Die optimization process of zinc-aluminum alloy die-casting optical module and aluminum alloy processing die

Abstract

The embodiment of the invention relates to the technical field of aluminum alloy processing, and discloses a die optimization process of a zinc-aluminum alloy die-casting optical module and an aluminum alloy processing die, the method comprises the following steps: obtaining material attribute information of a to-be-processed aluminum alloy and material data of the die; according to the material data of the to-be-machined aluminum alloy and the material attribute information of the mold, a geometric model of the mold is established, and grid division is carried out; performing die-casting simulation analysis in finite element analysis software, and simulating a die-casting process to obtain a simulation result; the simulation result is input into a parameter optimization model, and adjustment parameters of a sprue, a runner system and a cooling water channel of the mold are obtained; and according to the adjustment parameters of the sprue, the runner system and the cooling water channel of the mold, a target mold is manufactured. By means of the mode, high precision of mold design is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of aluminum alloy processing, and more particularly to a mold optimization process for a zinc-aluminum alloy die-cast optical module and an aluminum alloy processing mold. Background Art

[0002] Currently, aluminum alloy is one of the most widely used metal materials in industries such as aviation. For example, it is widely used in aircraft fuselage structural parts (skins, stringers, bulkheads, etc.), engine components, landing gear, and airborne equipment housings. Therefore, the zinc-aluminum alloy die-casting process is widely used in the manufacture of precision optical modules. Aluminum alloy parts have the characteristics of light weight, high specific stiffness, and corrosion resistance, but they also have poor ductility and resilience. Therefore, in order to improve the production efficiency and product quality of aluminum alloy parts, it is necessary to study aluminum alloy processing molds to improve the precision of aluminum alloy processing technology.

[0003] The existing mold design process often suffers from problems such as short mold life and low molding quality. The inventors of this application have discovered that traditional mold design relies on trial and error, which is prone to problems such as filling defects and thermal stress concentration. In the existing technology, although finite element simulation can assist in optimization, it has the following shortcomings:

[0004] 1. It is difficult to balance meshing accuracy and computational efficiency, and complex surfaces are prone to simulation distortion;

[0005] 2. Gate parameter adjustment lacks quantitative basis and relies on manual experience and judgment;

[0006] 3. Insufficient coupling analysis of temperature field and stress field makes it difficult to predict the risk of thermal cracking.

[0007] Therefore, there is an urgent need for a mold optimization method that integrates intelligent algorithms and dynamic grid encryption to adjust mold parameters and improve the product quality of aluminum alloy products. Summary of the Invention

[0008] In view of the above problems, the embodiments of the present invention provide a mold optimization process for a zinc-aluminum alloy die-cast optical module and an aluminum alloy processing mold, which are used to solve the problem of low mold design accuracy in the prior art.

[0009] According to one aspect of an embodiment of the present invention, a mold optimization process for a zinc-aluminum alloy die-cast optical module is provided, the process comprising:

[0010] Obtaining material property information of the aluminum alloy to be processed and material data of the mold; the material property information includes material flow, rebound and stress distribution data; the material data includes physical and mechanical property data and geometric parameters;

[0011] According to the material data of the aluminum alloy to be processed and the material property information of the mold, a geometric model of the mold is established and meshed; the geometric model includes a cavity, a gate, a runner, and a cooling water channel;

[0012] Perform die-casting simulation analysis in finite element analysis software to simulate the die-casting process and obtain simulation results; the simulation results include animation of the molten metal flow front, filling process analysis information, solidification information, and stress distribution information and temperature information of the mold;

[0013] Inputting the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system and cooling water channel of the mold;

[0014] The target mold is manufactured according to the adjustment parameters of the gate, runner system and cooling water channel of the mold.

[0015] In an optional manner, obtaining material property information of the aluminum alloy to be processed and material data of the mold includes:

[0016] Testing the aluminum alloy to be processed to obtain material property information of the aluminum alloy to be processed;

[0017] The material data of the mold is determined according to the material property information and processing requirements of the aluminum alloy to be processed.

