Optimization design method and system for precise mold cooling system

Abstract

The invention discloses an optimal design method and system for a precise mold cooling system, and relates to the technical field of mold design and manufacturing, the method comprises the following steps: obtaining structure parameters of a precise mold and thermophysical parameters of an injection molding material, and constructing a three-dimensional thermodynamic simulation model; performing temperature field dynamic simulation based on the three-dimensional thermodynamic simulation model, and identifying a hot spot distribution area on the surface of a mold cavity; and performing multi-objective optimization according to the hot spot distribution area, performing iterative optimization on topological structure parameters of the cooling channel in the precision mold according to an optimization result, and generating a cooling channel optimization scheme of the precision mold. The technical problems that in the design of a precise mold cooling system, temperature distribution is not uniform, and cooling efficiency is low are solved, and the technical effects that the temperature field is homogenized, the cooling efficiency is improved, the defect rate of injection molding parts is reduced, and the production period is shortened are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of mold design and manufacturing, and in particular to an optimization design method and system for a precision mold cooling system. Background Art

[0002] During the precision mold injection molding process, the design quality of the cooling system is crucial to molding efficiency and product accuracy. Conventional precision mold cooling system design relies primarily on experience or static simulation, achieving heat dissipation through fixed cooling channels. This approach can meet basic requirements for simple molds or stable operating conditions, but as precision molds become more complex and demanding, their limitations are becoming increasingly apparent:

[0003] On the one hand, traditional designs do not fully combine geometric features such as mold surface curvature and wall thickness distribution with the thermophysical properties of the injection molding material (such as phase change temperature), resulting in a mismatch between the cooling channel layout and the mold's thermal conductivity characteristics, which easily forms hot spots on the cavity surface and causes local overheating; on the other hand, static simulation cannot simulate the dynamic fluctuations of the temperature field during the injection molding cycle, and lacks a multi-objective optimization mechanism for the cooling channel topology (such as the balance between temperature uniformity and flow resistance), resulting in low cooling efficiency and a high defect rate of injection molded parts. Summary of the Invention

[0004] This application provides an optimization design method and system for a precision mold cooling system, which is used to solve the technical problems of uneven temperature distribution and low cooling efficiency in the design of a precision mold cooling system.

[0005] The first aspect of the present application provides an optimization design method for a precision mold cooling system, the method comprising: obtaining structural parameters of the precision mold and thermal physical properties of the injection molding material, and constructing a three-dimensional thermodynamic simulation model; performing dynamic simulation of the temperature field based on the three-dimensional thermodynamic simulation model, and identifying the hotspot distribution area on the mold cavity surface; performing multi-objective optimization based on the hotspot distribution area, and iteratively optimizing the topological structure parameters of the cooling channel in the precision mold based on the optimization results to generate an optimization solution for the cooling channel of the precision mold.

[0006] The second aspect of the present application provides an optimization design system for a precision mold cooling system, the system comprising: a three-dimensional model construction module for obtaining the structural parameters of the precision mold and the thermal physical properties of the injection molding material, and constructing a three-dimensional thermodynamic simulation model; a hotspot distribution area identification module for performing dynamic simulation of the temperature field based on the three-dimensional thermodynamic simulation model, and identifying the hotspot distribution area on the mold cavity surface; a cooling optimization scheme generation module for performing multi-objective optimization based on the hotspot distribution area, iteratively optimizing the topological structure parameters of the cooling channel in the precision mold based on the optimization results, and generating a cooling channel optimization scheme for the precision mold.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] This application constructs a three-dimensional thermodynamic simulation model by obtaining the structural parameters of the precision mold (surface curvature, wall thickness) and the thermal physical properties of the injection molding material. The hotspot distribution area on the cavity surface is identified through dynamic simulation of the temperature field. Multi-objective optimization is performed around the hotspot coordinates and temperature extremes. The cooling channel topology parameters are iteratively optimized to generate candidate solutions. Combined with fluid-solid coupling simulation verification and temperature difference gradient evaluation, the cooling channel optimization solution that meets the expected temperature difference threshold is screened out, thereby accurately solving the problem of uneven temperature distribution in the mold cavity, improving cooling efficiency and injection molding quality, making the design of the precision mold cooling system more scientific and reliable, and achieving the technical effects of temperature field homogenization and improved cooling efficiency, reducing the defect rate of injection molded parts and shortening the production cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 It is a flow chart of the optimization design method of the precision mold cooling system provided in the embodiment of the present application.

[0011] Figure 2 It is a structural diagram of the optimization design system of the precision mold cooling system provided in the embodiment of the present application.

[0012] Explanation of the accompanying drawings: three-dimensional model construction module 1, hot spot distribution area identification module 2, cooling optimization plan generation module 3. DETAILED DESCRIPTION

[0013] This application provides an optimization design method and system for a precision mold cooling system, which is used to solve the technical problems of uneven temperature distribution and low cooling efficiency in the design of a precision mold cooling system.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the optimization design method of the precision mold cooling system, wherein the method includes:

[0017] Step A100: Obtain the structural parameters of the precision mold and the thermophysical parameters of the injection molding material, and construct a three-dimensional thermodynamic simulation model.

[0018] In one embodiment of the present application, first, three-dimensional laser scanning is used to obtain surface curvature data, ultrasonic thickness measurement is used to obtain wall thickness data, and spatial alignment is performed to obtain precision mold structure parameters. The specific steps are described in detail in A110-A130.

