Magnesium alloy semi-solid forming method and system

Abstract

The application discloses a magnesium alloy semi-solid forming method and system, and the method comprises the following steps: obtaining target physical parameter data of a magnesium alloy to be processed; extracting general physical parameter data matched with set physical parameter data of a sample magnesium alloy from the target physical parameter data, and determining specific physical parameter data of the target physical parameter data except the general physical parameter data; obtaining general simulation data matched with the general physical parameter data from a simulation database, wherein the simulation database is used for storing semi-solid forming simulation data of the sample magnesium alloy, and the semi-solid forming simulation data is obtained by using CAE to perform semi-solid forming simulation according to the set physical parameter data; and performing semi-solid forming simulation based on the specific physical parameter data and the general simulation data by using CAE to obtain a semi-solid forming simulation result, wherein the simulation result is used for optimizing semi-solid forming process parameters of the magnesium alloy to be processed, so that the simulation operation amount can be reduced, and the simulation operation time length can be shortened.

Description

Technical Field

[0001] This application relates to the field of simulation technology, and in particular to a method and system for semi-solid forming of magnesium alloys. Background Technology

[0002] Magnesium alloys have advantages such as high specific stiffness, high specific strength, light weight, and good resistance to electromagnetic radiation, and are therefore widely used in automobiles, aerospace, and electronic products.

[0003] Semi-solid forming of magnesium alloys is an advanced metal processing technology that combines the advantages of liquid forming (such as die casting) and solid forming (such as forging) to produce high-precision, high-performance magnesium alloy parts at lower forming temperatures.

[0004] In related technologies, to ensure the processing effect of magnesium alloy semi-solid forming, simulation analysis is usually performed on the magnesium alloy semi-solid forming process. This allows for optimization of the process based on the simulation results, achieving better processing results. However, such simulation analysis typically involves a large amount of computation and takes a long time. Summary of the Invention

[0005] To address the aforementioned technical problems, this application proposes a semi-solid forming method and system for magnesium alloys, which can reduce the amount of simulation computation and shorten the simulation computation time.

[0006] In a first aspect, embodiments of this application provide a method for semi-solid forming of magnesium alloys, comprising:

[0007] Obtain the target physical parameter data of the magnesium alloy to be processed;

[0008] From the target physical parameter data, extract the general physical parameter data that matches the set physical parameter data of the sample magnesium alloy, and determine the specific physical parameter data in the target physical parameter data other than the general physical parameter data;

[0009] From the simulation database, obtain general simulation data that matches the general physical parameter data, wherein the simulation database is used to store the semi-solid forming simulation data of the sample magnesium alloy, and the semi-solid forming simulation data is obtained by using computer-aided engineering (CAE) to perform semi-solid forming simulation based on the set physical parameter data.

[0010] Based on the specific physical parameter data and the general simulation data, CAE is used to perform semi-solid forming simulation to obtain the semi-solid forming simulation results of the magnesium alloy to be processed. The semi-solid forming simulation results are used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed.

[0011] Optionally, the target physical parameter data includes first parameter information for each of multiple parameter types, and the set physical parameter data includes second parameter information for each of the multiple parameter types;

[0012] The step of extracting general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data includes:

[0013] For each piece of the first parameter information, determine its parameter type, and determine the similarity between the first parameter information and the second parameter information belonging to the same parameter type.

[0014] Based on the similarity and the preset similarity threshold, the first parameter information of each of the multiple parameter types is filtered;

[0015] The general physical parameter data are determined based at least on the selected first parameter information.

[0016] Optionally, the plurality of parameter types includes a specific parameter type, and the determination of the general physical parameter data based at least on the selected first parameter information includes:

[0017] The general physical parameter data is generated based on the first parameter information that does not belong to the specific parameter type among the selected first parameter information.

[0018] Optionally, the plurality of parameter types include at least one of the following: viscosity, liquidus temperature, solidus temperature, density, specific heat capacity, thermal conductivity, yield stress, and coefficient of thermal expansion;

[0019] Where the plurality of parameter types include at least viscosity, a specific parameter type among the plurality of parameter types includes viscosity.

[0020] Optionally, each of the first parameter information included in the general physical parameter data is contained in the selected first parameter information;

[0021] The step of obtaining general simulation data that matches the general physical parameter data from the simulation database includes:

[0022] For each first parameter information included in the general physical parameter data, in the simulation database, based on the second parameter information whose similarity with the first parameter information is higher than the preset similarity threshold, matching simulation data is retrieved.

[0023] The general simulation data is determined based on the simulation data matched with the first parameter information included in the general physical parameter data.

[0024] Optionally, the step of using CAE to perform semi-solid forming simulation based on the specific physical parameter data and the general simulation data to obtain the semi-solid forming simulation results of the magnesium alloy to be processed includes:

[0025] Based on the general simulation data, determine the initial state information of the CAE simulation;

[0026] Based on the initial state information of the CAE simulation and the specific physical parameter data, a semi-solid forming simulation is performed using CAE to obtain the semi-solid forming simulation results.

[0027] Optionally, the step of using CAE to perform semi-solid forming simulation based on the initial state information of the CAE simulation and the specific physical parameter data, and obtaining the semi-solid forming simulation results, includes:

[0028] Based on the CAE simulation initial state information, the initial conditions of the CAE software are set.

[0029] Based on the specific physical parameter data, the material properties of the set CAE software are updated;

[0030] The updated CAE software was used to perform semi-solid forming simulation, and the semi-solid forming simulation results were obtained.

