CN113127973B - Multi-material intelligent material selection method, system and electronic equipment based on CAE simulation technology - Google Patents

Abstract

The invention discloses a multi-material intelligent material selection method, system and electronic equipment based on CAE simulation technology. The method includes: embedding an initial CAE model of a material selection object, and obtaining candidate materials matching the performance parameters of target materials from a material database , build the initial material set; build a topology optimization model according to the preset optimization parameters, initial material set and initial CAE model and solve the model to obtain the optimization result; build the CAE model set according to the optimization result, and according to each CAE included in the CAE model set The material properties in the model are used to construct an intermediate material set; the multi-dimensional index evaluation is performed according to the CAE model set and the intermediate material set, and the target material set with marks is obtained; a single-objective optimal design model is constructed according to the target material set and the model is solved, and the obtained The optimal solution result determines the material selection scheme. The invention realizes the intelligent material selection and the intelligent generation of the material selection scheme, improves the material selection efficiency, and reduces the material selection cost.

Description

Multi-material intelligent material selection method, system and electronic equipment based on CAE simulation technology

technical field

The invention belongs to the technical field of auxiliary engineering simulation, and in particular relates to a multi-material intelligent material selection method, system and electronic equipment of CAE simulation technology.

Background technique

Taking the engineering product as an automobile as an example, the raw material cost of producing a car accounts for 53% of the production cost. It can be seen that the rational selection of materials, scientific processing and precise design are directly related to the cost and quality of automobile products. With the continuous innovation and development of technology and related products, the replacement of automotive products is more frequent and the structure is more complex, which brings more frequent and difficult material selection. The traditional material selection method is repeated, verified and modified continuously through manual experience and experimental means, resulting in high time, energy consumption and material cost, and it is far from meeting the requirements of technological development.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a multi-material intelligent material selection method, system and electronic device based on CAE simulation technology, so as to solve the above-mentioned technical problems existing in the traditional material selection method.

Based on the above purpose, in the first aspect, the present invention provides a multi-material intelligent material selection method based on CAE simulation technology, including:

Embed the initial CAE model of the material selection object, and obtain the candidate materials matching the performance parameters of the target material from the preset material database to construct the initial material set;

Build 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;

Build a CAE model set according to the optimization result, and build an intermediate material set according to the material properties in each CAE model included in the CAE model set;

Perform multi-dimensional index evaluation according to the CAE model set and the intermediate material set to obtain a marked target material set;

A single-objective optimal design model is constructed according to the target material set and the model is solved, and the material selection scheme is determined according to the optimal solution result obtained by the solution.

Preferably, the preset optimization parameters include optimization constraints and optimization objectives; the topology optimization model is constructed according to the preset optimization parameters, the initial material set and the initial CAE model, and the topology optimization model is obtained by solving Optimization results, including:

Setting optimization constraints; the optimization constraints include rigidity constraints and strength constraints of the material selection object;

Setting an optimization goal; the optimization goal includes the largest volume, the lightest weight, and the best material distribution of the material selection object;

Modify material properties in the initial CAE model according to the initial material set, and construct M topology optimization models according to the optimization constraints and the optimization objective;

Solve each of the topology optimization models to obtain corresponding optimization results.

Preferably, the multi-dimensional index evaluation is performed according to the CAE model set and the intermediate material set to obtain a marked target material set, including:

Build a multi-dimensional indicator system; the multi-dimensional indicator system includes performance indicators, molding indicators, lightweight indicators, cost indicators, technology maturity indicators and other indicators;

Based on the CAE model and the multi-dimensional indicator system, perform multi-dimensional indicator evaluation on each of the candidate materials in the intermediate material set through a preset indicator analysis interface to obtain a corresponding weight factor;

Preprocessing the weighting factors of each dimension index;

The preprocessed weighting factors are marked into the intermediate material set to generate a marked target material set.

Preferably, the single-objective optimal design model is constructed according to the target material set and the model is solved, and the material selection scheme is determined according to the optimal solution result obtained by the solution, including:

Construct a single-objective optimal design model according to the target material set and the weight factors of each of the dimension indicators;

The single-objective optimal design model is solved through a heuristic optimization algorithm to obtain the optimal solution result.

