Rapid high-precision simulation construction method for die-casting structural member
By determining the range of variation of target material parameters for die-cast structural parts, and optimizing the parameters using experimental design and global optimization methods, the problem of insufficient simulation accuracy for die-cast structural parts was solved, achieving high-precision simulation construction and parameter universality.
Patent Information
- Application Number
- CN202511479696.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-16
- Publication Date
- 2026-02-10
AI Technical Summary
In the current die-casting process, the parameters of die-cast structural parts are not accurately obtained, resulting in differences in the microstructure of the material at different locations, causing non-uniformity of mechanical properties, which affects the accuracy of simulation and optimization design.
By determining the range of target material parameter variations for the die-cast structural parts in the simulation group, experimental design methods were used to sample and calculate response data, objective function constraints were set, global optimization was performed, and material parameters were optimized to minimize the difference between simulation and experimental results. The accuracy of the optimal parameters was verified through confidence intervals.
It improves the simulation accuracy of die-cast structural parts, reduces the impact of performance inhomogeneity on simulation errors, achieves closed-loop optimization and parameter universality, and is applicable to die-cast structural parts of the same batch.
Smart Images
Figure CN121502992A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of materials testing, computer-aided engineering (CAE) simulation and parameter optimization, and in particular to a method for rapid and high-precision simulation construction of die-cast structural parts. Background Technology
[0002] Die casting, as a highly efficient and precise metal forming technology, has been widely used in the automotive industry, electronic equipment, aerospace and other fields. In particular, in recent years, with the continuous increase in the demand for lightweight vehicles, the application of die-cast structural parts such as aluminum alloys and magnesium alloys in vehicle body structures has been increasing.
[0003] Die casting, by injecting molten metal into a mold cavity under high pressure and achieving rapid cooling and shaping, can produce parts with complex geometries and high dimensional accuracy. However, due to factors such as the flow of molten metal, differences in cooling rate, and porosity distribution during die casting, the microstructure of the material may vary at different locations, leading to inhomogeneities in mechanical properties.
[0004] In the automotive manufacturing industry, especially in the design of body structural components, CAE simulation technology has become a key tool for predicting part performance and optimizing design schemes. By constructing finite element models, it simulates the mechanical response (stress distribution, displacement deformation, fatigue life, etc.) of die-cast structural components under actual working conditions (such as load, vibration, and impact), which can significantly shorten the product development cycle. However, the parameters of die-cast structural components obtained by existing technologies are still not accurate enough. Summary of the Invention
[0005] The main objective of this application is to propose a rapid and high-precision simulation construction method for die-cast structural parts, so as to improve the parameter accuracy of die-cast structural parts.
[0006] To achieve the above objectives, one aspect of this application proposes a rapid and high-precision simulation construction method for die-cast structural parts, the method comprising the following steps: Determine the target variation range of the target material parameters for the die-cast structural parts in the simulation group; wherein, the target variation range serves as the search space; The experimental design method was used to sample within the search space, and the response data of the sample points were obtained through simulation calculation. The first force and displacement curve of the simulated die-cast structural component is determined based on the sample point response data, and an objective function is set as a constraint to minimize the difference between the first force and displacement curve and the second force and displacement curve of the experimental die-cast structural component. Under the constraints of the objective function, a global optimization is performed in the search space to obtain an approximate optimal solution for the target material parameters; The prediction accuracy is verified using the finite element model with the approximate optimal solution. The optimal parameter values of the target material parameters are determined based on preset conditions and the prediction accuracy.
[0007] In some embodiments, determining the target variation range of the target material parameters for the simulated die-cast structural component includes the following steps: The simulation group of die-cast structural parts is sampled in different zones according to the stress distribution to obtain the candidate variation range of multiple candidate material parameters; Establish a finite element model of the die-cast structural component of the simulation group, and set the initial values of the finite element model according to each of the candidate variation ranges; A parameter sensitivity analysis is performed on the finite element model with the initial values set, and then a target material parameter is selected from each of the candidate material parameters, and the target variation range of the target material parameter is obtained.
