Construction system and method of integrated die-casting material simulation model based on mechanical property failure characteristic analysis
By constructing an integrated die-casting material simulation model based on mechanical performance failure characteristics analysis, the simulation prediction error caused by the differences in mechanical properties of integrated die-casting body parts is solved, precise collision prediction and performance optimization are achieved, and the R&D cycle is shortened.
Patent Information
- Application Number
- CN202510563583.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-30
- Publication Date
- 2025-08-19
AI Technical Summary
The mechanical properties of integrated die-cast body parts in different regions are significantly different, resulting in large differences in the results of traditional simulation calculations and actual performance, especially in the predictions in collision and durability simulation, and the existing technology lacks effective solutions.
A integrated die-casting material simulation model based on mechanical performance failure characteristics analysis is constructed. Through model building modules, simulation modules, prediction modules and model correction modules, grid division, flow, temperature field simulation and mechanical performance prediction of integrated die-casting are realized, and the prediction accuracy is met by correcting the model accuracy.
It realizes accurate prediction of the collision performance of the integrated cabin body, supports lean design and performance optimization, and shortens the R&D cycle.
Smart Images
Figure CN120509166A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of vehicle simulation, and in particular relates to a system and method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis. Background Art
[0002] Unlike traditional steel bodies that use stamping and welding to assemble multiple small components, integrated die-cast bodies use high-pressure die-casting technology to integrate multiple body structural parts. This innovative process significantly reduces the number of parts and welding steps, significantly improving production efficiency and body lightweighting. However, the resulting process complexity also leads to significant performance challenges. Since integrated die-cast body parts are typically large in size, thin in wall thickness, and complex in structure, the internal structure and microstructure of the parts will vary significantly during the high-speed filling of the mold cavity by molten metal, affected by factors such as the cooling rate gradient, the layout of the pouring system, and the uneven mold temperature field. As a result, the mechanical properties of different parts of the molded parts vary, that is, the mechanical properties of different regions of the same part are different.
[0003] This spatially distributed difference in mechanical properties presents a significant challenge when simulating the performance of an integrated vehicle body. In traditional simulation processes, engineers typically use a single material to impart uniform mechanical parameters to parts. This simplified approach often results in significant discrepancies between simulated results and actual performance, particularly during collision and durability simulations. In collision simulations, this can lead to inaccurate predictions of energy absorption paths. In durability simulations, the inability to accurately simulate the fatigue characteristics of stress concentration areas can lead to significant errors in the life predictions of key structural components. This is a major industry pain point in the development of integrated vehicle body performance simulation, and no effective solution currently exists. Summary of the Invention
[0004] In order to solve the problem of simulation accuracy of regional collision performance of integrated die-cast parts, the present invention proposes a system and method for constructing an integrated die-cast material simulation model based on mechanical performance failure characteristic analysis. The model can be used to accurately predict the collision performance of the integrated cabin and body, realize the lean design of the integrated die-cast body, and effectively optimize the performance to cope with the development cycle.
[0005] A system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis to achieve one of the objectives of the present invention includes:
[0006] Model building module: used to mesh and set parameters of the geometric model of the integrated die casting in the casting simulation software; iteratively simulate the flow, temperature field and solidification process of the integrated die casting until the performance standards are met, thereby obtaining a simulation model of the integrated die casting;
[0007] Model simulation module: used to perform die casting filling and solidification simulation using the simulation model to obtain simulation results of flow and temperature distribution of die casting parts during filling and solidification processes;
[0008] Prediction module: used to obtain the predicted value of the mechanical properties of each grid according to the simulation results; divide the integrated die casting into physical areas according to the predicted value of the mechanical properties of each grid, and obtain the predicted value of the mechanical properties of each physical area;
[0009] Model correction module: used to compare the predicted value and actual value of the mechanical properties of each physical area obtained from each simulation result to obtain a comparison result; based on the comparison result, the predicted value of the mechanical properties is corrected so that the model meets the prediction accuracy; when the prediction accuracy is met, the model obtained is an integrated die-casting material simulation model based on the analysis of mechanical property failure characteristics.