Preferably, the single-objective optimization design model is:

Among them, ρ(x ₁ ,x ₂ ,Λ,x _n ) is the single-objective optimization design model, β is the smoothing factor, g(y _i )(i=1,2,Λ,m) is the target value of the design target and The absolute value of the difference between the current values, g(y _i ) is:

g(y _i )=|V _i -R _i |,

Among them, _Vi is the target value of the design target _, and Ri is the current value of the design target.

Preferably, the heuristic optimization algorithm is a particle swarm optimization algorithm; the single-objective optimization design model is solved through the heuristic optimization algorithm to obtain the optimal solution result, including:

Obtain the best current value in the single-objective optimal design model by using the particle swarm optimization algorithm;

Detecting whether the material selection scheme corresponding to the best current value is a satisfactory scheme;

If the solution is not satisfied, obtain all the design constraints and design objectives in the non-satisfied solution, and obtain the best target value;

Determine whether the current number of iterations is less than or equal to the preset iteration threshold;

If yes, go back to the step: obtain the best current value in the single-objective optimization design model through the particle swarm optimization algorithm;

If not, it is determined that there is no optimal solution result.

In the 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 to embed the initial CAE model of the material selection object, and obtain the candidate materials matching the performance parameters of the target material from the preset material database to construct the initial material set;

a topology optimization module, 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;

a material set processing module, configured to construct a CAE model set according to the optimization result, and construct an intermediate material set according to the material properties in each CAE model included in the CAE model set;

A multi-dimensional evaluation module, configured to perform multi-dimensional index evaluation through the CAE model according to the CAE model set and the intermediate material set, and obtain a marked target material set;

The scheme generation module is used for constructing a single-objective optimal design model according to the target material set and solving the model, and determining a material selection scheme according to the optimal solution result obtained by the solving.

Preferably, the topology optimization module includes:

A constraint unit, used for setting optimization constraints; the optimization constraints include rigidity constraints and strength constraints of the material selection object;

A target unit, used for setting an optimization target; the optimization target includes the largest volume, the lightest weight, and the best material distribution of the material selection object;

a topology optimization unit, modifying material properties in the initial CAE model according to the initial material set, and constructing M topology optimization models according to the optimization constraints and the optimization objective;

The topology optimization solving unit solves each of the topology optimization models to obtain corresponding optimization results.

Preferably, the multi-dimensional evaluation module includes:

a system construction unit for constructing a multi-dimensional indicator system; the multi-dimensional indicator system includes performance indicators, molding indicators, lightweight indicators, cost indicators, technology maturity indicators and other indicators;

An evaluation unit, configured to perform multi-dimensional index evaluation on each of the candidate materials in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system, and obtain corresponding weighting factors ;

a preprocessing unit, configured to preprocess the weighting factor of each dimension index;

A labeling unit, configured to label the preprocessed weighting factors into the intermediate material set to generate a target material set containing the label.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements any one of the above when executing the program The multi-material intelligent material selection method based on CAE simulation technology.

The multi-material intelligent material selection method, system and electronic device based on the CAE simulation technology of the present invention first embed the CAE model, intelligently select materials from the material database to construct a material set, and then combine the CAE solver and the topology optimization model to obtain the optimized CAE Then, based on the optimized CAE model, the multi-dimensional index evaluation is carried out, and the marked material set is constructed. Finally, the satisfiability is solved through the single-objective optimization design model, and the material selection scheme is obtained. The invention combines the material database technology with the CAE simulation technology to construct a multi-dimensional index evaluation system, which can realize the programming, quantification, dataization and intelligence of the material selection process, and at the same time build a single-objective optimization design model, which can realize the material selection of engineering products. The intelligent generation of the scheme improves the efficiency of material selection, reduces the cost of material selection, and can meet the needs of early design.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

1 is a flowchart of a multi-material intelligent material selection method based on CAE simulation technology in an embodiment of the present invention;

2 is a flowchart of step S20 of the multi-material intelligent material selection method based on CAE simulation technology in an embodiment of the present invention;

3 is a flowchart of step S40 of the multi-material intelligent material selection method based on CAE simulation technology in an embodiment of the present invention;

