Vehicle frame optimization simulation method and system based on discreteness and storage medium

Abstract

The invention discloses a discreteness-based frame optimization simulation method and system and a storage medium, and the method comprises the steps: setting a first parameter value and a first correction coefficient in a simulation model, the first parameter value being determined based on simulation analysis and a first weighting parameter, the first correction coefficient is obtained based on the material discreteness and the contribution degree of the manufacturing process to the structural strength; a second parameter value and a second correction coefficient in the simulation model are set, the second parameter value is determined based on simulation analysis and a second weighting parameter, and the second correction coefficient is obtained based on the contribution degree of each key size to the influence of the structural strength; performing simulation analysis on the frame based on the first parameter value and the second parameter value; and correcting and optimizing the simulation result based on the first correction coefficient and the second correction coefficient. According to the discreteness-based frame optimization simulation method and system and the storage medium disclosed by the invention, the reliability of the frame structure design can be remarkably improved, so that the frame structure can better adapt to the uncertainty in actual production and manufacturing while meeting the strength requirement.

Description

Technical Field

[0001] The present application relates to the technical field of commercial vehicle chassis, and in particular to a discrete-based frame optimization simulation method, a discrete-based frame optimization simulation system, and a storage medium. Background Art

[0002] Currently, vehicle frame structure simulations are primarily based on standard material values ​​and design dimensional values. This approach is idealistic because it doesn't fully incorporate two key factors in the manufacturing process: material discreteness and dimensional tolerance. In reality, material discreteness can arise from differences in raw material batches, heat treatment processes, and other factors, all of which can affect the actual strength of the vehicle frame. Dimensional tolerances involve multiple considerations, including component machining accuracy and cumulative assembly errors. Ignoring these factors in simulations can lead to significant deviations between the design and actual performance. This strength impact cannot be easily ignored in many practical application scenarios. Summary of the Invention

[0003] The purpose of the present invention is to provide a discrete-based frame optimization simulation method, system and storage medium, which can significantly improve the reliability of the frame structure design, so that it can better adapt to the uncertainties in actual production and manufacturing while meeting the strength requirements.

[0004] The present invention provides the following solutions:

[0005] According to one aspect of the present invention, a discrete-based vehicle frame optimization simulation method is provided, the discrete-based vehicle frame optimization simulation method comprising:

[0006] Setting a first parameter value and a first correction coefficient in the simulation model, wherein the first parameter value is determined based on simulation analysis and a first weighting parameter, and the first correction coefficient is obtained based on the contribution of material discreteness and manufacturing process to structural strength;

[0007] Setting a second parameter value and a second correction coefficient in the simulation model, wherein the second parameter value is determined based on simulation analysis and a second weighting parameter, and the second correction coefficient is obtained based on the contribution of each key dimension to the structural strength;

[0008] Performing simulation analysis on the vehicle frame based on the first parameter value and the second parameter value;

[0009] The simulation results are corrected and optimized based on the first correction coefficient and the second correction coefficient.

[0010] Optionally, setting a first parameter value and a first correction coefficient in the simulation model includes:

[0011] Collect the mechanical parameters of the raw materials in the scene, form a database, and conduct preliminary analysis to form a normal distribution diagram of each parameter;

[0012] Determine the value of each parameter based on the target life of the design object and the normal distribution diagram of each parameter;

[0013] Based on simulation analysis, the contribution of raw material discreteness and manufacturing process to structural strength is determined, thereby determining the weighting parameters;

[0014] Based on simulation analysis and weighted parameters, the material parameter values ​​in the final simulation model are determined.

[0015] Optionally, collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter, including:

[0016] For the same type of material, the mechanical parameters of different batches of raw materials are collected to form a database, and preliminary analysis is performed to form a batch normal distribution diagram of each parameter.

[0017] Optionally, collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter, including:

[0018] For molded parts made of the same type of material but using different processes, samples are taken and the corresponding material parameters are collected to form a database. A preliminary analysis is then performed to form a process normal distribution diagram of each parameter under each process.