[0010] A further technical solution includes: a method for obtaining a predicted value of the mechanical properties of each grid according to the simulation results includes:
[0011] Calculate the die-casting defect distribution field and secondary dendrite spacing distribution field of each grid cell based on the simulation results;
[0012] A predicted value of the tensile strength of each grid is obtained according to the die-casting defect distribution field and the secondary dendrite spacing distribution field.
[0013] Furthermore, the calculation method of the predicted value of the tensile strength of each grid includes:
[0014] σb=β0+β1×N p +β2×D p +β3×N C +β4×D c +β5×N s +β6×D s +β7×λ+ε
[0015] Where, σb: tensile strength of grid unit; β0: intercept of the model; β1~β7: regression coefficients; N p : number of pores, indicating the total number of pores in the grid unit; D p : the average pore diameter of all pores in the grid cell; N C : number of cold shuts, indicating the total number of cold shuts in the grid unit; D c : the average size of the cold shut in the grid cell; N s : the total number of shrinkage cells in the grid; D s : the average size of shrinkage porosity in a grid cell; λ: the secondary dendrite spacing of a grid cell; ε: represents the random error.
[0016] Furthermore, the predicted value of the tensile strength of each grid is corrected, and the correction method includes:
[0017] If the shrinkage size is greater than the set size or the porosity is greater than the set ratio or the cold shut length threshold is greater than the set threshold, the grid cell will be marked as a "defect-sensitive grid";
[0018] For mesh elements marked as "defect-sensitive meshes", the correction methods for the tensile strength prediction values include:
[0019] σb'=σb×(1-a1×ρ)
[0020] σb' is the corrected tensile strength prediction; ρ is the defect density; and a1 is an empirical value derived from experimental fitting, industry standards, or theoretical simplification. The technical effect is that for defect-sensitive meshes with significant defects, their tensile strength needs to be reduced based on the defect density, making the predicted tensile strength closer to the actual value.
[0021] Furthermore, if the number of "defect-sensitive grids" is greater than or equal to the set number and is continuously distributed, the continuously distributed area that meets this condition is marked as a "defect monitoring area." For grid cells within the "defect monitoring area," the correction method for the predicted tensile strength value includes:
[0022] σb'=σb×(1-a1×ρ)×a2
[0023] a2 is a positive value less than 1 related to the length of the defect band. The technical effect is that, due to the continuous distribution and high density of defects in the "defect monitoring area," the tensile strength is further reduced by a2 based on the defect-sensitive grid correction. This reflects the performance degradation of the high-risk failure area, making the predicted tensile strength closer to the actual value.
[0024] Furthermore, the calculation method of the secondary dendrite spacing includes:
[0025] Get the local solidification time t of each grid cell f ;
[0026] The secondary dendrite arm spacing λ is calculated according to the following formula:
[0027] λ=(M×t f ) n
[0028] M is the grain coarsening coefficient related to the alloy composition and is calibrated by differential scanning calorimetry (DSC) experiments; n is the grain growth factor.
[0029] Furthermore, the local coagulation time t f The calculation methods include:
[0030] The temperature field of the casting solidification process is simulated by the casting simulation software to obtain the temperature data of the integrated casting at different time points;
[0031] Obtaining a temperature-time curve at each position of the integrated casting during the solidification process according to the temperature data at different time points;
[0032] Identifying the grid unit's solidification start and complete solidification moments according to the temperature-time curve;
[0033] The local solidification time of the grid cell is obtained by subtracting the start solidification time from the complete solidification time.
[0034] Furthermore, the method of dividing the physical regions of the integrated die casting according to the predicted value of the mechanical properties of each grid includes:
[0035] The predicted tensile strength values of all grid cells are sorted by size to obtain a sequence of grid cells arranged in order;
[0036] According to the preset proportional interval, the sorted grid cell sequence is divided into continuous intervals, each interval contains a corresponding proportion of grid cells (for example, when the total number of grids is N, the first 10%N grids are the first interval, the next 10%N grids are the second interval, and so on); the physical area corresponding to the grid cells in each interval is the physical area after division.
