PVC Ball Rapid Prototyping Cooling System

Abstract

The present invention relates to the technical field of PVC ball processing, specifically to a PVC ball rapid prototyping cooling system, the system comprising: a cooling flow analysis module to obtain the initial physical parameter data of the PVC ball mold and the cooling medium, simulate and analyze the flow of the cooling medium in the PVC ball mold, and generate flow analysis results by setting boundary conditions and adjusting the simulation environment to match the physical characteristics of the current cooling process. In the present invention, by combining the improved Navier-Stokes equations and machine learning optimization technology in the cooling flow analysis module, the system can more accurately simulate the flow characteristics of the cooling medium in the mold, and automatically adjust the layout, size and distribution of the cooling channel to ensure maximum cooling efficiency while maintaining or improving product quality. Through the comprehensive statistical thermal analysis framework in the efficiency impact analysis module, the system can accurately identify and adjust the key parameters that affect the cooling efficiency, and achieve fine control of the cooling process.

Description

Technical Field

[0001] The invention relates to the technical field of PVC leather ball processing, in particular to a PVC leather ball rapid prototyping cooling system. Background Art

[0002] The field of PVC ball processing technology is the process of manufacturing balls using polyvinyl chloride materials, including design, molding, processing, decoration and finishing. PVC is widely used in the production of various ball products, such as toy balls, sports balls, etc., due to its durability, flexibility and cost-effectiveness. As the market's requirements for the quality and diversity of PVC balls increase, related processing technologies are also constantly improving and developing.

[0003] Among them, the PVC ball rapid prototyping cooling system achieves rapid production by shortening the time from prototyping to cooling of the PVC ball. At the same time, accelerating the cooling process can effectively reduce the production cycle, increase output, and ensure that the physical properties and surface quality of the finished ball meet the standards. In addition, the rapid prototyping cooling system also helps to reduce energy consumption and production costs, which is of great significance to improving the economic benefits and environmental sustainability of PVC ball production.

[0004] Although the existing technology has made significant progress in the field of PVC ball processing and manufacturing, improving production efficiency and reducing energy consumption, there are still limitations in the accuracy of the optimization of cooling channel design. When dealing with the internal flow of complex molds, traditional computational fluid dynamics simulations are difficult to accurately capture all the subtle factors that affect cooling efficiency and product quality, which in turn affects production efficiency and product quality. In addition, although the cooling process of PVC ball molding can be controlled to a certain extent by adjusting the flow rate and temperature of the cooling medium, it is still difficult to respond to real-time changes in the production process. The lack of flexible and adaptive cooling process control leads to uneven stress, deformation or other defects in the product, further affecting the performance and consistency of the product. Summary of the invention

[0005] The purpose of the invention is to solve the shortcomings in the prior art and to propose a PVC leather ball rapid prototyping cooling system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a PVC leather ball rapid prototyping cooling system, the system comprising:

[0007] The cooling flow analysis module obtains the initial physical parameter data of the PVC ball mold and the cooling medium, simulates and analyzes the flow of the cooling medium in the PVC ball mold, and generates flow analysis results by setting boundary conditions and adjusting the simulation environment to match the physical characteristics of the current cooling process;

[0008] The channel design optimization module performs intelligent design optimization of the layout, diameter and distribution of the cooling channel of the PVC ball mold based on the flow state analysis results, matches the predetermined cooling efficiency index, and generates an optimized channel configuration;

[0009] The efficiency impact analysis module measures and analyzes the surface temperature of the PVC ball mold and the flow rate of the cooling medium during the cooling process based on the optimized channel configuration, identifies key factors affecting the cooling efficiency, and obtains key cooling parameters;

[0010] The temperature control strategy adjustment module dynamically adjusts and analyzes the cooling temperature gradient of the PVC ball mold based on the key cooling parameters, and generates an adjusted temperature control plan;

[0011] The weight influence determination module analyzes the influence of each parameter in the cooling process of the PVC ball mold based on the adjusted temperature control scheme, determines the priority of the parameter in the cooling efficiency optimization, and generates a parameter importance analysis result;

[0012] The cooling configuration comprehensive optimization module performs comprehensive adjustment analysis on the cooling equipment configuration of the PVC ball mold based on the parameter importance analysis results and refers to the balance between cooling efficiency and finished product quality to generate an optimized cooling configuration;

[0013] The production process intelligent monitoring module monitors the real-time data of the PVC ball molding and cooling process, automatically adjusts the production parameters through the optimized cooling configuration, and generates an intelligent monitoring strategy.

[0014] The present invention has improvements in that the flow state analysis result includes a flow velocity distribution diagram, a pressure distribution diagram and a temperature distribution diagram of each point inside the PVC ball mold, the optimized channel configuration includes the adjusted number of channels, the channel layout plan and the value of the diameter size of each channel, the key cooling parameters include the medium flow rate, the medium inlet and outlet temperature difference and the cooling cycle time required for the PVC ball mold to achieve uniform cooling, the adjusted temperature control plan includes the set temperature control point, the temperature variation range of each control point and the adjustment time interval, the parameter importance analysis result includes the impact score of each cooling parameter on the product appearance quality and structural integrity, and the adjustment priority of the parameter during the cooling process, the optimized cooling configuration includes the selection of the cooling medium type, the pressure setting of the cooling equipment and the cooling cycle adjustment plan after referring to the cooling efficiency and product quality, and the intelligent monitoring strategy includes real-time monitoring data items, data collection frequency and automatic adjustment production parameter rules obtained according to data analysis.

