CN113158514A - Automobile body material structure matching lightweight design method, system and storage medium - Google Patents

Automobile body material structure matching lightweight design method, system and storage medium Download PDF

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CN113158514A
CN113158514A CN202110345933.4A CN202110345933A CN113158514A CN 113158514 A CN113158514 A CN 113158514A CN 202110345933 A CN202110345933 A CN 202110345933A CN 113158514 A CN113158514 A CN 113158514A
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王勇
刘角
孙光永
庞通
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Central South University
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Abstract

The invention discloses a matching lightweight design method, a matching lightweight design system and a matching lightweight storage medium for an automobile body material structure, wherein the thickness and the material mark of the automobile body thin-wall structure are taken as design parameters, the goal of taking the B column intrusion amount, the B column intrusion speed, the B column specific energy absorption or the automobile body structure weight as design targets is achieved, and the purpose of reducing the automobile weight while ensuring that the automobile crashworthiness is not reduced is achieved.

Description

Automobile body material structure matching lightweight design method, system and storage medium
Technical Field
The invention relates to the field of automobile structure design, in particular to a matching lightweight design method, a matching lightweight design system and a storage medium for an automobile body material structure.
Background
The automobile industry is one of the important post industries in the contemporary industrial field, and with the rapid development of the automobile industry, the environmental pollution, the energy shortage and the traffic safety problem caused by the automobile industry are increasingly highlighted. Environmental protection, energy saving and safety have become major problems in the development of the automobile industry at present, and these problems are closely related to the weight reduction of automobiles. The world aluminium association studies show that: when the self weight of the automobile is reduced by 10 percent, the fuel consumption and the emission can be respectively reduced by 6 to 8 percent and 5 to 6 percent; every 1 liter of oil consumption is reduced, the emission of carbon dioxide is correspondingly reduced by 2.45 kg. From the perspective of safety, the braking distance can be effectively reduced by reducing the weight of the automobile, so that the accident rate is reduced. The light weight of the automobile is still endless no matter from the aspects of energy conservation and emission reduction or safety, and the research on materials, forming and design related to the light weight of the automobile is always the leading edge and the hot spot in the automobile industry.
The design of the light weight of the automobile means that the weight of the automobile is reasonably reduced under the condition that the performance indexes of the automobile such as crashworthiness, safety, stability and smoothness are not reduced and the manufacturing cost is not increased. One way to achieve light weight is to optimize the topological structure, size and shape parameters of automobile parts by advanced design means such as multidisciplinary optimization starting from the automobile structure, compound, thin-walled and cavitate parts and the like.
In fact, in order to ensure the crashworthiness and reduce the weight of the motor vehicle body structure, it is possible to consider not only optimizing the dimensional parameters of the different components of the motor vehicle body structure, but also to achieve this by the rational distribution of the different materials for the different components. For example, when designing a vehicle body to be lightweight, it is generally desirable to minimize the amount of intrusion and the rate of intrusion of the B-pillar of the vehicle when a side collision occurs in order to ensure the safety of the occupants. From an optimization point of view, the problem has two characteristics: 1) both continuously variable (i.e., thin-walled structure thickness) and discretely variable (i.e., component material); 2) the two performance indexes of the intrusion amount and the intrusion speed of the automobile B column have no specific mathematical expression and can be obtained only through finite element analysis or physical experiments. Therefore, such a design problem considering both the size of the component and the material of the component is generally called a vehicle body multi-material structure matching design.
In designing automobiles for light weight, the entire design process is usually described as an optimization problem, and a specific light weight design is realized by solving the optimization problem. In the described optimization problem, physical properties of some parts, such as collision energy absorption, collision peak force, part quality and the like, are used as performance indexes to measure the performances of crashworthiness, safety and the like of the automobile, and parameters of part size, structure, material and the like are taken as design parameters. By adjusting design parameters, the performance indexes are maximized/minimized or meet certain design requirements, and the aim of realizing the light weight of the automobile without reducing various performances of the automobile can be achieved. The matching design problem of the multi-material structure of the vehicle body mainly comprises the following three characteristics:
black box: the optimization problem described for automotive lightweighting is typically not provided with a displayed expression. This means that when solving such optimization problems, often only the objective function response corresponding to a set of parameters can be known, and mathematical properties such as gradients, second derivatives, etc. cannot be obtained.
Expensive: for many performance indexes, only simulation tools such as finite element analysis or actual physical experiments can obtain specific numerical values. This process can be time and financial intensive. Therefore, it is not practical to evaluate some performance indexes many times.
Two or more variable types are included: the optimization problem described for automotive body multi-material structural matching designs may involve both continuous variables such as part size and discrete variables such as material selection.