[0008] In some embodiments, determining the first force-displacement curve of the simulated die-cast structural component based on the sample point response data includes the following steps: Based on the response data of the sample points, plot the engineering stress and strain curves; The engineering stress-strain curve is converted into a real stress-strain curve using the following expression: ε t = ln(1 + ε e ); σ t = σ e ×(1 + ε e ); Where, ε t For realistic response, ε e For engineering strain, σ t For the actual stress, σ e For engineering stress; The actual stress-strain curve is determined as the first force-displacement curve.
[0009] In some embodiments, the step of searching for a globally optimal solution for the target material parameters in the search space under the constraints of the objective function to obtain an approximate optimal solution includes the following steps: Generate finite element solution files; Using a solver under the constraints of the objective function, a global optimization is performed in the search space to solve the finite element solution file, thereby obtaining the approximate optimal solution for the target material parameters.
[0010] In some embodiments, verifying the prediction accuracy of the finite element model with the approximate optimal solution includes the following steps: The prediction accuracy of the finite element model is verified using the sample point response data that were not involved in the inverse optimization.
[0011] In some embodiments, determining the optimal parameter value of the target material parameter based on preset conditions and the prediction accuracy includes the following steps: If the prediction accuracy reaches the preset condition, then the current approximate optimal solution is taken as the optimal parameter value of the target material parameter; If the prediction accuracy does not meet the preset condition, the process returns to the step of sampling in the search space using the design of experiments method and obtaining the sample point response data through simulation calculation, until the prediction accuracy meets the preset condition, and then the current approximate optimal solution is taken as the optimal parameter value of the target material parameter.
[0012] In some embodiments, the preset condition is that the difference between the first force-displacement curve and the second force-displacement curve is less than or equal to 5% of the first force-displacement curve.
[0013] To achieve the above objectives, another aspect of this application proposes a rapid and high-precision simulation construction device for die-cast structural parts, the device comprising: The search space determination unit is used to determine the target variation range of the target material parameters of the die-cast structural parts in the simulation group; wherein, the target variation range is used as the search space. The experimental sampling unit is used to sample within the search space using experimental design methods and obtain sample point response data through simulation calculations. The constraint setting unit is used to determine the first force and displacement curve of the simulated group die-cast structural component based on the sample point response data, and set the objective function as a constraint to minimize the difference between the first force and displacement curve and the second force and displacement curve of the experimental group die-cast structural component. The parameter solving unit is used to search for a globally optimal solution for the target material parameters in the search space under the constraints of the objective function. The accuracy verification unit is used to verify the prediction accuracy of the finite element model with the approximate optimal solution set. The parameter determination unit is used to determine the optimal parameter values of the target material parameters based on preset conditions and the prediction accuracy.
[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.
[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.
