Die temperature balance control system and method thereof

Abstract

The invention provides a mold temperature balance control system and method. The system comprises a temperature balance control middle table, a model construction module, a data processing module, a temperature prediction module, an output quantity calculation module and a temperature balance control module. The model building module is used for building a mold heat balance dynamic model based on mold material thermophysical properties, mold structure characteristics, mold and workpiece contact characteristics, cooling liquid flow characteristics and machining process parameters; the data processing module obtains and fuses the multi-dimensional mold temperature data to obtain mold temperature distribution state information; the temperature prediction module inputs the mold temperature distribution state information into the mold heat balance dynamic model to obtain a mold temperature prediction result; the output quantity calculation module is combined with a fuzzy logic control algorithm to calculate the optimal control output quantity; the temperature balance control module controls the power regulation amount based on the optimal control output amount. According to the invention, the accuracy and stability of mold temperature control are realized, and the mold product quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of computers, and in particular to a mold temperature balance control system and a method thereof. Background Art

[0002] During mold manufacturing and use, balanced control of mold temperature has a vital impact on product quality and production efficiency.

[0003] The existing mold temperature balance control methods mainly include simple temperature threshold control methods and predictive control methods based on a single heat conduction model. The simple temperature threshold control method sets a temperature sensor inside or on the surface of the mold. When the detected temperature is higher or lower than the preset temperature threshold, the heating or cooling device is started to adjust the mold temperature. Therefore, the temperature can only be simply switched on and off, and it is impossible to fine-tune it according to the actual heat demand of the mold. However, in actual production, the heat transfer characteristics of different areas of the mold are different. The above control method is likely to cause large local temperature fluctuations, affecting the quality of the mold product. The predictive control method based on a single heat conduction model uses a heat conduction mathematical model to predict the temperature change trend of the mold during the processing according to the material properties, structural dimensions, and processing parameters of the mold, and then adjust the heating or cooling strategy in advance. However, there are many complex factors in the actual mold processing process, such as changes in the contact thermal resistance between the mold and the workpiece, the uneven flow of the coolant, and the slight differences in the internal structure of the mold. These factors are difficult to fully and accurately reflect in a single heat conduction model. Therefore, in practical applications of the predictive control method based on a single heat conduction model, there is often a certain deviation between the temperature predicted by the model and the actual temperature of the mold, resulting in local imbalance of the mold temperature and affecting the quality of the mold product. Summary of the invention

[0004] The present invention provides a mold temperature balance control system and method thereof, aiming to achieve accuracy and stability in controlling mold temperature and improve mold product quality.

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

[0006] The model building module is used to establish a multi-factor coupled mold thermal balance dynamic model based on the thermophysical properties of the mold material, the mold structure characteristics, the contact characteristics between the mold and the workpiece, the coolant flow characteristics and the processing parameters;

[0007] The data processing module is used to obtain multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations of the mold, and 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 state 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 represents 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 combined with the fuzzy logic control algorithm; the input variable of the fuzzy logic control algorithm is the deviation between the predicted temperature and the target temperature of the mold in different areas and the deviation change rate, and the output variable is the optimal control output of the temperature balance device;

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

[0011] In a second aspect, the present invention further provides a mold temperature balance control method, which is implemented based on the mold temperature balance control system described in the first aspect, and the mold temperature balance control method includes:

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

[0013] The multi-dimensional mold temperature data is integrated to obtain mold temperature distribution state information;

[0014] Inputting the mold temperature distribution state information into a mold thermal balance dynamic model to obtain a mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result represents the temperature change trend of different areas of the mold in the future;

[0015] Based on the mold temperature prediction result combined with the fuzzy logic control algorithm, the optimal control output is calculated; the input variable of the fuzzy logic control algorithm is the deviation between the predicted temperature and the target temperature in different areas of the mold and the deviation change rate, and the output variable is the optimal control output of the temperature balance device;

[0016] controlling a power adjustment amount of the temperature balancing device based on the optimal control output amount;

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

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

[0019] When the temperature balancing device is a heating rod, 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] Among them, P max represents the maximum power of the heating rod, and u represents the optimal control output;

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

[0023]

[0024] Among them, O represents the final valve opening, O min and Q max Respectively represent the minimum valve opening and the maximum valve opening, u max Indicates the maximum value range of the valve opening.