[0031] Optionally, the step of calling the updated CAE software to perform semi-solid forming simulation includes:

[0032] Select the corresponding incremental simulation mode based on the updated material properties in the material property update;

[0033] The updated CAE software is invoked, and the selected incremental simulation mode is used to perform semi-solid forming simulation.

[0034] Optionally, selecting the corresponding incremental simulation mode based on the updated material properties in the material property update includes:

[0035] Based on the updated material properties in the material property update, the incremental strategy selector is invoked to select the selected incremental simulation mode from multiple incremental simulation modes, wherein the incremental strategy selector is constructed based on a reinforcement learning algorithm.

[0036] Secondly, embodiments of this application provide a magnesium alloy semi-solid forming system, comprising:

[0037] The data acquisition module is used to acquire the target physical parameter data of the magnesium alloy to be processed;

[0038] The data segmentation module is used to extract general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data, and to determine specific physical parameter data other than the general physical parameter data in the target physical parameter data.

[0039] A general simulation data retrieval module is used to retrieve general simulation data that matches the general physical parameter data from a simulation database. The simulation database is used to store the semi-solid forming simulation data of the sample magnesium alloy. The semi-solid forming simulation data is obtained by performing semi-solid forming simulation using computer-aided engineering (CAE) based on the set physical parameter data.

[0040] The simulation module is used to perform semi-solid forming simulation using CAE based on the specific physical parameter data and the general simulation data, and to obtain the semi-solid forming simulation results of the magnesium alloy to be processed. The semi-solid forming simulation results are used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed.

[0041] In summary, the embodiments of this application have at least the following beneficial effects:

[0042] In this embodiment of the application, target physical parameter data of the magnesium alloy to be processed is obtained; general physical parameter data matching the set physical parameter data of the sample magnesium alloy is extracted from the target physical parameter data, and specific physical parameter data other than the general physical parameter data in the target physical parameter data are determined; general simulation data matching the general physical parameter data is obtained from a simulation database, wherein the simulation database is used to store semi-solid forming simulation data of the sample magnesium alloy, and the semi-solid forming simulation data is obtained by performing semi-solid forming simulation using computer-aided engineering (CAE) based on the set physical parameter data; based on the specific physical parameter data and the general simulation data, semi-solid forming simulation is performed using CAE to obtain the semi-solid forming simulation result of the magnesium alloy to be processed, wherein the semi-solid forming simulation result is used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed, thereby reducing the amount of simulation computation and shortening the simulation computation time. Attached Figure Description

[0043] Figure 1 This is a schematic flowchart of the magnesium alloy semi-solid forming method provided in the embodiments of this application;

[0044] Figure 2 This is a schematic diagram of the structure of the magnesium alloy semi-solid forming system provided in the embodiments of this application;

[0045] Figure 3 This is a schematic diagram of the structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments / examples are only a part of the embodiments / examples of this application, and not all of the embodiments / examples. Based on the embodiments / examples in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0047] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more. In the description of this application, the term "comprising" and its variations are open-ended, meaning "including but not limited to." The term "based on" means "at least partially based on." The term "according to" means "at least partially according to." The term "one embodiment / example" means "at least one embodiment / example"; the term "another embodiment / example" means "at least one additional embodiment / example"; the term "some embodiments / examples" means "at least some embodiments / examples."

[0048] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0049] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing specific embodiments only and is not intended to limit the application. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0050] The following explains some terms and concepts used in the embodiments of this application:

[0051] Computer-aided engineering (CAE) is an interdisciplinary numerical simulation and analysis technique that can be used to perform simulation analyses such as fluid dynamics analysis, process simulation, and multiphysics coupling analysis (such as fluid-structure interaction, thermo-mechanical coupling, etc.).

[0052] Firstly, see [the following] Figure 1 The diagram shows a flow chart of a semi-solid forming method for magnesium alloys provided in an embodiment of this application. The method includes steps S101-S104, as detailed below.

[0053] S101, Obtain the target physical parameter data of the magnesium alloy to be processed.

[0054] In some examples, the target physical parameter data may include at least one of the following parameters of the magnesium alloy to be processed: first viscosity information, first liquidus temperature information, first solidus temperature information, first density information, first specific heat capacity information, first thermal conductivity information, first yield stress information, and first thermal expansion coefficient information.

[0055] In some examples, the magnesium alloy to be processed may be a batch of magnesium alloy that is about to be processed by a semi-solid forming process.

[0056] S102, extract general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data, and determine specific physical parameter data other than the general physical parameter data in the target physical parameter data.

[0057] In some examples, this embodiment essentially divides the target physical parameter data into two parts: one part consists of general physical parameter data that matches the set physical parameter data of the sample magnesium alloy, and the other part consists of specific physical parameter data other than the general physical parameter data. It is easy to understand that the general physical parameter data refers to data with a high degree of correlation to the set physical parameter data of the sample magnesium alloy (in other words, it can refer to physical parameters that are highly similar between the magnesium alloy to be processed and the sample magnesium alloy), while the specific physical parameter data is data that is different from the general physical parameter data and has a lower degree of correlation (in other words, it can refer to physical parameters unique to the magnesium alloy to be processed that distinguish it from the sample magnesium alloy).

