Thin-wall die casting die flow simulation data mixed extraction and efficient reconstruction method

Abstract

The invention relates to the field of data extraction, and discloses a thin-wall die casting die flow simulation data mixed extraction and efficient reconstruction method, which comprises the following steps: step 1, carrying out data extraction on a die flow simulation result of a die casting, and outputting a simulation result which comprises point cloud data; step 2, performing grid division on the die casting, discretizing a geometry of the die casting into a plurality of tetrahedrons with specific geometric features and topological relations so as to form a grid model, performing coordinate system unification on point cloud data obtained by data extraction and the grid model, establishing a spatial mapping relation, and forming a die casting model; and removing the tetrahedrons which do not contain the point cloud data. Therefore, the data extraction accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of data extraction, specifically to a method for hybrid extraction and efficient reconstruction of simulation data of thin-walled die casting mold flow. Background Technology

[0002] With the improvement of people's living standards and the rise of new energy vehicles, the demand for automobiles is increasing, and the demand for die-cast parts is constantly expanding and becoming more complex, requiring shorter R&D cycles for new products. Against this backdrop, mold flow simulation technology has become a key link in die-casting process development. By simulating the filling, cooling and solidification processes of molten metal, it provides key data input for evaluating casting quality, predicting defects, and for subsequent crash performance simulation.

[0003] However, directly using the massive amounts of raw data generated by mold flow simulation for collision analysis presents significant challenges. There are two reasons for this: First, the mold flow model of the die-casting process for components includes auxiliary structures such as the gating system, slag bag, and cooling system. These auxiliary structures are crucial for process optimization, but the data they generate becomes redundant information in subsequent analyses of the mechanical properties of the casting itself. It is necessary to remove these auxiliary structures before data extraction. Second, the mesh of mold flow simulation is fine, with a node density far exceeding that of the Eulerian mesh used in collision simulation. Since the two focus on different scales, the former needs to be rationally compressed to achieve a similar amount of mesh information.

[0004] Therefore, a hybrid data extraction and efficient reconstruction method for thin-walled die casting data is needed to preprocess the numerical simulation results of the die casting process, intelligently remove process system data that is irrelevant to the casting body, and compress the data to reduce the complexity of the mold flow simulation data and reduce the difficulty of subsequent collision simulation data extraction and analysis. Summary of the Invention

[0005] The present invention aims to provide a method for hybrid extraction and efficient reconstruction of mold flow simulation data for thin-walled die castings, so as to reduce the complexity of mold flow simulation data and reduce the difficulty of subsequent collision simulation data extraction and analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for hybrid extraction and efficient reconstruction of simulation data of thin-walled die casting mold flow, comprising the following steps: Step 1: Extract data from the mold flow simulation results of the die casting and output the simulation results, including point cloud data. Step 2: Mesh the die casting. Discretize the geometry of the die casting into several tetrahedrons with specific geometric features and topological relationships to form a mesh model. Use the point cloud data obtained from data extraction and the mesh model to establish a coordinate system and spatial mapping relationship to form the die casting model. Remove tetrahedrons that do not contain point cloud data.

[0007] The beneficial effects of this plan are: Since the object of flow simulation is fluid, the model information is usually a shell without thickness. Taking the most commonly used STEP neutral format file as an example, this file consists of three variables: file description, file name, and file mode. Through specific text formats and entity data expression methods, different systems can convert their own data into specific expressions in the neutral file, thereby realizing data exchange between different systems. The STEP file establishes an effective entity expression structure for different parts of the data, classifying bodies, shells, faces, and vertices, establishing hierarchical relationships, and describing these relationships in a specific format. The data segment stores three parts of entity data information: geometric information, topological information, and geometric entity model information. Parsing an existing STEP document reveals that the entire structure is composed of shells, and the geometric cross-sectional shape of the part resembles a "donut-shaped ring shell," belonging to non-fluid geometry. The outer surface of this geometry is closed, but the interior has no volume, and the shell has no thickness. Due to these characteristics, Boolean operations cannot correctly handle such shells, causing geometry-based Boolean algorithms to fail in this situation. Therefore, this scheme employs a multiphysics coupling analysis method based on finite element discretization to accurately extract the coordinates of the part, obtaining point cloud data, which is then processed. Unlike directly meshing the original model, the main object of meshing in this scheme is the point cloud data output from the model flow simulation. Its core purpose is to directly extract the data to improve the accuracy of data extraction, rather than increasing the accuracy of the simulation results. Its purpose, object, and stage differ from conventional meshing. Specifically, firstly, HyperMesh software is used to mesh the part into tetrahedrons; then, the point cloud data obtained from the model flow simulation and the mesh model are unified in coordinate system. The Boolean relationship between the point set and the tetrahedron is calculated using spatial geometric algorithms (such as octree search and ray casting) to establish a precise spatial mapping relationship.

