Metal Composite Molding Die Stress Optimization System Based on Finite Element Analysis

Abstract

The present invention relates to the field of computer-aided design, and particularly to a stress optimization system for a metal composite forming die based on finite element analysis, including: changing the values of die-casting parameters and adjusting the model mesh multiple times, obtaining the stress change coefficient a caused by the change of the model mesh and the stress change coefficient b caused by the change of the die-casting parameters each time when the values of the die-casting parameters are changed and the model mesh is adjusted adjacent to each other, making the change amount of the die-casting parameters positively correlated with b each time the die-casting parameters are changed, and making the mesh density adjustment amount positively correlated with a each time the model mesh is adjusted. After changing the values of the die-casting parameters multiple times, the die-casting parameters when the stress distribution is the smallest are used as the optimized die-casting parameters. The present invention makes the finite element analysis process more efficient by adjusting the model mesh distribution and the values of the die-casting parameters in real time.

Description

Technical Field

[0001] The present invention relates to the field of computer-aided design, and particularly to a stress optimization system for metal composite forming dies based on finite element analysis. Background Art

[0002] Finite element analysis is a common method for stress optimization of metal composite forming dies. It creates a three-dimensional model based on computer-aided design software, then simulates and predicts the stress distribution based on this three-dimensional model, and performs stress optimization. The conventional stress optimization method based on finite element analysis is to set different optimization parameters, continuously perform finite element analysis on the three-dimensional model under multiple optimization parameters, and then determine the optimal optimization parameters.

[0003] However, in the process of continuously performing multiple finite element analyses, due to the inappropriate model grid distribution of the three-dimensional model, the efficiency of the finite element analysis stress optimization process is low (that is, the computing power consumption is large, the calculation time is long, and the stress optimization result may not be optimal either); for example, when the model grid is locally too dense, the computing power consumption of the finite element analysis is large and the calculation time is long, and if the model grid is locally too sparse, the predicted stress distribution may have obvious errors. More notably, when there are more optimization parameters set, the efficiency brought about by the inappropriate model grid distribution described in the above process will be even lower, resulting in the inability to efficiently obtain comprehensive and accurate optimal optimization parameters, and further resulting in the stress optimization result obtained from the entire finite element analysis process not being optimal. Summary of the Invention

[0004] To solve the above problems, the present invention provides a stress optimization system for metal composite forming dies based on finite element analysis.

[0005] The stress optimization system for metal composite forming dies based on finite element analysis of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a stress optimization system for metal composite forming dies based on finite element analysis, and the system includes the following modules:

[0007] A finite element analysis module, configured to perform finite element analysis on the model of the die and the die-casting parameters to obtain a stress distribution, and the position points where the stress in the stress distribution is greater than and not greater than a preset threshold th are respectively defined as target points and non-target points;

[0008] A model grid adjustment module, configured to change the values of the die-casting parameters and adjust the model grid multiple times;

[0009] Wherein, the stress distribution and model obtained by changing the die-casting parameters and adjusting the model grid for the i-th time are respectively denoted as 、 ; the model obtained by adjusting the model grid for the (i + 1)-th time is denoted as ;

[0010] Perform finite element analysis on the model and the die-casting parameters after the (i + 1)-th change to obtain the stress distribution , and obtain Regions where the target points increase relative to ; The stress change coefficient a caused by the change of the model mesh is positively correlated with the correlation between the stress increase amount and the grid density increase amount in the region; The stress change coefficient b caused by the change of the die-casting parameters is positively correlated with the correlation between the stress increase amount and the grid density decrease amount in the region;

[0011] When the difference between a and b is greater than the preset difference, after changing the value of th, use the model mesh adjustment module to recalculate a and b;

[0012] When changing the die-casting parameters for the (i + 2)-th time, make the change amount of the die-casting parameters be positively correlated with b. The (i + 2)-th adjustment of the model mesh includes: increasing at the grid density at the target points in , and the grid density adjustment amount is positively correlated with a, and reducing the grid density at the non-target points in

[0013] The die-casting parameter optimization module, after changing the values of the die-casting parameters multiple times, uses the die-casting parameters when the stress distribution is the smallest as the optimized die-casting parameters.

[0014] Preferably, the step of increasing at the grid density at the target points in includes the following specific steps:

[0015] On the model , obtain all the grids where the target points in are located, denoted as the first grids, record the number of the first grids as K1, record the sum of the areas of all the first grids as S1, and record the grid density adjustment amount when increasing the grid density at the target points in as , indicating an average increase of grids per unit area, let , and split each first grid into N1 grids, where N1 is rounded to the nearest integer.

[0016] Preferably, the step of reducing the grid density at the non-target points in

[0017] On the model , obtain The grids where all non-target points are located are denoted as the second grids. Denote the number of the second grids as K2, the sum of the areas of all the second grids as S2, and the reduction amount of the grid density when reducing the grid density at non-target points as Let , and N2 = K2 - , where Round to the nearest integer, and reduce the K2 second grids to N2 grids; The value of is equal to the grid density adjustment amount.

[0018] Preferably, the stress change coefficient a caused by the change of the model grid is positively correlated with the correlation between the stress increase amount and the grid density increase amount in the region, and the specific steps are as follows:

[0019] For any position point in the region where the target points increase, obtain the stress of this position point in , obtain the stress of this position point in , and take as the stress increase amount of this position; obtain the grid density of this position point on , obtain the grid density of this position point on , and take as the grid point density increase amount of this position point;

[0020] For all position points where the grid point density increase amount is greater than the preset percentage, the Pearson correlation coefficient between the stress increase amounts of all position points in the region where the target points increase and the grid point density increase amounts is denoted as the first correlation; the product of the first correlation and the first preset parameter k1 is denoted as a.

[0021] Preferably, the stress change coefficient b caused by the change of the die-casting parameters is positively correlated with the correlation between the stress increase amount and the grid density reduction amount in the region, and the specific steps are as follows:

[0022] For any position point in the region where the target points increase, obtain the stress of this position point in , obtain the stress of this position point in , and take as the stress increase amount of this position; obtain the grid density of this position point on , obtain the grid density of this position point on , and take as the grid density reduction amount of this position point;

[0023] For all position points where the increase in grid point density is greater than a preset percentage, the Pearson correlation coefficient between the stress increase of all position points in the increased area of the target points and the decrease in grid density is denoted as the second correlation, and the product of the second correlation and the second preset parameter k2 is denoted as b.

[0024] Preferably, when the difference between a and b is greater than a preset difference, changing the value of th includes the following specific steps:

[0025] When the difference between a and b is greater than a preset difference, if the number of target points is greater than the number of non-target points, then increase the value of th, and the increased th is th + ft, where ft represents the change amount of th; if the number of target points is less than or equal to the number of non-target points, then decrease the value of th, and the decreased th is th - ft.

[0026] Preferably, the (i + 2)-th change of die-casting parameters includes the following specific steps:

[0027] The die-casting parameters obtained after the (i + 1)-th change of die-casting parameters are denoted as , and the die-casting parameters are regarded as parameter points composed of pouring temperature, injection speed, and holding time. Randomly select a parameter point on the spherical surface centered on the die-casting parameters with the change amount of die-casting parameters as the die-casting parameters after the (i + 2)-th change.