[0019] Optionally, collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter, including:

[0020] For the same type of material, mechanical parameters of different batches of raw materials are collected to form a database, and preliminary analysis is performed to form a batch normal distribution diagram of each parameter;

[0021] For molded parts made of the same type of material but using different processes, samples are taken and the corresponding material parameters are collected to form a database. A preliminary analysis is then performed to form a process normal distribution diagram of each parameter under each process.

[0022] Optionally, setting the first parameter value and the first correction coefficient in the simulation model further includes:

[0023] Combining the influencing factors such as the discreteness of raw materials and manufacturing process, the first correction coefficient of each material is summarized.

[0024] Optionally, setting a second parameter value and a second correction coefficient in the simulation model includes:

[0025] Determine critical dimensions;

[0026] Analyze the distribution of tolerance zones of key dimensions, count the characteristic values ​​of each dimension, and form a normal distribution diagram of each dimension parameter;

[0027] Determine the parameter values ​​of each dimension based on the target life of the object of interest and the normal distribution diagram of each dimension;

[0028] Based on simulation analysis, the contribution of each key dimension to the structural strength is determined, thereby determining the weighting parameters;

[0029] Based on simulation analysis and weighted parameters, the final simulation results are determined.

[0030] Optionally, setting the second parameter value and the second correction coefficient in the simulation model further includes:

[0031] Combined with the dimensional tolerance analysis, the second correction coefficient is summarized.

[0032] According to two aspects of the present invention, a discrete-based vehicle frame optimization simulation system is provided, the discrete-based vehicle frame optimization simulation system comprising:

[0033] A first setting module is configured to set a first parameter value and a first correction coefficient in the simulation model, wherein the first parameter value is determined based on simulation analysis and a first weighting parameter, and the first correction coefficient is obtained based on the contribution of material discreteness and manufacturing process to structural strength;

[0034] A second setting module is used to set a second parameter value and a second correction coefficient in the simulation model, wherein the second parameter value is determined based on the simulation analysis and the second weighting parameter, and the second correction coefficient is obtained based on the contribution of each key dimension to the structural strength;

[0035] A simulation analysis module, configured to perform simulation analysis on the vehicle frame based on the first parameter value and the second parameter value;

[0036] The optimization module is used to correct and optimize the simulation results based on the first correction coefficient and the second correction coefficient.

[0037] According to three aspects of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the discrete-based frame optimization simulation method as described above is implemented.

[0038] Through the above solution, the following beneficial technical effects are achieved:

[0039] The purpose of the present invention is to realize a frame simulation optimization method based on discreteness through the following technical solutions. Taking a commercial vehicle as an example, first, a material performance database is established to record the changes in the mechanical properties of different batches of materials, and reasonable material parameters are determined in combination with statistical analysis; secondly, in dimensional design, not only the design values ​​are adopted, but also a reasonable tolerance range is set in combination with the process level, and the cumulative effect of these tolerances in the frame structure is simulated to ensure that the simulation model is closer to the actual manufacturing state; then, by defining a set of comprehensive evaluation criteria that include the influence of material discreteness and dimensional tolerance, the reliability of the frame structure design is improved, so that it can better adapt to the uncertainties in actual production and manufacturing while meeting the strength requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention;

[0041] Figure 2 is a flowchart of a first setting operation in a discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention;

[0042] Figure 3 is a flowchart of a first setting operation in a discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention;

[0043] Figure 4 is a flowchart of a first setting operation in a discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention;

[0044] Figure 5 is a flow chart of a second setting operation in the discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention;

[0045] Figure 6 is a flow chart of a second setting operation in the discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention;

[0046] Figure 7 It is a structural diagram of a discrete-based vehicle frame optimization simulation system provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] Figure 1FIG1 is a flow chart of a discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention. Figure 1 The discrete-based frame optimization simulation method includes the following steps:

[0049] S11, setting a first parameter value and a first correction coefficient in the simulation model, wherein the first parameter value is determined based on simulation analysis and a first weighted parameter, and the first correction coefficient is obtained based on the contribution of material discreteness and manufacturing process to structural strength.