It is worth noting that the vehicle body multi-material structure matching design problem has both black box and expensive characteristics. These two characteristics make it impossible to evaluate a large number of performance indicators of the automobile parts, and also to use the traditional gradient-based optimization method. In order to effectively deal with the design problem of the automobile structure with black boxes and expensive characteristics, a plurality of optimization algorithms based on a proxy model are proposed in the past decade. However, most of the methods are directed to design problems involving only continuous design parameters, and methods involving multiple variable types simultaneously, such as vehicle body multi-material structure matching design, are rare. Existing approaches to include multiple variable types employ only a single kind of proxy model. This approach is often difficult to effectively approximate objective functions and constraints with multiple variable types, and further difficult to effectively guide the algorithm to find a high-quality optimal solution.
Disclosure of Invention
The invention aims to solve the technical problem that aiming at the defects of the prior art, the invention provides a method, a system and a storage medium for designing the matching lightweight of the automobile body material structure, so as to optimize the automobile body structure and achieve the aim of reducing the weight of an automobile.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a matching lightweight design method for a multi-material structure of an automobile body comprises the following steps:
s1, establishing and preliminarily solving the following optimization problems:
min:f(x)
s.t.c1(x)≤T1
c2(x)≤T2
c3(x)≤T3
x＝[xthick,xmat]
Figure BDA0003000812800000031
Figure BDA0003000812800000032
Figure BDA0003000812800000033
Figure BDA0003000812800000034
wherein x is a design parameter, xthickFor vectors composed of thickness design parameters, xmatIs a vector composed of material design parameters, f (x) is an objective function, c1(x) As a first constraint, c2(x) As a second constraint, c3(x) As a third constraint, T1，T2And T3Are indexes that three constraint conditions need to satisfy, LiAnd UiLower and upper limits of the design parameter for the ith thickness, M1～MpEach representing p different materials, n1For the number of thickness parameters to be optimized, n2Number of parameters for the grade of the material to be optimized;
s2, converting the optimization problem into the following problems:
Figure BDA0003000812800000035
Figure BDA0003000812800000036
Figure BDA0003000812800000037
Figure BDA0003000812800000038
Figure BDA0003000812800000039
Figure BDA00030008128000000310
s3, solving the problem of the step S2 by using sequential quadratic programming to obtain a solution
Figure BDA00030008128000000311
This solution represents the optimal thin-wall structure thickness design parameter given by the local search, which will then follow
Figure BDA00030008128000000312
And
Figure BDA00030008128000000313
combined into a new solution
Figure BDA00030008128000000314
The solution is the optimal design parameter given by the local search.
The invention has the advantages that:
s1 focuses on obtaining optimal solutions for both continuous and discrete variables. Solving the optimization problem established in S1 can quickly locate the region where the optimal solution is located, providing an initial solution of higher quality for subsequent optimization. The initial solution can provide a relatively excellent lightweight design scheme for the subsequent optimization process, and a relatively light vehicle body lightweight design scheme is obtained under the condition that the design indexes are met as far as possible.
S2 focuses on obtaining a high quality continuous variable solution. In S3, the optimization problem established in S2 is solved, and the optimal solution can be converged to a good one. On the basis of the solution obtained in S1, namely the automobile lightweight design scheme, a better and light automobile body lightweight design scheme meeting design indexes is further and quickly found.