[0028] Preferably, the change amount of die-casting parameters is b×R, where R is a preset value.

[0029] Preferably, the grid density adjustment amount is: min1 + a×(max1 - min1), where min1 represents the preset minimum value of the grid density adjustment amount, and max1 represents the preset maximum value of the grid density adjustment amount.

[0030] Preferably, the change amount of th is negatively correlated with |a - b|.

[0031] The beneficial effects of the technical solution of the present invention are:

[0032] On the one hand, the present invention adjusts the distribution of the model grid in real time and dynamically, and increases the grid density at the target points in the stress distribution (i.e., at the positions with higher stress), so as to improve the accuracy of the finite element analysis results in the stress abnormal area, and avoid the problem that the stress abnormal area (such as the area with larger stress) cannot be detected in time during subsequent stress optimization (i.e., in the die-casting parameter optimization module), resulting in the stress abnormal area still existing in the die-cast part after stress optimization. By reducing the grid density at non-target points (i.e., at the positions with lower stress), the problem of excessive grid in the normal stress area leading to large computing power consumption and long calculation time can be avoided. On the other hand, the values of the die-casting parameters are adjusted in real time and dynamically, which can avoid wasting excessive computing power and calculation time in the finite element analysis process corresponding to multiple changes of the die-casting parameters in a local small range when stress abnormal areas are likely to appear; when stress abnormal areas are not likely to appear, the die-casting parameters can be adjusted more precisely and continuously to ensure that the die-casting parameters at the optimal stress can be obtained comprehensively as a whole subsequently. At this time, the problem that the optimal die-casting parameters cannot be obtained due to large changes in the die-casting parameters is avoided at the cost of computing power and calculation time.

[0033] Furthermore, the present invention dynamically adjusts the value of th, so that when the values of the die-casting parameters are changed multiple times and the model grid is adjusted, the division of the target points and non-target points is more reasonable, so that it can be distinguished or clarified whether the increase in the target points is caused by the change of the model grid or the change of the die-casting parameters, and avoid regarding the positions or areas that do not belong to abnormal stress in actual production as the positions or areas of abnormal stress (i.e., divided into target points), or causing the positions or areas that belong to abnormal stress in actual production not to be regarded as the positions or areas of abnormal stress (i.e., not divided into target points), thereby resulting in relatively inaccurate optimized die-casting parameters and relatively inefficient calculation processes (such as more wasted computing power and longer calculation time). BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is the framework structure diagram of the stress optimization system of the metal composite molding die based on finite element analysis provided by an embodiment of the present invention;

[0036] Figure 2 It is the schematic diagram of the die model of the stress optimization system of the metal composite molding die based on finite element analysis provided by an embodiment of the present invention. Detailed Implementation Manner

[0037] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manner, structure, features, and effects of the stress optimization system for metal composite forming molds based on finite element analysis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0039] The following specifically describes the specific solution of the stress optimization system for metal composite forming molds based on finite element analysis provided by the present invention in combination with the accompanying drawings.

[0040] Embodiment 1:

[0041] Please refer to Figure 1 , which shows the framework structure diagram of the stress optimization system for metal composite forming molds based on finite element analysis provided by an embodiment of the present invention. The system includes the following modules: a finite element analysis module, a model mesh adjustment module, and a die-casting parameter optimization module. The system runs on a computer device.

[0042] The finite element analysis module is used to predict the stress distribution of the model through finite element analysis based on the three-dimensional model of the metal composite forming mold and the die-casting parameters of the mold.

[0043] The model mesh adjustment module is used to change the values of the die-casting parameters and adjust the model mesh multiple times. Each time the die-casting parameters are changed, the model mesh is adjusted once and a finite element analysis is performed again. On the one hand, it avoids obvious errors in the predicted stress caused by unreasonable model mesh distribution (for example, the mesh in some areas of the model is too dense and in some areas is too sparse); on the other hand, it avoids the problem that the model mesh occupies or wastes too much computing power during the process of changing the values of the die-casting parameters multiple times, resulting in too long calculation or being unable to perform more finite element analyses in a short time, thereby ensuring that stress optimization can be carried out from more finite element analysis processes subsequently.

[0044] The die-casting parameter optimization module is used to obtain the optimal die-casting parameters, make them suitable for the current metal composite forming mold, and make it have the optimal stress distribution.

[0045] Specifically:

[0046] The finite element analysis module includes the following steps:

[0047] Step S101: Create a 3D model of the metal composite forming die using computer-aided design software.

[0048] In this embodiment, a 3D model of the die (hereinafter referred to as the model) is designed using computer-aided design software (such as CAD). In other embodiments, software such as Maya or 3dMax can be used to design the 3D model of the die. This embodiment does not make specific limitations. Figure 2 A model of a die is shown, which is designed using CAD.

[0049] The model in this embodiment is composed of triangular mesh elements and mesh points. In other embodiments, the model can be divided into hexahedron or tetrahedron units. In this embodiment, HyperMesh software is used to divide the model mesh. CAD, Maya, HyperMesh, etc. are all existing computer-aided design software, and the principles and operation methods thereof are not described in this embodiment.

[0050] It should be noted that the above-mentioned software related to computer-aided design has been pre-installed in the computer device.

[0051] Step S102: Based on the model of the die and the die-casting parameters of the die, predict the stress distribution of the model through finite element analysis.

[0052] Import the model of the die (for example, a model in STEP or IGES format) into finite element analysis software (such as ANSYS SpaceClaim computer-aided design software), and at the same time set the material properties and boundary conditions.

[0053] As an example, when die-casting zinc alloy, the material properties and boundary conditions refer to the specific heat capacity, thermal conductivity, solid / liquidus temperature, etc. of zinc alloy and the elastic modulus, Poisson's ratio, etc. of die steel. In this embodiment, SKD61 hot work die steel is used for the die-casting die. These materials have good hot hardness, wear resistance and thermal fatigue resistance, and can meet the requirements of zinc alloy die-casting.

[0054] The die-casting parameters in this embodiment refer to three index data: pouring temperature, injection speed, and holding time. In some other embodiments, when die-casting is carried out in stages, the number of stages, the pouring temperature, injection speed, holding time and other index data in each stage can be used together as the die-casting parameters.

[0055] In one embodiment, the initial values of the die-casting parameters are as follows: the pouring temperature is 400 °C, the injection speed is 70 m / s, and the holding time is 1.2 s. In other embodiments, the initial values of the die-casting parameters can be set to other values, which are not specifically limited here. In this embodiment, in order to eliminate the interference of the dimension and order of magnitude of the die-casting parameters, the above initial values in the die-casting parameters are set as the ratio of the pouring temperature of 400 °C to the maximum pouring temperature of 420 °C, the ratio of the injection speed of 70 m / s to the maximum injection speed of 80 m / s, and the ratio of the holding time of 1.2 s to the maximum holding time of 3 s.

