CN107330172A - White body modularity based on modular product family platform - Google Patents

Abstract

The invention belongs to the field of automobile body-in-white structure design, and relates to a body-in-white module design method based on a modular product family platform. In the conceptual design stage of the body-in-white, the present invention introduces the idea of modularization to improve the sharing degree of parts in the product family under the premise of comprehensively considering the performance and cost of the body, and guarantees the codes in the topology diagram corresponding to the structural assembly model Optimizing, combined with genetic algorithm for multi-level and multi-objective optimization, realizes module sharing among models of the same level or even across levels in the product family, improves the versatility of parts and greatly reduces the aspect cost. The present invention provides designers with a body assembly design idea based on the concept of modularization that considers the realization of parts sharing in the concept design stage of the body, and realizes the optimal design of the body assembly for the entire product family. It has more important practical significance in the process of orientation design.

Description

Body-in-white module design method based on modular product family platform

technical field

The invention relates to a method for dividing and screening body-in-white structure modules of cross-level vehicle models based on a modular product family platform used in the concept design stage, belonging to the technical field of body design, and mainly used for the whole series under the same product family at the initial stage of design. The body structure of the vehicle model is designed for module division and assembly method.

Background technique

With the development of science and technology and the increasingly mature economic environment, world-class automobile manufacturers have begun to adopt a modular R&D model under the trend of global integration, thereby reducing the cost of production, manufacturing, maintenance and even material transportation, and shortening the cost of new models. At the same time, it can meet the diverse needs of different consumer groups and even different consumer individuals through customization, which brings huge advantages to enterprises. At present, the new platform developed based on modular technology has even become the main selling point of various models on the market. However, the modularization technology of my country's automobile industry started late, and the low modular design ability has become a major bottleneck restricting the development of my country's automobile industry.

Automobile body design can be divided into two stages: conceptual design and detailed design. Among them, the layout plan and structural performance of the whole vehicle are determined in the concept design stage, which determines 70% of the overall cost; and the advantage of the automobile platform strategy lies in the realization of a high degree of commonality of parts and expandability of the body structure. Therefore, ideas based on modular design, manufacturing and production should be introduced in the concept design stage.

Contents of the invention

In the conceptual design stage, the present invention comprehensively considers multiple vehicle body performance indicators, and proposes a body-in-white structural module assembly design method based on a modular manufacturing method on the basis of the inventive method (patent number CN105787221A) proposed by the inventor. , under the premise of ensuring the performance of the body structure, the body is divided into assembly structures based on module manufacturing, and then the modules are classified and screened according to the calculation and optimization results, providing designers with a way to realize the modular design of the body-in-white ideas.

Technical scheme of the present invention:

The body-in-white module design method based on the modular product family platform, the steps are as follows:

(1) Establishing an optimization model for a single car model: On the basis of the proposed inventive method (patent number CN105787221A), a mathematical model is established for each car model of the same product family. In order not to affect the following description, a brief description is as follows:

Taking the coordinate system of the body-in-white model as the reference, take several points in the X direction and Y direction (bottom plate) and Y direction and Z direction (side wall), and divide the body-in-white into blocks according to these points, that is, divide it into several sub-boards And the sub-beam; take the divided sub-components as nodes, and the connection relationship between sub-components as edges, establish a topological relationship G=(V, E), where V={V ₁ , V ₂ ,..., V _p , ..., V _P }, E={E ₁ , E ₂ , ..., E _q , ..., E _Q }. In the formula, {V ₁ , V ₂ ,..., V _p ,..., V _P } represent a group of nodes, there are P nodes in total, p is the node number, {E ₁ , E ₂ ,..., E _q ,..., E _Q } represent a group of edges, there are Q edges in total, and _q is the number of the edge; define a group of segmentation vectors γ=(γ ₁ , γ ₂ ,...,γ _q ,...,γ _Q ): When γ _q is 0, it means that the edge E _q in the topological relationship is removed, and when it is 1, it means that the edge is retained, then the split vector γ can be used to express An assembly method; with γ as the design variable, the body stiffness, manufacturing cost, and assembly cost are optimized as the optimization objectives, and the objective functions are:

F _{body stiffness} = displacement (G (V, E (γ)))

In the formula, the _body stiffness function F is measured by the maximum displacement of the finite element model calculation results of the structure corresponding to the topology graph G(V, E(γ)): the greater the displacement, the greater the structural deformation, the smaller the stiffness; Comp( k, G(V, E(γ))) represents the kth sub-component in the model after the vehicle structure is divided according to G(V, E(γ)); the smaller the mold area, the _{manufacturing} cost F of the sub-component is represented The lower the _cost , the fewer the number of solder joints, and the lower the _assembly cost F of the representative structure. Then the optimization model corresponds to:

The optimization model is a multi-objective optimization problem, the optimization independent variable is a binary vector composed of 0 and 1, and the genetic algorithm can be directly used for optimization calculation without special coding process, and the iterative population and iterative algebra need to go through several trial calculations Determined according to convergence. According to relevant research, for general engineering optimization problems, it can be set that the replacement rate of offspring to parent is 50%, the probability of crossover is 90%, and the probability of mutation is 10%, and the average fitness function change rate of the population does not exceed 3% as the convergence condition. After trial calculation, for general car models, the population size is 200, and the iteration algebra is 100 generations, which can meet the convergence requirements. Through optimization, the best assembly method of a single model can be obtained;

(2) On the basis of the optimization model of a single car model, the search for optimization is carried out, and the assembly design of multiple car models is considered within the product family at the same time.

The assembly structure is optimized for each bicycle model, and the optimization results are compared. Expand the population size in the optimization model of a single car model to ensure that there are enough individuals with the same assembly structure or only partially different individuals in each generation of the population when different models are optimized in parallel (after many trials, the population size should be at least Reach 3-4 times of the optimization model of a single car model). Select these individuals as the initial solution for the next iteration until the optimization converges; for n car models, it is divided into m segment structures, each segment structure is composed of α sub-components, and each segment assembly method will be determined by (2α-1) codes, which is The optimization model is:

In the formula Represents the comparison of the assembly methods of the corresponding positions of the two models. If the two are the same, it is counted as 0, and if they are different, it is counted as 1. Then The smaller the size, the closer the assembly structure is; by selecting the guaranteed solution among the populations of different models, the assembly scheme design based on the idea of modularization of multiple models can be realized.

(3) The body structure components that have completed the assembly design will be classified as modules. In the method of the present invention, module is divided into following four classes and is screened step by step:

Parameter module: a module that needs to be redesigned when designing a new model;

Universal module: a module that can be used universally among all models;

Flexible modules: modules that require local adjustments;

Personality module: a module that is common among similar models and not common between different types of models.

(4) First select the personality module: according to the vehicle type and the structural characteristics of the body, the personality module can be directly selected;

(5) Next, select the parameter module: when designing a new model with a certain model as the prototype, the size can be changed in multiple positions. According to the assembly result, select the coordinate point u=(u _min , u _max ) of each module in the redesign direction, where u=x, y, z, u _min is the minimum coordinate value in the corresponding direction, and u _max is the maximum Coordinate value; for two adjacent modules R and R+1, if there is u _{R max} >u _{R+1 min} , then in this direction, there is a vehicle that can be changed by changing the size of modules R and R+1 The location of the size. If u _{R max} =u _{R+1 min} , the variation of the overall vehicle size can be achieved by changing only one of the modules R or R+1. If there are three adjacent modules, u _{R max} ＞u _{R+1 min} , u _{R+1 max} ＞u _{R+2 min} , u _{R max} ＞u _{R+2 min} , the body structure should be checked at the corresponding position When the size is changed, the three modules R, R+1 and R+2 need to be changed at the same time; thus, the positions where the body size can be adjusted and the corresponding modules that need to be changed can be found. When manufacturing a new model, choosing different adjustment positions will increase different additional costs. The concept design stage mainly considers the manufacturing cost F _{manufacturing cost} and F _{assembly cost} . Since the size change at this time will not be too large and may be uncertain, the vehicle body performance function F _{body stiffness is} used as a constraint for verification, and the predefined stiffness is required to be satisfied Select the location with the smallest additional cost as the main redesign area, and the corresponding module that needs to be modified is the parameter module. If the location does not meet performance requirements, go back and reselect. Then the optimization model can be expressed as:

(6) Finally select the flexible module; constrain all unscreened modules to be general modules, that is, each design parameter has t _{model 1} = t _{model 2} =...= t _{model n} . Under the current constraints, each vehicle model is optimized, and the optimization goal is to maximize the vehicle weight Module mass (Comp k) and body performance F _{body stiffness} . According to the designer's needs, the selection range and optimal solution of the two optimization goals are set, and they are consistent with the design requirements. and Carry out a comparison, and release the constraints of t _{model 1} = t _{model 2} =...= t _{model n} according to the solution situation and ΔS _t value (that is, the design parameters do not need to be consistent with the corresponding parameters in other models): if the optimization of a certain model result And at least one = is not established (if both = are established, the requirements have been met), then release the design parameter constraints corresponding to the module with the lowest ΔS _t in this model, and this module becomes a flexible module; on the contrary, if the optimization of a certain model result And if at least one = is not established, the design parameter constraint corresponding to the module with the highest ΔS _t is released, and this module becomes a flexible module. After changing the constraints, enter the next round of iteration until all models meet the design requirements, and the remaining unselected modules are general modules, and finally complete the screening of all types of modules.

Due to the adoption of the above technical solutions, the present invention has the following advantages: 1. The present invention divides the assembly structure of the body-in-white structure based on the body performance, assembly and manufacturing costs, and simultaneously considers the division of the assembly structure of cross-level vehicle models based on module sharing, The product family design based on the modular platform is realized; 2. The division result of the assembly structure is classified and screened by modules, and the sharing of body parts is further determined and improved; 3. Compared with the sensitivity method, the present invention, It is no longer limited to parameter-flexible product family design, but further realizes the modular configuration product family design of standardized interchangeable modules, which is more suitable for the current production and manufacturing models of various automobile companies.

Description of drawings

Fig. 1 is application method of the present invention carries out three kinds of example car models under the same product family of modular design, wherein:

Figure 1(a) is the body-in-white structure of a certain sedan model;

Figure 1(b) is the body-in-white structure of a hatchback model;

Figure 1(c) is the body-in-white structure of an SUV model.

Fig. 2 is the body-in-white floor model of the present invention's implementation optimization design, wherein:

Figure 2(a) is the body-in-white floor of the smaller sedan and hatchback models;

Figure 2(b) is the body-in-white floor of a larger SUV model.

Fig. 3 is the corresponding topological connection diagram after pre-segmenting the backplane by applying the method of the present invention, wherein:

Figure 3(a) is the corresponding topology diagram of the floor of the sedan model and the hatchback model;

Figure 3(b) is the corresponding topological diagram of the floor of the SUV model.

Figure 4 shows three possible assembly methods based on the modular manufacturing method.

Figure 4(a) lengthens the bottom plate by adding modules;

Figure 4(b) lengthens the bottom plate in the way of stretching modules;

Figure 4(c) is to lengthen the bottom plate by stretching modules and adding modules at the same time.

Fig. 5 is the floor module segmentation method obtained by applying the method of the present invention, wherein:

Fig. 5(a) is the floor division method of the sedan model and the hatchback model;

Figure 5(b) shows the floor division method of SUV models.