4 is a flowchart of step S50 of the multi-material intelligent material selection method based on CAE simulation technology in an embodiment of the present invention;

5 is a schematic block diagram of a multi-material intelligent material selection system based on CAE simulation technology in an embodiment of the present invention;

6 is a schematic block diagram of a topology optimization module of a multi-material intelligent material selection system based on CAE simulation technology in an embodiment of the present invention;

7 is a schematic block diagram of a multi-dimensional evaluation module of a multi-material intelligent material selection system based on CAE simulation technology in an embodiment of the present invention;

FIG. 8 is a schematic diagram of an electronic device in an embodiment of the present invention.

Detailed ways

In order to make those skilled in the art better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only Embodiments are part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In one embodiment, as shown in Figure 1, a multi-material intelligent material selection method based on CAE simulation technology is provided, comprising the following steps:

In step S10, the initial CAE model of the material selection object is embedded, and candidate materials matching the performance parameters of the target material are obtained from the preset material database, and an initial material set A is constructed.

In this embodiment, the target material performance parameters include, but are not limited to, conventional material performance and use performance, and the like.

During the material set generation process, the initial CAE (Computer Aided Engineering) model of the material selection object is embedded, and the CAE model contains material properties. After obtaining the target material performance parameters arbitrarily set by the specified user based on experience and the characteristics of competing products After that, candidate materials that meet performance parameters such as conventional performance and service performance are automatically screened out through the retrieval function of the preset material database, so that the initial material set A to be evaluated is composed of all candidate material combinations. The designated user may be a product design engineer.

Preferably, different types of materials can be used for comparison, and the material set generation process of step S10 can be repeatedly performed. For example, if the competitor 1 is an aluminum alloy, an aluminum alloy evaluation set A ₁ can be generated; the competitor 2 is a high-strength steel, Then a high-strength steel evaluation set A ₂ can be generated again; if the competitor 3 is a composite material, a composite material evaluation set A ₃ can be generated again. At this time, the final generated initial material set A to be evaluated can be expressed as:

A=A ₁ +A ₂ +A ₃ (1)

In step S20, a topology optimization model is constructed according to the preset optimization parameters, the initial material set A, and the initial CAE model, and an optimization result is obtained by solving the topology optimization model.

In this embodiment, the preset optimization parameters include optimization constraints and optimization objectives. At this time, as shown in FIG. 2 , step S20 includes the following steps:

Step S201, setting optimization constraints, the optimization constraints include performance constraints such as rigidity constraints and strength constraints of the material selection object.

Step S202, setting an optimization objective, the optimization objective includes objectives such as the largest volume, the lightest weight, and the best material distribution of the material selection object.

Step S203, modify the material properties in the initial CAE model according to the initial material set A, and build M topology optimization models according to optimization constraints and optimization objectives.

In step S204, each topology optimization model is solved 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 process of structural topology optimization, the material properties in the initial CAE model are modified according to the initial material set A, and a topology optimization model matching the number of candidate materials in the initial material set A is derived by combining optimization constraints and optimization objectives. The optimization model is optimized and solved, and the optimization results corresponding to each topology optimization model are recorded. At this time, the optimized CAE model can be obtained through analysis from the optimization results.

In step S30, a CAE model set M is constructed according to the optimization result, and an intermediate material set B is constructed according to the material properties in each CAE model included in the CAE model set M.

In this embodiment, based on the optimization results obtained in step S20, k (k≥1) initial CAE models to be modified are determined, and the initial CAE models are modified according to the corresponding optimized CAE models, and then k modified initial CAE models are and N-k (N is the number of candidate materials in the initial material set A) unmodified initial CAE models are combined to form the CAE model set M, and the material properties in each CAE model included in the CAE model set M are recorded to obtain the intermediate material. set B.

In step S40, multi-dimensional index evaluation is performed according to the CAE model set M and the intermediate material set B, and a target material set A' containing marks is obtained.

In the multi-dimensional evaluation process, based on the CAE model set M and the intermediate material set B in step S30, the CAE models of the CAE model set M are respectively called to perform multi-dimensional index evaluation on each candidate material in the intermediate material set B. The weight factor of the dimension index is marked, and the weight factor corresponding to each dimension index is marked into the intermediate material set B, and finally a marked target material set A' is generated.