[0058] In some examples, there may be multiple sample magnesium alloys, each with corresponding set physical parameter data. Thus, the aforementioned general physical parameter data may refer to the data in the target physical parameter data that has a high degree of correlation with the set physical parameter data of at least one of the multiple sample magnesium alloys.

[0059] It is understood that "higher" in any embodiment of this application may mean higher than the corresponding threshold, and / or may mean one or more of the highest values ​​in the descending order.

[0060] In some examples, the degree of association, similarity, and / or similarity in any embodiment of this application can be determined by calculating the cosine similarity of their respective information features.

[0061] S103, Obtain general simulation data matching the general physical parameter data from the simulation database, wherein the simulation database is used to store the semi-solid forming simulation data of the sample magnesium alloy, and the semi-solid forming simulation data is obtained by performing semi-solid forming simulation using computer-aided engineering (CAE) based on the set physical parameter data.

[0062] In some examples, the simulation database described above pre-stores semi-solid forming simulation data of sample magnesium alloys, which may include at least one standard magnesium alloy and / or at least one processed magnesium alloy, which refers to a magnesium alloy that was previously subjected to semi-solid forming processing.

[0063] In some examples, the semi-solid forming simulation data for each sample magnesium alloy can be obtained by directly calling CAE software to perform semi-solid forming simulation based on the set physical parameter data for each sample magnesium alloy. Here, for the standard magnesium alloy, since it serves as the simulation benchmark, the set physical parameter data of the standard magnesium alloy can be used directly to perform a complete semi-solid forming simulation, so that the generated semi-solid forming simulation data is more accurate. For the processed magnesium alloy, since it is also the magnesium alloy to be processed previously, the semi-solid forming simulation process for it can refer to the various embodiments of semi-solid forming simulation for magnesium alloys to be processed in this application, and the semi-solid forming simulation results corresponding to the processed magnesium alloy are used as the corresponding semi-solid forming simulation data. Thus, each obtained semi-solid forming simulation result can be stored in the simulation database to facilitate the reuse of simulation results and enrich the data content of the database. At this time, the method may also include: storing the obtained semi-solid forming simulation results of the magnesium alloy to be processed into the simulation database.

[0064] S104. Based on the specific physical parameter data and the general simulation data, CAE is used to perform semi-solid forming simulation to obtain the semi-solid forming simulation results of the magnesium alloy to be processed. The semi-solid forming simulation results are used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed.

[0065] In this embodiment, the target physical parameter data of the magnesium alloy to be processed is first divided into general physical parameter data that matches the sample magnesium alloy and specific physical parameter data other than the general physical parameter data. Then, general simulation data that matches the general physical parameter data is obtained from the pre-stored semi-solid forming simulation data. Combined with the specific physical parameter data, CAE is used to perform semi-solid forming simulation. This allows the general simulation data to be reused directly during simulation, avoiding the need for CAE to perform a complete semi-solid forming simulation of the magnesium alloy to be processed based on the target physical parameter data. This reduces the amount of simulation computation and shortens the simulation time, so as to complete the simulation quickly and improve the production efficiency of magnesium alloy semi-solid forming processing.

[0066] In some examples, CAE software can be used to build a CAE geometric model (which may include structures such as molds and blanks) based on a semi-solid forming scenario (such as die casting and extrusion). The CAE input data obtained by integrating general simulation data with specific physical parameter data is assigned to the CAE geometric model, and the multiphysics coupling simulation function in the CAE software is called to perform semi-solid forming simulation on the assigned CAE geometric model to obtain the above-mentioned semi-solid forming simulation results.

[0067] Among them, the above-mentioned multiphysics coupling simulation function can be used to simulate at least one of the following: flow filling process (can be used to simulate the flow front of magnesium alloy slurry in the mold cavity and / or whether the filling is complete), thermodynamic behavior (can be used to simulate the temperature field change, solidification process, potential hot spots and / or shrinkage defects, etc. during the molding process), stress / strain field (can be used to simulate the corresponding thermal stress distribution, residual stress and / or deformation (warping) during the part cooling process, etc.).

[0068] In some examples, the above semi-solid forming simulation results may include at least one of the following: defect diagnosis results (which can be used to identify the location and severity of defects such as inadequate filling, air entrapment and / or shrinkage cavities in the part formed after the semi-solid forming simulation of the magnesium alloy to be processed), and field variable distribution results (which can be used to display the distribution cloud map of the corresponding temperature, pressure, velocity and / or stress in the part formed after the semi-solid forming simulation of the magnesium alloy to be processed).

[0069] In some examples, the semi-solid forming process parameters of the magnesium alloy to be processed can be specifically optimized and adjusted based on the semi-solid forming simulation results. For instance, if the defect diagnosis results indicate insufficient filling, the injection speed, mold temperature, and / or gate design in the semi-solid forming process parameters can be adjusted; if the defect diagnosis results indicate shrinkage cavities or porosity, the holding pressure and / or holding time in the semi-solid forming process parameters can be optimized. In specific adjustments, the controlled variable method can be used to first randomly adjust one parameter corresponding to the existing problem and then obtain new semi-solid forming simulation results. By judging whether the problem still exists or has been improved in the new semi-solid forming simulation results, it can be determined whether the adjustment of that parameter is appropriate, thereby optimizing that parameter and further optimizing the entire semi-solid forming process parameters. In this embodiment, it is easy to understand that since the process parameters need to be optimized by controlling variables, that is, multiple simulations are required. At this time, the problems of large amount of computation and long computation time in related technologies become more prominent. Accordingly, in this case, this embodiment can significantly reduce the amount of simulation computation and shorten the simulation computation time, thereby greatly improving the production efficiency of magnesium alloy semi-solid forming processing.

