A digital mold simulation analysis method and system

Abstract

The present invention relates to the field of simulation technology, and specifically, to a digital mold simulation analysis method and system. It includes the following steps: collecting geometric data of the mold, determining edge devices, and using edge devices to collect and preprocess real-time operation data of the mold; establishing a three-dimensional model of the mold on a cloud platform based on the collected mold geometry data; running simulation algorithms on edge devices to predict physical phenomena of the mold; the cloud platform adjusts the mold design based on the real-time operation data and simulation results uploaded by the edge devices, and transmits the adjusted decision results to the edge devices. In this digital mold simulation analysis method and system, the synergy of edge computing and cloud computing is utilized to realize real-time monitoring, simulation prediction, and dynamic optimization of mold design, and ensure that resource allocation can meet current task requirements without over-configuration, thereby improving resource utilization and cost-effectiveness.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a digital mold simulation analysis method and system. Background Art

[0002] With the increasing requirements for product quality, production efficiency and cost control. The traditional trial-and-error mold design and manufacturing process is time-consuming and costly, and it is difficult to meet the rapid iteration and customization needs of modern industry. The development of high-performance computing technology enables complex simulation tasks to be completed in a short time. The emergence of cloud computing has further broken the geographical and resource limitations, so that companies do not need to invest a lot of money to build their own high-performance computing centers, but can rent cloud services on demand, greatly reducing computing costs and improving resource utilization. In the traditional mold design and simulation analysis process, data processing and analysis are often performed on central servers or workstations, which may lead to data transmission delays and slow response speeds, especially when processing large amounts of data. Therefore, a digital mold simulation analysis method and system are provided. Summary of the invention

[0003] The object of the present invention is to provide a digital mold simulation analysis method and system to solve the problems of data transmission delay and slow response speed mentioned in the above background technology.

[0004] To achieve the above object, the present invention provides a digital mold simulation analysis method, comprising the following steps:

[0005] S1. Collect the geometric data of the mold, determine the edge device, use the edge device to collect and preprocess the real-time operation data of the mold, and upload the preprocessed real-time operation data to the cloud platform;

[0006] S2. On the cloud platform, based on the collected mold geometry data, use CAD software to build a three-dimensional model of the mold, and divide the three-dimensional model into several small units to form a grid. At the same time, define the initial conditions and boundary conditions of the mold on the cloud platform;

[0007] S3, running the simulation algorithm on the edge device to predict the physical phenomena of the mold, and at the same time, the edge device transmits the simulation results to the cloud platform;

[0008] S4. The cloud platform adjusts the mold design according to the real-time operation data and simulation results uploaded by the edge device, and transmits the adjusted decision results to the edge device to achieve closed-loop control.

[0009] As a further improvement of the technical solution, in S1, the cloud platform can determine the target resource allocation in advance, and dynamically adjust the cloud resources according to the target resource allocation of the simulation task, as follows:

[0010] ;

[0011] in, Allocate resources to the target; For in time The predicted load; For safety margin; for resource efficiency; Allocate for minimum resources; for budget constraints;

[0012] The calculation demand factor is introduced into the calculation formula of the above target resource allocation for optimization, specifically:

[0013] ;

[0014] in, To calculate demand; is the scale of the simulation task; The amount of computation required for each unit size; is the duration of the simulation task;

[0015] ;

[0016] in, Allocate resources to the optimized target;

[0017] ;

[0018] in, for the adjusted resource allocation; Allocate resources for the current time; is the current load; is the scaling factor.

[0019] As a further improvement of the technical solution, the real-time operation data collected in S1 includes temperature, pressure and vibration data of the mold.

[0020] As a further improvement of the technical solution, in S2, the three-dimensional model is divided into a number of small units to form a grid, as follows:

[0021] S21. Select the mesh type and define the mesh size according to the geometric characteristics of the model and analysis requirements;

[0022] S22, calculate the size of each unit, and check the distortion and aspect ratio of the unit;

[0023] S23. Optimize the mesh through multiple iterations.

[0024] As a further improvement of the technical solution, the network is optimized through multiple iterations based on a grid optimization algorithm in S23, as follows:

[0025] ;

[0026] in, For grid nodes new location; For Node Current location; is the weight factor; is the number of neighbor nodes; For Node The set of neighbors of ; Neighbor node location.