[0016] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0017] The embodiments of this application include at least the following beneficial effects: This application provides a method, apparatus, electronic device, and storage medium for rapid and high-precision simulation of die-cast structural components. The solution involves determining the target variation range of the target material parameters for the simulated die-cast structural components; this target variation range serves as the search space; sampling is performed within the search space using a design-of-experiments method, and sample point response data is obtained through simulation calculations; the first force-displacement curve of the simulated die-cast structural components is determined based on the sample point response data, and an objective function is set as a constraint to minimize the difference between the first force-displacement curve and the second force-displacement curve of the experimental die-cast structural components; under the constraint of the objective function, global optimization is performed in the search space to obtain an approximate optimal solution for the target material parameters; the prediction accuracy is verified on the finite element model with the approximate optimal solution; and the optimal parameter values of the target material parameters are determined based on preset conditions and prediction accuracy. This application achieves optimal parameter combination determination through experimental simulation benchmarking by comparing the differences between the first and second force-displacement curves, thereby reducing the impact of uneven performance of the die-cast structural components on simulation errors and realizing closed-loop parameter adjustment and optimization; the approximate optimal solution is verified through preset conditions to improve the applicability of the optimal parameter values of the target material parameters to the same batch of die-cast structural components. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart illustrating a rapid and high-precision simulation construction method for die-cast structural parts provided in this application embodiment; Figure 2An example flowchart of a rapid and high-precision simulation construction method for die-cast structural parts provided in this application embodiment; Figure 3 Example flowchart of the method for constructing material cards for die-cast structural parts provided in the embodiments of this application; Figure 4 A flowchart for obtaining material parameters provided in this application embodiment; Figure 5 A flowchart illustrating the optimized material parameters provided in this application embodiment; Figure 6 A schematic diagram of a rapid and high-precision simulation construction device for die-cast structural parts provided in this application embodiment; Figure 7 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0022] Before providing a detailed description of the embodiments of this application, some related technologies involved in the embodiments of this application will be described first, as follows: The accuracy of simulations largely depends on the reliability of the input material parameters, especially key mechanical properties such as elastic modulus, yield strength, tensile strength, and elongation. Therefore, accurately obtaining these parameters and establishing a model that truly reflects material properties is a prerequisite for achieving high-precision simulations. Existing simulation technologies primarily use experimental averages for performance prediction. However, averages often fail to reflect the overall performance of die-cast structural components. For example, the mechanical properties of high-stress areas (such as suspension connection points) and low-stress areas (such as sidewalls) in automotive body die-cast structural components can differ by 10%-20%. Using uniform averages can lead to simulation results in high-stress areas being overly conservative (indicating insufficient actual strength), increasing the risk or cost of lightweight vehicle design. Furthermore, existing technologies lack a closed-loop 'parameter optimization-verification' process. The universality of optimized parameters (whether they can cover the same batch of die-cast structural components) has not been verified through confidence intervals, resulting in poor applicability of material cards in mass production.
[0023] This application determines the range of variation of elastic modulus, yield strength, tensile strength and elongation by taking samples from multiple different locations of the same die-cast structural part for tensile testing. The optimal parameter combination is determined by benchmarking the test simulation, thereby reducing the impact of uneven performance of the die-cast structural part on the simulation error and realizing closed-loop adjustment and optimization of parameters. The application also improves the applicability of the material card to the same batch of die-cast structural parts by verifying the confidence interval.
[0024] This application provides a method, apparatus, electronic device, and storage medium for rapid and high-precision simulation construction of die-cast structural parts, relating to the field of materials testing technology. The method, apparatus, electronic device, and storage medium provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the server can also be a node server in a blockchain network; the software can be an application implementing a rapid and high-precision simulation construction method for die-cast structural parts, but is not limited to the above forms.
[0025] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0026] Reference Figure 1 This application provides a method for rapid and high-precision simulation construction of die-cast structural parts. This method may include, but is not limited to, steps S100 to S150, as follows: S100: Determine the target variation range of the target material parameters for the die-cast structural parts in the simulation group; wherein, the target variation range serves as the search space; S110: Samples are taken in the search space using experimental design methods, and the response data of the sample points are obtained through simulation calculations; S120: Determine the first force and displacement curve of the simulated group die-cast structural component based on the sample point response data, and set an objective function as a constraint to minimize the difference between the first force and displacement curve and the second force and displacement curve of the experimental group die-cast structural component. S130: Under the constraints of the objective function, search the search space to perform global optimization and obtain an approximate optimal solution for the target material parameters; S140: Verify the prediction accuracy of the finite element model with the approximate optimal solution set; S150: Determine the optimal parameter values of the target material parameters based on preset conditions and the prediction accuracy.
[0027] Optionally, determining the target variation range of the target material parameters for the simulated die-cast structural parts includes the following steps: The simulation group of die-cast structural parts is sampled in different zones according to the stress distribution to obtain the candidate variation range of multiple candidate material parameters; Establish a finite element model of the die-cast structural component of the simulation group, and set the initial values of the finite element model according to each of the candidate variation ranges; A parameter sensitivity analysis is performed on the finite element model with the initial values set, and then a target material parameter is selected from each of the candidate material parameters, and the target variation range of the target material parameter is obtained.