[0056] Based on the model of the mold, die-casting parameters, as well as material properties and boundary conditions, the die-casting process of zinc alloy is simulated by finite element analysis software to predict the stress distribution of the mold in the mold-opening stage. The stress distribution described is the stress magnitude at each position point on the model surface.

[0057] In the stress distribution of this embodiment, the position points where the stress is greater than the preset threshold th are defined as target points, and the position points where the stress is less than or equal to the preset threshold th are non-target points. This process divides the stress distribution into two parts for subsequent analysis and processing. One part has relatively high stress (i.e., target points), and these stresses are more likely to cause springback or deformation in the die-cast parts; the other part has relatively low stress (i.e., non-target points), and these stresses are less likely to cause springback or deformation in the die-cast parts. In this embodiment, th = 40 MPa is taken as an example for description. In other embodiments, th can be set to other values, which are not specifically limited in this embodiment.

[0058] As an example, the method for obtaining each position point on the model surface is as follows:

[0059] The model is in a three-dimensional space of a computer three-dimensional design software (such as CAD, Maya, 3DMax). The three-dimensional space is equally divided into cubic grids with equal volumes. In this embodiment, the volume of each cubic grid is 1 cubic millimeter. In other embodiments, the volume of each cubic grid can be set to other values, which are not specifically limited in this embodiment. The center point of the cubic grid passed by the grid of the model is recorded as a position point of the grid of the model (this position point is a three-dimensional coordinate point). In other embodiments, the center point of the model grid within each cubic grid can also be used as the position point of the grid of the model.

[0060] As another example, the method for obtaining each position point on the model surface is as follows:

[0061] Since the mesh of the model is the surface of the model, using the method of model UV unwrapping, the mesh of the three-dimensional model is flattened and rendered into a two-dimensional image. In this embodiment, the size of the flattened and rendered image is 2048×2048. At this time, each pixel point on the image represents each position point on the model surface (which is a two-dimensional coordinate point). Model UV unwrapping is a well-known method and will not be specifically described in this embodiment.

[0062] The model mesh adjustment module is used to change the values of the die-casting parameters and adjust the model mesh multiple times, specifically including:

[0063] Step S201, record the initial die-casting parameters as (that is, the initial value of the die-casting parameters described in step S102), record the initial model as (that is, the model obtained in step S101), and record the initial stress distribution as (that is, the stress distribution obtained in step S102).

[0064] For the convenience of subsequent description, record as the value of the die-casting parameters after the 0th change of the die-casting parameters; record as the model obtained after the 0th adjustment of the model mesh; is recorded as the stress distribution obtained after the 0th change of the die-casting parameter value and adjustment of the model mesh.

[0065] The above process of obtaining , , is recorded as: the process of the 0th change of the die-casting parameter value and adjustment of the model mesh (abbreviated as the 0th process).

[0066] The first change of the die-casting parameter value and adjustment of the model mesh, including the process (abbreviated as the first process):

[0067] On the basis of change the value of , and record the result as , indicating the value of the die-casting parameters after the first change of the die-casting parameters;

[0068] Adjust the mesh density of the model . The specific method is: for the target points and non-target points in (the specific method is shown in step S102), increase the mesh density at the target points and decrease the mesh density at the non-target points on , and record the obtained model as , indicating the model obtained after the first adjustment of the model mesh.

[0069] Then for the model of the mold And the die-casting parameters of the mold Perform finite element analysis to predict the stress distribution of the mold in the mold opening stage , which represents the stress distribution obtained after the first change in the die-casting parameter values and adjustment of the model mesh

[0070] The second change in the die-casting parameter values and adjustment of the model mesh, including the process (abbreviated as the second process):

[0071] At Based on, change The value of, and the result is recorded as , which represents the value of the die-casting parameters after the second change in the die-casting parameters

[0072] Adjust the model The mesh density on, and the specific method is: for The target points and non-target points in (see step S102 for the specific method), at Increase the mesh density at the target points and decrease the mesh density at the non-target points on, and the obtained model is recorded as , which represents the model obtained after the second adjustment of the model mesh

[0073] Then, perform finite element analysis on the mold model And the die-casting parameters of the mold To predict the stress distribution of the mold in the mold opening stage , which represents the stress distribution obtained after the second change in the die-casting parameter values and adjustment of the model mesh

[0074] And so on, perform the process of the i-th change in the die-casting parameter values and adjustment of the model mesh (abbreviated as the i-th process), and the stress distribution and model obtained after the i-th change in the die-casting parameters and adjustment of the model mesh are respectively recorded as 、 , and the obtained die-casting parameters are recorded as .

[0075] Then, the process of the (i + 1)-th change in the die-casting parameter values and adjustment of the model mesh includes (abbreviated as the (i + 1)-th process):

[0076] At Based on, change The value of, and the result is recorded as , which represents the value of the die-casting parameters after the (i + 1)-th change in the die-casting parameters

[0077] Adjust the model The mesh density on, and the specific method is: for The target points and non-target points in (see step S102 for the specific method), at Increase the grid density at the target points and decrease the grid density at non-target points, and the resulting model is denoted as , which represents the model obtained after the (i + 1)-th adjustment of the model grid.

[0078] Then, for the model of the mold and the die-casting parameters of the mold perform finite element analysis to predict the stress distribution of the mold in the mold opening stage , which represents the stress distribution obtained after the (i + 1)-th change of the die-casting parameter values and adjustment of the model grid.

[0079] Subsequently, continue in this way by analogy, and this embodiment will not be elaborated further.

[0080] As an example, the process of changing the die-casting parameter values and adjusting the model grid is set to 50 times, that is, stop after the 50th change of the die-casting parameter values and adjustment of the model grid. In other embodiments, 50 here can be replaced by other values, and this embodiment does not make specific limitations.

[0081] As another example, limit the process of changing the die-casting parameter values and adjusting the model grid multiple times within 8 hours, that is, end after the steps of the model grid adjustment module are executed for 8 hours.

[0082] The specific problem considered above is that in finite element analysis, the grid density of the model significantly affects the accuracy of the stress distribution prediction results. Among them, increasing the grid density can significantly improve the accuracy of the stress distribution, but when the grid density is too high, it will increase the computational amount (i.e., computing power) during finite element analysis. Especially in this embodiment, the die-casting parameter values need to be changed multiple times, and a finite element analysis needs to be performed each time the die-casting parameters are changed. If the grid density is too high, there will be a huge computational amount and it will take a long time.

[0083] In the above process of this step, increase the grid density at the target points in the stress distribution on the model and decrease the grid density at non-target points to achieve real-time adjustment of the model density (or real-time redistribution of the model grid distribution); by increasing the grid density at the target points in the stress distribution (i.e., at the positions with higher stress), improve the accuracy of the finite element analysis results in the stress abnormal area, so that subsequent stress optimization (i.e., in the die-casting parameter optimization module) can avoid the problem that the stress abnormal area (such as the area with larger stress) cannot be detected in time, resulting in the problem that the components die-cast after stress optimization still have stress abnormal areas. By reducing the grid density at non-target points (i.e., at the positions with lower stress), avoid the problem of large computing power consumption and long computing time due to excessive grids in the stress normal area.

