Mold Temperature Balance Control System and Method

Abstract

This invention provides a mold temperature balance control system and method. The system includes a temperature balance control platform, a model building module, a data processing module, a temperature prediction module, an output calculation module, and a temperature balance control module. The model building module establishes a dynamic model of mold thermal balance based on the thermophysical properties of the mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters. The data processing module acquires and fuses multi-dimensional mold temperature data to obtain mold temperature distribution information. The temperature prediction module inputs the mold temperature distribution information into the dynamic model of mold thermal balance to obtain the mold temperature prediction result. The output calculation module calculates the optimal control output using a fuzzy logic control algorithm. The temperature balance control module adjusts the power based on the optimal control output. This invention achieves accurate and stable mold temperature control, improving the quality of mold products.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and specifically to a mold temperature balance control system and method. Background Technology

[0002] In the process of mold manufacturing and use, the balanced control of mold temperature has a crucial impact on product quality and production efficiency.

[0003] Existing mold temperature balance control methods mainly include simple temperature threshold control and predictive control methods based on a single heat conduction model. Simple temperature threshold control methods use temperature sensors inside or on the surface of the mold. When the detected temperature is higher or lower than a preset threshold, heating or cooling devices are activated to regulate the mold temperature. Therefore, this method only allows for simple on / off temperature control and cannot finely adjust the temperature according to the actual thermal requirements of the mold. However, in actual production, different areas of the mold have different heat transfer characteristics, and the above control method can easily cause large local temperature fluctuations, affecting the quality of the molded product. Predictive control methods based on a single heat conduction model establish a mathematical model of heat conduction. Based on the mold's material properties, structural dimensions, and processing parameters, the method predicts the temperature change trend of the mold during processing and adjusts the heating or cooling strategy in advance. However, many complex factors exist in actual mold processing, such as changes in the contact thermal resistance between the mold and the workpiece, uneven flow of coolant, and subtle differences in the internal structure of the mold. These factors are difficult to fully and accurately represent in a single heat conduction model. Therefore, in practical applications, the predictive control method based on a single heat conduction model often results in a certain deviation between the temperature predicted by the model and the actual temperature of the mold, leading to local temperature imbalance in the mold and affecting the quality of the mold products. Summary of the Invention

[0004] This invention provides a mold temperature balance control system and method, which aims to achieve accurate and stable control of mold temperature and improve the quality of mold products.

[0005] In a first aspect, the present invention provides a mold temperature balance control system, comprising a temperature balance control platform, a model building module, a data processing module, a temperature prediction module, an output calculation module, and a temperature balance control module; the temperature balance control platform is connected to the model building module, the data processing module, the temperature prediction module, the output calculation module, and the temperature balance control module respectively, and manages and controls each module;

[0006] The model building module is used to establish a dynamic model of mold thermal balance based on the thermophysical properties of mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters, using a combination of factors.

[0007] The data processing module is used to acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold, and to fuse the multi-dimensional mold temperature data to obtain mold temperature distribution status information.

[0008] The temperature prediction module is used to input the mold temperature distribution status information into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different areas of the mold in the future.

[0009] The output calculation module is used to calculate the optimal control output based on the mold temperature prediction result and the fuzzy logic control algorithm. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device.

[0010] The temperature balance control module is used to control the power adjustment of the temperature balance device based on the optimal control output.

[0011] In a second aspect, the present invention also provides a mold temperature balance control method, implemented based on the mold temperature balance control system described in the first aspect, the mold temperature balance control method comprising:

[0012] Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold;

[0013] The multi-dimensional mold temperature data are fused to obtain mold temperature distribution status information;

[0014] The mold temperature distribution information is input into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different regions of the mold in the future.

[0015] Based on the mold temperature prediction results and the fuzzy logic control algorithm, the optimal control output is calculated. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device.

[0016] The power regulation of the temperature balancing device is controlled based on the optimal control output.

[0017] The dynamic model of mold thermal balance is a multi-factor coupled mold thermal balance prediction model based on the thermophysical properties of mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters.

[0018] According to the mold temperature balance control method provided in the embodiment of the present invention, the temperature balance device includes a heating rod and a flow regulating valve in a cooling pipe; the step of controlling the power adjustment amount of the temperature balance device based on the optimal control output includes:

[0019] For a heating rod as the temperature balancing device, the actual power of the heating rod is determined based on the optimal control output and the maximum power of the heating rod. The specific calculation formula is as follows:

[0020] ;

[0021] in, This indicates the maximum power of the heating element. Indicates the optimal control output;

[0022] For the temperature balancing device being a flow regulating valve, based on the optimal control output and the range of the minimum, maximum, and maximum valve openings of the flow regulating valve, the final valve opening is determined to adjust the cooling power. The specific calculation formula is as follows:

[0023] ;

[0024] in, Indicates the final valve opening. and These represent the minimum valve opening and the maximum valve opening, respectively. This indicates the maximum range of valve opening.