[0025] According to the mold temperature balance control method provided by an embodiment of the present invention, the optimal control output is calculated based on the mold temperature prediction result combined with a fuzzy logic control algorithm, including:

[0026] Fuzzify the deviation and the deviation change rate into different fuzzy linguistic variables;

[0027] Based on the preset membership function corresponding to the deviation and the deviation change rate, fuzzy reasoning is performed in combination with fuzzy linguistic variables with different deviations and deviation change rates to obtain a first fuzzy output result corresponding to the deviation and a second fuzzy output result corresponding to the deviation change rate, respectively;

[0028] A defuzzification operation is performed based on the first fuzzy output result and the second fuzzy output result, and the fuzzy output result is converted into an optimal control output of the temperature balancing device.

[0029] According to the mold temperature balance control method provided by an embodiment 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] Inputting the mold temperature distribution state information into the mold thermal balance dynamic model, and performing calculations according to the mold temperature distribution state information through the comprehensive thermal balance influence function to obtain an initial prediction result;

[0031] Discretizing the time in the initial prediction result with a preset time step, and calculating the mold temperature prediction results at different moments in the future in combination with the comprehensive thermal balance influence function;

[0032] Among them, the comprehensive thermal balance influence function can be expressed as:

[0033]

[0034] Among them, T distribute represents the mold temperature distribution information, t represents time, P process represents the processing power, t process Represents the processing time, R contact represents the contact thermal resistance, k represents the thermal conductivity, h represents the mold wall thickness, v coolant Indicates the coolant flow rate, C coolant represents the heat capacity of the coolant, ω represents the frequency parameter related to the mold thermal cycle, T stable Indicates the stable temperature reference value that the mold is expected to reach, L i and k i are the length and thermal conductivity of the corresponding heat conduction path, respectively.

[0035] According to the mold temperature balance control method provided by an embodiment of the present invention, the multi-dimensional mold temperature data is integrated to obtain mold temperature distribution state information, including:

[0036] Determine the spatial thermal influence weight ω of each sensor location i ; Among them, the spatial thermal influence weight ω i Based on the distance d from each sensor position to the preset key heat flow node of the mold i And the rate of change of temperature gradient at each sensor location Determine, the spatial thermal influence weight ω i The calculation formula is as follows:

[0037]

[0038] Among them, k1 and k2 are weight adjustment coefficients predetermined according to the mold material and structure;

[0039] The temperature data matrix T constructed by the collected multi-dimensional mold temperature data is fused and calculated to obtain the mold temperature distribution state information T distribute , the specific formula is as follows:

[0040]

[0041] Where n represents the number of sensor installation positions on the mold; t ij The element in the i-th row and j-th column of the temperature data matrix T represents the temperature value collected at the i-th sensor position at the j-th sampling time; Represents the mean of the temperature data collected at the i-th sensor position at m moments.

[0042] According to the mold temperature balance control method provided by an embodiment of the present invention, there are n sensor installation positions on the mold, and data collected at m consecutive sampling moments, and the multi-dimensional mold temperature data collected by temperature sensors installed at multiple positions of the mold is obtained, including:

[0043] Get the temperature value collected at the i-th sensor position at the j-th sampling time;

[0044] Based on the temperature value collected at the i-th sensor position at the j-th sampling time, a temperature data matrix T is constructed to obtain multi-dimensional mold temperature data;

[0045] Among them, the temperature data matrix T can be expressed as:

[0046]

[0047] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing any of the above-mentioned mold temperature balance control methods.

[0048] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, wherein the storage medium stores a computer software program, and when the computer software program is executed by a processor, the mold temperature balance control method as described in any one of the above is implemented.

[0049] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described mold temperature balance control methods.

[0050] The mold temperature balance control system provided by the embodiment of the present invention sets high-precision temperature sensors at multiple key parts of the mold, and obtains mold temperature distribution state information by fusing data, so as to comprehensively and accurately collect mold temperature data. At the same time, by establishing a multi-factor coupled mold thermal balance dynamic model, the influence of multiple factors on the mold temperature, such as the thermophysical properties of the mold material, the mold structure characteristics, the mold and workpiece contact characteristics, the coolant flow characteristics and the processing parameters, is comprehensively considered. Compared with the traditional single heat conduction model, it can more truly reflect the heat transfer process of the mold in the actual processing process, and further input the accurate mold temperature distribution state information into the multi-factor coupled mold thermal balance dynamic model to predict the temperature change trend, and then use the fuzzy logic control algorithm to calculate the optimal control output of each heating and cooling unit according to the prediction result. The fuzzy logic control algorithm uses the deviation between the predicted temperature and the target temperature of different areas of the mold and the deviation change rate as input variables, and can flexibly adjust the power adjustment amount of the heating or cooling unit according to the actual change of the mold temperature, avoiding the temperature fluctuation problem caused by the simple switch control of the traditional threshold control method, and realizing the refined adjustment of the mold temperature, thereby realizing the accuracy and stability of controlling the mold temperature and improving the quality of the mold product. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0053] Figure 3 An embodiment diagram of an electronic device provided by an embodiment of the present invention;