[0019] Then, a differential scanning calorimeter is used to obtain the thermophysical properties of the injection molding material. The precision mold structure parameters are integrated to establish a matrix and identify the initial position of the cooling channel. The initial position and material parameters are combined to perform non-uniform grid division and construct a three-dimensional thermodynamic simulation model. The specific steps are described in detail in A140-A160.

[0020] Step A200: Performing a dynamic simulation of the temperature field based on the three-dimensional thermodynamic simulation model to identify the hotspot distribution area on the mold cavity surface.

[0021] In the embodiment of the present application, the hot spot distribution area refers to the area where the surface temperature of the mold cavity is abnormally increased, which is identified after performing dynamic simulation of the temperature field of the three-dimensional thermodynamic simulation model.

[0022] Optionally, retrieve the injection cycle parameters and analyze the melt temperature curve, synchronize them to the simulation model for multi-cycle iterative calculation, extract the transient temperature data to generate a spatiotemporal distribution matrix, and use this to identify the hotspot distribution area on the mold cavity surface. The specific steps are detailed in A210-A240.

[0023] Step A300: performing multi-objective optimization based on the hotspot distribution area, iteratively optimizing the topological structure parameters of the cooling channel in the precision mold based on the optimization results, and generating an optimization solution for the cooling channel of the precision mold.

[0024] In the embodiments of the present application, the topological structure parameters refer to the geometric characteristic parameters such as the layout shape, direction, spacing, and aperture of the cooling channels in the precision mold.

[0025] In one embodiment of the present application, the hot spot area is traversed to extract the target coordinates and temperature extremes and perform temperature difference optimization, the cooling channel topology structure is iteratively optimized to generate a candidate set, and the cooling channel optimization solution is screened out through transient heat conduction simulation and fitness analysis. The specific steps are described in detail in A310-A350.

[0026] Furthermore, step A100 in the method provided in the embodiment of the present application includes:

[0027] A110: Use a 3D laser scanner to perform multi-point scanning on the cavity of a precision mold to obtain surface curvature distribution data.

[0028] A120: Use ultrasonic thickness gauges to perform traversal inspections on various areas of precision molds to obtain multi-zone wall thickness data.

[0029] A130: spatially aligning the profile curvature distribution data with the multi-zone wall thickness data to obtain the structural parameters of the precision mold.

[0030] In the embodiments of this application, surface curvature is geometric characteristic data reflecting the degree of curvature of various parts of the cavity surface, obtained by scanning the cavity of a precision mold at multiple points using a 3D laser scanner. This data is used to characterize parameters such as the concave-convex shape and curvature radius of the cavity surface. Multi-zone wall thickness is thickness data obtained at different locations by traversing various areas of the precision mold using an ultrasonic thickness gauge. This data is used to describe the wall thickness distribution differences of various parts of the mold (such as the core and cavity plate).

[0031] Specifically, when obtaining precision mold structural parameters, the mold cavity is first scanned at multiple points using a 3D laser scanner. 3D laser scanning technology can rapidly acquire three-dimensional coordinate data of an object's surface in a non-contact manner. By densely sampling different locations in the cavity, the curvature distribution of the mold surface can be accurately acquired. For convex or concave areas of complex surfaces, the scanner generates high-density point cloud data. After processing, this data is converted into numerical information reflecting the curvature changes in each part, such as the curvature radius and curvature change trends. This data intuitively presents the geometric characteristics of the cavity surface.

[0032] Next, an ultrasonic thickness gauge is used to perform a thorough inspection of each area of ​​the mold. The ultrasonic thickness gauge calculates the thickness by emitting ultrasonic waves and measuring their reflection time at the interface of different media. It can penetrate mold materials (such as metal) to achieve non-destructive testing. During the inspection process, key parts of the mold, such as the core and cavity plate, are measured point by point at a certain grid spacing to obtain multi-zone wall thickness data. For example, for locations where the wall thickness may suddenly change, such as the edge area of ​​the mold and near the reinforcement ribs, the thickness gauge can accurately record the specific thickness value, forming a wall thickness distribution matrix for each area, clearly reflecting the thickness differences at different locations of the mold.

[0033] Subsequently, the surface curvature distribution data and the multi-zone wall thickness data are spatially aligned. Spatial alignment is to unify data from different sources into the same coordinate system through coordinate transformation, ensuring that the two types of data correspond one-to-one in spatial position. In the specific operation, with the design reference coordinate system of the mold as a reference, the surface curvature data obtained by laser scanning and the wall thickness data obtained by ultrasonic thickness measurement are coordinate transformed and matched. For example, the curvature value of a certain measuring point is associated with the wall thickness value at its location to form a composite data point containing spatial position, curvature characteristics and wall thickness characteristics. In this way, the discrete geometric feature data is integrated into complete mold structure parameters to fully describe the three-dimensional geometric shape of the mold.

[0034] Through the above steps, leveraging the high-precision geometric measurement capabilities of 3D laser scanning and the non-destructive testing advantages of ultrasonic thickness measurement, combined with spatial registration technology to achieve data fusion, the precise mold structural parameters including surface curvature and wall thickness distribution were accurately obtained. This laid the data foundation for the subsequent construction of a high-precision 3D thermodynamic simulation model, ensuring that the model can truly reflect the actual geometric structure of the mold, thereby improving the accuracy and effectiveness of the cooling system design.

[0035] Furthermore, step A100 in the method provided in the embodiment of the present application includes:

[0036] A140: Use a differential scanning calorimeter to test the phase change temperature of the injection molding material to obtain the thermal physical property parameters of the injection molding material.