[0027] As a further improvement of the present technical solution, in S2, the initial conditions and boundary conditions of the mold are defined, wherein the initial conditions include the initial temperature, initial pressure and initial vibration data of the mold; and the boundary conditions include thermal boundary conditions, mechanical boundary conditions and contact conditions.

[0028] As a further improvement of the technical solution, in S3, predicting the physical phenomena of the mold includes predicting the temperature distribution in the mold, the flow of the fluid in the mold, and the deformation of the mold.

[0029] As a further improvement of the present technical solution, adjusting the mold design in S4 includes adjusting the cooling channel layout, material thickness and material shape.

[0030] As a further improvement of the technical solution, the adjustment mold design is based on an objective function, wherein the objective function includes the weighted influence of cooling time, cost, weight and structural strength, as follows:

[0031] ;

[0032] in, For cooling time; The quality of the mold material; The temperature drop required for the mold material to cool from the molten state to the demoulding temperature; is the convective heat transfer coefficient between the mold surface and the cooling medium; It is the effective area of ​​contact between the mold surface and the cooling medium; is the temperature drop of the cooling medium;

[0033] ;

[0034] in, For cost; For material cost; is the processing cost; for energy costs; For maintenance costs;

[0035] ;

[0036] in, is weight; is the total volume of the mold; is the density of the material;

[0037] ;

[0038] in, For structural strength; is the force acting on the mold; is the effective bearing area; is the yield strength of the material;

[0039] but:

[0040] ;

[0041] in, is the objective function; is the weight of the cooling time; is the weight of cost; is the weight of the weight; is the weight of the structural strength.

[0042] On the other hand, the present invention provides a digital mold simulation analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned digital mold simulation analysis method.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. This digital mold simulation analysis method and system utilizes the synergy of edge computing and cloud computing to achieve real-time monitoring, simulation prediction and dynamic optimization of mold design, including data collection, model building, simulation prediction and closed-loop control, covering the comprehensive process from data source to cloud platform.

[0045] 2. In the digital mold simulation analysis method and system, the dynamic adjustment strategy is used to ensure that resources are allocated on demand, avoid resource waste, and improve the overall resource utilization efficiency. The computing demand factor is introduced into the target strategy allocation for optimization to ensure that resource allocation can meet the current task requirements without over-configuration, thereby improving resource utilization and cost-effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0048] See also Figure 1 As shown, this embodiment provides a digital mold simulation analysis method, including the following steps:

[0049] S1. Collect the geometric data of the mold, determine the edge device, use the edge device to collect and preprocess the real-time operation data of the mold, and upload the preprocessed real-time operation data to the cloud platform; the real-time operation data includes the temperature, pressure and vibration data of the mold;

[0050] Edge devices are responsible for real-time data collection and preliminary processing, while the cloud platform is responsible for computationally intensive tasks such as model building, meshing, and complex simulation calculations. This division of labor can ensure efficient and accurate mold simulation analysis;

[0051] The cloud platform can determine the target resource allocation in advance and dynamically adjust the cloud resources according to the target resource allocation of the simulation task, as follows:

[0052] ;

[0053] in, Allocate resources to the target; For in time The predicted load is the load at a certain point in the future predicted by the machine learning model; As a safety margin, in order to cope with forecast errors, the actual allocated resources will be slightly higher than the forecast demand; is resource efficiency, which is the amount of computation that can be processed per unit of resource per unit of time; Allocate for minimum resources; It is a budget constraint, which limits the maximum amount of resources that can be allocated;

[0054] It is the predicted load taking into account the safety margin; It is the theoretical resource requirement based on the predicted load and resource efficiency calculation; is to ensure that the allocated resources do not fall below the minimum resource requirements; is to ensure that the allocated resources do not exceed the budget constraints.

