CN117077287A

CN117077287A - Method and device for optimizing large die castings of vehicle body

Info

Publication number: CN117077287A
Application number: CN202311034459.9A
Authority: CN
Inventors: 苏永雷
Original assignee: Xiaomi Automobile Technology Co Ltd
Current assignee: Xiaomi Automobile Technology Co Ltd
Priority date: 2023-08-16
Filing date: 2023-08-16
Publication date: 2023-11-17

Abstract

The disclosure relates to a method and a device for optimizing a large die casting of a vehicle body, and relates to the technical field of vehicle engineering, wherein the method comprises the following steps: establishing a vehicle body model, wherein the vehicle body model comprises a nonlinear part model and a linear part model; establishing a topology model, wherein the topology model comprises a large die casting topology model of a vehicle body; optimizing the topology model according to a multi-model optimization method to obtain an optimized model; and executing parameter optimization operation of the large die casting of the vehicle body based on the optimization model to obtain a combined proxy model. The optimization method and the system can accurately and effectively optimize the large die casting of the vehicle body by establishing the vehicle body model comprising the linear part model and the nonlinear part model.

Description

Method and device for optimizing large die castings of vehicle body

Technical Field

The disclosure relates to the technical field of vehicle engineering, in particular to a method and a device for optimizing a large die casting of a vehicle body.

Background

The large die casting of the automobile body is a main construction of the automobile, and can comprise a large die casting of an automobile cabin area, a large die casting of an automobile rear, and the like. The automobile cabin domain mainly comprises cabin longitudinal beams (shotgun), damping towers, auxiliary frames and other structures; the rear large casting of the automobile is not only a key component of the lower automobile body, but also a rear suspension to provide support, and is closely related to collision performance, NVH performance and running performance. Along with the promotion of the wave tide of large die casting, the integrated casting process and the design are the common choices of reducing the cost, improving the production efficiency, reducing factory equipment and shortening the number of parts for the train rabbet.

Disclosure of Invention

In order to overcome the problems in the related art, the present disclosure provides a method and apparatus for optimizing a large die casting of a vehicle body.

According to a first aspect of an embodiment of the present disclosure, there is provided a method for optimizing a die casting for a large vehicle body, including:

establishing a vehicle body model, wherein the vehicle body model comprises a nonlinear part model and a linear part model;

establishing a topology model, wherein the topology model comprises a large die casting topology model of a vehicle body;

optimizing the topology model according to a multi-model optimization method to obtain an optimized model;

and executing parameter optimization operation of the large die casting of the vehicle body based on the optimization model to obtain a combined proxy model.

Optionally, the large die casting of the body includes a cabin domain and/or a rear large casting.

Optionally, the building a vehicle body model includes:

establishing a pure vehicle body model;

dividing the pure vehicle body model into at least two sub-models according to the position of the cabin domain and/or the rear large casting to be optimized, wherein the cabin domain and/or the rear large casting is contained in one of the sub-models, and the at least two sub-models are connected through a superunit boundary point; and

the sub-model not containing the cabin domain and/or rear large casting to be optimized is processed as a superunit sub-model, so that the pure body model forms a hybrid model.

Optionally, when the large die casting of the vehicle body is the cabin domain, selecting a vehicle body a column, a left threshold and a right threshold, cutting off the pure vehicle body model along an approximate YZ plane, and dividing the pure vehicle body model into the nonlinear part model containing the cabin domain and the linear part model not containing the cabin domain.

Optionally, when the large die casting of the vehicle body is the large rear casting, selecting a rear side position of a C column or a B column of the vehicle body, a left threshold and a right threshold, cutting off the pure vehicle body model along an approximate YZ plane, and dividing the pure vehicle body model into the nonlinear part model containing the large rear casting and the linear part model not containing the large rear casting.

Optionally, the building a vehicle body model further includes:

chassis superunit models, powertrain superunit models, battery pack system superunit models, and closure and other accessory superunit models are added on the basis of the hybrid model to form the body model.

Optionally, the building a topology model includes:

establishing a dynamic conceptual feasible design space of an integrated die casting forming framework of the large die casting of the vehicle body to obtain a topological model of the large die casting of the vehicle body;

The topology model is optimized according to a multi-model optimization method to obtain an optimized model, which comprises the following steps:

taking the topological model of the large die casting of the vehicle body as a shared design variable, taking the rigidity, the modal, the dynamic rigidity and the collision performance of the vehicle body as optimization constraints, and executing the multi-model optimization method by taking the minimum volume fraction of the topological model of the large die casting of the vehicle body as an optimization target.

Optionally, the performing the parameter optimization operation of the large die casting of the vehicle body based on the optimization model, to obtain a combined proxy model, includes:

and taking the thickness design parameters of the large-sized die casting of the automobile body as shared design variables, taking the rigidity, the modal, the dynamic rigidity and the collision performance of the automobile body as optimization constraints, and taking the minimum total mass of the large-sized die casting of the automobile body as an optimization target to execute parameter optimization operation to obtain the combined proxy model.

and taking the optimization model as input, taking the attention performance of each working condition as output, and calling a solver corresponding to each working condition to carry out simulation calculation to obtain the combined proxy model.

According to a second aspect of embodiments of the present disclosure, there is provided a die casting optimizing apparatus for a large-sized vehicle body, including:

a first build module configured to build a vehicle body model, the vehicle body model comprising a nonlinear part model and a linear part model;

a second build module configured to build a topology model, the topology model comprising a large die casting topology model of the vehicle body;

the first optimization module is configured to optimize the topology model according to a multi-model optimization method to obtain an optimized model;

and the second optimization module is configured to execute parameter optimization operation of the large die casting of the vehicle body based on the optimization model to obtain a combined proxy model.

According to the embodiment of the disclosure, the optimization of the large die casting of the vehicle body can be accurately and effectively realized by establishing the vehicle body model comprising the nonlinear part model and the linear part model. Specifically, a vehicle body model is built, the vehicle body model can comprise a nonlinear part model and a linear part model, and a topology model is built, the topology model can comprise a vehicle body large die casting topology model, on the basis, the topology model is optimized according to a multi-model optimization method to obtain an optimized model, and then parameter optimization operation of the vehicle body large die casting is performed based on the optimized model to obtain a combined proxy model, so that optimization of the vehicle body large die casting can be stably and rapidly achieved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.

Fig. 1 is a flow chart illustrating a method of optimizing a die cast for a large body of a vehicle according to an exemplary embodiment.

