Intelligent manufacturing execution method for regulating rubber molding temperature
By constructing a virtual thermal potential energy model and energy efficiency audit factor, the output duty cycle of power electronic switching devices is adjusted in real time, which solves the uncertainty and disturbance problem of mold temperature control in the rubber vulcanization molding process, and realizes precise temperature control and stable rubber molding.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO DOUSH HYDRAULIC
- Filing Date
- 2026-03-30
- Publication Date
- 2026-06-05
AI Technical Summary
In existing rubber vulcanization molding processes, mold temperature control suffers from physical time delays and uncertainties in energy output caused by environmental disturbances, as well as jumps in heat transfer boundaries and a lack of identification of endogenous heat sources in materials, making it difficult to achieve precise temperature control.
By acquiring the bus feedback voltage and real-time induced current to calculate the energy efficiency audit factor, a virtual thermal potential energy model is constructed. The output duty cycle of the power electronic switching devices is adjusted in real time to achieve coordinated control of mechanical load changes and power output. Endogenous disturbance power is identified and compensated, and the thermal potential energy saturation threshold is dynamically adjusted to achieve energy balance control.
It achieves precise control of mold temperature, reduces temperature fluctuations, avoids the risk of overheating, and improves the stability and efficiency of rubber molding.
Smart Images
Figure CN122143249A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an intelligent manufacturing execution method for regulating the molding temperature of rubber, belonging to the field of power distribution and regulation technology. Background Technology
[0002] Currently, in rubber vulcanization molding processes, maintaining a precise and constant global mold temperature is a core prerequisite for ensuring the crosslinking density of the product. The commonly used method is proportional-integral-derivative (PID) control based on feedback from a single-point thermistor. This involves collecting temperature signals from specific locations within the mold and adjusting the output duty cycle of the heating actuator to maintain the mold temperature at a preset target value. The physical essence of the mold heating process is the conversion of electrical energy into the enthalpy state within a large thermal inertia metal medium. Constrained by the laws of heat conduction and energy conservation, its temperature rise rate depends on the difference between the Joule heat injected per unit time and the heat dissipated from the environment. Due to the low thermal conductivity of rubber and the thermal inertia of large-volume metal molds, there is a physical time lag between the energy output of the heating actuator and the temperature changes captured by the sensor, resulting in the feedback signal failing to accurately represent the true enthalpy state inside the mold in real time.
[0003] In manufacturing scenarios pursuing high production capacity, existing temperature control methods struggle to balance heating rate and thermal stability. Simply increasing the sampling frequency on the execution side or adding more sensors—linear improvements—cannot resolve the logical disconnect between control commands and physical enthalpy conversion. Specifically, the following defects exist: 1. Uncertainty in execution-side energy output: fluctuations in the factory grid voltage and the shift in heating component resistance with temperature rise cause nonlinear deviations between the controller's commanded power and the actual heat energy absorbed by the mold; 2. Condition-induced abrupt changes in heat transfer boundaries: at the moment of mold closure and injection, high pressure causes a nonlinear decrease in the contact thermal resistance between the material and the mold core, rendering the preset static heat dissipation model ineffective; 3. Lack of identification of intrinsic heat sources within the material: the exothermic effect of the vulcanization reaction cannot be effectively offset by conventional feedback loops. Increased risk of overheating during the later stages of molding; to address inherent physical defects, existing technologies attempt to improve the stability of materials to temperature control deviations without changing the control method through soft optimization methods such as material modification or specific post-processing. For example, Chinese invention patent CN104892963B discloses a fluororubber molded article and its manufacturing method, which aims to change the molecular structure and reduce the glass transition temperature of the material by impregnating the cross-linked fluororubber in a fluorocarbon compound or its aqueous solution, thereby improving low-temperature characteristics. Attempts to use simple algorithm compensation strategies often fall into model drift because they cannot identify energy efficiency fluctuations on the execution side. This implicit constraint, stemming from the loss of energy accuracy on the execution side and the jump in thermal resistance induced by operating conditions, has become a common technical bottleneck hindering the improvement of the efficiency of intelligent rubber manufacturing.