[0054] Figure 4 An embodiment diagram of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

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

[0056] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0057] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0058] Optional, see Figure 1 As shown, Figure 1 It is a structural 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 center, a model building module, a data processing module, a temperature prediction module, an output calculation module and a temperature balance control module.

[0059] Among them, the temperature balance control middle platform in the design example of the present invention is respectively connected to the model construction module, data processing module, temperature prediction module, output calculation module and temperature balance control module to manage and control each module.

[0060] Optionally, the model building module obtains the mold material thermophysical properties, mold structural characteristics, mold-workpiece contact characteristics, coolant flow characteristics and processing technology parameters of the mold transmitted by the temperature balance control center, and establishes a multi-factor coupled mold thermal balance dynamic model based on the mold material thermophysical properties, mold structural characteristics, mold-workpiece contact characteristics, coolant flow characteristics and processing technology parameters.

[0061] The thermophysical properties of mold materials include the thermal conductivity, specific heat and density of mold materials. Thermal conductivity determines the speed of heat conduction inside the material, specific heat reflects the amount of heat required to increase the temperature of a unit mass of material by one degree, and density affects the heat capacity of the material. The mold structural characteristics refer to the geometric shape of the mold, such as thickness and complex cavity structure. The contact characteristics between the mold and the workpiece include the roughness of the mold surface, the hardness of the workpiece material, and the pressure during the processing. The flow characteristics of the coolant include the flow rate, specific heat, density and geometry of the cooling channel (diameter, length, etc.). Processing parameters include processing speed, processing power, etc.

[0062] Optionally, the data processing module obtains multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations of 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. Among them, the mold temperature prediction result in the embodiment of the present invention represents the temperature change trend of different areas of the mold in the future.

[0063] Optionally, the output calculation module calculates the optimal control output based on the mold temperature prediction result combined with the fuzzy logic control algorithm. The input variables of the fuzzy logic control algorithm in the embodiment of the present invention are the deviation between the predicted temperature of the mold in different areas and the target temperature and the deviation change rate, and the output variable is the optimal control output of the temperature balance device. Furthermore, the temperature balance control module controls the power adjustment amount of the temperature balance device based on the optimal control output.

[0064] In the embodiment of the present invention, high-precision temperature sensors are arranged at multiple key positions of the mold, and the data is fused to obtain the mold temperature distribution state information, so that the mold temperature data can be collected comprehensively and accurately. At the same time, by establishing a multi-factor coupled mold thermal balance dynamic model, the influence of multiple factors such as the thermophysical properties of the mold material, the mold structure characteristics, the mold and workpiece contact characteristics, the coolant flow characteristics and the processing parameters on the mold temperature are comprehensively considered, and the heat transfer process of the mold in the actual processing process can be more truly reflected. The accurate mold temperature distribution state information is further input into the multi-factor coupled mold thermal balance dynamic model to predict the temperature change trend, and the fuzzy logic control algorithm is used to calculate the optimal control output of each heating and cooling unit according to the prediction result. The fuzzy logic control algorithm uses the deviation between the predicted temperature and the target temperature of different areas of the mold and the deviation change rate as input variables, and can flexibly adjust the power adjustment amount of the heating or cooling unit according to the actual change of the mold temperature, thereby realizing the fine adjustment of the mold temperature, thereby realizing the accuracy and stability of controlling the mold temperature and improving the quality of the mold product.

[0065] Optional, see Figure 2 , Figure 2 : is a flow chart of the mold temperature balance control method provided by the present invention. The execution subject of the mold temperature balance control method in the embodiment of the present invention is the mold temperature balance control system, therefore, the mold temperature balance control method includes:

[0066] Step 10: Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations of the mold.