[0055] The calculation demand factor is introduced into the calculation formula of the above target resource allocation for optimization, specifically:

[0056] ;

[0057] in, To calculate demand; is the scale of the simulation task, which can be the number of entities, grid points, particles, etc. involved in the simulation. The unit may vary depending on the simulation type. The amount of computation required per unit scale, such as the number of computational operations per grid point, particle, or entity, in units of (floating-point operations per second) or any other unit of measure of computational complexity; is the duration of the simulation task;

[0058] ;

[0059] in, Allocate resources to the optimized target;

[0060] This formula takes into account two sources of load: one is based on the computational requirements of the current task. , and the other is future load prediction based on historical data The formula first takes the larger of the two to ensure that resource allocation can meet the higher demand. Then, it compares the resource efficiency Combined, calculate the theoretical resource requirements and add a safety margin to deal with potential prediction errors. Next, by comparing and budget cap Comparison can be made to ensure that resource allocation is neither too low nor too high, thus finding a balance between cost control and service quality.

[0061] ;

[0062] in, for the adjusted resource allocation; Current resource allocation can be obtained directly from the cloud platform’s monitoring system, as it reflects the amount of resources currently being used by the system; is the current load; is the scaling factor; the scaling factor determines how quickly the resource is adjusted. For example, if Then resource adjustments will be made gradually, with each adjustment reaching only half the distance to the target; if , the resource will be adjusted to the target value immediately. In practical applications, The choice needs to balance resource utilization efficiency and service quality to avoid over- or under-allocation of resources.

[0063] S2. On the cloud platform, based on the collected mold geometry data, use CAD software to build a three-dimensional model of the mold, and divide the three-dimensional model into several small units to form a grid. At the same time, define the initial conditions and boundary conditions of the mold on the cloud platform;

[0064] In S2, the three-dimensional model is divided into several small units to form a grid, as follows:

[0065] S21. Select the mesh type and define the mesh size according to the geometric characteristics of the model and analysis requirements;

[0066] Select the mesh type based on the geometric complexity of the model and the type of analysis (such as fluid flow, heat conduction, structural analysis, etc.). Common mesh types include structured meshes (such as hexahedral meshes), unstructured meshes (such as tetrahedral meshes), and hybrid meshes (combining the advantages of structured and unstructured meshes);

[0067] Determining the size of each mesh element can be done through global mesh sizing or local refinement, where local refinement uses smaller elements in areas where higher resolution is required (e.g., sharp corners, high stress areas, high heat flow areas);

[0068] S22. Calculate the size of each element, ensure sufficient mesh density in key areas to capture details while maintaining computational efficiency, and check the distortion and aspect ratio of the element;

[0069] Distortion is often used to describe how much the element shape deviates from the ideal shape. For triangular and quadrilateral elements, distortion can be defined as the ratio of the smallest interior angle to the largest interior angle. For tetrahedral elements, distortion can be defined as the ratio of the smallest dihedral angle to the largest dihedral angle.

[0070] Ideally, if the element is a square or equilateral triangle, then the minimum and maximum angles are equal and the distortion is equal to 1, but as the deviation from the element shape increases, the distortion decreases until it approaches zero, indicating that the element is severely distorted.

[0071] The aspect ratio describes the proportional relationship between the lengths of the cell sides. For a two-dimensional rectangular cell, the aspect ratio AR AR Defined as the length of the longest side in the cell The length of the shortest side The ratio of

[0072] An ideal cell (such as a square) has an aspect ratio of 1, meaning that all sides are the same length. For non-square rectangular cells, an aspect ratio greater than 1 means that the cell is stretched in some direction. In the three-dimensional case, for hexahedral cells, the aspect ratio can be defined as the ratio of the longest side to the shortest side of the cell, or more generally, it can be defined based on the ratio of the dimensions in the direction of the major axis of the cell.

[0073] In meshing, a good mesh usually requires that the distortion of all cells is close to 1 and the aspect ratio is close to 1, which means that the cells are close to squares or regular hexahedrons, which helps improve the accuracy and stability of numerical solutions. However, in practical applications, in order to balance computational efficiency and accuracy, the mesh may have a higher aspect ratio or lower distortion in some areas, especially in areas where higher resolution details are required.

[0074] S23, optimize the grid through multiple iterations; ensure that there is sufficient grid density in key areas, while maintaining lower density in other areas to save computing resources; optimize the network through multiple iterations based on the grid optimization algorithm, the grid optimization algorithm is Smoothing is to reduce mesh distortion by moving nodes to the average position of their neighboring nodes. , its new location It can be calculated by the following formula:

[0075] ;

[0076] in, For grid nodes new location; For Node Current location; is a weight factor used to control the degree of mixing between the original position and the average position. Usually, represents complete smoothness, while This means no smoothing is performed; is the number of neighbor nodes; For Node The set of neighbors of ; Neighbor node location.