Fig. 2 is an exemplary diagram showing a multi-model optimization method among large die casting optimization methods for a vehicle body according to an exemplary embodiment.

Fig. 3 is an exemplary diagram illustrating parameter optimization performed in a method for optimizing a die cast for a large vehicle body according to an exemplary embodiment.

Fig. 4 is a block diagram illustrating a large die casting optimizing apparatus for a vehicle body according to an exemplary embodiment.

Fig. 5 is a block diagram of an electronic device, according to an example embodiment.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

In the description of the present disclosure, terms such as "first," "second," and the like are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. In addition, unless otherwise stated, in the description with reference to the drawings, the same reference numerals in different drawings denote the same elements.

Although operations or steps are described in a particular order in the figures in the disclosed embodiments, it should not be understood as requiring that such operations or steps be performed in the particular order shown or in sequential order, or that all illustrated operations or steps be performed, to achieve desirable results. In embodiments of the present disclosure, these operations or steps may be performed serially; these operations or steps may also be performed in parallel; some of these operations or steps may also be performed.

Fig. 1 is a flowchart illustrating a method of optimizing a large die casting for a vehicle body according to an exemplary embodiment, which includes steps S110 to S140 as shown in fig. 1.

In step S110, a vehicle body model is built, which includes a nonlinear partial model and a linear partial model.

In some embodiments, building the body model may include building a clean body model; dividing the pure vehicle body model into at least two sub-models according to the position of the large die casting of the vehicle body to be optimized, wherein the large die casting of the vehicle body is contained in one of the sub-models, and the at least two sub-models are connected through superunit boundary points; and processing the sub-model which does not contain the large die casting of the vehicle body to be optimized into a superunit sub-model so as to enable the pure vehicle body model to form a hybrid model, thus effectively reducing the calculation scale and the optimization efficiency of the model, increasing the iterative analysis efficiency and reducing the influence of interference orders.

In the disclosed embodiments, the large die cast body may include a cabin area and/or a rear large casting. When the large die casting of the vehicle body is a cabin domain, the process of establishing the vehicle body model can be to establish a pure vehicle body model; dividing the pure vehicle body model into at least two sub-models according to the position of a cabin domain to be optimized, wherein the cabin domain is contained in one of the sub-models, and the at least two sub-models are connected through superunit boundary points; and processing the sub-model not containing the cabin domain to be optimized as a superunit sub-model so that the pure body model forms a hybrid model.

Alternatively, when the large die casting of the vehicle body is a rear large casting, the process of building the vehicle body model may be building a clean vehicle body model; dividing the pure vehicle body model into at least two sub-models according to the position of a rear large casting to be optimized, wherein the rear large casting is contained in one of the sub-models, and the at least two sub-models are connected through superunit boundary points; and processing the sub-model not containing the rear large casting to be optimized into a superunit sub-model so that the pure body model forms a hybrid model.

The method for creating the hybrid model is called superunit method, specifically, in a finite element integral model, a partial model is cut, and finite element analysis is used to extract specific mechanical properties through modular expression, matrix expression and transfer function expression, and this process is called superunit generation (polycondensation). Then, when the whole model is analyzed, the cut part model is replaced by the expressions, and the operation method is called superunit method (superunit) or direct matrix input method (DMIG), the expressions are called superunits, and the part of the whole model except the superunits is called residual structure.

The most significant use of the superunit is to greatly reduce the calculation cost, improve the analysis efficiency, complete the analysis of huge calculation by using limited calculation resources, and greatly reduce the degree of freedom of the whole model, so the calculation amount is relatively lower, and the superunit can be used for carrying out more complex analysis. Therefore, more parts or subsystems of complex large-scale structural members, such as an airplane, a ship and the like, are split and manufactured into superunits, so that the whole analysis task can be successfully completed, the multi-application of the current automobile field is applied to shortening of the model calculation period and optimization calculation, particularly in multi-disciplinary optimization and light optimization analysis, the calculation analysis optimization efficiency is remarkably improved, the solving process of the whole model is approximately divided into two parts, namely, the first step is that the superunits (superelement generation run) are generated; the second step is residual structure calculation (residual run).

When the integral model of the vehicle body is built based on the superunit method, in order to better reduce the number of boundary points, improve the efficiency and facilitate the operation, the separation position of the pure vehicle body model can be determined according to the type of the large die casting of the vehicle body.

Specifically, when a large die casting of a vehicle body is a cabin domain, the embodiment of the disclosure may select a vehicle body a column, a left threshold and a right threshold, cut off the pure vehicle body model (white vehicle body model) along an approximate YZ plane, and divide the pure vehicle body model (white vehicle body model) into a nonlinear part model including the cabin domain and a linear part model not including the cabin domain on the basis of the cut-off. In this process, the whole B column should be kept as completely as possible in part of the model.

Alternatively, when the large die casting of the vehicle body is a rear large casting, the embodiment of the disclosure may select the rear side position of the C-pillar or the B-pillar of the vehicle body, the left and right thresholds, cut the pure vehicle body model (white vehicle body model) along the approximately YZ plane, and on this basis, divide the pure vehicle body model (white vehicle body model) into a nonlinear part model including the rear large casting and a linear part model not including the rear large casting.

It should be noted that the superunit involved in the process of establishing the vehicle body model may be a second-level superunit. For example, a second level superunit may be used to construct the body-in-white hybrid model E. The existing boundary of the vehicle body model does not exist objectively, and boundary points are reasonably defined according to specific conditions in the embodiment of the disclosure. The defining principle of the boundary can be as follows: the number of boundary points is reduced as much as possible; different treatments are respectively carried out on the linear region and the nonlinear region of the collision model, namely, the nonlinear part is not processed, remains as a residual structure, and only the linear part is processed as a superunit.

Further, building the body model may also include adding chassis superunit models, powertrain superunit models, battery pack system superunit models, and closures and other accessory superunit models on the basis of the hybrid model to form the body model. Therefore, the simulation scene is more real, the structure is more accurate, and the higher optimization analysis effect is guaranteed. Here, the superunit involved in the build process of the vehicle body model may be a first-level superunit.