Figure 6 shows the assembly and division results of the body side wall and the dimensional change position during redesign.

Fig. 7 is the design scheme obtained after applying the method of the present invention to the entire passenger compartment, wherein:

Figure 7(a) is the module design scheme of the sedan model;

Figure 7(b) is the modular design scheme of the hatchback model;

Figure 7(c) is the module design scheme of the SUV model.

detailed description

The present invention will be described in detail below in conjunction with the accompanying drawings and technical solutions.

As shown in Figure 1, there are three models of the sedan (Figure 1a), hatchback (Figure 1b) and SUV (Figure 1c) in the same product family. The other two models are larger in size, with an axial gap of up to 300mm. The method of the present invention is mainly used to carry out assembly design based on modular manufacturing for "cross-class" vehicle models in the traditional sense with large size differences as shown in the figure. For "same level" vehicle models with smaller size differences, the method of the present invention can be simplified and applied according to actual conditions.

According to the actual manufacturing and assembly process level, the manufacturing unit pre-segmentation of the body-in-white bottom plate is carried out. According to the symmetry of the body structure, only the left half-body body can be used as the research object. Taking the bottom plate as an example, the division status is shown in the red dotted line in Figure 2 . Select appropriate segmentation points in the X-axis and Y-axis directions of the coordinate system where the body model is located to divide the body into x and y segments along the two directions, and the pre-segmentation unit has (x×y) blocks in total. By controlling the number of x and y, the number of pre-divided blocks of the bottom plate can be controlled. For cross-level models, the pre-segmentation units usually differ by x blocks to 2x blocks in the y direction.

The pre-segmented structure graph of each model floor is transformed into a corresponding topological connection graph. The vertices in the topology graph correspond to the pre-segmented units, and the edges in the topology graph correspond to the connection relationship between each unit. Numbering in sequence according to the direction of the X-axis and Y-axis, the sequence number of the unit set is V ₁ , V ₂ , ..., V _P , the sequence number of the connection relationship set is E ₁ , E ₂ , ..., E _Q , subcomponent 4 The coordinates of a vertex are a, b, c, d are the numbers of the four clockwise nodes of the sub-board, and x, y, z are the coordinates of the vertices. Based on this, a one-to-one corresponding topology model is established for each model to be studied in the product family, as shown in Figure 3, where Figure 3a is the floor topology relationship diagram of hatchback and sedan models, and Figure 3b is the floor topology of SUV models relation chart.

As far as the actual structure is concerned, there are two possibilities for the connection relationship of a part: stamping as a whole and stamping separately and then welding. In the topology diagram, when the edge connecting the two nodes exists, it means that the two pre-segmented units belong to the same part, and there is no weld; when the edge connecting the two nodes does not exist, it means that the two pre-segmented units are disconnected and then welded , there is a weld. It can be seen that in the topology graph, an array composed of a set of 0 and 1 variables can be used as the segmentation vector of the structure. When the variable is 1, it means that the corresponding edge exists, and when it is 0, it means that the edge does not exist. The group of arrays is also used as the individual code of the genetic algorithm in the optimization calculation.

The structural performance indicators that need to be considered in the concept design stage of the body need to include at least body stiffness (affecting driving experience, NVH performance, and safety performance, etc.), assemblability (affecting manufacturing difficulty, assembly cost, and structural reliability, etc.), and manufacturability ( Assess manufacturing risk and cost) in three aspects. In the method of the present invention, these three performance indexes are taken as optimization targets, and the assembly mode of the structure is solved. The evaluation methods of various performance indicators are as follows:

1. Body stiffness: under the same load, use the displacement at the predefined nodes in the finite element model to evaluate, the greater the deformation, the worse the stiffness;

2. Manufacturability: The manufacturing cost is approximated by the mold area, and the mold area of the kth sub-component is approximated as:

3. Assemblability: Measured approximately by the number of solder joints, the distance between solder joints is set to 30mm in this method, then the number of solder joints of the lth weld in the structure is: (y direction) or (x direction).