[0025] According to the mold temperature balance control method provided in the embodiment of the present invention, the step of calculating the optimal control output based on the mold temperature prediction result combined with a fuzzy logic control algorithm includes:

[0026] The deviation and the rate of change of deviation are fuzzified into different fuzzy linguistic variables;

[0027] Based on the preset membership function corresponding to the deviation and the rate of change of deviation, fuzzy inference is performed by combining fuzzy linguistic variables with different deviations and rates of change of deviation, and the first fuzzy output result corresponding to the deviation and the second fuzzy output result corresponding to the rate of change of deviation are obtained respectively.

[0028] Based on the first fuzzy output result and the second fuzzy output result, a defuzzification operation is performed to convert the fuzzy output result into the optimal control output of the temperature balancing device.

[0029] According to the mold temperature balance control method provided in the embodiments of the present invention, the mold thermal balance dynamic model includes a comprehensive thermal balance influence function; the mold temperature distribution state information is input into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model, including:

[0030] The mold temperature distribution information is input into the mold thermal balance dynamic model, and the initial prediction result is obtained by calculating based on the mold temperature distribution information through the comprehensive thermal balance influence function.

[0031] The time in the initial prediction result is discretized with a preset time step, and the mold temperature prediction result at different future times is calculated by combining the comprehensive thermal balance influence function.

[0032] The comprehensive thermal balance influence function can be expressed as:

[0033] ;

[0034] in, This indicates the temperature distribution status information of the mold. Indicates time, Indicates processing power. Indicates processing time. Indicates contact thermal resistance. Indicates thermal conductivity, Indicates the mold wall thickness. Indicates the coolant flow rate. Indicates the heat capacity of the coolant. This represents a frequency parameter related to the mold's thermal cycle. This indicates the reference value for the stable temperature that the mold is expected to achieve. and These correspond to the length of the heat conduction path and the thermal conductivity, respectively. Indicates the reference temperature.

[0035] According to the mold temperature balance control method provided in the embodiment of the present invention, the step of fusing the multi-dimensional mold temperature data to obtain mold temperature distribution state information includes:

[0036] Determine the spatial thermal influence weights for each sensor location. Among them, the weight of space thermal influence Based on the distance from each sensor location to the preset key heat flow node of the mold and the rate of change of temperature gradient at each sensor location Determine the weight of the space thermal influence. The calculation formula is as follows:

[0037] ;

[0038] in, and It is a weight adjustment coefficient predetermined based on the mold material and structure;

[0039] A temperature data matrix was constructed from the collected multi-dimensional mold temperature data. By performing fusion calculations, the temperature distribution status information of the mold is obtained. The specific formula is as follows:

[0040] .

[0041] in, Indicates the number of sensor mounting positions on the mold; Representing temperature data matrix The Middle Line number The elements of the column represent the first... The sensor location is at the [number]th [location]. Temperature values ​​collected at each sampling time; Indicates the first Data collected from each sensor location The average of temperature data at each time point.

[0042] According to the mold temperature balance control method provided in the embodiment of the present invention, the mold has Each sensor installation location, in The data collected at consecutive sampling times, namely, the multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold, includes:

[0043] Get the The sensor location is at the [number]th [location]. Temperature values ​​collected at each sampling time;

[0044] Based on the The sensor location is at the [number]th [location]. A temperature data matrix is ​​constructed from the temperature values ​​collected at each sampling time. This yields multi-dimensional mold temperature data;

[0045] Among them, the temperature data matrix It can be represented as:

[0046] .

[0047] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby implementing the mold temperature balance control method as described above.

[0048] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the mold temperature balance control method described above.

[0049] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the mold temperature balance control method as described above.

[0050] The mold temperature balance control system provided in this invention sets high-precision temperature sensors at multiple key parts of the mold and fuses the data to obtain mold temperature distribution information. This allows for comprehensive and accurate acquisition of mold temperature data. Furthermore, by establishing a multi-factor coupled dynamic model of mold thermal balance, it comprehensively considers the influence of various factors on mold temperature, including the thermophysical properties of the mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters. Compared to traditional single heat conduction models, this model more realistically reflects the heat transfer process during actual processing. The accurate mold temperature distribution information is further input into the multi-factor coupled dynamic model for temperature trend prediction. Then, a fuzzy logic control algorithm is used to calculate the optimal control output of each heating and cooling unit based on the prediction results. The fuzzy logic control algorithm uses the deviation between the predicted temperature and the target temperature in different areas of the mold, as well as the rate of deviation change, as input variables. This allows for flexible adjustment of the power regulation of the heating or cooling units based on the actual temperature changes, avoiding the temperature fluctuation problems caused by the simple on / off control of traditional threshold control methods. This achieves refined adjustment of mold temperature, thereby improving the accuracy and stability of mold temperature control and enhancing the quality of mold products. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the mold temperature balance control system provided by the present invention;