[0037] Furthermore, the method for obtaining the predicted value of the mechanical properties of each physical region includes: using the distance from each grid cell to the centroid of the region as a weight, calculating the average tensile strength of the region as the corrected tensile strength of the region. That is:
[0038]
[0039] Where, w i Indicates the distance from the ith grid cell in a physical area to the center of mass of the area; σb i represents the tensile strength of the i-th grid cell in a physical area; σb 区域 That is w i The tensile strength of the physical area; n represents the number of grid cells in the physical area.
[0040] A further technical solution also includes: a method for obtaining a simulation model of an integrated die casting includes:
[0041] Determine the output result type and specific characteristics of the simulation model according to the desired output result type, wherein the output result type includes: flow condition of molten metal, temperature distribution, and stress analysis;
[0042] Use software to design 3D models of integrated die castings;
[0043] Export the 3D model as a geometric model in a format supported by casting simulation software;
[0044] Importing the geometric model into casting simulation software;
[0045] In the casting simulation software, a corresponding casting material is selected according to the actual material of the integrated die casting, and the corresponding physical and thermal property parameters of the material are input;
[0046] Meshing the imported geometric model;
[0047] Set the boundary conditions, gating system and cooling system of the casting process in the casting simulation software;
[0048] Simulate the flow, temperature field and solidification process of the casting in the casting simulation software, start the simulation, and obtain the simulation results of the flow field and temperature field;
[0049] Whether there are defects is determined based on the simulation results, and design optimization is performed based on the defect results until the model reaches the required performance standards, thereby obtaining a simulation model of the integrated die casting.
[0050] A method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis to achieve the second objective of the present invention includes:
[0051] Meshing and parameter setting are performed on the geometric model of the integrated die-casting in the casting simulation software; the flow, temperature field, and solidification process of the integrated die-casting are simulated iteratively until the performance standard is met, thereby obtaining a simulation model of the integrated die-casting;
[0052] The simulation model is used to perform die casting filling and solidification simulation to obtain simulation results of the flow and temperature distribution of the die casting during the filling and solidification process;
[0053] Obtaining a predicted value of the mechanical properties of each grid according to the simulation results; dividing the integrated die casting into physical regions according to the predicted value of the mechanical properties of each grid to obtain a predicted value of the mechanical properties of each physical region;
[0054] The predicted value and actual value of the mechanical properties of each physical area obtained from each simulation result are compared to obtain a comparison result; the predicted value of the mechanical properties is corrected according to the comparison result so that the model meets the prediction accuracy; the model obtained when the prediction accuracy is met is the integrated die-casting material simulation model based on the analysis of mechanical property failure characteristics.
[0055] A non-transitory computer-readable storage medium for achieving the third purpose of the present invention stores a computer program, which, when executed by a processor, implements the steps of the method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis.
[0056] A computer program product for achieving the fourth purpose of the present invention includes a computer program / instruction, which, when executed by a processor, implements the steps of the method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis.
[0057] The beneficial effects of the present invention include:
[0058] The present invention models and simulates different areas of the same part according to actual mechanical properties to better reflect complex mechanical behaviors and ensure that the impact resistance of different areas can be simulated and optimized according to actual needs, thereby achieving accurate prediction of the integrated cabin and body collision performance. The regional performance prediction scheme realizes the lean design of the integrated die-cast body, effectively optimizes the development cycle of performance response, and shortens the vehicle R&D cycle. BRIEF DESCRIPTION OF THE DRAWINGS
[0059] Figure 1 It is a flow chart of an embodiment of the method of the present invention. DETAILED DESCRIPTION
[0060] The following specific embodiments are provided to explain the technical solutions of the present invention so that those skilled in the art can understand the present invention. The scope of protection of the present invention is not limited to the specific implementation structures described below. Any implementation schemes created by those skilled in the art that include the technical solutions of the present invention but differ from the following specific implementation schemes are also within the scope of protection of the present invention.