[0084] In summary, the above process requires multiple finite element analyses when optimizing stress. In multiple finite element analyses, by reasonably redistributing the density distribution of the model mesh, while ensuring the efficiency of the finite element analysis (that is, reducing the large computing power consumption and computing time), the situation where the stress abnormal area cannot be optimized and removed due to stress prediction errors is avoided.

[0085] Step S202: For the process of changing the values of die-casting parameters and adjusting the model mesh twice in succession, such as the process of changing the values of die-casting parameters and adjusting the model mesh for the i-th and (i + 1)-th times, the following processing is performed:

[0086] (1) First, it should be noted that when performing finite element analysis on the i-th and (i + 1)-th processes, the die-casting parameters and the model mesh density used are different. Therefore and the distribution of target points in may be different, that is the target points in are relative to the target points of which have changed.

[0087] First, obtain relative to the area where the target points have increased, denoted as That is the target points in the area in are increased compared to the target points in in this area, indicating an increase in the target stress (that is, the number of position points with higher stress increases).

[0088] (2) Then, estimate the stress change coefficient a caused by the change of the model mesh, and estimate the stress change coefficient b caused by the change of the die-casting parameters.

[0089] In this regard, it should be noted that this step takes into account that the increase in target points (equivalent to the increase in stress abnormal position points) is caused by the change of the model mesh on the one hand and the die-casting parameters on the other hand; the larger the stress change coefficient a, the more likely it is caused by the change of the model mesh; the larger the stress change coefficient b, the more likely it is caused by the change of the die-casting parameters.

[0090] Furthermore, it should be noted that if within the area where the target points increase (that is, within the area ), the increase in stress is caused by the increase in mesh density, it indicates that the stress change coefficient a is larger; if within the area where the target points increase (that is, within the area ), the mesh density decreases but the stress increases significantly, then it indicates that the stress change coefficient b is larger.

[0091] Therefore, a is positively correlated with the first correlation, and the first correlation refers to within the area where the target points increase (that is, within the area The correlation between the increase in (internal) stress and the increase in mesh density; b is positively correlated with the second correlation, where the second correlation refers to the correlation between the increase in stress within the area where the target points increase (i.e., within the area and the decrease in mesh density.

[0092] Specifically, when the area does not exist, let a = b = 0.25.

[0093] (3) After obtaining the change coefficient a and the change coefficient b, the process of changing the values of the die-casting parameters and adjusting the model mesh for the (i + 2)-th time (referred to as the (i + 2)-th process for short) can be executed, including:

[0094] Based on , change the value of , and record the result as , which represents the value of the die-casting parameters after changing the die-casting parameters for the (i + 2)-th time. Among them, when changing to , the change amount of is positively correlated with b.

[0095] Adjust the mesh density on the model . The specific method is: for the target points and non-target points in , increase the mesh density at the target points and decrease the mesh density at the non-target points on , and record the obtained model as , which represents the model obtained after adjusting the model mesh for the (i + 2)-th time. Among them, when increasing the mesh density at the target points on , the adjustment amount of the mesh density is positively correlated with a.

[0096] In this process, the change amount of is positively correlated with b, indicating that: if it was easier to cause or induce an increase in target points (i.e., it was easier to generate an abnormal stress area) when changing the die-casting parameters previously (i.e., during the i-th and (i + 1)-th processes), then a larger change in is required during the (i + 2)-th process, to avoid wasting excessive computing power and computing time on the finite element analysis process corresponding to multiple changes in the die-casting parameters in a small local range when an abnormal stress area is likely to appear. If it was less likely to cause or induce an increase in target points when changing the die-casting parameters previously (i.e., during the i-th and (i + 1)-th processes), then a smaller change in is required during the (i + 2)-th process, so that when an abnormal stress area is less likely to appear, the die-casting parameters can be adjusted more finely and continuously to ensure that the die-casting parameters with the optimal stress can be obtained comprehensively as a whole later. At this time, sacrificing computing power and computing time is used to avoid the problem that the optimal die-casting parameters cannot be obtained due to a large change in the die-casting parameters.

[0097] The adjustment amount of the grid density is positively correlated with a, indicating that: if it was easier to cause or induce an increase in target points during the previous adjustment of the model grid density (during the i-th and (i + 1)-th adjustments of the model grid density), then a larger increase in the grid density is required. That is, when stress anomaly regions are likely to appear, further redistribution is carried out, such that the increase amount of the grid density is larger. The purpose is to improve the accuracy of stress prediction by increasing the grid density of the model, so as to be able to more accurately distinguish target points and non-target points subsequently, and enable a more reasonable allocation or adjustment of the grid density subsequently. If it was less likely to cause or induce an increase in target points during the previous change of the model grid density (equivalent to no obvious stress anomaly regions), then a smaller increase in the model grid density is required. The reason is that while avoiding wasting excessive computing power and computing time, it can prevent the situation where the grid density is unreasonable during subsequent redistribution or adjustment due to the existence of large stress prediction errors. The situation where the grid density is unreasonable refers to the case where the grid density in the region with large stress (or stress anomaly) is low, resulting in the inability to further accurately predict stress, and the case where the grid density in the region with small stress (or normal stress) is high, resulting in waste of computing power and computing time during finite element analysis.

[0098] So far, the model grid adjustment module makes the following summary:

[0099] H1: The model grid adjustment module continuously changes the values of the die-casting parameters and adjusts the model grid, and continuously obtains the stress distribution. Each process performs a finite element analysis once, and a stress distribution is obtained for each die-casting parameter.

[0100] H2: Continuously changing the values of the die-casting parameters and adjusting the model grid is a recursive process, that is, the next process of changing the values of the die-casting parameters and adjusting the model grid is based on the die-casting parameters, model, and stress distribution obtained in the previous time (after changing the values of the die-casting parameters and adjusting the model grid).

[0101] H3: Each time the values of the die-casting parameters are changed and the model grid is adjusted, the change amount of the die-casting parameters and the increase amount of the model grid are obtained from the variation coefficient a and the variation coefficient b, where the variation coefficient a and the variation coefficient b are obtained from the previous two processes of changing the values of the die-casting parameters and adjusting the model grid;

[0102] For example, the variation coefficient a and the variation coefficient b used in the (i + 2)-th process are obtained from the i-th and (i + 1)-th times. That is:

[0103] The variation coefficient a and the variation coefficient b used in the 2nd process are obtained from the 0th and 1st times; the variation coefficient a and the variation coefficient b used in the 3rd process are obtained from the 1st and 2nd times, and so on.

[0104] Specifically, when the die-casting parameters are changed multiple times and the values of each index data in the die-casting parameters exceed the upper and lower boundaries of the expected value range, then the values of the index data are set to the values corresponding to the upper and lower boundaries of the value range. For example, the pouring temperature is 390 - 420 °C, the injection speed is 30 - 80 m / s, and the holding pressure time is 0.5 - 3 s.