Here, the chassis superunit model may be represented by a chassis superunit model a, the chassis divides the chassis system into a front part and a rear part, the front part may include a front suspension system front brake system and the like, the rear part may include a rear suspension system, a rear brake system, a rear subframe and the like, and the front part and the rear part of the chassis and the vehicle body system may be respectively connected through a suspension or a bushing as boundary points of the subsystem, where the boundary points are objectively existing boundaries, and the number of boundary points is small to facilitate the establishment of the chassis system superunit model; the power assembly superunit model can be represented by a power assembly superunit model B, and the front electric drive system and the front cabin domain suspension attachment point can be used as boundary points of a subsystem, wherein the boundary points are objectively existing boundaries, the number of the boundary points is small, the construction of the electric drive system superunit model is convenient, meanwhile, the suspension attachment points of the rear electric drive system and the chassis rear auxiliary frame system are used as boundary points of the subsystem, the boundary points are objectively existing boundaries, and the number of the boundary points is small, so that the construction of the rear electric drive system superunit model is convenient; the superunit model of the battery pack system can be expressed as a superunit model C of the battery pack system, and the embodiment of the disclosure can take the bolt connection point of the battery pack system and the vehicle body system as a boundary point of a subsystem, wherein the boundary point is an objectively existing boundary, and the number of the boundary points is small, so that the superunit model of the battery pack system is convenient to establish; the closure and other accessory superunit model may be represented as closure and other accessory superunit model D, where the closure may include a front cover, back door or trunk lid, front door, rear door, other accessories may include front row seats, rear row seats, etc., such a subsystem may be connected to the vehicle body by bolts or latches, the disclosed embodiments may use the bolt or latch connection points as boundary points for the subsystem, which are objectively existing boundaries, a small number facilitating the creation of the corresponding superunit model.

In summary, the present disclosure describes specific modeling steps of a vehicle body model by superunit method using a large die casting of a vehicle body as an example of a cabin domain, and for convenience of explanation, the present disclosure defines a running direction of a vehicle as an X-direction, a height direction of the vehicle as a Z-direction, and a width direction of the vehicle as a Y-direction, and the specific steps are as follows:

firstly, establishing a pure vehicle body model; then selecting a vehicle body A column, a left threshold and a right threshold, cutting off a pure vehicle body model along an approximate YZ plane, dividing the pure vehicle body model into a nonlinear part model 1 containing a cabin domain and a linear part model 2 not containing the cabin domain, wherein the B column in cutting off completely belongs to one part of models.

Superunit boundary points are then defined based on model 1. Based on the section of the A column of the vehicle body, a rbe unit positioned in the section area of the left A column of the model 1 is generated, the rbe unit belongs to the model 1, the node of the section unit is a slave point of rbe2, the centroid of the selected slave point is a main point i of rbe2, the main point i is taken as a common main point position, the node of the corresponding section of the model 2 is grasped as the slave point, a new rbe2 is generated, the new rbe unit belongs to the model 2, and the cut-off part of the model is re-sewn up through two rbe2 connections. Mapping the principal point i of the rbe unit on the left side of the model 1 to the right side area of the model 1 about the y=0mm plane, generating a principal point ii which is symmetrical about the y=0mm plane, and establishing two new rbe units based on the principal point ii, wherein the two new rbe units are respectively connected with the sections of the model 1 and the model 2, rbe2 connected with the model 1 belongs to the model 1, rbe2 connected with the model 2 belongs to the model 2, and the generated new connection ensures bilateral symmetry in the whole vehicle state. Similarly, the front door sill section also generates rbe units and corresponding main points iii and iv, and specific steps are the same as the main points i and ii of the a pillar section, and are not described in detail in this disclosure.

Further, at the boundary point definition of the front windshield glass, based on the model 1, grabbing all nodes of the section of the front windshield glass with rbe2, wherein rbe units belong to the model 1, the intersection point of the plane with Y=0mm and the cut-off edge is defined as a main point v of rbe units, the main point v is taken as a common main point position, all nodes of the corresponding section edge of the model 2 are grabbed as auxiliary points, a new rbe2 is generated, the rbe units belong to the model 2, and the cut-off part of the model is stitched again through connection of two rbe2 units. The above five principal points are then boundary points of the body model 1 and the model 2, so that the two sub-models separated from each other, that is, the model 1 and the model 2 are reconnected by the superunit boundary point.

Then, in performing modal analysis on the hybrid model obtained by re-stitching the model 1 and the model 2, the present disclosure uses the cabin domain as an optimization object, adaptively performs superunit analysis using the model 2 as a superunit object, and the superunit model output format general template setting method is a default output H3D format superunit, and if a PCH format is to be further generated, parameter settings PARAM, EXTOUT, DMIGPCH are added to the finite element analysis section. In addition, the general template setting method of the superunit output information comprises the following steps: if the superunit internal node is an output point or an observation point, the superunit internal node is connected through a plot and outlines a structural outline, and generates an elset together with the internal unit to be output, so as to define an output statement MODEL=elset, NONE, NORIGID. Further, the cabin domain to be optimized is located in a residual structure model, namely, in the model 1, the residual structure model calculates a priority reference H3D format superunit; the elset consists of two parts, one part is a plot and the other part is an element of residual calculation in the group, specifically, the residual structure calculation refers to the H3D superunit file grammar: assgn, H3DDMIG, FILENAME, H3DFILENAME.H3D; reference PCH superunit file syntax: INCLUDE, PCHFILENAME.PCH.

The method is characterized in that the mixed model of the pure vehicle body combined by the superunit model is subjected to modal analysis by the method, and compared with the pure vehicle body model which is not combined by the superunit model, the analysis frequency ranges of the mixed model and the pure vehicle body model are consistent, and then the modal comparison is performed, so that the modal order of the mixed model of the pure vehicle body combined by the superunit model is obviously reduced, the finite element model of the pure vehicle body model which is not combined by the superunit model has 62-order modes, and the modal order of the mixed model of the pure vehicle body combined by the superunit model is 38-order or 39-order, so that the modal order is greatly reduced, the problem that the corresponding order layer cannot be accurately grasped due to higher modal order is avoided, the automatic jump order is caused, and the accuracy of a modal analysis result is influenced is solved.

According to different application scenes and statistical carding, the embodiment of the method can carry out statics analysis, dynamics analysis and mode division with prestress based on the setting of a dynamic reduction CBN algorithm and a FREE interface method, namely, a finite element model object is generated into a superunit by adopting the CBN algorithm and the FREE interface method.