[0050] S12, setting a second parameter value and a second correction coefficient in the simulation model, wherein the second parameter value is determined based on simulation analysis and a second weighting parameter, and the second correction coefficient is obtained based on the contribution of each key dimension to the structural strength.

[0051] S13: Perform simulation analysis on the vehicle frame based on the first parameter value and the second parameter value.

[0052] S14, performing correction and optimization on the simulation result based on the first correction coefficient and the second correction coefficient.

[0053] It's well known that the mechanical properties of the frame materials used during the frame design process will significantly impact the frame's strength, stiffness, and other mechanical properties. The frame's strength, stiffness, and other properties also significantly impact the overall safety of the frame during subsequent use. In other words, the mechanical properties of the frame material have a crucial impact on the overall safety of the frame.

[0054] However, during the frame mechanical performance simulation process, it is difficult to accurately grasp the material mechanical performance parameters. For example, it is very common that the actual mechanical properties of the raw materials provided by the raw material supplier may exceed or fail to meet the material mechanical performance requirements in one or more parameters.

[0055] On the other hand, the dimensional tolerances between the various components in the frame structure also have a crucial impact on the overall safety of the frame.

[0056] It should be understood that the tolerances between different parts, as well as the mismatch between tolerances, can have a cumulative effect in actual vehicle frame applications, ultimately causing unsafe use issues such as vibration and noise in the finished frame. Therefore, effectively simulating the cumulative effects of these tolerances will be an important aspect of the technical solution of this application.

[0057] Through simulation experiments, the discreteness of the above-mentioned mechanical properties and the cumulative effect of dimensional tolerances are simulated, and eventually a complete and scientific evaluation standard for frame materials can be formed.

[0058] This evaluation standard includes a correction factor for each mechanical property of the material and another correction factor obtained from the tolerance analysis of the size.

[0059] Through the above series of simulation operations, the reliability of the frame structure design can be significantly improved, so that it can better adapt to the uncertainties in actual production and manufacturing while meeting the strength requirements.

[0060] The determination of the mechanical properties of the material is accomplished by establishing a material parameter database.

[0061] It should be noted that the establishment of material parameter data requires reference to the mechanical properties of materials from different batches.

[0062] For example, the structural strength parameter of a material may be 50 for the first batch of aluminum alloy materials arriving at the factory. However, the structural strength of the second batch of aluminum alloy materials arriving at the factory may drop to 30. This means that even raw materials with the same material and signal delivered from different batches may exhibit a certain degree of dispersion in their specific mechanical performance parameters.

[0063] By recording the original mechanical properties of different batches entering the factory, we can effectively address the performance dispersion of materials between different batches. For example, by statistically analyzing the mechanical properties of materials with the same project across different batches, we can obtain a normal distribution diagram of the material's mechanical properties, thereby effectively determining the dispersion of raw materials between different batches and their contribution to the final structural strength.

[0064] In addition to sampling the mechanical parameters of different batches of materials, it is also necessary to sample the mechanical performance parameters of the same material after different processing processes. The sampling and analysis process is similar to the statistical analysis process of the mechanical performance parameters of different batches of raw materials.

[0065] The simulation process for tolerance accumulation is similar to that for material parameter discreteness. First, each critical dimension is collected, followed by a tolerance band for each critical dimension. Statistical analysis of the tolerance band parameters yields a statistical distribution diagram of the dimensional parameters. Based on this statistical distribution diagram, the critical dimensional parameter values ​​are determined. The contribution of each critical dimensional parameter to the final structural strength is then determined. Based on this contribution, the corresponding weighting parameters are assigned to each critical dimensional parameter. Finally, simulation analysis and weighting parameters are used to obtain the simulation results.

[0066] Figure 2 This is a flow chart of the first setting operation in the discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention. Figure 2 , setting the first parameter value and the first correction coefficient in the simulation model includes the following steps:

[0067] S21, collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter.

