solver and a topology optimization model to obtain an optimized CAE model, further performing multi-dimensional index evaluation on the basis of the optimized CAE model, constructing a material set containing a mark, and finally performing satisfiability solution through a single-target optimization design model to obtain a material selection scheme. According to the embodiment, the material database technology and the CAE simulation technology are combined, a multi-dimensional index evaluation system is constructed, the programming, the quantification, the datamation and the intellectualization of the material selection process can be realized, a single-target optimization design model is constructed, the intelligent generation of the material selection scheme of the engineering product can be realized, the material selection efficiency is improved, the material selection cost is reduced, and the early-stage design requirements can be met.
In an embodiment, a multi-material intelligent material selection system based on a CAE simulation technology is provided, and the multi-material intelligent material selection system based on the CAE simulation technology corresponds to the multi-material intelligent material selection method based on the CAE simulation technology in the above embodiments one to one. As shown in fig. 5, the multi-material intelligent material selection system based on the CAE simulation technology includes a material set generation module 110, a topology optimization module 120, a material set processing module 130, a multi-dimensional evaluation module 140, and a scheme generation module 150, and the detailed description of each functional module is as follows:
the material set generation module 110 is configured to embed an initial CAE model of the material selection object, acquire an alternative material matched with the target material performance parameter from a preset material database, and construct an initial material set;
the topology optimization module 120 is configured to construct a topology optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solve the topology optimization model to obtain an optimization result;
the material set processing module 130 is configured to construct a CAE model set according to the optimization result, and construct an intermediate material set according to material attributes in each CAE model included in the CAE model set;
the multidimensional evaluation module 140 is configured to perform multidimensional index evaluation according to the CAE model set and the intermediate material set to obtain a target material set containing a marker;
and the scheme generation module 150 is used for constructing a single-target optimization design model according to the target material set, solving the model, and determining a material selection scheme according to an optimal solution result obtained by solving.
Further, as shown in fig. 6, the topology optimization module 120 includes a constraint unit 121, an objective unit 122, a topology optimization unit 123, and a topology optimization solving unit 124, and the detailed description of each functional unit is as follows:
a constraint unit 121 for setting optimization constraints; the optimization constraints comprise rigidity constraints and strength constraints of the material selection objects;
a goal unit 122 for setting an optimization goal; the optimization target comprises the maximum volume, the lightest weight and the optimal material distribution of the material selection object;
the topology optimization unit 123 modifies the material attributes in the initial CAE model according to the initial material set, and constructs M topology optimization models according to optimization constraints and optimization targets;
and the topology optimization solving unit 124 is used for solving each topology optimization model to obtain a corresponding optimization result.
Further, as shown in fig. 7, the multi-dimensional evaluation module 140 includes a system building unit 141, an evaluation unit 142, a preprocessing unit 143, and a marking unit 144, and the detailed description of each functional unit is as follows:
the system building unit 141 is used for building a multi-dimensional index system; the multi-dimensional index system comprises a performance index, a forming index, a lightweight index, a cost index, a technical maturity index and other indexes;
the evaluation unit 142 is configured to perform multidimensional index evaluation on each candidate material in the intermediate material set through a preset index analysis interface based on the CAE model and a multidimensional index system to obtain a corresponding weight factor;
a preprocessing unit 143, configured to preprocess the weight factors of the dimension indexes;
a marking unit 144, configured to mark the preprocessed weighting factors into the intermediate material set, so as to generate a target material set containing a mark.
Further, the solution generating module 150 includes a design model constructing unit and a design model solving unit, and the detailed description of each functional unit is as follows:
the design model building unit is used for building a single-target optimization design model according to the target material set and the weight factors of the dimensional indexes;
and the design model solving unit is used for solving the single-target optimization design model through a heuristic optimization algorithm to obtain an optimal solution result.
The system of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
In addition, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program that is stored in the memory and can be run on the processor, and when the processor executes the computer program, the multi-material intelligent material selection method based on the CAE simulation technology according to any one of the embodiments is implemented.
Fig. 8 is a schematic diagram illustrating a more specific hardware structure of an electronic device provided in this embodiment, where the electronic device may include: a processor 100, a memory 200, an input/output interface 300, a communication interface 400, and a bus 500. Wherein the processor 100, the memory 200, the input/output interface 300 and the communication interface 400, the bus 500 enable a communication connection within the device between each other.
The processor 100 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solution provided by the embodiment of the present invention.
The Memory 200 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 200 may store an operating system and other application programs, and when the technical solution provided by the embodiment of the present invention is implemented by software or firmware, the relevant program codes are stored in the memory 200 and called to be executed by the processor 100.
The input/output interface 300 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 400 is used for connecting a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable, etc.) (or in a wireless mode (such as mobile network, WIFI, bluetooth, etc.).
It should be noted that although the above-mentioned device only shows the processor 100, the memory 200, the input/output interface 300, the communication interface 400 and the bus 500, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the present disclosure, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of brevity.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made without departing from the spirit or scope of the embodiments of the present invention are intended to be included within the scope of the disclosure.