[0067] Optionally, the embodiment of the present invention installs temperature sensors at different key positions of the mold. The selection of these positions should comprehensively consider factors such as the geometric shape of the mold, the characteristics of the heat flow distribution, and areas where large 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, around the cooling channel, etc., to obtain multi-dimensional data that can fully reflect the temperature of the mold. The data collected by the sensor may include temperature values ​​corresponding to different times, and sensors at different positions constitute a spatial temperature monitoring network, and the dimensions of the collected data can be expressed as a matrix. In one embodiment, there are n sensor installation positions on the mold, and data is collected at m consecutive sampling times. Then the collected temperature data matrix T can be expressed as:

[0068]

[0069] Among them, t ij represents the temperature value collected by the i-th sensor position at the j-th sampling time. In the embodiment of the present invention, the acquisition frequency of each sensor can be adaptively adjusted according to the actual thermal response characteristics and processing requirements of the mold. The embodiment of the present invention is based on the adaptive frequency adjustment function f adjust (t prev ,Δt max ) is adjusted, and the adaptive frequency adjustment function is based on the temperature t collected at the previous moment prev And the maximum allowable temperature change time interval Δt max Dynamically determine the sampling interval of the next moment, the formula is as follows:

[0070] t prev =Δt max / [1+exp(-(t prev -t ref ))].

[0071] Among them, t ref 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 the 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 fusion algorithm based on the spatial thermal weight distribution in the embodiment of the present invention first determines the spatial thermal influence weight ω of each sensor position i , spatial thermal influence weight ω i The distance d from this position to the preset key heat flow node of the mold (such as the center of the heat concentration area, the node where the cooling channel plays a key role, etc.) i And the temperature gradient change rate at that location The calculation formula is as follows:

[0074]

[0075] Among them, k1 and k2 are weight adjustment coefficients predetermined according to the mold material and structure.

[0076] Furthermore, the collected temperature data matrix T is fused and calculated, and the mold temperature distribution state information T distribute It can be obtained by the following formula:

[0077]

[0078] in, It represents the mean value of the temperature data collected at the i-th sensor position at m moments. Therefore, the fusion algorithm based on spatial thermal weight distribution can more scientifically reflect the temperature distribution state of the entire mold, and comprehensively considers the influence of spatial position and temperature change characteristics.

[0079] Step 30: input 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.

[0080] Optionally, a pre-trained mold thermal balance dynamic model is embedded in the mold temperature balance control system of an embodiment of the present invention, wherein the mold thermal balance dynamic model is a multi-factor coupled mold thermal balance prediction model established based on the thermophysical properties of the mold material, the mold structural characteristics, the contact characteristics between the mold and the workpiece, the coolant flow characteristics and the processing technology parameters. The mold temperature prediction result output by the mold thermal balance dynamic model characterizes the temperature change trend of different areas of the mold in the future.

[0081] Therefore, the mold thermal balance dynamic model in the embodiment of the present invention can be understood as a complex model established by integrating multiple factors. Its internal operating mechanism involves the thermophysical properties of the mold material (such as thermal conductivity k, specific heat capacity c, etc.), mold structure characteristics (such as wall thickness h, heat conduction path length L corresponding to the geometric shape of each part, etc.), mold and workpiece contact characteristics (contact thermal resistance Rcontact ), coolant flow characteristics (coolant flow rate v coolant 、Coolant heat capacity C coolant etc.) and processing parameters (such as processing power P process , processing time t process In the embodiment of the present invention, the comprehensive thermal balance influence function F in the mold thermal balance dynamic model balance (T distribute ,t) describes the comprehensive influence of these factors on the mold temperature at a certain time t, the comprehensive thermal balance influence function F balance (T distribute ,t) are as follows:

[0082]

[0083] Where ω represents the frequency parameter related to the mold thermal cycle, T stable Indicates the stable temperature reference value that the mold is expected to reach, L i and k i are the length and thermal conductivity of the corresponding heat conduction path, respectively.

[0084] Substitute the mold temperature distribution state information into the comprehensive thermal balance influence function F balance (T distribute ,t), by discretizing time t and calculating it step by step (for example, iterating with a small time step Δt), the mold temperature prediction results T at different moments in the future are obtained. predict (t), the specific calculation process is as follows:

[0085] T predict (t+Δt)=T predict (t)+F balance (T predict (t),t)·Δt.

[0086] Therefore, the temperature change trend of different areas of the mold in the future is obtained through iterative calculation, that is, the mold temperature prediction result is obtained.

[0087] Step 40, based on the mold temperature prediction result combined with the fuzzy logic control algorithm, calculate the optimal control output.