[0070] In one optional implementation, the target physical parameter data includes first parameter information for each of multiple parameter types, and the set physical parameter data includes second parameter information for each of the multiple parameter types;

[0071] The step of extracting general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data includes:

[0072] For each piece of the first parameter information, determine its parameter type, and determine the similarity between the first parameter information and the second parameter information belonging to the same parameter type.

[0073] Based on the similarity and the preset similarity threshold, the first parameter information of each of the multiple parameter types is filtered;

[0074] The general physical parameter data are determined based at least on the selected first parameter information.

[0075] In some examples, the selected first parameter information can be directly used as general physical parameter data.

[0076] In some examples, there can be N types of sample magnesium alloys, where N is an integer greater than 1. Each sample magnesium alloy has corresponding set physical parameter data. Thus, the set physical parameter data for each sample magnesium alloy contains multiple second parameter information corresponding to that sample magnesium alloy. These multiple second parameter information correspond one-to-one with the aforementioned multiple parameter types. Furthermore, for each first parameter information, under its respective parameter type, each sample magnesium alloy can have one second parameter information belonging to the aforementioned parameter type. Therefore, this first parameter information can correspond to N similarities.

[0077] Following the previous example, the above-mentioned filtering of the first parameter information of each of the multiple parameter types based on the similarity and the preset similarity threshold may include: for any one of the first parameter information of each of the multiple parameter types, if at least one of the N similarities corresponding to it is greater than the preset similarity threshold, then the first parameter information is added to the filtered first parameter information; if none of the N similarities corresponding to the first parameter information is greater than the preset similarity threshold, then the first parameter information is not added to the filtered first parameter information.

[0078] In some examples, there may be only one type of magnesium alloy, such as a standard magnesium alloy. Therefore, each of the first parameter information corresponds to a similarity score. Thus, the above-mentioned filtering of the first parameter information of each of the multiple parameter types based on the similarity score and a preset similarity threshold may include: filtering out the first parameter information of each of the multiple parameter types whose similarity score is greater than the preset similarity threshold.

[0079] In one optional implementation, the plurality of parameter types includes a specific parameter type, and the determination of the general physical parameter data based at least on the selected first parameter information includes:

[0080] The general physical parameter data is generated based on the first parameter information that does not belong to the specific parameter type among the selected first parameter information.

[0081] In some examples, the first parameter information that does not belong to the specific parameter type among the filtered first parameter information can be used to form the general physical parameter data.

[0082] In this embodiment, since some specific parameter types are extremely important for the semi-solid molding process, if the first parameter information belonging to this specific parameter type is directly used as general physical parameter data, thereby excluding the first parameter information belonging to this specific parameter type from the specific physical parameter data, it will have a certain impact on the accuracy and reliability of the final semi-solid molding simulation results. Therefore, this embodiment specifically sets up a method to exclude the first parameter information belonging to this specific parameter type from the basis for generating general physical parameter data, thereby improving the accuracy and reliability of the final semi-solid molding simulation results.

[0083] It is understood that in this embodiment, if there is no first parameter information that does not belong to the specific parameter type among the first parameter information selected, the general physical parameter data can be generated directly based on the first parameter information selected, or further, the general physical parameter data can be directly constructed from the first parameter information selected.

[0084] In one alternative implementation, the plurality of parameter types include at least one of the following: viscosity, liquidus temperature, solidus temperature, density, specific heat capacity, thermal conductivity, yield stress, and coefficient of thermal expansion.

[0085] Where the plurality of parameter types include at least viscosity, a specific parameter type among the plurality of parameter types includes viscosity.

[0086] In this embodiment, viscosity is extremely important for semi-solid molding processes because it can affect the shear energy consumption of slurry preparation, determine injection pressure and / or filling capacity, indirectly affect solidification behavior (e.g., high viscosity limits convection and changes the temperature field), and even affect defect formation (e.g., high viscosity makes it difficult to vent and easily forms pores). However, if the first parameter information corresponding to viscosity is directly used as general physical parameter data, thereby excluding the first parameter information belonging to that viscosity from the specific physical parameter data, the final semi-solid molding simulation results will have certain errors in shear energy consumption, injection pressure and / or filling capacity, solidification behavior, and defect formation in slurry preparation. Therefore, this embodiment specifically excludes the first parameter information belonging to that viscosity from the generation basis of general physical parameter data, thereby reducing the simulation computation load and shortening the simulation computation time, while also reducing the errors in the final semi-solid molding simulation results in shear energy consumption, injection pressure and / or filling capacity, solidification behavior, and defect formation in slurry preparation.

[0087] It should be noted that the liquidus temperature (Tliquid) can be used to determine the initial heating / cooling temperature; generally, the slurry needs to be below Tliquid to form a solid phase. The solidus temperature (Tsolid) can be used to determine the temperature at which complete solidification occurs; the difference between Tliquid and Tsolid affects the solidification range and slurry stability. Density can be used to calculate flow inertia, gravitational settling, and filling rate. Specific heat capacity can be used to determine the energy required for heating / cooling the material, affecting heating and cooling rates. Thermal conductivity can be used to determine the rate of heat transfer, facilitating the determination of solidification rate and temperature field distribution. Viscosity can be used to determine slurry flowability, filling capacity, and pressure requirements. Yield stress can be used to determine the non-Newtonian properties specific to semi-solid slurries; generally, below this yield stress, the slurry behaves as a solid, while above it, it begins to flow; it can be used to determine the initial injection pressure. The coefficient of thermal expansion can be used to determine the shrinkage behavior during cooling, which is related to residual stress and deformation.