[0061] The embodiment of the present invention provides a method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis, such as Figure 1 As shown, the following steps are included:
[0062] Step 1: Use casting simulation software to establish a simulation model of the integrated die casting, including the following steps:
[0063] 1.1. Define project goals
[0064] Determine the output type of the simulation based on the desired output type, including: selecting the flow analysis module to analyze the flow, temperature distribution, and stress of the molten metal. Flow analysis is used to understand the flow of molten metal in the mold, which helps optimize the gating system design and avoid defects such as insufficient pouring and cold shuts. Temperature distribution is used to determine the location of hot spots during solidification to prevent defects such as shrinkage cavities and porosity. Stress analysis is used to predict the stress generated in the casting during solidification, providing a basis for avoiding deformation and cracking.
[0065] Because different materials have different physical and thermal properties that affect simulation results, shape and size determine the geometric characteristics of the model and are the basis for creating an accurate simulation model. Therefore, this also includes determining the specific characteristics of the die casting (such as material, shape, size, etc.).
[0066] 1.2. Create the geometric model
[0067] According to the shape and size of the die-casting, use CAD software (such as SolidWorks, AutoCAD, etc.) to design a 3D model of the integrated die-casting, ensure the accuracy of the details of the 3D model, including gates, cooling channels, vents, etc., and export the 3D model as a geometric model in a format supported by casting simulation software (such as STL, STEP, etc.).
[0068] 1.3. Importing the Geometry Model
[0069] Import the geometric model into casting simulation software (such as MAGMA or ProCAST), ensuring that the scale and unit of the model are set correctly.
[0070] 1.4. Set material properties
[0071] In the casting simulation software, the corresponding casting material (such as aluminum, zinc alloy, etc.) is selected according to the actual material of the integrated die-casting, and the corresponding physical and thermal properties of the material are input, including: melting point, density, thermal conductivity, viscosity, etc.
[0072] 1.5. Create a grid
[0073] Mesh the imported geometric model to ensure that the quality and density of the mesh are suitable for simulation requirements.
[0074] 1.6. Define boundary conditions and initial conditions
[0075] The boundary conditions of the casting process are set in the casting simulation software. The boundary conditions include: pouring temperature, mold temperature, ambient temperature, and initial conditions are defined, including the initial state of the material.
[0076] The pouring temperature determines the initial energy state and fluidity of the molten metal as it enters the mold. Excessively high temperatures can lead to excessive oxidation of the molten metal, shortening mold life and causing defects such as shrinkage and porosity in the casting. Excessively low temperatures can result in poor molten metal fluidity, potentially leading to problems such as under-pouring and cold shuts. By setting the appropriate pouring temperature, we can simulate the flow and solidification behavior of molten metal during the actual pouring process and predict the quality of the casting.
[0077] The right mold temperature allows the molten metal to cool evenly in the mold, helping to reduce stress concentration, deformation, and cracking in the casting. At the same time, mold temperature also affects the heat exchange between the molten metal and the mold, which in turn affects the solidification time and microstructure of the casting.
[0078] Ambient temperature affects the heat dissipation conditions of castings during solidification. Changes in ambient temperature alter the rate of heat exchange between the casting and its surroundings, affecting the casting's cooling rate and temperature distribution. By adjusting the ambient temperature, you can more realistically simulate the solidification process of castings in different environments, providing a basis for process optimization.
[0079] The initial state of a material includes information such as its initial temperature and phase state. The initial temperature influences the temperature history during pouring and solidification, which in turn affects solidification time and microstructure formation. The initial phase state, on the other hand, affects the phase transformation behavior of the molten metal during solidification, which in turn affects the mechanical properties of the casting. Accurately setting the initial state of a material helps improve the accuracy of simulation results and better predict the quality and performance of castings.
[0080] 1.7. Set up the pouring system
[0081] The pouring system and cooling system are set up in the casting simulation software. The pouring system includes a gate, runner, and riser to ensure smooth flow of molten metal, effectively discharge cavity gas, control shrinkage defects, and avoid the generation of bubbles and inclusions.
[0082] After building the simulation model, it also includes:
[0083] 1.8. Run the simulation
[0084] The flow, temperature field and solidification process of the casting are simulated in the casting simulation software. The simulation is started and the calculation is completed to obtain the simulation results of the flow field and temperature field.
