Optimization method, system and device for mold machining and storage medium

Abstract

The invention relates to the technical field of mold machining optimization, and discloses a mold machining optimization method, system and device and a storage medium. The method comprises the steps that design parameters of a mold are obtained and input into simulation software, and a mold product model is generated; performing parameter optimization through mold flow analysis to obtain optimized design parameters; actual production parameters are calculated according to the optimized parameter values, and key parameter values in the production process are recorded; and finally exporting and displaying an optimization result, and determining an optimal design value. The method has the following effects that through simulation analysis and multiple iterative optimization, the efficiency of mold design and the injection molding process is effectively improved, the precision and quality of product forming are ensured, and intelligent monitoring and optimized display of the production process are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of mold processing, and in particular to an optimization method, system, device and storage medium for mold processing. Background Art

[0002] At present, molds are widely used in injection molding. Their function is to form a cavity for filling plastic melt through the contour on the injection molding machine. The screw of the injection molding machine injects the uniformly molten plastic melt into the mold cavity through the pushing action. After the plastic is solidified and shaped in the mold, it is ejected and automatically demolded, and the mold then completes a working cycle. The quality of mold design not only affects the injection molding cycle, but also directly affects the production cost of the product. Therefore, mold design has become one of the key links in the injection molding process.

[0003] In the existing technology, mold design mostly relies on experience. Although the design can be simulated and verified to a certain extent through mold flow analysis software, these analyses are often limited to simple simulations of the plastic flow state and do not fully cover the diverse conditions at each stage of the injection molding process. At the same time, the setting of injection molding machine parameters still mainly relies on experience. There is a lack of scientific basis for the setting of key parameters such as pressure, speed and temperature, making it difficult to fully cope with the complex molding process.

[0004] The existing technology for mold design results and injection molding parameter settings often fails to fully consider the dynamic flow state and variable processing conditions during the injection molding process, which leads to the inability to ensure the optimality of the design under different molding conditions and increases the risk of quality fluctuations during the production process. Therefore, how to ensure molding accuracy and product quality by optimizing design and parameter settings has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a mold processing optimization method, system, device and storage medium to solve the problems that the existing mold design and injection molding process parameter setting rely on experience, are insufficiently optimized, and are difficult to cope with complex molding processes.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for optimizing mold processing, comprising: Obtaining design parameters of the mold, and inputting the design parameters into simulation software to establish a mold product model; Performing mold flow analysis according to the mold product model to obtain mold flow analysis results; According to the mold flow analysis results, optimization calculation is performed to obtain optimized parameter values; Calculating the optimized parameter values ​​to obtain actual production parameter values; wherein the actual production parameter values ​​include actual injection pressure, actual cooling time, actual filling time and actual temperature distribution; Repeat the optimization calculation and record each actual production parameter value corresponding to each design parameter in the optimization process; Each of the actual production parameter values ​​is exported, a preferred design value is determined from all the actual production parameter values, and the optimized parameter values, the actual production parameter values, and the preferred design values ​​are displayed.

[0007] Preferably, the step of obtaining the design parameters of the mold includes: Acquiring flow channel design parameters, wherein the flow channel design parameters include the size of the inflow flow channel, the length of the main flow channel, and the distribution ratio of the outflow flow channel; Acquire cavity size parameters, wherein the cavity size parameters include the depth and width of the cavity and the layout of the cooling system; Acquiring process parameters during the injection molding process, wherein the process parameters include injection pressure, injection speed and cooling time; The sprue position parameters are obtained. The sprue position is set above the cavity and the flow direction and speed of the plastic melt are controlled by the sprue position.

[0008] Preferably, the process of establishing the mold product model includes: Importing the design parameters into the simulation software, and generating a preliminary mold product model based on the input runner design parameters, cavity size parameters and injection molding process parameters; According to the fluid flow path, cavity wall thickness and cooling channel distribution contained in the preliminary mold product model, detailed design operations are performed to obtain the mold product model.

[0009] Preferably, a mold flow analysis is performed according to the mold product model to obtain a mold flow analysis result, including: In the simulation software, the fluid simulation module is used to simulate the flow of the mold product model and analyze the flow path, flow velocity, and pressure changes of the plastic melt in the mold cavity; Combined with the flow channel design parameters, analyze the flow of the fluid from the inlet flow channel to the outlet flow channel, especially the filling speed and flow uniformity of the fluid in the cavity; Simulate the mold cooling system, analyze the temperature distribution and cooling time during melt cooling, and evaluate the impact of different cooling areas on molded products; Based on the simulation results, injection molding defects such as cavitation, trapped air, underfilling and overflow are detected, and these problems are marked and recorded for further design optimization.

[0010] Preferably, according to the mold flow analysis results, an optimization calculation is performed to obtain optimized parameter values, including: Determining key parameters in the mold flow analysis results, wherein the key parameters include fluid pressure, cooling time, weld line depth and flow path; Substituting the key parameters into a preset optimization calculation model, wherein the optimization calculation model is used to calculate the optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution; Comparing parameters at different heights and positions, and using the optimization calculation model to obtain an optimal value, wherein the optimal value is used to ensure flow uniformity and cooling rate consistency of the plastic melt; According to the optimal value, adjusting the key parameters of the mold design, the key parameters including flow channel design parameters, cavity size parameters and cooling system layout parameters; The optimization calculation is performed again until the key parameters meet the set process requirement values, thereby obtaining the optimized parameter values.