[0088] In some examples, the first parameter information for each of the above parameter types includes at least one of the following: first viscosity information, first liquidus temperature information, first solidus temperature information, first density information, first specific heat capacity information, first thermal conductivity information, first yield stress information, and first thermal expansion coefficient information.

[0089] In some examples, the second parameter information for each of the above parameter types includes at least one of the following: second viscosity information, second liquidus temperature information, second solidus temperature information, second density information, second specific heat capacity information, second thermal conductivity information, second yield stress information, and second thermal expansion coefficient information.

[0090] In one optional implementation, each of the first parameter information included in the general physical parameter data is contained in the filtered first parameter information;

[0091] The step of obtaining general simulation data that matches the general physical parameter data from the simulation database includes:

[0092] For each first parameter information included in the general physical parameter data, in the simulation database, based on the second parameter information whose similarity with the first parameter information is higher than the preset similarity threshold, matching simulation data is retrieved.

[0093] The general simulation data is determined based on the simulation data matched with the first parameter information included in the general physical parameter data.

[0094] In some examples, there can be N types of sample magnesium alloys, where N is an integer greater than 1. Each sample magnesium alloy has corresponding set physical parameter data. Thus, the set physical parameter data for each sample magnesium alloy contains multiple second parameter information corresponding to that sample magnesium alloy. These multiple second parameter information correspond one-to-one with the aforementioned multiple parameter types. Furthermore, for each first parameter information, under its respective parameter type, each sample magnesium alloy can have one second parameter information belonging to the aforementioned parameter type. Therefore, this first parameter information can correspond to N similarities.

[0095] Continuing with the previous example, for each first parameter information included in the general physical parameter data, among the N similarities corresponding to that first parameter information, M may have similarities higher than a preset similarity threshold. Thus, the second parameter information matched by that first parameter information can be M second parameter information entries corresponding one-to-one with the aforementioned M similarities, where 0 ≤ M ≤ N. When M is 0, it indicates that none of the N similarities corresponding to that first parameter information are greater than the preset similarity threshold.

[0096] In some examples, determining the general simulation data based on the simulation data matched by the first parameter information included in the general physical parameter data may include: constructing the general simulation data by matching the simulation data matched by the first parameter information included in the general physical parameter data.

[0097] In one optional implementation, the step of using CAE to perform semi-solid forming simulation based on the specific physical parameter data and the general simulation data to obtain the semi-solid forming simulation results of the magnesium alloy to be processed includes:

[0098] Based on the general simulation data, determine the initial state information of the CAE simulation;

[0099] Based on the initial state information of the CAE simulation and the specific physical parameter data, a semi-solid forming simulation is performed using CAE to obtain the semi-solid forming simulation results.

[0100] In some examples, the aforementioned CAE simulation initial state information may include at least one of the following: initial field conditions and a reference solution. The aforementioned general simulation data can be used to characterize the temperature field distribution, velocity field distribution, pressure field distribution, and / or the boundary conditions of the CAE geometric model. Thus, the aforementioned temperature field distribution, velocity field distribution, pressure field distribution, and / or the boundary conditions of the CAE geometric model can be used as the aforementioned initial field conditions. The reference solution can be used to indicate the reference value of the aforementioned initial field conditions. Both the initial field conditions and the reference solution can be obtained by analyzing the temperature field distribution, velocity field distribution, pressure field distribution, and / or the boundary conditions of the CAE geometric model characterized by the general simulation data. Alternatively, since the general simulation data originates from the semi-solid forming simulation data of the sample magnesium alloy, the initial field conditions and the reference solution can actually be recorded in the semi-solid forming simulation data of the sample magnesium alloy.

[0101] In one optional implementation, the step of performing semi-solid forming simulation using CAE based on the initial state information of the CAE simulation and the specific physical parameter data, and obtaining the semi-solid forming simulation results, includes:

[0102] Based on the CAE simulation initial state information, the initial conditions of the CAE software are set.

[0103] Based on the specific physical parameter data, the material properties of the set CAE software are updated;

[0104] The updated CAE software was used to perform semi-solid forming simulation, and the semi-solid forming simulation results were obtained.

[0105] In some examples, the aforementioned general simulation data can be used to characterize the temperature field distribution, velocity field distribution, pressure field distribution, and / or the boundary conditions of the CAE geometric model. Thus, these boundary conditions can be used as the initial field conditions to set the initial conditions for the CAE software. In this embodiment, by setting the initial conditions for the CAE software, it is possible to avoid the CAE software starting its calculations from a cold start, thereby significantly improving the computation / convergence speed of the CAE software in subsequent simulation operations.

[0106] If the initial state information of the CAE simulation includes the reference solution, the initial conditions of the CAE software can be set according to the reference solution. Here, the reference solution refers to the semi-solid forming reference simulation result corresponding to the magnesium alloy to be processed. In other words, it is equivalent to allowing the set CAE software to start calculation from a simulation result used for reference. It should be understood that the general simulation starts from zero and goes through multiple iterations to approximate the optimal solution to obtain the final simulation result. However, this embodiment allows the set CAE software to start calculation from a simulation result used for reference, avoiding multiple iterations of simulation from zero in the early stage, thereby greatly improving the calculation / convergence speed of the CAE software during simulation calculation.