[0037] A150: Fusing the profile curvature distribution data with the multi-zone wall thickness data to establish a mold structure parameter matrix, and identifying initial position information of the cooling channel according to the mold structure parameter matrix.

[0038] A160: The thermophysical property parameters of the injection molding material are integrated into the mold structure parameter matrix and combined with the initial position information of the cooling channel to perform non-uniform grid division to construct the three-dimensional thermodynamic simulation model.

[0039] In the embodiment of the present application, the phase change temperature test is a process of testing the injection molding material using a differential scanning calorimeter to obtain the thermal physical property parameters of the injection molding material.

[0040] Optionally, when constructing a three-dimensional thermodynamic simulation model, it is first necessary to obtain the thermophysical properties of the injection molding material. The injection molding material is subjected to a phase change temperature test using a differential scanning calorimeter (DSC), which can accurately measure the heat flow changes of the material during heating or cooling. For example, during the test, the material sample is heated to a molten state at a constant rate and the heat flow curve is recorded. Key parameters such as the melting temperature and crystallization temperature of the material can be obtained, and thermal characteristic data such as melting enthalpy (the amount of heat absorbed or released during phase change per unit mass of material) and crystallinity (the proportion of crystalline phase in the material) can be calculated. These parameters directly reflect the heat conduction and phase change behavior of the material during the injection molding process, and are the core input for describing the thermophysical properties of the material in the simulation model.

[0041] Then process the mold structure parameters. The surface curvature distribution data obtained by three-dimensional laser scanning is fused with the multi-zone wall thickness data obtained by ultrasonic thickness measurement. During the data fusion process, the spatial coordinates of the mold are used as a reference, and the curvature value and wall thickness value of each measuring point are mapped to a unified grid node to form a mold structure parameter matrix containing information such as position coordinates, curvature radius, and wall thickness dimensions. For example, for a certain coordinate point (x, y, z) on the surface of the mold cavity, the matrix records its curvature radius R and wall thickness h. In this way, discrete geometric data is converted into a structured matrix form. Based on this matrix, combined with the basic rules of cooling system design, such as the minimum distance between the cooling channel and the cavity surface, avoiding stress concentration areas, etc., the initial position information of the cooling channel is identified, such as preliminarily planning the channel direction and distribution in corner areas with larger curvature or areas with thicker wall thickness.

[0042] Finally, non-uniform meshing is performed. The thermophysical properties of the injection molding material (such as melting enthalpy and crystallinity) are integrated into the mold structure parameter matrix so that each mesh node has both geometric characteristics and material thermal properties. For areas near the initial position of the cooling channel and potential hot spots of the mold, such as areas with large wall thickness or sudden changes in curvature, a smaller mesh size is used for local encryption, while a larger mesh is used in areas with simple structure and uniform heat conduction to balance the calculation accuracy and efficiency. For example, in areas with wall thickness h>5mm, the mesh size is set to 0.5mm×0.5mm×0.5mm, while in areas with wall thickness h≤5mm, the mesh size is expanded to 1mm×1mm×1mm. Through this non-uniform partitioning method, a three-dimensional thermodynamic simulation model that can accurately reflect the geometric characteristics of the mold and the thermal behavior of the material is constructed.

[0043] Through the above steps, a differential scanning calorimeter is used to obtain the material's thermophysical properties. Data fusion technology is combined to establish a mold structure parameter matrix that includes geometric and thermal characteristics. Non-uniform meshing is used to highlight the calculation accuracy of key areas. Ultimately, a high-precision three-dimensional thermodynamic simulation model is constructed, providing a reliable analysis basis for dynamic simulation of temperature fields and optimization of cooling channels. This ensures that the simulation results can accurately predict the heat conduction process within the mold, thereby improving the scientific nature and effectiveness of cooling system design.

[0044] Furthermore, step A200 in the method provided in the embodiment of the present application includes:

[0045] A210: Retrieve injection molding cycle parameters, perform melt temperature record analysis according to the injection molding cycle parameters, and construct melt temperature curve parameters.

[0046] A220: Synchronize the melt temperature curve parameters to the three-dimensional thermodynamic simulation model, perform multi-cycle iterative calculation according to the injection cycle parameters, and obtain multi-cycle thermal temperature simulation fluctuation data.

[0047] A230: Extracting transient temperature data of cavity surface nodes based on the multi-cycle thermal temperature simulation fluctuation data to generate a temperature field spatiotemporal distribution matrix.

[0048] A240: Identify hot spots according to the temperature field spatiotemporal distribution matrix to determine the hot spot distribution area on the mold cavity surface.

[0049] Specifically, when performing dynamic simulation of the temperature field based on a three-dimensional thermodynamic simulation model, first, the injection molding cycle parameters are retrieved. The injection molding cycle parameters include key process parameters such as injection time, holding time, and cooling time. At the same time, the temperature change of the melt during the injection molding process is recorded in real time by a temperature sensor. For example, during the injection molding stage, the melt temperature is usually maintained at 200-280°C. The specific value is adjusted by technical personnel in this field according to the material type. By analyzing these temperature data, curve parameters reflecting the change of melt temperature over time, such as heating rate, peak temperature, cooling rate, etc., are constructed to form a complete melt temperature curve.