[0028] Optionally, determining the first force-displacement curve of the simulated die-cast structural component based on the sample point response data includes the following steps: Based on the response data of the sample points, plot the engineering stress and strain curves; The engineering stress-strain curve is converted into a real stress-strain curve using the following expression: ε t = ln(1 + ε e ); σ t = σ e ×(1 + ε e ); Where, ε t For realistic response, ε e For engineering strain, σ t For the actual stress, σ e For engineering stress; The actual stress-strain curve is determined as the first force-displacement curve.
[0029] Optionally, the step of searching for a globally optimal solution for the target material parameters in the search space under the constraints of the objective function to obtain an approximate optimal solution includes the following steps: Generate finite element solution files; Using a solver under the constraints of the objective function, a global optimization is performed in the search space to solve the finite element solution file, thereby obtaining the approximate optimal solution for the target material parameters.
[0030] Optionally, verifying the prediction accuracy of the finite element model with the approximate optimal solution includes the following steps: The prediction accuracy of the finite element model is verified using the sample point response data that were not involved in the inverse optimization.
[0031] Optionally, determining the optimal parameter values of the target material parameters based on preset conditions and the prediction accuracy includes the following steps: If the prediction accuracy reaches the preset condition, then the current approximate optimal solution is taken as the optimal parameter value of the target material parameter; If the prediction accuracy does not meet the preset condition, the process returns to the step of sampling in the search space using the design of experiments method and obtaining the sample point response data through simulation calculation, until the prediction accuracy meets the preset condition, and then the current approximate optimal solution is taken as the optimal parameter value of the target material parameter.
[0032] Optionally, the preset condition is that the difference between the first force-displacement curve and the second force-displacement curve is less than or equal to 5% of the first force-displacement curve.
[0033] The following sections will provide a detailed description and explanation of some optional embodiments of this application, using specific application examples.
[0034] The method for constructing material cards for die-cast structural components in this embodiment may include: performing finite element modeling of the die-cast structural component, analyzing and obtaining the stress distribution of the model, as detailed in the following reference. Figure 2 .
[0035] Two die-cast structural components were defined as a "simulation group" (for parameter optimization) and an "experimental control group" (for model accuracy verification), respectively. Based on the stress distribution in multiple regions of the two die-cast structural components, multiple samples were taken. The variation range of parameters such as elastic modulus, yield strength, tensile strength and elongation at break of the material was obtained through standard mechanical tests. A finite element model of the die-cast structural components was established, and the average value of the parameters was selected as the initial value for the parameter input of the finite element model material.
[0036] A parameter sensitivity analysis is performed on the model to identify several key material parameters (target material parameters) that have the most significant impact on the target performance (such as maximum stress and displacement). The range of variation obtained from the parameter sensitivity analysis is used as the initial range of variation for the key material parameters, which serves as the search space for subsequent optimization.
[0037] The Design of Experiments (DOE) method is used to sample within a designed search space, and the response of the sample points is obtained through simulation calculation. For each test specimen, the test result (force-displacement curve) serves as an optimization objective. An objective function is set to minimize the difference between the simulation curve and the experimental curve. Key material parameters are identified as design variables, and parameter constraints are defined as the range of variation obtained from the tests. Genetic Algorithm (GA) is used first for global optimization to obtain an approximate optimal solution. Then, Sequential Quadratic Programming (SQP) is used for local fine-tuning to drive the surrogate model to perform iterative calculations and find the best-matching "optimal" combination of material parameters for each specimen. A portion of the experimental data that was not involved in the back-optimization is used to verify the prediction accuracy of the simulation model. A prediction bandwidth of 95% confidence interval is set. If the simulation results meet the model accuracy requirements, the results are considered reasonable; otherwise, a different Design of Experiments (DOE) method is selected, the optimization algorithm is adjusted, and parameter optimization is performed again to achieve closed-loop optimization adjustment until a parameter set that meets the accuracy requirements is obtained.