Finally, in order to ensure that the simulation scene is more real, a chassis superunit model, a power assembly superunit model, a battery pack system superunit model, a closure member and other accessory superunit models are added to the hybrid model established by the superunit method. It will be appreciated from the foregoing that the battery pack system and body system may be bolted, and that the subsystems involved in the closure and other accessories may also be bolted to the body. The bolt connection can adopt 'rbe 2+beam+ rbe' type connection, and a common node of the superunit part rbe and a beam unit is a boundary point for generating a superunit model, and the embodiment of the disclosure can adopt a CBN algorithm and a FIX interface method to convert a finite element model of a battery pack into the superunit model. In addition, the suspension, bushing and latch connections employ "BUSH" unit type connections, either end of which serve as boundary points for generating superunit models.

As such, the present disclosure constructs a vehicle body model by simultaneously referencing a hybrid model, a chassis superunit model, a powertrain superunit model, a battery pack system superunit model, and a closure and other accessory superunit models, and performs relevant operating condition analysis on the vehicle body model. The relevant working condition analysis can comprise typical stiffness analysis, modal analysis, dynamic stiffness analysis, collision analysis and the like.

When the rigidity analysis and the modal analysis are performed, the embodiment of the disclosure can construct a first reduced model of bending torsional rigidity and modal working conditions of the large die casting of the vehicle body, wherein the first reduced model=the superunit model C+the white vehicle body mixed model E of the battery pack system. Based on the first reduced model, modal analysis and bending stiffness analysis are carried out, the reduced model is compared with a finite element model which is not subjected to superunit reduction, and boundary conditions set by simulation analysis of the two models are completely consistent, as shown in the following table 1.

TABLE 1

The Base-finite element modeling in the above table 1 is based on a vehicle body model which is not built by the superunit method, the Opt-hybrid modeling is based on a vehicle body model which is built by the superunit method, and is formed by adding a chassis superunit model, a power assembly superunit model, a battery pack system superunit model, a closure member and other accessory superunit models, and the modeling methods of the two vehicle body models are used for respectively analyzing working conditions, and by combining the table 1, the performance deviation of the two modeling modes is very small, and the modeling method can realize high fidelity.

Optionally, in performing dynamic stiffness analysis, embodiments of the present disclosure may construct a second reduced model of dynamic stiffness conditions of a large die cast part of a motor vehicle body, the second reduced model = battery pack system superunit model C + closure and other accessory superunit model D + body in white hybrid model E. Based on the construction of the second reduction model, dynamic stiffness analysis of the attachment point of the large die casting of the vehicle body is carried out, and the comparison reduction model and the corresponding finite element model which is not subjected to superunit reduction can know that boundary conditions set by simulation analysis of the two models are completely consistent. The attachment point dynamic stiffness simulation results are compared in table 2 below.

TABLE 2

By comparing the two models in table 2, and taking the dynamic stiffness of the attachment point at the position of the shock absorption tower as an example, it can be known that the performance deviation of the two models is very small, so that the modeling method in the embodiment of the disclosure can realize high fidelity.

Optionally, in performing the collision analysis, the disclosed embodiments may construct a third reduced model of the large die cast body in motion collision conditions, the third reduced model = chassis superunit model a + powertrain superunit model B + battery pack system superunit model C + closure and other accessory superunit model D + body in white hybrid model E. Based on the third reduced model, applying equivalent static load to the corresponding section, analyzing collision working conditions, comparing the reduced model with the corresponding finite element model which is not subjected to superunit reduction, and finding that the collision deformation modes of the two models are identical and the performance results of the corresponding positions such as speed, deformation, intrusion and the like are identical. Therefore, the collision reduction model modeling method in the embodiment of the present disclosure can achieve high fidelity.

In addition, it should be noted that, the single-round analysis efficiency of the vehicle body model formed by adding the chassis superunit model, the power assembly superunit model, the battery pack system superunit model, the closure member and other accessory superunit models into the hybrid model established by the superunit method can be improved by 8 times, so that the scene of the optimization analysis requiring multi-sample analysis or multiple iterations can be better supported, and the specific comparison results are shown in the following table 3.

TABLE 3 Table 3

In summary, the total time consumption of the single simulation analysis of the three reduced models is known through the table 3, compared with the single round analysis of the corresponding finite element model, the efficiency can be improved by 8 times, and the scene of the optimization analysis requiring multi-sample analysis or multiple iterations can be better supported.

The large die casting of the vehicle body in the present disclosure may be a cabin domain, a rear large casting, or both cabin domain and rear large casting. For example, the second reduced model may be a reduced model of the nacelle domain dynamic stiffness operating mode, or may be a reduced model of the rear large casting dynamic stiffness operating mode. For another example, the third reduced model may be a reduced model of cabin domain collision conditions, or may be a reduced model of rear large casting collision conditions. The specific choice of which large die casting of the vehicle body to optimize is not specifically limited and can be selected according to practical situations.

In step S120, a topology model is established, the topology model including a die cast topology model of a large body.

As an optional mode, in the process of establishing the topological model, the embodiment of the disclosure can be used for obtaining the topological model of the large die casting of the vehicle body through the dynamic concept feasible design space of the integrated die casting forming framework of the large die casting of the vehicle body. On the basis, the topology model is optimized according to the multi-model optimization method, and an optimization model is obtained.

As a specific implementation, when the large die cast of the vehicle body is a cabin domain, the disclosed embodiments may create a dynamic conceptually viable design space for a cabin domain integrated die cast molding architecture. Wherein the dynamic concept feasible design space is a set of all potential solutions, and the construction process is mainly divided into two steps: the first step is to construct a conceptual design space in a static arrangement scene based on 3D data of a vehicle body, an electric drive system, a chassis and other functional parts in a cabin domain on the premise of meeting the minimum clearance requirement of the arrangement space of the related parts. And secondly, on the basis of the space constructed in the first step, performing analysis such as wheel jump analysis, acceleration, braking and the like under the maximum shock absorber stroke according to the selected suspension type and hard point position, identifying the motion envelope of the control arm and other hand pieces, further reducing the design space range, and avoiding the feasible design space of the concept of the auxiliary frame under the dynamic arrangement scene obtained by the motion envelope. On the basis, the concept feasible design space is divided into 4mm second-order body unit grids, and the 4mm second-order body unit grids are used as topology domains of the cabin domain integrated die-casting molding framework and are used for multi-model topology optimization.

The non-topological domain can adopt finite metadata of basic vehicles, reference vehicles, platform vehicles and conceptual design vehicle types, and bolts, bonding or other welding connection modes between the non-topological domain and the topological domain can be kept unchanged. And carrying out model reduction on the collision linear region in the non-topological domain according to the working condition reduction method in the second step.