[0018] In an optional manner, the processing technology includes one of CNC processing, cutting processing, and forming processing; and determining the material data of the mold according to the material property information and processing requirements of the aluminum alloy to be processed includes:

[0019] Based on the material property information and processing requirements of the aluminum alloy to be processed, the physical and mechanical properties data and mold geometric dimensions of the mold are determined in a preset mold material database; the physical and mechanical properties data of the mold include the density, thermal conductivity, specific heat capacity, solidus-liquidus temperature, elastic modulus, Poisson's ratio and thermal expansion coefficient of the mold material.

[0020] In an optional manner, the material property information of the aluminum alloy to be processed includes finished product size information corresponding to the aluminum alloy to be processed; and a geometric model of the mold is established based on the material data of the aluminum alloy to be processed and the material property information of the mold, and meshing is performed, including:

[0021] Based on the finished product size information of the aluminum alloy to be processed, CAD modeling is performed and the die-casting mold runner is optimized using 3DQuickPress to determine the basic dimensions of the mold body of the mold; wherein, a fillet with a radius greater than a preset threshold is retained at the gate;

[0022] Determining the mold cavity and pouring system of the mold according to the processing information;

[0023] Assigning the physical and mechanical property data of the mold to the cavity and the mold body to obtain a geometric model of the mold;

[0024] The geometric model is meshed using a hybrid network technology to obtain a meshed geometric model.

[0025] In an optional manner, meshing the geometric model using a hybrid network technology to obtain a meshed geometric model includes:

[0026] Determine the initial coarse mesh; the cavity / flow channel area uses a hexahedron-dominated mesh, the complex curved surface uses a tetrahedron mesh, and the thin-walled structure uses a prismatic layer mesh;

[0027] Use CNN to analyze the simulation results of the initial coarse grid and identify high gradient areas;

[0028] Encrypting the grid density in the high gradient area according to a preset encryption step size;

[0029] The geometric model after mesh encryption is further analyzed using CNN for simulation results, and high gradient areas are further identified. The mesh is then dynamically encrypted until the target accuracy is reached, and the geometric model after meshing is obtained.

[0030] In an optional manner, the parameter optimization model includes a U-Net model, a parameter optimization model, and a generative adversarial network; inputting the simulation results into the parameter optimization model to obtain adjustment parameters of the gate, runner system, and cooling water channel of the mold includes:

[0031] Inputting the molten metal flow front animation into a U-Net model to detect information about uneven filling, air entrainment, or short shots in the molten metal;

[0032] When uneven filling, air entrainment or short shot occurs in the molten metal, a prompt message is generated, which includes a gate position or injection speed indicating the problem.

[0033] In an optional manner, inputting the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system, and cooling water channel of the mold further includes:

[0034] The stress distribution cloud map and temperature cloud map are respectively input into the generative adversarial network to obtain high-precision stress distribution cloud map and high-precision temperature cloud map.

[0035] According to another aspect of an embodiment of the present invention, an aluminum alloy processing mold is provided. The aluminum alloy processing mold is processed using the mold optimization process for the zinc-aluminum alloy die-casting optical module.

[0036] The embodiment of the present invention obtains the material property information of the aluminum alloy to be processed and the material data of the mold, establishes a geometric model of the mold according to the material data of the aluminum alloy to be processed and the material property information of the mold, and performs meshing; performs die-casting simulation analysis in finite element analysis software to simulate the die-casting process and obtain simulation results; inputs the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system and cooling water channel of the mold; and manufactures a target mold according to the adjustment parameters of the gate, runner system and cooling water channel of the mold, which can effectively improve the manufacturing accuracy of the mold and make it more reasonable, so that the use of the mold for aluminum alloy processing is more adapted to the characteristics and processing technology of the current aluminum alloy material, thereby improving the manufacturing accuracy.

[0037] The above description is only an overview of the technical solutions of the embodiments of the present invention. In order to more clearly understand the technical means of the embodiments of the present invention, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the drawings. In the drawings:

[0039] Figure 1 A schematic diagram illustrating a process flow of a mold optimization process for a zinc-aluminum alloy die-cast optical module provided by an embodiment of the present invention is shown;

[0040] Figure 2 A schematic diagram of the process of establishing a geometric model in a mold optimization process for a zinc-aluminum alloy die-cast optical module provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0041] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0042] Figure 1 The flowchart of the mold optimization process of the zinc-aluminum alloy die-casting optical module provided by the embodiment of the present invention is shown. Figure 1 As shown, the process includes the following steps:

[0043] Step 110: Obtain material property information of the aluminum alloy to be processed and material data of the mold.

