RPS optimization method, apparatus and equipment based on static analysis and virtual fixture

Abstract

This invention discloses an RPS optimization method, apparatus, and equipment based on static analysis and virtual fixtures, relating to the field of vehicle design and manufacturing. The method includes: developing an initial RPS for a component based on RPS design principles and establishing a finite element model for the component to perform static analysis; constructing a virtual fixture, assembling the component onto the virtual fixture and aligning it with the initial RPS, and verifying the repeatability of the component's positioning; determining the optimization target area based on the static analysis results and the deformation of each region of the component, and adding RPS points to the determined optimization target area to obtain the optimized RPS. This application can shorten the development cycle and reduce costs, and provides a direction and theoretical basis for improving product accuracy.

Description

Technical Field

[0001] This application relates to the field of vehicle design and manufacturing, specifically to an RPS optimization method, apparatus, and equipment based on static analysis and virtual fixtures. Background Technology

[0002] With the research and practice of RPS (Reference Point System) theory, numerous product designers and process engineers both domestically and internationally have summarized and discussed their experiences. Among these summaries and discussions, RPS has achieved widespread consensus in adhering to principles such as the six-point positioning criterion, coordinate parallelism criterion, positioning rigidity, and datum continuity.

[0003] In practice, especially for thin-plate parts, there is often considerable debate regarding the number and location of RPS (Resistant Press Positioners). Too few RPS placements increase the difficulty of springback adjustment during stamping and result in poor measurement stability. Too many placements not only increase manufacturing costs but also fail to accurately reflect the true condition of the parts, and increase the adjustment cycle and difficulty during the body-in-white precision adjustment phase. Currently, RPS design during the design phase largely relies on engineering experience. During the manufacturing phase, the rationality of the positioning is verified through repetitive operations on the parts on the fixture, leading to increased trial-and-error costs and extended timelines. Summary of the Invention

[0004] This application provides an RPS optimization method, apparatus, and equipment based on static analysis and virtual fixtures, which can shorten the development cycle and reduce costs, and provide rectification direction and theoretical basis for improving product accuracy.

[0005] In a first aspect, embodiments of this application provide an RPS optimization method based on static analysis and virtual fixtures, the RPS optimization method based on static analysis and virtual fixtures comprising:

[0006] Based on the RPS design principle, an initial RPS is developed for the component objects, and a finite element model is established for the component objects to perform static analysis.

[0007] Construct a virtual fixture, assemble the component objects onto the virtual fixture and correspond them to the initial RPS, and verify the positioning repeatability of the component objects;

[0008] Based on the static analysis results and the deformation of each region of the component object, the optimization target region is determined, and RPS points are added to the determined optimization target region to obtain the optimized RPS.

[0009] In conjunction with the first aspect, in one implementation, the step of developing an initial RPS for component objects based on the RPS design principle specifically includes:

[0010] Based on the RPS design principles, an initial RPS is developed for the component objects. The RPS design principles include six-point positioning criteria, coordinate parallelism criteria, positioning stiffness, and datum continuity.

[0011] The selection and determination of datum planes and datum holes on the component object are used to constrain the six degrees of freedom of the component object.

[0012] In conjunction with the first aspect, in one implementation method, establishing a finite element model of the component object for static analysis specifically includes:

[0013] Import the geometric model of the component object into the static finite element analysis software;

[0014] Based on the size and surface complexity of the component object, define the mesh attributes and divide it according to the preset mesh size to establish the finite element model of the component object;

[0015] Based on the finite element model of the component object, static analysis is performed on the component object to obtain the deformation of the self-weight of each region of the component object.

[0016] In conjunction with the first aspect, in one implementation, the construction of the virtual fixture, assembling the component object onto the virtual fixture and corresponding it to the initial RPS, and verifying the repeatability of the component object's positioning, specifically includes:

[0017] A virtual fixture is constructed based on deviation analysis software. Component objects are assembled onto the virtual fixture through the constructed DCS points and correspond to the initial version of the RPS.