Transform this design into a mathematical multi-objective optimization problem, the optimization variables are the position of the segmentation point and the connection of each edge in the topology graph, and the constraints are the size, stiffness, cost and other aspects of the segmentation unit involved in actual manufacturing. The objective The function converts the above three performance indicators into:

F _{body stiffness} = min {displacement (G (V, E (γ)))}

Several car models involved in the design are optimized according to the optimization model. Combined with the genetic evolution algorithm suitable for multi-objective optimization problems, the optimal Pareto solution set can be obtained. Each individual in the solution set is a segmentation vector, which corresponds to a floor segmentation method. In a group of segmentation vectors, each segment coded by γ=0 or 1 can correspond to an assembly method corresponding to a position. As shown in Figure 4, the three different combinations of E ₆ -E ₁₂ can correspond to three different assembly methods of this section of the bottom plate, and the codes of other parts remain unchanged to ensure the commonality of the corresponding parts among several different assembly methods . Since there is usually a floor module difference between cross-level models, the module sharing degree can be solved in this way. After the optimal solution set is obtained for each model, individuals with consistent local characteristics are selected from the solution set, and the final result is selected according to the design requirements, so that module sharing can be realized among cross-level models. Figure 5 is the result obtained.

Using the above method, the body assembly method based on modular design can be obtained. However, it is still uncertain which parts to be assembled can be shared and which cannot be shared. Therefore, the method of the present invention further proposes classification of several modules and corresponding screening methods. Taking the side wall as shown in Figure 6 as an example, it is an assembly method based on modular design calculated according to the above method. When designing a new model with this as a reference, some of the modules can be replaced on the premise of keeping the overall structure unchanged. If the wheelbase needs to be lengthened, the effect can be achieved by replacing the modules 2 and 3, and the modules 4 and 5 can be replaced, while other modules remain unchanged. Considering the performance requirements of the body structure, some modules also need to be thickened and thinned. Therefore, the modules can be roughly divided into four categories: individual modules that are obviously different between different models, parameter modules that have large changes in size, general-purpose modules that do not require any changes, and flexible modules that only change the thickness of the board. Among them, the common module and the flexible module belong to the shared module, and the personality module can be shared among different subdivision models of the same large class of vehicle, such as the special module for the sedan model, etc.

Parameter modules basically cannot be shared between any models, and the additional cost is the highest; personality modules can usually be shared between different subdivision models under the same category of models, and the additional cost is relatively high; Minor adjustments and modifications can be made to the mold or punch, and the extra cost is low; the general module can be used by all models, and the extra cost is the lowest.

Using the results obtained from the modular design method proposed above, the personality modules can be found directly based on observations, without the need to study the method of screening personality modules.

The parameter module is a module that needs to be redesigned and replaced when designing a new car model. Therefore, when selecting a parameterized module, the manufacturing cost and assembly cost when replacing the module should be taken as the optimization goal, and the performance of the body is the constraint, that is, the guarantee On the premise of no loss of body performance, the cost is minimized. According to the dimensional change position involved in the model design, the cost corresponding to the replacement module is prioritized, and the low-cost position is given priority to replace the module, that is, it is set as a parameter module. As shown in Figure 6, when the wheelbase direction of the vehicle body changes, it can be changed from four positions I, II, III, and IV respectively; when the vehicle body height direction changes, it can be changed from two positions i and ii. The axial direction III and the height i direction are respectively the positions with the lowest cost when replacing modules in the two directions, and modules 4 and 5 are given priority as axial parameter modules, and 1, 2, and 4 are height-oriented parameter modules.

After selecting the module with the lowest cost as the parameterized module, it is necessary to optimize each model to see if it can meet the performance and weight requirements. If it cannot be satisfied, it is necessary to re-select the module with the second lowest cost as the parameter module, and perform single-model optimization again, and so on, until the optimization result meets the requirements.