Preferably, as shown in Figure 3, step S40 specifically includes the following steps:

In step S401, a multi-dimensional index system is constructed, and the multi-dimensional index system includes performance indicators, molding indicators, lightweight indicators, cost indicators, technology maturity indicators and other indicators.

Step S402 , based on the CAE model and the multi-dimensional index system, perform multi-dimensional index evaluation on each candidate material in the intermediate material set B through a preset index analysis interface, and obtain the corresponding weighting factor R _i .

Step S403, preprocessing the weighting factor R _i of each dimension index. Among them, the preprocessing includes forward processing and dimensionless processing, which is used to process the diverse and multi-dimensional weighting factors R _i into consistent and dimensionless standard weighting factors.

Step S404, marking the preprocessed weighting factor R _i into the intermediate material set B to generate a marked target material set A'.

In this embodiment, the preset indicator analysis interfaces include a performance CAE analysis interface, a forming CAE analysis interface, a topology optimization analysis interface, a cost analysis interface, a technology maturity analysis interface, and other scalable indicator analysis interfaces.

That is, for each candidate material in the intermediate material set B, call the corresponding CAE model, and use each indicator analysis interface to evaluate each dimension indicator to obtain the corresponding weight factor R _i , and then compare each weight factor R _i with Each candidate material is corresponding and marked into the intermediate material set B, thereby obtaining the target material set A'. Among them, each indicator analysis interface corresponds to a dimension indicator.

Further, in an embodiment, in order to preliminarily screen the most suitable material, after step S404, the following steps may be included:

First, each weighting factor associated with each candidate material in the marked target material set A' is input into the preset material selection evaluation model, and the comprehensive index weights output by the preset material selection evaluation model are obtained; wherein, the preset material selection evaluation model Can be:

F=∑R _i *f _j (2)

In formula (2), F is the weight of the comprehensive index, R _i (i=1,2,Λ,I) is the weight factor of the candidate material in each dimension index, _fj (j=1,2,Λ,J) The index value for the specific performance 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 materials whose comprehensive index weight F is equal to or greater than the weight threshold are determined as the most suitable materials.

In step S50, a single-objective optimal design model is constructed according to the target material set A' and the model is solved, and a material selection scheme is determined according to the optimal solution result obtained by the solution.

Preferably, as shown in Figure 4, step S50 includes the following steps:

In step S501, a single-objective optimal design model is constructed according to the target material set A' and the weighting factors of each dimension index. Among them, the single-objective optimization model can be:

In formula (3), ρ(x ₁ , x ₂ , Λ, x _n ) is the single-objective optimization design model, β is the smoothing factor, and g(y _i ) (i=1,2,Λ,m) is the design objective The absolute value of the difference between the target value and the current value of , g(y _i ) can be expressed as:

g(y _i )=|V _i -R _i | (4)

In formula (4), _Vi is the target value of the design target _, and Ri is the current value of the design target.

Understandably, the design variable x _i in the single-objective optimal design model ρ(x ₁ ,x ₂ ,Λ,x _n ) is the candidate material in the target material set A′, and the current value R _i of the design objective _yi is The weighting factor of the candidate material in the selected evaluation dimension.

In step S502, the single-objective optimization design model is solved 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 1: Obtain the best current value in the single-objective optimal design model through the particle swarm optimization algorithm.

Step 2: Check whether the material selection scheme corresponding to the best current value is a satisfactory scheme.

Step 3, if the solution is not satisfied, obtain all the design constraints and design objectives in the non-satisfied solution, and obtain the optimal target value; otherwise, output the satisfiable solution as the optimal solution result.

Step 4: Determine whether the current iteration number is less than or equal to a preset iteration threshold.

Step 5, if yes, return to step S5021, otherwise, determine that there is no optimal solution result.

In the optimization solution process, firstly, the best current value R best in the single-objective optimization design model is found by the particle swarm optimization algorithm, and the satisfiability of the material selection scheme corresponding to the _best current value R _best is detected. When , the satisfiable scheme is output as the solution result; when the material selection scheme is an unsatisfactory scheme, record all design constraints and design objectives in the unsatisfied scheme, and find the best target value V _best .