[0015] The present invention is improved in that the cooling flow state analysis module comprises:

[0016] The initial data acquisition submodule obtains the initial physical parameter data of the PVC ball mold and the cooling medium, including temperature, pressure and density, and integrates them into a database to generate an initial parameter set;

[0017] The flow pattern simulation adjustment submodule uses the Navier-Stokes equation based on the initial parameter set and combines the genetic algorithm to automatically adjust the simulation environment parameters, including the cooling medium flow rate and temperature range, and adjusts the boundary condition position, performs dynamic streamline tracking, and generates simulation adjustment results;

[0018] The cooling effect prediction submodule optimizes the cooling medium path through the simulation adjustment results and applies the principles of fluid dynamics, predicts the internal temperature distribution of the PVC ball mold, analyzes the cooling uniformity, and generates flow analysis results.

[0019] The present invention is improved in that the channel design optimization module comprises:

[0020] The flow data application submodule uses the flow analysis results to analyze the flow characteristics of the cooling medium in the PVC ball mold, identify the areas where the mold cooling demand is concentrated, and analyze the areas with uneven temperature distribution, select the starting point for channel layout optimization, and generate layout key point analysis results;

[0021] The channel intelligent layout submodule replans the channel layout, including direction, position, shape and size, based on the analysis results of the layout key points, with reference to cooling uniformity and manufacturing feasibility, and generates an optimized layout plan;

[0022] The optimization design output submodule integrates the optimized layout scheme, analyzes the relationship between cooling effect and finished product quality, adjusts the channel design, matches the preset cooling efficiency target, and generates an optimized channel configuration.

[0023] The present invention is improved in that the efficiency impact analysis module includes:

[0024] The mold temperature monitoring submodule is based on the optimized channel configuration, combined with the surface temperature data of the PVC ball mold, and performs real-time monitoring and analysis to generate a mold surface temperature data set;

[0025] The medium flow rate analysis submodule analyzes the flow rate change of the cooling medium inside the PVC ball mold based on the mold surface temperature data set, determines the influence of the flow rate on the cooling efficiency, and generates the flow rate influence analysis result;

[0026] The efficiency key parameter extraction submodule is based on the flow rate influence analysis results, comprehensively examines the PVC ball mold temperature, cooling medium speed and temperature difference, and uses variance analysis to identify the key factors affecting the cooling efficiency and their interactions, assists in calibrating the cooling efficiency influencing factors, optimizes the cooling cycle, and obtains the key cooling parameters.

[0027] The present invention is improved in that the temperature control strategy adjustment module includes:

[0028] The cooling parameter analysis submodule analyzes the relationship between the cooling medium flow rate and the mold temperature, as well as the change of the temperature gradient during the cooling process, based on the key cooling parameters and the temperature data inside and outside the PVC ball mold, and generates a temperature gradient analysis result;

[0029] The local temperature adjustment submodule identifies the uneven cooling area in the temperature control of the PVC ball mold based on the temperature gradient analysis result, designs a local temperature adjustment scheme, adjusts the flow rate and temperature of the cooling medium, and generates a temperature adjustment strategy;

[0030] The temperature control optimization solution generation submodule analyzes the adjusted temperature control effect based on the temperature adjustment strategy, optimizes the overall temperature control strategy of the PVC ball mold, and generates an adjusted temperature control solution.

[0031] The present invention is improved in that the weight impact determination module comprises:

[0032] The cooling parameter influence analysis submodule collects the influence data of each cooling parameter on the temperature control of the PVC ball mold based on the adjusted temperature control scheme, and generates the parameter influence analysis results by comparing the influence of the parameters on the cooling efficiency;

[0033] The parameter priority assessment submodule analyzes the importance of each parameter to the molding quality based on the parameter influence analysis results, divides the parameter priorities according to the influence, and generates parameter priority assessment results;

[0034] The key parameter sorting submodule sorts all cooling parameters according to the priority based on the parameter priority assessment result, determines the order of optimization and adjustment, and generates parameter importance analysis results.

[0035] The present invention is improved in that the cooling configuration comprehensive optimization module comprises:

[0036] The quality efficiency analysis submodule refers to the parameter importance analysis results, analyzes the impact of each cooling parameter configuration on the quality and production efficiency of the finished product, as well as the balance point between efficiency and quality in the cooling process, and generates a quality efficiency balance analysis result;

[0037] The configuration adjustment analysis submodule selects key configuration items that affect cooling efficiency and finished product quality based on the quality-efficiency balance analysis results, formulates adjustment strategies for the configuration items, and generates a configuration adjustment plan;

[0038] The cooling equipment optimization design submodule optimizes the cooling equipment according to the configuration adjustment scheme, the selection of cooling medium and the adjustment of temperature control parameters, and generates an optimized cooling configuration.

[0039] The present invention is improved in that the production process intelligent monitoring module comprises:

[0040] The real-time data tracking submodule continuously collects temperature, pressure and flow rate data of the molding and cooling processes based on the optimized cooling configuration to generate a real-time monitoring data set;

[0041] The parameter dynamic adjustment submodule uses the real-time monitoring data set to automatically identify parameters that deviate from the target range, automatically adjust them, and generate dynamic adjustment records;

[0042] The monitoring strategy formulation submodule analyzes the adjustment effect, optimizes the monitoring logic and parameter adjustment strategy, and generates an intelligent monitoring strategy based on the dynamic adjustment record.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are:

[0044] In the present invention, by combining the improved Navier-Stokes equations and machine learning optimization technology in the cooling flow analysis module, the system can more accurately simulate the flow characteristics of the cooling medium in the mold, and automatically adjust the layout, size and distribution of the cooling channel to ensure maximum cooling efficiency while maintaining or improving product quality, significantly improving the accuracy and optimization efficiency of the cooling channel design. Through the comprehensive statistical thermal analysis framework in the efficiency impact analysis module, the system can accurately identify and adjust the key parameters that affect the cooling efficiency, and achieve fine control of the cooling process. Not only does it improve production efficiency and reduce costs, but it also ensures the consistency and predictability of product quality by dynamically adjusting and optimizing cooling parameters, effectively avoiding the generation of uneven stress, deformation or other defects inside the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The module diagram of the cooling system for rapid prototyping of a PVC ball proposed by the present invention;

[0046] Figure 2 The system framework diagram of the PVC leather ball rapid prototyping cooling system proposed by the present invention;

[0047] Figure 3 A schematic diagram of a cooling flow state analysis module in a PVC ball rapid prototyping cooling system provided by the present invention;

[0048] Figure 4 This is a schematic diagram of a channel design optimization module in a PVC ball rapid prototyping cooling system proposed by the present invention;

[0049] Figure 5 A schematic diagram of an efficiency impact analysis module in a PVC ball rapid prototyping cooling system proposed by the present invention;

[0050] Figure 6 A schematic diagram of a temperature control strategy adjustment module in a PVC ball rapid prototyping cooling system proposed by the present invention;

[0051] Figure 7 A schematic diagram of a weight influence determination module in a PVC ball rapid prototyping cooling system proposed by the present invention;

[0052] Figure 8 A schematic diagram of a cooling configuration comprehensive optimization module in a PVC leather ball rapid prototyping cooling system proposed by the present invention;

[0053] Fig. 9 The present invention provides a schematic diagram of an intelligent monitoring module for the production process in a PVC leather ball rapid prototyping cooling system. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. Example

[0056] See also Figure 1 The present invention provides a technical solution: a PVC ball rapid prototyping cooling system, the system comprising:

[0057] The cooling flow analysis module obtains the initial physical parameter data of the PVC ball mold and the cooling medium, simulates and analyzes the flow of the cooling medium in the PVC ball mold, and generates flow analysis results by setting boundary conditions and adjusting the simulation environment to match the physical characteristics of the current cooling process;

[0058] The channel design optimization module performs intelligent design optimization of the layout, diameter and distribution of the cooling channel of the PVC ball mold based on the flow state analysis results, matches the predetermined cooling efficiency index, and generates an optimized channel configuration;

[0059] The efficiency impact analysis module measures and analyzes the surface temperature of the PVC ball mold and the flow rate of the cooling medium during the cooling process based on the optimized channel configuration, identifies the key factors affecting the cooling efficiency, and obtains the key cooling parameters;

[0060] The temperature control strategy adjustment module dynamically adjusts and analyzes the cooling temperature gradient of the PVC ball mold based on key cooling parameters and generates an adjusted temperature control plan;

[0061] The weighted influence determination module analyzes the influence of each parameter in the cooling process of the PVC ball mold based on the adjusted temperature control scheme, determines the priority of the parameter in the cooling efficiency optimization, and generates the parameter importance analysis results;

[0062] The cooling configuration comprehensive optimization module conducts comprehensive adjustment and analysis on the cooling equipment configuration of the PVC ball mold based on the results of parameter importance analysis and the balance between cooling efficiency and finished product quality, and generates an optimized cooling configuration.

[0063] The intelligent production process monitoring module monitors the real-time data of the PVC ball molding and cooling process, automatically adjusts production parameters through optimized cooling configuration, and generates intelligent monitoring strategies.

[0064] The flow analysis results include the velocity distribution diagram, pressure distribution diagram and temperature distribution diagram of each point inside the PVC ball mold. The optimized channel configuration includes the adjusted number of channels, channel layout plan and the value of the diameter size of each channel. The key cooling parameters include the medium flow rate, medium inlet and outlet temperature difference and cooling cycle time required for uniform cooling of the PVC ball mold. The adjusted temperature control plan includes the set temperature control point, the temperature variation range of each control point and the adjustment time interval. The parameter importance analysis results include the impact score of each cooling parameter on the product appearance quality and structural integrity, as well as the adjustment priority of the parameter during the cooling process. The optimized cooling configuration includes the selection of cooling medium type, the pressure setting of the cooling equipment and the adjustment plan of the cooling cycle after referring to the cooling efficiency and product quality. The intelligent monitoring strategy includes real-time monitoring data items, data collection frequency and automatic adjustment rules for production parameters based on data analysis.

[0065] See also Figure 2 and Figure 3 , cooling flow analysis module includes:

[0066] The initial data acquisition submodule obtains the initial physical parameter data of the PVC ball mold and the cooling medium including temperature, pressure and density, and integrates them into a database. The specific process of generating the initial parameter set is as follows;

[0067] The initial data acquisition submodule is based on the initial physical parameters of the PVC ball mold and the cooling medium. It uses SQL database commands to insert data, including INSERT INTO statements to enter temperature, pressure, density and other data. It uses SELECT statements combined with WHERE conditions to filter data to ensure data accuracy. It also combines GROUP BY statements to classify and summarize data to generate an initial parameter set.

[0068] The flow simulation adjustment submodule uses the Navier-Stokes equation based on the initial parameter set and combines genetic algorithms to automatically adjust the simulation environment parameters, including the cooling medium flow rate and temperature range, as well as the boundary condition position, to perform dynamic streamline tracking, and generate the simulation adjustment results. The specific process is as follows;

[0069] The flow simulation adjustment submodule is based on the initial parameter set and adopts the Navier-Stokes equation to perform fluid dynamics simulation through the OpenFOAM software package. The simulation environment parameters are set including the location of the boundary conditions with the defineBoundaryConditions command. The cooling medium flow rate and temperature range are adjusted through the adjustFlowParameters function. The dynamic mesh technology dynamicMesh is applied to simulate the flow of the cooling medium in the mold and generate simulation adjustment results.