[0044] The material property information of the embodiment of the present invention includes material flow, rebound and stress distribution data; the material data includes physical and mechanical property data and geometric parameters.

[0045] Specifically, the aluminum alloy to be processed is tested to obtain material property information of the aluminum alloy to be processed; and the material data of the mold is determined based on the material property information and processing requirements of the aluminum alloy to be processed. In an embodiment of the present invention, the processing technology includes one of CNC machining, cutting, and forming. According to the material property information and processing requirements of the aluminum alloy to be processed, the physical and mechanical property data and mold geometric dimensions of the mold are determined in a preset mold material database; the physical and mechanical property data of the mold include the density, thermal conductivity, specific heat capacity, solidus-liquidus temperature, elastic modulus, Poisson's ratio, and thermal expansion coefficient of the mold material. For example, in one embodiment of the present invention, the material to be processed is: ZL101A aluminum alloy (measured parameters: liquidus 615°C, solidus 555°C, dynamic viscosity 2.8 Pa·s). Correspondingly, the mold material is H13 hot work die steel (thermal conductivity 24.3 W / m·K, yield strength 1450 MPa). The rebound coefficient of the aluminum alloy measured by a high-temperature tensile testing machine is 0.12, and the solidification latent heat data is obtained by DSC analysis.

[0046] Step 120: Establishing a geometric model of the mold and performing meshing according to the material data of the aluminum alloy to be processed and the material property information of the mold.

[0047] In the embodiments of the present invention, the mold's geometric model includes a mold body, which includes a cavity, a gate, runners, and cooling channels. The cavity is the hollow portion of the mold used to shape the product. It is the core component of the mold and determines the final product's appearance and dimensional accuracy. The gate is the channel connecting the runner to the cavity and serves as the entrance for molten metal to enter the cavity, controlling the inflow rate and direction of the molten metal. For example, the size and shape of the gate can adjust the filling speed of the molten metal, avoiding problems such as air entrainment, splashing, or insufficient filling caused by overfilling. It also reduces turbulence in the molten metal and facilitates subsequent cleaning. The gate is typically designed to be easily removable, facilitating removal of residual gate portions after product formation, thereby improving the product's appearance quality and dimensional accuracy. The material property information of the aluminum alloy to be processed includes the corresponding dimensional information of the finished product. For example, the aluminum alloy casting is an optical module housing with dimensions of 80 mm × 50 mm × 10 mm and a wall thickness of 1.2 mm. The corresponding mold size is 200mm×150mm×80mm, with a 20mm processing allowance reserved on one side.

[0048] Among them, such as Figure 2 As shown, the embodiment of the present invention includes the following steps:

[0049] Step 210: Based on the finished product size information corresponding to the aluminum alloy to be processed, the basic size of the mold body of the mold is determined through CAD modeling and combined with 3DQuickPress to optimize the die-casting mold flow channel; wherein, a fillet with a radius greater than a preset threshold is retained at the gate.

[0050] Step 220: Determine the mold cavity and pouring system based on the processing information. Assign the physical and mechanical performance data of the mold to the cavity and the mold body to obtain the geometric model of the mold. Mesh the geometric model using hybrid network technology to obtain a meshed geometric model. In an embodiment of the present invention, CAD software is used to create a geometric model of the mold, including the cavity, gate, runner, cooling water channel and other parts to ensure the accuracy and integrity of the model. In one embodiment of the present invention, the cooling water channel is designed as a straight-through water channel with a diameter of φ8mm, 15mm from the cavity surface, and a spacing of 30mm; a spiral water channel is added to the hot zone in the special area, with a pitch of 12mm and a diameter of φ6mm. The ejection system is an ejector with a diameter of φ3mm, and the spacing between the edge of the casting and the root of the reinforcement is 25mm; a 10mm safety margin is reserved for the ejection stroke.