[0085] 1.9. Result Analysis and Optimization
[0086] Analyze the simulation results to examine the flow diagram, temperature distribution, filling time, etc. to determine if there are defects (such as cold shuts, air holes, deformation, etc.) and optimize the design based on the results. The model can be improved by adjusting parameters such as pouring speed, temperature, and mold design.
[0087] 1.10. Generating Reports
[0088] Record simulation results and generate reports containing key data and visualization images to facilitate subsequent discussions and modifications.
[0089] 1.11. Iterative Optimization
[0090] Design iterations are performed based on the simulation results, and the above steps are repeated until the simulation model meets the required performance standards.
[0091] Through the above steps, a simulation model of the integrated die casting can be established in the casting simulation software.
[0092] Step 2: Get simulation results
[0093] After the simulation model of the integrated die-casting part that meets the performance standards after iterative optimization is established in step 1, simulation is performed using casting simulation software (such as MAGMA, ProCAST, SolidCast, etc.) to obtain simulation results of the flow and temperature distribution of the integrated die-casting part during the filling and solidification processes in the simulation environment.
[0094] Step 3: Predict the mechanical properties of die castings
[0095] According to the simulation results obtained in step 2, the die-casting defect distribution field and the secondary dendrite spacing distribution field are calculated to predict the mechanical properties of the die-casting, such as tensile strength, yield strength and elongation. The mechanical property of concern in this embodiment is tensile strength; the prediction of the mechanical properties of the die-casting is achieved by statistically analyzing the distribution of defects in the casting process and their influence on the microstructure of the material, that is, by calculating the defect distribution field and the secondary dendrite spacing distribution field of the die-casting. In the die-casting process, common defect types include pores, cold shuts, and inclusions. These defects will affect the strength, toughness and ductility of the material. The defect types of concern in this embodiment are: pores, cold shuts and shrinkage; the distribution of the three defects of pores, cold shuts and shrinkage is calculated in the casting simulation software to predict the location, size and number of defects. The growth of secondary dendrites affects the microstructure of the material, and thus affects the mechanical properties of the material. Generally speaking, smaller secondary dendrite spacing usually results in better mechanical properties. Therefore, the die-casting defect distribution field and the secondary dendrite spacing distribution field can be used to predict the mechanical properties of die-castings. The specific prediction methods include:
[0096] 3.1. Calculation of secondary dendrite spacing
[0097] The local solidification time of each grid cell is obtained, and the secondary dendrite arm spacing λ is calculated using the Furer-Wunderlin secondary dendrite arm side melting model in the casting simulation software. f ) n , λ is the secondary dendrite spacing; M is the grain coarsening coefficient related to the alloy composition, which is calibrated by differential scanning calorimetry (DSC) experiment; n is the grain growth factor, which defaults to 0.3 and can be corrected according to the JMatPro solidification kinetics model; t f is the local solidification time of each grid unit. Through this model, the secondary dendrite spacing of each grid unit of the die casting can be calculated.
[0098] In some embodiments, the local solidification time t f The methods for obtaining include:
[0099] The casting simulation software simulates the temperature field during the solidification process of the integrated casting. During the simulation, the software outputs the temperature data of the integrated casting at different time points based on the material properties, geometry, boundary conditions, and other factors set by the die casting. This simulation can generate temperature data at various locations of the integrated casting during the solidification process, namely the temperature-time curve.
[0100] According to the temperature-time curve, the starting solidification time and the complete solidification time of each grid unit are extracted to calculate the local solidification time.
[0101] In this embodiment, the solid phase ratio threshold method (solid phase ratio f_s ≥ 0.01 determines the start of solidification, and solid phase ratio f_s ≥ 0.99 determines the end of solidification) or the temperature derivative method (when dT / dt < -10 ° C / s, the start of solidification is triggered, and when dT / dt > -1 ° C / s, the end of solidification is determined) is used to extract the start solidification time t_start and the complete solidification time t_end of each grid cell;
[0102] The solidification time of each grid unit is obtained by subtracting the start solidification time from the complete solidification time, and a calculation model for the regional secondary dendrite spacing is established.