Claims (7)
1. A multi-material intelligent material selection method based on a CAE simulation technology is characterized by comprising the following steps:
embedding an initial CAE model of a material selection object, acquiring an alternative material matched with the performance parameters of the target material from a preset material database, and constructing an initial material set;
constructing a topological optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solving the topological optimization model to obtain an optimization result;
constructing a CAE model set according to the optimization result, and constructing an intermediate material set according to the material attribute in each CAE model contained in the CAE model set;
performing multi-dimensional index evaluation according to the CAE model set and the intermediate material set to obtain a target material set containing a mark; the marks are weight factors corresponding to all dimension indexes;
constructing a single-target optimization design model according to the target material set, solving the model, and determining a material selection scheme according to an optimal solution result obtained by solving; the method comprises the following steps: constructing a single-target optimization design model according to the target material set and the weight factors of the dimension indexes; the single-target optimization design model is as follows:
where ρ (x)1,x2,Λ,xn) Design model, g (y), for single-target optimizationi) (i ═ 1,2, Λ, m) is the absolute value of the difference between the target and current values of the design target, β is the smoothing factor, and n is the design variable xiM is the design target yiThe number of (c), g (y)i) Comprises the following steps:
g(yi)＝|Vi-Ri|，
wherein, ViTarget value for design purposes, RiIs a current value of a design objective;
solving the single-target optimization design model through a heuristic optimization algorithm to obtain the optimal solution result; the heuristic optimization algorithm is a particle swarm optimization algorithm, and the method comprises the following steps:
obtaining the optimal current value in the single-target optimization design model through a particle swarm optimization algorithm;
detecting whether a material selection scheme corresponding to the optimal current value is a satisfiable scheme;
if the scheme is not satisfied, acquiring all design constraints and design targets in the scheme which is not satisfied, and acquiring an optimal target value; judging whether the current iteration times are less than or equal to a preset iteration threshold value or not;
if yes, returning to the step: obtaining the optimal current value in the single-target optimization design model through a particle swarm optimization algorithm; if not, determining that no optimal solution result exists.
2. The CAE simulation technology-based multi-material intelligent material selection method according to claim 1, wherein the preset optimization parameters comprise optimization constraints and optimization objectives;
the constructing a topology optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solving the topology optimization model to obtain an optimization result comprises the following steps:
setting optimization constraints; the optimization constraints comprise rigidity constraints and strength constraints of the material selection object;
setting an optimization target; the optimization target comprises the maximum volume, the lightest weight and the optimal material distribution of the material selection object;
modifying the material attributes in the initial CAE model according to the initial material set, and constructing M topological optimization models according to the optimization constraints and the optimization targets;
and solving each topological optimization model to obtain a corresponding optimization result.
3. The CAE simulation technology-based multi-material intelligent material selection method of claim 1, wherein the multi-dimensional index evaluation is performed according to the CAE model set and the intermediate material set to obtain a labeled target material set, and the method comprises the following steps:
constructing a multi-dimensional index system; the multi-dimensional index system comprises a performance index, a forming index, a lightweight index, a cost index, a technical maturity index and other indexes;
performing multi-dimensional index evaluation on each alternative material in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system to obtain a corresponding weight factor;
preprocessing the weight factors of the dimension indexes;
marking the preprocessed weight factors into the intermediate material set to generate a marked target material set.
4. The utility model provides a many materials intelligence material selection system based on CAE emulation technique which characterized in that includes:
the material set generation module is used for embedding an initial CAE model of the material selection object, acquiring an alternative material matched with the performance parameters of the target material from a preset material database and constructing an initial material set;
the topological optimization module is used for constructing a topological optimization model according to preset optimization parameters, the initial material set and the initial CAE model, and solving the topological optimization model to obtain an optimization result;
the material set processing module is used for constructing a CAE model set according to the optimization result and constructing an intermediate material set according to the material attribute in each CAE model contained in the CAE model set;
the multi-dimensional evaluation module is used for carrying out multi-dimensional index evaluation through the CAE model according to the CAE model set and the intermediate material set to obtain a target material set containing a mark; the marks are weight factors corresponding to all dimension indexes;
the scheme generation module is used for constructing a single-target optimization design model according to the target material set, solving the model and determining a material selection scheme according to an optimal solution result obtained by solving; the scheme generation module comprises:
the design model building unit is used for building a single-target optimization design model according to the target material set and the weight factors of the dimension indexes;
and the design model solving unit is used for solving the single-target optimization design model through a heuristic optimization algorithm to obtain the optimal solution result.
5. The CAE simulation technology-based multi-material intelligent material selection system of claim 4, wherein the topology optimization module comprises:
a constraint unit for setting an optimization constraint; the optimization constraints comprise rigidity constraints and strength constraints of the material selection object;
a target unit for setting an optimization target; the optimization target comprises the maximum volume, the lightest weight and the optimal material distribution of the material selection object;
the topology optimization unit modifies the material attributes in the initial CAE model according to the initial material set and constructs M topology optimization models according to the optimization constraints and the optimization targets;
and the topological optimization solving unit is used for solving each topological optimization model to obtain a corresponding optimization result.
6. The CAE simulation technology-based multi-material intelligent material selection system of claim 5, wherein the multi-dimensional evaluation module comprises:
the system construction unit is used for constructing a multi-dimensional index system; the multi-dimensional index system comprises a performance index, a forming index, a lightweight index, a cost index, a technical maturity index and other indexes;
the evaluation unit is used for carrying out multi-dimensional index evaluation on each alternative material in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system to obtain a corresponding weight factor;
the preprocessing unit is used for preprocessing the weight factors of the dimension indexes;
and the marking unit is used for marking the preprocessed weighting factors into the intermediate material set to generate a marked target material set.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the CAE simulation technology-based multi-material intelligent material selection method according to any one of claims 1 to 3 when executing the program.
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