[0050] Next, the melt temperature curve parameters are synchronized with the 3D thermodynamic simulation model, and multi-cycle iterative calculations are performed according to the injection cycle parameters. The simulation model simulates the heat conduction process of the mold during each injection cycle. For example, during the injection phase of the first cycle, the model inputs high-temperature boundary conditions based on the melt temperature curve and calculates the temperature rise process of the mold cavity surface. During the cooling phase, the model calculates the heat dissipation process from the mold to the cooling medium in combination with the initial design parameters of the cooling channel. By repeatedly executing the simulation calculation for multiple cycles, such as simulating 50-100 consecutive cycles, multi-cycle thermal temperature simulation fluctuation data is obtained. This data contains the temperature changes of various parts of the mold at different times during each cycle.

[0051] Then, the transient temperature data of the cavity surface nodes are extracted from the multi-cycle thermal temperature simulation fluctuation data. Transient temperature data refers to the specific temperature value of each node at different time points. For example, for the node with the cavity surface coordinate (x0, y0, z0), its temperature value at the 10th, 20th, and 30th time points in each cycle is extracted to form the temperature time series of the node. After integrating the transient temperature data of all nodes, a spatiotemporal distribution matrix of the temperature field is generated. The matrix uses the spatial coordinates (x, y, z) and time t as indexes to record the temperature values ​​at the corresponding positions and moments. For example, the matrix element T(x, y, z, t) represents the temperature at the coordinates (x, y, z) at time t.

[0052] Finally, hotspot identification is performed according to the spatiotemporal distribution matrix of the temperature field. The standard for hotspot identification is usually set as an area where the temperature exceeds a certain threshold of the average temperature (such as 15-20°C above the average temperature). By traversing each element in the matrix, the node groups whose temperature is continuously higher than the threshold are identified. These node groups constitute the hotspot distribution area on the mold cavity surface. For example, if it is found in the spatiotemporal distribution matrix that the temperature of a certain area in the cooling stage for 10 consecutive cycles is higher than the average temperature by 20°C, then the area is determined to be a hotspot area.

[0053] Through the above steps, the injection cycle parameters are used to drive the simulation model for multi-cycle iterative calculations. Combined with the spatiotemporal distribution analysis of transient temperature data, the hotspot distribution area on the mold cavity surface is accurately located, providing clear optimization targets for the subsequent multi-objective optimization of the cooling channel topology structure. This achieves accurate mapping from dynamic simulation data to key problem areas, improving the targetedness and effectiveness of cooling system optimization.

[0054] Furthermore, step A300 in the method provided in the embodiment of the present application includes:

[0055] A310: traverse the hotspot distribution area to perform multi-target extraction, determine the target hotspot coordinates and the target temperature extreme value, and there is a corresponding relationship between the target hotspot coordinates and the target temperature extreme value.

[0056] A320: Optimizing the temperature difference of the target hotspot coordinates based on the target temperature extreme value to obtain an optimization result.

[0057] A330: Iteratively optimize the topology parameters of the cooling channel according to the optimization result to generate a candidate topology set.

[0058] A340: traversing the candidate topology structure set to perform transient heat conduction simulation, obtaining cavity surface temperature difference data, performing fitness analysis based on the cavity surface temperature difference data, and constructing multiple fitness values.

[0059] A350: Arrange the cavity surface temperature difference data in descending order based on the multiple fitness values, screen the candidate topology structure set according to the temperature difference sequence, and construct the cooling channel optimization solution for the precision mold.

[0060] Specifically, when performing multi-objective optimization on a hotspot distribution area, first, traverse the area to extract the key optimization objectives. By analyzing the spatiotemporal distribution matrix of the temperature field, the spatial three-dimensional coordinates (x, y, z) of each hotspot area and its corresponding temperature extremes (such as the maximum temperature T max ), establish a one-to-one correspondence between the target hotspot coordinates and the temperature extremes. For example, in the hotspot area A, the temperature extremes at the coordinates (10,20,5) are determined to be 120°C, and at the coordinates (15,25,8) is 115°C, forming a two-tuple data set containing position and temperature {(x1,T1),(x2,T2),...,(x n ,T n )}, providing clear target parameters for subsequent optimization.

[0061] Next, the temperature difference is optimized based on the target temperature extreme value. Set the target cooling temperature T target (usually the ideal working temperature of the mold material, such as 60-80°C), calculate the temperature difference of each target hot spot ΔT = T max -T target , taking ΔT as the optimization objective function, by adjusting the spatial distance and angle between the cooling channel and the hotspot area, ΔT is gradually reduced to near zero. For example, for a hotspot with a temperature extreme of 120°C, setting T target =70°C, the target temperature difference optimization value is 50°C. Through iterative calculation of the layout adjustment of the cooling channel, the temperature of the hot spot area is gradually reduced to the target range, forming preliminary optimization results. For example, it is recommended that the minimum distance between the cooling channel and the hot spot is 15mm, and the angle between the channel direction and the heat flow direction is 45°.

[0062] Then, the cooling channel topology parameters are iteratively optimized based on the optimization results. Topology parameters include channel direction, spacing, aperture, etc. Through parametric modeling methods, such as using the sketch constraint function in two-dimensional drawing software, the optimization results are converted into specific geometric parameter adjustments. For example, for a certain hot spot area, the original straight channel is adjusted to a spiral type to enhance heat dissipation, or branch channels are added in dense hot spot areas to form a set of candidate topology structures with different layouts. Each candidate solution corresponds to a specific set of topological parameters (such as channel length L = 100mm, aperture d = 8mm, spacing s = 20mm, etc.).