[0051] Step 230: After the basic geometric model is designed, the geometric model in CAD format is imported into the finite element analysis software and then meshed. In the embodiment of the present invention, the appropriate mesh type (such as tetrahedral mesh, hexahedral mesh, etc.) and mesh density are selected according to the complexity of the geometric model and the analysis accuracy requirements. After meshing the geometric model, a finite element mesh is generated. In the gate, thick-walled parts, etc., the mesh is appropriately encrypted by gradually encrypting the mesh to improve the analysis accuracy. Specifically, the initial coarse mesh is determined; in the cavity and runner areas, the hexahedral dominant mesh is used, the complex curved surface is used with a tetrahedral mesh, and the thin-walled structure is used with a prismatic layer mesh. In the simulation results of the initial coarse mesh, CNN is used to identify high-gradient areas; the mesh density of the high-gradient areas is encrypted according to a preset encryption step; the simulation results of the meshed geometric model are further analyzed by CNN, and the high-gradient areas are further identified, and the mesh is dynamically encrypted until the target accuracy is reached, thereby obtaining a meshed geometric model. Among them, the mesh quality can be checked, and when the distortion is less than 0.85, the aspect ratio is less than 15, and the unit volume mutation rate is less than 30%, the mesh quality is determined to be qualified. In an embodiment of the present invention, a feature sample is constructed, and the feature sample includes a mold sample and a label annotation of the area to be encrypted. The feature sample data is trained in the CNN to obtain a trained CNN. Among them, the characteristic parameters of the model are input into the trained CNN, and the simulation results of the initial coarse grid are analyzed to obtain the current high gradient area of the mold, and the high gradient area is meshed and encrypted. By setting the model to dynamically adjust the grid, the workload of manually adjusting the grid can be effectively reduced, and the accuracy of the simulation can be improved.

[0052] Step 130: Perform die-casting simulation analysis in finite element analysis software to simulate the die-casting process and obtain simulation results.

[0053] The simulation results include animation of the molten metal flow front, filling process analysis information, solidification information, and mold stress distribution information and temperature information.

[0054] Specifically, in the embodiment of the present invention, the VOF (Volume of Fluid) model is used for flow analysis to track the flow front of the molten metal. The heat transfer analysis is coupled with the solidification latent heat release, and the interface thermal resistance is set. The heat transfer coefficient of the mold-casting interface is 5000W / m 2·K. Stress analysis uses an elastic-plastic model to consider the thermal-mechanical coupling effect of the mold and casting. Among them, filling process analysis: observe the metal liquid filling sequence through flow front animation, identify the "stagnant zone" and "jet stream", where the stagnant zone is the area where the filling time difference is greater than 10%, and the jet stream is the area where the flow rate is greater than 40m / s. Extract the air volume distribution map and locate the gas hole risk area, where the area with a gas volume fraction greater than 5% is defined as the gas hole risk area. Solidification and stress analysis: By drawing a temperature gradient cloud map, identify the hot spot and shrinkage risk area, where the hot spot is the area with a cooling rate of less than 50℃ / s. Extract the mold stress distribution and mark the maximum equivalent stress area. If it is greater than 800MPa, the structure needs to be optimized.

[0055] Step 140: Input the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system, and cooling water channel of the mold.

[0056] The parameter optimization model is trained based on historical aluminum alloy processing data and historical mold parameters. In an embodiment of the present invention, the parameter optimization model includes a U-Net model, a parameter optimization model, and a generative adversarial network. The historical aluminum alloy processing data includes animation samples of the molten metal flow front, stress distribution cloud map samples, temperature cloud map samples, and simulation data samples.

[0057] The molten metal flow front animation is fed into a U-Net model to detect uneven filling, air entrainment, or short shots within the molten metal. The U-Net model is pre-trained using samples of the molten metal flow front animation. When uneven filling, air entrainment, or short shots occur, a prompt is generated, including information indicating the problematic gate location or injection speed. If the flow front does not synchronously reach the end of the cavity, the gate position is adjusted using the "flow balance index."

[0058] The stress distribution cloud map and the temperature cloud map are respectively input into the generative adversarial network to obtain a high-precision stress distribution cloud map and a high-precision temperature cloud map; wherein the generative adversarial network is pre-trained based on stress distribution cloud map samples and temperature cloud map samples.