[0105] The die-casting parameter optimization module, and the specific method of this module is:

[0106] After changing the values of the die-casting parameters multiple times, the die-casting parameters when the stress distribution is the smallest are used as the optimized die-casting parameters.

[0107] The optimized die-casting parameters describe that: for the die provided by the finite element analysis module, when die-casting is carried out using this die, die-casting is carried out using the optimized die-casting parameters, so that the obtained die-cast part has less stress, and the die-cast part can meet the production requirements. For example, when the die-cast part is integrally formed with other metal parts, the product integrally formed will not be unqualified due to the area with excessive stress in the die-cast part.

[0108] It should be noted that the optimized die-casting parameters are dimensionless and without magnitude. The index data such as the pouring temperature, injection speed, and holding pressure time in the optimized die-casting parameters need to be multiplied by the maximum pouring temperature, maximum injection speed, and maximum holding pressure time respectively, so as to obtain the die-casting parameters with dimension and magnitude.

[0109] As an example, the minimum stress distribution means:

[0110] Obtain the mean value of the stresses at all position points in the stress distribution, and regard the stress distribution with the smallest mean value as the minimum stress distribution.

[0111] As another example, the minimum stress distribution means:

[0112] Obtain the mean value of the stresses at all target points, and regard the stress distribution with the smallest mean value as the minimum stress distribution.

[0113] Thus, Example 1 ends.

[0114] The present invention provides a comparative example, and the stress optimization method included in this comparative example is:

[0115] Set different die-casting parameters artificially (die-casting parameters refer to pouring temperature, injection speed, and holding pressure time). For example, randomly select 50 die-casting parameters within a pouring temperature range of 390 - 420 °C, an injection speed range of 30 - 80 m / s, and a holding pressure time range of 0.5 - 3 s. Use each die-casting parameter and the model of the same mold to perform finite element analysis respectively, obtain the stress distribution of each die-casting parameter, and finally take the die-casting parameter with the smallest stress distribution as the optimized die-casting parameter.

[0116] This comparative example is an existing or conventional method for stress optimization. The problem with this method is that it is difficult to comprehensively obtain optimized die-casting parameters when the artificially set die-casting parameter distribution is unreasonable or the number of die-casting parameters is small. If the number of artificially set die-casting parameters is large, it will face huge computational amounts and calculation times, especially when each die-casting parameter contains a large number of index dimensions. In summary, this comparative example is difficult to efficiently obtain relatively optimal die-casting parameters.

[0117] Compared with the comparative example, on the one hand, by adjusting the distribution of the model mesh in real time and dynamically, increasing the mesh density at the target points in the stress distribution (i.e., at the positions with higher stress), to improve the accuracy of the finite element analysis results in the stress abnormal area, so as to avoid the problem that the stress abnormal area (such as the area with larger stress) cannot be detected in time during subsequent stress optimization (i.e., in the die-casting parameter optimization module), resulting in stress abnormal areas still existing in the die-cast parts after stress optimization. By reducing the mesh density at non-target points (i.e., at the positions with lower stress), to avoid the problem of large computing power consumption and long calculation time caused by too many meshes in the stress normal area. On the other hand, adjusting the values of the die-casting parameters in real time and dynamically can avoid wasting too much computing power and calculation time in the finite element analysis process corresponding to changing the die-casting parameters multiple times in a small local range when stress abnormal areas are likely to appear; when stress abnormal areas are not likely to appear, it can adjust the die-casting parameters more finely and continuously to ensure that the die-casting parameters with the optimal stress can be obtained comprehensively as a whole. At this time, sacrificing computing power and calculation time to avoid the problem that the optimal die-casting parameters cannot be obtained due to large changes in the die-casting parameters.

[0118] Example Two:

[0119] This example is a further improvement of the model mesh adjustment module in Example One, specifically an improvement in the calculation process of the change coefficient a and the change coefficient b.

[0120] Referring back to Example 1, it can be seen that: if the increase in stress within the region where the target points increase is due to an increase in mesh density, it indicates that the stress change coefficient a is larger; if within the region where the target points increase, the mesh density decreases but the stress increases significantly, then it indicates that the stress change coefficient b is larger. Therefore, a is positively correlated with the first correlation, where the first correlation refers to the correlation between the increase in stress and the increase in mesh density within the region where the target points increase; b is positively correlated with the second correlation, where the second correlation refers to the correlation between the increase in stress and the decrease in mesh density within the region where the target points increase.

[0121] However, the above process in Example 1 may be inaccurate. The so-called inaccurate situation means that: the increase in stress within the region where the target points increase, and the decrease in mesh density but significant increase in stress within the region where the target points increase, these two points may be caused simultaneously by changes in the model mesh and changes in die-casting parameters. At this time, it is difficult to distinguish or clarify whether the increase in target points is caused by changes in the model mesh or changes in die-casting parameters. At this time, the process of changing the values of die-casting parameters and adjusting the model mesh in Example 1 is unreliable (that is, the optimized die-casting parameters obtained in Example 1 are not optimal and the calculation process is not efficient).

[0122] This embodiment takes into account that when the difference between a and b is large, it is very easy to distinguish whether the increase in target points is caused by changes in the model mesh or changes in die-casting parameters. For example, when a is larger than b, it means that the increase in target points is more obviously caused by changes in the model mesh. Even if a part of it is caused by changes in die-casting parameters, its influence is small and can be ignored. That is, at this time, implementing according to the method in Example 1 is reliable (that is, the optimized die-casting parameters obtained in Example 1 are relatively optimal and the calculation process is relatively efficient).

[0123] When the difference between a and b is less than the preset difference, it means that the difference between a and b is not large, indicating that it is difficult to distinguish or clarify whether the increase in target points is caused by changes in the model mesh or changes in die-casting parameters. The reason may be that the preset threshold th set in Example 1 is inaccurate. For example, when the preset threshold th is set too large or too small, it will cause positions or regions that are not abnormal stress in actual production to be regarded as positions or regions of abnormal stress (that is, classified as target points), or it will cause positions or regions that are abnormal stress in actual production not to be regarded as positions or regions of abnormal stress (that is, not classified as target points). As a result, it is difficult to distinguish or clarify whether the increase in target points obtained by a and b in Example 1 is caused by changes in the model mesh or changes in die-casting parameters, and further leads to the situation where the optimized die-casting parameters obtained in Example 1 are relatively inaccurate and the calculation process is relatively inefficient (such as wasting more computing power and taking a longer calculation time).

[0124] In this embodiment, when the difference between a and b is detected to be less than the preset difference, the value of th is changed, and then the model grid adjustment module is used again to recalculate a and b. For the specific process, refer to step (2) of step S202 in Embodiment 1. In this embodiment, the preset difference is taken as 0.18 for description. In other embodiments, it can be set to other values, which is not specifically limited in this embodiment.

[0125] It should be noted that during the process of recalculating a and b using the model grid adjustment module, the changed value of th needs to be used to divide the target points and non-target points.