[0063] Subsequently, transient heat conduction simulation is performed on the candidate topology set. The heat conduction process of each candidate solution during the injection molding cycle is simulated by simulation software to obtain the temperature difference data of each node on the cavity surface (such as the maximum temperature difference ΔT max , average temperature difference ΔT avg For example, the ΔT of candidate 1 is max 18℃, ΔT avg is 10℃; ΔT of candidate 2 max 15℃, ΔT avg The fitness analysis was performed based on the temperature difference data, and the temperature difference was converted into a fitness value using a normalization method (e.g., fitness value = 1-ΔT max / initial maximum temperature difference), so that the larger the value is, the better the cooling effect is, and a data set containing the fitness values ​​of each scheme is constructed.

[0064] Finally, candidate solutions are sorted in descending order based on their fitness values ​​to identify the optimal solution. For example, after sorting the fitness values ​​from high to low, the top three solutions are selected for further analysis of engineering indicators such as hydraulic pressure drop and processing difficulty. The minimum spanning tree algorithm is then used to optimize channel connection paths. For example, the coordinates of the channel nodes in the optimal topology are extracted and connected using the shortest path principle to reduce fluid resistance. Ultimately, a cooling channel optimization solution with the best overall performance is constructed.

[0065] Through the above steps, taking the temperature extreme value of the hot spot area as the core optimization target, combined with iterative optimization, simulation verification and fitness screening mechanism, the whole process of intelligent design of the cooling channel topology structure from parameter adjustment to solution generation is realized, which accurately solves the problem of uneven temperature distribution of the mold and improves the heat dissipation efficiency and engineering feasibility of the cooling system.

[0066] Furthermore, step A400 in the method provided in the embodiment of the present application includes:

[0067] A410: Execute the cooling channel optimization solution to perform fluid-solid coupling simulation verification and generate simulation verification results.

[0068] A420: Record the temperature difference based on the simulation verification results and construct temperature difference gradient parameters.

[0069] A430: Set an expected temperature difference threshold, determine whether the temperature difference gradient parameter reaches the expected temperature difference threshold, and generate a cooling parameter configuration table when the temperature difference gradient parameter reaches the expected temperature difference threshold.

[0070] A440: Update the cooling channel optimization solution according to the cooling parameter configuration table, and output the cooling channel optimization update strategy.

[0071] In the embodiment of the present application, fluid-solid coupling refers to dividing the mold system into a cooling medium flow area and a mold body when performing simulation verification of the cooling channel optimization plan, and synchronously calculating the coupling process of the cooling medium flow and mold heat conduction by analyzing the heat transfer data at the interface between the two.

[0072] Specifically, after generating the cooling channel optimization plan, first, the plan is verified by fluid-solid coupling simulation. Fluid-solid coupling simulation requires dividing the mold system into a fluid domain and a solid domain, namely the cooling medium flow area and the mold body. For the fluid domain, the grid cutting technology is used to discretize the fluid area in the cooling channel into tetrahedral or hexahedral grids. For example, a cooling channel with an aperture of 8mm is divided into a hexahedral grid with a side length of 1mm to accurately capture the details of the fluid flow; for the solid domain, combined with the hot spot distribution area identified in the early stage, local grid encryption is performed in areas with large wall thickness or complex curvature, such as using a 0.5mm grid spacing in areas with a wall thickness of 10mm to ensure the accuracy of heat conduction calculations. Through the transfer of fluid-solid interface data, such as the heat flux density described by Newton's cooling law, the heat transfer caused by the flow of the cooling medium and the temperature distribution of the solid domain of the mold are calculated simultaneously to generate simulation verification results containing cavity surface temperature field data (such as the temperature value of each node) and cooling hydraulic pressure drop data (such as the inlet and outlet pressure difference).

[0073] Next, the temperature difference gradient parameters are constructed based on the simulation verification results. The temperature values ​​of each node on the cavity surface are extracted from the temperature field data, and statistics such as the maximum temperature difference and the average temperature difference are calculated to form the temperature difference gradient parameters. For example, if the simulation results show that the highest temperature on the cavity surface is 95°C and the lowest temperature is 65°C, then the maximum temperature difference is 30°C and the average temperature difference is 20°C. At the same time, combined with the cooling hydraulic pressure drop data, such as the pressure loss of 0.5MPa, it is evaluated whether the flow resistance of the cooling system is within the process allowable range.

[0074] Next, the desired temperature difference threshold is set. For example, based on the injection molding material characteristics and product precision requirements, a maximum temperature difference of ≤15°C and an average temperature difference of ≤10°C are set. The temperature difference gradient parameters are then compared with the thresholds. If the current solution's maximum temperature difference is 30°C, exceeding the desired threshold, the topology needs to be adjusted by returning to the optimization step. If, after a simulation, the maximum temperature difference drops to 12°C and the average temperature difference drops to 8°C, both meeting the threshold requirements, the cooling parameter configuration table generation process is triggered. The optimized cooling channel geometry parameters, such as orientation, aperture, and spacing, as well as fluid parameters such as cooling medium flow rate and temperature, are recorded to form a standardized parameter configuration file.

[0075] Finally, the optimization plan is updated according to the cooling parameter configuration table. For example, the channel spacing is adjusted from 20mm to 18mm to enhance heat dissipation, or the cooling medium type is changed to improve heat exchange efficiency. The cooling channel optimization update strategy containing specific parameter adjustment content is output as a basis for mold processing and production debugging.

[0076] Through the aforementioned fluid-solid coupling simulation verification, temperature gradient assessment, and parameter configuration process, heat conduction analysis is combined with fluid mechanics calculations to form a closed-loop control of design, simulation, verification, and iteration. This ensures that the cooling channel optimization solution not only meets the temperature uniformity requirements but also complies with the flow resistance constraints in actual production. Ultimately, a data-driven verification mechanism improves the engineering feasibility of the solution and the overall performance of the cooling system.

