CN107330172B - Body-in-white module design method 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 a body-in-white, the invention introduces the modularization idea of improving the sharing degree of parts in a product family on the premise of comprehensively considering the performance and the cost of the body, performs characteristic and optimization on codes in a topological graph corresponding to a structural assembly model, performs multi-level and multi-target optimization by combining a genetic algorithm, realizes the module sharing between the same-level vehicle models and even cross-level vehicle models in the product family, improves the universality of the parts on the premise of ensuring the performance of the body, and greatly reduces the cost in each aspect. The invention provides a car body assembly design idea based on a modularization idea for realizing the sharing of parts in the car body conceptual design stage for designers, realizes the car body assembly optimization design of the whole product family, and has important practical significance in the process of car body reverse improvement and forward design.

Description

Design method of body-in-white module based on modular product family platform

technical field

The invention relates to a method for dividing and screening body-in-white structural modules of a cross-class vehicle body based on a modular product family platform used in the conceptual design stage, belonging to the technical field of car body design, and is mainly used for evaluating all systems under the same product family in the early design stage. The body structure of the model is designed for module division and assembly.

Background technique

With the development of science and technology and the growing maturity of the 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 also meet the diversified needs of different consumer groups and even different consumers through customization, which brings huge advantages to enterprises. At present, new platforms based on modular technology have even become the main selling point of various models on the market. However, the modular technology of my country's auto industry started late, and the low modular design capability has become a major bottleneck restricting the development of my country's auto industry.

The car 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 conceptual design stage, which determines 70% of the overall cost; and the advantage of the vehicle platform strategy lies in the realization of a high degree of commonality of components and scalability of the body structure. Therefore, ideas based on modular design, manufacturing and production should be introduced at the conceptual design stage.

SUMMARY OF THE INVENTION

In the conceptual design stage, the present invention comprehensively considers a number of vehicle body performance indicators, and on the basis of the invention method (patent number CN105787221A) proposed by the inventor before, proposes a body-in-white structure module assembly design method based on a modular manufacturing method , Under the premise of ensuring the performance of the body structure, the body is divided into the assembly structure based on module manufacturing, and then the modules are classified and screened according to the calculation optimization results, which provides 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) Establish an optimization model of a single vehicle model: On the basis of the proposed invention method (Patent No. CN105787221A), a mathematical model is established for each single vehicle model under the same product family. In order not to affect the following description, a brief description is as follows:

Based on the coordinate system where the body-in-white model is located, take several points in the X and Y directions (bottom plate) and the Y and Z directions (side walls) respectively, and divide the body-in-white into blocks according to these points, that is, into several sub-boards and sub-beams; take the sub-components after the block as nodes, and the connection relationship between the 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 } represents a group of nodes, there are P nodes in total, p is the node number, {E ₁ , E ₂ ,..., _{E q , . 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 segmentation vector γ can be used to express An assembly method; taking γ as the design variable, and optimizing the body stiffness, manufacturing cost, and assembly cost as the optimization objectives, the objective functions are:

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

In the formula, the body stiffness function F The _{body stiffness} is measured by the maximum displacement of the finite element model calculation result of the structure corresponding to the topology map G(V, E(γ)): the larger the displacement, the larger 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 die area, the _{manufacturing} cost F of the sub-component The lower the _cost , the less 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. The genetic algorithm can be directly used for optimization calculation without special coding process. Iterative population and iterative algebra need to be calculated after several trials. Determined based on convergence. According to relevant research, for general engineering optimization problems, the replacement rate of the offspring to the parent can be set at 50%, the crossover probability is 90%, and the mutation probability 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 models, the population size is 200, and the iteration algebra is 100 generations to 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 model, carry out the search for optimization, and realize the assembly design of multiple models in the product family at the same time.