After the personality module and parameter module are determined, the flexible module needs to be screened out, and the rest are shared modules. After replacing the parameter module of the body structure, the flexible module is mainly used to make some adjustments in thickness and local details, so that the new model can meet the requirements of stiffness and light weight. Therefore, performance indicators such as body stiffness and weight are the criteria mainly used to screen flexible modules.

If all the remaining parts are regarded as general-purpose modules after the parameter module is selected, two problems may occur: 1. Some models use thick general-purpose modules, resulting in heavy body weight, which does not meet the lightweight requirements; 2. Some models use thinner general-purpose modules, resulting in poor body performance, which does not meet the requirements of stiffness and strength. In view of these two possible problems, the method of the present invention proposes a "weak constraint, strong target" method to gradually release the general module constraints to the flexible module. In this part of the optimization problem, there are two main constraints: platform constraints (constraints that the same design parameters are consistent among different models) when parallel optimization is performed among different models, and performance constraints in the optimization problem of each model. "Weak constraint" refers to the second type, that is, in the optimization process, the constraint that is a function of the body performance is weakened, and it is regarded as an objective function and the weight of the body is optimized at the same time, and then the platform constraint is gradually released according to the result.

The problem in this part of the work is that it is impossible to choose to release the appropriate parameter variable as the design variable of the flexible module. Constraints, that is, there are unreasonable constraints in the model) It is difficult to control the optimization direction when optimizing under equality constraints, and there are difficulties in convergence. The method of converting it into an objective function by weakening the constraints and then controlling the optimization direction by releasing the constraints can obtain a relatively broad solution set space, which is more convenient to adjust the optimization solution set according to the needs of the designer, and at the same time, after each iteration, the According to the result, the variable that should release the platform constraints next time can be selected, so that the selection of the flexible adjustment module can be realized while strictly ensuring the performance of the vehicle body.

In the method of the present invention, the constraint is released mainly according to the size of ΔS _t . ΔS _t =S×Δt, S is the area of the module, and Δt is the thickness of the plate required to increase the performance index of the module by 1 unit, which is obtained by optimizing the sensitivity function obtained at the current point. ΔS _t is the increased material weight required to provide relative 1 unit stiffness. When using the method of the present invention, at first the current modules to be screened are regarded as general modules, that is, the same part is consistent among all models, and then each model is optimized in parallel, and compared with the performance when the modular design is not considered. Compared. If the performance of a certain model is qualified but the weight of the vehicle is too heavy to meet the predetermined lightweight requirements, the parts with high ΔS _t in the model are selected as flexible modules and the weight of the parts is reduced, that is, at the cost of the least performance sacrifice Maximize the weight reduction, and then optimize again to continue to screen the remaining parts; if a certain model has a light weight but unqualified performance, then filter the parts with low ΔS _t in the model as flexible modules and Components are reinforced to enhance structural performance at the expense of minimal weight gain. And so on, until all models meet the predetermined requirements.

For the three models in the invention calculation example, the optimization calculation is carried out according to the above method in turn, and a design method can be obtained as shown in Figure 7, where Figure 7(a) is the module design result of the sedan model, and Figure 7(b) is The module design results of hatchback models, Figure 7(c) shows the module design results of SUV models. The blue part in the figure is a general module, the red part is a parameter module, the yellow part is a personality module, and the green part is a flexible module. The parts sharing rate and lightweight results of the optimized design are shown in Table 1. It can be seen from the table that compared with the lightweight results of individual optimization of each model, the lightweight results based on modular design have a certain loss, but they are all controlled below 10%, while the sharing rate has increased to 40% or even 60%. It can effectively reduce the cost of all aspects of the enterprise.