[0070] The Navier-Stokes equation is combined with the genetic algorithm according to the improved formula I:

[0071]

[0072] Simulate the flow of cooling medium in the mold and generate simulation adjustment results;

[0073] Among them, u is the fluid velocity vector, which represents the speed and direction of the fluid moving inside the mold. Time is used to represent the time variable in the fluid dynamics simulation process, which is key to tracking the changes of fluid state over time. Fluid density refers to the mass of unit volume of fluid under specific conditions. Changes in fluid density directly affect the flow characteristics of the fluid. Fluid pressure refers to the pressure state of the fluid at a certain point and is one of the important factors that determine the direction and speed of fluid flow. It is the dynamic viscosity coefficient, which describes the magnitude of the viscosity inside the fluid and affects the relative movement speed between fluid layers. It is the flow rate adjustment coefficient, which is determined by comparative analysis between fluid dynamics simulation and current cooling effect, and adjusted according to the genetic algorithm optimization results. It is used to accurately control the fluid velocity in the simulation to match the flow characteristics of the actual cooling medium. It is the density adjustment value, which is determined by performing regression analysis on the experimental data of the effect of temperature changes inside the mold on fluid density. It reflects the change in fluid density caused by temperature changes or other factors, and is used to improve the adaptability and accuracy of the simulation. The pressure adjustment value is determined by analyzing the deviation between the mold internal pressure measurement data and the theoretical pressure distribution. Adjustments are made based on the local pressure changes caused by the mold internal structure to ensure that the pressure distribution simulation is closer to the actual situation. It is the viscosity adjustment coefficient, which is determined by comparing the experimental data of fluid viscosity under various cooling media and temperature conditions with the theoretical value. It takes into account the influence of cooling medium type and temperature on fluid viscosity and is used to adjust the dynamic viscosity coefficient in order to more accurately simulate the flow state of the fluid.

[0074] The execution process is as follows:

[0075] S1: Set the initial flow rate adjustment coefficient , density adjustment value , pressure adjustment value , and the viscosity adjustment factor At the same time, this value is based on previous experimental data or empirical estimation.

[0076] S2: Using the initial parameters, substitute into Formula I , , and The first round of fluid dynamics simulation was carried out to obtain preliminary simulation results of the cooling medium flow in the mold.

[0077] S3: Based on the cooling efficiency and temperature distribution uniformity obtained by simulation, the performance indicators of the simulation results, such as cooling time, temperature gradient, etc., are calculated.

[0078] S4: Use the performance index of the simulation results as the fitness function of the genetic algorithm and generate new parameters through selection, crossover and mutation operations , , and .

[0079] S5: Using the parameters optimized by the genetic algorithm, repeatedly perform the fluid dynamics simulation of Formula I to obtain updated simulation results.

[0080] S6: Evaluate the performance index of the new round of simulation results and compare them with the results of the previous round. If the results meet the predetermined convergence conditions (such as the improvement of the performance index is less than the set threshold), the optimization process ends; otherwise, return to step S4 to continue iteration.

[0081] S7: Once the convergence conditions are met, the current parameters , , and And the corresponding simulation results are taken as the final optimization results.

[0082] The cooling effect prediction submodule optimizes the cooling medium path by simulating the adjustment results and applying the principles of fluid dynamics, predicts the internal temperature distribution of the PVC ball mold, analyzes the cooling uniformity, and generates the flow analysis results. The specific process is as follows;

[0083] The cooling effect prediction submodule uses ANSYS Fluent to perform fluid dynamics analysis based on the simulation adjustment results, sets the cooling medium path optimization parameters including path length and fluid resistance, specifies temperature distribution uniformity as the optimization target through the defineOptimizationCriteria function, and uses the temperature monitoring point setting function tempMonitorPoints to track the temperature changes at key locations inside the mold and generate flow analysis results.

[0084] See also Figure 2 and Figure 4 , the channel design optimization module includes:

[0085] The flow data application submodule uses the flow analysis results to analyze the flow characteristics of the cooling medium in the PVC ball mold, identify the areas where mold cooling needs are concentrated, analyze the areas with uneven temperature distribution, select the starting point for channel layout optimization, and generate the specific process of layout key point analysis results as follows;

[0086] Based on the flow analysis results, the flow data application submodule analyzes the flow characteristics of the cooling medium in the PVC ball mold. The findPeaks function is executed by Matlab software to identify the temperature distribution peak in the mold, which represents the area with concentrated cooling demand. The contour function is used to draw the temperature distribution diagram to identify the uneven temperature area. The starting point of the channel layout optimization is selected through the setOptimizationStartPoint command to generate the layout key point analysis results.

[0087] The channel intelligent layout submodule replans the channel layout, including direction, position, shape and size, based on the analysis results of the key points of the layout, referring to the cooling uniformity and manufacturing feasibility, and generates the optimized layout plan in the following specific process;

[0088] The channel intelligent layout submodule uses SolidWorks software for 3D modeling based on the results of layout key point analysis. The API function SetLayoutParameters is used to set the parameters of channel direction, position, shape and size. Fluid dynamics simulation is performed through Dassault Systemes' SIMULIA to test cooling uniformity and manufacturing feasibility. The fmincon function in OptimizationToolbox is used to optimize the channel layout parameters and generate an optimized layout solution.

[0089] The optimization design output submodule integrates the optimized layout scheme, analyzes the relationship between cooling effect and finished product quality, adjusts the channel design, matches the preset cooling efficiency target, and generates the specific process of the optimized channel configuration as follows;

[0090] The optimization design output submodule integrates the optimized layout scheme, uses ANSYS Workbench for simulation analysis, sets the Boundary Conditions in the Simulation environment as the preset cooling efficiency target, adjusts the DesignPoint settings through the Goal DrivenOptimization module to match the preset cooling efficiency target, executes the solution method for iterative optimization, and generates the optimized channel configuration.