优化模型为：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 vehicle model to ensure that there are enough individuals with the same assembly structure or only locally different individuals in each generation of populations when different vehicle models are optimized in parallel (after multiple trial calculations, the population size should be at least up to 3-4 times in a single vehicle model optimization model). These individuals are selected as the initial solution of the next iteration until the optimization converges; for n models, it is divided into m sections of structure, each section of the structure is composed of α sub-components, then the assembly method of each section will be determined by (2α-1) codes, which is

The optimized model is:

代表两个车型相应位置的装配方式比对，二者相同时计0，不相同计1，则

越小代表装配结构越相近；通过在不同模型的种群之间选取保征解，实现多个模型的基于模块化思想的装配方案设计。in the formula

It represents the comparison of the assembly methods of the corresponding positions of the two models. If the two are the same, the time is 0, and the time is 1 if they are different.

The smaller the value, the more similar the assembly structure is; by selecting the guarantee solution between the populations of different models, the assembly scheme design based on the modular idea of multiple models is realized.

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

Parameter modules: modules that need to be redesigned when designing new models;

Common module: a common module that can be used between all models;

Flexible modules: modules that require local adjustment;

Personality module: a module that is common among similar models, but not common among different models.

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

选取附加成本最小的位置作为主要重设计区域，对应需要改动的模块即为参数模块。如果该位置不能满足性能需求，则返回重新选择。则优化模型可表示为：(5) Secondly, the parameter module is selected: when a certain model is used as a prototype car to design a new model, the size can be changed in multiple positions. According to the assembly results, 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 dimensions of the modules R and R+1 size location. If u _{R max} = u _{R+1 min} , then the change in overall vehicle size can be achieved by changing only one of the modules R or R+1. If there are three adjacent modules, and u _{R max} >u _{R+1 min} , u _{R+1 max} >u _{R+2 min} , u _{R max} >u R+2 min , and u R max >u R+1 min , u R max >u _{R+2 min} , then the body structure should be carried out at the corresponding position. When the size changes, the three modules R, R+1 and R+2 need to be changed at the same time; thus, the position 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. In the conceptual design stage, the manufacturing cost F _{manufacturing cost} and F _{assembly cost} are mainly considered. Since the dimensional change at this time will not be too large and may be uncertain, the body performance function F _{body stiffness is} used as a constraint for verification, and the predefined stiffness is required to be met

The position with the least additional cost is selected as the main redesign area, and the corresponding module that needs to be changed is the parameter module. If the location does not meet the performance requirements, return to re-selection. Then the optimization model can be expressed as:

模块质量(Comp k)和车身性能F_车身刚度。根据设计者需求设定两个优化目标的选择区间及最优解，并与设计要求的

及

进行比对，根据解的情况及ΔS_t值释放其t_车型1＝t_车型2＝…＝t_车型n的约束(即设计参数无需再与其他车型内相应参数保持一致)：如果某车型的优化结果

且至少有一个＝不成立(如果两个＝均成立，则已经满足要求)，则释放该车型中ΔS_t最低的模块所对应的设计参数约束，该模块成为柔性模块；反之，如果某车型的优化结果

且至少有一个＝不成立，则释放ΔS_t最高的模块所对应的设计参数约束，该模块成为柔性模块。更改约束后，进入下一轮迭代，直到所有车型满足设计要求，余下未被选择的模块即为通用模块，最终完成全部种类模块的筛选。(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} . Optimize each vehicle model under the current constraints, and the optimization objective is to maximize vehicle weight

Module mass (Comp k) and body performance F _{body stiffness} . The selection interval and optimal solution of the two optimization objectives are set according to the designer's needs, and are consistent with the design requirements.

and

Carry out 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 of a certain model result

And at least one = is not established (if both = are established, the requirements have been met), release the design parameter constraints corresponding to the module with the lowest ΔS _t in the model, and the module becomes a flexible module; on the contrary, if the optimization of a certain model result

And at least one = does not hold, then release the design parameter constraint 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 are general modules, and finally the screening of all types of modules is completed.

The present invention has the following advantages due to the adoption of the above technical solutions: 1. The present invention divides the body-in-white structure into an assembly structure based on body performance, assembly and manufacturing costs, and considers the assembly structure division of cross-class 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, which further determines and improves the sharing of body parts; 3. Compared with the sensitivity method, the present invention has It is no longer limited by the parameter expansion type product family design, but further realizes the modular configuration type product family design of standardized interchangeable modules, which is more suitable for the current manufacturing mode of various automobile enterprises.