[0018] Manufacturing tolerances are assigned to the virtual fixture, and the measurement points of the component objects are solved. The positioning repeatability of the component objects is verified by tolerance simulation based on deviation analysis software.

[0019] In conjunction with the first aspect, in one implementation method, the assumptions established when performing tolerance simulation based on deviation analysis software include:

[0020] The component objects are modeled and analyzed based on the rigid body assumption. The tolerance distribution of the component objects adopts normal distribution and uniform distribution respectively for molded parts and machined parts. The model building of the component objects is based on the actual assembly sequence. The component objects ignore the effects of welding deformation, painting deformation, assembly deformation, vibration and thermal deformation.

[0021] In conjunction with the first aspect, in one implementation, the verification of the positioning repeatability of the component object specifically includes:

[0022] The standard deviation of the component object is calculated based on the preset standard deviation calculation formula, and the calculated standard deviation is compared with the tolerance zone value of the component:

[0023] If the standard deviation is not greater than the tolerance zone value of the part, it means that the repeatability of the virtual fixture meets the requirements;

[0024] If the standard deviation is greater than the tolerance zone value of the part, it means that the repeatability of the virtual fixture does not meet the requirements;

[0025] The specific formula for calculating the preset standard deviation is as follows:

[0026]

[0027] in, Indicates standard deviation, Represents the first component object The measured values ​​at each measuring point This represents the average value of all measured values ​​at all measuring points. This indicates the number of measurement points.

[0028] In conjunction with the first aspect, in one implementation, the step of determining the optimization target area based on the static analysis results and the deformation of each region of the component object, and adding RPS points to the determined optimization target area to obtain the optimized RPS, specifically includes:

[0029] Based on the static analysis results, the deformation of each region of the component object is obtained according to its own weight. The region with deformation greater than the set value is identified as the optimization target region.

[0030] Add RPS points to the defined optimization target area and combine them with the initial RPS to obtain the optimized RPS.

[0031] Secondly, embodiments of this application provide an RPS optimization device based on static analysis and virtual fixtures, the RPS optimization device based on static analysis and virtual fixtures comprising:

[0032] The formulation unit is used to formulate an initial RPS for component objects based on the RPS design principle, and to establish a finite element model for component objects for static analysis.

[0033] The verification unit is used to construct a virtual fixture, assemble the component objects onto the virtual fixture and correspond to the initial RPS, and verify the positioning repeatability of the component objects.

[0034] The optimization unit is used to determine the optimization target area based on the static analysis results and the deformation of each region of the component object, and to add RPS points on the determined optimization target area to obtain the optimized RPS.

[0035] Thirdly, embodiments of this application provide an RPS optimization device based on static analysis and virtual fixtures. The RPS optimization device based on static analysis and virtual fixtures includes a processor, a memory, and an RPS optimization program based on static analysis and virtual fixtures stored in the memory and executable by the processor. When the RPS optimization program based on static analysis and virtual fixtures is executed by the processor, it implements the steps of the RPS optimization method based on static analysis and virtual fixtures described above.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium storing an RPS optimization program based on static analysis and a virtual fixture, wherein when the RPS optimization program based on static analysis and a virtual fixture is executed by a processor, it implements the steps of the RPS optimization method based on static analysis and a virtual fixture described above.

[0037] The beneficial effects of the technical solutions provided in this application include:

[0038] During the design phase, an initial RPS (Reference Positioning System) is first developed based on project experience and RPS layout principles. Then, gravity deformation analysis of the components is performed using static finite element analysis software to identify areas affected by gravity in advance, optimize support points, and eliminate some of the gravity effects. Simultaneously, virtual fixtures are constructed using deviation analysis software to verify the repeatability of positioning. When verifying repeatability, the standard deviation is compared with the tolerance zone to make a judgment. When the standard deviation is not greater than the tolerance zone value of the part, the repeatability of the fixture is considered fully acceptable; otherwise, the process is iterated continuously until a compliant RPS file is finally developed. This effectively reduces the discrepancies in RPS layout between stamping and welding disciplines, and also reduces the time for subsequent stamping die and welding fixture debugging, thereby shortening the development cycle and reducing costs. Furthermore, it provides a direction and theoretical basis for improving product accuracy. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the RPS optimization method based on static analysis and virtual fixtures proposed in this application.