[0077] In S2, the initial conditions and boundary conditions of the mold are defined, wherein the initial conditions include the initial temperature, initial pressure and initial vibration data of the mold; and the boundary conditions include thermal boundary conditions, mechanical boundary conditions and contact conditions.

[0078] Thermal boundary conditions include convection, radiation, and conduction boundary conditions, which determine the heat exchange between the mold and the surrounding environment or other objects. For example, the heat exchange efficiency between the mold and the cooling medium (such as cooling water) directly affects the cooling time. Mechanical boundary conditions include fixed boundaries, free boundaries, applied forces or moments, etc., which affect the stress state and deformation of the mold. When designing a mold, correctly setting the mechanical boundary conditions can help predict the stress distribution and fatigue life of the mold during use. In a multi-component system, contact conditions describe the interaction between different components, including contact pressure, friction coefficient, and contact stiffness.

[0079] Accurate initial conditions and boundary conditions can ensure that the simulation model reflects the actual situation as much as possible and reduce prediction errors. Correct application of initial conditions and boundary conditions in simulation can replace some physical experiments and save cost and time.

[0080] S3. Run the simulation algorithm on the edge device to predict the physical phenomena of the mold. At the same time, the edge device transmits the simulation results to the cloud platform. Predicting the physical phenomena of the mold includes predicting the temperature distribution in the mold, the flow of the fluid in the mold, and the deformation of the mold.

[0081] S4. The cloud platform adjusts the mold design based on the real-time operation data and simulation results uploaded by the edge device, and transmits the adjusted decision results to the edge device to achieve closed-loop control. Among them, adjusting the mold design includes adjusting the cooling channel layout, material thickness, and material shape.

[0082] Optimization of cooling channel layout focuses on improving the temperature distribution of the mold to reduce cooling time and avoid thermal stress; adjustment of material thickness aims to optimize the mechanical properties of the mold, such as strength and stiffness; shape optimization may involve structural optimization and fluid dynamics optimization to improve the mechanical properties or fluid flow performance of the mold.

[0083] Adjusting the mold design is based on an objective function, where the objective function includes the weighted effects of cooling time, cost, weight, and structural strength, as follows:

[0084] ;

[0085] in, For cooling time; The quality of the mold material; The temperature drop required for the mold material to cool from the molten state to the demoulding temperature; is the convective heat transfer coefficient between the mold surface and the cooling medium; It is the effective area of ​​contact between the mold surface and the cooling medium; is the temperature drop of the cooling medium;

[0086] Shortening the cooling time means that a molding cycle can be completed faster, thereby improving productivity. By adjusting the mold design, such as changing the layout of the cooling channels or increasing the number of cooling channels, the heat transfer efficiency can be optimized, thereby reducing the cooling time;

[0087] ;

[0088] in, For cost; For material cost; is the processing cost; for energy costs; For maintenance costs;

[0089] When optimizing the design, the goal is to reduce costs while ensuring the performance and service life of the mold;

[0090] ;

[0091] in, is weight; is the total volume of the mold; is the density of the material;

[0092] Weight of mold It may affect its convenience of handling and installation, as well as energy consumption (such as in an automated production line). Reducing the weight of the mold can reduce energy consumption and improve operating efficiency without affecting its structural integrity;

[0093] ;

[0094] in, For structural strength; is the force acting on the mold; is the effective bearing area; is the yield strength of the material;

[0095] Structural strength Ensure that the mold can withstand the expected load without failure during use. When designing, it is necessary to ensure that the mold has sufficient strength to withstand the pressure during the molding process, while avoiding the cost increase caused by over-design.

[0096] but:

[0097] ;

[0098] in, is the objective function; is the weight of the cooling time; is the weight of cost; is the weight of the weight; is the weight of the structural strength.

[0099] Objective Function Combining all the above factors, different weights are assigned to reflect the relative importance of each indicator in the overall optimization. The choice of weights depends on the specific application scenario and priority. For example, in some cases, cost may be the most important consideration, while in other cases, cooling time or structural strength may be more critical.