[0004] Therefore, the technical problem to be solved by this invention is how to construct an energy balance control mechanism that can penetrate physical time delay and offset environmental disturbances by real-time auditing of energy flow on the execution side and dynamic reshaping of the heat transfer model under injection pressure conditions. Summary of the Invention
[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A smart manufacturing execution method for regulating rubber molding temperature, comprising the following steps:
[0006] Step S101: Obtain the bus feedback voltage and real-time induced current of the controlled heat load circuit on the distribution side;
[0007] Step S102: Calculate the instantaneous physical power of the power conversion execution terminal based on the bus feedback voltage and real-time induced current, and define the ratio of the instantaneous physical power to the preset command power as the energy efficiency audit factor;
[0008] Step S103: The instantaneous physical power is weighted and corrected using the energy efficiency audit factor to obtain the physical energy value of the controlled object within a sampling period, and a virtual thermal potential energy model characterizing the real-time thermal enthalpy state of the controlled object is constructed based on the cumulative amount of physical energy value over time.
[0009] Step S104: Receive the load feedback signal characterizing the change in external mechanical operating conditions, and adjust the thermal potential energy saturation threshold used to determine state switching in the virtual thermal potential energy model according to the amplitude change rate of the load feedback signal in the sampling time sequence, so as to realize the coordination between the mechanical action load change and the electrical energy output logic.
[0010] Step S105: Calculate the algebraic difference between the measured temperature change rate of the controlled object and the theoretical temperature rise rate derived from the virtual thermal potential energy model, generate the energy deviation residual, and determine the endogenous disturbance power inside the controlled object based on the energy deviation residual.
[0011] Step S106: Based on the virtual heat increment, the adjusted thermal potential energy saturation threshold, and the endogenous disturbance power, modify the output duty cycle parameters of the power electronic switching devices in the controlled heat load circuit in real time, and generate a power regulation command for the controlled heat load circuit.
[0012] Preferably, the method for establishing the energy efficiency audit factor in step S102 is as follows: based on the formula The energy efficiency audit factor was calculated; among which, Let i be the energy efficiency audit factor for the i-th sampling period. This is the bus feedback voltage. To sense the current in real time, This is the preset command power.
[0013] Preferably, the method of adjusting the thermal potential energy saturation threshold in step S104 includes: when the slope of the load feedback signal exceeds the preset pressure threshold, reducing the integral step size of the accumulator in the virtual thermal potential energy model, so as to trigger the downward adjustment of the output duty cycle parameter in advance before the controlled object generates thermal overshoot.
[0014] Preferably, step S105 includes: defining the identified endogenous disturbance power as the endogenous electrical energy input component of the controlled heat load circuit; and performing a weighted subtraction operation on the energy output item of the power regulation command according to the endogenous electrical energy input component to reduce the total amount of electrical energy drawn by the controlled heat load circuit from the external power grid.
[0015] Preferably, the method further includes the following steps: Step S501, extracting the natural cooling data of the power conversion execution terminal during the energy supply interruption period; Step S502, calculating the real-time heat dissipation characteristic parameters under the current operating condition in reverse based on the decay slope of the controlled object state quantity changing with time in the natural cooling data.
[0016] Preferably, the method further includes the following steps: Step S601: Iteratively correct the heat dissipation term factor in the virtual thermal potential energy model using real-time heat dissipation characteristic parameters, so that the virtual thermal potential energy model can adapt to the evolution of hardware loss of the power conversion execution terminal.
[0017] Preferably, the power regulation command is applied to the control components in the controlled heat load circuit via a pulse width modulation signal to control the on / off duration of electrical energy in the controlled heat load circuit.
[0018] Preferably, the virtual thermal potential energy model characterizes the global real-time thermal enthalpy state of the controlled object by integrating the physical energy value over time.
[0019] Preferably, the method for receiving the load feedback signal in step S104 is to call the oil pressure sensor data or in-mold pressure sensor data of the injection equipment in real time through the manufacturing execution system.