As a specific implementation, when the large die cast of the vehicle body is a rear large cast, the disclosed embodiments may create a dynamic conceptually feasible design space for the rear large cast integrated die cast molding architecture. The rear large casting integrates sheet metal schemes such as a traditional longitudinal beam, a wheel cover, a floor and a cross beam, a system in spatial connection with the rear large casting is provided with a power system, a chassis system, a soft and hard interior decoration system, an exterior decoration system and the like, and parts of the systems can limit the design space of the large casting from three directions of XYZ. Specifically, a total of 40 parts are required to be installed on a rear large casting, 137 installation points are required to be installed, the installation point modes relate to bolt connection, metal plate clamps, thread clamps and the like, and relate to grounding wire connection and bear the grounding function. In addition, 4 connection modes of steel and aluminum of the automobile body are SPR self-piercing riveting, bolting, FDS hot melting self-tapping screwing and structural adhesive respectively. In addition, the sealing foam at the upper cover of the compressed battery of the rear large casting plays a role in sealing the inside and outside of the vehicle and bears the requirements of safety collision, compression resistance, torsion resistance, durability of strength, vibration transmission reduction and the like. The construction process of the rear large casting is similar to that of the cabin domain, and a detailed description is omitted here.

In the embodiment of the disclosure, the reduced model of the dynamic concept design space of the large die casting of the vehicle body can comprise three reduced models, and the three reduced models can be reduced models of collision working conditions respectively; stiffness and modal condition reduction models; dynamic stiffness operating mode reduction model. The method and the device can perform nonlinear topological optimization on a collision working condition reduction model, and linear topological optimization on a rigidity, modal working condition reduction model and dynamic rigidity working condition reduction model, so that multimode topological optimization can be realized by the embodiment of the disclosure.

In step S130, the topology model is optimized according to the multi-model optimization method, so as to obtain an optimized model.

As an alternative, embodiments of the present disclosure may optimize a topology model based on a multi-model optimization (MMO) method. The multi-model optimization can simultaneously consider a plurality of calculation models, the calculation models can share part of design variables, and the embodiments of the disclosure can obtain the same or similar optimization results based on the shared design variables.

Through the above description, three working conditions related to the large die casting (cabin domain/rear large casting) of the vehicle body are respectively the rigidity and the modal working conditions of the vehicle body; dynamic stiffness conditions; collision conditions. The three working conditions are different in calculation model constitution, so that all analysis working conditions cannot be directly covered based on one model, and therefore, a multi-model optimization MMO method is needed to develop topology optimization.

Specifically, the embodiment of the disclosure can take a topological domain of a large die casting (cabin domain/rear large casting) of a vehicle body as a shared design variable; defining rigidity, mode, dynamic rigidity and collision performances as optimization constraints; the multi-model topological optimization MMO is developed by taking the minimum topological domain volume fraction as an optimization target, and a specific optimization flow is shown in figure 2. As shown in fig. 2, cl is a vehicle body rigidity and modal performance target, c2 is a dynamic rigidity performance target of each attachment point of a large die casting (cabin domain/rear large casting) of the vehicle body, and c3 is a compliance performance target of a collision equivalent static load working condition.

After the topology optimization result is obtained, the embodiment of the disclosure can perform topology path interpretation based on the topology optimization result to complete the primary structural design of the integrated die casting forming framework of the large die casting (cabin domain/rear large casting) of the vehicle body, and update the topology domain of each working condition reduction model into primary structural design data to obtain an optimization model. The structure of the updated reduced model large die casting (cabin domain/rear large casting) is still a rough preliminary design, and specific parameter optimization needs to be further carried out, namely, step S140 is counted.

In step S140, a parametric optimization operation of the large die casting of the vehicle body is performed based on the optimization model, resulting in a combined proxy model.

As an alternative, after the optimization model is obtained, the embodiments of the present disclosure may perform a parameter optimization operation of the large die casting of the vehicle body based on the optimization model, resulting in a combined proxy model. Through the above description, three kinds of working conditions related to the large die casting (cabin domain/rear large casting) of the automobile body are different in calculation model composition, so that in the process of executing the parameter optimization operation of the large die casting of the automobile body based on the optimization model, the embodiment of the disclosure can take the thickness design parameters of the large die casting of the automobile body (cabin domain/rear large casting) as shared design variables; defining rigidity, mode, dynamic rigidity and collision performances as optimization constraints; and carrying out multidisciplinary parameter optimization by taking the minimum total mass of design variables as an optimization target, wherein a specific optimization flow is shown in fig. 3. C as shown in FIG. 3 _l Is the object of rigidity and modal performance of the vehicle body, c ₂ For the dynamic stiffness performance target of each attachment point of the cabin domain, c ₃ The performance target of the nonlinear collision working condition can be speed, position, intrusion quantity and the like.

In some embodiments, the structure of the large die casting (cabin domain/rear large casting) of the vehicle body can be improved from a topological type to a preliminary engineering design scheme through the optimization, and the improvement degree of the design data of the large die casting (cabin domain/rear large casting) of the vehicle body is greatly improved. Aiming at the data characteristics of the stage, the collision working condition adopts a standard nonlinear collision analysis working condition, and in order to improve the optimization efficiency, the method and the device can adaptively reduce the whole vehicle collision model.

Specifically, for the reduction of subsystems of nonlinear collision analysis conditions, the embodiments of the present disclosure may employ LS-DYNA software to divide the model into a substructure and a residual structure. The subsystem division and boundary point setting are consistent with the vehicle body model. On the basis, boundary points are defined as data transmission nodes of the substructure analysis, and specifically, the boundary points are defined by an INTERFACE_COMPONENT keyword so as to generate a superunit format file supported by collision nonlinear solution. Here, the reduced model ii of the reconstructed large die cast (cabin/rear large cast) collision condition = chassis superunit model a + powertrain superunit model B + battery pack system superunit model C + closure and other accessory superunit model D + body-in-white hybrid model E.

It should be noted that, the reduced models of the bending stiffness, the modal working condition and the dynamic stiffness working condition still adopt the modeling methods of the working conditions, and only the large die casting (cabin domain/rear large casting) of the vehicle body is updated to the preliminary structural design data so as to obtain the corresponding reduced model ii.