[0008] 2. The data volume of mold flow simulation results is enormous. This solution processes this data by dividing it into tetrahedrons and then removing redundant data, thereby improving the efficiency of subsequent data processing. Taking the mold flow simulation results from the software FLOW-3D as an example, the presence of auxiliary structures such as the gating system, slag pot, and cooling system in the die-casting model increases the difficulty of subsequent analysis and data extraction. In this solution, tetrahedrons that do not contain point cloud data are removed, i.e., auxiliary structures such as the gating system, slag pot, and cooling system are removed, thereby improving the accuracy of data extraction and achieving data reduction of the numerical simulation results of the die-casting process.

[0009] Furthermore, in step one, during the mold flow simulation, the liquidus temperature, solidus temperature, and latent heat of fusion are input, and the temperature field distribution at the end of filling is used as the initial value. Solidification data is output, including solidus velocity, thermal gradient, and cooling rate. The solidification time is calculated based on the solidification data.

[0010] Furthermore, in step one, porosity is extracted from data based on the fluid dynamics shrinkage model. The analysis objective of the fluid dynamics shrinkage model is: when the material shrinks during solidification, the pressure drops to the critical pressure, and the shrinkage pores formed at this time.

[0011] Furthermore, in step one, porosity is extracted from the data based on a simplified shrinkage model, which is used to evaluate the solidification shrinkage volume of each isolated liquid region in each time step of the die casting.

[0012] Furthermore, in step one, the simulation results include temperature field information during solidification. Based on the temperature field information during solidification, redundant information is removed, including duplicate data, molds, and the gating system.

[0013] Furthermore, in step two, the method for determining whether a tetrahedron contains point cloud data is as follows: Calculate the directed volume of the cloud data coordinates to each face of the tetrahedron and the total volume of the tetrahedron. If the sum of the directed volumes of each face of the tetrahedron equals the directed volume of the tetrahedron, and the directed volumes of each face of the tetrahedron have the same sign as the directed volume of the tetrahedron, then the cloud data coordinates are within the tetrahedron.

[0014] Furthermore, in step two, the method for determining whether a tetrahedron contains point cloud data is as follows: If non-negative weights exist , , , If the following equation is satisfied, then the cloud data coordinates P lie within a tetrahedron: D; ; A, B, C, D - Coordinates of four points on a tetrahedron.

[0015] Furthermore, step three involves simplifying the data using a neighborhood aggregation method based on virtual sphere scanning: Step 3.1: Using the virtual sphere as a tool, scan all the grid nodes within the virtual sphere; Step 3.2: Perform geometric mean calculation on the grid nodes within the virtual sphere, and compress these grid nodes into a new node; Step 3.3: The virtual sphere moves in steps not exceeding its radius, gradually covering the surface of the part until surface sampling is completed.

[0016] Furthermore, in step three, the formula for calculating the virtual sphere radius γ is: , 5; d avg- - The average distance from each point in the point cloud data to its nearest neighbor; α - Scale factor.

[0017] Definitions: Solidification time: The time it takes for a metal to begin solidifying within a given unit (tetrahedral range).

[0018] This solution also has the following effects: 1. FLOW-3D's method for calculating solidification: The temperature field distribution at the end of filling is used as the initial value, and then shrinkage cavities are calculated. To activate the solidification model, specify the liquidus temperature, solidus temperature, and latent heat of fusion in the properties. In the simplest solidification model, latent heat is released linearly as the material cools from the liquidus to the solidus temperature. In the solidification model, the solver calculates solidification-related data including solidus velocity, thermal gradient, and cooling rate. By default, the calculated temperature is the solidus temperature of the fluid. Solidification data is determined based on different temperatures.