The specific implementation process of step S1 includes:
a1, establishing the following RBF proxy model for the objective function f (x):
Figure BDA0003000812800000041
wherein xlParameters are designed for the l-th group in the archive set,
Figure BDA0003000812800000042
is a Gaussian kernel function, dis (x, x)l) Denotes x and xlThe distance between the two or more of the two or more,
Figure BDA0003000812800000043
wherein
Figure BDA0003000812800000044
Denotes x and xlThe vector difference in the thickness variation,
Figure BDA0003000812800000045
is x and xlThe xor operation of the vector of the material brand,
Figure BDA00030008128000000414
is composed of
Figure BDA00030008128000000413
And
Figure BDA0003000812800000047
the vector is formed, wherein | · | | represents a 2-norm, N is the number of design parameters stored in an archive set, and A is { x ═ x |, in the archive setl,yl,(cl,1,cl,2,cl,3)|l＝1,...,N}，
Figure BDA0003000812800000048
(cl,1,cl,2,cl,3) The constraint function values for the l-th set of design parameters,
Figure BDA0003000812800000049
an ith thickness parameter representing the ith set of design parameters,
Figure BDA00030008128000000410
j material grade variable, y, representing the l set of design parameterslDesigning the objective function value of the parameter for the l-th group, wlIs a weight;
for constraint c1(x)，c1(x) And c1(x) Establishing three RBF proxy models
Figure BDA00030008128000000411
And
Figure BDA00030008128000000412
for the target function f (x), establishing the following gradient lifting tree proxy model on the basis of a design parameter filing set:
Figure BDA0003000812800000051
t (,) is a regression tree, ΘmIs a parameter of the mth regression tree, M is the total number of regression trees,
Figure BDA0003000812800000052
for constraint c1(x)，c1(x) And c1(x) Establishing three gradient lifting tree agent models
Figure BDA0003000812800000053
Figure BDA0003000812800000054
And
Figure BDA0003000812800000055
sorting the parameters in the ant colony algorithm colony according to the function value of the objective function, and distributing ordinal rank(s) to each group of parameters, wherein s represents the ant colony algorithm colony P ═ { x ═sThe s-th set of parameters in 1., K },
Figure BDA0003000812800000056
Figure BDA0003000812800000057
an ith thickness parameter representing an s-th set of design parameters,
Figure BDA0003000812800000058
a jth material grade variable representing a set s of design parameters; assigning weights to each set of parameters in an ant colony algorithm population
Figure BDA0003000812800000059
q is an ant colony algorithm parameter, and K is an ant colony algorithm population scale; design variable of ith thickness in generation of h-th offspring
Figure BDA00030008128000000510
First, the probability is calculated
Figure BDA00030008128000000511
And randomly selecting a thickness parameter of a thin-wall structural part from the ant colony algorithm colony according to the probability, and recording the thickness parameter as mujThen according to a Gaussian distribution
Figure BDA00030008128000000512
Generating a thin-wall structure thickness parameter corresponding to the offspring, wherein
Figure BDA00030008128000000513
Xi is an ant colony algorithm parameter; material grade variable at the jth offspring from which the h offspring was generated
Figure BDA00030008128000000514
According to the probability psRandomly selecting a material grade from an ant colony algorithm colony, and giving a probability of 0.1 to change the material grade into any other material grade; the above process is repeated until after H offspring are generated, all of which are stored in the set O ═ xh1,. H, H ], wherein
Figure BDA00030008128000000515
Represents the h-th offspring expressing a set of design parameters.
A2, evaluating all descendants in the set O by using all the agent models, namely substituting the descendants in the set into the established agent models one by one to obtain the corresponding agent models
Figure BDA00030008128000000516
Figure BDA00030008128000000517
And
Figure BDA00030008128000000518
a value of (d); then, two sets of design parameters are selected from O according to these values, specifically: if there is a sufficient condition in O
Figure BDA0003000812800000061
The design parameter of (1), then selecting the one satisfying the condition from O
Figure BDA0003000812800000062
The design parameter with the smallest value is selected otherwise
Figure BDA0003000812800000063
The design parameter with the smallest value; if present, is
Figure BDA0003000812800000064
The design parameter of (1), then selecting the one satisfying the condition from O
Figure BDA0003000812800000065
The design parameter with the smallest value is selected otherwise
Figure BDA0003000812800000066
The design parameter with the smallest value; after the selection is finished, randomly selecting a group of design parameters from O; using an objective function f (x) and a constraint c1(x)，c2(x)，c3(x) And evaluating the three selected groups of design parameters, and storing the three groups of design parameters, the target functions and the constraint condition values into a design parameter filing set A.
The specific implementation process of step S2 is
A3, determining the best solution in archive set A, and recording as
Figure BDA0003000812800000067
Wherein
Figure BDA0003000812800000068
The parameters are designed for the thickness of the film,
Figure BDA0003000812800000069
is a material brand;
a4, finding all material trade mark variables and
Figure BDA00030008128000000610
the same design parameters and the thickness variables of these design parameters are stored in an archive set AlocalPerforming the following steps; according to AlocalAnd establishing a plurality of continuous RBF proxy models for the objective function and the constraint condition to obtain the problem of the step S2.
Compared with the prior art, the invention has the following technical effects:
1. in step a1, the present invention uses a mixture variable to simultaneously use two proxy models: the RBF proxy model and the gradient lifting tree proxy model process target functions and constraint conditions with various variable types. This has the advantage that the RBF proxy model and the gradient-lifting tree proxy model can be adapted to different types of variables, i.e. the RBF proxy model is adapted to handle objective functions and constraints with continuous variables and the gradient-lifting tree model is adapted to handle objective functions and constraints with discrete variables. Due to the matching problem of the multi-material structure of the automobile body to be solved, namely, the optimal values of the dimension parameters and the material parameters in the automobile body structure are obtained simultaneously, and the optimal values comprise continuous variables and discrete variables. Therefore, using both proxy models at the same time is more helpful in dealing with such problems. Furthermore, when the weight of the automobile is reduced, the method can help designers find excellent automobile weight reduction design schemes more easily, namely, extremely light automobile bodies can be obtained under the condition that the requirement of crashworthiness is met.