[0068] S22, determining the values ​​of each parameter in combination with the target life of the design object and the normal distribution diagram of each parameter.

[0069] S23, based on simulation analysis, determine the contribution of raw material discreteness and manufacturing process to the structural strength, thereby determining the weighting parameters.

[0070] S24, based on the simulation analysis and weighted parameters, determining the material parameter values ​​in the final simulation model.

[0071] It should be understood that Figure 2 The purpose of the setting process of the first parameter value shown is to simulate the contribution of raw material discreteness and different manufacturing processes to the structural strength of the frame.

[0072] Raw materials have a variety of mechanical parameters. For example, the mechanical parameters of the raw materials used to make a bicycle frame can include yield strength, tensile strength, elongation, etc. These mechanical parameters are affected by both the material's discreteness and the manufacturing process.

[0073] For example, the elongation of a bar material after stamping and bending will be significantly different. The first parameter value and the first correction coefficient setting are designed to simulate these effects through a simulation process.

[0074] To simulate the aforementioned effects, mechanical parameters can be collected under different scenarios. This can include collecting original mechanical parameters from different batches to simulate the effect of material discreteness on structural strength. Alternatively, mechanical parameters can be collected from materials after undergoing different processing steps to simulate the effect of different manufacturing processes on structural strength.

[0075] When determining parameter values, we consulted the parameter's normal distribution diagram. A normal distribution diagram expresses the possible range of values ​​for a parameter in the form of a probability distribution. Therefore, when determining specific parameter values, referring to the normal distribution diagram can effectively screen out values ​​that fall outside the normal range, ensuring that the final values ​​obtained are more consistent with the norm.

[0076] When determining the values ​​of mechanical parameters, the target lifespan of the design object is also considered. Generally speaking, the longer the target lifespan of the design object, the more stringent the requirements for the various mechanical properties of the material. Therefore, the allowable range of values ​​for the mechanical parameters on a normal distribution plot should be narrower. Conversely, the shorter the target lifespan of the design object, the less stringent the requirements for various mechanical properties of the material. Therefore, the allowable range of values ​​for the mechanical parameters on a normal distribution plot should be wider.

[0077] Figure 3 This is a flow chart of the first setting operation in the discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention. Figure 3 , setting the first parameter value and the first correction coefficient in the simulation model includes the following steps:

[0078] S31, for the same type of material, collect the mechanical parameters of different batches of raw materials, form a database, and perform preliminary analysis to form a batch normal distribution diagram of each parameter.

[0079] S32, for molded parts made of the same type of material but with different processes, sampling is performed, and corresponding material parameters are collected to form a database. A preliminary analysis is then performed to form a process normal distribution diagram of each parameter under each process.

[0080] S33, determining the values ​​of the parameters in combination with the target life of the design object and the normal distribution diagram of the parameters.

[0081] S34, based on simulation analysis, determine the contribution of raw material discreteness and manufacturing process to the structural strength, thereby determining the weighting parameters.

[0082] S35, based on the simulation analysis and the weighted parameters, determining the material parameter values ​​in the final simulation model.

[0083] In this embodiment, the obtained normal distribution diagrams of mechanical parameters are clearly divided into batch normal distribution diagrams and process normal distribution diagrams. The batch normal distribution diagram is specifically used to characterize the influence of the material dispersion between different batches on the values ​​of the material mechanical parameters. The process normal distribution diagram is specifically used to characterize the influence of the material processing technology on the values ​​of the material mechanical parameters.

[0084] In the normal distribution diagram acquisition operation, the batch normal distribution diagram and the process normal distribution diagram are acquired separately, so that more accurate data can be obtained on the influence of material discreteness and processing technology on mechanical parameters.

[0085] Since both the batch and process normal distribution diagrams are obtained during the normal distribution diagram acquisition process, the specific values ​​of the material mechanical parameters can be determined by referring to the two different normal distribution diagrams. This makes the determination of the mechanical parameter values ​​more accurate.