[0019] 2. The shrinkage model is based solely on solutions to the energy equations for the metal and the die. Porosity is predicted by evaluating the solidification shrinkage volume of each isolated liquid region at each time step in the die casting. This volume is then subtracted from the top of the liquid region based on the amount of liquid metal available in the cells from which the liquid is removed. The “top” of the liquid region is defined by the direction of gravity. This approach is supported by the fact that, in many cases, fluid flow in the solidified metal is negligible. In such cases, porosity formation is primarily controlled by metal cooling and gravity. Gravity-induced replenishment typically occurs on timescales much smaller than the total solidification time.

[0020] 3. After the part is meshed, if a certain tetrahedron contains geometric information originating from the part geometry, it means that the die-cast model point set located within the space of that small unit is located within the part geometry and can be considered a valid component of the part; otherwise, it can be determined that the die-cast model point set located within the space of that small tetrahedron is outside the part geometry. In this case, these point sets outside the geometry usually correspond to structures that do not need to be retained, such as the gating system added during the part casting process, and can be removed from the die-cast model.

[0021] Generally, in order to accurately determine the position of a point inside the mold within the part, the volume method and the centroid coordinate method are two commonly used and efficient determination methods, which are determined by the specific position of the point on the tetrahedron.

[0022] When using the centroid coordinate method for determination, the result is obtained through linear calculation, which has low requirements for floating-point precision and fast calculation speed, making it suitable for large-scale point set calculations. However, when dealing with degenerate tetrahedrons in complex geometries, the centroid coordinate method may lead to misjudgments.

[0023] The part is a shell with a complex shape, which makes each tetrahedron a closed unit with a different shape. Considering the possibility of degenerate tetrahedrons, a combination of volume method and centroid coordinate method is used to determine the position of the point. The algorithm is more robust, the data acquisition is more stable, and the data calculation rate is improved.

[0024] 4. This scheme is based on a composite spatial partitioning strategy using quadratic axis-aligned bounding boxes (AABB). The adopted double bounding box spatial acceleration strategy significantly reduces the time complexity of nearest neighbor search through a collaborative mechanism of module-level coarse screening and unit-level precise positioning. Research shows that a multi-level spatial partitioning strategy can optimize the O(n) linear query complexity of traditional single index structures to the O(logn) logarithmic level.

[0025] 5. After preprocessing the FLOW-3D mold flow simulation results, the data volume is simplified. In the mold flow simulation process, the die casting uses a high-resolution discretization mesh generation method, which has a high number of nodes and makes mesh topology transformation difficult. Data reduction aims to remove duplicate data, compress data, and reduce data storage space, in order to prepare for subsequent machine learning and LSDYNA mesh mapping.

[0026] This solution employs a neighborhood aggregation method based on sphere scanning for data simplification. This includes the following four steps: 1) Sphere scanning: Starting from a point on the outer surface of the part, a virtual sphere is used to scan nodes in 3D space, selecting all mesh nodes within the sphere; 2) Neighborhood aggregation: The geometric mean of the nodes within the sphere is calculated, compressing these nodes into a new node. The neighborhood mean is used as the new point, ensuring the smoothness and geometric properties of the reduced mesh are maintained; 3) Iterative coverage: Using the sphere as a window, and moving the sphere in steps not exceeding its radius, the surface of the part is gradually covered until surface sampling is complete; 4) Mesh reduction: The total number of nodes is reduced through node aggregation operations within the sphere, forming a new simplified mesh. Attached Figure Description

[0027] Figure 1 This is a flowchart of Example 1; Figure 2 The seven degenerate tetrahedrons listed; Figure 3 This is a porosity distribution diagram after data compression for Example 1; Figure 4 This is the output information after compressing the porosity data from Example 1; Figure 5 This is a distribution diagram of solidification time after data compression in Example 1; Figure 6 This is the output information after compressing the solidification time data of Example 1. Detailed Implementation