2. In step A2, we use two metrics, namely
Figure BDA00030008128000000611
And
Figure BDA00030008128000000612
and judging the advantages and the disadvantages of different solutions. Wherein the content of the first and second substances,
Figure BDA0003000812800000071
focusing on satisfying constraints, i.e.
Figure BDA0003000812800000072
While
Figure BDA0003000812800000073
Emphasis is placed on the objective function. This helps to guide the algorithm into the feasible domain quickly for the descendants generated in A1, and further boost the value of the objective function based thereon. Furthermore, the mode helps designers to find the automobile lightweight design scheme meeting design requirements quickly, design efficiency is improved, and design period is shortened.
3. In steps A3 and a4, we use only a part of the solutions in the archive set to create the optimization problem mentioned in S2, which can effectively focus on obtaining a high-precision problem model in a local scope, thereby improving the efficiency of obtaining a high-quality solution. Furthermore, the method can further improve the quality of light weight design and obtain more excellent automobile light weight design scheme.
Before step a3, the three sets of design parameters are incorporated into the population, and then sorted according to the objective function values and constraint condition values corresponding to the three sets of design parameters, where the sorting method is as follows: first, for
Figure BDA0003000812800000074
Is ordered from large to small, then for
Figure BDA0003000812800000075
The design parameters of (2) are sorted from large to small according to the objective function value
Figure BDA0003000812800000076
Is arranged to
Figure BDA0003000812800000077
Before the design parameters are sorted, deleting the three groups of design parameters before sorting from the population after sorting is finished, namely finishing elite selection, and storing the three groups of design parameters after sorting, target functions of the three groups of design parameters and constraint condition values into a design parameter filing set A.
The invention also provides a matching lightweight design system of the multi-material structure of the automobile body, which comprises computer equipment; the computer device is configured or programmed for performing the steps of the above-described method.
As an inventive concept, the present invention also provides a computer-readable storage medium storing a program; the program is configured for performing the steps of the above-described method.
Compared with the prior art, the invention has the technical effects that:
1. the invention can deal with the design problem of light weight of the automobile with continuous variable (such as the structural size of a certain part of the automobile) and discrete variable (such as the material selection of a certain part of the automobile);
1. the invention reasonably utilizes a plurality of agent models and can obtain a high-quality design scheme in a short design period.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of design parameters.
Detailed Description
The method proposed by the present invention is now exemplified by the design of a vehicle body structure for weight reduction as shown in fig. 2The description is given. In this example, the thickness and material grade of the five thin-walled structural members are set as design parameters. The purpose of the embodiment is to reduce the intrusion amount of the B column of the automobile as much as possible by adjusting the thickness and the material grade of the five thin-wall structural components, and simultaneously ensure that the intrusion speed of the B column, the integral weight of the side body structure and the structural energy absorption do not exceed T respectively1，T2And T3. Referring to fig. 1, the implementation steps of this embodiment are as follows:
step 1: establishing an optimization problem;
step 2: initializing an algorithm;
and step 3: establishing an agent model;
and 4, step 4: generating offspring;
and 5: selecting a multi-agent model in an auxiliary mode;
step 6: updating the population;
and 7: the agent model assists the local search;
and 8: and continuously executing the steps of 2, 3, 4, 5, 6 and 7 until the termination condition is met.
In step 1, the design parameters are determined by selecting a number of thin-walled structural components from the body of the automobile, using the thicknesses of these components as continuous design parameters, and using the material grades as discrete design parameters, as shown in fig. 2. The design target determining method is that one index of B column intrusion amount, B column intrusion speed, B column energy absorption or automobile body structure weight is used as a target function, other three indexes are used as constraint conditions, and the indexes are smaller than a certain value, so that the automobile crashworthiness is maximized under the condition that the low automobile weight is not improved, or the automobile weight is minimized under the condition that the automobile crashworthiness is not reduced. These indices can be obtained by finite element analysis software, such as LS-DYNA. Finally, the following optimization problem is established:
min:f(x)
s.t.c1(x)≤T1
c2(x)≤T2
c3(x)≤T3
x＝[xthick,xmat]
Figure BDA0003000812800000091
Figure BDA0003000812800000092
Figure BDA0003000812800000093
Figure BDA0003000812800000094
wherein x is a set of design parameters, xthickFor vectors composed of thickness design parameters, xmatIs a vector composed of material design parameters, f (x) is an objective function, i.e., B-pillar intrusion, c1(x) The first constraint, i.e., the B-pillar invasion speed, c2(x) A second constraint, i.e. the weight of the side structure, c3(x) For the third constraint, i.e. B-pillar energy absorption, the objective function and the function values for all constraints can be obtained by finite element analysis, T1，T2And T3Three constraints are respectively required to satisfy, i.e. the B-pillar intrusion speed of the vehicle is required not to exceed T1The energy absorption of the B column does not exceed T2Weight on side not exceeding T3，LiAnd UiLower and upper limits of the design parameter for the ith thickness, M1～M5Respectively represent five high-strength steels with the grades of DP440, DP500, DP600, DP780 and DP 980.