[0020] Compared with the prior art, the beneficial effects of the present invention are:
[0021] 1. In intelligent manufacturing of rubber molding temperature, by real-time monitoring of the heating circuit current and bus voltage, and by calculating the energy efficiency conversion factor between the instantaneous physical power and command power of the heating actuator, a real-time audit mechanism for the power supply side is constructed. This enables online weighted correction of the power input items based on the energy efficiency conversion factor when calculating the virtual heat increment of the mold, thereby offsetting the energy injection error caused by industrial grid voltage fluctuations or physical shifts in the resistance of heating components with temperature rise, and ensuring the physical consistency between the power distribution logic and the actual Joule heat absorbed by the mold.
[0022] 2. The injection pressure characterization signal sent by the manufacturing execution system is mapped to a heat transfer boundary correction coefficient, and the weight of the heat dissipation coefficient in the energy accumulator is dynamically adjusted using this coefficient. This enables predictive sensing of sudden changes in contact thermal resistance inside the mold during the injection stage. The system can increase the sensitivity of the thermal potential energy saturation judgment in advance before the actual overshoot feedback of the mold temperature is generated by reducing the integral step size of the accumulator. This achieves cross-domain coordination between changes in mechanical load and electrical output logic, avoiding the risk of local temperature rise pulses induced by a surge in pressure.
[0023] 3. In the steady-state production section, by extracting the energy deviation residual between the measured temperature change rate and the theoretical temperature rise rate predicted by the virtual thermal potential energy model, the implicit heat source of material sulfidation exothermics is actively identified. This internal reaction heat is treated as a pseudo-external input of the system and weighted to offset it, thereby guiding the control loop to automatically reduce the output duty cycle of the actuator. This completes the smooth control connection from external electric heating to internal material reaction exothermics, eliminating the risk of temperature runaway caused by heat accumulation in the later stage of sulfidation without relying on additional sensing devices. Attached Figure Description
[0024] Fig. 1 This is a flowchart illustrating the intelligent control process of rubber molding temperature using the virtual thermal potential energy model of this invention.
[0025] Fig. 2 This invention provides a system control topology and signal interaction diagram that integrates energy efficiency auditing and operating condition coordination. Detailed Implementation
[0026] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.
[0027] A smart manufacturing execution method for regulating rubber molding temperature includes the following steps:
[0028] Step S101: Obtain the bus feedback voltage and real-time induced current of the controlled heat load circuit on the distribution side;
[0029] Step S102: Calculate the instantaneous physical power of the power conversion execution terminal based on the bus feedback voltage and real-time induced current, and define the ratio of the instantaneous physical power to the preset command power as the energy efficiency audit factor;
[0030] Step S103: The instantaneous physical power is weighted and corrected using the energy efficiency audit factor to obtain the physical energy value of the controlled object within a sampling period, and a virtual thermal potential energy model characterizing the real-time thermal enthalpy state of the controlled object is constructed based on the cumulative amount of physical energy value over time.
[0031] Step S104: Receive the load feedback signal characterizing the change in external mechanical operating conditions, and adjust the thermal potential energy saturation threshold used to determine state switching in the virtual thermal potential energy model according to the amplitude change rate of the load feedback signal in the sampling time sequence, so as to realize the coordination between the mechanical action load change and the electrical energy output logic.
[0032] Step S105: Calculate the algebraic difference between the measured temperature change rate of the controlled object and the theoretical temperature rise rate derived from the virtual thermal potential energy model, generate the energy deviation residual, and determine the endogenous disturbance power inside the controlled object based on the energy deviation residual.
[0033] Step S106: Based on the virtual heat increment, the adjusted thermal potential energy saturation threshold, and the endogenous disturbance power, modify the output duty cycle parameters of the power electronic switching devices in the controlled heat load circuit in real time, and generate a power regulation command for the controlled heat load circuit.
[0034] Preferably, the method for establishing the energy efficiency audit factor in step S102 is as follows: based on the formula The energy efficiency audit factor was calculated; among which, Let i be the energy efficiency audit factor for the i-th sampling period. This is the bus feedback voltage. To sense the current in real time, This is the preset command power.