[0028] The following detailed description illustrates the specific implementation method: Example 1 A method for hybrid extraction and efficient reconstruction of mold flow simulation data for thin-walled die casting parts, the flowchart of which is shown below. Figure 1 As shown, it includes the following steps: Step 1: Data Extraction. Research indicates that aluminum alloys are lightweight, highly ductile, and corrosion-resistant. However, during die casting, they are prone to defects such as porosity, shrinkage, and hot cracking. Based on the priority of factors affecting die casting quality, the main factors influencing the quality are solidification time, porosity, and flow length. Since the flow length varies depending on the pouring position, its impact on die casting is usually not considered. Only when high-pressure die casting is used, where the molten metal flows and cools rapidly, must the impact of flow length on die casting quality be considered. This embodiment uses aluminum subframes and employs low-pressure casting; therefore, the main considerations are the effects of solidification time and porosity on the die casting. 1) In the mold flow simulation, the liquidus temperature, solidus temperature and latent heat of fusion are input, and the temperature field distribution at the end of filling is used as the initial value to output solidification data, which includes solidus linear velocity, thermal gradient and cooling rate. 2) Porosity is extracted from data based on a simplified shrinkage model. The simplified shrinkage model is used to evaluate the solidification shrinkage volume of each isolated liquid region in each time step of the die casting. Then, the volume is subtracted from the top of the liquid region based on the amount of liquid metal available in the cells from which the liquid is removed. The “top” of the liquid region is defined by the direction of gravity. The shrinkage model can quickly simulate the shrinkage of complex die castings. The volume does not include the evolution of gas. The porosity volume is determined by the cooling rate and the solid / liquid density difference. 3) Extract data from the mold flow simulation results of the die casting and output the simulation results, which include point cloud data of porosity and solidification data; 4) Calculate the solidification time based on solidification data. The solidification time will be calculated based on the metal temperature difference at the beginning and end of the time cycle. If the metal temperature difference is less than the design temperature difference, the solidification time will be set to the current time. When the metal temperature difference is greater than the design temperature difference, it is considered to have solidified. Remove redundant information, including duplicate data, molds, and gating systems. Step 2, Data Preprocessing: Use HyperMesh software to mesh the die casting, discretizing the geometry of the die casting into several tetrahedrons with specific geometric features and topological relationships to form a mesh model. Then, use coordinate system unification between the point cloud data obtained from data extraction and the mesh model to establish a spatial mapping relationship and form a die casting model. HyperMesh software was used to mesh the part, forming a mesh model that accurately describes the spatial characteristics of the geometry, transforming the part from a shell structure into a "pseudo-solid" model. Three mesh sizes were used in the part meshing process, as shown in the table below. In the Python programming environment, the key data after part meshing in the *.k file was parsed to accurately obtain detailed information about each tetrahedron in the model, including its constituent nodes, elements, and topological relationships. This laid the foundation for in-depth comparison and accurate calculation of the geometric features (such as shape, size, curvature, etc.) and topological relationships (such as adjacency, containment, intersection, etc.) of the point set and the part geometry.

[0029] Remove tetrahedrons that do not contain point cloud data; Specifically, calculate the directed volume of the cloud data coordinates to each face of the tetrahedron and the total volume of the tetrahedron. If the sum of the directed volumes of each face of the tetrahedron equals the directed volume of the tetrahedron, and the directed volumes of each face of the tetrahedron have the same sign as the directed volume of the tetrahedron, then it means that the cloud data coordinates are inside the tetrahedron.