[0107] Furthermore, the reference solution can be used to indicate reference values ​​for different field distributions (e.g., temperature field distribution, velocity field distribution, and / or pressure field distribution). Since semi-solid forming involves strong nonlinear problems such as non-Newtonian fluids, phase changes, and multiphase flows, general numerical solutions are prone to divergence. Therefore, this embodiment can provide a physically self-consistent field distribution by setting a reasonable reference solution, thereby avoiding numerical oscillations or divergences caused by unreasonable initial guesses, thus improving the simulation success rate and reducing the number of times parameters need to be adjusted due to non-convergence.

[0108] In some examples, the updated material property in the above material property update may refer to the material property value of the first parameter information of each parameter type contained in the specific physical parameter data.

[0109] In some examples, the CAE software selected in this application embodiment is generally required to support complex physical models such as non-Newtonian fluids, multiphase flow, and phase change heat transfer in order to better realize semi-solid forming simulation. For example, the CAE software may include at least one of the following: ANSYS Fluent (customizable rheological model), Polyflow (customizable rheological model), MAGMAsoft (casting-specific software with built-in semi-solid forming module), ProCAST (supports multiphysics simulation of die casting and semi-solid forming), and COMSOL Multiphysics (semi-solid model can be customized through its CFD and heat transfer modules).

[0110] In one optional implementation, the step of calling the updated CAE software to perform semi-solid forming simulation includes:

[0111] Select the corresponding incremental simulation mode based on the updated material properties in the material property update;

[0112] The updated CAE software is invoked, and the selected incremental simulation mode is used to perform semi-solid forming simulation.

[0113] In some examples, the selected incremental simulation mode can be chosen from multiple incremental simulation modes based on the updated material properties in the material property update. Here, the multiple incremental simulation modes may include short-term transient simulation incremental simulation mode, steady-state restart incremental simulation mode, and / or sensitivity analysis incremental simulation mode. Specifically, when process conditions are similar and only material fine-tuning is required, the steady-state restart incremental simulation mode can be selected. This mode instructs the simulation to be re-run using the initial field conditions characterized by the initial conditions of the CAE software as the initial field, considering the values ​​of the fine-tuned material properties, to observe the impact of changes in the values ​​of the fine-tuned material properties on the steady state. When there are significant material differences, the short-term transient simulation incremental simulation mode can be selected. This mode instructs the simulation to be re-run using the adjusted material property values, starting from the injection stage of the semi-solid molding process. During the re-simulation, the temperature field distribution can be derived from general simulation data, ensuring that the temperature field distribution in the set CAE software remains unchanged. When material differences are concentrated in a few parameters, a sensitivity analysis incremental simulation mode can be selected. This sensitivity analysis incremental simulation mode can be used to indicate that the above-mentioned short-term transient simulation incremental simulation mode should be run first to analyze the sensitivity of the few parameters to key simulation results (such as maximum pressure and filling time), and then the general simulation data can be directly corrected accordingly to obtain the semi-solid forming simulation results.

[0114] In one optional implementation, selecting the corresponding incremental simulation mode based on the updated material properties in the material property update includes:

[0115] Based on the updated material properties in the material property update, the incremental strategy selector is invoked to select the selected incremental simulation mode from multiple incremental simulation modes, wherein the incremental strategy selector is constructed based on a reinforcement learning algorithm.

[0116] In some examples, the incremental policy selector can be pre-trained using reinforcement learning algorithms (such as the A3C algorithm) to analyze the updated material properties during material property updates. This allows it to identify which of the following scenarios—"similar process conditions with only minor material adjustments," "significant material differences," or "material differences concentrated in a few parameters"—is more likely to be selected, thus choosing the corresponding incremental simulation mode. It is easy to understand that the specific process of training using reinforcement learning algorithms is a mature and widely used technique, and will not be elaborated upon here.

[0117] Secondly, correspondingly, the embodiments of this application also provide a magnesium alloy semi-solid forming system, which can realize all the processes of the magnesium alloy semi-solid forming method provided in the above embodiments.

[0118] See Figure 2 The diagram shows a schematic representation of a magnesium alloy semi-solid forming system provided in an embodiment of this application. The magnesium alloy semi-solid forming system includes:

[0119] Data acquisition module 201 is used to acquire the target physical parameter data of the magnesium alloy to be processed;

[0120] The data segmentation module 202 is used to extract general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data, and to determine specific physical parameter data other than the general physical parameter data in the target physical parameter data.

[0121] The general simulation data retrieval module 203 is used to retrieve general simulation data that matches the general physical parameter data from the simulation database. The simulation database is used to store the semi-solid forming simulation data of the sample magnesium alloy. The semi-solid forming simulation data is obtained by performing semi-solid forming simulation using computer-aided engineering (CAE) based on the set physical parameter data.

[0122] The simulation module 204 is used to perform semi-solid forming simulation using CAE based on the specific physical parameter data and the general simulation data, and to obtain the semi-solid forming simulation results of the magnesium alloy to be processed. The semi-solid forming simulation results are used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed.

[0123] In one optional implementation, the target physical parameter data includes first parameter information for each of multiple parameter types, and the set physical parameter data includes second parameter information for each of the multiple parameter types;

[0124] The step of extracting general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data includes:

[0125] For each piece of the first parameter information, determine its parameter type, and determine the similarity between the first parameter information and the second parameter information belonging to the same parameter type.