Table 1 Modular design results

Through the above example study, we obtained a block assembly method of body-in-white designed based on the modular concept. Under the premise of satisfying and optimizing the body performance constraints, manufacturing costs, and assembly costs, this block method improves the versatility of parts among different product individuals in the same product family, and greatly reduces automobile manufacturing, parts transportation, etc. aspects of the cost. The results can provide designers with one or even a variety of car body modular manufacturing solutions to meet the designer's need to increase the degree of component sharing.

Under the current trend of taking modular thinking as the main research and development direction, in the concept design stage, it is necessary to design and optimize the assembly structure under the premise of considering the modular research and development method, and such research and development ideas must involve multiple models It directly affects the time and cost of all links from research and development to production to sales in a relatively long product family life cycle of the enterprise. The design plan in the conceptual design stage requires the designer to weigh various factors such as process requirements, cost control, and structural performance, and minimize various costs in each link while ensuring product performance. The method of the invention provides a new idea for the designer in the conceptual design stage of the car body, realizes the division, classification and screening of various modules in the car body structure, can realize the module sharing mode based on the entire product family, and improves the degree of module sharing , to reduce the cost, this method is of great significance in both the reverse design and the forward design of the body.

The specific examples have been listed above to set forth in detail that the present invention realizes the vehicle body design of the modular product family platform based on the modular design idea. These examples are only for explaining the principle of the present invention and its implementation, rather than limiting the present invention. Those skilled in the art can make more modifications and improvements without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions shall belong to the category of the present invention and be limited by the claims of the present invention.

Claims (1)