[0040] Figure 2 A schematic diagram of the initial version of RPS;

[0041] Figure 3 This is a schematic diagram showing the distribution of measuring points;

[0042] Figure 4 This is a schematic diagram of the optimized RPS;

[0043] Figure 5 This is a schematic diagram of the functional modules of the RPS optimization device based on static analysis and virtual fixtures in this application;

[0044] Figure 6 This is a schematic diagram of the hardware structure of an RPS optimization device based on static analysis and virtual fixtures. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0047] Firstly, this application provides an RPS optimization method based on static analysis and virtual fixtures. During the design phase, the self-weight deformation of the parts is analyzed by static finite element analysis software, and the positioning stability of the parts in the fixture state is analyzed by deviation analysis software. The two methods are mutually verified, which effectively reduces the discrepancies between stamping and welding disciplines regarding RPS layout. It also reduces the time for subsequent stamping die and welding tooling debugging, thereby shortening the development cycle and reducing costs. Furthermore, it provides a direction for improvement and theoretical basis for enhancing the product's accuracy.

[0048] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the RPS optimization method based on static analysis and virtual fixtures proposed in this application. Figure 1 As shown, the RPS optimization method based on static analysis and virtual fixtures includes:

[0049] S1: Based on the RPS design principle, formulate an initial RPS for the component objects and establish a finite element model for the component objects to perform static analysis;

[0050] Furthermore, in one embodiment, an initial RPS is developed for the component objects based on the RPS design principle, specifically including:

[0051] S101: Based on the RPS design principles, a preliminary RPS is formulated for the component objects. The RPS design principles include six-point positioning criteria, coordinate parallelism criteria, positioning stiffness, and datum continuity.

[0052] S102: Select and determine the reference planes and reference holes on the component object to constrain the six degrees of freedom of the component object.

[0053] In one possible implementation, a specific example will be given using the inner door panel of a certain vehicle model as the component object.

[0054] Firstly, for the inner door panel, an initial version of the RPS was developed based on principles such as the six-point positioning criterion, coordinate parallelism criterion, positioning stiffness, and datum continuity. Figure 2 As shown, reference surfaces A1, A2, A3, and A4 are selected on the functional surfaces of the part, which are the main stamping surfaces, offering good strength and precision while avoiding weld points and being evenly distributed. Reference holes B and C are located in the middle area of ​​the part, and the distance between them should ideally exceed 2 / 3 of the total length of the part. Reference surfaces A1, A2, A3, and A4 restrict the translational motion of the inner door panel along the Y-axis, its rotation around the X-axis, and its rotation around the Z-axis; reference hole B restricts the translational degrees of freedom of the front door along the X and Z directions; reference hole C restricts the rotational degree of freedom of the front door around the Y-axis. In this way, all six degrees of freedom of the inner door panel are fully constrained.

[0055] Furthermore, in one embodiment, a finite element model is established for the component object to perform static analysis, specifically including:

[0056] S111: Import the geometric model of the component object into the static finite element analysis software; the static finite element analysis software can be CATIA GSA, that is, import the geometric model of the component object into the finite element analysis module of CATIA GSA;

[0057] S112: Define mesh attributes and divide the mesh according to the preset mesh size based on the size and surface complexity of the component object to establish a finite element model of the component object; furthermore, it can be divided according to an absolute size of 10mm to establish a finite element model.

[0058] S113: Based on the finite element model of the component object, perform static analysis on the component object to obtain the deformation of the self-weight of each region of the component object.