[0059] The filling process analysis information, solidification information, high-precision stress distribution cloud map and high-precision temperature cloud map are input into the multi-task analysis model to obtain the porosity, thermal cracking probability and shrinkage risk analysis results; wherein, the multi-task analysis model is pre-trained based on simulation data samples.

[0060] The embodiment of the present invention uses a parameter optimization model to more efficiently and accurately adjust the parameters of the mold structure according to the simulation results.

[0061] Step 150: manufacturing a target mold according to adjustment parameters of the mold's gate, runner system, and cooling water channel.

[0062] High-precision CNC machining equipment, such as CNC milling machines or EDM machines, is used to precisely machine the gate according to the design drawings. The gate's cross-sectional area, length, and angle must meet optimized parameter requirements, and machining accuracy must be within the allowable tolerance range. For complex gate shapes, such as those for tangential feed, multi-axis machining may be required to ensure machining accuracy and surface quality.

[0063] In this embodiment of the present invention, CNC machining equipment is used to process the flow channels, including circular and trapezoidal channels, as well as the "T-balancers" at the channel branches. Based on adjustment parameters, the cross-sectional shape, size, and position of the flow channels are precisely controlled to ensure that the processed flow channels meet the optimized parameter requirements. The additional curved guide blocks are machined using CNC milling machines or electrical discharge machining equipment to ensure that their shape and size meet the design requirements and perfectly integrate with the flow channels.

[0064] Among them, for conventional cooling water channels, CNC machining equipment is used for drilling and milling to ensure that the diameter, spacing and position of the water channels meet the design requirements. During the machining process, attention is paid to controlling the machining accuracy and surface roughness to ensure the smooth flow of the cooling water channels and the cooling effect. For special water channel structures, such as spiral cooling water channels and variable cross-section water channels, the embodiments of the present invention are obtained by additive manufacturing or laser processing. In this way, complex water channel shapes and structures can be achieved, and the machining accuracy and quality of the cooling water channels can be improved.

[0065] In this embodiment of the present invention, after the target mold is manufactured, it is debugged, including the gating system and cooling system. If defects are found in the mold or performance does not meet the requirements, the mold's gate, runner system, or cooling water channel are partially adjusted or redesigned based on the defect type and location.

[0066] The embodiment of the present invention obtains the material property information of the aluminum alloy to be processed and the material data of the mold, establishes a geometric model of the mold according to the material data of the aluminum alloy to be processed and the material property information of the mold, and performs meshing; performs die-casting simulation analysis in finite element analysis software to simulate the die-casting process and obtain simulation results; inputs the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system and cooling water channel of the mold; and manufactures a target mold according to the adjustment parameters of the gate, runner system and cooling water channel of the mold, which can effectively improve the manufacturing accuracy of the mold and make it more reasonable, so that the use of the mold for aluminum alloy processing is more adapted to the characteristics and processing technology of the current aluminum alloy material, thereby improving the manufacturing accuracy.

[0067] The algorithm or demonstration provided herein are not inherently relevant to any particular computer, virtual system or other equipment. Various general-purpose systems may also be used together with the teachings based on this. According to the above description, it is apparent that the structure required for constructing this type of system. In addition, the embodiment of the present invention is not directed to any specific programming language yet. It should be understood that various programming languages can be utilized to realize the content of the present invention described herein, and the above description of specific languages is for the purpose of disclosing the best mode of the present invention.

[0068] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0069] Similarly, it should be understood that in order to streamline the present invention and facilitate understanding of one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0070] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed so far can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) can be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0071] It should be noted that the above embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by a person skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. A mold optimization process for zinc-aluminum alloy die-casting optical modules, characterized in that: The process comprises: Obtaining material property information of the aluminum alloy to be processed and material data of the mold; the material property information includes material flow, rebound and stress distribution data; the material data includes physical and mechanical property data and geometric parameters; According to the material data of the aluminum alloy to be processed and the material property information of the mold, a geometric model of the mold is established and meshed; the geometric model includes a cavity, a gate, a runner, and a cooling water channel; Perform die-casting simulation analysis in finite element analysis software to simulate the die-casting process and obtain simulation results; the simulation results include animation of the molten metal flow front, filling process analysis information, solidification information, and stress distribution information and temperature information of the mold; Inputting the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system and cooling water channel of the mold; the parameter optimization model is trained based on historical aluminum alloy processing data and historical mold parameters; The target mold is manufactured according to the adjustment parameters of the gate, runner system and cooling water channel of the mold.