[0011] Preferably, the optimized parameter values ​​are calculated to obtain actual production parameter values, including: The optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution are preliminarily calculated using the following formula: in, is a fixed value, and its value range is [4, 6]. To optimize the value, is the actual value of the sprue position, The actual value assigned to the flow rate into the flow channel, is the actual value of the depth of the primary air cooling zone, is the actual value of the width of the primary air cooling zone, is the actual value of the cooling time at the midpoint of the cavity, is the actual value of the width of the secondary air cooling zone; The optimal value of cooling time for weld line depth is calculated by the following formula: in, is the actual value, that is, the distance between the weld line and the midpoint of the cavity obtained in the mold flow analysis. is the cooling time obtained in mold flow analysis, is the cooling time of the secondary air cooling zone, is an optimized value of cooling time to weld line depth, and the optimized value can be used to adjust the cooling time in the design to ensure that the weld line depth meets the process requirements; Furthermore, based on the preliminary calculation of the optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution, the above parameters are finely optimized using the following formula: in, is the area where the flow velocity is 0 in mold flow analysis, is the actual value of the sprue position, The actual value assigned to the flow rate into the flow channel, is the actual value of the depth of the first-stage air cooling zone, is the actual value of the width of the first-stage air cooling zone, is the actual value of the width of the secondary air cooling zone, It is the area where the flow velocity is 0 in mold flow analysis.

[0012] Preferably, calculating the optimized parameter value to obtain the actual production parameter value includes: The actual injection pressure is calculated by the following formula: in, is the actual injection pressure, To optimize the calculated pressure value, is the injection cross-sectional area of ​​the injection molding machine, is the cavity cross-sectional area of ​​the mold; The actual cooling time is calculated by the following formula: in, is the actual cooling time, To optimize the cooling time obtained, It is a constant related to the cooling process and is used to describe the combined influence of mold material properties, environmental conditions and other factors in the cooling time calculation. is the mold wall thickness, is the thermal conductivity of the material; The actual filling time is calculated by the following formula: in, is the actual filling time, is the volume of the mold cavity, is the injection flow rate of the plastic melt; The actual temperature distribution is calculated by the following formula: in, is the actual production temperature, To optimize the temperature value, is the temperature change difference.

[0013] Preferably, the optimization calculation is repeated, and each actual production parameter value corresponding to each design parameter in the optimization process is recorded, including: Recording the key parameter values ​​generated during each optimization iteration, wherein the key parameters include flow channel design parameters, cavity size parameters, injection molding process parameters, cooling time, weld line depth, temperature distribution, and fluid flow path; Recording the production parameters after each iteration in detail, including actual injection pressure, actual cooling time, actual filling time, and actual temperature distribution; The design parameters in each iteration are associated with the actual production parameter values, and the changes in the design parameters each time are recorded to form an optimization calculation record table.

[0014] Preferably, the step of deriving each of the actual production parameter values, determining a preferred design value from all the actual production parameter values, and displaying the optimized parameter values, the actual production parameter values, and the preferred design values ​​comprises: Exporting actual production parameter values ​​recorded in each optimization iteration through a data export tool, wherein the actual production parameter values ​​include actual injection pressure, actual cooling time, actual filling time, and actual temperature distribution; According to the derived actual production parameter values, all production parameter values ​​are analyzed using preset calculation rules to determine the preferred design value of each design parameter, wherein the preferred design value is used to indicate the state in which each design parameter achieves the best effect in production; The optimized parameter values, the derived actual production parameter values ​​and the determined preferred design values ​​are displayed, and the changes in each parameter and the final design selection are presented in the form of charts or tables.

[0015] In a second aspect, the present invention provides a mold processing optimization system, comprising: Data acquisition module, used to obtain mold design parameters and mold flow analysis results; An optimization calculation module is used to perform optimization calculation according to the design parameters to obtain optimized design parameter values; An analysis module, used to perform further optimization calculations based on the mold flow analysis results and the optimized design parameter values ​​to obtain final optimization parameters; The data output module is used to output the final optimization parameters, or input the final optimization parameters into a pre-trained model to generate a final optimization design solution.

[0016] Display module: used to visualize the optimized parameter values, actual production parameter values ​​and preferred design values.

[0017] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-mentioned mold processing optimization methods when executing the computer program.

[0018] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned mold processing optimization methods.

[0019] Compared with the prior art, the present invention has the following beneficial effects: First, through a data-driven multiple iterative optimization method, accurate optimization of mold design parameters and injection molding process parameters is achieved. Different from the prior art that relies on experience for setting, the present invention combines the mold flow analysis results and scientifically optimizes key parameters such as runner design, cooling time, and weld line depth through an optimization calculation model. Through multiple optimization calculations, the changes in each design parameter and the corresponding actual production parameters are recorded to ensure that each iteration can be dynamically adjusted according to actual conditions. This not only reduces the trial and error time in the design process, but also improves the accuracy of the design, allowing the mold design and injection molding process to better cope with complex molding conditions.

[0020] Secondly, the present invention greatly improves the efficiency of the design and production process through a modular optimization system. The optimization system includes a data acquisition module, an optimization calculation module, an analysis module, a data output module and a display module. The modules work together to achieve automated processing from data acquisition to the final optimized design solution. Unlike the manual adjustment and verification required in the prior art, the present invention can quickly calculate the optimal parameters through an automated system, reducing the operational burden. At the same time, the display module visualizes the optimized parameters, actual production parameters and preferred design values, helping to more intuitively analyze the optimization effect, thereby making more scientific decisions during the design process.

[0021] In addition, the characteristics of dynamic optimization and flexible adjustment make the present invention highly adaptable when dealing with different production conditions. The system continuously adjusts key parameters through the optimization data generated in each iteration to ensure the optimal fluid flow uniformity and cooling time in actual production. Compared with the defect of the prior art that cannot cope with complex dynamic conditions, the present invention realizes the fine optimization of multiple parameters such as flow channel design, cooling time and temperature distribution through the optimization calculation model, significantly reducing the quality fluctuation in the injection molding process and improving the yield rate.

[0022] Furthermore, the information recording and tracking capabilities of the present invention have also been significantly improved. By recording the parameter changes in each optimization step, the present invention can provide accurate traceability basis for design and production, facilitate analysis of the actual impact of each parameter in production, and further enhance the control capability in the production process. In addition, the system can automatically derive the actual production parameters in each iteration, and determine the optimal design values ​​based on these data to ensure that the final optimized design meets production requirements.