[0052] Figure 2 This is a schematic flowchart of the mold temperature balance control method provided by the present invention;

[0053] Figure 3 An embodiment diagram of the electronic device provided in this invention;

[0054] Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0057] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0058] Optional, see below Figure 1 As shown, Figure 1 This is a schematic diagram of the mold temperature balance control system provided by the present invention. The mold temperature balance control system includes a temperature balance control platform, a model construction module, a data processing module, a temperature prediction module, an output calculation module, and a temperature balance control module.

[0059] In this invention, the temperature balance control platform is connected to the model building module, data processing module, temperature prediction module, output calculation module, and temperature balance control module, respectively, and manages and controls each module.

[0060] Optionally, the model building module acquires the thermophysical properties of the mold material, the structural features of the mold, the contact characteristics between the mold and the workpiece, the flow characteristics of the coolant, and the processing parameters of the mold transmitted by the temperature balance control platform. Based on the thermophysical properties of the mold material, the structural features of the mold, the contact characteristics between the mold and the workpiece, the flow characteristics of the coolant, and the processing parameters, a dynamic model of mold thermal balance coupled with multiple factors is established.

[0061] The thermophysical properties of mold materials include thermal conductivity, specific heat capacity, and density. Thermal conductivity determines the rate of heat conduction within the material, specific heat capacity reflects the amount of heat required to raise the temperature of a unit mass of material by one degree Celsius, and density affects the material's heat capacity. Mold structural characteristics refer to the mold's geometry, such as thickness and complex cavity structures. Mold-workpiece contact characteristics include mold surface roughness, workpiece material hardness, and pressure during processing. Coolant flow characteristics include coolant flow rate, specific heat capacity, density, and the geometry of cooling channels (diameter, length, etc.). Processing parameters include processing speed and processing power.

[0062] Optionally, the data processing module acquires multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold, and fuses the multi-dimensional mold temperature data to obtain mold temperature distribution status information. Optionally, the temperature prediction module inputs the mold temperature distribution status information into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model. In this embodiment, the mold temperature prediction result characterizes the temperature change trend of different regions of the mold over future time.

[0063] Optionally, the output calculation module calculates the optimal control output based on the mold temperature prediction result combined with a fuzzy logic control algorithm. In this embodiment, the input variables of the fuzzy logic control algorithm are the deviation between the predicted and target temperatures of the mold in different regions and the rate of change of this deviation; the output variable is the optimal control output of the temperature balancing device. Further, the temperature balancing control module controls the power adjustment of the temperature balancing device based on the optimal control output.

[0064] This invention incorporates high-precision temperature sensors at multiple key locations on the mold and fuses the data to obtain mold temperature distribution information. This allows for comprehensive and accurate acquisition of mold temperature data. Furthermore, by establishing a multi-factor coupled dynamic model of mold thermal balance, it comprehensively considers the influence of various factors on mold temperature, including the thermophysical properties of the mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters. This model more realistically reflects the heat transfer process during actual processing. The accurate mold temperature distribution information is then input into the multi-factor coupled dynamic model for temperature trend prediction. A fuzzy logic control algorithm is used to calculate the optimal control output for each heating and cooling unit based on the prediction results. The fuzzy logic control algorithm uses the deviation between the predicted and target temperatures in different areas of the mold, as well as the rate of change of this deviation, as input variables. This allows for flexible adjustment of the power regulation of the heating or cooling units based on the actual temperature changes, achieving refined temperature control and thus improving the accuracy and stability of mold temperature control, ultimately enhancing the quality of mold products.

[0065] Optional, refer to Figure 2 , Figure 2 This is a flowchart illustrating the mold temperature balance control method provided by the present invention. In this embodiment of the invention, the executing entity of the mold temperature balance control method is the mold temperature balance control system; therefore, the mold temperature balance control method includes:

[0066] Step 10: Obtain multi-dimensional mold temperature data collected by temperature sensors installed in multiple locations on the mold.

[0067] Optionally, in embodiments of the present invention, temperature sensors are installed at different key locations on the mold. The selection of these locations should comprehensively consider factors such as the mold's geometry, heat flow distribution characteristics, and areas where significant temperature differences may occur. For example, for an injection mold with a complex cavity structure, sensors are installed on the cavity wall, near the gate, and around the cooling channels to obtain multi-dimensional data that comprehensively reflects the mold's temperature. The data collected by the sensors may include temperature values ​​at different times, and sensors at different locations constitute a spatial temperature monitoring network, whose collected data dimensions can be represented as a matrix. In one embodiment, the mold has... Each sensor installation location, in Data collected at consecutive sampling times, then the collected temperature data matrix It can be represented as:

[0068] .