[0030] When using the volume method for determination, the method is simple and not limited to whether the tetrahedron's geometric shape is regular. It is robust and suitable for calculating complex geometric shapes. However, when there are many tetrahedrons, the computational load is large and the efficiency is low. Step 3: Time Complexity Optimization: After extracting and preprocessing the simulation data, which covers 900,000 coordinate points, each point requires traversing all tetrahedrons. Without optimization, the total time complexity is at least O(900,000 * 76,000). Therefore, spatial indexing and algorithm optimization are needed to reduce the time complexity. This embodiment proposes a composite spatial partitioning strategy based on quadratic axis-aligned bounding boxes (AABB). The core algorithm flow includes: Step 3.1: Perform a primary AABB spatial partitioning on the die-cast model, decomposing it into 16 topologically related sub-regions to reduce the number of tetrahedrals required to traverse in a single spatial query. Step 3.2: Take the inherent bounding box of each tetrahedron in each sub-region as a secondary AABB cell, implement binary tree structure reorganization using KD tree data structure, construct a spatial recursive subdivision index mechanism, and accelerate spatial tetrahedron query. Step 4: Data Compression: Data simplification is performed using a neighborhood aggregation method based on virtual sphere scanning. Step 3.1: Using the virtual sphere as a tool, scan all the grid nodes within the virtual sphere; In point cloud data processing, the selection of the virtual sphere radius and scanning step size has a significant impact on the accuracy of the results. The sphere radius can be selected based on the density of the point cloud to ensure that the sphere can encompass an appropriate number of points. When considering actual data compression requirements, if a 3D geometric model of the part is provided, it can be converted into coordinate point cloud data, and the sphere radius can be dynamically adjusted according to the information content of the converted data. If no 3D model information is available, the part thickness can be used as the default sphere radius. Both methods take into account applications in different scenarios, have flexibility and applicability, and help improve the efficiency and accuracy of point cloud processing. The formula for calculating the radius γ of the virtual sphere is: , 5; d avg- - The average distance from each point in the point cloud data to its nearest neighbor; α - Scale factor; The ratio of scanned points to the total number of point clouds is the coverage rate C. After data reduction, the coverage rate should satisfy: 95% ≤ C ≤ 99%. Step 3.2: Perform geometric mean calculation on the grid nodes within the virtual sphere, and compress these grid nodes into a new node; Step 3.3: The virtual sphere moves in steps not exceeding its radius, gradually covering the surface of the part until surface sampling is completed; The material of the parts in this project is AlSi7Mg. 0.3 The part weighs 17kg, has dimensions of 704.4mm × 1036mm × 225mm, and a minimum thickness of approximately 2mm. The minimum thickness is used as the radius and scan step size of the virtual sphere. After data simplification, the data points remain within the expected range while ensuring geometric accuracy. The retained nodes fully represent the geometric characteristics, and areas with high curvature are not oversimplified. The porosity distribution map after data compression is shown below. Figure 3 As shown, the output information is as follows Figure 4 As shown in the figure, the solidification time distribution after data compression is as follows: Figure 5 As shown, the output information is as follows Figure 6 As shown; The error assessment after data compression and dimensionality reduction adopts a multidimensional statistical assessment framework. The reduced data is reconstructed in three dimensions. While meeting the expected range, the geometric accuracy of the results must be guaranteed. It is necessary to check whether the retained nodes can fully express the geometric characteristics and whether the regions with high curvature have been oversimplified. Step 5: Data Assembly: The "porosity" and "solidification time" data are merged using the KDTree structure retrieval method to form a standard format document.

[0031] Example 2 Based on Examples 2 and 1, in step two, the method for determining whether a tetrahedron contains point cloud data also includes the centroid coordinate method: If non-negative weights exist , , , If the following equation is satisfied, then the cloud data coordinates P lie within a tetrahedron: D; ; A, B, C, D - Coordinates of four points on a tetrahedron.

[0032] When using the centroid coordinate method for determination, the result is obtained through linear calculation, which has low requirements for floating-point precision and fast calculation speed, making it suitable for large-scale point set calculations; however, when facing situations such as... Figure 2 When dealing with degenerate tetrahedrons in complex geometries, the centroid coordinate method may lead to misjudgments. The part is a shell with a complex shape, which makes each tetrahedron a closed unit with a different shape. Considering the possibility of degenerate tetrahedrons, a combination of "volume method + centroid coordinate method" is used to determine the position of the point. The algorithm is more robust, the data acquisition is more stable, and the data calculation speed is improved. When the point set appears at corners or flanges, these places have a large height-to-width ratio. Theoretically, the volume method or the centroid coordinate method can be used to determine the position. However, in actual judgment, the judgment may fail because the mesh is relatively flat or thin. Therefore, the "distance between the point and the part" is used to determine the position of the remaining points.