[0023] Finally, the system integration and ease of operation of the present invention provide great convenience for users. Through integration with electronic devices and computer programs, the optimization method can be easily executed through electronic devices, and the program in the storage medium ensures the flexible deployment and portability of the optimization system. This not only improves production efficiency, but also enables the system to be applied in a wide range of production environments to meet the needs of different mold designs and injection molding processes.

[0024] In summary, the present invention demonstrates significant technical advantages in multiple iterative optimization, system automation processing, flexible adjustment, data tracking and ease of operation, overcomes the shortcomings of the prior art, can significantly improve the accuracy of mold design, production efficiency and yield rate, and has broad industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process flow of the mold processing optimization method provided by the first embodiment of the present invention; Figure 2 It is a structural schematic diagram of the mold processing optimization method provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] Reference Figure 1 The first embodiment of the present invention provides a method for optimizing mold processing, comprising the following steps: S11, obtaining design parameters of the mold, and inputting the design parameters into simulation software to establish a mold product model; S12, performing mold flow analysis according to the mold product model to obtain a mold flow analysis result; S13, according to the mold flow analysis results, perform optimization calculations to obtain optimized parameter values; S14, calculating the optimized parameter values ​​to obtain actual production parameter values, wherein the actual production parameter values ​​include actual injection pressure, actual cooling time, actual filling time and actual temperature distribution; S15, repeat the optimization calculation and record each actual production parameter value corresponding to each design parameter in the optimization process; S16, exporting each of the actual production parameter values, determining the preferred design value from all the actual production parameter values, and displaying the optimized parameter values, the actual production parameter values, and the preferred design value.

[0028] In step S11, the design parameters of the mold are obtained and input into the simulation software to establish a mold product model, including: Importing the design parameters into the simulation software, and generating a preliminary mold product model based on the input runner design parameters, cavity size parameters and injection molding process parameters; According to the fluid flow path, cavity wall thickness and cooling channel distribution contained in the preliminary mold product model, detailed design operations are performed to obtain the mold product model.

[0029] It should be noted that, in step S11, the acquisition of design parameters includes: Determination of runner design parameters: The runner is the channel for the melt to enter the mold cavity. The runner design parameters include the size of the inlet runner, the length of the main runner, the cross-sectional shape and the distribution ratio of the outlet runner. The above parameters determine the uniformity and flow rate of the melt flow. Reasonable runner design can reduce defects caused by weld lines, cavitation and flow instability. According to the size and shape of the product, the size of the runner needs to be accurately determined to ensure that the melt fills the mold smoothly.

[0030] Setting of cavity size parameters: The size of the cavity directly affects the shape accuracy of the product. The cavity size parameters include the depth, width, volume, contour and layout of the cooling system. The geometric shape of the product and the molding characteristics of the material should be considered during design. The distribution of cooling channels should also be reasonably designed according to the size of the cavity to ensure rapid and uniform cooling and avoid warping and internal stress.

[0031] Setting of injection molding process parameters: Injection molding process parameters include injection molding pressure, injection molding speed, mold temperature and cooling time. The above parameters affect the fluidity of the melt, mold filling rate and cooling process. When designing, it is necessary to comprehensively consider the melting characteristics of the plastic material, the geometric shape of the product and the capacity of the injection molding equipment to ensure the matching of the process parameters and the mold design.

[0032] Determination of the sprue position: The sprue is the entrance for the melt to enter the cavity, and the rationality of its position affects the flow path and the position of the weld line. According to the geometric structure of the product, the melt flow characteristics and the cavity layout, the sprue should be set in the area that is most conducive to uniform fluid distribution, reduce the generation of weld lines, and optimize the filling time and pressure distribution.

[0033] In addition, in step S11, after obtaining the design parameters, all the design parameters are input into the simulation software. The simulation software automatically generates a preliminary model of the mold according to the input flow channel design parameters, cavity size parameters and injection molding process parameters, including the following steps: S111, inputting all the determined design parameters into the simulation software one by one to generate a preliminary mold model structure to ensure that each part meets the actual production conditions, wherein the design parameters include runner design parameters, cavity size parameters, process parameters during the injection molding process, and sprue position parameters; S112, the simulation software automatically constructs a preliminary three-dimensional mold model based on the above parameters. The model covers the runners, cavities and cooling channels of the mold, and all geometric and physical properties are accurately constructed based on the input parameters.

[0034] S113, after generating the preliminary model, adjust the key structure of the model according to actual needs. For example, adjust the cooling channel layout to improve cooling efficiency, or optimize the flow channel shape to ensure uniform melt flow. The cooling system design needs to ensure that the cooling speed of each area is consistent to avoid quality defects caused by uneven cooling.

[0035] S114, through multiple simulation iterations to optimize the model, discover and correct potential problems such as cooling efficiency and melt filling path, gradually improve the accuracy of the model, and ensure that the parameters are verified and optimized.

[0036] S115, finally determining the model for mold flow analysis, the model has met the preset requirements of runner design, cavity structure and cooling system, and is ready for the next step of fluid flow simulation.

[0037] In step S12, mold flow analysis is performed according to the mold product model to obtain mold flow analysis results, including: In the simulation software, the fluid simulation module is used to simulate the flow of the mold product model and analyze the flow path, flow velocity, and pressure changes of the plastic melt in the mold cavity; Combined with the flow channel design parameters, analyze the flow of the fluid from the inlet flow channel to the outlet flow channel, especially the filling speed and flow uniformity of the fluid in the cavity; Simulate the mold cooling system, analyze the temperature distribution and cooling time during melt cooling, and evaluate the impact of different cooling areas on molded products; Based on the simulation results, injection molding defects such as cavitation, trapped air, underfilling and overflow are detected, and these problems are marked and recorded for further design optimization.

[0038] In step S12, a fluid simulation module is used to perform flow simulation on the mold product model, including: The three-dimensional preliminary mold model generated in step S11 is imported into the fluid simulation module as the geometric basis for simulation analysis.

[0039] According to the selected plastic material, the rheological parameters and injection molding process conditions in the molten state are input to ensure that the parameter settings are consistent with the actual production. The rheological parameters include viscosity, density and thermal conductivity, and the process conditions include injection pressure, injection speed and mold temperature.