Description of drawings

Fig. 1 is three example car models under the same product family using the method of the present invention for modular design, wherein:

Figure 1(a) is the body-in-white structure of a 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 model of the present invention's implementation optimization design, wherein:

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

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

Fig. 3 is the corresponding topology connection diagram after applying the method of the present invention to pre-segment the base plate, wherein:

Figure 3(a) is the topological map corresponding to the bottom plate of the sedan and hatchback models;

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

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

Figure 4(a) is to lengthen the base plate by adding modules;

Figure 4(b) is to lengthen the bottom plate by stretching the module;

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

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

Figure 5(a) is the floor division of the sedan and hatchback models;

Figure 5(b) is the floor division method of the SUV model.

Fig. 6 shows the results of assembly and division of the side panels of the body and the position of dimensional changes 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 module design scheme of the hatchback model;

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

Detailed ways

The present invention is further described in detail below with reference to the accompanying drawings and technical solutions.

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

According to the actual manufacturing and assembly process level, the manufacturing unit of the body-in-white floor is pre-segmented. According to the symmetry of the body structure, only the left half body can be taken as the research object. Taking the floor as an example, the segmentation status is shown by the red dot-dash line in Figure 2. . In the X-axis and Y-axis directions of the coordinate system where the vehicle body model is located, appropriate segmentation points are selected to divide the vehicle body into x and y segments along the two directions respectively, and the pre-segmentation unit has a total of (x×y) blocks. By controlling the number of x and y you can control the number of pre-segmented blocks on the backplane. For cross-class models, the pre-segmentation units typically differ by x blocks to 2x blocks in the y direction.

a、b、c、d为子板顺时针方向四个节点编号，x、y、z为顶点坐标。依此对产品族内每个待研究车型建立起一一对应的拓扑模型，如图3所示,其中图3a为两厢车型和三厢车型的底板拓扑关系图，图3b为SUV车型的底板拓扑关系图。The pre-segmented structure diagram of the bottom plate of each vehicle model is transformed into a corresponding topological connection relationship diagram. The vertices in the topology diagram correspond to the pre-segmented units, and the edges in the topology diagram correspond to the connection relationships between the units. According to the direction of X-axis and Y-axis, the numbering is performed in sequence. The unit sets are sorted and numbered as V ₁ , V ₂ , ..., V _P , and the connection relationship sets are sorted and numbered as E ₁ , E ₂ , ..., E _Q , and the number of sub-components of k number four The coordinates of the vertices are

a, b, c, and d are the four node numbers in the clockwise direction of the sub-board, and x, y, and z are the vertex coordinates. Based on this, a one-to-one correspondence topology model is established for each model to be studied in the product family, as shown in Figure 3, in which Figure 3a is the floor topology diagram of the hatchback and sedan, and Figure 3b is the floor topology of the SUV model. relation chart.

For the actual structure, 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 elements 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 elements 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 set to 1, it means that the corresponding edge exists, and when it is set to 0, it means that the edge does not exist. This 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 conceptual design stage of the body should at least include 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 risks and costs) in three aspects. In the method of the present invention, the 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: use the displacement at the predefined nodes in the finite element model to evaluate under the same load, the greater the deformation, the worse the stiffness;

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

(y向)或

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

(y direction) or

(x-direction).

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

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

Several models involved in the design are optimized according to the optimization model. Combining with the genetic evolution algorithm suitable for multi-objective optimization problems, the optimal Pareto solution set can be obtained, and each individual in the solution set is a segmentation vector, which corresponds to a floor segmentation method. In a set of segmentation vectors, each segment of the code with γ=0 or 1 can correspond to an assembly method corresponding to the 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 section of floor module between cross-class models, the module sharing degree can be solved in this way. After the optimal solution set is obtained for each model, the individuals with consistent local characteristics are selected from the solution set, and then the final result is selected according to the design requirements, so that module sharing can be realized among cross-class models. Figure 5 shows the obtained results.