1. A body-in-white module design method based on a modular product family platform, characterized in that the steps are as follows: (1) Establish an optimization model for a single car model: Taking the coordinate system of the body-in-white model as the reference, take several points in the X direction and Y direction, as well as the Y direction and Z direction, and divide the body-in-white into blocks according to these points, that is, into several sub-boards and sub-beams; The sub-components of are nodes, and the connections between sub-components are edges, and the topological relationship G=(V, E) is established, where V={V ₁ , V ₂ ,..., V _p ,..., V _P } , E={E ₁ , E ₂ ,..., E _q ,..., E _Q }; where {V ₁ , V ₂ ,..., V _p ,..., V _P } represent A group of nodes, a total of P nodes, p is the node number, {E ₁ , E ₂ , ..., E _q , ..., E _Q } represents a group of edges, a total of Q edges, q is the number of the edge ;Define a set of segmentation vectors for the original graph G composed of binary variables γ _q γ={γ ₁ ,γ ₂ ,...,γ _q ,...,γ _Q }: when γ _q is 0, it represents the topology The edge E _q in the relationship is removed, and when it is 1, it means that the edge is reserved, then the segmentation vector γ is used to express an assembly method; with γ as the design variable, the body stiffness, manufacturing cost, and assembly cost are optimized as the optimization objectives. The objective functions are: F _{body stiffness} = displacement (G (V, E (γ))) In the formula, the _body stiffness function F is measured by the maximum displacement of the finite element model calculation results of the structure corresponding to the topology graph G(V, E(γ)): the greater the displacement, the greater the structural deformation, the smaller the stiffness; Comp( k, G(V, E(γ))) represents the kth sub-component in the model after the vehicle structure is divided according to G(V, E(γ)); the smaller the mold area, the _{manufacturing} cost F of the sub-component is represented The lower the _cost , the fewer the number of solder joints, the lower the _assembly cost F of the representative structure; the optimization model corresponds to: The optimization model is a multi-objective optimization problem, the optimization independent variable is a binary vector composed of 0 and 1, and the genetic algorithm is used for optimization calculation. The iterative population and the iterative algebra need to be determined according to the convergence after several trials; set the offspring The replacement rate of the parent generation is 50%, the crossover probability is 90%, and the mutation probability is 10%. The convergence condition is that the average fitness function change rate of the population does not exceed 3%. Through optimization, the best assembly method of a single model is obtained; (2) On the basis of the optimization model of a single car model, the search for optimization is carried out, and the assembly design of multiple car models is considered in the product family at the same time: Optimize the assembly structure of each single vehicle model, and compare the optimization results; expand the population size in the optimization model of a single vehicle model to ensure that there are enough assemblies in each generation of populations when different models are optimized in parallel Individuals with the same structure or only partial differences appear; these individuals are selected as the initial solution for the next iteration until the optimization converges; for n models, they are divided into m segments, and each segment is composed of α sub-components, then each segment assembly method will be determined by (2α-1) codes, which is The optimization model is: In the formula Represents the comparison of the assembly methods of the corresponding positions of the two models. If the two are the same, it is counted as 0, and if they are different, it is counted as 1. Then The smaller the size, the closer the assembly structure is; by selecting the guaranteed solution among the populations of different models, the assembly scheme design based on the idea of modularization of multiple models can be realized; (3) the vehicle body structure components that complete the assembly design will be classified as modules, and in the method of the present invention, the modules are divided into the following four categories and gradually screened: Parameter module: a module that needs to be redesigned when designing a new model; Universal module: a module that can be used universally among all models; Flexible modules: modules that require local adjustments; Personality modules: modules that are common among similar models, but not common between different types of models; (4) First select the personality module: directly select the personality module according to the vehicle type and the structural characteristics of the vehicle body; (5) Next, select parameter modules: when designing a new model with a certain model as a prototype, change the size at multiple positions; according to the assembly results, select the coordinate point u=(u _min , u _max ), where u=x, y, z, u _min is the minimum coordinate value in the corresponding direction, u _max is the maximum coordinate value; for two adjacent modules R and R+1, if u _{R max} ＞u _R+1min , then in this direction, there is a position that can change the size of the whole vehicle by changing the size of modules R and R+1; if u _{R max} ＝u _R+1min , then it can be One of R+1 is used to change the size of the vehicle; if there are three adjacent modules, u _{R max} >u _R+1min , u _R+1max >u _R+2min , u _{R max} >u _{R+ 2min} , when changing the size of the body structure at the corresponding position, the three modules R, R+1 and R+2 need to be changed at the same time; thus, find out the position where the body size is adjusted and the corresponding modules that need to be changed; When manufacturing a new car model, selecting different adjustment positions will increase different additional costs. In the conceptual design stage, the manufacturing cost F _{manufacturing cost} and F _{assembly cost} are mainly considered; since the size change at this time will not be too large and may be uncertain, the body The _{body stiffness} of the performance function F is verified as a constraint, and it is required to meet the predefined stiffness Select the location with the smallest additional cost as the main redesign area, and the corresponding module that needs to be changed is the parameter module; if the location cannot meet the performance requirements, return to reselect; then the optimization model is expressed as: (6) Select the flexible module at last; Constrain all unscreened modules to be general modules, namely each design parameter has t _{car model 1} =t _{car model 2} =...=t _{car model n} ; Under the current constraints, each car model is optimized, and the optimization target To maximize vehicle weight Module quality (Comp k) and body performance F _{body stiffness} ; according to the designer's needs, set the selection interval and optimal solution of two optimization goals, and match the design requirements and Carry out a comparison, and release the constraints of t _{model 1} = t _{model 2} =...= t _{model n} according to the solution and ΔS _t value, that is, the design parameters do not need to be consistent with the corresponding parameters in other models: if the optimization result of a certain model and And at least one = is not established, if both = are established, the requirements have been met, then release the design parameter constraints corresponding to the module with the lowest ΔS _t in this model, and this module becomes a flexible module; on the contrary, if the optimization result of a certain model and And at least one = is not established, then release the design parameter constraints corresponding to the module with the highest ΔS _t , and this module becomes a flexible module; after changing the constraints, enter the next round of iteration until all models meet the design requirements, and the remaining unselected modules It is a general-purpose module, and finally completes the screening of all types of modules.