[0126] For example, when performing the (i + 2)-th change of the die-casting parameter values and adjustment of the model grid, if the difference between a and b is greater than the preset difference, then change the value of th, and then re-obtain the region where the target points increase ; where the method for obtaining the region where the target points increase is the same as that in step S202 of the model grid adjustment module in Embodiment 1, and specifically includes:

[0127] Re-obtain the target points and non-target points in based on the changed th (see step S102 of Embodiment 1 for details), re-obtain the target points and non-target points in based on the changed th, and then obtain the region where there are increased target points relative to , which is denoted as .

[0128] After recalculating a and b, continue to change the die-casting parameter values and adjust the model grid according to step (3) of step S202 in Embodiment 1; for example, after recalculating a and b, when is changed to , the change amount of is positively correlated with the recalculated b; when adjusting the model to , when increasing the grid density at the target points on , the adjustment amount of the grid density is positively correlated with the recalculated a.

[0129] As an example, when it is detected that the difference between a and b is less than the preset difference threshold, the method for changing the value of th includes:

[0130] When the difference between a and b is greater than the preset difference, if the number of target points is greater than the number of non-target points, then increase the value of th, and the increased th is th + ft, where ft represents the change amount of th. If the number of target points is less than or equal to the number of non-target points, then decrease the value of th, and the decreased th is th - ft.

[0131] As an alternative example, the method for obtaining the change amount ft of th is: set ft to 0.1×th.

[0132] As a preferred example, the method for obtaining the change amount ft of th is: set ft to 0.5 - |a - b|×th, where |a - b| represents the difference between a and b, and the smaller this difference is, the larger ft is, indicating that a larger change in th is more needed.

[0133] Specifically, when ft is less than or equal to 0, set ft to 0, and when ft is greater than 0.5×th, set ft = 0.5×th.

[0134] Specifically, if there is no target point or non - target point in this embodiment, instead of calculating a and b, directly change the value of th, and at this time, set the change amount ft of th to 0.1×th.

[0135] In this embodiment, by dynamically adjusting the value of th, when changing the values of die - casting parameters and adjusting the model grid multiple times, it is ensured that the division of target points and non - target points is more reasonable, so that it can be distinguished or clarified whether the increase in target points is caused by the change of the model grid or the change of die - casting parameters, avoiding regarding the positions or regions that do not belong to abnormal stress in actual production as positions or regions of abnormal stress (i.e., dividing them into target points), or not regarding the positions or regions that belong to abnormal stress in actual production as positions or regions of abnormal stress (i.e., not dividing them into target points). This can avoid the problems that the optimized die - casting parameters obtained in the first embodiment are relatively inaccurate and the calculation process is relatively inefficient (for example, wasting more computing power and taking a longer calculation time).

[0136] Embodiment Three:

[0137] In this embodiment, based on Embodiment Two, the following method is further included:

[0138] As an example, after recalculating a and b, the following steps are further included: when the difference between a and b is still less than the preset difference threshold, then recalculate a and b again, and so on, until the difference between a and b is greater than or equal to the preset difference, and then continue to change the values of die - casting parameters and adjust the model grid according to step (3) of step S202 in Embodiment One.

[0139] Furthermore, when after repeating the calculation of a and b several times (for example, after 10 times), the difference between a and b is still less than the preset difference, at this time, obtain the values of a and b when the difference between a and b is the largest among these several times, and for the value of th changed when taking these values of a and b, re - calculate a and b using the model grid adjustment module again.

[0140] Embodiment Four:

[0141] As described in Embodiment 1, when performing the process of changing the values of die-casting parameters and adjusting the model grid for the (i + 2)-th time, the steps included are as follows:

[0142] (1) On the basis of , change the value of , and record the result as . Among them, when changing to , the change amount of is positively correlated with b.

[0143] (2) For the target points and non-target points in , increase the grid density at the target points and decrease the grid density at the non-target points on , and record the obtained model as . Among them, when increasing the grid density at the target points on , the adjustment amount of the grid density is positively correlated with a.

[0144] In this embodiment, as an example, when increasing the grid density at the target points on , the methods included are as follows:

[0145] On the model , obtain the grids where all target points are located, which is denoted as the first grids, that is, each first grid contains at least one target point. Denote the number of the first grids as K1, denote the sum of the areas of all the first grids as S1, and use the following method to increase the grid density at the target points:

[0146] Define a parameter , which represents the adjustment amount of the grid density during the (i + 2)-th process of adjusting the model grid, that is, the average number of grids to be increased per unit area is . Then represents that: it is necessary to expand K1 first grids by grids. Then each first grid needs to be split into grids (N1 needs to be rounded to the nearest integer).

[0147] As an example, the method for splitting each first grid into N1 grids is as follows:

[0148] Randomly generate N1 position points within each first grid, and use the Thiessen polygon algorithm to generate N1 grids based on the N1 position points.

[0149] As another example, the method for splitting each first grid into N1 grids is as follows:

[0150] Randomly generate N1×100 position points within each first grid. Use the K-Means clustering algorithm to cluster the N1×100 position points into N1 categories, obtain the center points of all position points in each category, and get N1 center points. Use the Thiessen polygon algorithm to generate N1 grids based on the N1 center points. In other embodiments, 100 described here can be replaced with other values, and this embodiment does not make specific limitations.

[0151] In this embodiment, as an example, on reduce the grid density at non-target points, and the methods included are:

[0152] On the model obtain all the grids where non-target points are located, denoted as the second grids, that is, each second grid contains at least one non-target point. Denote the number of second grids as K2, and denote the sum of the areas of all second grids as S2. Use the following method to reduce the grid density at non-target points:

[0153] Define a parameter , which represents the reduction amount of grid density in the process of adjusting the model grid for the (i + 2)-th time, that is, the average number of grids to be reduced per unit area is grids. Then represents that: it is necessary to reduce grids from the K2 second grids ( needs to be rounded to an integer), then the reduced number of grids is N2 = K2 - , that is, it is necessary to reduce the K2 second grids to N2 grids.

[0154] In this embodiment , where represents the scaling factor. In this embodiment, it is described with pp = 1 as an example, that is, the reduction amount of grids per unit area is equal to the increase amount of grids per unit area. In other embodiments, pp can also be set to other values, such as 1.2 or 0.8, and this embodiment does not make specific limitations.

[0155] As an example, reducing the K2 second grids to N2 grids includes:

[0156] Obtain all the center points of the K2 second grids to get K2 center points. Use the K-Means clustering algorithm to cluster the K2 center points into N2 categories. Each category contains several grids. Merge all the grids in each category into one grid. For example, delete the common edges and vertices of all the grids in each category, and then merge all the grids in each category into one grid.

[0157] Specifically, when N2 > K2 × 0.5, it means that too many grids are reduced. At this time, let N2 = K2 × 0.5 (rounded to the nearest integer). In other embodiments, 0.5 of the number of times can be set to other values, such as 1.0. This embodiment does not make specific limitations.

[0158] In the above process of this embodiment, by increasing the grid density at the target point and reducing the grid density at non-target points on , the obtained model is denoted as .