2. The process according to claim 1, characterized in that Obtain the material property information of the aluminum alloy to be processed and the material data of the mold, including: Testing the aluminum alloy to be processed to obtain material property information of the aluminum alloy to be processed; The material data of the mold is determined according to the material property information and processing requirements of the aluminum alloy to be processed.

3. The process according to claim 2, characterized in that The processing technology includes one of CNC processing, cutting processing, and forming processing; the material data of the mold is determined according to the material property information and processing requirements of the aluminum alloy to be processed, including: Based on the material property information and processing requirements of the aluminum alloy to be processed, the physical and mechanical properties data and mold geometric dimensions of the mold are determined in a preset mold material database; the physical and mechanical properties data of the mold include the density, thermal conductivity, specific heat capacity, solidus-liquidus temperature, elastic modulus, Poisson's ratio and thermal expansion coefficient of the mold material.

4. The process according to claim 3, characterized in that The material property information of the aluminum alloy to be processed includes the finished product size information corresponding to the aluminum alloy to be processed; According to the material data of the aluminum alloy to be processed and the material property information of the mold, a geometric model of the mold is established and meshing is performed, including: Based on the finished product size information of the aluminum alloy to be processed, CAD modeling is performed and the die-casting mold runner is optimized using 3DQuickPress to determine the basic dimensions of the mold body of the mold; wherein, a fillet with a radius greater than a preset threshold is retained at the gate; Determining the mold cavity and pouring system of the mold according to the processing information; Assigning the physical and mechanical property data of the mold to the cavity and the mold body to obtain a geometric model of the mold; The geometric model is meshed using a hybrid network technology to obtain a meshed geometric model.

5. The process according to claim 4, characterized in that Meshing the geometric model using a hybrid network technology to obtain a meshed geometric model includes: Determine the initial coarse mesh; the cavity / flow channel area uses a hexahedron-dominated mesh, the complex curved surface uses a tetrahedron mesh, and the thin-walled structure uses a prismatic layer mesh; Use CNN to analyze the simulation results of the initial coarse grid and identify high gradient areas; Encrypting the grid density in the high gradient area according to a preset encryption step size; The geometric model after mesh encryption is further analyzed using CNN for simulation results, and high gradient areas are further identified. The mesh is then dynamically encrypted until the target accuracy is reached, and the geometric model after meshing is obtained.

6. The process according to claim 1, characterized in that The parameter optimization model includes a U-Net model, a parameter optimization model, and a generative adversarial network; the simulation results are input into the parameter optimization model to obtain adjustment parameters of the gate, runner system, and cooling water channel of the mold, including: Inputting the molten metal flow front animation into a U-Net model to detect information about uneven filling, air entrainment, or short shots in the molten metal; wherein the U-Net model is pre-trained based on molten metal flow front animation samples; When uneven filling, air entrainment or short shot occurs in the molten metal, a prompt message is generated, which includes a gate position or injection speed indicating the problem.

7. The process according to claim 6, characterized in that The step of inputting the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system, and cooling water channel of the mold further includes: The stress distribution cloud map and the temperature cloud map are respectively input into a generative adversarial network to obtain a high-precision stress distribution cloud map and a high-precision temperature cloud map; wherein the generative adversarial network is pre-trained based on stress distribution cloud map samples and temperature cloud map samples.

8. The process according to claim 7, characterized in that Inputting the simulation results into a parameter optimization model to obtain adjustment parameters of the gate, runner system, and cooling water channel of the mold, further comprising: The filling process analysis information, solidification information, high-precision stress distribution cloud map and high-precision temperature cloud map are input into the multi-task analysis model to obtain porosity, thermal cracking probability and shrinkage risk analysis results; wherein, the multi-task analysis model is pre-trained based on simulation data samples.

9. An aluminum alloy processing mold, characterized in that: The aluminum alloy processing mold is obtained by using the mold optimization process for the zinc-aluminum alloy die-casting optical module according to any one of claims 1 to 8.

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