[0103] 3.2 Calculation of defect distribution field
[0104] Using the built-in defect criteria of the casting simulation software (such as the pore formation criterion and the shrinkage Niyama criterion), a statistical overall analysis of the distribution of three types of defects, namely pores, cold shuts and shrinkage, in each grid unit is performed to obtain the defect type and size of each grid unit of the casting and form a defect distribution field.
[0105] 3.3 Prediction of mechanical properties
[0106] The relationship between each defect value, secondary dendrite spacing and tensile strength σb was obtained through regression analysis, as follows:
[0107] σb=β0+β1×N p +β2×D p +β3×N C +β4×D c +β5×N s +β6×D s +β7×λ+ε
[0108] σb: tensile strength of the grid element, in MPa;
[0109] β0: The intercept of the regression analysis model, which means that when all independent variables (Np 、D p 、N C 、D c 、N s 、D s ) is an estimated value of the tensile strength when ) is zero;
[0110] β1~β7: regression coefficients obtained through regression analysis;
[0111] N p : Number of stomata, indicating the total number of stomata in the grid cell;
[0112] D p : average pore diameter, in mm, representing the average diameter of all pores in the grid cell;
[0113] N C : number of cold shuts, indicating the total number of cold shuts in the grid cell;
[0114] D c : average size of cold shut, in mm, indicating the average size of cold shut in the grid unit;
[0115] N s : shrinkage number, which indicates the total number of shrinkages in the grid cell;
[0116] D s : average shrinkage size, in mm, represents the average shrinkage size in the grid cells;
[0117] λ: secondary dendrite spacing, in μm, representing the secondary dendrite spacing of the grid unit;
[0118] ε: Error term, representing the random error of the model.
[0119] Through the above formula, the predicted value of the tensile strength in the mechanical properties of each grid unit can be obtained.
[0120] The method for determining the values of each regression coefficient β1~β7 includes: constructing a data set containing defect parameters (number of pores, average diameter, number of cold shuts, average size, number of shrinkages, average size), secondary dendrite spacing and other independent variables and tensile strength dependent variable from simulation data (obtaining defect parameters, secondary dendrite spacing and corresponding tensile strength simulation values of each grid unit under different process parameters through casting simulation software) and experimental data (tensile strength measured by tensile test of actual die-cast specimens); after data preprocessing (eliminating outliers, etc.), using a multivariate linear regression model, solving the regression coefficient matrix through statistical software or algorithm, and verifying the significance and goodness of fit of the model; when the die-casting material, process parameters, etc. change, it is necessary to re-collect data and calculate each coefficient according to the above steps to ensure the adaptability of the model; for example, in the embodiment, the aluminum alloy die-casting simulation is fitted with the experimental data, and the specific regression coefficient is calculated by software and meets the goodness of fit requirements, thereby achieving accurate value of the regression coefficient.
[0121] In some embodiments, the method further includes correcting the predicted value of the tensile strength of each grid, wherein the correction method includes:
[0122] Grid cells with shrinkage size greater than the set size (e.g., 2 mm), porosity greater than the set ratio (e.g., 5%), or cold shut length threshold greater than the set threshold (e.g., 3 mm) are marked as "defect-sensitive grids";
[0123] If the number of "defect sensitive grids" is greater than or equal to the set number (e.g., 100) and is continuously distributed (e.g., forming a defect band larger than 3 mm), the continuously distributed area that meets this condition will be marked as a "defect monitoring area," i.e., a high-risk failure area.
[0124] For the grid cells belonging to the "defect-sensitive grid", the predicted tensile strength value σb needs to be multiplied by the first correction coefficient k1: the calculation method of the first correction coefficient k1 includes: k1 = 1-a1×defect density; a1 is a positive value less than 1, and its value is combined with experimental fitting, industry standards or theoretical simplified empirical values. In this embodiment, the preferred range of a1 is (0, 0.2), and the preferred value is 0.1;
[0125] For the grid cells belonging to the "defect monitoring area", the predicted tensile strength value σb needs to be multiplied by the second correction coefficient k2: the calculation method of the second correction coefficient k2 includes: k2 = (1-a1×defect density)×a2; a2 is a positive value less than 1 related to the length of the defect band.
[0126] If a grid cell has multiple defects at the same time (such as shrinkage and pores), the density percentage of each defect needs to be calculated separately, and the maximum value is taken to correct the tensile strength prediction value σb.