[0086] In other words, the process of determining the mechanical parameter values ​​simultaneously references the batch normal distribution diagram of the material's mechanical parameters, the material's process normal distribution diagram, and the target life of the design object. This process is undoubtedly more accurate than determining the values ​​based solely on a single normal distribution diagram.

[0087] Figure 4 This is a flow chart of the first setting operation in the discrete-based vehicle frame optimization simulation method provided by one or more embodiments of the present invention. Figure 4 , setting the first parameter value and the first correction coefficient in the simulation model includes the following steps:

[0088] S41, for the same type of material, collect the mechanical parameters of different batches of raw materials to form a database, and perform preliminary analysis to form a batch normal distribution diagram of each parameter.

[0089] S42, for molded parts made of the same type of material but with different processes, sampling is performed, and corresponding material parameters are collected to form a database. A preliminary analysis is then performed to form a process normal distribution diagram of each parameter under each process.

[0090] S43, determining the values ​​of the parameters in combination with the target life of the design object and the normal distribution diagram of the parameters.

[0091] S44, based on simulation analysis, determine the contribution of raw material discreteness and manufacturing process to structural strength, thereby determining weighting parameters.

[0092] S45, based on the simulation analysis and the weighted parameters, determining the material parameter values ​​in the final simulation model.

[0093] S46, combining the influencing factors such as raw material discreteness and manufacturing process, summarizes the first correction coefficient of each material.

[0094] The process of summarizing the first correction factor of the inductive material is given by the following formula:

[0095] σ s =W i *σ sci +W j *σ sgj =a*σ s标

[0096] Among them, σ srepresents the modified yield strength, σ sci represents the yield strength determined from the batch normal distribution plot, σ sgj represents the yield strength determined by the process normal distribution diagram, W i and W j They represent weighting coefficients, a represents the first correction coefficient, σ s标 Indicates the standard yield strength.

[0097] The yield strength corrected by the above formula takes into account the material's discreteness and the impact of the manufacturing process on structural strength. This corrected yield strength more accurately reflects the material's actual state, ensuring frame strength while better accommodating the uncertainties of actual production.

[0098] Figure 5 This is a flow chart of the second setting operation in the discrete-based frame optimization simulation method provided by one or more embodiments of the present invention. Figure 5 , setting the second parameter value and the second correction coefficient in the simulation model includes the following steps:

[0099] S51, determine the critical dimensions.

[0100] S52, analyze the distribution of tolerance bands of key dimensions, count the characteristic values ​​of each dimension, and form a normal distribution diagram of each dimension parameter.

[0101] S53 , determining the parameter values ​​of each dimension based on the target life of the object of interest and the normal distribution diagram of each dimension.

[0102] S54, based on simulation analysis, determining the contribution of each key dimension to the structural strength, thereby determining the weighting parameters.

[0103] S55: Determine the final simulation result based on the simulation analysis and weighted parameters.

[0104] Depend on Figure 5 The second setting operation shown is mainly intended to simulate the contribution of the material's dimensional tolerance to the vehicle frame structure, and then determine the material's mechanical parameters after considering the cumulative effect of the dimensional tolerance.

[0105] First, the critical dimensions of the part need to be determined. For example, for the cross member of the frame, the critical dimensions are the thickness and width of the cross member.

[0106] After obtaining the critical dimensions of the part, the tolerance zone distribution of the critical dimensions is statistically analyzed. By statistically analyzing the tolerance zone distribution, a normal distribution diagram of the dimensional parameters can be obtained.

[0107] Next, the parameter values ​​of the critical dimensions are determined based on the obtained normal distribution diagram of the critical dimensions and the target life of the design object.

[0108] After determining the parameter values ​​for each critical dimension, simulation analysis can be used to determine the contribution of each critical dimension to the overall structural strength of the frame. Based on the contribution of each critical dimension, the weighted parameters for the corresponding stress calculated for each critical dimension are determined.

[0109] Once the weighted parameters corresponding to each component are determined, the final value of the mechanical parameter can be determined based on the weighted parameters.