[0088] Optionally, the input variable of the fuzzy logic control algorithm in the embodiment of the present invention is the predicted temperature T of the mold in different areas. predict (r) (where r represents different areas of the mold) and the target temperature T target (r) deviation e(r) and the rate of change of deviation The output variable is the optimal control output u of the temperature balancing device.

[0089] The calculation formula of the deviation e(r) in the embodiment of the present invention is: e(r)=T predict (r)-T target (r), the rate of change of the deviation is calculated by taking the difference of the deviations of adjacent prediction moments and dividing it by the time interval, i.e.

[0090] Furthermore, the fuzzification rule set in the embodiment of the present invention is to convert the deviation e(r) and the deviation change rate Fuzzification is performed into different fuzzy language variables, such as setting language values ​​such as "negative large (NL)", "negative middle (NM)", "negative small (NS)", "zero (ZE)", "positive small (PS)", "positive middle (PM)", and "positive large (PL)". The corresponding membership function can adopt a custom form such as a trapezoidal membership function or a Gaussian membership function. For example, for the "negative large (NL)" membership function μ NL (e) can be defined as (taking the trapezoidal membership function as an example):

[0091]

[0092] Among them, e max and e mid It is a threshold parameter set according to the actual control accuracy and temperature range.

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

[0094] Finally, through the defuzzification operation, the fuzzy output is converted into the optimal control output u of the accurate temperature balance device. The defuzzification formula of the embodiment of the present invention is based on the centroid method. For example, the membership degree corresponding to the fuzzy output result is μ i , the corresponding optimal control output fuzzy value is u i (u i represents different fuzzy inference result numbers), then the optimal control output calculation formula is:

[0095] u i =(∑ i μ i ·u i ) / (∑ i μ i ).

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

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

[0098] Optionally, after receiving the calculated optimal control output u, the temperature balancing device (such as a heating rod, a flow control valve in a cooling pipe, etc.) of the embodiment of the present invention needs to perform an effective power adjustment execution operation. For a heating device such as a heating rod, its actual power P actual The relationship with the controlled output u can be obtained through the power regulation function P actual (u) is used to express the specific formula as follows:

[0099]

[0100] Among them, P max is the maximum power of the heating rod. actual (u) relationship, converting the control output into actual adjustable power, thereby changing the intensity of heating or cooling, achieving precise control of the mold temperature, making it change towards the target temperature, and maintaining the thermal equilibrium state of the mold.

[0101] For the flow control valve of the cooling pipeline, the valve opening can also be adjusted according to the flow rate of the coolant and the cooling power. The specific formula for the relationship between the valve opening O and the control output u is as follows:

[0102]

[0103] Among them, O min and O max They are the minimum valve opening and the maximum valve opening, u max It is the maximum value range of the valve opening, which realizes effective control of the mold temperature and ensures that the mold is always in a suitable thermal balance state during the processing.

[0104] In the embodiment of the present invention, high-precision temperature sensors are arranged at multiple key positions of the mold, and the data is fused to obtain the mold temperature distribution state information, so that the mold temperature data can be collected comprehensively and accurately. At the same time, by establishing a multi-factor coupled mold thermal balance dynamic model, the influence of multiple factors such as the thermophysical properties of the mold material, the mold structure characteristics, the mold and workpiece contact characteristics, the coolant flow characteristics and the processing parameters on the mold temperature are comprehensively considered, and the heat transfer process of the mold in the actual processing process can be more truly reflected. The accurate mold temperature distribution state information is further input into the multi-factor coupled mold thermal balance dynamic model to predict the temperature change trend, and the fuzzy logic control algorithm is used to calculate the optimal control output of each heating and cooling unit according to the prediction result. The fuzzy logic control algorithm uses the deviation between the predicted temperature and the target temperature of different areas of the mold and the deviation change rate as input variables, and can flexibly adjust the power adjustment amount of the heating or cooling unit according to the actual change of the mold temperature, thereby realizing the fine adjustment of the mold temperature, thereby realizing the accuracy and stability of controlling the mold temperature and improving the quality of the mold product.

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

[0106] 1. Consider the key factors that affect the thermal balance of the mold: The thermal balance of the mold is affected by multiple factors, mainly including the thermophysical properties of the mold material, the mold structure characteristics, the contact characteristics between the mold and the workpiece, the coolant flow characteristics, and the processing parameters.