Using the above method, a 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 the classification of several modules and the corresponding screening methods. Taking the side wall shown in FIG. 6 as an example, it is an assembly method based on modular design calculated according to the above method. When designing a new model based on this reference, some 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 modules 2 and 3, and modules 4 and 5 can be replaced, while other modules remain unchanged. Considering the requirements of body structure performance, some modules also need to be thickened and thinned. Therefore, the modules can be roughly divided into four categories: personality modules that are obviously different between different models, parameter modules with large changes in size, general modules that do not require any changes, and flexible modules that only change the thickness of the board. Among them, the general module and the flexible module belong to the shared module, and the individual module can be shared among different subdivision models of the same general model, such as the special module for the sedan model.

The parameter module basically cannot be shared between any models, and the extra cost is the highest; the personality module can usually be shared among different subdivided models under the same type of car, and the extra cost is high; the flexible module only needs to concave the stamping die. The mold or punch is slightly adjusted and modified, 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, personality modules can be found directly based on observations, and there is no 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 model. Therefore, when screening the parameterized module, the manufacturing cost and assembly cost when replacing the module should be the optimization goal, and the body performance should be the constraint. On the premise of no loss of body performance, the cost is minimized. The cost of replacing the module is prioritized according to the size change position involved in the model design, and the low-cost position is given priority for module replacement, that is, the parameter module is set. 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, and when the vehicle height direction changes, it can be changed from two positions i and ii. The axial direction III and the height i direction are the positions with the lowest cost when replacing modules in the two directions respectively, then modules 4 and 5 are given priority to be the axial parameter modules, and modules 1, 2, and 4 are the height parameter modules.

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

After the personality module and parameter module are determined, the flexible modules need to be screened out, and the rest are shared modules. After the replacement of the parameter module of the body structure, the flexible module is mainly used to adjust part of the 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 mainly used to screen flexible modules.

If all the remaining components are regarded as general modules after the parameter module is selected, there may be two problems: 1. Some models use thick general modules, resulting in heavier body weight, which does not meet the lightweight requirements; 2. Some models use thin general modules, resulting in poor body performance, which does not meet the requirements of stiffness and strength. Aiming at these two possible problems, the method of the present invention proposes a method of gradually releasing the constraints of general modules to flexible modules with "weak constraints and strong goals". In this part of the optimization problem, there are two main constraints: platform constraints (constraints that the same design parameters are consistent between different models) when parallel optimization is performed between 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 vehicle body performance is weakened, and it is regarded as the objective function and the vehicle body weight to be optimized at the same time, and then the platform constraint is gradually released according to the result.

The main problem of this part of the work is that it is impossible to choose and release the appropriate parameter variables as the design variables of the flexible module. When multiple models are optimized in parallel, when there are unknown constraints on different models (the flexible module selection needs to be released gradually Constraints, that is, there are still unreasonable constraints in the model), it is difficult to control the optimization direction for optimization under equality constraints, and there are difficulties in convergence. The method of weakening the constraints and converting them into objective functions and then controlling the optimization direction by releasing the constraints can obtain a wider solution set space, which is more convenient to adjust the optimization solution set according to the needs of the designer. According to the results, the next variable to release the constraints of the platform can be selected, thereby realizing the screening of flexible adjustment modules 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 module by 1 unit of performance index, which is obtained from the sensitivity function obtained by optimizing the current point. ΔS _t is the added weight of material required to provide relative 1 unit stiffness. When using the method of the present invention, the current modules to be screened are regarded as general modules at first, that is, the same component is kept consistent among all models, and then each model is optimized in parallel, and is 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 cannot meet the predetermined lightweight requirements, the components with high ΔS _t in the model will be screened as flexible modules and the weight of the components will be reduced, that is, at the cost of minimum performance sacrifice. Maximize weight reduction, and then optimize again to screen the remaining components; if a certain model has a lighter weight but unqualified performance, the components with low ΔS _t in the model are screened as flexible modules and the Components are reinforced, i.e., structural performance is enhanced with minimal added weight. And so on until all models meet the predetermined requirements.