[0059] Continuing with the example of the door inner panel, the boundary conditions for the static analysis of the door inner panel are shown in Table 1:

[0060] Table 1 Boundary conditions for static analysis

[0061]

[0062] After performing static analysis on the finite element model of the inner door panel, the deformation due to the panel's own weight can be obtained. In practical applications, based on the deformation results of the door panel's own weight, it can be seen that the maximum deformation in the middle of the part has reached 1.35 mm, concentrated in the upper section of the window frame, and about 0.5 mm in the middle area of ​​the door panel, all exceeding the expected target of 0.2 mm. Therefore, it is advisable to add RPS points in the upper section of the window frame and the middle area of ​​the door panel in the future. This application summarizes the target of the maximum deformation of the part not exceeding 0.2 mm based on project experience.

[0063] S2: Construct a virtual fixture, assemble the component objects onto the virtual fixture and correspond them to the initial RPS, and verify the positioning repeatability of the component objects;

[0064] Furthermore, in one embodiment, a virtual fixture is constructed, and the component objects are assembled onto the virtual fixture and correspond to the initial RPS. The repeatability of the positioning of the component objects is verified, specifically including:

[0065] S201: Construct a virtual fixture based on deviation analysis software. Assemble the component objects onto the virtual fixture using the constructed DCS (Distributed Control System), and ensure it corresponds to the initial RPS (Relevant Planning Specification). The deviation analysis software can be 3DCS software. By using 3DCS software to construct the virtual fixture tooling, the repeatability of component positioning is verified.

[0066] S202: Assign manufacturing tolerances to the virtual fixture and solve for the measurement points of the component object, and verify the positioning repeatability of the component object by tolerance simulation based on deviation analysis software.

[0067] The following explanation continues using the door inner panel as an example. First, the door inner panel is assembled onto a virtual fixture using the constructed DCS points, corresponding to the initial RPS mentioned above. Then, the virtual fixture is assigned the corresponding manufacturing tolerances. Finally, the measurement points of the door inner panel are calculated. In specific applications, the target tolerance for holes and profiles on the door inner panel is ±0.5, i.e., IT=1. The fixture's accuracy is as follows: ① Tooling positioning surface and support surface profile tolerance is 0.2; ② Locating pin position tolerance is Φ0.1; ③ Locating pin dimensional tolerance is -0.05 / 0.

[0068] In this application, when performing tolerance simulation based on deviation analysis software, the established assumptions include: the component objects are modeled and analyzed based on the rigid body assumption; the tolerance distribution of the component objects adopts a normal distribution and a uniform distribution according to the molded parts and machined parts, respectively; the model building of the component objects is based on the actual assembly sequence; and the component objects ignore the effects of welding deformation, painting deformation, assembly deformation, vibration and thermal deformation.

[0069] The following assumptions are made when performing tolerance simulation in 3DCS: (1) All parts are modeled and analyzed based on the rigid body assumption; (2) The tolerance distribution of parts adopts normal distribution and uniform distribution respectively according to the molded parts and machined parts; (3) The model is built with reference to the actual assembly sequence; (4) Welding deformation, painting deformation and assembly deformation are not considered; (5) Vibration and thermal deformation are not considered.

[0070] Furthermore, in one embodiment, the repeatability of the positioning of the component object is verified, wherein the verification of the repeatability of positioning specifically includes:

[0071] The standard deviation of the component object is calculated based on the preset standard deviation calculation formula, and the calculated standard deviation is compared with the tolerance zone value of the component:

[0072] If the standard deviation is not greater than the tolerance zone value of the part, it means that the repeatability of the virtual fixture meets the requirements;

[0073] If the standard deviation is greater than the tolerance zone value of the part, it means that the repeatability of the virtual fixture does not meet the requirements;

[0074] The specific formula for calculating the preset standard deviation is as follows:

[0075]

[0076] in, Indicates standard deviation, Represents the first component object The measured values ​​at each measuring point This represents the average value of all measured values ​​at all measuring points. This indicates the number of measurement points. In one example, It equals 10000.