Because the preliminary design scheme of the large die casting (cabin domain/rear large casting) of the automobile body obtained by the embodiment is an integrated structure, the rib starting parts are all thick, so as to meet the casting process requirements, and the parameter optimization is inconvenient to directly carry out. Therefore, the embodiment of the disclosure provides a rib lifting thickness equivalent method for a casting process structure. Wherein, the self-adaptive equivalent thickness calculation formula is:

wherein T (x) is the self-adaptive equivalent thickness; v is the total volume of the units on the fascia; a is the area of the fascia.

Optionally, the parameterized thickness parameters of the large die casting (cabin domain/rear large casting) of the vehicle body are often more or can exceed 1000, and if all the parameters participate in optimization, a large number of calculation samples are needed to achieve higher prediction accuracy. Therefore, the embodiment of the disclosure can sort the volumes of the ribs or the basal planes from large to small, and select the parameter of 10% before sorting as the design variable of subsequent optimization. Specifically, the sheet metal, the rib surface and the base surface are simultaneously used as design variables for sensitivity analysis, and sensitivity about torsional rigidity is mainly examined, namely, the sheet metal structure and the casting structure of the whole automobile body participate in screening and optimizing simultaneously.

In addition, embodiments of the present disclosure may employ quality regularized sensitivity analysis to avoid direct sensitivity analysis resulting in large components having higher sensitivity than other components. In performing a quality normalized sensitivity analysis, embodiments of the present disclosure may add MASS instructions to a sensitivity analysis output statement, such as

OUTPUT, H3DGAUGE, FL, MASS. On the basis, quality regularization is ordered from large to small, 10% of thickness parameters before ordering are selected to improve the performance, and 10% of thickness parameters after ordering are selected to reduce the weight.

Further, in the thickness parameters of the first 10% and the last 10%, the thickness value is taken as the starting rib or the base surface of the upper limit value and the lower limit value of the casting process: if the thickness parameter sensitivity is higher and the value reaches the upper limit value of the process, setting the upper limit thickness parameter as a thickness design value, and not further participating in optimization; if the thickness parameter sensitivity is lower and the value is the lower limit value of the process, setting the lower limit thickness parameter as a thickness design value, and not further participating in optimization; other thickness parameters participate in the optimization.

As an optional mode, in the process of performing parameter optimization operation of a large die casting of a vehicle body based on an optimization model to obtain a combined proxy model, the embodiment of the disclosure may take the optimization model as input, take the attention performance of each working condition as output, and call a solver corresponding to each working condition to perform simulation calculation to obtain the combined proxy model. Wherein the combined proxy model may also be referred to as a proxy model, the mathematical model of the proxy model may be expressed as:

Wherein f (x) is a real model,for the proxy model, ε (x) is the approximation error. Common proxy models include polynomial response surfaces, kriging functions, radial basis functions, and the like.

Here, the representation of the polynomial response surface may be:

wherein n is _v To design the dimension of the variable, x _i As the i-th variable, the undetermined coefficient beta ₀ ，β _i ，β _ii ，β _ij Determined by least squares regression.

Alternatively, the Kriging function may be expressed as:

wherein g (x) is a polynomial global approximation model, the local deviation term Z (x) is zero in mean and sigma in variance ² The approximation ability of KRG is mainly determined by the local bias term Z (x), a non-zero covariance, random process. The covariance matrix of Z (x) can be expressed as:

wherein R is a Gaussian correlation function,is a symmetric correlation matrix. In addition, the gaussian correlation function can be expressed as:

alternatively, the radial odd function may be expressed as:

the weight coefficient vector w can be solved as follows:

where φ (r) is a radial function and r is the Euclidean distance between sample points.

Because a single proxy model has a strong predictive power for all optimization problems, the combined proxy model in the embodiments of the present disclosure has a more stable approximation power. The embodiment of the disclosure can select a polynomial response surface, a radial basis function and a Kriging function to participate in the construction of a combined model, and a Cross-validation (CV) precision verification method is adopted to obtain the precision evaluation value of each agent model. In addition, a weight coefficient is calculated according to the precision evaluation value, and a combined agent model for static stiffness calibration is determined through linear weighting, wherein the concrete expression is as follows:

Wherein m is participation construction combined proxy modelIs->Number, w _i (x) Is a weight coefficient.

In addition, the CV evaluates the single agent model approximation accuracy using a Root Mean Square Error (RMSE) criterion expressed as follows:

wherein f _j (x) For the sample point real model value,n is a proxy model value _t To verify the sample point number of the agent model accuracy.

In the embodiment of the disclosure, the weight coefficient calculation may adopt CV-RMSE criterion, specifically, the weight coefficient w _i For the design variables, with the root mean square error minimized as the objective function, the combined proxy model calculation process can be translated into:

wherein f _ENk (x) For the sample point real model value,to combine proxy model values, N _e To verify the accuracy of the combined proxy model.

Further, when parameter optimization is performed, ls-opt software can be adopted in the embodiment of the disclosure, three working conditions including a collision reduction model ii, a bending rigidity and modal reduction model ii and a dynamic rigidity reduction model ii are integrated, the integrated workflow takes design variables (sharing thickness) of the optimization model as input, attention performance of each working condition as output, and simulation calculation is performed by calling solvers corresponding to each working condition. And performing simulation debugging based on the constructed workflow, performing single sample analysis and calculation, and ensuring that the analysis result of the association flow is consistent with the direct calculation result, so that the workflow debugging can be ensured to be successful. In addition, the embodiment of the disclosure can also perform analysis and calculation of a plurality of samples, so that the association flow can be ensured to drive the automatic iterative update of the design variables.

The embodiment of the disclosure can be distributed with a plurality of parallel cluster computing modes to improve the computing efficiency, and is simultaneously used for high-performance computer clusters under Linux clusters, windows clusters and Windows-Linux heterogeneous conditions, and an Open PBS parallel resource management system is adopted for high-efficiency parallel computing. In addition, for a plurality of workstations connected with a local area network, the distributed parallel computing can be realized by adopting an OPS method, and the multi-working-condition parallel computing resource efficient distribution can be realized.

Alternatively, for high-time-consuming analysis conditions (such as nonlinear analysis of collision-reduction model ii), embodiments of the present disclosure may be assigned to high-performance workstation parallel computations; for the lower time-consuming analysis working conditions, the method can be distributed to high-performance workstation parallel computing and can also be distributed to local workstation parallel computing. The high-time-consuming analysis working condition is often a determining factor for limiting the calculation time of all samples, the most efficient parallel analysis mode needs to be adopted, and the low-time-consuming analysis working condition can select different parallel analysis modes.