[0159] Furthermore, in this embodiment, as an example, when increasing the grid density at the target point on , the adjustment amount of the grid density is positively correlated with a, including:

[0160] where the adjustment amount of the grid density is also the parameter defined above in this embodiment, namely ;

[0161]

[0162] min1 and max1 are two different preset parameters greater than 0, and max1 is greater than min1. In this embodiment, a is greater than or equal to 1 and less than or equal to 0. At this time, represents the minimum value. In this embodiment, taking as an example for description, max1 represents the maximum value. In this embodiment, max1 = 5 is taken as an example for description.

[0163] In other embodiments, min1 and max1 can be set to other values. This embodiment does not make specific limitations.

[0164] In this embodiment, as an example, when changing to , the change amount of is positively correlated with b, including:

[0165] Considering that includes multiple indicators, such as pouring temperature, injection speed, and holding pressure time. All indicators are regarded as a vector (for example, a three-dimensional vector composed of pouring temperature, injection speed, and holding pressure time). At this time, is represented as a vector in the space composed of all indicators, that is, a parameter point in the space.

[0166] Taking the parameter point represented by as the center, a parameter point is randomly selected on the spherical surface with a radius of b × R. This parameter point is used as the value of. Where R is a preset value. b × R represents the change to The change amount. In this embodiment, R = 0.1 is taken as an example for description. In other embodiments, R can be set to other values, as long as it is greater than 0 and less than 1.

[0167] In the above steps of this embodiment, taking the process of changing the values of die-casting parameters and adjusting the model grid for the (i + 2)-th time as an example, the specific adjustment method for the model grid density on the model is given, that is, the specific methods involved in increasing the grid density at the target point, the specific methods involved in reducing the grid density at non-target points, the calculation method of the change amount, and the calculation method of the increase amount of the grid density are described above in this embodiment. The calculation method.

[0168] It should be emphasized again that, as described in H3 in the model grid adjustment module of Embodiment 1, the change coefficient a and the change coefficient b used in the (i + 2)-th process are obtained from the i-th and (i + 1)-th times, that is, the change amount and the increase amount of the grid density depend on the i-th and (i + 1)-th processes. Specifically, the change coefficient a and the change coefficient b used in the first process cannot be obtained, or rather, the change coefficient a and the change coefficient b used in the first process do not need to be obtained. Therefore, in the first process, the change amount and the increase amount of the grid density cannot be obtained by using the above method in this embodiment.

[0169] In this embodiment, in the process of changing the values of die-casting parameters and adjusting the model grid for the first time, the change amount and the increase amount of the grid density are obtained as follows:

[0170] Set the change amount of to 0.5×R, that is, randomly select a parameter point on the spherical surface with a radius of 0.5×R centered on the parameter point represented by . This parameter point is used as the value of , that is, the changed value of .

[0171] The increase amount of the grid density is set to 1. In other embodiments, can also be set to other values, which is not specifically limited in this embodiment.

[0172] Specifically, when the target point is exactly at a grid point, the determination method for the grid where the target point is located is as follows: For all grids sharing this grid point, obtain the center points of these grids, and take the grid with the closest Euclidean distance between the center point and the target point as the grid where the target point is located.

[0173] Example 5:

[0174] As described in Example 1, in the process of changing the values of the die-casting parameters and adjusting the model grid for the i-th and (i + 1)-th times, and were obtained, and relative to the area where the target points increase was obtained, denoted as ; the stress change coefficient a caused by the change of the model grid was estimated, and the stress change coefficient b caused by the change of the die-casting parameters was estimated. a is positively correlated with the first correlation, and the first correlation refers to the correlation between the stress increase amount and the grid density increase amount within the area where the target points increase (i.e., within the area ); b is positively correlated with the second correlation, and the second correlation refers to the correlation between the stress increase amount and the grid density decrease amount within the area where the target points increase (i.e., within the area ).

[0175] In this embodiment, as an example, a is positively correlated with the first correlation, and the first correlation refers to the correlation between the stress increase amount and the grid density increase amount within the area where the target points increase (i.e., within the area ), and the method includes:

[0176] For any position point within the area where the target points increase, the stress of this position point in was obtained, and the stress of this position point in was obtained, and was taken as the stress increase amount of this position.

[0177] For any position point within the area where the target points increase. The grid density of this position point on was obtained, and the grid density of this position point on was obtained, and was taken as the grid point density increase amount of this position point.

[0178] The Pearson correlation coefficient between the stress increase amounts of all positions within the area where the target points increase and the grid point density increase amounts of all positions is denoted as the first correlation; the product of the first correlation and the first preset parameter k1 is denoted as a (note that when a is less than 0, a is set to 0). In this embodiment, k1 = 1 is taken as an example for description, and in other embodiments, k1 can be set to other values, which is not specifically limited in this embodiment.

[0179] As another example, a is positively correlated with the first correlation, and the first correlation refers to the area where the target points increase (i.e., the area The correlation between the increase in (internal) stress and the increase in grid density, including the method:

[0180] Only consider the position points where the increase in grid point density is greater than 1%. The Pearson correlation coefficient between the stress increase at these position points in the area where the target points increase and the increase in grid point density at all positions is denoted as the first correlation.

[0181] Specifically, if there are no position points where the increase in grid point density is greater than 1%, then replace 1% with 0.9%. If there are no position points where the increase in grid point density is greater than 0.9%, then replace 0.9% with 0.8%, and so on until there are some.

[0182] In this embodiment, as an example, b is positively correlated with the second correlation. The second correlation refers to the correlation between the stress increase in the area where the target points increase (i.e., in the area within) and the decrease in grid density, including the method:

[0183] For any position point in the area where the target points increase, obtain the stress at this position point in , obtain the stress at this position point in , and use as the stress increase at this position point.

[0184] For any position point in the area where the target points increase. Obtain the grid density at this position point on , obtain the grid density at this position point on , and use as the grid density decrease at this position point.

[0185] The Pearson correlation coefficient between the stress increases at all positions in the area where the target points increase and the grid density decreases at all positions is denoted as the second correlation. The product of the second correlation and the second preset parameter k2 is denoted as b (note that when b is less than 0.01, set b to 0.01). In this embodiment, k2 = 1 is used for description, and in other embodiments, k2 can be set to other values, which are not specifically limited in this embodiment.

[0186] As an example, b is positively correlated with the second correlation. The second correlation refers to the correlation between the stress increase in the area where the target points increase (i.e., in the area within) and the decrease in grid density, including the method:

[0187] Only consider the position points where the reduction in grid density is greater than 1%. The Pearson correlation coefficient between the increase in stress at these positions within the region where the target points increase and the reduction in grid density at all positions is denoted as the second correlation.

[0188] Specifically, if there are no position points where the reduction in grid density is greater than 1%, then replace 1% with 0.9%. If there are no position points where the reduction in grid density is greater than 0.9%, then replace 0.9% with 0.8%, and so on until there are some.