[0077] Specifically, when verifying repeatability, the standard deviation of the component object is calculated according to the above formula and compared with the tolerance zone value of the part. If If the tolerance is ≤ IT / 16, the repeatability of the inspection fixture is deemed fully acceptable and meets the requirements. Here, IT represents the tolerance zone.

[0078] The selection of measuring points must ensure that they are arranged in the X, Y, and Z directions. The selected locations should ideally be on flat and stable features, avoiding locations with rounded corners of the parts. Furthermore, there should be no fewer than 3 measuring points in the first reference direction, no fewer than 2 measuring points in the second reference direction, and no fewer than 1 measuring point in the third reference direction. In one example, the measuring points are as follows: Figure 3 As shown, the first datum Y has 4 measuring points, the first datum Z has 2 measuring points, and the third datum X has 2 measuring points.

[0079] The analysis results of the 3DCS software on positioning stability are shown in Table 2 below. Based on Table 2, it can be seen that at Y1 and Y2... It is greater than IT / 16 (IT / 16 = 1 / 16), which is consistent with the results of the gravity analysis above.

[0080] Table 2 Standard deviation of measurements on virtual fixtures

[0081]

[0082] S3: Based on the static analysis results and the deformation of each region of the component object, determine the optimization target region, and add RPS points on the determined optimization target region to obtain the optimized RPS.

[0083] Furthermore, in one embodiment, based on the static analysis results and the deformation of each region of the component object, an optimization target region is determined, and RPS points are added to the determined optimization target region to obtain the optimized RPS, specifically including:

[0084] S301: Based on the static analysis results, obtain the deformation of each region of the component object according to its self-weight, and determine the region with deformation greater than the set value as the optimization target region;

[0085] S302: Add RPS points to the defined optimization target area and combine them with the initial RPS to obtain the optimized RPS.

[0086] The following explanation continues using the door inner panel as an example. Based on the static analysis results above, in Figure 2 Based on this, A5 and A6 are added to the upper section of the window frame, and A7 is added to the middle section of the door panel, thus obtaining the optimized RPS, such as... Figure 4 As shown, this is to reduce the effects of gravitational deformation.

[0087] Static analysis was performed on the optimized door inner panel RPS. Other boundary conditions are referenced in Table 1. Based on the door deformation results after analysis, it can be seen that the maximum deformation of the part is 0.0216, which meets the target requirements.

[0088] Meanwhile, a 3DCS analysis was performed on the optimized door inner panel RPS, and the results are shown in Table 3 below. It can be seen that all points... All are less than IT / 16 (IT / 16 = 1 / 16), which meets the requirements for part positioning stability.

[0089] Table 3 Standard deviation of measurements on the virtual fixture (after optimization)

[0090]

[0091] The RPS optimization method based on static analysis and virtual fixtures in this application embodiment first formulates an initial RPS based on project experience and RPS layout principles during the design phase. Then, gravity deformation analysis of the parts is performed using static finite element analysis software to identify the areas affected by gravity in advance, optimize support points, and eliminate some of the gravity effects. Simultaneously, virtual fixtures are constructed using deviation analysis software to verify the repeatability of positioning. When verifying repeatability, the judgment is based on the comparison between the standard deviation and the tolerance zone. When the standard deviation is not greater than the tolerance zone value of the part, the repeatability of the fixture is considered fully acceptable; otherwise, the process is iterated continuously until a compliant RPS file is finally formulated. This effectively reduces the discrepancies in RPS layout between stamping and welding disciplines, and also reduces the time for subsequent stamping die and welding fixture debugging, thereby shortening the development cycle and reducing costs. Furthermore, it provides a direction and theoretical basis for improving product accuracy.

[0092] Secondly, embodiments of this application also provide an RPS optimization device based on static analysis and virtual fixtures.

[0093] In one embodiment, reference is made to Figure 5 , Figure 5 This is a schematic diagram of the functional modules of the RPS optimization device based on static analysis and virtual fixtures in this application. Figure 5 As shown, the RPS optimization device based on static analysis and virtual fixtures includes a design unit, a verification unit, and an optimization unit.