[0035] Preferably, the method of adjusting the thermal potential energy saturation threshold in step S104 includes: when the slope of the load feedback signal exceeds the preset pressure threshold, reducing the integral step size of the accumulator in the virtual thermal potential energy model, so as to trigger the downward adjustment of the output duty cycle parameter in advance before the controlled object generates thermal overshoot.
[0036] Preferably, step S105 includes: defining the identified endogenous disturbance power as the endogenous electrical energy input component of the controlled heat load circuit; and performing a weighted subtraction operation on the energy output item of the power regulation command according to the endogenous electrical energy input component to reduce the total amount of electrical energy drawn by the controlled heat load circuit from the external power grid.
[0037] Comparison of measured temperature change rate of controlled object during steady-state production stage Theoretical temperature rise rate derived from virtual thermal potential energy model The algebraic difference is confirmed as the energy deviation residual, based on the equivalent heat capacity constant. Calculate the power of intrinsic disturbances As the output duty cycle parameter Deductions, using formulas Determine the power adjustment command. The ratio of basic energy supply, Rated physical power for the controlled heat load circuit.
[0038] Preferably, the method further includes the following steps: Step S501, extracting the natural cooling data of the power conversion execution terminal during the energy supply interruption period; Step S502, calculating the real-time heat dissipation characteristic parameters under the current operating condition in reverse based on the decay slope of the controlled object state quantity changing with time in the natural cooling data.
[0039] Preferably, the method further includes the following steps: Step S601: Iteratively correct the heat dissipation term factor in the virtual thermal potential energy model using real-time heat dissipation characteristic parameters, so that the virtual thermal potential energy model can adapt to the evolution of hardware loss of the power conversion execution terminal.
[0040] Preferably, the power regulation command is applied to the control components in the controlled heat load circuit via a pulse width modulation signal to control the on / off duration of electrical energy in the controlled heat load circuit.
[0041] Preferably, the virtual thermal potential energy model characterizes the global real-time thermal enthalpy state of the controlled object by integrating the physical energy value over time.
[0042] The controlled object is in a quasi-adiabatic initial state, and the rated power is injected into the controlled heat load circuit. And maintain calibration time Record the measured temperature rise ΔT of the controlled object, and establish the equivalent heat capacity constant based on the ratio of the total injected physical energy to the measured temperature rise. This data is then stored in the parameter register, converting the accumulated energy value E of the virtual thermal potential energy model into an equivalent temperature state quantity. .
[0043] Preferably, the method for receiving the load feedback signal in step S104 is to call the oil pressure sensor data or in-mold pressure sensor data of the injection equipment in real time through the manufacturing execution system.
[0044] Example 1: In a continuous intelligent manufacturing scenario for large-size, multi-cavity precision rubber seals, the system faces frequent fluctuations in the production workshop's power grid bus voltage due to the start-up and shutdown of large actuators, as well as nonlinear thermal resistance jumps accompanying the high-pressure mold closing action of the injection mechanism. The technical solution of this invention addresses these issues by acquiring the bus feedback voltage of the controlled thermal load circuit on the distribution side when the controlled thermal load circuit is in the heating phase. and real-time induced current Based on bus feedback voltage With real-time induced current The product of the power conversion and execution terminal is used to calculate the instantaneous physical power and then compared with the preset command power. The ratio is determined as the energy efficiency audit factor. This allows energy input quantities that were originally determined solely by instructions to be evaluated through energy efficiency audit factors. The physical energy value is corrected to be equivalent to physical Joule heat, thereby offsetting input disturbances caused by voltage fluctuations or resistance shifts in heating components due to temperature rise at the source.