[0069] in, Indicates the first The sensor location is at the [number]th [location]. The temperature values ​​collected at each sampling time. In this embodiment of the invention, the sampling frequency of each sensor can be adaptively adjusted according to the actual thermal response characteristics of the mold and the processing requirements. This embodiment of the invention is based on an adaptive frequency adjustment function. Adjustments are made, and the adaptive frequency adjustment function is based on the temperature collected at the previous moment. and the maximum allowable time interval for temperature changes The sampling interval for the next moment is dynamically determined by the following formula:

[0070] .

[0071] in, It is a reference temperature threshold. Therefore, the adaptive frequency adjustment function can automatically increase the acquisition frequency when the temperature changes rapidly, so as to capture temperature changes more accurately.

[0072] Step 20: Fuse the multi-dimensional mold temperature data to obtain mold temperature distribution status information.

[0073] Optionally, for data fusion, the embodiment of the present invention uses a fusion algorithm based on spatial thermal weight allocation, which first determines the spatial thermal influence weight of each sensor location. Space thermal influence weight The distance from this location to the pre-designed critical heat flow nodes of the mold (such as the center of the heat concentration area, nodes where the cooling channel plays a key role, etc.). and the rate of change of temperature gradient at that location The relevant calculation formula is as follows:

[0074] .

[0075] in, and It is a weight adjustment coefficient predetermined based on the mold material and structure.

[0076] Furthermore, the collected temperature data matrix Perform fusion calculations to obtain mold temperature distribution information. It can be obtained through the following formula:

[0077] .

[0078] in, Indicates the first Data collected from each sensor location The average temperature data at each moment is used to more scientifically reflect the overall temperature distribution of the mold by using a fusion algorithm based on spatial thermal weighting, taking into account the influence of spatial location and temperature change characteristics.

[0079] Step 30: Input the mold temperature distribution status information into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model.

[0080] Optionally, the mold temperature balance control system of this embodiment of the invention is embedded with a pre-trained mold thermal balance dynamic model. The mold thermal balance dynamic model is a multi-factor coupled mold thermal balance prediction model based on the thermophysical properties of mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters. The mold temperature prediction results output by the mold thermal balance dynamic model characterize the temperature change trend of different areas of the mold in the future.

[0081] Therefore, the dynamic model of mold thermal balance in this embodiment of the invention can be understood as a complex model established by integrating multiple factors, and its internal operating mechanism involves the thermophysical properties of the mold material (such as thermal conductivity). Specific heat capacity etc.), mold structural features (such as wall thickness) The length of the heat conduction path corresponding to the geometry of each part. etc.), contact characteristics between mold and workpiece (contact thermal resistance) Coolant flow characteristics (coolant flow rate) Coolant heat capacity (etc.) and processing parameters (such as processing power) Processing time The coupling calculation of (etc.). In this embodiment of the invention, the comprehensive thermal balance influence function in the mold thermal balance dynamic model. Describe at a certain moment The combined effect of these factors on mold temperature, and the combined thermal balance effect function. Specifically, it is as follows:

[0082] .

[0083] in, This represents a frequency parameter related to the mold's thermal cycle. This indicates the reference value for the stable temperature that the mold is expected to achieve. and These are the length of the corresponding heat conduction path and the thermal conductivity, respectively.

[0084] Substitute the mold temperature distribution information into the comprehensive thermal balance influence function. By time Discretize and compute step by step (e.g., with very small time steps) (Through iteration) to obtain the mold temperature prediction results at different future times. The specific calculation process is as follows:

[0085] .

[0086] Therefore, by iteratively calculating, the temperature change trend of different regions of the mold in the future can be obtained, which is the mold temperature prediction result.

[0087] Step 40: Calculate the optimal control output based on the mold temperature prediction results and the fuzzy logic control algorithm.

[0088] Optionally, the input variable of the fuzzy logic control algorithm in this embodiment of the invention is the predicted temperature of the mold in different regions. (in (representing different areas of the mold) and target temperature deviation and the rate of change of deviation The output variable is the optimal control output of the temperature balancing device. .

[0089] Among them, the deviation of the embodiments of the present invention The calculation formula is: The rate of change of deviation is calculated by subtracting the deviations from those at adjacent prediction times and dividing by the time interval. .