[0107] 1. Thermophysical properties of mold materials: The thermal conductivity k, specific heat c and density ρ of mold materials play a key role in the conduction, storage and distribution of heat. Different materials will show different temperature response characteristics under the same heat input. For example, materials with high thermal conductivity can transfer heat faster, making the temperature distribution relatively more uniform, but the heat dissipation is also fast; materials with large specific heat capacity can store more heat and the temperature change is relatively milder.

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

[0109]

[0110] Among them, T1 represents the temperature of a certain area of ​​the current mold, and T0 represents a reference base temperature.

[0111] 2. Mold structural features: The structure of the mold determines the heat conduction path, heat concentration area, etc. For example, the wall thickness h affects the heat transfer speed in different parts, and the complex cavity structure will cause heat accumulation or form a thermal gradient. In order to quantify the impact of structural characteristics on thermal balance, the embodiment of the present invention sets a structural thermal resistance coefficient. The structural thermal resistance coefficient is related to the wall thickness of each part, the heat conduction path length L, and the corresponding cross-sectional area A. The calculation formula of the structural thermal resistance coefficient is as follows:

[0112]

[0113] Among them, i represents different structural parts of the mold, H max Indicates the maximum wall thickness of the entire mold.

[0114] 3. Contact characteristics between mold and workpiece: There is contact thermal resistance R when the mold contacts the workpiece contact The contact thermal resistance is related to factors such as the surface roughness of the workpiece and the contact pressure. The size of the contact thermal resistance directly affects the efficiency of heat transfer between the mold and the workpiece. Therefore, the embodiment of the present invention sets the contact heat transfer coefficient to describe the influence of the contact characteristics on the thermal balance. The calculation formula of the contact heat transfer coefficient is:

[0115]

[0116] Among them, P contact is the actual contact pressure, P max is the maximum achievable contact pressure, ΔT contact is the temperature difference between the mold and the workpiece contact interface, T ref is the preset reference temperature.

[0117] 4. Coolant flow characteristics: The coolant flows in the cooling channel of the mold, and its flow rate v collant , heat capacity C collant And the convection heat transfer coefficient h between the coolant and the mold wall conv Determines the cooling effect. A coolant with a fast flow rate, large heat capacity and high convection heat transfer coefficient can more effectively remove heat from the mold. The embodiment of the present invention sets the coolant cooling efficiency coefficient to quantify its impact. The calculation formula of the coolant cooling efficiency coefficient is as follows:

[0118]

[0119] Where, ΔT coolant is the temperature difference between the coolant inlet and outlet, T inlet is the coolant inlet temperature.

[0120] 5. Processing parameters: Processing parameters such as processing power P process , processing time t process And the processing frequency fprocess (For periodic processing) heat will be continuously input into the mold, which is an important external heat source factor affecting the thermal balance of the mold. The embodiment of the present invention sets a processing heat input coefficient to comprehensively represent the influence of these parameters on thermal balance. The calculation formula of the processing heat input coefficient is:

[0121]

[0122] Among them, T process is the average temperature of the key parts of the mold during processing, T avg is the preset average temperature reference value.

[0123] 2. Basic equation framework for building mold thermal balance dynamic model

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

[0125] In one embodiment, the heat source entering this area is mainly the heat generated by the processing process (from I process embodied) and the heat transferred by heat conduction with other regions (related to thermal conductivity k, temperature gradient The heat flowing out of this area is mainly transferred by convection with the coolant (by E coolant The change in internal energy is related to the material's specific heat capacity c, density ρ and temperature change rate. The thermal equilibrium dynamic equation is constructed as follows:

[0126]

[0127] Furthermore, a comprehensive coupling coefficient C is introduced couple To describe various factors (such as R m , R s , C contat The influence of the interaction between factors (such as thermal conductivity, thermal conductivity, etc.) on thermal equilibrium is a complex function of the coefficients of various factors. The comprehensive coupling coefficient C couple The calculation formula is as follows:

[0128]

[0129] Then the thermal equilibrium dynamic equation is corrected to:

[0130]

[0131] 3. Discretization of the model and solution method

[0132] The above constructed thermal equilibrium dynamic equation is a partial differential equation. In order to perform numerical calculations and simulations in practice, it needs to be discretized. The embodiment of the present invention uses the finite difference method for discretization, dividing the spatial area of ​​the mold into a number of small grid units (for example, uniform grid division is used, and the grid sizes are Δx, Δy, and Δz corresponding to the three dimensions of space respectively), and time is also discretized into time steps Δt. For the temperature T at a certain point in space (i, j, k) (corresponding to the index of the grid unit) and time n (corresponding to the time step number), the discretized temperature is expressed as