[0091] See also Figure 2 and Figure 5 , the efficiency impact analysis module includes:

[0092] The mold temperature monitoring submodule is based on the optimized channel configuration, combined with the PVC ball mold surface temperature data, and performs real-time monitoring and analysis to generate the mold surface temperature data set. The specific process is as follows:

[0093] The mold temperature monitoring submodule is based on the optimized channel configuration. It uses a Python script to call the matplotlib library to draw a real-time monitoring graph of the mold surface temperature. It reads the temperature sensor data through the serial communication library pySerial, and uses the numpy library for data preprocessing, including filtering and denoising. It executes the real-time data update function updateRealTimeTemperaturePlot to reflect the changes in the mold surface temperature and generate a mold surface temperature data set.

[0094] The medium flow rate analysis submodule analyzes the flow rate changes of the cooling medium inside the PVC ball mold based on the mold surface temperature data set, determines the impact of the flow rate on the cooling efficiency, and generates the specific process of the flow rate impact analysis results as follows;

[0095] The medium flow rate analysis submodule is based on the mold surface temperature data set and uses the R language to execute the lm function for linear regression analysis. The cooling medium flow rate is used as the independent variable and the mold surface temperature is used as the dependent variable. The correlation coefficient between the flow rate and the temperature is calculated. The model evaluation indicators, including R-squared and p-value, are obtained through the summary function to determine the impact of the flow rate on the cooling efficiency and generate the flow rate impact analysis results.

[0096] The efficiency key parameter extraction submodule is based on the flow rate impact analysis results, comprehensively examines the PVC ball mold temperature, cooling medium speed and temperature difference, and uses variance analysis to identify the key factors affecting cooling efficiency and their interactions, assists in calibrating the cooling efficiency influencing factors, optimizes the cooling cycle, and obtains the specific process of key cooling parameters.

[0097] The efficiency key parameter extraction submodule is based on the flow rate influence analysis results. SPSS software is used to perform variance analysis. The mold temperature, cooling medium speed and temperature difference are set as independent variables through the Define Factorial Design interface, and the cooling efficiency is set as the dependent variable. The Analyze function is executed to identify the influence of different variable combinations on the cooling efficiency through Post Hoc Tests, assist in calibrating the factors affecting the cooling efficiency, adjust the Analysis Parameters to optimize the cooling cycle, and obtain the key cooling parameters.

[0098] The variance analysis method is based on the improved formula II:

[0099]

[0100] Calculate the key factors affecting cooling efficiency and their interactions, assist in calibrating the factors affecting cooling efficiency, and obtain key cooling parameters;

[0101] Among them, MS is the mean square difference between groups, indicating the magnitude of the variation between different cooling parameter combinations, reflecting the difference in mold cooling efficiency under different parameter settings. It is the mean squared deviation within the group, indicating the degree of variation of the internal data of the same cooling parameter combination, and is used to measure the volatility of cooling efficiency under the same conditions. Adjusted weights for mold temperature, determined through experimental data analysis and expert evaluation, quantify the relative importance of mold temperature’s effect on cooling efficiency. It is the cooling medium velocity adjustment bonus, which is determined by comparing the experimental results of cooling efficiency under various flow rates. It is used to make weighted adjustments to the mean square difference between groups according to the specific impact of the cooling medium flow rate on the cooling efficiency. It is the temperature difference adjustment coefficient, which is determined by analyzing the experimental data of the influence of the temperature difference between the mold and the cooling medium on the cooling time. The influence of the temperature difference between the mold and the cooling medium on the cooling efficiency is considered, and the mean square difference within the group is adjusted. The cooling efficiency adjustment bonus is determined by evaluating the parameter changes required to achieve multiple cooling efficiency standards. It is a weighted adjustment of the within-group variation based on the cooling efficiency target value, reflecting the difficulty of achieving a specific cooling efficiency standard.

[0102] The execution process is as follows:

[0103] The experiment collected data on the PVC ball mold temperature, cooling medium velocity, temperature difference between the mold and the cooling medium, and cooling efficiency.

[0104] Statistical software was used to analyze the collected data, especially the effects of mold temperature and cooling medium velocity on cooling efficiency, as well as the effect of temperature difference on cooling efficiency.

[0105] According to the data analysis results, the mold temperature adjustment weight is determined through expert evaluation and experimental data analysis , cooling medium speed adjustment bonus , temperature difference adjustment coefficient , and Cooling Efficiency Modification Bonus .

[0106] Substitute the determined weight coefficient and addition coefficient into Formula II, perform the variance analysis process, calculate the improved F value, and evaluate the comprehensive impact of different cooling parameters on cooling efficiency.

[0107] According to the results of variance analysis, the key factors affecting the cooling efficiency and their interactions are identified to assist in calibrating the factors affecting the cooling efficiency.

[0108] Based on the analysis results, the cooling channel design of the PVC ball mold, the flow rate and temperature settings of the cooling medium, and other related cooling parameters are optimized to achieve higher cooling efficiency and uniformity of temperature distribution.