In step 2, the following parameters will be initialized:
ant colony algorithm parameters: the ant colony algorithm population scale K is included to generate two constants q and ξ for the offspring design parameters.
Ant colony algorithm population: comprises a series of automobile body structure parameters and design function response values thereof, which are marked as P ═ xs|s1, K, wherein
Figure BDA0003000812800000095
Figure BDA0003000812800000096
An ith thickness parameter representing an s-th set of design parameters,
Figure BDA0003000812800000097
the jth material grade variable representing the s-th set of design parameters. The structural parameters of the automobile body are shown in fig. 2 specifically, and are the thickness of the thin-wall structural components of the automobile body and the material grade of each thin-wall structural component. The design function response value can be specifically the B column waist line intrusion amount, the B column waist line intrusion speed, the B column energy absorption and the B column specific energy absorption, and can be obtained by finite element analysis. In the subsequent step, the population will store the K sets of design parameters evaluated by finite element analysis with the best performance index.
Designing a parameter archive set: comprises a series of automobile body structure parameters and design function response values thereof, which are marked as A ═ xl,yl,(cl,1,cl,2,cl,3) 1., N }, wherein
Figure BDA0003000812800000098
ylAn objective function value for the first set of design parameters, (c)l,1,cl,2,cl,3) The constraint function values for the l-th set of design parameters,
Figure BDA0003000812800000101
a jth thickness parameter representing a ith set of design parameters,
Figure BDA0003000812800000102
the jth material grade variable representing the ith set of design parameters.
In step 3, two proxy models, namely an RBF model and a gradient lifting tree model, are respectively established for the objective function and each constraint condition. The method comprises the following specific steps:
for the objective function, the following RBF proxy model is established on the basis of the design parameter archive set
Figure BDA0003000812800000103
Wherein xlA parameter, dis (x, x), is designed for group i in the archive setl) Denotes x and xlA distance therebetween, in particular
Figure BDA0003000812800000104
Wherein
Figure BDA0003000812800000105
Denotes x and xlThe vector difference in the thickness variation,
Figure BDA0003000812800000106
is x and xlThe xor operation of the vector of the material brand,
Figure BDA0003000812800000107
is composed of
Figure BDA0003000812800000108
And
Figure BDA0003000812800000109
the vector of which, | · | |, represents a 2-norm. w is alFor the weight, the specific calculation method is as follows:
w＝(ΦTΦ)-1(ΦTy)
wherein w ═ w1,...,wN) Is a weight vector, y ═ y1,...,yN) For the objective function response values stored in the design parameter archive set, Φ is the following matrix:
Figure BDA00030008128000001010
similarly, for constraint c1(x)，c2(x) And c3(x) In a similar mannerMethod for establishing three RBF proxy models
Figure BDA00030008128000001011
And
Figure BDA00030008128000001012
in particular to
Figure BDA00030008128000001013
Figure BDA00030008128000001014
Figure BDA00030008128000001015
Wherein the weight is
Figure BDA00030008128000001016
And
Figure BDA00030008128000001017
calculated in the following manner, respectively:
wc1＝(ΦTΦ)-1(ΦTc1)
wc2＝(ΦTΦ)-1(ΦTc2)
wc3＝(ΦTΦ)-1(ΦTc3)
wherein c is1＝(c1,1,...,cN,1)，c2＝(c1,2,...,cN,2) And c3＝(c1,3,...,cN,3) Instead of y ═ y1,...,yN) A vector of constraint response values stored in the data archive set.