[0077] Furthermore, step A410 in the method provided in the embodiment of the present application includes:

[0078] A411: Determine the fluid domain and solid domain according to the cooling channel optimization scheme, mesh the fluid domain to obtain fluid mesh data, mesh the solid domain, perform local encryption based on the hotspot distribution area, and obtain solid mesh data.

[0079] A412: Perform transfer analysis based on the fluid grid data and the solid grid data to determine fluid-solid interface data.

[0080] A413: Execute the cooling channel optimization scheme according to the fluid-solid interface data to perform multi-dimensional simulation, and generate cavity surface temperature field simulation data and cooling fluid pressure drop simulation data.

[0081] A414: Verify and correct the cavity surface temperature field simulation data according to the cooling hydraulic pressure drop simulation data to generate the simulation verification result.

[0082] In one embodiment, when performing the fluid-solid coupling simulation verification of the cooling channel optimization solution, first, the model is domain-divided. According to the optimized cooling channel layout, the mold system is clearly divided into a fluid domain (the internal space of the channel through which the cooling medium flows) and a solid domain (the mold body structure). For the fluid domain, an adaptive grid cutting technology is used. For example, a cooling channel with a diameter of 8 mm is divided into a hexahedral grid with a spacing of 1 mm along the axial direction to ensure that the grid density in the fluid flow direction is sufficient to capture turbulent details; for the solid domain, for the hot spot distribution areas identified in the early stage, such as areas with a wall thickness greater than 6 mm or a curvature radius less than 5 mm, a local grid encryption strategy is used to reduce the grid size from the conventional 2 mm to 0.5 mm to improve the accuracy of the heat conduction calculation. Through the above operations, fluid grid data and solid grid data are obtained respectively.

[0083] Next, the transfer analysis of the fluid-solid interface data is carried out. The fluid-solid interface is the contact surface where the cooling medium and the mold body exchange heat. The data transfer relationship needs to be established based on Fourier's heat conduction law and Newton's cooling formula. Specifically, the heat transfer rate per unit area is determined by calculating the temperature gradient and heat flux density at the interface between the fluid domain grid node and the solid domain grid node. For example, if the fluid temperature at a node on the interface is 25°C, the solid temperature is 80°C, and the heat transfer coefficient is 5000W / (m 2 K), the heat flux is 5000×(80-25)=275000W / m 2 ,This data is input into the simulation model as a boundary condition to ensure that the heat exchange process between the fluid and the solid is accurately simulated.

[0084] Then, a multi-dimensional simulation is performed based on the fluid-solid interface data. During the simulation, the temperature field and fluid mechanics parameters are calculated simultaneously. Regarding the temperature field, the temperature changes at each node on the cavity surface are continuously monitored, generating temperature field simulation data containing time-space coordinates. For example, the temperature of a node is 75°C at the 10th second of the cooling phase, and drops to 68°C at the 20th second. Regarding fluid mechanics, the flow pressure drop of the cooling medium within the channel is calculated, generating hydraulic pressure drop simulation data, such as an inlet pressure of 1.2 MPa, an outlet pressure of 0.8 MPa, and a pressure drop of 0.4 MPa. Through multi-physics field coupling calculations, a comprehensive evaluation of the thermal and flow properties of the cooling system is achieved.

[0085] Finally, the temperature field simulation data is verified and corrected using the cooling hydraulic pressure drop simulation data. If the hydraulic pressure drop data shows abnormal flow resistance, such as pressure drop exceeding the process upper limit of 1MPa, it is necessary to check whether the local turbulence is aggravated due to unreasonable mesh division or channel layout, and adjust the solid domain mesh density or channel curvature parameters accordingly, and recalculate the temperature field distribution. For example, if the pressure drop is reduced to 0.6MPa after correction, and the maximum temperature difference on the cavity surface is reduced from 22°C to 18°C, the current simulation data is considered credible, and then a simulation verification result including temperature uniformity index and flow resistance index is generated.

[0086] Through the above steps, with the help of refined meshing of the fluid and solid domains, heat transfer analysis of the fluid-solid interface, and cross-validation of multi-dimensional simulation data, a comprehensive performance evaluation of the cooling channel optimization scheme was achieved, ensuring that the simulation results not only accurately reflect the temperature distribution characteristics but also comply with the fluid mechanics constraints in actual production. Ultimately, a data-driven verification mechanism is used to improve the engineering reliability of the cooling system design and the feasibility of the optimization scheme.

[0087] In summary, the optimization design method for the precision mold cooling system provided in the embodiments of the present application has the following technical effects:

[0088] This application constructs a three-dimensional thermodynamic simulation model by obtaining the structural parameters of the precision mold (surface curvature, wall thickness) and the thermophysical properties of the injection molding material (phase change temperature, etc.), identifies the hotspot distribution area on the cavity surface through dynamic simulation of the temperature field, performs multi-objective optimization around the hotspot coordinates and temperature extremes, iteratively optimizes the cooling channel topology parameters to generate candidate solutions, combines fluid-solid coupling simulation verification and temperature difference gradient evaluation, and screens out cooling channel optimization solutions that meet the expected temperature difference threshold, thereby accurately solving the problem of uneven temperature distribution in the mold cavity, improving cooling efficiency and injection molding quality, making the design of the precision mold cooling system more scientific and reliable, and achieving the technical effects of temperature field homogenization and improved cooling efficiency, reducing the defect rate of injection molded parts and shortening the production cycle.