[0109] See also Figure 2 and Figure 6 , the temperature control strategy adjustment module includes:

[0110] The cooling parameter analysis submodule analyzes the relationship between the cooling medium flow rate and the mold temperature, as well as the change in temperature gradient during the cooling process, based on key cooling parameters and combined with the temperature data inside and outside the PVC ball mold. The specific process of generating the temperature gradient analysis results is as follows:

[0111] The cooling parameter analysis submodule is based on key cooling parameters and combines the temperature data inside and outside the PVC ball mold. It uses the SciPy library in the Python script to calculate the Pearson correlation coefficient, analyzes the relationship between the cooling medium flow rate and the mold temperature, and uses the numpy library to perform numerical differentiation of the temperature gradient change, calculates the rate of change of temperature over time, and generates the temperature gradient analysis results.

[0112] Based on the temperature gradient analysis results, the local temperature adjustment submodule identifies the uneven cooling area in the temperature control of the PVC ball mold, designs the local temperature adjustment plan, adjusts the flow rate and temperature of the cooling medium, and generates the specific process of the temperature adjustment strategy as follows;

[0113] Based on the temperature gradient analysis results, the local temperature adjustment submodule uses the pdepe function in MATLAB software to solve the partial differential equation, simulates the temperature distribution in the PVC ball mold, identifies the area with significant temperature gradient changes, that is, uneven cooling areas, and sets the optimal adjustment strategy for the cooling medium flow rate and temperature by adjusting the TolFun property in the solverOptions parameter to generate a temperature adjustment strategy.

[0114] The temperature control optimization solution generation submodule analyzes the adjusted temperature control effect based on the temperature adjustment strategy, and optimizes the overall temperature control strategy of the PVC ball mold. The specific process of generating the adjusted temperature control solution is as follows;

[0115] The temperature control optimization solution generation submodule is based on the temperature adjustment strategy. SolidWorks Flow Simulation is used to perform fluid dynamics and heat conduction simulations. Initial Conditions is set as the mold temperature control conditions before adjustment. The temperature control targets of the adjustment area are located through the Goals function. Parametric Study is run to analyze the impact of the adjustment strategy on the mold temperature control effect. Boundary Conditions is adjusted to optimize the overall temperature control strategy of the PVC ball mold, and the adjusted temperature control solution is generated.

[0116] See also Figure 2 and Figure 7 , the weight impact determination module includes:

[0117] The cooling parameter influence analysis submodule collects the influence data of each cooling parameter on the temperature control of the PVC ball mold based on the adjusted temperature control scheme. By comparing the influence of the parameters on the cooling efficiency, the specific process of generating the parameter influence analysis results is as follows;

[0118] Based on the adjusted temperature control scheme, the cooling parameter influence analysis submodule uses a Python script to execute the DataFrame constructor in the pandas library to integrate the cooling parameter data, uses the numpy library for numerical calculations, and executes the pearsonr function through the stats module in the scipy library to calculate the correlation coefficient between each cooling parameter and mold temperature control, so as to measure the influence of the parameter on the cooling efficiency and generate the parameter influence analysis results.

[0119] The parameter priority assessment submodule analyzes the importance of each parameter to the molding quality based on the parameter influence analysis results, and divides the parameter priorities according to the influence. The specific process of generating the parameter priority assessment results is as follows:

[0120] The parameter priority assessment submodule uses the R language to execute the lm function for multiple linear regression analysis based on the parameter influence analysis results. The molding quality is used as the dependent variable and the cooling parameters are used as the independent variables. The statistical significance of each parameter is evaluated through the summary function, including the coefficient estimate and the p-value. The parameter priority is divided according to the size of the estimated coefficient and the significance of the p-value to generate the parameter priority assessment results.

[0121] The key parameter sorting submodule sorts all cooling parameters according to the priority based on the parameter priority assessment results, determines the order of optimization and adjustment, and generates the specific process of parameter importance analysis results as follows;

[0122] The key parameter sorting submodule is based on the parameter priority assessment results. It uses the Java language to execute the Collections.sort method to sort all cooling parameters according to their priorities. The Comparator interface is used to define the sorting rules to ensure that the sorting process conforms to the priority assessment results. The order of optimization and adjustment is determined according to the priority order, and the parameter importance analysis results are generated.

[0123] See also Figure 2 and Figure 8 , the cooling configuration comprehensive optimization module includes:

[0124] The quality efficiency analysis submodule refers to the parameter importance analysis results to analyze the impact of each cooling parameter configuration on the quality of the finished product and production efficiency, as well as the balance point between efficiency and quality in the cooling process. The specific process for generating the quality efficiency balance analysis results is as follows;

[0125] The quality and efficiency analysis submodule is based on the results of parameter importance analysis. It uses Excel software to execute the VLOOKUP function to match the relevant data of each cooling parameter with the finished product quality and production efficiency. It uses the pivot table function to analyze the comprehensive impact of different cooling parameter configurations on the finished product quality and production efficiency. It performs conditional formatting to highlight the balance point between efficiency and quality, and generates quality and efficiency balance analysis results.

[0126] The configuration adjustment analysis submodule selects key configuration items that affect cooling efficiency and finished product quality based on the quality-efficiency balance analysis results, formulates adjustment strategies for configuration items, and generates the specific process of configuration adjustment solutions as follows;

[0127] The configuration adjustment analysis submodule is based on the results of the quality-efficiency balance analysis and uses the fmincon function in the MATLAB software to perform constrained optimization calculations. The objective function is set to maximize production efficiency while ensuring that the quality of the finished product is not lower than the predetermined standard. The algorithm parameter options are modified to adjust the accuracy and speed of the optimization process. According to the optimization results, key configuration items that affect the cooling efficiency and finished product quality are selected to generate a configuration adjustment plan.