For the objective function, the following gradient lifting tree agent model is established on the basis of a design parameter filing set
Figure BDA0003000812800000111
Wherein T (·,. cndot.) is a regression tree, ΘmThe parameter of the mth regression tree is calculated as follows
Figure BDA0003000812800000112
Wherein
Figure BDA0003000812800000113
Similarly, for constraint c1(x)，c2(x) And c3(x) Establishing three gradient lifting tree models in a similar way
Figure BDA0003000812800000114
And
Figure BDA0003000812800000115
the method comprises the following specific steps:
Figure BDA0003000812800000116
Figure BDA0003000812800000117
Figure BDA0003000812800000118
gradient lifting tree model parameters
Figure BDA0003000812800000119
And
Figure BDA00030008128000001110
the method comprises the following steps:
Figure BDA00030008128000001111
Figure BDA00030008128000001112
Figure BDA00030008128000001113
wherein
Figure BDA00030008128000001114
Figure BDA00030008128000001115
In step 4, a mixed variable ant colony Algorithm (ACO) is employedMV) Generating offspring, each offspring representing a set of design parameters and represented as
Figure BDA00030008128000001116
Wherein
Figure BDA00030008128000001117
The ith thickness design parameter representing the h-th offspring,
Figure BDA00030008128000001118
the jth material design parameter representing the h descendant is generated as follows. Firstly, sorting parameters in the ant colony algorithm colony according to the response value of the design function, wherein the sorting mode is specifically that firstly, the parameters in the ant colony algorithm colony are sorted
Figure BDA00030008128000001119
The design parameters of (1) are ordered according to the value of the objective function from large to small, and then
Figure BDA0003000812800000121
Is provided withMeasure a parameter according to
Figure BDA0003000812800000122
Is sorted from small to large and will finally
Figure BDA0003000812800000123
Is arranged to
Figure BDA0003000812800000124
And a rear face. After sorting, distributing ordinal rank(s) for each group of parameters, wherein s represents the s-th group of parameters in the ant colony algorithm colony; subsequently assigning weights to each set of parameters in the ant colony algorithm population
Figure BDA0003000812800000125
Wherein q is an ant colony algorithm parameter, and K is an ant colony algorithm population scale; firstly, sorting parameters in the ant colony algorithm colony according to the response value of the design function, and distributing ordinal rank(s) to each group of parameters, wherein s represents the ant colony algorithm colony P ═ { x ═sThe s-th set of parameters in 1., K },
Figure BDA0003000812800000126
Figure BDA0003000812800000127
an ith thickness parameter representing an s-th set of design parameters,
Figure BDA0003000812800000128
the jth material grade variable representing the s-th set of design parameters. Subsequently assigning weights to each set of parameters in the ant colony algorithm population
Figure BDA0003000812800000129
Wherein q is an ant colony algorithm parameter, and K is an ant colony algorithm population scale; then, in the generation
Figure BDA00030008128000001210
First, the probability is calculated
Figure BDA00030008128000001211
And randomly selecting a thickness parameter of a thin-wall structural part from the ant colony algorithm colony according to the probability, and recording the thickness parameter as mujThen according to a Gaussian distribution
Figure BDA00030008128000001212
Generating a thin-wall structure thickness parameter corresponding to the offspring, wherein
Figure BDA00030008128000001213
Xi is an ant colony algorithm parameter; material grade variable in generating offspring
Figure BDA00030008128000001214
According to the probability psA material brand is randomly selected from the ant colony algorithm population and given a probability of 0.1 so that it can be mutated to any other material brand. The above process is repeated until after H offspring are generated, all of which are stored in the set O ═ xh1,. H, H ], wherein
Figure BDA00030008128000001215
In step 5, all descendants in the set O are evaluated by using the agent model established in step 3, specifically, the descendants in the set are gradually brought into the agent model established in step 3, and the corresponding agent models are obtained
Figure BDA00030008128000001216
And
Figure BDA00030008128000001217
the value of (c). Then, two sets of design parameters are selected from O according to these values, specifically: if there is a sufficient condition in O
Figure BDA0003000812800000131
The design parameter of (1), then selecting the one satisfying the condition from O
Figure BDA0003000812800000132
The design parameter with the smallest value is selected otherwise
Figure BDA0003000812800000133
The design parameter with the smallest value; similarly, if present
Figure BDA0003000812800000134
The design parameter of (1), then selecting the one satisfying the condition from O
Figure BDA0003000812800000135
The design parameter with the smallest value is selected otherwise
Figure BDA0003000812800000136
The smallest value of the design parameter. After the selection is finished, a group of design parameters is randomly selected from the O. Next, the objective function f (x) and the constraint c are used1(x)，c2(x)，c3(x) And evaluating the three selected groups of design parameters, and storing the three groups of design parameters, the target functions and the constraint condition values into a design parameter filing set.
In step 6, the population is updated with elite selection. Specifically, the three sets of design parameters selected in step 5 are incorporated into the population, and then sorted according to their objective function values and constraint condition values by a sorting method of first combining the three sets of design parameters into the population
Figure BDA0003000812800000137
Is ordered from large to small, then for
Figure BDA0003000812800000138
The design parameters are sorted from large to small according to the objective function value
Figure BDA0003000812800000139
Is arranged to
Figure BDA00030008128000001310
Before design parameters of. And deleting the three groups of design parameters from the population after the sorting is finished, and finally finishing the elite selection.