[0133] Discretize each term in the dynamic equation of thermal balance, such as the heat conduction term Discretization form of (taking one dimension as an example, three dimensions can be similarly expanded):

[0134]

[0135] in, It is the equivalent thermal conductivity at the corresponding grid interface, which can be obtained by interpolating the thermal conductivity of adjacent grid units. Substituting the discretized terms into the dynamic equation of thermal balance, we can get a set of algebraic equations about the temperature of each grid unit at different time steps. Then, an iterative method (such as Gauss-Seidel iteration method, etc.) can be used to solve this set of algebraic equations. The initial condition can be set to the initial temperature distribution of the mold (which can be obtained through the initial measurement value of the sensor or empirical estimation), and the boundary conditions are set according to the actual boundary heat exchange of the mold (such as convection heat exchange between the outer surface of the mold and the environment, and the fixed temperature boundary with the wall of the cooling channel, etc.).

[0136] By continuously iterating and solving the temperature value of each grid unit 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 established mold thermal balance dynamic model can be used to achieve dynamic prediction of the mold thermal state.

[0137] See also Figure 3 , Figure 3 FIG. 1 is an embodiment diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present 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, the following steps are implemented:

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

[0139] The multi-dimensional mold temperature data is integrated to obtain the mold temperature distribution status information;

[0140] 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; the mold temperature prediction result represents the temperature change trend of different areas of the mold in the future;

[0141] Based on the mold temperature prediction results combined with 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 in different areas of the mold and the deviation change rate, and the output variable is the optimal control output of the temperature balance device;

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

[0143] See also Figure 4 , Figure 4 Detailed description of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. 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, the following steps are implemented:

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

[0145] The multi-dimensional mold temperature data is integrated to obtain the mold temperature distribution status information;

[0146] 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; the mold temperature prediction result represents the temperature change trend of different areas of the mold in the future;

[0147] Based on the mold temperature prediction results combined with 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 in different areas of the mold and the deviation change rate, and the output variable is the optimal control output of the temperature balance device;

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

[0149] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can 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 of the mold;

[0151] The multi-dimensional mold temperature data is integrated to obtain the mold temperature distribution status information;

[0152] 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; the mold temperature prediction result represents the temperature change trend of different areas of the mold in the future;

[0153] Based on the mold temperature prediction results combined with 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 in different areas of the mold and the deviation change rate, and the output variable is the optimal control output of the temperature balance device;

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

[0155] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0156] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment 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, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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 center, 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 center is respectively connected to the model building module, the data processing module, the temperature prediction module, the output calculation module and the temperature balance control module to manage and control each module; The model building module is used to establish a multi-factor coupled mold thermal balance dynamic model based on the thermophysical properties of the mold material, the mold structure characteristics, the contact characteristics between the mold and the workpiece, the coolant flow characteristics and the processing parameters; The data processing module is used to obtain multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations of the mold, and 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 state 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 represents 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 combined with the fuzzy logic control algorithm; the input variable of the fuzzy logic control algorithm is the deviation between the predicted temperature and the target temperature of the mold in different areas and the deviation change rate, and the output variable is the optimal control output of the temperature balance device; The temperature balance control module is used to control the power adjustment amount of the temperature balance device based on the optimal control output amount.

2. A mold temperature balance control method, implemented based on the mold temperature balance control system according to claim 1, characterized in that: The mold temperature balance control method comprises: Acquire multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations of the mold; The multi-dimensional mold temperature data is integrated to obtain mold temperature distribution state information; Inputting the mold temperature distribution state information into a mold thermal balance dynamic model to obtain a mold temperature prediction result output by the mold thermal balance dynamic model; the mold temperature prediction result represents the temperature change trend of different areas of the mold in the future; Based on the mold temperature prediction result combined with the fuzzy logic control algorithm, the optimal control output is calculated; the input variable of the fuzzy logic control algorithm is the deviation between the predicted temperature and the target temperature in different areas of the mold and the deviation change rate, and the output variable is the optimal control output of the temperature balance device; controlling a power adjustment amount of the temperature balancing device based on the optimal control output amount; The mold thermal balance dynamic model is a multi-factor coupled mold thermal balance prediction model established based on the thermophysical properties of the mold material, the mold structure characteristics, the contact characteristics between the mold and the workpiece, the coolant flow characteristics and the processing technology parameters.