[0094] The formulation unit is used to formulate an initial RPS for the component object based on the RPS design principle, and to establish a finite element model of the component object for static analysis; the verification unit is used to construct a virtual fixture, assemble the component object on the virtual fixture and correspond it with the initial RPS, and verify the positioning repeatability of the component object; the optimization unit is used to determine the optimization target area based on the static analysis results and the deformation of each region of the component object, and to add RPS points on the determined optimization target area to obtain the optimized RPS.

[0095] The functions of each module in the RPS optimization device based on static analysis and virtual fixtures correspond to the steps in the RPS optimization method embodiment based on static analysis and virtual fixtures. Their functions and implementation processes will not be described in detail here.

[0096] Thirdly, embodiments of this application provide an RPS optimization device based on static analysis and virtual fixtures. The RPS optimization device based on static analysis and virtual fixtures can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0097] Reference Figure 6, Figure 6 This is a schematic diagram of the hardware structure of the RPS optimization device based on static analysis and virtual fixtures involved in the embodiments of this application. In the embodiments of this application, the RPS optimization device based on static analysis and virtual fixtures may include a processor, memory, communication interface, and communication bus.

[0098] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0099] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces for interconnecting devices within the RPS optimization equipment based on static analysis and virtual fixtures, as well as interfaces for interconnecting the RPS optimization equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0100] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0101] The processor can be a general-purpose processor, which can call the RPS optimization program based on static analysis and virtual fixture stored in memory and execute the RPS optimization method based on static analysis and virtual fixture provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the RPS optimization program based on static analysis and virtual fixture is called can refer to the various embodiments of the RPS optimization method based on static analysis and virtual fixture of this application, and will not be repeated here.

[0102] Those skilled in the art will understand that Figure 6 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0103] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0104] The present application stores an RPS optimization program based on static analysis and virtual fixture on a computer-readable storage medium, wherein when the RPS optimization program based on static analysis and virtual fixture is executed by a processor, the steps of the RPS optimization method based on static analysis and virtual fixture as described above are implemented.

[0105] The method implemented when the RPS optimization program based on static analysis and virtual fixture is executed can be referred to in various embodiments of the RPS optimization method based on static analysis and virtual fixture of this application, and will not be repeated here.

[0106] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0107] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0108] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0109] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0110] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0111] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0112] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An RPS optimization method based on static analysis and virtual fixtures, characterized in that, The RPS optimization method based on static analysis and virtual fixtures includes: Based on the RPS design principle, an initial RPS is developed for the component objects, and a finite element model is established for the component objects to perform static analysis. Construct a virtual fixture, assemble the component objects onto the virtual fixture and correspond them to the initial version of RPS, and verify the positioning repeatability of the component objects; Based on the static analysis results and the deformation of each region of the component, the optimization target region is determined, and RPS points are added to the determined optimization target region to obtain the optimized RPS; Specifically, establishing a finite element model of the component object for static analysis includes: Import the geometric model of the component object into the static finite element analysis software; Based on the size and surface complexity of the component object, define the mesh attributes and divide it according to the preset mesh size to establish the finite element model of the component object; Based on the finite element model of the component object, static analysis is performed on the component object to obtain the deformation of the self-weight of each region of the component object. Specifically, the process of determining the optimization target area based on the static analysis results and the deformation of each region of the component object, and adding RPS points to the determined optimization target area to obtain the optimized RPS, includes: Based on the static analysis results, the deformation of each region of the component object is obtained according to its own weight. The region with deformation greater than the set value is identified as the optimization target region. Add RPS points to the defined optimization target area and combine them with the initial RPS to obtain the optimized RPS.

2. The RPS optimization method based on static analysis and virtual fixtures as described in claim 1, characterized in that, The initial RPS for component objects based on the RPS design principle specifically includes: Based on the RPS design principles, an initial RPS is developed for the component objects. The RPS design principles include six-point positioning criteria, coordinate parallelism criteria, positioning stiffness, and datum continuity. The selection and determination of datum planes and datum holes on the component object are used to constrain the six degrees of freedom of the component object.