[0110] Taking the final determination of material stress as an example, the material stress obtained by considering the cumulative effect of the dimensional tolerances of each part is given by the following formula:

[0111] σ=N i* σ ti +N j* σ Lj

[0112] Where σ represents the material stress obtained by considering the cumulative effect of the dimensional tolerances of each part, σ ti represents the material stress of the i-th value in the thickness direction, σ Lj Indicates the material stress of the jth value in the width direction, N i is the weighting parameter in the thickness direction, N j is the weighting coefficient in the width direction.

[0113] Figure 6 This is a flow chart of the second setting operation in the discrete-based frame optimization simulation method provided by one or more embodiments of the present invention. Figure 6 , setting the second parameter value and the second correction coefficient in the simulation model includes the following steps:

[0114] S61, determine the critical dimensions.

[0115] S62, analyze the distribution of tolerance bands of key dimensions, count the characteristic values ​​of each dimension, and form a normal distribution diagram of each dimension parameter.

[0116] S63 , determining the parameter values ​​of each dimension based on the target life of the object of interest and the normal distribution diagram of each dimension.

[0117] S64: Based on simulation analysis, determine the contribution of each key dimension to the structural strength, thereby determining the weighted parameters.

[0118] S65: Determine the final simulation result based on the simulation analysis and the weighted parameters.

[0119] S66, combined with dimensional tolerance analysis, summarizes the second correction coefficient.

[0120] The second correction factor for the material is given by the following formula:

[0121] σ=N i *σ ti +N j *σ Lj =b*σ 计

[0122] Where σ represents the corrected material stress, σ ti represents the material stress of the i-th value in the thickness direction, σ lj Indicates the material stress of the jth value in the width direction, N i is the weighting parameter in the thickness direction, N j is the weighting coefficient in the width direction, b represents the second correction coefficient, σ 计 represents the material stress obtained from the simulation.

[0123] Figure 7 This is an architecture diagram of a discrete-based vehicle frame optimization simulation system provided by one or more embodiments of the present invention. Figure 7 The discrete-based frame optimization simulation system includes: a first setting module 701 , a second setting module 702 , a simulation analysis module 703 , and an optimization module 704 .

[0124] The first setting module 701 is used to set a first parameter value and a first correction coefficient in the simulation model, where the first parameter value is determined based on simulation analysis and a first weighted parameter, and the first correction coefficient is obtained based on the contribution of material discreteness and manufacturing process to structural strength.

[0125] The second setting module 702 is used to set a second parameter value and a second correction coefficient in the simulation model, where the second parameter value is determined based on simulation analysis and a second weighting parameter, and the second correction coefficient is obtained based on the contribution of each key dimension to the structural strength.

[0126] The simulation analysis module 703 is used to perform simulation analysis on the vehicle frame based on the first parameter value and the second parameter value.

[0127] The optimization module 704 is used to perform correction optimization on the simulation result based on the first correction coefficient and the second correction coefficient.

[0128] It is worth noting that although only some basic functional modules are disclosed in the embodiment of the present invention, it does not mean that the composition of the present system is limited to the above basic functional modules. On the contrary, what this embodiment wants to express is that on the basis of the above basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with the existing technology to form an infinite number of embodiments or technical solutions. In other words, this system is open rather than closed. Just because this embodiment only discloses individual basic functional modules, it cannot be considered that the scope of protection of the claims of the present invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above devices are described in terms of functions, which are divided into various units and modules. Of course, when implementing the present invention, the functions of each unit and module can be implemented in the same or one or more software and / or hardware.

[0129] The present invention also provides a computer-readable storage medium, comprising: a computer program that can be executed by a vehicle is stored therein, and when the computer program is run on the vehicle, the vehicle executes the steps of the vehicle monitoring method.