[0089] Example 2, as Figure 2 As shown, based on the same inventive concept as the aforementioned embodiment 1, the embodiment of the present application provides an optimization design system for a precision mold cooling system, the system comprising:

[0090] The three-dimensional model building module 1 is used to obtain the structural parameters of the precision mold and the thermal physical properties of the injection molding material, and to build a three-dimensional thermodynamic simulation model.

[0091] The hotspot distribution area identification module 2 performs a temperature field dynamic simulation based on the three-dimensional thermodynamic simulation model to identify the hotspot distribution area on the mold cavity surface.

[0092] The cooling optimization scheme generation module 3 is used to perform multi-objective optimization according to the hot spot distribution area, iteratively optimize the topological structure parameters of the cooling channel in the precision mold according to the optimization results, and generate a cooling channel optimization scheme for the precision mold.

[0093] Furthermore, the three-dimensional model building module 1 is used to perform the following steps:

[0094] The cavity of the precision mold is scanned at multiple points using a three-dimensional laser scanner to obtain surface curvature distribution data; each area of ​​the precision mold is traversed and detected using an ultrasonic thickness gauge to obtain multi-zone wall thickness data; the surface curvature distribution data is spatially aligned with the multi-zone wall thickness data to obtain the structural parameters of the precision mold.

[0095] Furthermore, the three-dimensional model building module 1 is used to perform the following steps:

[0096] A differential scanning calorimeter is used to test the phase change temperature of the injection molding material to obtain the thermophysical properties of the injection molding material; the surface curvature distribution data and the multi-zone wall thickness data are fused to establish a mold structure parameter matrix, and the initial position information of the cooling channel is identified according to the mold structure parameter matrix; the thermophysical properties of the injection molding material are fused into the mold structure parameter matrix and combined with the initial position information of the cooling channel to perform non-uniform grid division to construct the three-dimensional thermodynamic simulation model.

[0097] Furthermore, the hotspot distribution area identification module 2 is configured to perform the following steps:

[0098] Retrieve the injection molding cycle parameters, perform melt temperature record analysis according to the injection molding cycle parameters, and construct melt temperature curve parameters; synchronize the melt temperature curve parameters to the three-dimensional thermodynamic simulation model, perform multi-cycle iterative calculation according to the injection molding cycle parameters, and obtain multi-cycle thermal temperature simulation fluctuation data; extract the transient temperature data of the cavity surface nodes based on the multi-cycle thermal temperature simulation fluctuation data, and generate a temperature field spatiotemporal distribution matrix; perform hotspot identification according to the temperature field spatiotemporal distribution matrix, and determine the hotspot distribution area on the mold cavity surface.

[0099] Furthermore, the cooling optimization solution generation module 3 is used to perform the following steps:

[0100] The hotspot distribution area is traversed to perform multi-target extraction, and the target hotspot coordinates and the target temperature extreme values ​​are determined, where the target hotspot coordinates correspond to the target temperature extreme values; the target hotspot coordinates are temperature-difference optimized based on the target temperature extreme values ​​to obtain optimization results; the topological structure parameters of the cooling channel are iteratively optimized according to the optimization results to generate a candidate topological structure set; the candidate topological structure set is traversed to perform transient heat conduction simulation to obtain cavity surface temperature difference data, fitness analysis is performed based on the cavity surface temperature difference data, and multiple fitness values ​​are constructed; the cavity surface temperature difference data are arranged in descending order based on the multiple fitness values, and the candidate topological structure set is screened according to the temperature difference sequence to construct the cooling channel optimization solution for the precision mold.

[0101] Furthermore, the cooling optimization solution generation module 3 is used to perform the following steps:

[0102] Execute the cooling channel optimization scheme to perform fluid-solid coupling simulation verification and generate simulation verification results; record the temperature difference based on the simulation verification results and construct a temperature difference gradient parameter; set an expected temperature difference threshold, determine whether the temperature difference gradient parameter reaches the expected temperature difference threshold, and generate a cooling parameter configuration table when the temperature difference gradient parameter reaches the expected temperature difference threshold; update the cooling channel optimization scheme according to the cooling parameter configuration table, and output a cooling channel optimization update strategy.

[0103] Furthermore, the cooling optimization solution generation module 3 is used to perform the following steps:

[0104] According to the cooling channel optimization scheme, the fluid domain and the solid domain are determined, the fluid domain is meshed to obtain fluid mesh data, the solid domain is meshed, and local encryption is performed in combination with the hot spot distribution area to obtain solid mesh data; based on the fluid mesh data and the solid mesh data, a transfer analysis is performed to determine the fluid-solid interface data; according to the fluid-solid interface data, the cooling channel optimization scheme is executed to perform multi-dimensional simulation to generate cavity surface temperature field simulation data and cooling hydraulic pressure drop simulation data; according to the cooling hydraulic pressure drop simulation data, the cavity surface temperature field simulation data is verified and corrected to generate the simulation verification result.

[0105] The optimization design system for a precision mold cooling system provided by an embodiment of the present invention can execute the optimization design method for a precision mold cooling system provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0106] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0107] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The optimization design method of precision mold cooling system is characterized by: The method comprises: Obtain the structural parameters of the precision mold and the thermophysical properties of the injection molding material, and build a three-dimensional thermodynamic simulation model; Performing a dynamic simulation of the temperature field based on the three-dimensional thermodynamic simulation model to identify hotspot distribution areas on the mold cavity surface; Multi-objective optimization is performed based on the hotspot distribution area, and the topological structure parameters of the cooling channel in the precision mold are iteratively optimized based on the optimization results to generate an optimization solution for the cooling channel of the precision mold.