For the three models in the example of the invention, 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, in which Figure 7(a) is the design result of the sedan model module, and Figure 7(b) is Figure 7(c) shows the module design result of the SUV model. The blue part in the figure is the general module, the red part is the parameter module, the yellow part is the personality module, and the green part is the 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 optimized for each model individually, the lightweight results based on modular design and manufacturing 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 study of the above examples, we obtained a block assembly method of body-in-white based on the modular concept design. Under the premise of satisfying and optimizing the body performance constraints, manufacturing costs, and assembly costs, this block method improves the versatility of parts and components between different product individuals within the same product family, and greatly reduces automobile manufacturing, parts transportation, etc. cost in terms of. The results can provide designers with one or more solutions for modular body manufacturing, and meet the designers' needs to increase the sharing of parts.

Under the current trend of taking the modular idea as the main research and development direction, the design and optimization of the assembly structure must be carried out in the conceptual design stage under the premise of considering the modular research and development method, and such a research and development idea must involve multiple models. It directly affects the time and cost of all links from research and development to production to sales in the long life cycle of a product family. The design scheme in the conceptual design stage requires the designer to weigh various factors such as process requirements, cost control, structural performance, etc., 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 vehicle body, realizes the division, classification and screening of various modules in the vehicle body structure, and can obtain a module sharing method based on the entire product family and improve the degree of module sharing. , reduce the cost, this method is of great significance in both the reverse design and the forward design of the body.

The specific examples are listed above to describe in detail the vehicle body design of the modular product family platform based on the modular design idea of the present invention. Without departing from the spirit and scope of the present invention, those skilled in the art can also make further modifications and improvements. Therefore, all equivalent technical solutions should belong to the scope of the present invention and be defined by the various claims of the present invention.

Claims (1)

1. A body-in-white module design method based on a modular product family platform is characterized by comprising the following steps:

(1) establishing an optimization model of a single vehicle type:

taking a plurality of points in the X direction, the Y direction and the Z direction respectively by taking a coordinate system where the white body model is located as a reference, and partitioning the white body into blocks according to the points, namely, a plurality of sub-plates and sub-beams; taking the sub-components after being blocked as nodes, and taking the connection relation between the sub-components as edges, establishing a topological relation G ═ V, E, wherein V ═ { V ═ V₁，V₂，...，V_p，...，V_P}，E＝{E₁，E₂，...，E_q，...，E_Q}; wherein, { V₁，V₂，...，V_p，...，V_PRepresents a group of nodes, P nodes in total, P being the node number, { E₁，E₂，...，E_q，...，E_QRepresents a group of edges, and has Q edges in total, and Q is the number of the edges; defining a set of binary variables gamma_qThe division vector γ (γ) for the original G is composed₁，γ₂，…，γ_q，…，γ_Q): when gamma is_qAn edge E in the topological relation is represented when the value is 0_qRemoved, 1 indicates that the edge remains, and the split vector γ is used to express an assembly; and (3) optimizing by taking gamma as a design variable and taking the rigidity of the vehicle body, the manufacturing cost and the assembly cost as optimization targets, wherein the objective functions are respectively as follows:

F_{vehicle body stiffness}Displacement (G (V, E (gamma)))

Body stiffness function F in formula_{Vehicle body stiffness}Measured by the maximum displacement of the finite element model calculation result of the corresponding structure of the topological graph G (V, E (gamma)): the larger the displacement is, the larger the structural deformation is, the smaller the rigidity is; comp (k, G (V, E (gamma))) represents the kth sub-component in the model divided by G (V, E (gamma)) in the whole vehicle structure; the smaller the die area, the lower the manufacturing cost F of the sub-part_{Manufacturing cost}The lower the number of solder joints, the lower the assembly cost F of the structure_{Assembly cost}The lower; the optimization model corresponds to:

the optimization model is a multi-objective optimization problem, the optimization independent variable is a binary vector consisting of 0 and 1, the optimization calculation is carried out by using a genetic algorithm, and the iterative population and the iterative algebra are determined according to the convergence condition after being tried out for several times; setting the replacement rate of filial generations to parent generations to be 50%, the cross probability to be 90% and the mutation probability to be 10%, and taking the change rate of the population average fitness function not more than 3% as a convergence condition; obtaining the optimal assembly mode of a single vehicle type through optimization;