[0130] Specifically, the computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, device or device.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A frame optimization simulation method based on discreteness, characterized in that: The frame optimization simulation method based on discreteness includes: Setting a first parameter value and a first correction coefficient in the simulation model, wherein the first parameter value is determined based on simulation analysis and a first weighting parameter, and the first correction coefficient is obtained based on the contribution of material discreteness and manufacturing process to structural strength; Setting a second parameter value and a second correction coefficient in the simulation model, wherein the second parameter value is determined based on simulation analysis and a second weighting parameter, and the second correction coefficient is obtained based on the contribution of each key dimension to the structural strength; Performing simulation analysis on the vehicle frame based on the first parameter value and the second parameter value; The simulation results are corrected and optimized based on the first correction coefficient and the second correction coefficient.

2. The method according to claim 1, characterized in that Setting a first parameter value and a first correction coefficient in the simulation model includes: Collect the mechanical parameters of the raw materials in the scene, form a database, and conduct preliminary analysis to form a normal distribution diagram of each parameter; Determine the value of each parameter based on the target life of the design object and the normal distribution diagram of each parameter; Based on simulation analysis, the contribution of raw material discreteness and manufacturing process to structural strength is determined, thereby determining the weighting parameters; Based on simulation analysis and weighted parameters, the material parameter values ​​in the final simulation model are determined.

3. The method according to claim 2, characterized in that Collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter, including: For the same type of material, the mechanical parameters of different batches of raw materials are collected to form a database, and preliminary analysis is performed to form a batch normal distribution diagram of each parameter.

4. The method according to claim 2, characterized in that Collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter, including: For molded parts made of the same type of material but using different processes, samples are taken and the corresponding material parameters are collected to form a database. A preliminary analysis is then performed to form a process normal distribution diagram of each parameter under each process.

5. The method according to claim 2, characterized in that Collect the mechanical parameters of the raw materials in the scene, form a database, and perform preliminary analysis to form a normal distribution diagram of each parameter, including: For the same type of material, mechanical parameters of different batches of raw materials are collected to form a database, and preliminary analysis is performed to form a batch normal distribution diagram of each parameter; For molded parts made of the same type of material but using different processes, samples are taken and the corresponding material parameters are collected to form a database. A preliminary analysis is then performed to form a process normal distribution diagram of each parameter under each process.

6. The method according to claim 2, characterized in that Setting a first parameter value and a first correction coefficient in the simulation model also includes: Combining the influencing factors of raw material discreteness and manufacturing process, the first correction coefficient of each material is summarized.

7. The method according to claim 1, characterized in that Setting a second parameter value and a second correction coefficient in the simulation model includes: Determine critical dimensions; Analyze the distribution of tolerance zones of key dimensions, count the characteristic values ​​of each dimension, and form a normal distribution diagram of each dimension parameter; Determine the parameter values ​​of each dimension based on the target life of the object of interest and the normal distribution diagram of each dimension; Based on simulation analysis, the contribution of each key dimension to the structural strength is determined, thereby determining the weighting parameters; Based on simulation analysis and weighted parameters, the final simulation results are determined.

8. The method according to claim 7, characterized in that Setting a second parameter value and a second correction coefficient in the simulation model also includes: Combined with the dimensional tolerance analysis, the second correction coefficient is summarized.

9. A discrete-based frame optimization simulation system, characterized in that: The discrete-based frame optimization simulation system includes: A first setting module is configured to set a first parameter value and a first correction coefficient in the simulation model, wherein the first parameter value is determined based on simulation analysis and a first weighting parameter, and the first correction coefficient is obtained based on the contribution of material discreteness and manufacturing process to structural strength; A second setting module is used to set a second parameter value and a second correction coefficient in the simulation model, wherein the second parameter value is determined based on the simulation analysis and the second weighting parameter, and the second correction coefficient is obtained based on the contribution of each key dimension to the structural strength; A simulation analysis module, configured to perform simulation analysis on the vehicle frame based on the first parameter value and the second parameter value; The optimization module is used to correct and optimize the simulation results based on the first correction coefficient and the second correction coefficient.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the discrete-based frame optimization simulation method according to any one of claims 1 to 8 is implemented.

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