[0038] like Figure 3 As shown, the die-casting structural component material card construction method of this embodiment specifically includes the following steps: Material parameters were obtained by taking multiple samples from different areas and conducting standard mechanical tests.
[0039] A finite element model was established, and parameter sensitivity analysis was performed on the model to identify several material parameters that have the most significant impact on the target performance. In this embodiment, the initial range of variation for the material parameters was set based on the range of variation obtained from testing, providing a search space for subsequent optimization.
[0040] The Design of Experiments (DOE) method is used to sample within the designed search space, and the response of the sample points is obtained through simulation calculation. Key parameters are optimized to find the "optimal" combination of material parameters.
[0041] The prediction accuracy of the simulation model is verified using a portion of the experimental data that was not used in the inverse optimization. If the accuracy is not met, the optimization process is reconstructed to achieve closed-loop optimization adjustment.
[0042] like Figure 4 As shown, the process for obtaining material parameters specifically includes the following steps: S11. Name the two die-cast structural parts to be tested as the simulation group and the experimental control group. Establish the finite element model of the die-cast structural parts. Based on the finite element hotspot analysis, determine the high stress area. According to the finite element analysis results, the two die-cast structural parts are divided into multiple regions with the same surface area from high to low stress level. Samples are taken multiple times in each region and grouped for standard mechanical property tests to obtain the engineering stress-strain curves of multiple sets of samples.
[0043] S12. Engineering stress-strain curves underestimate the true stress state of the material during the plastic deformation stage. Therefore, they need to be converted into true stress-strain curves using formulas to ensure the accuracy of parameter input during the plastic stage of subsequent simulations. Data processing is performed on the simulation group to convert the engineering stress-strain curves into true stress-strain curves. The conversion expression is as follows: ε t = ln(1 + ε e ); σ t = σ e ×(1 + ε e ); Where, ε t For realistic response, ε e For engineering strain, σ t For the actual stress, σ e For engineering stress.
[0044] S13. For the simulation group, multiple sets of material parameters are obtained by processing the real stress-strain curves to determine the variation range of each material parameter of the die-cast structural part, providing a search space for subsequent optimization, and setting the parameter mean value as the initial value of the finite element model and optimization input.
[0045] For the experimental group, the force-displacement curve data obtained from the experiment were extracted and used as an evaluation criterion to verify the confidence level of the simulation model.
[0046] like Figure 5 As shown, the process of establishing an optimization process, finding the "optimal" combination of material parameters, and achieving closed-loop optimization adjustment specifically includes the following steps: S21. Input the finite element solution file into the optimization process and associate the solver with the optimization software.
[0047] S22. Using Design of Experiments (DOE) sampling, set the initial value of each parameter to the average value of the parameter range determined in S12 as the initial sample point, and calculate the sample point response of the initial value.
[0048] S23. Set the parameter sampling range to the range determined by the experiment. The optimization objective is that the difference in the area of the force-displacement curves between the simulation group and the experimental group is ≤5% (the area calculation interval is "0 to fracture displacement"), while satisfying the peak force error ≤3%. Calculate and find the "optimal" combination of material parameters that minimizes the error between the experimental and simulation curves.
[0049] S24. Use the data from the experimental group to verify the combination of material parameters in the simulation. Set the error range of the confidence interval to 5% of the simulated force-displacement curve. Determine the confidence level of the experimental group to be 95% by comparing it with the data from the benchmark experimental group. If the data from the experimental group meets the confidence interval, obtain the die-casting material parameter card. If it does not meet the confidence interval, switch the sampling method and rerun the optimization until the confidence level reaches 95% and obtain the material parameter card.