[0033] Example 3 The difference between Example 3 and Example 1 is that in step one, porosity is extracted based on the fluid dynamics shrinkage model. The analysis objective of the fluid dynamics shrinkage model is: when the material shrinks during solidification, the pressure drops to the critical pressure, and the shrinkage pores formed at this time.

[0034] The principle of the model is that when the material solidifies, it shrinks and some tension is generated at some locations inside the material. It draws in the surrounding liquid in the form of negative pressure to make up for the volume lost due to shrinkage. If the surrounding liquid is restricted and cannot flow, the pressure will continue to drop, further shrinking until the critical pressure is reached, forming a shrinkage pore at this point [9]. At the same time, whether the dissolved gas in the molten metal is released is also related to the critical pressure at this point. The first principle model is more accurate in simulating the pore formation phenomenon, but the computational load is very large. In each time step, the numerical algorithm involves the complete solution of the momentum and energy equations. The simulation time step is determined by a combination of various relevant standards. Compared with the total solidification time of the casting, the time step may be very large or very small.

[0035] Example 4 The difference from Example 1 is that in step three, a composite spatial partitioning strategy based on quadratic axis-aligned bounding boxes (AABB) is used. The core algorithm process includes: Step 1: Divide the geometry into AABB space. AABB space includes primary AABB space partitioning and secondary AABB space partitioning. The specific steps for performing AABB space partitioning include: b1. Use a common axis-aligned bounding box to surround the entire space; b2. Divide the interior of the bounding box into several sub-regions, and enclose each sub-region with several primary bounding boxes; b3. Use several secondary bounding boxes to enclose the tetrahedrons in the sub-region.

[0036] The primary AABB space partitioning divides the geometry of the die casting into several topologically related sub-regions; the primary bounding box is the smallest cuboid parallel to the XYZ coordinate axes, which can just enclose the sub-regions. The secondary AABB spatial partitioning divides the sub-region into several tetrahedral units; the secondary bounding box is the smallest cuboid parallel to the XYZ coordinate axes, which can just enclose the tetrahedral units.

[0037] The secondary AABB spatial partitioning method divides the subregion into several tetrahedral units as follows: The geometry is meshed to form a network model, and the geometry is discretized into several tetrahedrons with geometric features and topological relationships. The coordinate system of the point set of the simulation data and the mesh model is unified, and the Boolean relationship between the point set and the tetrahedral elements is established through spatial geometric algorithms.

[0038] KDTree is used to construct binary tree structure reorganization within subregions; Constructing binary tree structure reorganization within a subregion using KDTree includes the following steps: Step a1: Calculate the variance of the point set on each coordinate axis of the simulation data in the sub-region, and select the coordinate axis with the largest variance; Step a2: Use the median of all data points on the selected coordinate axis as the split point; Step a3: Create a plane perpendicular to the selected coordinate axis at the dividing point to divide the current space into two parts. Step a4: Recursively repeat steps 1.1-1.3 for the two parts of the space until the number of data points in the subspace is less than the set threshold or the depth of the tree reaches the preset value, then stop the partitioning and form leaf nodes.

[0039] Step 2: Determine whether the target coordinate point is within the sub-region. Traverse the tetrahedral cells of the sub-region and locate the target coordinates using a binary tree structure.

[0040] Specifically, the target coordinates are regarded as a special bounding box with a volume of 0. The special bounding box is then subjected to bounding box intersection detection with the primary bounding box in turn to filter out the sub-regions where the special bounding box is located. Then, the tetrahedral elements of the sub-regions are traversed, and the special bounding box is then subjected to bounding box intersection detection with the secondary bounding box in turn to filter out the tetrahedral elements where the special bounding box is located.