[0126] Based on the similarity and the preset similarity threshold, the first parameter information of each of the multiple parameter types is filtered;

[0127] The general physical parameter data are determined based at least on the selected first parameter information.

[0128] In one optional implementation, the plurality of parameter types includes a specific parameter type, and the determination of the general physical parameter data based at least on the selected first parameter information includes:

[0129] The general physical parameter data is generated based on the first parameter information that does not belong to the specific parameter type among the selected first parameter information.

[0130] In one alternative implementation, the plurality of parameter types include at least one of the following: viscosity, liquidus temperature, solidus temperature, density, specific heat capacity, thermal conductivity, yield stress, and coefficient of thermal expansion.

[0131] Where the plurality of parameter types include at least viscosity, a specific parameter type among the plurality of parameter types includes viscosity.

[0132] In one optional implementation, each of the first parameter information included in the general physical parameter data is contained in the filtered first parameter information;

[0133] The step of obtaining general simulation data that matches the general physical parameter data from the simulation database includes:

[0134] For each first parameter information included in the general physical parameter data, in the simulation database, based on the second parameter information whose similarity with the first parameter information is higher than the preset similarity threshold, matching simulation data is retrieved.

[0135] The general simulation data is determined based on the simulation data matched with the first parameter information included in the general physical parameter data.

[0136] In one optional implementation, the step of using CAE to perform semi-solid forming simulation based on the specific physical parameter data and the general simulation data to obtain the semi-solid forming simulation results of the magnesium alloy to be processed includes:

[0137] Based on the general simulation data, determine the initial state information of the CAE simulation;

[0138] Based on the initial state information of the CAE simulation and the specific physical parameter data, a semi-solid forming simulation is performed using CAE to obtain the semi-solid forming simulation results.

[0139] In one optional implementation, the step of performing semi-solid forming simulation using CAE based on the initial state information of the CAE simulation and the specific physical parameter data, and obtaining the semi-solid forming simulation results, includes:

[0140] Based on the CAE simulation initial state information, the initial conditions of the CAE software are set.

[0141] Based on the specific physical parameter data, the material properties of the set CAE software are updated;

[0142] The updated CAE software was used to perform semi-solid forming simulation, and the semi-solid forming simulation results were obtained.

[0143] In one optional implementation, the step of calling the updated CAE software to perform semi-solid forming simulation includes:

[0144] Select the corresponding incremental simulation mode based on the updated material properties in the material property update;

[0145] The updated CAE software is invoked, and the selected incremental simulation mode is used to perform semi-solid forming simulation.

[0146] In one optional implementation, selecting the corresponding incremental simulation mode based on the updated material properties in the material property update includes:

[0147] Based on the updated material properties in the material property update, the incremental strategy selector is invoked to select the selected incremental simulation mode from multiple incremental simulation modes, wherein the incremental strategy selector is constructed based on a reinforcement learning algorithm.

[0148] Thirdly, embodiments of this application provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above-mentioned embodiments.

[0149] Fourthly, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method described in any of the above-described embodiments.

[0150] Fifthly, embodiments of this application provide a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the preceding claims.

[0151] See Figure 3 The computer device in this embodiment includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301, such as a magnesium alloy semi-solid forming program. When the processor 301 executes the computer program, it implements the steps in the various magnesium alloy semi-solid forming method embodiments described above, for example... Figure 1 The steps S101-S104 are shown.

[0152] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0153] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0154] The processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 301 can be any conventional processor. The processor 301 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0155] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0156] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a non-transitory computer-readable storage medium. When the computer program is executed by the processor 301, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0157] In summary, the embodiments of this application have at least the following beneficial effects:

[0158] In this embodiment of the application, target physical parameter data of the magnesium alloy to be processed is obtained; general physical parameter data matching the set physical parameter data of the sample magnesium alloy is extracted from the target physical parameter data, and specific physical parameter data other than the general physical parameter data in the target physical parameter data are determined; general simulation data matching the general physical parameter data is obtained from a simulation database, wherein the simulation database is used to store semi-solid forming simulation data of the sample magnesium alloy, and the semi-solid forming simulation data is obtained by performing semi-solid forming simulation using computer-aided engineering (CAE) based on the set physical parameter data; based on the specific physical parameter data and the general simulation data, semi-solid forming simulation is performed using CAE to obtain the semi-solid forming simulation result of the magnesium alloy to be processed, wherein the semi-solid forming simulation result is used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed, thereby reducing the amount of simulation computation and shortening the simulation computation time.