[0050] This embodiment considers the impact of uneven performance of die-cast structural components on material properties. By sampling in different regions, the range of material parameter variations is determined. Sensitivity analysis is used to identify the parameters to be optimized. An optimization process is established to optimize the material parameters and obtain the optimal combination of material parameters that best reflects the overall performance of the die-cast structural components. Furthermore, considering that each die-cast structural component has performance differences due to errors in the die-casting process, two sets of die-cast structural components are used as a control group. Confidence intervals are set to ensure that the material parameter combination reaches a confidence level. Otherwise, the sampling method is switched for further optimization, forming a closed-loop optimization process. Compared with the prior art, this embodiment takes into account the impact of experimental errors on simulation data and the uneven performance of die-cast structural parts. It improves parameter accuracy through multiple sets of experiments and establishes an automatic parameter back-optimization process. It considers the performance differences between the same group of die-cast structural parts and realizes the prediction of the performance of the same group of die-cast structural parts through a set of material parameter combinations.
[0051] Unlike a single average value input, this embodiment samples multiple times in multiple regions (such as the surface area region) of the die-cast structural part to quantify the range of parameter changes (such as the elastic modulus fluctuation range), providing a physical constraint search space for optimization and solving the simulation deviation problem caused by the material inhomogeneity of the die-cast structural part.
[0052] By using a comparative design of simulation groups and experimental control groups (multiple die-cast structural parts), parameter combinations can predict the performance of die-cast structural parts in the same group, supporting mass applications (such as automotive production lines).
[0053] By introducing parameter sensitivity analysis to identify key variables (such as yield strength, which significantly affects displacement), we can avoid the curse of dimensionality caused by full-parameter optimization. The innovation lies in directly linking the sensitivity results to the DOE sampling range, thereby improving the targeting of optimization.
[0054] The model was validated using experimental data (control group) that was not used for optimization, and a prediction bandwidth of 95% confidence interval was set. If the target was not met, the sampling method was switched for optimization, thus achieving a closed loop of "test-optimization-validation".
[0055] The innovation of this embodiment is not a single technical point (such as multi-region sampling), but a full-chain combination innovation of "multi-region sampling quantification range → sensitivity analysis dimensionality reduction → GA+SQP hybrid optimization → 95% confidence interval verification". It solves the systemic problems of existing technologies such as "unrepresentative parameters, low optimization efficiency and lack of verification", rather than local improvement.
[0056] Reference Figure 6 This application also provides a rapid and high-precision simulation construction device for die-cast structural parts, which can realize the above-mentioned rapid and high-precision simulation construction method for die-cast structural parts. The device includes: The search space determination unit is used to determine the target variation range of the target material parameters of the die-cast structural parts in the simulation group; wherein, the target variation range is used as the search space. The experimental sampling unit is used to sample within the search space using experimental design methods and obtain sample point response data through simulation calculations. The constraint setting unit is used to determine the first force and displacement curve of the simulated group die-cast structural component based on the sample point response data, and set the objective function as a constraint to minimize the difference between the first force and displacement curve and the second force and displacement curve of the experimental group die-cast structural component. The parameter solving unit is used to search for a globally optimal solution for the target material parameters in the search space under the constraints of the objective function. The accuracy verification unit is used to verify the prediction accuracy of the finite element model with the approximate optimal solution set. The parameter determination unit is used to determine the optimal parameter values of the target material parameters based on preset conditions and the prediction accuracy.
[0057] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0058] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of this application. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
[0059] It is understood that the content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the methods of this application, and the beneficial effects achieved are the same as those achieved by the methods of this application.
[0060] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.
[0061] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of this application.
[0062] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.
[0063] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0064] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0065] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0066] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0068] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0069] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0070] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0071] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0072] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0073] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0075] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. A method for rapid and high-precision simulation construction of die-cast structural parts, characterized in that, The method includes the following steps: Determine the target variation range of the target material parameters for the die-cast structural parts in the simulation group; wherein, the target variation range serves as the search space; The experimental design method was used to sample within the search space, and the response data of the sample points were obtained through simulation calculation. The first force and displacement curve of the simulated die-cast structural component is determined based on the sample point response data, and an objective function is set as a constraint to minimize the difference between the first force and displacement curve and the second force and displacement curve of the experimental die-cast structural component. Under the constraints of the objective function, a global optimization is performed in the search space to obtain an approximate optimal solution for the target material parameters; The prediction accuracy is verified using the finite element model with the approximate optimal solution. The optimal parameter values of the target material parameters are determined based on preset conditions and the prediction accuracy.