[0045] During the specific timing of the mold injection operation, the system synchronously receives load feedback signals characterizing changes in external mechanical conditions. The manufacturing execution system calls up data from the hydraulic pressure sensor of the injection equipment in real time. Based on the rate of change of the load feedback signal's amplitude during the sampling timing, it identifies the physical trend of a sharp increase in internal pressure leading to a decrease in the thermal resistance at the material-mold core boundary. Correspondingly, it adjusts the thermal potential energy saturation threshold used to determine state switching in the virtual thermal potential energy model. By reducing the accumulator's integration step size, the system initiates logical braking before the temperature sensor detects an overshoot signal. Specifically, simply reducing the time step size of the numerical integration only improves the sampling resolution of the discrete control system to reduce loop dead zone delay. To achieve early triggering of logical braking without affecting the absolute value of accumulated physical energy by the algorithm step size, the control unit, while operating with a high-resolution step size, performs a high-frequency, dense comparison between the real-time accumulated thermal potential energy state quantity and the saturation threshold, which has been equivalently reduced by the pressure signal. This utilizes high-frequency detection on the time axis and the energy axis... The threshold compression synergistically shifts the judgment action of crossing the energy saturation boundary significantly forward on the real physical timeline. As the material enters the active period of cross-linking reaction, the system calculates the algebraic difference between the measured temperature change rate of the controlled object and the theoretical temperature rise rate derived from the virtual thermal potential energy model, generating energy deviation residuals. This identifies the endogenous disturbance power generated by the exothermic reaction of sulfurization within the controlled object and defines it as the endogenous electrical energy input component of the controlled heat load loop. The system performs weighted subtraction on the energy output item of the power adjustment command, achieving a smooth control connection from external electrical energy heating to internal material reaction exothermic reaction. Through the multi-dimensional energy flow dynamic allocation system jointly constructed by input-side energy efficiency audit, load-side pressure correction, and internal heat source residual compensation, the system reconstructs the originally passive and lagging temperature closed-loop regulation into a predictive saturation control based on energy conservation logic without relying on additional sensing devices. This ensures that the mold temperature field maintains an extremely narrow fluctuation window throughout the entire cycle and shortens the preheating time of a single sulfurization cycle.
[0046] Example 2: The intelligent manufacturing execution method for regulating rubber molding temperature according to the present invention is used to verify the vulcanization process of precision rubber damping components in automotive powertrain mounting systems. It operates in a flat vulcanizing unit environment with four independent temperature control loops, and the power distribution side of this unit integrates current and voltage acquisition sensor modules. The temperature sensor used in this test platform has a measurement accuracy of ±0.1℃, and the sampling frequency is confirmed to be 1kHz. The selection logic for this sampling frequency is based on the Nyquist-Shannon sampling theorem to perform high-rate oversampling of the power frequency signal, ensuring that no less than 20 discrete power sample points are obtained within one power frequency cycle, thereby accurately reconstructing the power waveform and eliminating aliasing errors. The test sets up the sample group, control group, and local feature control group with missing load feedback correction logic of the present invention, and injects Gaussian white noise with a signal-to-noise ratio of 20dB into the sensor input to simulate the electromagnetic interference environment in a real industrial scenario.
[0047] During the heating stage of the prototype of this invention, when the bus feedback voltage... When the voltage fluctuates between 360V and 400V, the control unit adjusts according to the formula. Determine the energy efficiency audit factor for the i-th sampling period. ,in, This is the bus feedback voltage. To sense the current in real time, As per the preset command power, actual measurement data shows that when 365.2V and At 12.4A, the calculated instantaneous physical power is 4528.48W. Under the condition of 5000W, the corresponding energy efficiency audit factor The value is 0.9057; this energy efficiency audit factor is used. The physical energy value entering the controlled object is weighted and corrected to ensure that the enthalpy state accumulated in the virtual thermal potential energy model is physically synchronized with the Joule heat actually output by the grid. When the vulcanizing unit performs the mold closing and injection action, causing the pressure inside the mold to rise from 0.2 MPa to 18.5 MPa within 1.5 seconds, the control unit receives a load feedback signal characterizing the change in