In step 7, the best solution in the archive set is first determined and noted as
Figure BDA00030008128000001311
Wherein
Figure BDA00030008128000001312
The parameters are designed for the thickness of the film,
Figure BDA00030008128000001313
is a material brand. Subsequently, all material grade variables and are found from archive set A
Figure BDA00030008128000001314
The same design parameters and the thickness variables of these design parameters are stored in an archive set AlocalIn (1). Then, according to AlocalThe method for establishing a plurality of continuous RBF proxy model construction methods for the objective function and the constraint condition is the same as the step 5, and the following optimization problem is finally obtained
Figure BDA00030008128000001315
Figure BDA00030008128000001316
Figure BDA00030008128000001317
Figure BDA00030008128000001318
Figure BDA00030008128000001319
Figure BDA00030008128000001320
Solving the problem using sequential quadratic programming to obtain a solution
Figure BDA00030008128000001321
This solution represents the optimal thin-wall structure thickness design parameter given by the local search, which will then follow
Figure BDA00030008128000001322
And
Figure BDA00030008128000001323
combined into a new solution
Figure BDA00030008128000001324
The solution is the optimal design parameter given by the local search. Finally, finite element analysis was used to evaluate solution xlocalI.e. according to the xlocalAnd the provided parameter setting is brought into the finite element analysis software LS-DYNA, and the simulation result obtained by the LS-DYNA is utilized to obtain the values of the target function and the constraint condition. The solution is then stored in a design parameter archive set and the population is updated using elite selection as in step 6. Notably, we put all evaluated solutions into the archive set. This is because the more data in the archive set, the more accurate the model is built, which in turn is more conducive to optimal solution.

Claims (6)

1. A matching lightweight design method for an automobile body material structure is characterized by comprising the following steps:
s1, establishing the following optimization problems:
Figure FDA0003000812790000011
wherein x is a design parameter，xthickFor vectors composed of thickness design parameters, xmatIs a vector composed of material design parameters, f (x) is an objective function, c1(x) As a first constraint, c2(x) As a second constraint, c3(x) As a third constraint, T1，T2And T3Are indexes that three constraint conditions need to satisfy, LiAnd UiLower and upper limits of the design parameter for the ith thickness, M1～MpEach representing p different materials, n1For the number of thickness parameters to be optimized, n2Number of parameters for the grade of the material to be optimized;
s2, converting the optimization problem into the following problems:
Figure FDA0003000812790000012
s3, solving the problem of the step S2 by using sequential quadratic programming to obtain a solution
Figure FDA0003000812790000013
This solution represents the optimal thin-wall structure thickness design parameter given by the local search, which will then follow
Figure FDA0003000812790000014
And
Figure FDA0003000812790000015
combined into a new solution
Figure FDA0003000812790000021
The solution is the optimal design parameter given by the local search.
2. The method for matching and designing the lightweight automobile body material structure according to claim 1, wherein the step S1 is realized by the following steps:
a1, establishing the following RBF proxy model for the objective function f (x):
Figure FDA0003000812790000022
wherein xlParameters are designed for the l-th group in the archive set,
Figure FDA0003000812790000023
is a Gaussian kernel function, dis (x, x)l) Denotes x and xlThe distance between the two or more of the two or more,
Figure FDA0003000812790000024
Figure FDA0003000812790000025
wherein
Figure FDA0003000812790000026
Denotes x and xlThe vector difference in the thickness variation,
Figure FDA0003000812790000027
is x and xlThe xor operation of the vector of the material brand,
Figure FDA0003000812790000028
is composed of
Figure FDA0003000812790000029
And
Figure FDA00030008127900000210
the vector is formed, wherein | · | | represents a 2-norm, N is the number of design parameters stored in an archive set, and A is { x ═ x |, in the archive setl,yl,(cl,1,cl,2,cl,3)|l＝1,...,N}，
Figure FDA00030008127900000211
(cl,1,cl,2,cl,3) The constraint function values for the l-th set of design parameters,
Figure FDA00030008127900000212
an ith thickness parameter representing the ith set of design parameters,
Figure FDA00030008127900000213
j material grade variable, y, representing the l set of design parameterslDesigning the objective function value of the parameter for the l-th group, wlIs a weight;
for constraint c1(x)，c1(x) And c1(x) Establishing three RBF proxy models
Figure FDA00030008127900000214
And
Figure FDA00030008127900000215
for the target function f (x), establishing the following gradient lifting tree proxy model on the basis of a design parameter filing set:
Figure FDA00030008127900000216
t (,) is a regression tree, ΘmIs a parameter of the mth regression tree, M is the total number of regression trees,
Figure FDA0003000812790000031
for constraint c1(x)，c1(x) And c1(x) Establishing three gradient lifting tree agent models
Figure FDA0003000812790000032
Figure FDA0003000812790000033
And
Figure FDA0003000812790000034