[0159] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware platforms, or it can be implemented entirely by hardware. Based on this understanding, all or part of the technical solutions of this application that contribute to the background technology can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0160] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A semi-solid forming method for magnesium alloys, characterized in that, include: Obtain the target physical parameter data of the magnesium alloy to be processed; From the target physical parameter data, extract general physical parameter data that matches the set physical parameter data of the sample magnesium alloy, and determine specific physical parameter data in the target physical parameter data other than the general physical parameter data; From the simulation database, obtain general simulation data that matches the general physical parameter data, wherein the simulation database is used to store the semi-solid forming simulation data of the sample magnesium alloy, and the semi-solid forming simulation data is obtained by using computer-aided engineering (CAE) to perform semi-solid forming simulation based on the set physical parameter data. Based on the specific physical parameter data and the general simulation data, CAE is used to perform semi-solid forming simulation to obtain the semi-solid forming simulation results of the magnesium alloy to be processed. The semi-solid forming simulation results are used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed. The step of using CAE to perform semi-solid forming simulation based on the specific physical parameter data and the general simulation data to obtain the semi-solid forming simulation result of the magnesium alloy to be processed includes: determining the initial state information of CAE simulation based on the general simulation data; and using CAE to perform semi-solid forming simulation based on the initial state information of CAE simulation and the specific physical parameter data to obtain the semi-solid forming simulation result. The step of performing semi-solid forming simulation using CAE based on the initial state information of the CAE simulation and the specific physical parameter data to obtain the semi-solid forming simulation result includes: setting the initial conditions of the CAE software based on the initial state information of the CAE simulation; updating the material properties of the set CAE software based on the specific physical parameter data; and calling the updated CAE software to perform semi-solid forming simulation to obtain the semi-solid forming simulation result. The step of calling the updated CAE software to perform semi-solid forming simulation includes: selecting the corresponding incremental simulation mode according to the updated material properties in the material property update; calling the updated CAE software and performing semi-solid forming simulation using the selected incremental simulation mode. The step of selecting the corresponding incremental simulation mode based on the updated material properties in the material property update includes: calling an incremental strategy selector based on the updated material properties in the material property update to select the selected incremental simulation mode from multiple incremental simulation modes, wherein the incremental strategy selector is constructed based on a reinforcement learning algorithm.

2. The method according to claim 1, characterized in that, The target physical parameter data includes first parameter information for each of multiple parameter types, and the set physical parameter data includes second parameter information for each of the multiple parameter types. The step of extracting general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data includes: For each piece of the first parameter information, determine its parameter type, and determine the similarity between the first parameter information and the second parameter information belonging to the same parameter type. Based on the similarity and the preset similarity threshold, the first parameter information of each of the multiple parameter types is filtered; The general physical parameter data are determined based at least on the selected first parameter information.

3. The method according to claim 2, characterized in that, The plurality of parameter types includes a specific parameter type, and the determination of the general physical parameter data based at least on the selected first parameter information includes: The general physical parameter data is generated based on the first parameter information that does not belong to the specific parameter type among the selected first parameter information.

4. The method according to claim 2 or 3, characterized in that, The multiple parameter types include at least one of the following: viscosity, liquidus temperature, solidus temperature, density, specific heat capacity, thermal conductivity, yield stress, and coefficient of thermal expansion; Where the plurality of parameter types include at least viscosity, a specific parameter type among the plurality of parameter types includes viscosity.

5. The method according to claim 2, characterized in that, Each of the first parameter information included in the general physical parameter data is contained in the selected first parameter information; The step of obtaining general simulation data that matches the general physical parameter data from the simulation database includes: For each first parameter information included in the general physical parameter data, in the simulation database, based on the second parameter information whose similarity with the first parameter information is higher than the preset similarity threshold, matching simulation data is retrieved. The general simulation data is determined based on the simulation data matched with the first parameter information included in the general physical parameter data.

6. A magnesium alloy semi-solid forming system, characterized in that, include: The data acquisition module is used to acquire the target physical parameter data of the magnesium alloy to be processed; The data segmentation module is used to extract general physical parameter data that matches the set physical parameter data of the sample magnesium alloy from the target physical parameter data, and to determine specific physical parameter data other than the general physical parameter data in the target physical parameter data. A general simulation data retrieval module is used to retrieve general simulation data that matches the general physical parameter data from a simulation database. The simulation database is used to store the semi-solid forming simulation data of the sample magnesium alloy. The semi-solid forming simulation data is obtained by performing semi-solid forming simulation using computer-aided engineering (CAE) based on the set physical parameter data. The simulation module is used to perform semi-solid forming simulation using CAE based on the specific physical parameter data and the general simulation data, and to obtain the semi-solid forming simulation results of the magnesium alloy to be processed. The semi-solid forming simulation results are used to optimize the semi-solid forming process parameters of the magnesium alloy to be processed. The step of using CAE to perform semi-solid forming simulation based on the specific physical parameter data and the general simulation data to obtain the semi-solid forming simulation result of the magnesium alloy to be processed includes: determining the initial state information of CAE simulation based on the general simulation data; and using CAE to perform semi-solid forming simulation based on the initial state information of CAE simulation and the specific physical parameter data to obtain the semi-solid forming simulation result. The step of performing semi-solid forming simulation using CAE based on the initial state information of the CAE simulation and the specific physical parameter data to obtain the semi-solid forming simulation result includes: setting the initial conditions of the CAE software based on the initial state information of the CAE simulation; updating the material properties of the set CAE software based on the specific physical parameter data; and calling the updated CAE software to perform semi-solid forming simulation to obtain the semi-solid forming simulation result. The step of calling the updated CAE software to perform semi-solid forming simulation includes: selecting the corresponding incremental simulation mode according to the updated material properties in the material property update; calling the updated CAE software and performing semi-solid forming simulation using the selected incremental simulation mode. The step of selecting the corresponding incremental simulation mode based on the updated material properties in the material property update includes: calling an incremental strategy selector based on the updated material properties in the material property update to select the selected incremental simulation mode from multiple incremental simulation modes, wherein the incremental strategy selector is constructed based on a reinforcement learning algorithm.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-5.

8. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method described in any one of claims 1-5.

9. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-5.

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