[0128] The cooling equipment optimization design submodule optimizes the cooling equipment according to the configuration adjustment plan, the selection of cooling medium and the adjustment of temperature control parameters, and generates the specific process of the optimized cooling configuration as follows;

[0129] The cooling equipment optimization design submodule uses SolidWorks 3D modeling software to design the cooling system according to the configuration adjustment plan, uses the fluid dynamics analysis module SimFlow to set the flow rate and temperature parameters of the cooling medium, adjusts the layout and size of the cooling channel according to the optimization results, performs simulation operations to test the effect of the adjusted design on improving the cooling efficiency, and generates an optimized cooling configuration.

[0130] See also Figure 2 and Fig. 9 , the intelligent monitoring module of the production process includes:

[0131] The real-time data tracking submodule continuously collects temperature, pressure and flow rate data of the molding and cooling processes based on the optimized cooling configuration. The specific process of generating the real-time monitoring data set is as follows:

[0132] Based on the optimized cooling configuration, the real-time data tracking submodule uses LabVIEW program to continuously monitor multiple types of sensor data such as temperature, pressure and flow rate. It reads sensor signals through DAQmx Read function, processes data sets with ArrayOperations function, and executes Write to Measurement File VI to record data in real time to generate real-time monitoring data sets.

[0133] The parameter dynamic adjustment submodule uses real-time monitoring data sets to automatically identify parameters that deviate from the target range and automatically adjust them. The specific process of generating dynamic adjustment records is as follows;

[0134] The parameter dynamic adjustment submodule adopts the real-time monitoring data set, uses the MATLAB script to execute the thresholding function to identify the parameters that deviate from the preset target range, automatically calculates the PID controller parameters through the pidTuner tool, adjusts the setPoint function to adjust the cooling medium flow rate and temperature in real time, and generates dynamic adjustment records.

[0135] The monitoring strategy formulation submodule analyzes the adjustment effect based on the dynamic adjustment records, optimizes the monitoring logic and parameter adjustment strategy, and generates the specific process of the intelligent monitoring strategy:

[0136] The monitoring strategy formulation submodule uses Python scripts to execute the DecisionTreeClassifier function in the sklearn library based on dynamic adjustment records to analyze the adjustment effect, optimizes the monitoring logic and parameter adjustment strategy through the fit and predict methods, analyzes the monitoring data in combination with the numpy and pandas libraries, determines new monitoring strategy parameters, and generates intelligent monitoring strategies.

[0137] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. PVC ball rapid prototyping cooling system, characterized by: The system comprises: The cooling flow pattern analysis module obtains the initial physical parameter data of the PVC ball mold and the cooling medium, simulates and analyzes the flow of the cooling medium in the PVC ball mold, and generates flow pattern analysis results; The cooling flow state analysis module comprises: The initial data acquisition submodule obtains the initial physical parameter data of the PVC ball mold and the cooling medium, including temperature, pressure and density, and integrates them into a database to generate an initial parameter set; The flow pattern simulation adjustment submodule uses the Navier-Stokes equation based on the initial parameter set and combines the genetic algorithm to automatically adjust the simulation environment parameters, including the cooling medium flow rate and temperature range, and adjusts the boundary condition position, performs dynamic streamline tracking, and generates simulation adjustment results; The Navier-Stokes equation is combined with the genetic algorithm according to the improved formula I: ； Simulate the flow of cooling medium in the mold and generate simulation adjustment results; Where u is the fluid velocity vector, For time, is the fluid density, is the fluid pressure, is the dynamic viscosity coefficient, is the flow rate adjustment factor, which is determined by comparing the fluid dynamics simulation with the current cooling effect. is the density adjustment value, which is determined by regression analysis of experimental data on the effect of temperature change inside the mold on fluid density. is the pressure adjustment value, which is determined by analyzing the deviation between the mold internal pressure measurement data and the theoretical pressure distribution. is the viscosity adjustment factor, which is determined by comparing the experimental data of fluid viscosity under various cooling media and temperature conditions with the theoretical value; The cooling effect prediction submodule optimizes the cooling medium path by applying the fluid dynamics principle through the simulation adjustment results, predicts the temperature distribution inside the PVC ball mold, analyzes the cooling uniformity, and generates the flow analysis results; The channel design optimization module performs intelligent design optimization of the layout, diameter and distribution of the cooling channel of the PVC ball mold based on the flow state analysis results, and generates an optimized channel configuration; The efficiency impact analysis module measures and analyzes the surface temperature of the PVC ball mold and the flow rate of the cooling medium during the cooling process based on the optimized channel configuration to obtain key cooling parameters; The efficiency impact analysis module includes: The mold temperature monitoring submodule is based on the optimized channel configuration, combined with the surface temperature data of the PVC ball mold, and performs real-time monitoring and analysis to generate a mold surface temperature data set; The medium flow rate analysis submodule analyzes the flow rate change of the cooling medium inside the PVC ball mold based on the mold surface temperature data set, determines the influence of the flow rate on the cooling efficiency, and generates the flow rate influence analysis result; Based on the flow rate influence analysis results, the efficiency key parameter extraction submodule comprehensively examines the PVC ball mold temperature, cooling medium velocity and temperature difference, and uses variance analysis to identify the key factors affecting cooling efficiency and their interactions, assists in calibrating cooling efficiency influencing factors, optimizes the cooling cycle, and obtains key cooling parameters; The variance analysis method is based on the improved formula II: ； Calculate the key factors affecting cooling efficiency and their interactions, assist in calibrating the factors affecting cooling efficiency, and obtain key cooling parameters; Among them, MS is the mean square difference between groups, MS is the within-group mean square difference, Adjust the weight for mold temperature, determined through experimental data analysis and expert evaluation, The cooling medium velocity adjustment bonus is determined by comparing the experimental results of cooling efficiency under various flow rates. is the temperature difference adjustment coefficient, which is determined by analyzing the experimental data of the effect of the temperature difference between the mold and the cooling medium on the cooling time. A cooling efficiency adjustment bonus is determined by evaluating the amount of parameter change required to achieve various cooling efficiency standards; The temperature control strategy adjustment module dynamically adjusts and analyzes the cooling temperature gradient of the PVC ball mold based on the key cooling parameters, and generates an adjusted temperature control plan; The weight influence determination module performs an analysis operation on the influence of each parameter in the cooling process of the PVC ball mold based on the adjusted temperature control scheme, and generates a parameter importance analysis result; The cooling configuration comprehensive optimization module performs comprehensive adjustment analysis on the cooling equipment configuration of the PVC ball mold based on the parameter importance analysis results to generate an optimized cooling configuration; The production process intelligent monitoring module monitors the real-time data of the PVC ball molding and cooling process, automatically adjusts the production parameters through the optimized cooling configuration, and generates an intelligent monitoring strategy.