3. The RPS optimization method based on static analysis and virtual fixtures as described in claim 1, characterized in that, The construction of the virtual fixture involves assembling the component objects onto the virtual fixture and corresponding them to the initial RPS, and verifying the repeatability of the component object's positioning. Specifically, this includes: A virtual fixture is constructed based on deviation analysis software. Component objects are assembled onto the virtual fixture through the constructed DCS points and correspond to the initial version of the RPS. Manufacturing tolerances are assigned to the virtual fixture, and the measurement points of the component objects are solved. The positioning repeatability of the component objects is verified by tolerance simulation based on deviation analysis software.

4. The RPS optimization method based on static analysis and virtual fixtures as described in claim 3, characterized in that, When performing tolerance simulation based on deviation analysis software, the established assumptions include: The component objects are modeled and analyzed based on the rigid body assumption. The tolerance distribution of the component objects adopts a normal distribution and a uniform distribution respectively for molded parts and machined parts. The model building of the component objects is based on the actual assembly sequence. The component objects ignore the effects of welding deformation, painting deformation, assembly deformation, vibration and thermal deformation.

5. The RPS optimization method based on static analysis and virtual fixtures as described in claim 3, characterized in that, The verification of the positioning repeatability of the component objects includes, specifically: The standard deviation of the component object is calculated based on the preset standard deviation calculation formula, and the calculated standard deviation is compared with the tolerance zone value of the component: If the standard deviation is not greater than the tolerance zone value of the part, it means that the repeatability of the virtual fixture meets the requirements; If the standard deviation is greater than the tolerance zone value of the part, it means that the repeatability of the virtual fixture does not meet the requirements; The specific formula for calculating the preset standard deviation is as follows: in, Indicates standard deviation, Represents the first component object The measured values ​​at each measuring point This represents the average value of all measured values ​​at all measuring points. This indicates the number of measurement points.

6. An RPS optimization device based on static analysis and virtual fixtures, characterized in that, The RPS optimization device based on static analysis and virtual fixtures includes: The formulation unit is used to formulate an initial RPS for component objects based on the RPS design principle, and to establish a finite element model for component objects for static analysis. The verification unit is used to construct a virtual fixture, assemble the component objects onto the virtual fixture and correspond to the initial RPS, and verify the positioning repeatability of the component objects. The optimization unit is used to determine the optimization target area based on the static analysis results and the deformation of each region of the component object, and to add RPS points on the determined optimization target area to obtain the optimized RPS; Specifically, establishing a finite element model of the component object for static analysis includes: Import the geometric model of the component object into the static finite element analysis software; Based on the size and surface complexity of the component object, define the mesh attributes and divide it according to the preset mesh size to establish the finite element model of the component object; Based on the finite element model of the component object, static analysis is performed on the component object to obtain the deformation of the self-weight of each region of the component object. Specifically, the process of determining the optimization target area based on the static analysis results and the deformation of each region of the component object, and adding RPS points to the determined optimization target area to obtain the optimized RPS, includes: Based on the static analysis results, the deformation of each region of the component object is obtained according to its own weight. The region with deformation greater than the set value is identified as the optimization target region. Add RPS points to the defined optimization target area and combine them with the initial RPS to obtain the optimized RPS.

7. An RPS optimization device based on static analysis and virtual fixtures, characterized in that, The RPS optimization device based on static analysis and virtual fixture includes a processor, a memory, and an RPS optimization program based on static analysis and virtual fixture stored in the memory and executable by the processor, wherein when the RPS optimization program based on static analysis and virtual fixture is executed by the processor, it implements the steps of the RPS optimization method based on static analysis and virtual fixture as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an RPS optimization program based on static analysis and virtual fixtures, wherein when the RPS optimization program based on static analysis and virtual fixtures is executed by a processor, it implements the steps of the RPS optimization method based on static analysis and virtual fixtures as described in any one of claims 1 to 5.

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