[0090] Furthermore, the fuzzification rule set in this embodiment of the invention is to fuzzify the deviation. and rate of change of deviation Fuzzification is achieved by assigning different fuzzy linguistic variables, such as setting linguistic values ​​like "negative large (NL)," "negative medium (NM)," "negative small (NS)," "zero (ZE)," "positive small (PS)," "positive medium (PM)," and "positive large (PL)." The corresponding membership functions can be user-defined, such as trapezoidal or Gaussian membership functions. For example, for deviation... "Negative Large (NL)" membership function It can be defined as (taking the trapezoidal membership function as an example):

[0091] .

[0092] in, and These are threshold parameters set based on actual control precision and temperature range.

[0093] The deviation change rate is also fuzzified. Fuzzy inference is performed based on the fuzzy inference rule table (custom rules, formulated based on experience and mold thermal control characteristics, such as when the deviation is "negatively large" and the deviation change rate is "negatively large", the optimal control output should be "positively large", etc.) to obtain fuzzy output results.

[0094] Finally, through defuzzification, the fuzzy output is transformed into the precise optimal control output of the temperature balancing device. The present invention provides a defuzzification formula based on the centroid method, for example, the membership degree corresponding to the fuzzy output result is... The corresponding optimal control output fuzzy value is ( (representing different fuzzy inference result numbers), then the formula for calculating the optimal control output is:

[0095] .

[0096] Therefore, the fuzzy logic control algorithm can reasonably calculate the optimal control output based on the mold temperature prediction to achieve precise temperature control.

[0097] Step 50: Control the power adjustment of the temperature balancing device based on the optimal control output.

[0098] Optionally, the temperature balancing device (such as a heating rod, a flow regulating valve in a cooling pipe, etc.) in this embodiment of the invention receives the calculated optimal control output. Afterwards, effective power adjustment must be performed. For heating devices like heating rods, the actual power... With control output The relationship can be obtained through the power adjustment function. To illustrate this, the specific formula is as follows:

[0099] .

[0100] in, This is the maximum power of the heating element. It is controlled via a power adjustment function. The relationship transforms the control output into actual adjustable power, thereby changing the intensity of heating or cooling, achieving precise control of the mold temperature, causing it to change towards the target temperature, and maintaining the thermal balance of the mold.

[0101] Similarly, the flow control valve in the cooling pipe can be adjusted to change the coolant flow rate and thus regulate the cooling power. With control output The specific formula for the relationship is as follows:

[0102] .

[0103] in, and These are the minimum valve opening and the maximum valve opening, respectively. It represents the maximum range of valve opening, enabling effective control of mold temperature and ensuring that the mold remains in a suitable thermal equilibrium state during processing.

[0104] This invention incorporates high-precision temperature sensors at multiple key locations on the mold and fuses the data to obtain mold temperature distribution information. This allows for comprehensive and accurate acquisition of mold temperature data. Furthermore, by establishing a multi-factor coupled dynamic model of mold thermal balance, it comprehensively considers the influence of various factors on mold temperature, including the thermophysical properties of the mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters. This model more realistically reflects the heat transfer process during actual processing. The accurate mold temperature distribution information is then input into the multi-factor coupled dynamic model for temperature trend prediction. A fuzzy logic control algorithm is used to calculate the optimal control output for each heating and cooling unit based on the prediction results. The fuzzy logic control algorithm uses the deviation between the predicted and target temperatures in different areas of the mold, as well as the rate of change of this deviation, as input variables. This allows for flexible adjustment of the power regulation of the heating or cooling units based on the actual temperature changes, achieving refined temperature control and thus improving the accuracy and stability of mold temperature control, ultimately enhancing the quality of mold products.

[0105] In one embodiment, the specific process of establishing the dynamic model of mold thermal balance is as follows:

[0106] I. Key factors affecting mold thermal balance: The thermal balance of a mold is affected by multiple factors, including the thermophysical properties of the mold material, the structural characteristics of the mold, the contact characteristics between the mold and the workpiece, the flow characteristics of the coolant, and the processing parameters.

[0107] 1. Regarding the thermophysical properties of mold materials: thermal conductivity of mold materials Specific heat capacity and density Materials play a crucial role in the conduction, storage, and distribution of heat. Different materials exhibit different temperature response characteristics under the same heat input. For example, materials with high thermal conductivity can transfer heat more quickly, resulting in a more uniform temperature distribution, but they also dissipate heat quickly; materials with high specific heat capacity can store more heat, and their temperature changes are relatively more gradual.

[0108] Therefore, this embodiment of the invention sets a material thermal response coefficient to comprehensively describe the influence of these properties on thermal equilibrium. The specific formula for the material thermal response coefficient is as follows:

[0109] .

[0110] in, This indicates the temperature of a certain area of ​​the mold. This represents a reference temperature.