[0127] The defect density calculation method includes:
[0128] For the “shrinkage” defect, the defect density = (shrinkage defect area within the grid unit / total volume of the grid unit) × 100%;
[0129] For "pore" defects, defect density = porosity;
[0130] For the “cold shut” defect, the defect density = (cold shut defect length / grid unit characteristic length) × 100%; the grid unit characteristic length is usually the size of the grid unit in the force direction.
[0131] In some embodiments, it also includes: conducting physical tests in a factory or laboratory simultaneously to obtain the actual tensile strength of each area, comparing it with the tensile strength predicted in the simulation results, and adjusting the simulation model according to the comparison results to improve the prediction accuracy of the mechanical properties. The adjustment method includes: the error between the simulated value and the actual value of the tensile strength is less than or equal to the set error (such as ≤10%); if this error is not met, then correcting the tensile strength prediction formula coefficient (β0~β7).
[0132] Based on the revised regression formula, the tensile strength of each region is recalculated, and the simulation model is iteratively updated until the simulation accuracy meets the requirements.
[0133] Step 4: Modify the material properties of each area of the simulation model
[0134] According to the predicted value of the tensile strength of each grid unit obtained in step 3, the predicted values of the tensile strength are sorted by size, and the physical area of the die casting is divided according to the grids corresponding to the sorted predicted values of the tensile strength; for example, if the division is performed at intervals of 10%, each 10% is divided into 1 area; for example, assuming that 1000 grids are divided, after sorting by the predicted values of the tensile strength, the physical areas corresponding to the first 100 grids are divided into area 1 from large to small; the physical areas corresponding to the 101st to 200th grids are divided into area 2; and so on.
[0135] The distance from each grid cell to the centroid of the region is used as the weight to calculate the average tensile strength of the region as the corrected tensile strength of the region.
[0136] In professional simulation software (such as ABAQUS, ANSYS, etc.), the corresponding tensile strength is assigned to each area, so as to obtain the final integrated die-casting material simulation model based on mechanical property failure characteristics analysis.
[0137] It should be understood that the order of execution of the steps in the above embodiments does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0138] An embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores a computer program. The computer program includes program instructions, which implement the various steps of the method described in the present invention when executed by a processor, and will not be repeated here.
[0139] The computer-readable storage medium may be the data transmission device provided in any of the aforementioned embodiments or an internal storage unit of a computer device, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., provided on the computer device.
[0140] Furthermore, the computer-readable storage medium may include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data to be output or that has been output.
[0141] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0142] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0143] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0145] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.
Claims
1. A system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis, characterized in that: include: Model building module: used to mesh and set parameters of the geometric model of the integrated die casting in the casting simulation software; iteratively simulate the flow, temperature field and solidification process of the integrated die casting until the performance standard is met, thereby obtaining a simulation model of the integrated die casting; Model simulation module: used to perform die casting filling and solidification simulation using the simulation model to obtain simulation results of flow and temperature distribution of the integrated die casting during the filling and solidification process; Calculation module: used to obtain the predicted value of the mechanical properties of each grid according to the simulation results; Dividing the integrated die casting into physical regions according to the predicted value of the mechanical properties of each grid, and obtaining the predicted value of the mechanical properties of each physical region; Model correction module: used to compare the predicted value and actual value of the mechanical properties of each physical area obtained from each simulation result to obtain a comparison result; According to the comparison results, the predicted values of mechanical properties are modified to make the model meet the prediction accuracy; The simulation model obtained when the prediction accuracy is met is the integrated die-casting material simulation model based on mechanical property failure characteristic analysis.
2. The system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis according to claim 1, characterized in that: The method for obtaining the predicted value of the mechanical properties of each grid according to the simulation results includes: Calculate the die-casting defect distribution field and secondary dendrite spacing distribution field of each grid cell based on the simulation results; A predicted value of the tensile strength of each grid is obtained according to the die-casting defect distribution field and the secondary dendrite spacing distribution field.