In summary, in constructing a combined proxy model, embodiments of the present disclosure may define design variables and value ranges and perform adaptive experimental design to construct a combined proxy model. Here, the thickness may be used as a design variable, and the design variable may have a value ranging from [1.8mm,5.0mm ] according to the requirement of the molding capability of the casting process, and the thickness may be continuously valued. The present disclosure may more efficiently construct a combined proxy model through adaptive experimental design.

As an example, the latin hypercube sampling method is adopted for initial sample points, 30 initial sample points are adopted, 10 self-adaptive sample points are added in each generation of the sampling process, the RBF+KRIG algorithm is selected to construct a proxy model, and the convergence criterion can be: the number of the maximum sample points reaches 180; the accuracy of the proxy model reaches 95%. And calculating convergence through 180 sample points, and constructing each performance agent model with the accuracy reaching 90%. And optimizing by adopting a sequence quadratic programming method (Sequential Quadratic Programming, SQP) based on the agent model to obtain an optimization scheme, and manufacturing detailed engineering design data according to the optimization scheme.

Based on the detailed engineering design data, the embodiment of the disclosure can further perform finite element analysis verification on all relevant working conditions of the design data of the large die casting (cabin area/rear large casting) of the vehicle body, a large amount of iterative optimization is not needed at this time, the finite element model is directly adopted for analysis, and aiming at the existing working conditions which do not meet the performance requirements, the weak area is identified by adopting a sensitivity analysis or strain energy analysis method, and the weak area is reinforced by means of material grade improvement, local area structure optimization and the like.

Specifically, in the following large castings as an example, the safety performance of the high/low speed balanced large castings is developed and verified, and in order to balance the performance requirements of low speed, GB and FMVSS, the embodiment of the disclosure can design a low-speed collision zone D1, a GB high-speed collision deformation zone D2 and a American standard collision deformation zone D3. Wherein, the total energy of the whole vehicle low-speed collision related to the low-speed collision deformation zone is 1/2mv ² The method comprises the steps that (1) F1 is adopted, the whole vehicle mass and the test speed are constant values, D1 values are determined based on a low-speed collision target, modeling and material accumulation after deformation, the average load of a low-speed collision deformation area is converted, the peak bearing capacity is defined based on the average load and a section design, no deformation of an extrusion longitudinal beam and a rear large casting is required to be ensured at a low speed (F3 is more than F2 is more than F1), and material and dimensional tolerance fluctuation is considered, wherein each gradient is at least more than 20KN; complete vehicle GB high-speed collision total energy 1/2mv related to GB high-speed collision deformation zone ² The method comprises the steps of (1) determining the mass and test speed of a whole vehicle as fixed values by (1) and (2) determining the D2 value by taking the arrangement states of a light vehicle, a heavy vehicle and a GB high-speed collision zone into consideration, and finally determining the F2 by taking the fact that the F2 is more than or equal to F1 plus 20KN in order to protect a large casting from damage after GB high-speed collision; the American standard collision deformation zone designs the cracking zone under the conditions of low-speed collision with GB high-speed collision and F3 being more than or equal to F2+20KN, so that the safety of passengers is protected.

Optionally, the embodiment of the disclosure can also be based on fatigue life assessment and verification of modal transient, and the modal transient fatigue simulation technology can effectively consider damages to the structure except road surface load, and meanwhile can consider the influence of road surface excitation frequency on resonance of the whole vehicle, especially for a rear vehicle body large die casting (cabin area/rear large casting), vibration fatigue damage occupies a relatively large area. In addition, the mechanical property differences of different areas of the large die casting (cabin area/rear large casting) of the automobile body are considered, the fatigue material curves of the different areas are correspondingly adjusted according to the mechanical property differences, so that the fatigue life of the large die casting (cabin area/rear large casting) of the automobile body in the whole automobile is accurately predicted, and smooth passing of a physical test is ensured.

Optionally, the embodiment of the disclosure can also identify and evaluate the problem of the large casting based on the performance decomposition of the whole vehicle, specifically, find the mode shape and deformation mode corresponding to the weak point frequency through the contribution quantity & ODS according to the NVH simulation analysis working condition and key performance indexes such as the mode and dynamic stiffness, find the weak point area according to the strain energy distribution, and locally design the reinforcing rib in the area. Therefore, if the problem area occurs in the large panel area in the middle of the rear large casting, the profile optimization is performed by taking ERP performance of the large panel profile deformation area as an optimization index, for example, the profile optimization is performed by taking the large panel as a design variable, taking the reinforcing rib fraction not higher than 0.2 as a constraint condition, and performing the optimization by taking the minimum ERP of the large panel profile deformation area as an optimization target, so that the reasonable reinforcing rib arrangement of the large panel area is obtained.

Optionally, the embodiment of the disclosure can also be based on the physical verification of the bench and the whole vehicle, specifically, the bench and the whole vehicle physical verification of the large die casting (cabin area/rear large casting) of the vehicle body are developed, and the design scheme is ensured to meet the performance requirements under various possible conditions.

In summary, the embodiment of the disclosure can comprehensively consider the collision performance, the NVH rigidity and the dynamic rigidity performance, and optimize the large die casting (cabin domain/rear large casting) of the vehicle body. Compared with NVH working conditions, the requirements of collision working conditions on the model are higher, and the vehicle body system modeling in the topological stage of the embodiment of the disclosure mainly takes the requirements of collision performance into consideration.

The collision performance core is a collision model order reduction and equivalent static load calculation method based on a model order reduction theory and a superunit technology model updating technology, and the model order reduction theory is mainly applied to structural collision analysis, so that on one hand, the collision analysis calculation cost can be reduced, on the other hand, the grid distortion problem caused by a low-density unit can be solved, and a new equivalent static load calculation method and a new model updating method are defined, so that the problems of high calculation cost and low optimization efficiency in solving structural large-deformation collision topological optimization due to the structural optimization method based on the equivalent static load are solved, meanwhile, the difference between a topological optimization result and a collision model is reduced, and the convergence of an algorithm is further improved. The method provided by the embodiment of the disclosure can comprise model decomposition, structural collision analysis, reduced equivalent static load calculation and the like.

FIG. 4 is a block diagram illustrating a large die casting optimizing apparatus for a vehicle body according to an exemplary embodiment. Referring to fig. 4, the apparatus includes a first setup module 410, a second setup module 420, a first optimization module 430, and a second optimization module 440.