[0040] The fluid simulation module numerically solves the fluid mechanics equations to simulate the flow path, flow velocity and pressure changes of the melt in the runner and cavity, and generates dynamic change simulations at different stages, including runner entry, cavity filling and cooling and solidification.

[0041] The simulation software generates a flow path diagram based on the flow data to show the flow trajectory of the melt in the cavity; the generated pressure distribution diagram and flow velocity change diagram are analyzed to confirm whether there is a local pressure that is too high or too low, ensuring that the melt is evenly distributed and effectively fills the cavity to avoid underfilling or overfilling.

[0042] Based on the analysis results, necessary optimization adjustments are made to ensure that the final design meets the expected process requirements.

[0043] In step S13, the optimization calculation is performed according to the mold flow analysis result to obtain the optimized parameter value, including: S131, determining key parameters in the mold flow analysis result, wherein the key parameters include fluid pressure, cooling time, weld line depth and flow path; S132, substituting the key parameters into a preset optimization calculation model, wherein the optimization calculation model is used to calculate the optimization values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution; S133, comparing parameters at different heights and different positions, and using the optimization calculation model to obtain an optimal value, wherein the optimal value is used to ensure flow uniformity and cooling speed consistency of the plastic melt; S134, adjusting the key parameters of the mold design according to the optimal value, wherein the key parameters include flow channel design parameters, cavity size parameters, and cooling system layout parameters; S135, performing optimization calculation again until the key parameters meet the set process requirement values, thereby obtaining optimized parameter values.

[0044] In step S131, key parameters are first extracted from the mold flow analysis results. The key parameters include fluid pressure, cooling time, weld line depth, and flow path, which play a decisive role in the flow, cooling, and molding process of the melt. Extracting the above parameters is the basis for subsequent optimization calculations, which can ensure that the most important parts of the model are optimized.

[0045] In step S132, these key parameters are then substituted into a preset optimization calculation model for calculation. The model is used to calculate the optimized values ​​of flow channel design, cooling time, fluid flow uniformity, and temperature distribution. The optimization calculation model adjusts the parameters based on a preset calculation formula to improve the efficiency and quality of mold forming.

[0046] It should be noted that the key parameters are substituted into the preset optimization calculation model to calculate the optimization values ​​of multiple parameters, including: The optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution are preliminarily calculated using the following formula: in, is a fixed value, and its value range is [4, 6]. To optimize the value, is the actual value of the sprue position, The actual value assigned to the flow rate into the flow channel, is the actual value of the depth of the primary air cooling zone, is the actual value of the width of the primary air cooling zone, is the actual value of the cooling time at the midpoint of the cavity, is the actual value of the width of the secondary air cooling zone; The optimal value of cooling time for weld line depth is calculated by the following formula: in, is the actual value, that is, the distance between the weld line and the midpoint of the cavity obtained in the mold flow analysis. is the cooling time obtained in mold flow analysis, is the cooling time of the secondary air cooling zone, is an optimized value of cooling time to weld line depth, and the optimized value can be used to adjust the cooling time in the design to ensure that the weld line depth meets the process requirements; Furthermore, based on the preliminary calculation of the optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution, the above parameters are finely optimized using the following formula: in, is the area where the flow velocity is 0 in mold flow analysis, is the actual value of the sprue position, The actual value assigned to the flow rate into the flow channel, is the actual value of the depth of the first-stage air cooling zone, is the actual value of the width of the first-stage air cooling zone, is the actual value of the width of the secondary air cooling zone, It is the area where the flow velocity is 0 in mold flow analysis.

[0047] In step S133, in order to ensure the accuracy of the results, it is necessary to compare key parameters at different heights and positions, including fluid pressure, cooling time, weld line depth, and flow path. Due to the complex geometry of the mold, the fluid flow and cooling process in each area are different, so it is necessary to measure and calculate the performance of these parameters in different areas through numerical analysis.

[0048] In step S133, by establishing parameter mappings at different heights and positions and comparing the parameter differences of each area one by one, the production performance of each part can be evaluated.

[0049] In step S133, special attention is paid to flow uniformity and cooling time. Paying attention to the above parameters can ensure that the parameters of each area meet the set standards, thereby determining the optimal parameter value of each area.

[0050] In step S134, after obtaining the optimal value, the key parameters of the mold can be adjusted, including flow channel design parameters, cavity size and cooling system layout. The purpose of these adjustments is to improve the overall performance of the mold design so that it meets the optimal production conditions obtained by optimization calculation.

[0051] In step S134, the key parameters of the mold are adjusted based on the optimal parameter values ​​obtained in step S133. Fine adjustments are made to key parameters such as flow channel design parameters, cavity size, and cooling system layout, including: According to the optimal fluid flow uniformity and pressure distribution, the size, shape and ratio of the inlet and outlet runners are adjusted to ensure that there is no obstruction or unevenness when the melt flows in the mold.

[0052] In order to match the flow path of the melt and the cooling requirements, the depth, width and wall thickness of the cavity can be adjusted to ensure that no deformation or stress concentration occurs during the product molding process.

[0053] By analyzing the cooling time and temperature distribution, the distribution and size of cooling channels can be optimized to achieve uniform cooling, reduce cooling time, and prevent product defects caused by uneven local cooling.

[0054] In step S135, in order to ensure the effectiveness of the optimization, multiple iterations must be performed. Each optimization calculation will further refine the key parameters until all parameter values ​​meet the preset process requirements, and finally the optimized parameter values ​​are obtained.

[0055] In step S135, in order to ensure the effectiveness of the final optimization result, multiple iterative optimization calculations are required, including: The optimized parameters obtained in the previous calculation are used for simulation analysis to verify the effect of these parameters under actual production conditions. The focus is on observing whether the uniformity of fluid flow, cooling rate and the position of weld lines meet the expected standards.