2. The rapid and high-precision simulation construction method for die-cast structural parts according to claim 1, characterized in that, Determining the target variation range of the target material parameters for the die-cast structural parts in the simulation group includes the following steps: The simulation group of die-cast structural parts is sampled in different zones according to the stress distribution to obtain the candidate variation range of multiple candidate material parameters; Establish a finite element model of the die-cast structural component of the simulation group, and set the initial values of the finite element model according to each of the candidate variation ranges; A parameter sensitivity analysis is performed on the finite element model with the initial values set, and then a target material parameter is selected from each of the candidate material parameters, and the target variation range of the target material parameter is obtained.
3. The rapid and high-precision simulation construction method for die-cast structural parts according to claim 1, characterized in that, Determining the first force-displacement curve of the simulated die-cast structural component based on the sample point response data includes the following steps: Based on the response data of the sample points, plot the engineering stress and strain curves; The engineering stress-strain curve is converted into a real stress-strain curve using the following expression: e t = ln(1 + e e ); s t = σ e ×(1 + e e ); Where, ε t For realistic response, ε e For engineering strain, σ t For the actual stress, σ e For engineering stress; The actual stress-strain curve is determined as the first force-displacement curve.
4. The rapid and high-precision simulation construction method for die-cast structural parts according to claim 1, characterized in that, The step of searching for an approximate optimal solution for the target material parameters in the search space under the constraints of the objective function includes the following steps: Generate finite element solution files; Using a solver under the constraints of the objective function, a global optimization is performed in the search space to solve the finite element solution file, thereby obtaining the approximate optimal solution for the target material parameters.
5. The rapid and high-precision simulation construction method for die-cast structural parts according to claim 1, characterized in that, The verification of the prediction accuracy of the finite element model with the approximate optimal solution includes the following steps: The prediction accuracy of the finite element model is verified using the sample point response data that were not involved in the inverse optimization.
6. A rapid and high-precision simulation construction method for die-cast structural parts according to any one of claims 1 to 5, characterized in that, Determining the optimal parameter values of the target material parameters based on preset conditions and the prediction accuracy includes the following steps: If the prediction accuracy reaches the preset condition, then the current approximate optimal solution is taken as the optimal parameter value of the target material parameter; If the prediction accuracy does not meet the preset condition, the process returns to the step of sampling in the search space using the design of experiments method and obtaining the sample point response data through simulation calculation, until the prediction accuracy meets the preset condition, and then the current approximate optimal solution is taken as the optimal parameter value of the target material parameter.
7. The rapid and high-precision simulation construction method for die-cast structural parts according to claim 6, characterized in that, The preset condition is that the difference between the first force and displacement curve and the second force and displacement curve is less than or equal to 5% of the first force and displacement curve.
8. A rapid and high-precision simulation construction device for die-cast structural parts, characterized in that, The device includes: The search space determination unit is used to determine the target variation range of the target material parameters of the die-cast structural parts in the simulation group; wherein, the target variation range is used as the search space. The experimental sampling unit is used to sample within the search space using experimental design methods and obtain sample point response data through simulation calculations. The constraint setting unit is used to determine the first force and displacement curve of the simulated group die-cast structural component based on the sample point response data, and set the objective function as a constraint to minimize the difference between the first force and displacement curve and the second force and displacement curve of the experimental group die-cast structural component. The parameter solving unit is used to search for a globally optimal solution for the target material parameters in the search space under the constraints of the objective function. The accuracy verification unit is used to verify the prediction accuracy of the finite element model with the approximate optimal solution set. The parameter determination unit is used to determine the optimal parameter values of the target material parameters based on preset conditions and the prediction accuracy.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.