external mechanical conditions and identifies the abrupt trend of heat transfer characteristics based on the rate of change of this signal. The controller lowers the thermal potential energy saturation threshold used to determine state switching in the virtual thermal potential energy model by 15%. The output duty cycle parameter is reduced from 85% to 62% before the actual temperature feedback from the mold, thus offsetting the reduction in contact thermal resistance caused by the increase in injection pressure. After entering the steady-state vulcanization stage, the system extracts the intrinsic disturbance power generated by the vulcanization reaction of the material by calculating the algebraic difference between the measured temperature change rate and the theoretical temperature rise rate derived from the virtual thermal potential energy model. In the actual physical reaction, the material not only releases latent chemical heat with the increase of vulcanization crosslinking depth, but its specific heat capacity also undergoes nonlinear evolution with the solidification of the polymer network structure. The change causes a physical boundary drift in the equivalent heat capacity constant originally set in the thermodynamic model. This system, by extracting the aforementioned energy deviation residual, does not essentially decouple the two types of thermal effects at the microscopic physical scale. Instead, it couples the sensible heat absorption and storage deviation caused by the nonlinear change in specific heat capacity with the latent heat released by the crosslinking reaction, uniformly converting them into a macroscopically equivalent generalized endogenous thermal disturbance. This ensures that the externally input electrical power adjustment can accurately and systematically offset the overall net enthalpy drift within the mold. As the degree of crosslinking increases, the identified endogenous electrical energy... The input component increased from 2.5W to 42.8W, and an equivalent subtraction was performed on the energy output term of the power adjustment command accordingly. The final performance statistics show that the static temperature field fluctuation deviation of the sample group of this invention was maintained at ±0.28℃, compared with ±1.45℃ of the control group, and its temperature control stability under the coupled environment of power supply fluctuation and transformer operation was enhanced. This confirms that by converting the hysteretic temperature feedback into energy flow prediction control based on the virtual thermal potential energy model, the influence of input power fluctuation and internal disturbance of the controlled object on energy balance can be effectively eliminated.
[0048] Example 3: This example combines Figs. 1-2 The method for intelligent manufacturing to regulate rubber molding temperature is explained, such as... Fig. 1As shown, step S101 obtains the bus feedback voltage and real-time induced current of the controlled heat load circuit on the distribution side. Step S102 calculates the instantaneous physical power based on the bus feedback voltage and real-time induced current, and defines the ratio of the instantaneous physical power to the preset command power as the energy efficiency audit factor. Step S103 uses the energy efficiency audit factor to weight and correct the instantaneous physical power to obtain the physical energy value, and constructs a virtual thermal potential energy model representing the real-time enthalpy state of the controlled object. Based on this, step S104 receives the load feedback signal and adjusts the thermal potential energy saturation threshold of the virtual thermal potential energy model according to its amplitude change rate to achieve coordination between mechanical load change and power output. Step S105 calculates the difference between the measured temperature change rate and the theoretical temperature rise rate to generate the energy deviation residual, and determines the internal disturbance power inside the controlled object. Finally, step S106 modifies the output duty cycle parameter to generate a power adjustment command based on the virtual heat increment, the adjusted thermal potential energy saturation threshold, and the internal disturbance power.
[0049] like Fig. 2 As shown, the core architecture of the system is centered on the control unit, which integrates four core functional modules: energy efficiency audit factor, power regulation command, thermal potential energy saturation threshold, and virtual thermal potential energy model. On the power distribution side, the bus feedback voltage and real-time induced current are collected and transmitted to the control unit through the input side audit path to establish the energy efficiency audit factor. On the pressure injection equipment side, the load feedback signal is extracted and applied to the control unit through the operating condition coordination path to adjust the thermal potential energy saturation threshold. The power regulation command generated by the control unit is transmitted to the power electronic switching devices in the controlled thermal load loop in the form of duty cycle parameters, thereby driving the controlled object containing endogenous disturbance power. The state of the controlled object is fed back to the virtual thermal potential energy model in the control unit in real time through the state feedback path, thus forming a closed-loop control topology.
[0050] Example 4: The intelligent manufacturing execution method for regulating rubber molding temperature according to the present invention, when used in a large-scale production line for heavy-duty vehicle rubber bushings, uses an initialization program built into the control unit to increment the energy accumulation register in the virtual thermal potential energy model at the start of each production shift. The temperature is reset to zero, and the measured temperature of the controlled object is read synchronously as the enthalpy reference point. During equipment operation, the control unit acquires the energy efficiency audit factor at a sampling frequency of 1kHz. The corrected physical energy value is then processed using a discretized cumulative calculation. When the vulcanizing unit is in the unloaded period between two vulcanizing cycles, the control unit initiates the heat dissipation parameter calibration program. By monitoring the temperature T of the controlled object after the energy supply interruption, the change in temperature over time ΔT is extracted within a preset observation period Δt, and then calculated according to the formula... Calculate real-time heat dissipation characteristic parameters Where C is the equivalent heat capacity constant of the controlled object, The heat dissipation power coefficient is given by ΔT, where ΔT is the temperature change and Δt is the observation duration. The control unit uses the calculated real-time heat dissipation characteristic parameters. The heat dissipation term in the virtual thermal potential energy model is modified to keep the model parameters dynamically synchronized with the physical heat dissipation conditions.
[0051] During the injection pressure operation, the control unit receives the load feedback signal from the injection mechanism, calculates its differential amplitude within the sampling period, and multiplies the differential amplitude by the pressure sensitivity coefficient to determine the correction amount for the thermal potential energy saturation threshold. Here, the pressure sensitivity coefficient is a dimensionless empirical gain characterizing the mapping relationship between a specific mold mechanical displacement state and internal heat transfer variations. It is obtained by the system calling multiple sets of pressure evolution gradients and measured temperature overshoot samples under injection pressure step conditions from the historical mold trial database, and optimizing them through linear fitting using the least squares method. Its essential physical meaning lies in differentiating the hydraulic dimension... The numerical values are directly converted across domains into a weighted percentage in the energy dimension used to reduce the thermal potential energy saturation threshold. When the control unit detects that the rise rate of the load feedback signal exceeds 5 MPa / s, it automatically reduces the thermal potential energy saturation threshold used to determine heating cessation in the virtual thermal potential energy model. This allows the energy output duty cycle of the controlled heat load loop to switch from a high level to a low level before the controlled object temperature overshoots, thereby offsetting the reduction in conduction thermal resistance caused by the increase in injection pressure. To map the identified endogenous disturbance power into the physical adjustment quantity of the controlled heat load loop, the control unit establishes the output duty cycle parameter. With endogenous disturbance power The algebraic cancellation relationship between them means that, within each control cycle, the control unit determines the algebraic cancellation relationship according to the formula. The instruction to determine the final duty cycle is as follows: This is the basic energy supply ratio derived from the virtual thermal potential energy model. This represents the power of the intrinsic disturbance. The rated physical power of the controlled heat load circuit is used to calculate the physical heat generated inside the controlled object as a deduction ratio from the external power grid input, thus maintaining the total energy input at a physical level. Under the condition that the output power of the heating actuator decreases by 15% due to aging, the above method uses real-time heat dissipation characteristic parameters... The periodic iterative updates maintain the accuracy of the virtual model's prediction of the enthalpy evolution trajectory inside the mold, ensuring that the dynamic control deviation of the mold temperature field meets the allowable tolerance of the precision rubber vulcanization process.
[0052] Example 5: When the system faces the need to replace a new type of vulcanizing mold or is deployed in a production environment with unknown physical properties, the control unit initiates a thermal characteristic calibration program, injecting a preset calibration power into the controlled heat load circuit under quasi-adiabatic conditions. And maintain a constant time The measured temperature rise of the controlled object is read, and the equivalent heat capacity constant C of the controlled object is determined by the ratio of the injected total physical energy to the measured temperature rise. After the heat supply is interrupted, the temperature decay rate of the controlled object is monitored to identify the initial heat dissipation characteristic parameters of the mold installation position. The control unit will obtain the equivalent thermal capacity constant C and the initial heat dissipation characteristic parameters. Write the parameter register of the virtual thermal potential energy model to the energy accumulation register. The integral increment calculation has a defined physical reference.