[0189] As an embodiment, obtain the grid density at this position point , and the method includes:

[0190] Obtain the grid where this position point is located. All grids adjacent to this grid are denoted as the first - order neighborhood grids of this grid (excluding this grid). All grids adjacent to the first - order neighborhood grids are denoted as the second - order neighborhood grids of this grid (excluding the first - order neighborhood grids). All grids adjacent to the second - order neighborhood grids are denoted as the third - order neighborhood grids of this grid (excluding the second - order neighborhood grids), and so on. In this embodiment, obtain the grid set composed of this grid, all its first - order neighborhood grids, and all its second - order neighborhood grids. The ratio of the number of grids in the grid set to the total area of the grids is denoted as the grid density , representing the average number of grids per unit area.

[0191] Specifically, when the average grid density of all position points within the grids where all target points are located is greater than the first preset density value, the grid density at all target points will no longer increase to avoid excessive grid density. In this embodiment, the first preset density value is equal to 1 grid per square millimeter. In other embodiments, the first preset density value can be set to other values, and this embodiment does not make specific limitations. Similarly, when the average grid density of all position points within the grids where all non - target points are located is less than the second preset density value, the grid density at all non - target points will no longer decrease to avoid too small grid density. In this embodiment, the second preset density value is equal to 0.00001 grid per square millimeter. In other embodiments, the first preset density value can be set to other values, and this embodiment does not make specific limitations.

[0192] The above - mentioned are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention. It should be noted that.

Claims

1. A stress optimization system for a metal composite forming die based on finite element analysis, characterized in that, The system includes the following modules: A finite element analysis module, which is used to perform finite element analysis on the model of the die and the die-casting parameters to obtain the stress distribution. The position points where the stress in the stress distribution is greater than and not greater than the preset threshold th are defined as target points and non-target points respectively; A model mesh adjustment module, which is used to change the values of the die-casting parameters and adjust the model mesh multiple times; Among them, the stress distribution and model obtained by changing the die-casting parameters and adjusting the model mesh for the $i$-th time are respectively denoted as , ; the model obtained by adjusting the model mesh for the $(i + 1)$-th time is denoted as ; For the model and the die-casting parameters after the (i + 1)-th change, perform finite element analysis to obtain the stress distribution , and obtain with respect to regions where the target points increase; the stress change coefficient a caused by the change in the model mesh is positively correlated with the correlation between the stress increase amount and the increase amount of the mesh density in the region; the stress change coefficient b caused by the change in the die-casting parameters is positively correlated with the correlation between the stress increase amount and the decrease amount of the mesh density in the region; When the difference between a and b is greater than the preset difference, after changing the value of th, use the model mesh adjustment module to recalculate a and b; When changing the die-casting parameters for the (i + 2)-th time, the change amount of the die-casting parameters is positively correlated with b. The (i + 2)-th adjustment of the model grid includes: adding at the grid density at the target points in, and the grid density adjustment amount is positively correlated with a, and reducing the grid density at the non-target points in; A die-casting parameter optimization module, after changing the values of the die-casting parameters multiple times, takes the die-casting parameters when the stress distribution is the smallest as the optimized die-casting parameters; The step of changing the value of th when the difference between a and b is greater than the preset difference includes the following specific steps: When the difference between a and b is greater than the preset difference, if the number of target points is greater than the number of non-target points, then increase the value of th, and the increased th is th + ft, where ft represents the change amount of th; if the number of target points is less than or equal to the number of non-target points, then decrease the value of th, and the decreased th is th - ft.

2. The stress optimization system for a metal composite forming die based on finite element analysis according to claim 1, wherein The above-mentioned increase on the grid density at the target point in, and the specific steps are as follows: On the model obtain the meshes at all target points in, denoted as the first meshes, denote the number of the first meshes as K1, denote the sum of the areas of all the first meshes as S1, and denote the mesh density adjustment amount when increasing the mesh density at the target points in as , indicating an average increase of meshes per unit area. Let , and split each of the first meshes into N1 meshes, where N1 is rounded to the nearest integer.

3. The stress optimization system for a metal composite forming die based on finite element analysis according to claim 1, characterized in that, The reduction of the grid density at non-target points in, including the following specific steps: On the model obtain all the meshes at non-target points, denoted as the second meshes. Denote the number of the second meshes as K2, denote the sum of the areas of all the second meshes as S2, and denote the reduction amount of the mesh density when reducing the mesh density at non-target points as Let , and N2 = K2 - , where round to the nearest integer, and reduce the K2 second meshes to N2 meshes; The value of is equal to the mesh density adjustment amount.

4. The stress optimization system for a metal composite forming die based on finite element analysis according to claim 1, characterized in that, The stress change coefficient a caused by the change of the model mesh is positively correlated with the correlation between the stress increase amount and the mesh density increase amount in the region, including the following specific steps: For any position point within the area where the target point increases, obtain the stress of this position point in , obtain the stress of this position point in , and use as the stress increment of this position point; obtain the mesh density of this position point on , obtain the mesh density of this position point on , and use as the mesh point density increment of this position point; For all position points where the increase amount of the grid point density is greater than the preset percentage, the Pearson correlation coefficient between the stress increase amount of all position points in the region where the target points increase and the grid point density increase amount is denoted as the first correlation; the product of the first correlation and the first preset parameter k1 is denoted as a.

5. The stress optimization system for the metal composite forming die based on finite element analysis according to claim 1, wherein The stress change coefficient b caused by the change of the die-casting parameters is positively correlated with the correlation between the stress increase amount and the mesh density decrease amount in the region, including the following specific steps: For any position point within the region where the target point increases, obtain the stress of this position point in , obtain the stress of this position point in , and take as the stress increment of this position point; obtain the mesh density of this position point on , obtain the mesh density of this position point on , and take as the mesh density reduction of this position point; For all position points where the decrease amount of the grid point density is greater than the preset percentage, the Pearson correlation coefficient between the stress increase amount of all position points in the region where the target points increase and the mesh density decrease amount is denoted as the second correlation, and the product of the second correlation and the second preset parameter k2 is denoted as b.

6. The stress optimization system for a metal composite forming die based on finite element analysis according to claim 1, wherein The (i + 2)-th change of the die-casting parameters includes the following specific steps: The die-casting parameters obtained after the (i + 1)-th change of the die-casting parameters are denoted as , and the die-casting parameters are regarded as the parameter points composed of the pouring temperature, injection speed, and holding time. Taking the die-casting parameters as the center and as the change amount of the die-casting parameters as the radius, a parameter point is randomly selected on the spherical surface as the die-casting parameters after the (i + 2)-th change.

7. The stress optimization system for a metal composite molding die based on finite element analysis according to claim 1 or 6, characterized in that, The change amount of the die-casting parameters is b×R, where R is a preset value.

8. The stress optimization system for metal composite forming dies based on finite element analysis according to any one of claims 1, 2, or 3, characterized in that The adjustment amount of the mesh density is: min1 + a×(max1 - min1), where min1 represents the preset minimum value of the mesh density adjustment amount, and max1 represents the preset maximum value of the mesh density adjustment amount.

9. The stress optimization system for a metal composite forming die based on finite element analysis according to claim 6, characterized in that, The change amount of th is negatively correlated with |a - b|.

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