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 a cooling pipeline; the power regulation amount of the temperature balancing device is controlled based on the optimal control output, including: When the temperature balancing device is a heating rod, 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: Among them, P max represents the maximum power of the heating rod, and u represents the optimal control output; When the temperature balancing device is a flow control valve, the final gate valve opening of the flow control valve is determined based on the optimal control output and the minimum valve opening, the maximum valve opening and the maximum value range of the valve opening of the flow control valve to adjust the cooling power. The specific calculation formula is as follows: Among them, O represents the final valve opening, O min and O max Respectively represent the minimum valve opening and the maximum valve opening, u max Indicates the maximum value range of the valve opening.

4. The mold temperature balance control method according to claim 2, characterized in that: The method of calculating the optimal control output based on the mold temperature prediction result and the fuzzy logic control algorithm includes: Fuzzify the deviation and the deviation change rate into different fuzzy linguistic variables; Based on the preset membership function corresponding to the deviation and the deviation change rate, fuzzy reasoning is performed in combination with fuzzy linguistic variables with different deviations and deviation change rates to obtain a first fuzzy output result corresponding to the deviation and a second fuzzy output result corresponding to the deviation change rate, respectively; A defuzzification operation is performed based on the first fuzzy output result and the second fuzzy output result, and the fuzzy output result is converted into an optimal control output of the temperature balancing device.

5. The mold temperature balance control method according to claim 2, characterized in that: 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: Inputting the mold temperature distribution state information into the mold thermal balance dynamic model, and performing calculations according to the mold temperature distribution state information through the comprehensive thermal balance influence function to obtain an initial prediction result; Discretizing the time in the initial prediction result with a preset time step, and calculating the mold temperature prediction results at different moments in the future in combination with the comprehensive thermal balance influence function; Among them, the comprehensive thermal balance influence function can be expressed as: Among them, T distribute represents the mold temperature distribution status information, t represents time, P process represents the processing power, t process Represents the processing time, R contact represents the contact thermal resistance, k represents the thermal conductivity, h represents the mold wall thickness, v coolant Indicates the coolant flow rate, C coolant represents the heat capacity of the coolant, ω represents the frequency parameter related to the mold thermal cycle, T stable Indicates the stable temperature reference value that the mold is expected to reach, L i and k i are the length and thermal conductivity of the corresponding heat conduction path, respectively.

6. The mold temperature balance control method according to claim 2, characterized in that: The multi-dimensional mold temperature data is integrated to obtain mold temperature distribution state information, including: Determine the spatial thermal influence weight ω of each sensor location i ; Among them, the spatial thermal influence weight ω i Based on the distance d from each sensor position to the preset key heat flow node of the mold i And the rate of change of temperature gradient at each sensor location Determine, the spatial thermal influence weight ω i The calculation formula is as follows: Among them, k1 and k2 are weight adjustment coefficients predetermined according to the mold material and structure; The temperature data matrix T constructed by the collected multi-dimensional mold temperature data is fused and calculated to obtain the mold temperature distribution state information T distribute , the specific formula is as follows: Where n represents the number of sensor installation positions on the mold; t ij The element in the i-th row and j-th column of the temperature data matrix T represents the temperature value collected at the i-th sensor position at the j-th sampling time; Represents the mean of the temperature data collected at the i-th sensor position at m moments.

7. The mold temperature balance control method according to any one of claims 2 to 6, characterized in that: There are n sensor installation positions on the mold, and data collected at m consecutive sampling moments. The multi-dimensional mold temperature data collected by temperature sensors installed at multiple locations of the mold includes: Get the temperature value collected at the i-th sensor position at the j-th sampling time; Based on the temperature value collected at the i-th sensor position at the j-th sampling time, a temperature data matrix T is constructed to obtain multi-dimensional mold temperature data; Among them, the temperature data matrix T can be expressed as:

8. An electronic device, comprising: A memory and a processor, characterized in that a computer software program is stored in the memory, and when the processor reads and executes the computer software program, the mold temperature balance control method as described in any one of claims 2 to 7 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores a computer software program, and when the computer software program is executed by a processor, the mold temperature balance control method according to any one of claims 2 to 7 is implemented.

10. A computer product, characterized in that: The computer product stores a computer software program, and when the computer software program is executed by a processor, the mold temperature balance control method according to any one of claims 2 to 7 is implemented.

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