[0100] By adjusting the design parameters , such as mold geometry, material selection, cooling channel layout, etc., can be iteratively optimized , until a mold design is found that achieves the best balance between cooling time, cost, weight and structural strength. Example 2

[0101] This embodiment provides a digital mold simulation analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the digital mold simulation analysis method.

[0102] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A digital mold simulation analysis method, characterized in that: The following steps are involved: S1. Collect the geometric data of the mold, determine the edge device, use the edge device to collect and preprocess the real-time operation data of the mold, and upload the preprocessed real-time operation data to the cloud platform; In S1, the cloud platform determines the target resource allocation in advance, and dynamically adjusts the cloud resources according to the target resource allocation of the simulation task, as follows: ； in, Allocate resources to the target; For in time The predicted load; For safety margin; for resource efficiency; Allocate for minimum resources; for budget constraints; The calculation demand factor is introduced into the calculation formula of the above target resource allocation for optimization, specifically: ； in, To calculate the demand; is the scale of the simulation task; The amount of computation required for each unit size; is the duration of the simulation task; ； in, Allocate resources to the optimized target; ； in, for the adjusted resource allocation; Allocate resources for the current time; is the current load; is the scaling factor; S2. On the cloud platform, based on the collected mold geometry data, use CAD software to build a three-dimensional model of the mold, and divide the three-dimensional model into several small units to form a grid. At the same time, define the initial conditions and boundary conditions of the mold on the cloud platform; S3, running the simulation algorithm on the edge device to predict the physical phenomena of the mold, and at the same time, the edge device transmits the simulation results to the cloud platform; S4. The cloud platform adjusts the mold design according to the real-time operation data and simulation results uploaded by the edge device, and transmits the adjusted decision results to the edge device to achieve closed-loop control; The adjustment mold design is based on an objective function, wherein the objective function includes weighted effects of cooling time, cost, weight, and structural strength, as follows: ； in, For cooling time; The quality of the mold material; The temperature drop required for the mold material to cool from the molten state to the demoulding temperature; is the convective heat transfer coefficient between the mold surface and the cooling medium; It is the effective area of ​​contact between the mold surface and the cooling medium; is the temperature drop of the cooling medium; ； in, For cost; For material cost; is the processing cost; for energy costs; For maintenance costs; ； in, is weight; is the total volume of the mold; is the density of the material; ； in, For structural strength; is the force acting on the mold; is the effective bearing area; is the yield strength of the material; but: ； in, is the objective function; is the weight of the cooling time; is the weight of cost; is the weight of the weight; is the weight of the structural strength.

2. The digital mold simulation analysis method according to claim 1 is characterized in that: The real-time operation data collected in S1 includes temperature, pressure and vibration data of the mold.

3. The digital mold simulation analysis method according to claim 2 is characterized in that: In S2, the three-dimensional model is divided into a number of small units to form a grid, as follows: S21. Select the mesh type and define the mesh size according to the geometric characteristics of the model and analysis requirements; S22, calculate the size of each unit, and check the distortion and aspect ratio of the unit; S23. Optimize the mesh through multiple iterations.

4. The digital mold simulation analysis method according to claim 3 is characterized in that: In S23, the network is optimized through multiple iterations based on a grid optimization algorithm, as follows: ； in, For grid nodes new location; For Node Current location; is the weight factor; is the number of neighbor nodes; For Node The set of neighbors of ; Neighbor node location.

5. The digital mold simulation analysis method according to claim 4 is characterized in that: In S2, the initial conditions and boundary conditions of the mold are defined, wherein the initial conditions include the initial temperature, initial pressure and initial vibration data of the mold; and the boundary conditions include thermal boundary conditions, mechanical boundary conditions and contact conditions.

6. The digital mold simulation analysis method according to claim 5 is characterized in that: In S3, predicting the physical phenomena of the mold includes predicting the temperature distribution in the mold, the flow of the fluid in the mold, and the deformation of the mold.

7. The digital mold simulation analysis method according to claim 6 is characterized in that: Adjusting the mold design in S4 includes adjusting the cooling channel layout, material thickness, and material shape.

8. A digital mold simulation analysis system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the digital mold simulation analysis method as described in any one of claims 1-7.

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