(2) the method is characterized by optimizing on the basis of a single vehicle model optimization model, and realizes the assembly design of multiple vehicle models simultaneously considered in a product family:

optimizing the assembly structure of each single vehicle type, comparing the optimization results, expanding the population scale in the optimization model of the single vehicle type so as to ensure that enough individuals with the same assembly structure or only locally different assemblies appear in each generation of population when different vehicle types are optimized in parallel, selecting the individuals as the initial solution of the next iteration until the optimization converges, and for n vehicle types, dividing the vehicle types into m sections of structures, wherein each section of structure is composed of α subcomponents, and each section of assembly mode is determined by (2 α -1) codes,

namely, it is

The optimization model is as follows:

in the formula

Representing the comparison of the assembly modes of corresponding positions of two vehicle types, the two are the same in time meter 0 and different in time meter 1, then

Smaller means more similar assembly structure; the method comprises the steps of selecting a guarantee solution among populations of different models to realize the modular thought-based assembly scheme design of a plurality of models;

(3) the vehicle body structure parts which are assembled and designed are taken as modules and classified, and the modules are classified into the following four classes and are screened step by step:

a parameter module: a module which needs to be redesigned when a new vehicle type is designed;

a general module: the module can be commonly used among all vehicle types;

a flexible module: a module requiring local adjustment;

a personality module: modules which are universal among similar vehicle types and not universal among different vehicle types;

(4) firstly, selecting a personality module: according to the type of the vehicle and the structural characteristics of the vehicle body, directly selecting individual modules;

(5) secondly, a parameter selection module: when a new model is designed by using a certain model as a prototype model, the size of the new model is changed at a plurality of positions; according to the assembly result, selecting a coordinate point u (u) of each module in the reset direction_min，u_max) Wherein u is x, y, z, u_minIs the minimum coordinate value in the corresponding direction, u_maxIs 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 position where the size of the entire vehicle can be changed by changing the sizes of the modules R and R + 1; if u is_{R max}＝u_{R+1 min}Then the change of the size of the whole vehicle can be realized by changing only one of the modules R or R + 1; if there are three adjacent modules, and there is u at the same time_{R max}＞u_{R+1 min}，u_{R+1 max}＞u_{R+2 min}，u_{R max}＞u_{R+2 min}When the size of the vehicle body structure is changed at the corresponding position, the three modules of R, R +1 and R +2 need to be changed simultaneously; therefore, the position for adjusting the size of the vehicle body and the corresponding module needing to be changed are found out; when a new vehicle model is manufactured, different additional costs can be increased by selecting different adjusting positions, and the manufacturing cost F is mainly considered in the concept design stage_{Manufacturing cost}And F_{Assembly cost}(ii) a Since the dimensional change at this time is not too great and may be uncertain, the body performance function F is used_{Vehicle body stiffness}Checked as constraints, requiring a predefined stiffness to be met

Selecting a position with the minimum additional cost as a main redesign area, wherein a corresponding module needing to be changed is a parameter module; if the position can not meet the performance requirement, returning to reselect; the optimization model is then expressed as:

(6) finally, selecting a flexible module; all the unscreened modules are constrained to be universal modules, i.e. each design parameter has t_{Vehicle type 1}＝t_{Vehicle type 2}＝…＝t_{Vehicle type n}(ii) a Optimizing each vehicle type under the current constraint, wherein the optimization target is the maximum vehicle weight

And body properties F_{Vehicle body stiffness}(ii) a Setting the selection interval and the optimal solution of two optimization targets according to the requirement of a designer and matching the design requirement

And

comparing according to the solution and the delta S_tValue releases its t_{Vehicle type 1}＝t_{Vehicle type 2}＝…＝t_{Vehicle type n}The design parameters do not need to be consistent with corresponding parameters in other vehicle models any more: if the optimization result of a certain vehicle type

And

if at least one of the delta S values is not satisfied, the delta S in the vehicle model is released_tThe design parameter constraint corresponding to the lowest module, which becomes the flexible module; on the contrary, if the optimization result of a certain vehicle type

And

and if at least one fails, releasing Δ S_tThe design parameter constraint corresponding to the highest module, which becomes the flexible module; and after the change is restrained, the next iteration is carried out until all the vehicle types meet the design requirements, the rest unselected modules are universal modules, and finally the screening of all the types of modules is finished.

Images