[0111] 2. Mold Structural Characteristics: The mold structure determines the heat conduction path, heat concentration areas, etc. For example, wall thickness. The thermal resistance coefficient affects the rate of heat transfer in different parts; complex cavity structures can cause heat accumulation or create thermal gradients. To quantify the impact of structural features on thermal balance, embodiments of this invention define a structural thermal resistance coefficient, which is related to the wall thickness of each part and the length of the heat conduction path. and the corresponding cross-sectional area The relevant formula for calculating the structural thermal resistance coefficient is as follows:

[0112] .

[0113] in, This indicates the different structural parts of the mold. This indicates the maximum wall thickness of the entire mold.

[0114] 3. Contact characteristics between mold and workpiece: There is contact thermal resistance when the mold and workpiece are in contact. Contact thermal resistance is related to factors such as workpiece surface roughness and contact pressure. The magnitude of contact thermal resistance directly affects the efficiency of heat transfer between the mold and the workpiece. Therefore, this embodiment of the invention sets a contact heat transfer coefficient to describe the influence of contact characteristics on thermal balance. The formula for calculating the contact heat transfer coefficient is:

[0115] .

[0116] in, It is the actual contact pressure. That is the maximum contact pressure that can be achieved. It is the temperature difference at the interface between the mold and the workpiece. This is the preset reference temperature.

[0117] 4. Coolant Flow Characteristics: The coolant flows in the cooling channels of the mold, and its flow rate... Heat capacity and the convective heat transfer coefficient between the coolant and the mold wall. The cooling effect is determined by the coolant's flow rate, heat capacity, and convective heat transfer coefficient. A coolant with a high flow rate, large heat capacity, and high convective heat transfer coefficient can more effectively remove heat from the mold. This invention embodiment sets a coolant cooling efficiency coefficient to quantify its impact. The calculation formula for the coolant cooling efficiency coefficient is as follows:

[0118] .

[0119] in, It is the temperature difference between the inlet and outlet of the coolant. It is the coolant inlet temperature.

[0120] 5. Processing parameters: Processing parameters such as processing power Processing time and processing frequency (For periodic processing) heat is continuously input into the mold, which is an important external heat source affecting the mold's thermal balance. This invention embodiment sets a processing heat input coefficient to comprehensively represent the influence of these parameters on the thermal balance. The formula for calculating the processing heat input coefficient is:

[0121] .

[0122] in, It is the average temperature of key parts of the mold during the processing. This is the preset average temperature reference value.

[0123] II. Basic Equation Framework for Constructing a Dynamic Model of Mold Thermal Balance

[0124] Based on the above quantitative analysis of key factors, the basic equations of the mold thermal balance dynamic model are constructed. Taking a small region of the mold (which can be regarded as a control volume) as an example, according to the law of conservation of energy, the amount of heat entering the region per unit time minus the amount of heat flowing out of the region equals the change in energy within the region.

[0125] In one embodiment, the heat entering the region primarily comes from heat generated during the processing (by... (reflected) and the heat transferred through thermal conduction to other areas (with thermal conductivity) Temperature gradient (and related factors); the heat flowing out of this area is mainly transferred through convective heat exchange with the coolant (by... (This is reflected in) heat conduction to other areas; the change in internal energy is related to the material's specific heat capacity. ,density and rate of temperature change The relevant dynamic equations for thermal equilibrium are as follows:

[0126] .

[0127] Furthermore, a comprehensive coupling coefficient is introduced. To describe each factor (such as , , The influence of the interaction between factors (etc.) on thermal equilibrium is a complex function of the coefficients of each factor, including the overall coupling coefficient. The calculation formula is as follows:

[0128] .

[0129] Then the dynamic equation of thermal equilibrium is modified as follows:

[0130] .

[0131] III. Model Discretization and Solution Method

[0132] The aforementioned thermal equilibrium dynamic equation is a partial differential equation. To enable numerical calculations and simulations in practice, it needs to be discretized. This embodiment of the invention employs the finite difference method for discretization, dividing the spatial region of the mold into several small mesh elements (e.g., using a uniform mesh with a mesh size of [missing information]). , , (Corresponding to the three dimensions of space respectively), and time is also discretized into time steps. Regarding temperature At a certain point in space (Corresponding grid cell index) and time (Corresponding to the time step number), its discretized temperature is expressed as: .