The first build module 410 is configured to build a body model including a nonlinear part model and a linear part model;

the second build module 420 is configured to build a topology model including a large die cast body topology model;

The first optimization module 430 is configured to optimize the topology model according to a multi-model optimization method to obtain an optimized model;

the second optimization module 440 is configured to perform a parametric optimization operation of the large die casting of the vehicle body based on the optimization model, resulting in a combined proxy model.

In some embodiments, the large die cast body includes a cabin domain and/or a rear large casting.

In some implementations, the first setup module 410 may include:

a body model building sub-module configured to build a clean body model;

the segmentation submodule is configured to divide the pure vehicle body model into at least two submodules according to the position of the cabin domain and/or the rear large casting to be optimized, wherein the cabin domain and/or the rear large casting is contained in one of the submodules, and the at least two submodules are connected through a superunit boundary point; and

a superunit processing sub-module configured to process sub-models that do not contain the cabin domain and/or rear large casting to be optimized as superunit sub-models, such that the clean body model forms a hybrid model.

In some embodiments, when the large die casting of the vehicle body is the cabin domain, selecting a vehicle body a pillar, a left threshold and a right threshold, cutting off the pure vehicle body model along an approximate YZ plane, and dividing the pure vehicle body model into the nonlinear partial model containing the cabin domain and the linear partial model not containing the cabin domain.

In some embodiments, when the large die casting of the vehicle body is the large rear casting, selecting a rear side position of a C column or a B column of the vehicle body, a left threshold and a right threshold, cutting off the pure vehicle body model along an approximate YZ plane, and dividing the pure vehicle body model into the nonlinear part model containing the large rear casting and the linear part model not containing the large rear casting.

In some embodiments, the first build module 410 may be further configured to add chassis superunit models, powertrain superunit models, battery pack system superunit models, and closures and other accessory superunit models to form the body model based on the hybrid model.

In some embodiments, the second building module 420 may be configured to build a dynamic conceptually feasible design space of a large-sized die casting integrated die casting architecture of the vehicle body, resulting in a topology model of the large-sized die casting of the vehicle body; the first optimization module 430 is configured to perform the multi-model optimization method with the volume fraction of the large die-cast body topology model as an optimization objective with the large die-cast body topology model as shared design variables and with body stiffness, modal, dynamic stiffness, and collision performance as optimization constraints.

In some embodiments, the second optimization module 440 is configured to perform a parametric optimization operation with the thickness design parameters of the large-sized die casting of the vehicle body as shared design variables, and with the vehicle body stiffness, the modal, the dynamic stiffness, and the collision performance as optimization constraints, with the total mass of the large-sized die casting of the vehicle body being the minimum as an optimization target, resulting in the combined proxy model.

In some embodiments, the second optimization module 440 is further configured to take the optimization model as input, take the attention performance of each working condition as output, and call the solver corresponding to each working condition to perform simulation calculation, so as to obtain the combined proxy model.

The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.

The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method for optimizing die castings for automotive body large scale provided by the present disclosure.

Fig. 5 is a block diagram illustrating an electronic device 500 for a method of optimizing a die cast for a large body of a vehicle, according to an exemplary embodiment. For example, electronic device 500 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.

Referring to fig. 5, an electronic device 500 may include one or more of the following components: a processing component 502, a memory 504, a power supply component 506, a multimedia component 508, an audio component 510, an input/output interface 512, a sensor component 514, and a communication component 516.

The processing component 502 generally controls overall operation of the electronic device 500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing assembly 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the large body die casting optimization method described above. Further, the processing component 502 can include one or more modules that facilitate interactions between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.

The memory 504 is configured to store various types of data to support operations at the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 504 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The power supply component 506 provides power to the various components of the electronic device 500. The power components 506 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 500.

The multimedia component 508 includes a screen between the electronic device 500 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front-facing camera and/or a rear-facing camera. When the electronic device 500 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 510 is configured to output and/or input audio signals. For example, the audio component 510 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 500 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 further comprises a speaker for outputting audio signals.

The input/output interface 512 provides an interface between the processing component 502 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 514 includes one or more sensors for providing status assessment of various aspects of the electronic device 500. For example, the sensor assembly 514 may detect an on/off state of the electronic device 500, a relative positioning of components such as a display and keypad of the electronic device 500, a change in position of the electronic device 500 or a component of the electronic device 500, the presence or absence of a user's contact with the electronic device 500, an orientation or acceleration/deceleration of the electronic device 500, and a change in temperature of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 516 is configured to facilitate communication between the electronic device 500 and other devices, either wired or wireless. The electronic device 500 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above-described method of die casting a large body.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 504, including instructions executable by processor 520 of electronic device 500 to perform the above-described method of optimizing die castings for large bodies of vehicles. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of optimizing die castings for large vehicle bodies when executed by the programmable apparatus.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. The optimizing method for the large die casting of the vehicle body is characterized by comprising the following steps of:

2. The method of claim 1, wherein the large die cast body comprises a cabin domain and/or a rear large casting.

3. The method of claim 2, wherein the building a body model comprises:

establishing a pure vehicle body model;

4. A method according to claim 3, wherein when the large die casting of the vehicle body is the cabin domain, selecting a vehicle body a pillar, left and right thresholds, truncating the clean vehicle body model along an approximate YZ plane, and dividing the clean vehicle body model into the nonlinear partial model containing the cabin domain and the linear partial model not containing the cabin domain.

5. A method according to claim 3, wherein when the large die casting of the vehicle body is the rear large casting, a vehicle body C-pillar or B-pillar rear side position, a left-right threshold, is selected, the clean vehicle body model is truncated along an approximately YZ plane, and the clean vehicle body model is divided into the nonlinear portion model including the rear large casting and the linear portion model not including the rear large casting.

6. A method according to claim 3, wherein said modeling the vehicle body further comprises:

7. The method according to any one of claims 1 to 6, wherein the establishing a topology model comprises:

8. The method according to any one of claims 1 to 6, wherein the performing the parameter optimization operation of the large die casting of the vehicle body based on the optimization model results in a combined proxy model, comprising:

9. The method according to any one of claims 1 to 6, wherein the performing the parameter optimization operation of the large die casting of the vehicle body based on the optimization model results in a combined proxy model, comprising:

10. The utility model provides a large-scale die casting optimizing device of automobile body which characterized in that includes: