Intelligent temperature control system for automobile part die-casting die

Abstract

The invention provides an intelligent temperature control system for an automobile part die-casting die, relates to the field of automobile part manufacturing, and aims to solve the problems that traditional manual temperature control is low in efficiency and poor in precision and cannot meet modern production requirements easily. The system comprises a temperature monitoring, control and execution mechanism and a human-computer interaction interface. The temperature monitoring module is calibrated by a standard platinum resistor temperature source and a least square method to ensure that the measurement is accurate and the deviation meets the requirement, acquires the temperature and converts the temperature into an electric signal, and transmits the electric signal to the temperature control module in the control cabinet of the die-casting machine through a shielded twisted pair. According to the stored die-casting process parameters, a cooling medium flow regulating valve of an executing mechanism regulates the flow speed, and a heating device controls the temperature in a partitioned mode. The man-machine interaction interface supports local and remote operation, displays data in real time and receives instructions, and finally effectively avoids the defects of mold sticking, shrinkage cavities, cracks, poor flowability and the like caused by improper temperature of the casting.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive component manufacturing, and particularly to an intelligent temperature control system for die-casting molds of automotive components. Background Art

[0002] During the die-casting production process of automotive components, the temperature of the die-casting mold has a crucial impact on the quality of the castings. If the temperature is too high, defects such as mold sticking, shrinkage cavities, and cracks will occur in the castings; if the temperature is too low, the fluidity of the castings will become poor, affecting the forming integrity of the castings. The traditional die-casting mold temperature control method is mostly manual experience adjustment. The operator observes the mold temperature and manually adjusts the flow rate of the cooling medium or the power of the heating device. This method is not only inefficient, but also difficult to guarantee the temperature control accuracy, and cannot meet the high-quality and high-precision production requirements of modern automotive components. With the rapid development of the automotive industry, higher requirements are put forward for the quality and production efficiency of automotive components. Therefore, it is of great practical significance to develop a system that can accurately and intelligently control the temperature of die-casting molds. Summary of the Invention

[0003] In view of this, the present invention provides the purpose and efficacy of an intelligent temperature control system for die-casting molds of automotive components, specifically including: a temperature monitoring module, a temperature control module, an actuator, and a human-machine interface; characterized in that: the temperature monitoring module is installed at the positions of the cavity, core, gate, and runner of the mold; the calibration process is completed through a built-in standard platinum resistance temperature source and a calibration algorithm based on the least squares method. This calibration algorithm is based on the following principle: Let the output temperature of the standard temperature source be T std , and the sensor measurement value be T measured . Multiple sets of data (T std1 , T measured1 ), (T std2 , T measured2 ), …, (T stdm , T measuredm ) are obtained through multiple measurements, and an error function is constructed. The least squares method is used to find the partial derivative of the error function with respect to the sensor calibration parameters and set it to zero, and the calibration parameters are obtained by solving the equations, so as to correct the sensor measurement data and make the deviation between the sensor measurement value T measured and the actual temperature value T actual satisfy |T measured - T actual|≤0.3°C; the temperature monitoring module transmits the temperature data collected by the sensor and converted into electrical signals to the temperature control module integrated with the electrical system and installed in the die-casting machine control cabinet through shielded twisted pair wires. The temperature control module sends control instructions to the cooling medium flow regulating valve located at the horizontal straight pipe section near the mold inlet of the cooling pipe and the actuator installed near the mold cavity and core through the industrial Ethernet. At the same time, the human-machine interface is connected to the temperature control module through a communication link, can display the temperature data, system status and receive the operator's instructions and feedback them to the temperature control module, and supports remote operation through the Internet via the SSL encryption protocol.

[0004] Further, the temperature control module includes: a central processor, a data storage unit and a control algorithm unit; the data storage unit stores the die-casting process, mold and casting material temperature parameters in an 8GB solid-state drive. The control algorithm unit calculates the adjustment value of the cooling medium flow or heating power according to the comparison result of the central processor using fuzzy control and neural network control algorithms and transmits it to the actuator; in the fuzzy control algorithm, a fuzzy set is established taking the temperature change rate as an example. For example, the membership function of the "high" fuzzy set the membership function of the "medium" fuzzy set the membership function of the "low" fuzzy set Through the fuzzy inference synthesis rule, such as using the maximum-minimum synthesis method, combined with the fuzzy sets of other input quantities such as temperature deviation and corresponding rules, the adjustment amount of the cooling medium flow or heating power is obtained; in the neural network control algorithm, it is assumed that the number of neurons in the input layer of the three-layer BP neural network is n in , including temperature T i and related process parameters the number of neurons in the hidden layer is n hid , the number of neurons in the output layer is n out , the cooling medium flow Q or heating power P is output, the weight matrix from the input layer to the hidden layer is W in-hid , the weight matrix from the hidden layer to the output layer is W hid-out , the bias vector of the hidden layer is b hid , the bias vector of the output layer is b out , the activation function uses the sigmoid function Then the output of the hidden layer the output of the output layer y = σ(W hid-out ·H + b out ), the neural network is trained with 1000 groups of actual die-casting process data, and the mean square error MSE is used as the loss function N is the number of training data groups, is the true value, is the predicted value, and the weight matrix and bias vector are continuously adjusted using the backpropagation algorithm to make the training error less than 0.05.

[0005] Furthermore, the actuator consists of a cooling medium flow regulating valve and a heating device; the cooling medium flow regulating valve is installed in the horizontal straight pipe section of the cooling pipe near the mold inlet, and the valve opening is adjusted according to the instruction to change and control the flow rate. The relationship between the valve opening and the flow rate is v = 0.02α + 0.1, where α is the opening degree from 0 to 100%, the accuracy reaches ±0.08 L / min, and it has a fault self-diagnosis function; the relationship between the valve opening and the flow rate is obtained through experimental calibration. The specific experimental process is as follows: at different valve opening degrees α1, α2,..., α s under which the flow rates v1, v2,..., v of the cooling medium are measured s are measured, and the curve fitting method, such as the least squares method to fit the straight line v = aα + b, is used to obtain a = 0.02 and b = 0.1; the fault self-diagnosis of the cooling medium flow regulating valve is achieved by monitoring the driving current I of the valve and the valve opening feedback signal α feedback is monitored, and the fault diagnosis algorithm based on the Bayesian network is used to judge whether the valve is working properly; assume that the nodes in the Bayesian network include valve status (normal or faulty), driving current status (normal or abnormal), valve opening feedback status (normal or abnormal), etc. The conditional probability distribution between nodes is obtained through a large amount of historical data statistics, such as etc. When the driving current I exceeds the normal range [I min = 0.5 A, I max = 2 A] and the deviation between the valve opening feedback signal α feedback and the command opening α command is greater than the set threshold δ = 5%, the valve fault probability is calculated according to the Bayesian network inference. If it is greater than the set value, it is determined that the valve has a fault, and the fault information is sent to the temperature control module for alarm prompt through the human-machine interface; the heating device uses nickel-chromium alloy heating wires, which are installed in sections near the mold cavity and core. They are divided into sections according to the cavity complexity (6 sections for complex cavities, 3 sections for simple ones) and the core length-diameter ratio (5 sections for a length-diameter ratio greater than 3, 3 sections for a length-diameter ratio less than 3). The heating power of each section can be independently controlled, and the adjustment range is 1 - 10 kW, and the heating efficiency is 92%; assume that the heating power of the jth section of the heating device is P j , the input electric energy is W input，j , and the actual output heat is Q actual，j , then the heating efficiency i.e., Q actual，j = 0.92W input，j .

[0006] Furthermore, the human-machine interaction interface is a 10-inch industrial touch screen installed on the operation console of the die-casting machine. It can display the die temperature data, set the temperature range, modify control parameters, and show the system status and alarm information in real time. It has functions of graphical display, permission management (e.g., "admin-engineer" can modify algorithm parameters, and "operator-normal" can only perform routine operations), and data recording. The data is stored in the InfluxDB time series database and backed up to a 1TB external hard drive daily. The graphical display is achieved by drawing a real-time temperature curve on the touch screen, with the time t as the abscissa and the temperature T as the ordinate. The temperature data transmitted by the temperature monitoring module in real time is plotted in the coordinate system in chronological order to form a continuous temperature change curve, which is convenient for operators to intuitively understand the dynamic changes of the die temperature. The permission management is realized through user account and password verification. The system pre-sets the permission levels of different user accounts. For example, the "admin-engineer" account corresponds to the authority of senior engineers and can modify the control algorithm parameters; the "operator-normal" account corresponds to the authority of ordinary operators and can only view the temperature data and perform routine temperature settings. The data recording function stores each temperature setting, parameter modification, temperature data, alarm information, etc. during the system operation in the InfluxDB database in a time series format. The storage format is {(timestamp1, data1), (timestamp2, data2),...}, where timestamp is the time stamp and data is the data corresponding to the time. The data in the database is backed up to a 1TB external hard drive daily to prevent data loss.

[0007] Furthermore, the communication between system modules is efficient and accurate. The temperature control module and the actuator communicate through industrial Ethernet with a delay within 0.08 s, and CRC-16 checksum is used to ensure data accuracy. Industrial Ethernet communication adopts the TCP / IP protocol stack. During data transmission, the data to be transmitted is encapsulated into data packets in a certain format, and each data packet contains information such as data content, source address, and destination address. The principle of CRC-16 checksum is as follows: Let the data to be checked be D, and the generating polynomial be G(x) = x 16 +x 15 +x 2 +1. Shift D left by 16 bits to get D' = D × 2 16 , then divide D' by G(x) to get the remainder R, and add R to R to get the data D with CRC checksum CRCTransmit; after the receiving end receives the data, perform a division operation on it using the same generating polynomial G(x). If the remainder is zero, it is considered that the data transmission is correct; otherwise, the data transmission is incorrect. Require the sending end to retransmit the data to ensure that the data transmission delay is within 0.08s and accurate; the fault diagnosis module is integrated into the temperature control module, judge faults based on the feedback of the monitoring module and the actuator, and predict faults using the LSTM algorithm. When the probability is greater than 0.7, give an early warning; in the LSTM algorithm, let the input sequence be x1, x2, …, x T , the input gate i at time step t t , forget gate f t , output gate o t and memory cell c t are calculated as follows:

[0008] i t = σ(W ix x t + W ih h t-1 + b i )

[0009] f t = σ(W fx x t + W fh h t-1 + b f )

[0010] o t = σ(W ox x t + W oh h t-1 + b o )

[0011] c t = f t · c t-1 + i t · tanh(W cx x t + W ch h t-1 + b c )

[0012] h t = o t · tanh(c t )

[0013] where W ix , W ih , W fx , W fh , W ox , W oh , W cx , Wch

[0014] is the weight matrix, and b i , and b f , and b o , and b c is the bias vector, σ is the sigmoid function, and tanh is the hyperbolic tangent function; by training historical temperature data, actuator status and other data, a fault prediction model is established. When the predicted fault probability is greater than 0.7, a warning message is sent through the man-machine interface to prompt the operator to perform maintenance.

[0015] Furthermore, the sensor of the temperature monitoring module has strong protection; it adopts a ceramic package of high-temperature and corrosion-resistant materials, with a working temperature of -20°C to 500°C and an IP67 protection level, which can effectively resist the adverse effects of the die-casting environment; the ceramic packaging material has a low thermal conductivity coefficient λ ceramic ≤1W / (m·K), which can reduce the influence of the external environmental temperature on the measurement accuracy of the sensor; at the same time, it has good chemical stability and can resist the erosion of corrosive gases and liquids that may be generated during the die-casting process; the IP67 protection level indicates that the sensor can completely prevent foreign objects from invading, and can be immersed in water 1 meter deep for within 30 minutes without affecting its normal operation, ensuring stable operation in the complex environment of the die-casting workshop.

[0016] Furthermore, the system has an energy recovery function; a heat pipe type energy recovery device is installed near the outlet end of the cooling pipe to recover the heat of the cooling medium, with an electric energy conversion efficiency of 30%, which can be stored or used for auxiliary heating to improve the energy utilization efficiency; the working principle of the heat pipe type energy recovery device is: the cooling medium flows through the evaporation section of the heat pipe, transfers heat to the working fluid in the heat pipe, causes the working fluid to evaporate into steam, and the steam flows to the condensation section of the heat pipe under the action of the pressure difference, releases heat to the power generation device (such as a thermoelectric generation module) in the condensation section, and after the steam condenses into a liquid, it flows back to the evaporation section under the action of gravity or capillary force to complete the cycle; let the recovered heat be Q recover , and the conversion efficiency to electric energy is η convert = 0.3, then the converted electric energy W recover = 0.3Q recover , and the recovered electric energy can be stored in the battery pack or directly used for the auxiliary heating device of the mold to reduce energy consumption.

[0017] Furthermore, the temperature control module has an adaptive learning ability. Based on the genetic algorithm, according to the temperature and process parameter changes during the die-casting process, it automatically adjusts the fuzzy control rules or the neural network weight matrix and bias vector to optimize the temperature control performance. In the genetic algorithm, the fuzzy control rule parameters or the neural network weight matrix and bias vector are encoded as chromosomes. Let the chromosome length be l and the initial population size be N. The population is continuously evolved through operations such as selection, crossover, and mutation. The selection operation uses the roulette wheel selection method. Let the fitness of the i-th chromosome be f i , and the total fitness of the population is Then the probability that the i-th chromosome is selected The crossover operation randomly selects two chromosomes for gene exchange with a crossover probability P c . For example, single-point crossover randomly selects a crossover point on the chromosome and exchanges the gene segments after the crossover point of the two chromosomes. The mutation operation randomly mutates the genes on the chromosome, such as adding or subtracting a random small amount to the gene value. By continuously evolving the population, the value of the fitness function (such as using the sum of squares of the deviation between the mold temperature and the set temperature as the fitness function) is minimized, thereby automatically adjusting the fuzzy control rules or the neural network weight matrix and bias vector to optimize the temperature control performance. m

[0018] Furthermore, the human-machine interface supports remote operation. Operators can remotely view the temperature, set parameters, etc. through the Internet on a device equipped with customized client software via the SSL encryption protocol to ensure data security. The SSL encryption protocol combines public key encryption and symmetric encryption. When the client and the server establish a connection, the server sends a digital certificate to the client. The certificate contains information such as the server public key. After the client verifies the legality of the certificate, it generates a random symmetric key, encrypts it with the server public key, and sends it to the server. The server decrypts it with the private key to obtain the symmetric key. Thereafter, both parties use this symmetric key for encryption and decryption during communication to ensure the security during data transmission, prevent data from being stolen or tampered with, and realize operations such as temperature viewing and parameter setting of the intelligent temperature control system by operators on remote devices.

[0019] Furthermore, the cooling pipe is wrapped with polyurethane foam heat insulation material with a thermal conductivity of 0.05 W / (m·K), which effectively reduces the heat loss of the cooling medium during transmission and stabilizes the cooling effect. Let the temperature of the cooling medium in the cooling pipe be T fluid , the ambient temperature be T env , the surface area of the cooling pipe be A pipe , and the thickness of the polyurethane foam insulation layer be d. According to the heat conduction law, the heat loss through the insulation layer per unit time Since the thermal conductivity of the polyurethane foam heat insulation material is λ insulate ​= 0.05 W / (m·K) is relatively low, which can effectively reduce heat loss, ensure the temperature stability of the cooling medium during transmission, and then stabilize the cooling effect of the mold.

[0020] Beneficial effects

[0021] In terms of temperature monitoring, its sensor layout is precise, which can comprehensively, timely and accurately collect the temperature data of each key part of the mold, providing a reliable basis for subsequent control. The temperature control module has an advanced algorithm, which can quickly and accurately calculate and adjust the control strategy according to the monitored data, realizing the precise control of the mold temperature. In the actuator, the cooling medium flow regulating valve and the heating device respond quickly, which can effectively change the cooling and heating states, ensuring the stability of the mold temperature. The human-machine interaction interface is convenient for operators to operate, the permission management improves the system security, the data recording provides materials for subsequent analysis, and the remote operation enhances the operation convenience. The following is a detailed introduction for you:

[0022] 1: The high-precision temperature sensors of the temperature monitoring module are reasonably arranged at the key parts of the mold. For example, K-type thermocouple temperature sensors are set every 30 mm on the cavity surface, and platinum resistance temperature sensors are arranged at specific intervals on the core. And it has a self-calibration function once every 2 hours, with an accuracy of ±0.3 °C, which can comprehensively, timely and accurately collect the temperature data, providing a reliable basis for the temperature control module and ensuring the precise capture of the mold temperature change.

[0023] 2: The temperature control module adopts fuzzy control or neural network control algorithms, and quickly and accurately calculates the adjustment values of the cooling medium flow or heating power according to the data of the temperature monitoring module. For example, the fuzzy control sets rules based on Mamdani reasoning, and the neural network control is trained with 1000 groups of data. It can adapt to different die-casting processes, precisely control the mold temperature, improve the quality of castings, and reduce defects caused by temperature problems.

[0024] 3: The cooling medium flow regulating valve of the actuator is installed at a specific position, and accurately adjusts the valve opening according to the instruction to control the flow, with an accuracy of ±0.08 L / min, and has a fault self-diagnosis function; the heating device uses nickel-chromium alloy heating wires installed in zones, and the heating power of each zone can be independently controlled, with an adjustment range of 1 - 10 kW and a heating efficiency of 92%, which can quickly respond to the instructions of the temperature control module, effectively change the cooling and heating states of the mold, and ensure the stability of the mold temperature.

[0025] 4: The human-machine interaction interface is a 10-inch industrial touch screen, and the installation position is convenient for operation. It can display information such as the mold temperature and system status in real time, and operators can conveniently set the temperature range and modify the control parameters. It has a graphical display for intuitive understanding of the temperature change, the permission management improves the system security, the data recording function provides materials for subsequent analysis, and it supports remote operation, greatly enhancing the operation convenience and improving the work efficiency.

[0026] 5: The communication between modules is efficient and accurate. The temperature control module and the actuator communicate through industrial Ethernet with a delay within 0.08 s, and CRC-16 checksum is used to ensure data accuracy. The fault diagnosis module is integrated into the temperature control module, and the LSTM algorithm is used to predict faults. When the probability is greater than 0.7, an early warning is issued, which can timely detect and solve problems, improving the reliability and stability of the system. The sensors of the temperature monitoring module have strong protection, a wide working temperature range and a high protection level, and can operate stably in a harsh die-casting environment. The cooling pipes are wrapped with heat-insulating materials with low thermal conductivity to stabilize the cooling effect and further ensure the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly introduced below.

[0028] The drawings in the following description only relate to some embodiments of the present invention and do not limit the present invention.

[0029] In the drawings:

[0030] Figure 1 is a schematic perspective view of the overall right front side of the intelligent temperature control system for die-casting molds of automotive parts according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments.

[0032] Embodiment: Please refer to Figures 1 to 1 as shown:

[0033] The present invention provides an intelligent temperature control system for die-casting molds of automotive parts, including a temperature monitoring module, a temperature control module, an actuator and a human-machine interface; characterized in that: the temperature monitoring module is installed at the cavity, core, gate and runner positions of the mold; the calibration process is completed through the built-in standard platinum resistance temperature source and the calibration algorithm based on the least squares method, and this calibration algorithm is based on the following principle: let the output temperature of the standard temperature source be T std , and the measured value of the sensor be T measured , and multiple groups of data (T std1 , T measured1 ), (T std2 , T measured2 ), …, (T stdm , T measuredm ) are obtained through multiple measurements, and an error function is constructed Using the least squares method to find the partial derivative of the error function with respect to the sensor calibration parameters and set it to zero, solve the equations to obtain the calibration parameters, so as to correct the sensor measurement data and make the sensor measurement value T measuredThe deviation from the actual temperature value T actual satisfies |T measured - T actual | ≤ 0.3 °C; the temperature monitoring module transmits the temperature data collected by the sensor and converted into an electrical signal to the temperature control module integrated with the electrical system and installed in the die-casting machine control cabinet through shielded twisted pair. The temperature control module sends control instructions to the cooling medium flow regulating valve near the horizontal straight pipe section of the mold inlet in the cooling pipeline and the actuator installed near the mold cavity and core through the industrial Ethernet. At the same time, the human-machine interface is connected to the temperature control module through a communication link, can display temperature data, system status and receive operator instructions and feedback them to the temperature control module, and supports remote operation through the Internet via the SSL encryption protocol.

[0034] Among them, the temperature control module includes: a central processor, a data storage unit, and a control algorithm unit; the data storage unit stores die-casting process and mold and casting material temperature parameters using an 8GB solid-state drive. The control algorithm unit calculates the adjustment value of the cooling medium flow or heating power according to the comparison result of the central processor using fuzzy control and neural network control algorithms and transmits it to the actuator; in the fuzzy control algorithm, taking the temperature change rate as an example to establish a fuzzy set, such as the membership function of the "high" fuzzy set the membership function of the "medium" fuzzy set the membership function of the "low" fuzzy set Through the fuzzy inference synthesis rule, such as using the maximum-minimum synthesis method, combined with the fuzzy sets of other input quantities such as temperature deviation and corresponding rules, the adjustment amount of the cooling medium flow or heating power is obtained; in the neural network control algorithm, it is assumed that the number of neurons in the input layer of the three-layer BP neural network is n in , including the temperature T i and related process parameters the number of neurons in the hidden layer is n hid , the number of neurons in the output layer is n out , the cooling medium flow Q or heating power P is output, the weight matrix from the input layer to the hidden layer is W in-hid , the weight matrix from the hidden layer to the output layer is W hid-out , the hidden layer bias vector is b hid , the output layer bias vector is b out , the activation function uses the sigmoid function Then the hidden layer output the output layer output y = σ(W hid-out ·H + b out ), the neural network is trained with 1000 groups of actual die-casting process data, and the mean square error MSE is used as the loss function N is the number of training data groups, is the true value, is the predicted value. The weight matrix and bias vector are continuously adjusted using the backpropagation algorithm to make the training error less than 0.05.

[0035] Among them, the actuator consists of a cooling medium flow regulating valve and a heating device; the cooling medium flow regulating valve is installed on the horizontal straight pipe section of the cooling pipeline near the mold inlet, and the valve opening is adjusted according to the instruction to change and control the flow rate. The relationship between the valve opening and the flow rate is v = 0.02α + 0.1, where α is the opening from 0 to 100%, the accuracy reaches ±0.08 L / min, and it has a fault self-diagnosis function; the relationship between the valve opening and the flow rate is obtained through experimental calibration. The specific experimental process is as follows: at different valve openings α1, α2,..., α s under which the flow rates v1, v2,..., v s of the cooling medium are measured. Using the curve fitting method, such as the least squares method to fit the straight line v = aα + b, we get a = 0.02 and b = 0.1; the fault self-diagnosis of the cooling medium flow regulating valve is achieved by monitoring the driving current I of the valve and the valve opening feedback signal α feedback and using the fault diagnosis algorithm based on the Bayesian network to determine whether the valve is working properly; assume that the nodes in the Bayesian network include valve status (normal or faulty), driving current status (normal or abnormal), valve opening feedback status (normal or abnormal), etc. The conditional probability distribution between each node is obtained through a large amount of historical data statistics, such as etc. When the driving current I exceeds the normal range [I min = 0.5 A, I max = 2 A] and the deviation between the valve opening feedback signal α feedback and the commanded opening α command is greater than the set threshold δ = 5%, the valve fault probability is calculated according to the Bayesian network inference. If it is greater than the set value, it is determined that the valve has a fault, and the fault information is sent to the temperature control module for alarm prompt through the human-machine interface; the heating device uses nickel-chromium alloy heating wires, which are installed in sections near the mold cavity and core. They are divided into zones according to the cavity complexity (6 zones for complex cavities, 3 zones for simple ones) and the core length-diameter ratio (5 zones for a length-diameter ratio greater than 3, 3 zones for less than 3). The heating power of each zone can be independently controlled, with an adjustment range of 1 - 10 kW and a heating efficiency of 92%; assume that the heating power of the j-th zone of the heating device is P j , the input electric energy is W input，j , and the actual output heat is Q actual，j , then the heating efficiency i.e., Q actual,j = 0.92W input,j .

[0036] Among them, the human-machine interaction interface is a 10-inch industrial touch screen, installed on the operation console of the die-casting machine; it can display the mold temperature data, set the temperature range, modify the control parameters, and display the system status and alarm information in real time. It has functions of graphical display, permission management (such as "admin-engineer" can modify algorithm parameters, and "operator-normal" can only perform routine operations), and data recording. The data is stored in the InfluxDB time series database and backed up to a 1TB external hard drive every day; the graphical display is achieved by drawing a real-time temperature curve on the touch screen. With time t as the abscissa and temperature T as the ordinate, the temperature data transmitted in real time by the temperature monitoring module is sequentially plotted in the coordinate system according to the time sequence to form a continuous temperature change curve, which is convenient for operators to intuitively understand the dynamic changes of the mold temperature; the permission management is realized through user account and password verification. The system pre-sets the permission levels of different user accounts. For example, the "admin-engineer" account corresponds to the authority of senior engineers and can modify the control algorithm parameters; the "operator-normal" account corresponds to the authority of ordinary operators and can only view the temperature data and perform routine temperature settings; the data recording function stores each temperature setting, parameter modification, as well as the temperature data and alarm information during the system operation process in the InfluxDB database in the time series format. The storage format is {(timestamp1,data1),(timestamp2,data2),...}, where timestamp is the time stamp and data is the data corresponding to the time. The data in the database is backed up to a 1TB external hard drive every day to prevent data loss.

[0037] Among them, the communication between the system modules is efficient and accurate; the temperature control module and the actuator communicate through the industrial Ethernet, with a delay within 0.08 s, and CRC-16 checksum is used to ensure data accuracy; the industrial Ethernet communication adopts the TCP / IP protocol stack. During the data transmission process, the data to be transmitted is encapsulated into data packets in a certain format. Each data packet contains information such as data content, source address, and destination address; the principle of CRC-16 checksum is as follows: Let the data to be checked be D, and the generating polynomial be G(x) = x 16 +x 15 +x 2 +1. Shift D to the left by 16 bits to get D′ = D × 2 16 , then divide D′ by G(x) to get the remainder R, and add R to R to get the data D with CRC checksum CRCTransmit; after the receiving end receives the data, perform a division operation on it using the same generating polynomial G(x). If the remainder is zero, it is considered that the data transmission is correct; otherwise, the data transmission is incorrect. The sending end is required to retransmit the data to ensure that the data transmission delay is within 0.08 s and accurate; the fault diagnosis module is integrated into the temperature control module, judges faults based on the feedback from the monitoring module and the actuator, and predicts faults using the LSTM algorithm. When the probability is greater than 0.7, an early warning is issued; in the LSTM algorithm, let the input sequence be x1, x2, …, x T , the input gate i at time step t t , forget gate f t , output gate o t and memory cell c t are calculated as follows:

[0038] i t = σ(W ix x t + W ih h t-1 + b i )

[0039] f t = σ(W fx x t + W fh h t-1 + b f )

[0040] o t = σ(W ox x t + W oh h t-1 + b o )

[0041] c t = f t · c t-1 + i t · tanh(W cx x t + W ch h t-1 + b c )

[0042] h t = o t · tanh(c t )

[0043] where W ix , W ih , W fx , W fh , W ox , W oh , W cx , Wch

[0044] is the weight matrix, and b i , and b f , and b o , and b c is the bias vector, σ is the sigmoid function, and tanh is the hyperbolic tangent function; by training historical temperature data, actuator status data, etc., a fault prediction model is established. When the predicted fault probability is greater than 0.7, a warning message is sent through the human-machine interface to prompt the operator to perform maintenance.

[0045] Among them, the sensor of the temperature monitoring module has strong protection; it uses a ceramic package of high-temperature and corrosion-resistant materials, with a working temperature of -20°C to 500°C and an IP67 protection level, which can effectively resist the adverse effects of the die-casting environment; the ceramic package material has a low thermal conductivity coefficient λ ceramic ≤1W / (m·K), which can reduce the influence of the external environmental temperature on the measurement accuracy of the sensor; at the same time, it has good chemical stability and can resist the erosion of corrosive gases and liquids that may be generated during die-casting; the IP67 protection level indicates that the sensor can completely prevent foreign objects from entering, and can be immersed in water 1 meter deep for less than 30 minutes without affecting its normal operation, ensuring stable operation in the complex environment of the die-casting workshop.

[0046] Among them, the system has an energy recovery function; a heat pipe type energy recovery device is installed near the outlet end of the cooling pipeline to recover the heat of the cooling medium, with an electric energy conversion efficiency of 30%, which can be stored or used for auxiliary heating to improve the energy utilization efficiency; the working principle of the heat pipe type energy recovery device is: the cooling medium flows through the evaporation section of the heat pipe, transfers heat to the working medium in the heat pipe, causes the working medium to evaporate into steam, and the steam flows to the condensation section of the heat pipe under the action of pressure difference, releases heat to the power generation device (such as a thermoelectric generation module) in the condensation section, and after the steam condenses into liquid, it flows back to the evaporation section under the action of gravity or capillary force to complete the cycle; let the recovered heat be Q recover , and the efficiency of converting it into electric energy is η convert =0.3, then the converted electric energy W recover =0.3Q recover , and the recovered electric energy can be stored in the battery pack or directly used for the auxiliary heating device of the mold to reduce energy consumption.

[0047] Among them, the temperature control module has self - adaptive learning ability; based on the genetic algorithm, according to the temperature and process parameter changes during the die - casting process, it automatically adjusts the fuzzy control rules or the neural network weight matrix and bias vector to optimize the temperature control performance; in the genetic algorithm, the fuzzy control rule parameters or the neural network weight matrix and bias vector are encoded as chromosomes. Let the chromosome length be \(l\) and the initial population size be \(N\). The population is continuously evolved through operations such as selection, crossover, and mutation; the selection operation uses the roulette wheel selection method. Let the fitness of the \(i\) - th chromosome be \(f\). i , and the total population fitness is Then the probability that the \(i\) - th chromosome is selected The crossover operation uses the crossover probability \(P\). c Randomly selects two chromosomes for gene exchange. For example, in single - point crossover, a crossover point is randomly selected on the chromosome, and the gene segments after the crossover point of the two chromosomes are exchanged; the mutation operation uses the mutation probability \(P\). m Randomly mutates the genes on the chromosome. For example, adding or subtracting a small random amount to the gene value; by continuously evolving the population, the value of the fitness function (such as taking the sum of squares of the deviation between the mold temperature and the set temperature as the fitness function) is minimized, thereby automatically adjusting the fuzzy control rules or the neural network weight matrix and bias vector to optimize the temperature control performance.

[0048] Among them, the human - machine interaction interface supports remote operation; operators can remotely view the temperature, set parameters, etc. through the Internet on a device equipped with customized client software and access it through the SSL encryption protocol to ensure data security; the SSL encryption protocol combines public - key encryption and symmetric encryption. When the client and the server establish a connection, the server sends a digital certificate to the client. The certificate contains information such as the server's public key. After the client verifies the legality of the certificate, it generates a random symmetric key, encrypts it with the server's public key, and sends it to the server. The server decrypts it with the private key to obtain the symmetric key. Thereafter, both parties use this symmetric key for encryption and decryption during communication to ensure the security during data transmission, prevent data from being stolen or tampered with, and realize operations such as temperature viewing and parameter setting of the intelligent temperature control system by operators on remote devices.

[0049] Among them, the cooling pipeline is wrapped with polyurethane foam thermal insulation material with a thermal conductivity of \(0.05W / (m\cdot K)\), which effectively reduces the heat loss of the cooling medium during transmission and stabilizes the cooling effect; let the temperature of the cooling medium in the cooling pipeline be \(T\). fluid , and the ambient temperature be \(T\). env , the surface area of the cooling pipeline be \(A\). pipe , the thickness of the polyurethane foam thermal insulation layer be \(d\). According to the heat conduction law, the heat loss through the thermal insulation layer per unit time Since the thermal conductivity of the polyurethane foam thermal insulation material is \(\lambda\). insulate= 0.05 W / (m·K) is relatively low, which can effectively reduce heat loss, ensure the temperature stability of the cooling medium during transmission, and thus stabilize the cooling effect of the mold. Comparative analysis of experimental data of the intelligent temperature control system - I. Comparison of temperature control accuracy

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] II. Comparison of casting quality - (I) Dimensional accuracy

[0056]

[0057]

[0058] (II) Metallographic structure and defects

[0059]

[0060]

[0061] Through the above detailed table comparison, the intelligent temperature control system has significant advantages over the traditional temperature control method in terms of temperature control accuracy, improvement of casting quality, and reduction of energy consumption, bringing higher efficiency, better product quality, and lower costs to die-casting production.

[0062] The specific usage mode and functions of this embodiment are as follows: The temperature monitoring module installs sensors calibrated by a standard platinum resistance temperature source and the least squares method at the positions of the mold cavity, core, gate, and runner, collects the temperature and converts it into an electrical signal, and transmits it to the temperature control module integrated in the electrical system of the die-casting machine control cabinet through shielded twisted-pair wires; the central processor of the temperature control module combines the die-casting process and material temperature parameters stored in the 8GB solid-state drive in the data storage unit, and uses the fuzzy control (constructing fuzzy sets and membership functions based on the temperature change rate, etc., and performing operations through the fuzzy inference synthesis rules) and neural network control (a three-layer BP neural network, trained with 1000 groups of data, using the mean square error as the loss function, and adjusting the weights and biases with the backpropagation algorithm) in the control algorithm unit to calculate the adjustment value of the cooling medium flow rate or heating power, and transmits it to the actuator; the cooling medium flow rate regulating valve of the actuator is installed on the horizontal straight pipe section near the mold inlet of the cooling pipeline, adjusts the flow rate according to the relationship between the opening and flow rate calibrated by experiments according to the instruction, and has a fault self-diagnosis function based on the Bayesian network algorithm. The heating device made of nickel-chromium alloy heating wire is installed in the vicinity of the mold cavity and core according to the complexity of the cavity and the length-diameter ratio of the core. The heating power of each partition can be independently controlled within 1 - 10kW, and the heating efficiency is 92%; the human-machine interface is a 10-inch industrial touch screen, installed on the die-casting machine operation console, which can locally display real-time data such as temperature, set parameters, graphically display, perform permission management and data recording (stored in the InfluxDB database and backed up to a 1TB mobile hard disk every day), and also supports operators to remotely view the temperature and set parameters on the device equipped with customized client software through the Internet via the SSL encryption protocol (combining public key and symmetric encryption); between the modules of the system, the temperature control module and the actuator communicate through the industrial Ethernet using the TCP / IP protocol stack, using CRC-16 check to ensure the accuracy of the data and the transmission delay is within 0.08s. The fault diagnosis module is integrated in the temperature control module, and uses the LSTM algorithm (performing operations based on the input gate, forget gate, output gate, and memory unit, etc.) to train and predict faults based on the monitoring and feedback of the actuator for historical data, and issues a warning when the probability is greater than 0.7; the sensors of the temperature monitoring module use ceramic-encapsulated high-temperature and corrosion-resistant materials, with a working temperature of -20°C to 500°C and an IP67 protection level; the system installs a heat pipe type energy recovery device near the outlet end of the cooling pipeline, which recovers the heat of the cooling medium with an efficiency of 30% to convert electrical energy for storage or auxiliary heating; the temperature control module automatically optimizes the temperature control performance based on the genetic algorithm (encoding the fuzzy control rules or neural network weight bias vectors, evolving the population through selection, crossover, and mutation operations to minimize the fitness function value); the cooling pipeline is wrapped with polyurethane foam heat insulation material with a thermal conductivity of 0.05W / (m·K), reducing heat loss according to the heat conduction law, and stabilizing the temperature of the cooling medium and the cooling effect of the mold.

Claims

1. An intelligent temperature control system for die-casting molds of automotive parts, comprising a temperature monitoring module, a temperature control module, an actuator and a human-machine interface; characterized in that: The temperature monitoring module is installed at the positions of the mold cavity, core, gate and runner; the calibration process is completed by an internal standard platinum resistance temperature source and a calibration algorithm based on the least squares method. The calibration algorithm is based on the following principle: Let the output temperature of the standard temperature source be T std , and the measured value of the sensor be T measured . Multiple groups of data (T std1 , T measured1 ) are obtained through multiple measurements, (T std2 , T measured2 ), …, (T stdm , T measuredm ), and an error function is constructed. Using the least squares method, the partial derivatives of the error function with respect to the sensor calibration parameters are calculated and set to zero. By solving the equations, the calibration parameters are obtained, so as to correct the sensor measurement data and make the deviation between the sensor measurement value T measured and the actual temperature value T actual satisfy |T measured - T actual | ≤ 0.3°C; the temperature monitoring module transmits the temperature data collected by the sensor and converted into electrical signals to the temperature control module integrated with the electrical system and installed in the die-casting machine control cabinet through shielded twisted pair wires. The temperature control module sends control commands to the cooling medium flow regulating valve located in the horizontal straight pipe section near the mold inlet of the cooling pipe and the actuator installed near the mold cavity and core via the industrial Ethernet. At the same time, the human-machine interface is connected to the temperature control module through a communication link, can display the temperature data, system status and receive the operator's instructions and feedback them to the temperature control module, and supports remote operation via the Internet through the SSL encryption protocol.

2. The intelligent temperature control system for die-casting molds of automobile parts according to claim 1, wherein The temperature control module includes: a central processing unit, a data storage unit, and a control algorithm unit; the data storage unit stores the temperature parameters of die-casting processes, molds, and casting materials using an 8GB solid-state drive. The control algorithm unit calculates the adjustment value of the cooling medium flow rate or heating power according to the comparison result of the central processing unit using fuzzy control and neural network control algorithms and transmits it to the actuator; in the fuzzy control algorithm, taking the temperature change rate as an example, fuzzy sets are established, such as the membership function of the "high" fuzzy set the membership function of the "medium" fuzzy set the membership function of the "low" fuzzy set Through the fuzzy inference synthesis rule, such as the max-min synthesis method, combined with the fuzzy sets of other input quantities such as temperature deviation and corresponding rules, the adjustment amount of the cooling medium flow rate or heating power is obtained; in the neural network control algorithm, it is assumed that the number of neurons in the input layer of a three-layer BP neural network is n in , including temperature T i and related process parameters the number of neurons in the hidden layer is n hid , the number of neurons in the output layer is n out , the cooling medium flow rate Q or heating power P is output, the weight matrix from the input layer to the hidden layer is W in-hid , the weight matrix from the hidden layer to the output layer is W hid-out , the bias vector of the hidden layer is b hid , the bias vector of the output layer is b out , and the sigmoid function is used as the activation function Then the output of the hidden layer The output of the output layer y = σ(W hid-out ·H + b out) . The neural network is trained with 1000 groups of actual die-casting process data, and the mean square error MSE is used as the loss function N is the number of training data groups, is the true value, is the predicted value. The weight matrix and bias vector are continuously adjusted using the backpropagation algorithm to make the training error less than 0.

05.

3. An intelligent temperature control system for a die-casting mold of automotive parts according to claim 1, characterized in that, The actuator consists of a cooling medium flow regulating valve and a heating device; the cooling medium flow regulating valve is installed in the horizontal straight pipe section of the cooling pipeline near the mold inlet, and according to the instruction, it adjusts the valve opening to change and control the flow rate. The relationship between the valve opening and the flow rate is v = 0.02α + 0.1, where α is the opening degree from 0 to 100%, the accuracy reaches ±0.08 L / min and it has a fault self-diagnosis function; the relationship between the valve opening and the flow rate is obtained through experimental calibration. The specific experimental process is as follows: at different valve opening degrees α1, α2, …, α s , measure the flow rates v1, v2, …, v s of the cooling medium, and use the curve fitting method, such as the least squares method to fit the straight line v = aα + b, and obtain a = 0.02 and b = 0.1; The fault self-diagnosis of the cooling + medium flow regulating valve monitors the drive current I of the valve and the valve opening feedback signal α feedback , and uses a fault diagnosis algorithm based on the Bayesian network to determine whether the valve is working properly; the nodes in the Bayesian network are set to include valve status (normal or faulty), drive current status (normal or abnormal), valve opening feedback status (normal or abnormal), etc. The conditional probability distribution between each node is obtained through a large number of historical data statistics, such as , etc. When the drive current I exceeds the normal range [I min = 0.5A, I max = 2A] and the deviation between the valve opening feedback signal α feedback and the command opening α command is greater than the set threshold δ = 5%, the valve fault probability is calculated according to the Bayesian network inference. If it is greater than the set value, it is determined that the valve has a fault, and the fault information is sent to the temperature control module for alarm prompt through the human-machine interface; The nickel-chromium alloy heating wire for the heating device is installed in sections near the mold cavity and core. It is divided into sections according to the complexity of the cavity (6 sections for complex cavities and 3 sections for simple ones) and the length-diameter ratio of the core (5 sections for a length-diameter ratio greater than 3 and 3 sections for a ratio less than 3). The heating power of each section can be independently controlled, with an adjustment range of 1 - 10 kW and a heating efficiency of 92%. Let the heating power of the j-th section of the heating device be P j , the input electric energy is W input，j , and the actual output heat is Q actual，j . Then the heating efficiency i.e., Q actual，j = 0.92W input，j .

4. An intelligent temperature control system for a die-casting mold of automotive parts according to claim 1, characterized in that, The human-machine interaction interface is a 10-inch industrial touch screen installed on the operation console of the die-casting machine. It can display the mold temperature data in real time, set the temperature range, modify the control parameters, and display the system status and alarm information. It has functions such as graphical display, permission management (e.g., "admin-engineer" can modify algorithm parameters, and "operator-normal" can only perform routine operations), and data recording. The data is stored in the InfluxDB time series database and backed up to a 1TB external hard drive every day. The graphical display is achieved by plotting the real-time temperature curve on the touch screen. With time t as the abscissa and temperature T as the ordinate, the temperature data transmitted by the temperature monitoring module in real time is plotted in the coordinate system in chronological order to form a continuous temperature change curve, which facilitates the operator to intuitively understand the dynamic change of the mold temperature. The permission management is achieved through user account and password verification. The system pre-sets the permission levels of different user accounts. For example, the "admin-engineer" account corresponds to the permissions of senior engineers and can modify the control algorithm parameters; the "operator-normal" account corresponds to the permissions of ordinary operators and can only view the temperature data and perform routine temperature settings. The data recording function stores each temperature setting, parameter modification, as well as the temperature data and alarm information during the system operation in the InfluxDB database in the time series format, and the storage format is {(timestamp1, data1), (timestamp2, data2),...}, where timestamp is the time stamp and data is the data corresponding to the time. The data in the database is backed up to a 1TB external hard drive every day to prevent data loss.

5. An intelligent temperature control system for a die-casting mold of automotive parts according to claim 1, characterized in that, The communication between the system modules is efficient and accurate; the temperature control module communicates with the actuator through the industrial Ethernet, with a delay within 0.08 s, and uses CRC-16 checksum to ensure data accuracy; the industrial Ethernet communication adopts the TCP / IP protocol stack. During the data transmission process, the data to be transmitted is encapsulated into data packets in a certain format, and each data packet contains information such as data content, source address, and destination address; the principle of CRC-16 checksum is as follows: Let the data to be checked be D, and the generating polynomial be G(x) = x 16 +x 15 +x 2 +1. Shift D left by 16 bits to get D' = D × 2 16 , then divide D' by G(x) to get the remainder R, and add R to R to get the data D CRC with CRC checksum for transmission; after the receiving end receives the data, perform division operation on it with the same generating polynomial G(x). If the remainder is zero, it is considered that the data transmission is correct, otherwise the data transmission is incorrect. The sending end is required to resend the data to ensure that the data transmission delay is within 0.08 s and accurate; the fault diagnosis module is integrated into the temperature control module, judges faults according to the feedback of the monitoring module and the actuator, and uses the LSTM algorithm to predict faults. When the probability is greater than 0.7, an early warning is issued; in the LSTM algorithm, let the input sequence be x1, x2,..., x T , the input gate i t , forget gate f t , output gate o t and memory cell c t are calculated as follows: i t = σ(W ix x t + W ih h t-1 + b i ) f t = σ(W fx x t + W fh h t-1 + b f ) o t = σ(W ox x t + W oh h t-1 + b o ) c t = f t ·c t-1 + i t ·tanh(W cx x t + W ch h t-1 + b c ) h t = o t ·tanh(c t ) Among them, W ix , W ih , W fx , W fh , W ox , W oh , W cx , W ch is the weight matrix, b i , b f , b o , b c is the bias vector, σ is the sigmoid function, and tanh is the hyperbolic tangent function; by training historical temperature data, actuator status and other data, a fault prediction model is established. When the predicted fault probability is greater than 0.7, a warning message is sent through the human-machine interface to prompt the operator to perform maintenance.

6. The intelligent temperature control system for die-casting molds of automotive parts according to claim 1, characterized in that, The sensor of the temperature monitoring module has strong protection; it uses a high-temperature and corrosion-resistant material with ceramic packaging, and the working temperature ranges from -20°C to 500°C. The protection level is IP67, which can effectively resist the adverse effects of the die-casting environment; the ceramic packaging material has a low thermal conductivity coefficient λ ceramic ≤1 W / (m·K), which can reduce the influence of the external environmental temperature on the measurement accuracy of the sensor; at the same time, it has good chemical stability and can resist the erosion of corrosive gases and liquids that may be generated during the die-casting process; the protection level of IP67 indicates that the sensor can completely prevent foreign objects from invading, and can be immersed in water 1 meter deep for within 30 minutes without affecting its normal operation, ensuring stable operation in the complex environment of the die-casting workshop.

7. An intelligent temperature control system for a die-casting mold of automotive parts according to claim 1, characterized in that, The system has an energy recovery function; a heat pipe type energy recovery device is installed near the outlet end of the cooling pipeline to recover the heat of the cooling medium. The efficiency of converting heat into electricity is 30%, and the recovered energy can be stored or used for auxiliary heating to improve the energy utilization efficiency. The working principle of the heat pipe type energy recovery device is as follows: the cooling medium flows through the evaporation section of the heat pipe, transferring heat to the working fluid inside the heat pipe, causing the working fluid to evaporate into steam. The steam flows to the condensation section of the heat pipe under the action of pressure difference, releasing heat to the power generation device (such as a thermoelectric generation module) in the condensation section. After the steam condenses into liquid, it flows back to the evaporation section under the action of gravity or capillary force to complete the cycle. Let the recovered heat be Q recover , and the efficiency of converting it into electricity is η convert = 0.

3. Then the electric energy W recover = 0.3Q recover . The recovered electric energy can be stored in the battery pack or directly used for the auxiliary heating device of the mold to reduce energy consumption.

8. An intelligent temperature control system for a die-casting mold of automobile parts according to claim 1, characterized in that, The temperature control module has the ability of adaptive learning; based on the genetic algorithm, according to the temperature and process parameter changes during the die-casting process, it automatically adjusts the fuzzy control rules or the neural network weight matrix and bias vector to optimize the temperature control performance; in the genetic algorithm, the fuzzy control rule parameters or the neural network weight matrix and bias vector are encoded as chromosomes. Let the chromosome length be l and the initial population size be N. The population is continuously evolved through operations such as selection, crossover, and mutation; for the selection operation, the roulette wheel selection method is adopted. Let the fitness of the i-th chromosome be f i , and the total fitness of the population is Then the probability that the i-th chromosome is selected For the crossover operation, with a crossover probability P c randomly select two chromosomes for gene exchange. For example, for single-point crossover, randomly select a crossover point on the chromosome and exchange the gene segments after the crossover point of the two chromosomes; for the mutation operation, with a mutation probability P m randomly mutate the genes on the chromosome. For example, add or subtract a random small amount from the gene value; by continuously evolving the population, minimize the value of the fitness function (such as using the sum of squares of the deviation between the mold temperature and the set temperature as the fitness function), so as to automatically adjust the fuzzy control rules or the neural network weight matrix and bias vector to optimize the temperature control performance.

9. An intelligent temperature control system for a die-casting mold of automotive parts according to claim 1, characterized in that, The human-machine interaction interface supports remote operation. The operator can access remotely through the Internet on a device installed with customized client software via the SSL encryption protocol to view the temperature, set parameters, etc., ensuring data security. The SSL encryption protocol combines public key encryption and symmetric encryption. When the client and the server establish a connection, the server sends a digital certificate to the client, and the certificate contains information such as the server public key. After the client verifies the legality of the certificate, it generates a random symmetric key, encrypts it with the server public key, and sends it to the server. The server decrypts it with the private key to obtain the symmetric key. Thereafter, both parties use this symmetric key for encryption and decryption during communication to ensure the security of the data transmission process, prevent data from being stolen or tampered with, and realize operations such as temperature viewing and parameter setting of the intelligent temperature control system by the operator on the remote device.

10. The intelligent temperature control system for die-casting molds of automotive parts according to claim 1, characterized in that, The cooling pipe is wrapped with polyurethane foam insulation material with a thermal conductivity of 0.05 W / (m·K), which can effectively reduce the heat loss during the transmission of the cooling medium and stabilize the cooling effect. Let the temperature of the cooling medium in the cooling pipe be T fluid , and the ambient temperature be T env . The surface area of the cooling pipe is A pipe . The thickness of the polyurethane foam insulation layer is d. According to the heat conduction law, the heat loss through the insulation layer per unit time Since the thermal conductivity λ insulate = 0.05 W / (m·K) of the polyurethane foam insulation material is relatively low, it can effectively reduce the heat loss and ensure the temperature stability of the cooling medium during the transmission process, thereby stabilizing the cooling effect of the mold.

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