[0133] Discretize the terms in the dynamic equation of thermal equilibrium, such as the heat conduction term. Discretized form (taking one dimension as an example, three dimensions can be similarly extended):

[0134]

[0135] in, This corresponds to the equivalent thermal conductivity at the mesh interface, which can be calculated by interpolation using the thermal conductivity of adjacent mesh elements. Substituting the discretized terms into the thermal balance dynamic equation yields a set of algebraic equations concerning the temperature of each mesh element at different time steps. These equations can then be solved using iterative methods (such as the Gauss-Seidel iterative method). The initial conditions can be set as the initial temperature distribution of the mold (obtainable through initial sensor measurements or empirical estimation), while the boundary conditions are set based on the actual boundary heat exchange of the mold (e.g., convective heat transfer between the mold's outer surface and the environment, and fixed temperature boundaries with the cooling channel walls).

[0136] By iteratively solving for the temperature value of each grid cell at each time step, the temperature distribution and changes of the mold during the entire processing process and at different subsequent times can be simulated. In other words, the dynamic prediction of the thermal state of the mold can be achieved by using the established dynamic model of mold thermal balance.

[0137] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps:

[0138] Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold;

[0139] Multi-dimensional mold temperature data are fused to obtain mold temperature distribution status information;

[0140] The mold temperature distribution information is input into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different regions of the mold in the future.

[0141] Based on the mold temperature prediction results and the fuzzy logic control algorithm, the optimal control output is calculated. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device.

[0142] The power regulation of the temperature balancing device is controlled based on the optimal control output.

[0143] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps:

[0144] Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold;

[0145] Multi-dimensional mold temperature data are fused to obtain mold temperature distribution status information;

[0146] The mold temperature distribution information is input into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different regions of the mold in the future.

[0147] Based on the mold temperature prediction results and the fuzzy logic control algorithm, the optimal control output is calculated. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device.

[0148] The power regulation of the temperature balancing device is controlled based on the optimal control output.

[0149] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the mold temperature balance control method provided by the above methods. The mold temperature balance control method includes:

[0150] Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold;

[0151] Multi-dimensional mold temperature data are fused to obtain mold temperature distribution status information;

[0152] The mold temperature distribution information is input into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different regions of the mold in the future.

[0153] Based on the mold temperature prediction results and the fuzzy logic control algorithm, the optimal control output is calculated. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device.

[0154] The power regulation of the temperature balancing device is controlled based on the optimal control output.

[0155] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mold temperature balance control system, characterized in that, It includes a temperature balance control platform, a model building module, a data processing module, a temperature prediction module, an output calculation module, and a temperature balance control module; the temperature balance control platform is connected to the model building module, the data processing module, the temperature prediction module, the output calculation module, and the temperature balance control module respectively, and manages and controls each module; The model building module is used to establish a dynamic model of mold thermal balance based on the thermophysical properties of mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters, using a combination of factors. The data processing module is used to acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold, and to fuse the multi-dimensional mold temperature data to obtain mold temperature distribution status information. The temperature prediction module is used to input the mold temperature distribution status information into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different areas of the mold in the future. The output calculation module is used to calculate the optimal control output based on the mold temperature prediction result and the fuzzy logic control algorithm. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device. The temperature balance control module is used to control the power adjustment of the temperature balance device based on the optimal control output. The mold thermal balance dynamic model includes a comprehensive thermal balance influence function; the step of inputting the mold temperature distribution state information into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model includes: The mold temperature distribution information is input into the mold thermal balance dynamic model, and the initial prediction result is obtained by calculating based on the mold temperature distribution information through the comprehensive thermal balance influence function. The time in the initial prediction result is discretized with a preset time step, and the mold temperature prediction result at different future times is calculated by combining the comprehensive thermal balance influence function. The comprehensive thermal balance influence function is expressed as follows: ； in, This indicates the temperature distribution status information of the mold. Indicates time, Indicates processing power. Indicates processing time. Indicates contact thermal resistance. Indicates thermal conductivity, Indicates the mold wall thickness. Indicates the coolant flow rate. Indicates the heat capacity of the coolant. This represents a frequency parameter related to the mold's thermal cycle. This indicates the reference value for the stable temperature that the mold is expected to achieve. and These correspond to the length of the heat conduction path and the thermal conductivity, respectively. Indicates the reference temperature.