2. The optimization design method for the precision mold cooling system according to claim 1, characterized in that: The process of obtaining the structural parameters of precision molds includes: Use a 3D laser scanner to scan the cavity of the precision mold at multiple points to obtain the surface curvature distribution data; Using ultrasonic thickness gauges to traverse and inspect each area of ​​the precision mold, obtaining multi-area wall thickness data; The surface curvature distribution data and the multi-zone wall thickness data are spatially aligned to obtain the structural parameters of the precision mold.

3. The optimization design method for the precision mold cooling system according to claim 2, characterized in that: Obtain the structural parameters of the precision mold and the thermophysical properties of the injection molding material, and build a three-dimensional thermodynamic simulation model. The method includes: A differential scanning calorimeter is used to test the phase transition temperature of the injection molding material to obtain the thermal physical property parameters of the injection molding material; fusing the profile curvature distribution data with the multi-zone wall thickness data to establish a mold structure parameter matrix, and identifying initial position information of the cooling channel according to the mold structure parameter matrix; The thermophysical property parameters of the injection molding material are integrated into the mold structure parameter matrix and combined with the initial position information of the cooling channel to perform non-uniform grid division to construct the three-dimensional thermodynamic simulation model.

4. The optimization design method for the precision mold cooling system according to claim 1, characterized in that: Performing a dynamic simulation of the temperature field based on the three-dimensional thermodynamic simulation model to identify the hotspot distribution area on the mold cavity surface includes: Retrieving injection molding cycle parameters, performing melt temperature record analysis according to the injection molding cycle parameters, and constructing melt temperature curve parameters; Synchronizing the melt temperature curve parameters to a three-dimensional thermodynamic simulation model and performing multi-cycle iterative calculations according to the injection cycle parameters to obtain multi-cycle thermal temperature simulation fluctuation data; Extracting transient temperature data of cavity surface nodes based on the multi-cycle thermal temperature simulation fluctuation data to generate a temperature field spatiotemporal distribution matrix; Hotspot identification is performed according to the spatiotemporal distribution matrix of the temperature field to determine the hotspot distribution area on the mold cavity surface.

5. The optimization design method for the precision mold cooling system according to claim 1, characterized in that: Multi-objective optimization is performed based on the hotspot distribution area, and topological structural parameters of the cooling channel in the precision mold are iteratively optimized based on the optimization results to generate an optimization solution for the cooling channel of the precision mold. The method includes: Traversing the hotspot distribution area to perform multi-target extraction, determining target hotspot coordinates and target temperature extremes, wherein the target hotspot coordinates and the target temperature extremes have a corresponding relationship; Performing temperature difference optimization on the target hotspot coordinates based on the target temperature extreme value to obtain an optimization result; Iteratively optimize the topological structure parameters of the cooling channel according to the optimization results to generate a candidate topological structure set; Traversing the candidate topology structure set to perform transient heat conduction simulation to obtain cavity surface temperature difference data, performing fitness analysis based on the cavity surface temperature difference data, and constructing multiple fitness values; The cavity surface temperature difference data are arranged in descending order based on the multiple fitness values, the candidate topology structure set is screened according to the temperature difference sequence, and the cooling channel optimization solution for the precision mold is constructed.

6. The optimization design method for a precision mold cooling system according to claim 1, wherein: After generating the cooling channel optimization plan, the method includes: Execute the cooling channel optimization scheme to perform fluid-solid coupling simulation verification and generate simulation verification results; Record the temperature difference based on the simulation verification result and construct the temperature gradient parameter; Setting an expected temperature difference threshold, determining whether the temperature difference gradient parameter reaches the expected temperature difference threshold, and generating a cooling parameter configuration table when the temperature difference gradient parameter reaches the expected temperature difference threshold; The cooling channel optimization scheme is updated according to the cooling parameter configuration table, and a cooling channel optimization update strategy is output.

7. The optimization design method for a precision mold cooling system according to claim 6, characterized in that: Executing the cooling channel optimization scheme to perform fluid-solid coupling simulation verification and generating simulation verification results, the method includes: Determine the fluid domain and the solid domain according to the cooling channel optimization scheme, mesh the fluid domain to obtain fluid mesh data, mesh the solid domain, perform local encryption based on the hotspot distribution area, and obtain solid mesh data; Performing a transfer analysis based on the fluid grid data and the solid grid data to determine fluid-solid interface data; Execute the cooling channel optimization scheme according to the fluid-solid interface data to perform multi-dimensional simulation, and generate cavity surface temperature field simulation data and cooling fluid pressure drop simulation data; The cavity surface temperature field simulation data is verified and corrected according to the cooling liquid pressure drop simulation data to generate the simulation verification result.

8. The optimization design system of precision mold cooling system is characterized by: The optimization design method for a precision mold cooling system according to any one of claims 1 to 7 is implemented, wherein the system comprises: 3D model building module, used to obtain the structural parameters of precision molds and the thermal physical properties of injection molding materials, and build a 3D thermodynamic simulation model; a hotspot distribution area identification module, which performs a dynamic simulation of the temperature field based on the three-dimensional thermodynamic simulation model to identify the hotspot distribution area on the mold cavity surface; The cooling optimization scheme generation module is used to perform multi-objective optimization according to the hot spot distribution area, iteratively optimize the topological structure parameters of the cooling channel in the precision mold according to the optimization results, and generate a cooling channel optimization scheme for the precision mold.

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