2. The PVC leather ball rapid prototyping cooling system according to claim 1, characterized in that: The flow analysis results include flow velocity distribution diagrams, pressure distribution diagrams and temperature distribution diagrams for each point inside the PVC ball mold; the optimized channel configuration includes the adjusted number of channels, channel layout scheme and the value of the diameter size of each channel; the key cooling parameters include the medium flow rate, medium inlet and outlet temperature difference and cooling cycle time required for the PVC ball mold to achieve uniform cooling; the adjusted temperature control scheme includes the set temperature control points, the temperature variation range of each control point and the adjustment time interval; the parameter importance analysis results include the impact score of each cooling parameter on the product appearance quality and structural integrity, and the adjustment priority of the parameter during the cooling process; the optimized cooling configuration includes the selection of cooling medium type, the pressure setting of the cooling equipment and the cooling cycle adjustment scheme after referring to the cooling efficiency and product quality; the intelligent monitoring strategy includes real-time monitoring data items, data collection frequency and automatic adjustment production parameter rules derived from data analysis.

3. The PVC leather ball rapid prototyping cooling system according to claim 1, characterized in that: The channel design optimization module includes: The flow data application submodule uses the flow analysis results to analyze the flow characteristics of the cooling medium in the PVC ball mold, identify the areas where the mold cooling demand is concentrated, and analyze the areas with uneven temperature distribution, select the starting point for channel layout optimization, and generate layout key point analysis results; The channel intelligent layout submodule replans the channel layout, including direction, position, shape and size, based on the analysis results of the layout key points, with reference to cooling uniformity and manufacturing feasibility, and generates an optimized layout plan; The optimization design output submodule integrates the optimized layout scheme, analyzes the relationship between cooling effect and finished product quality, adjusts the channel design, matches the preset cooling efficiency target, and generates an optimized channel configuration.

4. The PVC leather ball rapid prototyping cooling system according to claim 1, characterized in that: The temperature control strategy adjustment module includes: The cooling parameter analysis submodule analyzes the relationship between the cooling medium flow rate and the mold temperature, as well as the change of the temperature gradient during the cooling process, based on the key cooling parameters and the temperature data inside and outside the PVC ball mold, and generates a temperature gradient analysis result; The local temperature adjustment submodule identifies the uneven cooling area in the temperature control of the PVC ball mold based on the temperature gradient analysis result, designs a local temperature adjustment scheme, adjusts the flow rate and temperature of the cooling medium, and generates a temperature adjustment strategy; The temperature control optimization solution generation submodule analyzes the adjusted temperature control effect based on the temperature adjustment strategy, optimizes the overall temperature control strategy of the PVC ball mold, and generates an adjusted temperature control solution.

5. The PVC leather ball rapid prototyping cooling system according to claim 1, characterized in that: The weight impact determination module includes: The cooling parameter influence analysis submodule collects the influence data of each cooling parameter on the temperature control of the PVC ball mold based on the adjusted temperature control scheme, and generates the parameter influence analysis results by comparing the influence of the parameters on the cooling efficiency; The parameter priority assessment submodule analyzes the importance of each parameter to the molding quality based on the parameter influence analysis results, divides the parameter priorities according to the influence, and generates parameter priority assessment results; The key parameter sorting submodule sorts all cooling parameters according to the priority based on the parameter priority assessment result, determines the order of optimization and adjustment, and generates parameter importance analysis results.

6. The PVC leather ball rapid prototyping cooling system according to claim 1, characterized in that: The cooling configuration comprehensive optimization module includes: The quality efficiency analysis submodule refers to the parameter importance analysis results, analyzes the impact of each cooling parameter configuration on the quality and production efficiency of the finished product, as well as the balance point between efficiency and quality in the cooling process, and generates a quality efficiency balance analysis result; The configuration adjustment analysis submodule selects key configuration items that affect cooling efficiency and finished product quality based on the quality-efficiency balance analysis results, formulates adjustment strategies for the configuration items, and generates a configuration adjustment plan; The cooling equipment optimization design submodule optimizes the cooling equipment according to the configuration adjustment scheme, the selection of cooling medium and the adjustment of temperature control parameters, and generates an optimized cooling configuration.

7. The PVC leather ball rapid prototyping cooling system according to claim 1, characterized in that: The production process intelligent monitoring module includes: The real-time data tracking submodule continuously collects temperature, pressure and flow rate data of the molding and cooling processes based on the optimized cooling configuration to generate a real-time monitoring data set; The parameter dynamic adjustment submodule uses the real-time monitoring data set to automatically identify parameters that deviate from the target range, automatically adjust them, and generate dynamic adjustment records; The monitoring strategy formulation submodule analyzes the adjustment effect, optimizes the monitoring logic and parameter adjustment strategy, and generates an intelligent monitoring strategy based on the dynamic adjustment record.

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