[0056] According to the simulation analysis results, it is found that some parameters fail to meet the set process standards, such as the cooling speed in some areas is too fast or too slow, or the weld line position deviates from expectations. At this time, the parameters in the optimization calculation model will be adjusted again according to the results.

[0057] After each adjustment, the system will recalculate and generate new parameter values. Then, through another simulation, it will be verified whether these parameters are closer to or meet the process requirements. Each iteration aims to gradually reduce the parameter deviation until all parameters are within the set tolerance range.

[0058] After multiple iterations, the optimization calculation will gradually converge, the change range of the parameters will become smaller, and finally stabilize, which means that the optimal solution has been reached. When all key parameters, such as fluid pressure, cooling time and flow path, meet the preset requirements of the production process, the optimization process ends.

[0059] After multiple iterations, the optimization calculation will gradually converge, the change range of the parameters will become smaller, and finally stabilize, which means that the optimal solution has been reached. When all key parameters, such as fluid pressure, cooling time and flow path, meet the preset requirements of the production process, the optimization process ends.

[0060] Step S13 ensures that each parameter is fully verified and adjusted, and the final optimization result can meet the actual production requirements of complex mold design.

[0061] In step S14, the optimized parameter values ​​are calculated to obtain actual production parameter values, including: The actual injection pressure is calculated by the following formula: in, is the actual injection pressure, To optimize the calculated pressure value, is the injection cross-sectional area of ​​the injection molding machine, is the cross-sectional area of ​​the mold cavity. The above formula ensures that the injection pressure can accurately adapt to the size of the mold cavity, avoiding product defects such as underfilling or overfilling due to inappropriate pressure; The actual cooling time is calculated by the following formula: in, is the actual cooling time, To optimize the cooling time obtained, It is a constant related to the cooling process and is used to describe the combined influence of mold material properties, environmental conditions and other factors in the cooling time calculation. is the mold wall thickness, is the thermal conductivity of the material. The above formula can ensure that the cooling process reaches the best state and avoid internal stress or deformation of the product. It should be noted that the accurate cooling time is particularly critical in the case of different materials and mold thicknesses. The actual filling time is calculated by the following formula: in, is the actual filling time, is the volume of the mold cavity, is the injection flow rate of the plastic melt. Through the above formula, it can ensure that the mold cavity is evenly filled in the shortest time, thereby improving production efficiency and avoiding quality problems such as weld lines and cavitation caused by filling too fast or too slow; The actual temperature distribution is calculated by the following formula: in, is the actual production temperature, To optimize the temperature value, is the temperature change difference. The above formula can ensure that the temperature distribution at different positions in the mold is uniform during the cooling process, avoiding quality problems such as warping or surface defects caused by uneven temperature.

[0062] In step S15, the optimization calculation is repeated, and each actual production parameter value corresponding to each design parameter in the optimization process is recorded, including: Recording the key parameter values ​​generated during each optimization iteration, wherein the key parameters include flow channel design parameters, cavity size parameters, injection molding process parameters, cooling time, weld line depth, temperature distribution, and fluid flow path; Recording the production parameters after each iteration in detail, including actual injection pressure, actual cooling time, actual filling time, and actual temperature distribution; The design parameters in each iteration are associated with the actual production parameter values, and the changes in the design parameters each time are recorded to form an optimization calculation record table.

[0063] In each iteration and optimization process, the comparison value between the mold flow analysis result and the optimized value needs to be calculated by the following formula to ensure that the adjustment direction of each key parameter is correct: in, Represents the difference between the actual value and the optimized value. is the cooling time of the primary air cooling zone in mold flow analysis, is the depth of the first-stage air cooling zone, is the depth of the secondary air cooling zone, i.e. the area where the velocity is zero, The actual value representing the sprue position. The sprue position has an important influence on the flow path and uniformity of the melt, so it needs to be adjusted accurately. Indicates the actual value of the flow distribution into the runner. By adjusting the flow distribution, the melt is evenly distributed in the cavity to avoid local overfilling or underfilling. is the actual value of the depth of the primary air cooling zone. The depth of the air cooling zone has a direct impact on the temperature distribution during the cooling process. An air cooling zone that is too deep or too shallow will lead to uneven cooling, so it needs to be properly optimized. The actual value of the width of the primary air cooling zone. The width affects the cooling area and effect and is closely related to the cooling time. It is the cooling time of the midpoint of the cavity. Too long or too short cooling time will affect the molding quality. It reflects the temperature distribution inside the mold. It is the actual value of the width of the secondary air cooling zone. This parameter controls the efficiency of melt flow and cooling. Reasonable setting of the width can improve the uniformity of the cooling process.

[0064] Through the above comparison, the effectiveness of each optimization adjustment can be accurately evaluated, and these comparison values ​​can be recorded for subsequent reference. In addition to recording the changes in key parameters, it is also necessary to ensure that the change trends and correlations of all key parameters are fully analyzed during each iteration, including: 1. Dynamically record changes in key parameters: The parameter values ​​after each iteration are not just recorded once, but a dynamic recording system needs to be established to track the change process of each parameter. Through this dynamic recording, the fluctuation and adjustment direction of different parameters in each optimization iteration can be seen, so as to find out which parameters have a greater impact on the final result.

[0065] 2. Correlation analysis: There are correlations between different parameters. For example, the adjustment of flow channel design parameters will directly affect the fluid flow path and pressure distribution, and the change of cooling time will have a direct impact on the temperature distribution. Therefore, after recording each iteration, it is necessary to conduct a correlation analysis on these parameters to clarify how the adjustment of certain design parameters affects other parameters, so as to better control the optimization direction.

[0066] 3. Use of automated data analysis tools: In modern optimization systems, parameter changes are often recorded and analyzed through automated data analysis tools. These tools can generate detailed charts and reports to help intuitively understand the trend of each parameter. For example, by showing the relationship between cooling time and temperature distribution through a line chart or heat map, insufficient cooling problems in certain areas can be quickly discovered and targeted improvements can be made.