sorting the parameters in the ant colony algorithm colony according to the function value of the objective function, and distributing ordinal rank(s) to each group of parameters, wherein s represents the ant colony algorithm colony P ═ { x ═sThe s-th set of parameters in 1., K },
Figure FDA0003000812790000035
Figure FDA0003000812790000036
an ith thickness parameter representing an s-th set of design parameters,
Figure FDA0003000812790000037
a jth material grade variable representing a set s of design parameters; assigning weights to each set of parameters in an ant colony algorithm population
Figure FDA0003000812790000038
q is an ant colony algorithm parameter, and K is an ant colony algorithm population scale; design variable of ith thickness in generation of h-th offspring
Figure FDA0003000812790000039
First, the probability is calculated
Figure FDA00030008127900000310
And randomly selecting a thickness parameter of a thin-wall structural part from the ant colony algorithm colony according to the probability, and recording the thickness parameter as mujThen according to a Gaussian distribution
Figure FDA00030008127900000311
Generating a thin-wall structure thickness parameter corresponding to the offspring, wherein
Figure FDA00030008127900000312
Xi is an ant colony algorithm parameter; material grade variable at the jth offspring from which the h offspring was generated
Figure FDA00030008127900000313
According to the probability psRandomly selecting a material grade from an ant colony algorithm colony, and giving a probability of 0.1 to change the material grade into any other material grade; the above process is repeated until after H offspring are generated, all of which are stored in the set O ═ xh1,. H, H ], wherein
Figure FDA00030008127900000314
Represents the h-th offspring expressing a set of design parameters.
A2, evaluating all descendants in the set O by using all the agent models, namely substituting the descendants in the set into the established agent models one by one to obtain the corresponding agent models
Figure FDA00030008127900000315
Figure FDA00030008127900000316
And
Figure FDA00030008127900000317
a value of (d); then, two sets of design parameters are selected from O according to these values, specifically: if there is a sufficient condition in O
Figure FDA0003000812790000041
The design parameter of (1), then selecting the one satisfying the condition from O
Figure FDA0003000812790000042
The design parameter with the smallest value is selected otherwise
Figure FDA0003000812790000043
The design parameter with the smallest value; if present, is
Figure FDA0003000812790000044
The design parameter of (1), then selecting the one satisfying the condition from O
Figure FDA0003000812790000045
The design parameter with the smallest value is selected otherwise
Figure FDA0003000812790000046
The design parameter with the smallest value; after the selection is finished, randomly selecting a group of design parameters from O; using an objective function f (x) and a constraint c1(x)，c2(x)，c3(x) And evaluating the three selected groups of design parameters, and storing the three groups of design parameters, the target functions and the constraint condition values into a design parameter filing set A to obtain the optimization problem.
3. The method for designing an automobile body material with a matched structure and reduced weight according to claim 2,
the specific implementation process of step S2 includes:
a3, determining the best solution in archive set A, and recording as
Figure FDA0003000812790000047
Wherein
Figure FDA0003000812790000048
The parameters are designed for the thickness of the film,
Figure FDA0003000812790000049
is a material brand;
a4, finding all material trade mark variables and
Figure FDA00030008127900000410
the same design parameters, and setting themStoring the thickness variable of the metering parameter into an archive set AlocalPerforming the following steps; according to AlocalAnd establishing a plurality of continuous RBF proxy models for the objective function and the constraint condition to obtain the problem of the step S2.
4. The method for matching and designing the light weight of the automobile body material structure according to claim 3, wherein before step A3, the three sets of design parameters are incorporated into a population, and then the three sets of design parameters are sorted according to objective function values and constraint condition values corresponding to the three sets of design parameters, wherein the sorting method comprises the following steps: first, for
Figure FDA00030008127900000411
Is ordered from large to small, then for
Figure FDA00030008127900000412
The design parameters of (2) are sorted from large to small according to the objective function value
Figure FDA00030008127900000413
Is arranged to
Figure FDA00030008127900000414
Before the design parameters are sorted, deleting the three groups of design parameters before sorting from the population after sorting is finished, namely finishing elite selection, and storing the three groups of design parameters after sorting, target functions of the three groups of design parameters and constraint condition values into a design parameter filing set A.
5. A matching lightweight design system for an automobile body material structure is characterized by comprising computer equipment; the computer device is configured or programmed for carrying out the steps of the method according to one of claims 1 to 4.
6. A computer-readable storage medium characterized by storing a program; the program is configured for carrying out the steps of the method according to one of claims 1 to 4.
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