Then judge whether the current iteration number t is less than or equal to the preset iteration threshold T _max , when t≤T _max , return to step 1 to update the best current value R _best and the best target value V _best ; and when t>T _max , the iteration process ends.

Understandably, each dimension index corresponds to a single-objective optimization design model, and the particle swarm optimization algorithm is used to solve each single-objective optimization design model, and a series of satisfiable solutions can be obtained, and finally the final solution can be determined from a series of satisfiable solutions. material selection scheme.

Further, after step S502, the following steps may further include: when there is no optimal solution result, obtaining a satisfiable solution result, and determining a material selection scheme according to the satisfiable solution result.

That is, when the evaluation dimension is too large or the weight factor is not set properly, there may be no optimal solution results or the optimization solution time is too long. At this time, any satisfactory solution results can be found, and materials are selected based on the satisfactory solution results. Creative generation of scenarios.

The multi-material intelligent material selection method based on the CAE simulation technology of the above-mentioned embodiment firstly embeds the CAE model, and intelligently selects materials from the material database to construct a material set, and then combines the CAE solver and the topology optimization model to obtain the optimized CAE model, and further. Based on the optimized CAE model, the multi-dimensional index evaluation is carried out, and the material set with markers is constructed. Finally, the satisfiability is solved through the single-objective optimization design model, and the material selection scheme is obtained. The above embodiment combines material database technology with CAE simulation technology to build a multi-dimensional index evaluation system, which can realize the programming, quantification, dataization and intelligence of the material selection process, and at the same time build a single-objective optimization design model, which can realize engineering products The intelligent generation of the material selection scheme improves the efficiency of material selection, reduces the cost of material selection, and can meet the needs of early design.

In one embodiment, a kind of multi-material intelligent material selection system based on CAE simulation technology is provided, and the multi-material intelligent material selection system based on CAE simulation technology is in one-to-one correspondence with the multi-material intelligent material selection method based on CAE simulation technology in the above-mentioned embodiment. . As shown in FIG. 5 , the multi-material intelligent material selection system based on 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. Details are as follows:

The material set generation module 110 is used to embed the initial CAE model of the material selection object, and obtain candidate materials matching the performance parameters of the target material from the preset material database to construct an initial material set;

A topology optimization module 120, configured to construct a topology optimization model according to preset optimization parameters, an initial material set and an initial CAE model, and solve the topology optimization model to obtain an optimization result;

a material set processing module 130, configured to construct a CAE model set according to the optimization result, and construct an intermediate material set according to the material properties in each CAE model included in the CAE model set;

The multi-dimensional evaluation module 140 is configured to perform multi-dimensional index evaluation according to the CAE model set and the intermediate material set, and obtain the marked target material set;

The scheme generation module 150 is configured to construct a single-objective optimal design model according to the target material set and solve the model, and determine the material selection scheme according to the optimal solution result obtained by the solution.

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 solution unit 124. The detailed description of each functional unit is as follows:

The constraint unit 121 is used to set optimization constraints; the optimization constraints include rigidity constraints and strength constraints of the material selection object;

The target unit 122 is used to set an optimization target; the optimization target includes the largest volume, the lightest weight, and the best material distribution of the material selection object;

The topology optimization unit 123 modifies the material properties in the initial CAE model according to the initial material set, and builds M topology optimization models according to optimization constraints and optimization objectives;

The topology optimization solving unit 124 solves each topology optimization model to obtain corresponding optimization results.

Further, as shown in FIG. 7 , the multi-dimensional evaluation module 140 includes a system construction unit 141, an evaluation unit 142, a preprocessing unit 143 and a marking unit 144. The detailed description of each functional unit is as follows:

The system construction unit 141 is used to construct a multi-dimensional indicator system; the multi-dimensional indicator system includes performance indicators, molding indicators, lightweight indicators, cost indicators, technology maturity indicators and other indicators;

The evaluation unit 142 is configured to perform multi-dimensional index evaluation on each candidate material in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system, and obtain the corresponding weighting factor;

The preprocessing unit 143 is configured to preprocess the weighting factors of each dimension index;

The marking unit 144 is configured to mark the preprocessed weighting factors into the intermediate material set to generate a marked target material set.