[0067] 4. Optimize the creation of record tables: Categorize the key parameters generated in each iteration and create an optimization record sheet that summarizes the parameter changes in each iteration. The record sheet can not only serve as a traceability basis for the optimization process, but also provide a reference for subsequent decisions. For example, if a certain iteration causes the cooling time to exceed expectations, you can refer to the previous data in the optimization record sheet to find the source of the problem and make further optimizations.

[0068] In step S16, each of the actual production parameter values ​​is derived, a preferred design value is determined from all the actual production parameter values, and the optimized parameter values, the actual production parameter values, and the preferred design values ​​are displayed, including: Exporting actual production parameter values ​​recorded in each optimization iteration through a data export tool, wherein the actual production parameter values ​​include actual injection pressure, actual cooling time, actual filling time, and actual temperature distribution; According to the derived actual production parameter values, all production parameter values ​​are analyzed using preset calculation rules to determine the preferred design value of each design parameter, wherein the preferred design value is used to indicate the state in which each design parameter achieves the best effect in production; The optimized parameter values, the derived actual production parameter values ​​and the determined preferred design values ​​are displayed, and the changes in each parameter and the final design selection are presented in the form of charts or tables.

[0069] It should be further explained that in step S16, the actual production parameter values ​​recorded in each iteration process are first exported through the data export tool. The actual production parameter values ​​include the actual injection pressure, actual cooling time, actual filling time and actual temperature distribution, which are four critical indicators in the production process. Subsequently, the above data are used as the basis of the optimization process to further analyze and determine the preferred design values, including: 1. Data export and organization: The data after each iteration is summarized through the data export tool, and the actual production parameters under different process conditions are exported to a unified format for subsequent processing. The exported data is presented in a table to ensure that the value of each actual production parameter is clearly visible. The export tool should have the function of automatic sorting and marking to ensure data integrity and accuracy.

[0070] 2. Analysis and calculation of production parameters: After the data is exported, each production parameter is analyzed using the preset calculation rules. For example, based on the cooling time and injection pressure after each injection, the system can calculate the relationship between each parameter and the quality of the finished product, and screen out the parameter combination that can stably achieve the expected effect. The preset calculation rules are based on the optimization model setting, aiming to find the value that can achieve the best process effect in production.

[0071] 3. Determination of optimal design value: Based on the actual production parameter values ​​derived, the system automatically or manually determines the optimal design value for each parameter. The optimal design value represents the parameter setting that makes the mold work most efficiently and the product quality best in actual production. For example, for cooling time, by combining the performance of each cooling time in different iterations, the most appropriate cooling time value is calculated on the basis of ensuring cooling uniformity and the shortest cooling time. These optimal design values ​​are used to guide the final production parameter setting of the mold.

[0072] 4. Parameter display and chart generation: In order to intuitively present the optimization process and the changes in various parameters, the optimized parameter values, actual production parameter values, and the determined optimal design values ​​are displayed in the form of charts or tables. Through line charts, bar charts, etc., we can clearly see the changing trends of various parameters over time or the number of iterations, and find out the problems and improvement directions in the optimization process. These charts and tables can not only show the final results, but also provide data support for subsequent production decisions.

[0073] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0074] In one implementation, the present invention provides an implementation method based on mold processing optimization, which mainly includes the following steps: First, the design parameters of the mold are obtained and input into the simulation software to build the mold product model. In this step, the design parameters include flow channel design, cavity size and injection molding process parameters, which will directly affect the precision and quality of product molding.

[0075] Next, mold flow analysis is performed based on the generated mold product model to simulate the flow path, flow rate and temperature changes of the melt in the mold. The mold cooling system is analyzed through fluid simulation tools to find areas with insufficient cooling efficiency or uneven filling, providing basic data for subsequent optimization.

[0076] Then, based on the mold flow analysis results, optimization calculations are performed to obtain optimized parameter values. The optimization calculations adjust the design parameters through multiple iterations, such as the size of the flow channel, cooling time, weld line depth, etc., to ensure that the final design meets the predetermined process requirements.

[0077] After the optimization is completed, the optimized parameter values ​​are applied to actual production, the actual injection pressure, cooling time, filling time and temperature distribution are calculated, and the optimal design scheme is determined through data analysis tools to ensure that the mold performs at its best during the production process.

[0078] Finally, the display module presents the changes in various parameters and the optimization effects during the optimization process in the form of charts, which is easy to understand and adjust.

[0079] This implementation method effectively improves the accuracy of mold design and production efficiency, and can help manufacturing companies achieve better process control and finished product quality during complex mold processing.

[0080] In another implementation, the efficiency and accuracy of mold processing can be further improved by introducing an intelligent optimization algorithm. This method is based on machine learning and data-driven optimization technology and mainly includes the following steps: First, in the mold design stage, in addition to inputting basic design parameters, the optimal initial parameter settings can be predicted by intelligent algorithms based on historical production data. The algorithm learns the impact of different design parameters on product quality based on past production results, and thus recommends design parameters that better meet actual production needs.

[0081] Next, when performing mold flow analysis, the dynamic changes of melt flow and cooling process can be monitored in real time, and the simulation conditions can be adjusted using an intelligent feedback mechanism. This allows parameters to be modified instantly based on simulation results without having to re-enter parameters each time, shortening the optimization time.

[0082] Then, in the optimization calculation stage, the key parameters are automatically tuned by combining intelligent optimization methods such as genetic algorithms and particle swarm optimization algorithms. Through multiple iterations, the algorithm will gradually narrow the parameter range and eventually find the best parameter combination. This automated tuning process can further improve the optimization accuracy and reduce human intervention.

[0083] In addition, during the actual production stage, the intelligent system can monitor various production parameters in real time, fine-tune the design parameters based on actual production data, and adaptively adjust the injection pressure and cooling time according to changes in the production environment (such as temperature, pressure, etc.) to ensure the stability of the mold and product quality.

[0084] Ultimately, the optimized production parameters and design plans will automatically generate detailed reports through data analysis tools, and the parameter changes during the optimization process and the final design will be presented in a visual manner, helping to more clearly understand the key nodes and improvement directions of the optimization process.