2. A mold temperature balance control method, implemented based on the mold temperature balance control system as described in claim 1, characterized in that, The mold temperature balance control method includes: Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold; The multi-dimensional mold temperature data are fused to obtain mold temperature distribution status information; The mold temperature distribution information is input into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result characterizes the temperature change trend of different regions of the mold in the future. Based on the mold temperature prediction results and the fuzzy logic control algorithm, the optimal control output is calculated. The input variables of the fuzzy logic control algorithm are the deviation between the predicted temperature and the target temperature of the mold in different regions and the rate of change of the deviation. The output variable is the optimal control output of the temperature balancing device. The power regulation of the temperature balancing device is controlled based on the optimal control output. The mold thermal balance dynamic model is a multi-factor coupled mold thermal balance prediction model based on the thermophysical properties of mold material, mold structural features, mold-workpiece contact characteristics, coolant flow characteristics, and processing parameters. The mold thermal balance dynamic model includes a comprehensive thermal balance influence function; the step of inputting the mold temperature distribution state information into the mold thermal balance dynamic model to obtain the mold temperature prediction result output by the mold thermal balance dynamic model includes: The mold temperature distribution information is input into the mold thermal balance dynamic model, and the initial prediction result is obtained by calculating based on the mold temperature distribution information through the comprehensive thermal balance influence function. The time in the initial prediction result is discretized with a preset time step, and the mold temperature prediction result at different future times is calculated by combining the comprehensive thermal balance influence function. The comprehensive thermal balance influence function is expressed as follows: ； in, This indicates the temperature distribution status information of the mold. Indicates time, Indicates processing power. Indicates processing time. Indicates contact thermal resistance. Indicates thermal conductivity, Indicates the mold wall thickness. Indicates the coolant flow rate. Indicates the heat capacity of the coolant. This represents a frequency parameter related to the mold's thermal cycle. This indicates the reference value for the stable temperature that the mold is expected to achieve. and These correspond to the length of the heat conduction path and the thermal conductivity, respectively. Indicates the reference temperature.

3. The mold temperature balance control method according to claim 2, characterized in that, The temperature balancing device includes a heating rod and a flow regulating valve in the cooling pipe; the power regulation of the temperature balancing device based on the optimal control output includes: For a heating rod as the temperature balancing device, the actual power of the heating rod is determined based on the optimal control output and the maximum power of the heating rod. The specific calculation formula is as follows: ； in, This indicates the maximum power of the heating element. Indicates the optimal control output; For the temperature balancing device being a flow regulating valve, based on the optimal control output and the range of the minimum, maximum, and maximum valve openings of the flow regulating valve, the final valve opening is determined to adjust the cooling power. The specific calculation formula is as follows: ； in, Indicates the final valve opening. and These represent the minimum valve opening and the maximum valve opening, respectively. This indicates the maximum range of valve opening.

4. The mold temperature balance control method according to claim 2, characterized in that, The calculation of the optimal control output based on the mold temperature prediction result and the fuzzy logic control algorithm includes: The deviation and the rate of change of deviation are fuzzified into different fuzzy linguistic variables; Based on the preset membership function corresponding to the deviation and the rate of change of deviation, fuzzy inference is performed by combining fuzzy linguistic variables with different deviations and rates of change of deviation, and the first fuzzy output result corresponding to the deviation and the second fuzzy output result corresponding to the rate of change of deviation are obtained respectively. Based on the first fuzzy output result and the second fuzzy output result, a defuzzification operation is performed to convert the fuzzy output result into the optimal control output of the temperature balancing device.

5. The mold temperature balance control method according to claim 2, characterized in that, The process of fusing the multi-dimensional mold temperature data to obtain mold temperature distribution information includes: Determine the spatial thermal influence weights for each sensor location. Among them, the weight of space thermal influence Based on the distance from each sensor location to the preset key heat flow node of the mold and the rate of change of temperature gradient at each sensor location Determine the weight of the space thermal influence. The calculation formula is as follows: ； in, and It is a weight adjustment coefficient predetermined based on the mold material and structure; A temperature data matrix was constructed from the collected multi-dimensional mold temperature data. By performing fusion calculations, the temperature distribution status information of the mold is obtained. The specific formula is as follows: ； in, Indicates the number of sensor mounting positions on the mold; Representing temperature data matrix The Middle Line number The elements of the column represent the first... The sensor location is at the [number]th [location]. Temperature values ​​collected at each sampling time; Indicates the first Data collected from each sensor location The average of temperature data at each time point.

6. The mold temperature balance control method according to any one of claims 2 to 5, characterized in that, The mold has Each sensor installation location, in The data collected at consecutive sampling times, namely, the multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations on the mold, includes: Get the The sensor location is at the [number]th [location]. Temperature values ​​collected at each sampling time; Based on the The sensor location is at the [number]th [location]. A temperature data matrix is ​​constructed from the temperature values ​​collected at each sampling time. This yields multi-dimensional mold temperature data; Among them, the temperature data matrix Represented as: 。 7. An electronic device, comprising: The memory and processor are characterized in that the memory stores a computer software program, and when the processor reads and executes the computer software program, it implements the mold temperature balance control method as described in any one of claims 2 to 6.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores a computer software program, which, when executed by a processor, implements the mold temperature balance control method as described in any one of claims 2 to 6.

9. A computer product, characterized in that, The computer product stores a computer software program, which, when executed by a processor, implements the mold temperature balance control method as described in any one of claims 2 to 6.

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