Further, the solution generation module 150 includes a design model building 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 to build a single-objective optimal design model according to the target material set and the weight factors of each dimension index;

The design model solving unit is used to solve the single-objective optimization design model through the heuristic optimization algorithm to obtain the optimal solution result.

The systems in the foregoing embodiments are used to implement the corresponding methods in the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

In addition, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computing program stored in the memory and running on the processor, the processor implements any one of the foregoing implementations when the processor executes the program The multi-material intelligent material selection method based on CAE simulation technology described in this example.

FIG. 8 shows a schematic diagram of a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 100 , a memory 200 , an input/output interface 300 , a communication interface 400 and a bus 500 . The processor 100 , the memory 200 , the input/output interface 300 , the communication interface 400 , and the bus 500 realize the communication connection between each other within the device.

The processor 100 may be implemented by a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute related program to implement the technical solutions provided by the embodiments of the present invention.

The memory 200 may be implemented in the form of a ROM (Read Only Memory, read only memory), a RAM (Random Access Memory, random access memory), a static storage device, a dynamic storage device, and the like. The memory 200 may store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of the present invention through software or firmware, relevant program codes are stored in the memory 200 and invoked by the processor 100 for execution.

The input/output interface 300 is used for connecting input/output modules to realize information input and output. The input/output/module can be configured in the device as a component (not shown in the figure), or can be externally connected to the device to provide corresponding functions. 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, and the like.

The communication interface 400 is used to connect a communication module (not shown in the figure), so as to realize the communication interaction between the device and other devices. The communication module may implement communication through wired means (eg, USB, network cable, etc.), or may implement communication through wireless means (eg, mobile network, WIFI, Bluetooth, etc.).

Bus 500 includes a path that transfers information between the various components of the device (eg, processor 100, memory 200, input/output interface 300, and communication interface 400).

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 the specific implementation process, the device may also include necessary components for normal operation. other components. In addition, those skilled in the art can understand that, the above-mentioned device may only include components necessary to implement the solutions of the embodiments of the present specification, instead of all the components shown in the figures.

Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary, and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the spirit of the present disclosure, the above embodiments or Technical features in different embodiments can also be combined, steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of brevity.

Embodiments of the invention are intended to cover all such alternatives, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention shall be included within the protection scope of the present disclosure.

Claims (7)

1. a multi-material intelligent material selection method based on CAE simulation technology, is characterized in that, comprises: Embed the initial CAE model of the material selection object, and obtain the candidate materials matching the performance parameters of the target material from the preset material database to construct the initial material set; Build 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; Build a CAE model set according to the optimization result, and build an intermediate material set according to the material properties in each CAE model included in the CAE model set; Perform multi-dimensional index evaluation according to the CAE model set and the intermediate material set, and obtain a marked target material set; the marking is the weighting factor corresponding to each dimension index; Construct a single-objective optimal design model according to the target material set and solve the model, and determine a material selection scheme according to the optimal solution result obtained by the solution; this step includes: constructing a single-objective optimization design model according to the target material set and the weighting factors of each dimension index Objective optimization design model; the single objective optimization design model is:

Among them, ρ(x ₁ ,x ₂ ,Λ,x _n ) is the single-objective optimization design model, and g(y _i )(i=1,2,Λ,m) is the difference between the target value of the design target and the current value The absolute value of , β is the smoothing factor, n is the number of design variables x _i , m is the number of design targets y _i , and g(y _i ) is: g(y _i )=|V _i -R _i |, Among them, _Vi is the target value of the design target _, and Ri is the current value of the design target; The single-objective optimization design model is solved through a heuristic optimization algorithm, and the optimal solution result is obtained; the heuristic optimization algorithm is a particle swarm optimization algorithm, and this step includes: Obtain the best current value in the single-objective optimal design model by using the particle swarm optimization algorithm; Detecting whether the material selection scheme corresponding to the best current value is a satisfactory scheme; If it does not meet the scheme, obtain all the design constraints and design objectives that do not meet the scheme, and obtain the best target value; judge whether the current iteration number is less than or equal to the preset iteration threshold; If yes, go back to the step: obtain the best current value in the single-objective optimization design model through the particle swarm optimization algorithm; if not, determine that there is no optimal solution result. 2. the multi-material intelligent material selection method based on CAE simulation technology as claimed in claim 1, is characterized in that, described preset optimization parameter comprises optimization constraint and optimization objective; The building a topology optimization model according to the preset optimization parameters, the initial material set and the initial CAE model, and solving the topology optimization model to obtain an optimization result, including: Setting optimization constraints; the optimization constraints include rigidity constraints and strength constraints of the material selection object; Setting an optimization goal; the optimization goal includes the largest volume, the lightest weight, and the best material distribution of the material selection object; Modify material properties in the initial CAE model according to the initial material set, and construct M topology optimization models according to the optimization constraints and the optimization objective; Solve each of the topology optimization models to obtain corresponding optimization results. 3. The multi-material intelligent material selection method based on CAE simulation technology as claimed in claim 1, wherein the multi-dimensional index evaluation is carried out according to the CAE model set and the intermediate material set, and the target material containing the mark is obtained. set, including: Build a multi-dimensional indicator system; the multi-dimensional indicator system includes performance indicators, molding indicators, lightweight indicators, cost indicators, technology maturity indicators and other indicators; Based on the CAE model and the multi-dimensional indicator system, perform multi-dimensional indicator evaluation on each of the candidate materials in the intermediate material set through a preset indicator analysis interface to obtain corresponding weighting factors; Preprocessing the weighting factors of each dimension index; The preprocessed weighting factors are marked into the intermediate material set to generate a marked target material set. 4. a multi-material intelligent material selection system based on CAE simulation technology, is characterized in that, comprises: The material set generation module is used to embed the initial CAE model of the material selection object, and obtain the candidate materials matching the performance parameters of the target material from the preset material database to construct the initial material set; a topology optimization module, 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; a material set processing module, configured to construct a CAE model set according to the optimization result, and construct an intermediate material set according to the material properties in each CAE model included in the CAE model set; A multi-dimensional evaluation module, configured to perform multi-dimensional index evaluation through the CAE model according to the CAE model set and the intermediate material set, and obtain a marked target material set; the marking is the weighting factor corresponding to each dimension index ; A scheme generation module is used to construct a single-objective optimal design model according to the target material set and solve the model, and determine a material selection scheme according to the optimal solution result obtained by the solution; the scheme generation module includes: a design model construction unit, configured to construct a single-objective optimal design model according to the target material set and the weighting factors of each of the dimension indicators; A design model solving unit, configured to solve the single-objective optimal design model through a heuristic optimization algorithm to obtain the optimal solution result. 5. The multi-material intelligent material selection system based on CAE simulation technology as claimed in claim 4, is characterized in that, described topology optimization module comprises: A constraint unit, used for setting optimization constraints; the optimization constraints include rigidity constraints and strength constraints of the material selection object; A target unit, used for setting an optimization target; the optimization target includes the largest volume, the lightest weight, and the best material distribution of the material selection object; a topology optimization unit, modifying material properties in the initial CAE model according to the initial material set, and constructing M topology optimization models according to the optimization constraints and the optimization objective; The topology optimization solving unit solves each of the topology optimization models to obtain corresponding optimization results. 6. The multi-material intelligent material selection system based on CAE simulation technology as claimed in claim 5, wherein the multi-dimensional evaluation module comprises: a system construction unit for constructing a multi-dimensional indicator system; the multi-dimensional indicator system includes performance indicators, molding indicators, lightweight indicators, cost indicators, technology maturity indicators and other indicators; An evaluation unit, configured to perform multi-dimensional index evaluation on each of the candidate materials in the intermediate material set through a preset index analysis interface based on the CAE model and the multi-dimensional index system, and obtain corresponding weighting factors ; a preprocessing unit, configured to preprocess the weighting factor of each dimension index; A labeling unit, configured to label the preprocessed weighting factors into the intermediate material set to generate a target material set containing the label. 7. An electronic device, comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements any one of claims 1 to 3 when the processor executes the program The multi-material intelligent material selection method based on CAE simulation technology.

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