[0085] Through this implementation method, the intelligence and automation level of mold processing has been further improved, which not only shortens the optimization cycle, but also enables more accurate control of various parameters in the production process to ensure that the mold design and production quality reach the best level.

[0086] The following describes the working process of the present invention using a common scenario as an example. Figure 1 , which is a schematic diagram of the working scenario of the method of the present invention.

[0087] In this example, suppose a plastic product manufacturing company needs to optimize the injection molding process of a newly designed plastic shell. The company's design team first determines the preliminary mold design parameters based on the product's appearance and functional requirements, including basic data such as runner design, cavity size, injection pressure, and cooling time. Figure 1 In step S11, the team input these design parameters one by one into the simulation software, and the software generated a preliminary 3D mold product model to ensure that each design parameter is consistent with the actual production conditions.

[0088] In step S12, the design team used the fluid simulation module in the simulation software to simulate the flow path, flow rate and pressure changes of the melt. The simulation results showed that the melt flowed too fast in some areas, causing problems with the weld line. To this end, the designers adjusted the flow channel design and optimized the shape and distribution ratio of the flow channel to ensure that the melt can flow evenly in the cavity to avoid quality defects.

[0089] After completing the flow analysis, in step S13, the design team inputs the key parameters of the mold flow analysis (such as fluid pressure, cooling time, weld line depth, etc.) into the optimization calculation model of the present invention. Through iterative optimization, the system calculates the appropriate cooling system layout, injection pressure and temperature distribution, and generates optimized parameter values ​​to ensure that each production step meets the process requirements.

[0090] In step S14, the team applied the optimized parameter values ​​to the actual production process and verified the actual injection pressure, filling time and cooling time. With these optimized parameters, the team was able to monitor production data in real time during the actual injection process to ensure that all production parameters met the set standards, further improving production efficiency.

[0091] In step S15, the system records each design parameter and its corresponding actual production parameter value in detail during each iteration. By dynamically recording and analyzing these data, the system helps the team track the changes in each key parameter and ensure that the parameters finally determined after repeated optimization can be used in actual production.

[0092] Finally, in step S16, the team used the data export tool to export all actual production parameters and evaluated these parameters through the analysis tool. The system generated the optimal design values ​​based on these actual production data, and visualized the optimized parameters, actual production parameter values, and optimal design values. In the end, the designers clearly understood the changes in each parameter through charts, ensuring that all design goals were achieved and providing strong data support for subsequent mass production.

[0093] Through this scenario example, the present invention demonstrates how to improve the accuracy of mold design and production efficiency and ensure the stability of product quality by combining simulation analysis, optimization calculation and actual production data.

[0094] In summary, refer to Figure 2 The second embodiment of the present invention provides a mold processing optimization system, including: Data acquisition module, used to obtain mold design parameters and mold flow analysis results; An optimization calculation module is used to perform optimization calculation according to the design parameters to obtain optimized design parameter values; An analysis module, used to perform further optimization calculations based on the mold flow analysis results and the optimized design parameter values ​​to obtain final optimization parameters; The data output module is used to output the final optimization parameters, or input the final optimization parameters into a pre-trained model to generate a final optimization design solution.

[0095] Display module: used to visualize the optimized parameter values, actual production parameter values ​​and preferred design values.

[0096] Preferably, the data acquisition module can be further divided into multiple sub-modules, including: Parameter input submodule: It is used to input the provided mold design parameters (such as runner design parameters, cavity size parameters, injection molding process parameters, etc.) into the system one by one and perform standardization to ensure that the parameters are compatible with other data in the simulation and optimization calculation process.

[0097] Simulation data acquisition submodule: used to obtain mold flow analysis results from simulation software, including key data such as fluid flow path, fluid pressure distribution, cooling time and temperature distribution, to ensure that these data can provide a reliable basis for subsequent optimization calculations.

[0098] Preferably, the optimization calculation module may further include: Preliminary optimization submodule: Based on the design parameters input in the data acquisition module, a preliminary optimization calculation is first performed to obtain the preliminary optimization parameter values ​​related to the injection molding process (such as runner design, cooling time, etc.).

[0099] Iterative optimization submodule: It is used to further calculate the preliminary optimization results. It uses the preset optimization algorithm to repeatedly iterate and adjust the design parameters to ensure that the parameters tend to be optimal after each iteration until the set process requirements are met.

[0100] Preferably, the analysis module comprises: Correlation analysis submodule: By analyzing the correlation between each key parameter, especially the relationship between runner design and cooling system layout, injection pressure and temperature distribution, it ensures that the optimized parameters can be coordinated with each other in actual production to improve overall production efficiency.

[0101] Deviation analysis submodule: used to analyze the deviation between each optimization result and the target production parameters, and dynamically adjust the optimization strategy according to the degree of deviation to reduce errors and improve optimization accuracy.

[0102] Preferably, the data output module may include: Data export submodule: Export optimized design parameters and actual production parameters in a standard format for archiving or subsequent analysis.

[0103] Model application submodule: Input the optimization results into the pre-trained machine learning model to further improve the prediction and regulation accuracy of parameters in the production process and achieve more intelligent process control.

[0104] Preferably, the display module comprises: Visual display submodule: Visually display the optimized design parameters, actual production parameter values ​​and preferred design values, and generate charts, reports, etc. for intuitive analysis and decision-making.

[0105] Real-time monitoring and display submodule: It is used to display the changes of key parameters in real time during the actual production process, ensuring that the production process is consistent with the parameters of the optimized design, thereby further improving the accuracy and reliability of production.

[0106] Through the combination of these modules, the mold processing optimization system of the present invention can not only effectively improve the efficiency of mold design and injection molding process, but also provide complete production monitoring and optimization solution display to help make scientific decisions and adjustments.

[0107] It should be noted that a mold processing optimization system provided in an embodiment of the present invention is used to execute all process steps of a mold processing optimization method in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0108] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a mold processing optimization program. When the processor executes the computer program, the steps in the above-mentioned mold processing optimization method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module, the optimization calculation module, the analysis module, the data output module and the display module.