[0041] After traversing the tetrahedral cells in the sub-region, the selected tetrahedral cells are used as the calculation objects. The directed volume of the target coordinates and the small tetrahedron formed by each face of the tetrahedral cells is calculated. If the sum of the directed volumes of all the small tetrahedrons is equal to the volume of the original tetrahedron, and the signs of the directed volumes of all the small tetrahedrons are the same as the signs of the volume of the original tetrahedron, then it means that the target coordinates are within the tetrahedral cells. In practical tests, when processing a high-precision mesh model with 72,000 tetrahedral elements, the element retrieval time under complex conditions was reduced from 2 hours to 10 minutes using the traditional traversal algorithm, achieving a speedup of 12 times, while memory usage was reduced by 37%. This indexing system achieves an efficient spatial pruning mechanism through a spatial recursive subdivision strategy. When processing a high-precision mesh model containing 72,000 tetrahedral elements, based on the spatial partitioning characteristics of KDTree tree nodes, it can quickly eliminate non-candidate tetrahedrons that do not intersect with the query region, transforming the operation that originally required traversing the entire dataset into accessing a subset of tree nodes. Simultaneously, the construction method of secondary AABB cells effectively reduces the memory overhead of the index structure while ensuring spatial coverage accuracy, achieving a good balance between space utilization and query efficiency.

[0042] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A thin-walled die casting mold flow simulation data hybrid extraction and efficient reconstruction method, characterized in that, Includes the following steps: Step 1: Extract data from the mold flow simulation results of the die casting and output the simulation results, including point cloud data. Step 2: Mesh the die casting. Discretize the geometry of the die casting into several tetrahedrons with specific geometric features and topological relationships to form a mesh model. Use the point cloud data obtained from data extraction and the mesh model to establish a coordinate system and spatial mapping relationship to form the die casting model. Remove tetrahedrons that do not contain point cloud data.

2. The method of claim 1, wherein the method is characterized by: In step one, during the mold flow simulation, the liquidus temperature, solidus temperature, and latent heat of fusion are input. The temperature field distribution at the end of filling is used as the initial value, and solidification data is output. The solidification data includes solidus velocity, thermal gradient, and cooling rate. The solidification time is calculated based on the solidification data.

3. The method of claim 2, wherein the method is characterized by: In step one, porosity is extracted from data based on the fluid dynamics shrinkage model. The analysis objective of the fluid dynamics shrinkage model is to identify the shrinkage pores formed when the pressure drops to the critical pressure during the solidification of the material.

4. The method of claim 1, wherein the method is characterized by: In step one, porosity is extracted from the data based on a simplified shrinkage model, which is used to evaluate the solidification shrinkage volume of each isolated liquid region in each time step of the die casting.

5. The method of claim 1, wherein the method is characterized by: In step one, the simulation results include temperature field information during solidification. Based on the temperature field information during solidification, redundant information is removed, including duplicate data, mold, and gating system.

6. The method of claim 1, wherein the method is characterized by, In step two, the method for determining whether a tetrahedron contains point cloud data is as follows: Calculate the directed volume of the cloud data coordinates to each face of the tetrahedron and the total volume of the tetrahedron. If the sum of the directed volumes of each face of the tetrahedron equals the directed volume of the tetrahedron, and the directed volumes of each face of the tetrahedron have the same sign as the directed volume of the tetrahedron, then the cloud data coordinates are within the tetrahedron.

7. The method of claim 6, wherein the method is characterized by, In step two, the method for determining whether a tetrahedron contains point cloud data is as follows: If there is a non-negative weight , , , If the following formula is satisfied, the cloud data coordinates P are in the tetrahedron: D； ； A, B, C, D - Coordinates of four points on a tetrahedron.

8. The method of claim 7, wherein the method is characterized by, The process also includes step three, which uses a neighborhood aggregation method based on virtual sphere scanning to simplify the data. Step 3.1: Using the virtual sphere as a tool, scan all the grid nodes within the virtual sphere; Step 3.2: Perform geometric mean calculation on the grid nodes within the virtual sphere, and compress these grid nodes into a new node; Step 3.3: The virtual sphere moves in steps not exceeding its radius, gradually covering the surface of the part until surface sampling is completed.

9. The method of claim 8, wherein the method is characterized by, In step three, the formula for calculating the virtual sphere radius γ is: ， 5； d avg- - the average distance of each point in the point cloud data to its nearest neighbor; α - Scale factor.

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