3. The system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis according to claim 2, characterized in that: The calculation method for the predicted value of tensile strength of each grid includes: σb=β0+β1×N p +β2×D p +β3×N C +β4×D c +β5×N s +β6×D s +β7×λ+ε Where, σb: tensile strength of the grid; β0: intercept; β1~β7: regression coefficient; N p : the number of pores in the grid; D p : the average pore diameter of all pores in the grid; N C : the total number of cold shuts in the grid cell; D c : average size of cold shut in the grid; N s : the total number of shrinkages in the grid; D s : average size of shrinkage porosity in the grid; λ: secondary dendrite spacing of the grid; ε: random error.
4. The system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis according to claim 3, characterized in that: It also includes correcting the predicted value of the tensile strength of each grid. The correction method includes: If the shrinkage size is greater than the set size or the porosity is greater than the set ratio or the cold shut length threshold is greater than the set threshold, the grid cell will be marked as a "defect-sensitive grid"; For meshes marked as "defect-sensitive meshes", the correction methods for the tensile strength prediction values include: σb'=σb×(1-a1×ρ) σb' is the corrected predicted value of tensile strength; ρ is the defect density; a1 is the empirical value based on experimental fitting, industry standards or theoretical simplification.
5. The system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis according to claim 4, characterized in that: If the number of "defect sensitive grids" is greater than or equal to the set number and they are continuously distributed, the area that meets this condition will be marked as a "defect monitoring area"; For grid cells within the "defect monitoring area", the correction method for the tensile strength prediction value also includes: σb'=σb×(1-a1×ρ)×a2 a2 is a positive value less than 1 that is related to the length of the defective band.
6. The system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis according to claim 1, characterized in that: The method for obtaining a simulation model of an integrated die casting includes: Determine the output result type and specific characteristics of the simulation model according to the desired output result type, wherein the output result type includes: molten metal flow, temperature distribution, and stress analysis; Use software to design 3D models of integrated die castings; Export the 3D model as a geometric model in a format supported by casting simulation software; Importing the geometric model into casting simulation software; In the casting simulation software, a corresponding casting material is selected according to the actual material of the integrated die casting, and the corresponding physical and thermal property parameters of the material are input; Meshing the imported geometric model; Set the boundary conditions, gating system and cooling system of the casting process in the casting simulation software; Simulate the flow, temperature field, and solidification process of the integrated die casting in the casting simulation software, start the simulation, and obtain the simulation results of the flow field and temperature field; Whether there are defects is determined based on the simulation results, and design optimization is performed based on the defect results until the model meets the required performance standards, thereby obtaining a simulation model of the integrated die casting.
7. The system for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis according to claim 1, characterized in that: Methods for dividing the physical regions of an integrated die casting according to the predicted values of the mechanical properties of each grid include: Sort the predicted tensile strength values of all grids by size to obtain a sequence of grid cells arranged in order; According to the preset proportional interval, the sorted grid unit sequence is divided into continuous physical intervals, each physical interval contains grid units of corresponding proportions; the physical area corresponding to the grid in each interval is the physical area after division.
8. A method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis, characterized in that: include: Meshing and parameter setting are performed on the geometric model of the integrated die-casting in the casting simulation software; the flow, temperature field, and solidification process of the integrated die-casting are simulated iteratively until the performance standard is met, thereby obtaining a simulation model of the integrated die-casting; The simulation model is used to perform die casting filling and solidification simulation to obtain simulation results of the flow and temperature distribution of the die casting during the filling and solidification process; Obtaining a predicted value of the mechanical properties of each grid according to the simulation results; Dividing the integrated die casting into physical regions according to the predicted value of the mechanical properties of each grid, and obtaining the predicted value of the mechanical properties of each physical region; Comparing the predicted value of the mechanical properties of each physical area obtained from each simulation result with the actual value to obtain a comparison result; based on the comparison result, the predicted value of the mechanical properties is modified so that the model meets the prediction accuracy; The model obtained when the prediction accuracy is met is an integrated die-casting material simulation model based on the analysis of mechanical property failure characteristics.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis as described in claim 8 are implemented.
10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for constructing an integrated die-casting material simulation model based on mechanical property failure characteristic analysis as described in claim 8 are implemented.