[0109] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0110] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0111] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0112] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0113] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0114] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0115] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A mold processing optimization method, characterized in that: include: Obtaining design parameters of the mold, and inputting the design parameters into simulation software to establish a mold product model; Performing mold flow analysis according to the mold product model to obtain mold flow analysis results; According to the mold flow analysis results, optimization calculation is performed to obtain optimized parameter values; Calculating the optimized parameter values ​​to obtain actual production parameter values, wherein the actual production parameter values ​​include actual injection pressure, actual cooling time, actual filling time and actual temperature distribution; Repeat the optimization calculation and record each actual production parameter value corresponding to each design parameter in the optimization process; Each of the actual production parameter values ​​is exported, a preferred design value is determined from all the actual production parameter values, and the optimized parameter values, the actual production parameter values, and the preferred design values ​​are displayed.

2. The method for optimizing mold processing according to claim 1, characterized in that: The step of obtaining the design parameters of the mold includes: Acquiring flow channel design parameters, wherein the flow channel design parameters include the size of the inflow flow channel, the length of the main flow channel, and the distribution ratio of the outflow flow channel; Acquire cavity size parameters, wherein the cavity size parameters include the depth and width of the cavity and the layout of the cooling system; Acquiring process parameters during the injection molding process, wherein the process parameters include injection pressure, injection speed and cooling time; The sprue position parameters are obtained. The sprue position is set above the cavity and the flow direction and speed of the plastic melt are controlled by the sprue position.

3. The method for optimizing mold processing according to claim 1, characterized in that: The steps of establishing the mold product model include: Importing the design parameters into the simulation software, and generating a preliminary mold product model based on the input runner design parameters, cavity size parameters and injection molding process parameters; According to the fluid flow path, cavity wall thickness and cooling channel distribution contained in the preliminary mold product model, detailed design operations are performed to obtain the mold product model.

4. The method for optimizing mold processing according to claim 1, characterized in that: The step of performing optimization calculation according to the mold flow analysis result to obtain optimized parameter values ​​includes: Determining key parameters in the mold flow analysis results, wherein the key parameters include fluid pressure, cooling time, weld line depth and flow path; Substituting the key parameters into a preset optimization calculation model, wherein the optimization calculation model is used to calculate the optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution; Comparing parameters at different heights and positions, and using the optimization calculation model to obtain an optimal value, wherein the optimal value is used to ensure flow uniformity and cooling rate consistency of the plastic melt; According to the optimal value, adjusting the key parameters of the mold design, the key parameters including flow channel design parameters, cavity size parameters and cooling system layout parameters; The optimization calculation is performed again until the key parameters meet the set process requirement values, thereby obtaining the optimized parameter values.

5. The method for optimizing mold processing according to claim 4, characterized in that: Substituting the key parameters into a preset optimization calculation model to calculate the optimization values ​​of multiple parameters, including: The optimized values ​​of multiple parameters including flow channel design, cooling time, fluid flow uniformity and temperature distribution are preliminarily calculated using the following formula: in, is a fixed value, and its value range is [4, 6]. To optimize the value, is the actual value of the sprue position, The actual value assigned to the flow rate into the flow channel, is the actual value of the depth of the primary air cooling zone, is the actual value of the width of the primary air cooling zone, is the actual value of the cooling time at the midpoint of the cavity, is the actual value of the width of the secondary air cooling zone; The optimal value of cooling time for weld line depth is calculated by the following formula: in, is the actual value, that is, the distance between the weld line and the midpoint of the cavity obtained in the mold flow analysis. is the cooling time obtained in mold flow analysis, is the cooling time of the secondary air cooling zone, It is an optimized value of cooling time to weld line depth, and the optimized value can be used to adjust the cooling time in the design to ensure that the weld line depth meets the process requirements.

6. The method for optimizing mold processing according to claim 1, characterized in that: The step of calculating the optimized parameter value to obtain the actual production parameter value includes: The actual injection pressure is calculated by the following formula: in, is the actual injection pressure, To optimize the calculated pressure value, is the injection cross-sectional area of ​​the injection molding machine, is the cavity cross-sectional area of ​​the mold; The actual cooling time is calculated by the following formula: in, is the actual cooling time, To optimize the cooling time obtained, It is a constant related to the cooling process and is used to describe the combined influence of mold material properties, environmental conditions and other factors in the cooling time calculation. is the mold wall thickness, is the thermal conductivity of the material; The actual filling time is calculated by the following formula: in, is the actual filling time, is the volume of the mold cavity, is the injection flow rate of the plastic melt; The actual temperature distribution is calculated by the following formula: in, is the actual production temperature, To optimize the temperature value, is the temperature change difference.

7. The mold processing optimization method according to claim 1, characterized in that: Repeat the optimization calculation and record each actual production parameter value corresponding to each design parameter in the optimization process, including: Recording the key parameter values ​​generated during each optimization iteration, wherein the key parameters include flow channel design parameters, cavity size parameters, injection molding process parameters, cooling time, weld line depth, temperature distribution, and fluid flow path; Recording the production parameters after each iteration in detail, including actual injection pressure, actual cooling time, actual filling time, and actual temperature distribution; The design parameters in each iteration are associated with the actual production parameter values, and the changes in the design parameters each time are recorded to form an optimization calculation record table.

8. A mold processing optimization system, characterized in that: include: Data acquisition module, used to obtain mold design parameters and mold flow analysis results; An optimization calculation module is used to perform optimization calculation according to the design parameters to obtain optimized design parameter values; An analysis module, used to perform further optimization calculations based on the mold flow analysis results and the optimized design parameter values ​​to obtain final optimization parameters; A data output module, used to output the final optimization parameters, or input the final optimization parameters into a pre-trained model to generate a final optimization design solution; Display module: used to visualize the optimized parameter values, actual production parameter values ​​and preferred design values.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the mold processing optimization method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the mold processing optimization method according to any one of claims 1 to 7.

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