[0053] In manufacturing scenarios involving rubber materials with multiple exothermic gradients, to eliminate the influence of nonlinear thermal disturbances caused by crosslinking reactions of different materials, the system initiates a thermal characteristic calibration process before material production. By controlling the power conversion execution terminal, the controlled object is made to run at a preset heating slope. The initial deviation of the measured temperature change rate of the controlled object from the theoretical temperature rise rate of the virtual thermal potential energy model is extracted, and the judgment gain of the internal disturbance power of the controlled object is corrected using this value. The control unit adjusts the correction coefficient in the weight subtraction operation according to the identified exothermic intensity of the crosslinking reaction, converting the energy deviation residual into the endogenous electrical energy input component of the corresponding material type, so that the temperature field inside the mold is in a quasi-steady state during the vulcanization cycle.
[0054] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A smart manufacturing execution method for regulating rubber molding temperature, characterized in that, Includes the following steps: Step S101: Obtain the bus feedback voltage and real-time induced current of the controlled heat load circuit on the distribution side; Step S102: Calculate the instantaneous physical power of the power conversion execution terminal based on the bus feedback voltage and real-time induced current, and define the ratio of the instantaneous physical power to the preset command power as the energy efficiency audit factor; Step S103: The instantaneous physical power is weighted and corrected using the energy efficiency audit factor to obtain the physical energy value of the controlled object within a sampling period, and a virtual thermal potential energy model characterizing the real-time thermal enthalpy state of the controlled object is constructed based on the cumulative amount of physical energy value over time. Step S104: Receive the load feedback signal characterizing the change in external mechanical operating conditions, and adjust the thermal potential energy saturation threshold used to determine state switching in the virtual thermal potential energy model according to the amplitude change rate of the load feedback signal in the sampling time sequence, so as to realize the coordination between the mechanical action load change and the electrical energy output logic. Step S105: Calculate the algebraic difference between the measured temperature change rate of the controlled object and the theoretical temperature rise rate derived from the virtual thermal potential energy model, generate the energy deviation residual, and determine the endogenous disturbance power inside the controlled object based on the energy deviation residual. Step S106: Based on the virtual heat increment, the adjusted thermal potential energy saturation threshold, and the endogenous disturbance power, modify the output duty cycle parameters of the power electronic switching devices in the controlled heat load circuit in real time, and generate a power regulation command for the controlled heat load circuit.
2. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, The specific method for establishing energy efficiency audit factors in step S102 is as follows: based on the formula The energy efficiency audit factor was calculated; among which, Let i be the energy efficiency audit factor for the i-th sampling period. This is the bus feedback voltage. To sense the current in real time, This is the preset command power.
3. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, The method of adjusting the thermal potential energy saturation threshold in step S104 includes: when the slope of the load feedback signal exceeds the preset pressure threshold, reducing the integral step size of the accumulator in the virtual thermal potential energy model, so as to trigger the downward adjustment of the output duty cycle parameter in advance before the controlled object generates thermal overshoot.
4. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, Step S105 includes: defining the identified endogenous disturbance power as the endogenous electrical energy input component of the controlled heat load circuit; performing a weighted subtraction operation on the energy output item of the power regulation command according to the endogenous electrical energy input component, so as to reduce the total amount of electrical energy drawn by the controlled heat load circuit from the external power grid.
5. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, The method also includes the following steps: Step S501, extracting the natural cooling data of the power conversion execution terminal during the energy supply interruption period; Step S502, calculating the real-time heat dissipation characteristic parameters under the current operating condition in reverse based on the decay slope of the controlled object state quantity changing with time in the natural cooling data.
6. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 5, characterized in that, The method also includes the following steps: Step S601, using real-time heat dissipation characteristic parameters to iteratively correct the heat dissipation term factor in the virtual thermal potential energy model, so that the virtual thermal potential energy model can adapt to the evolution of hardware loss of the power conversion execution terminal.
7. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, The power regulation command acts on the control components in the controlled heat load circuit through a pulse width modulation signal to control the on and off duration of electrical energy in the controlled heat load circuit.
8. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, The virtual thermal potential energy model characterizes the global real-time thermal enthalpy state of the controlled object by integrating the physical energy value over time.
9. The intelligent manufacturing execution method for regulating rubber molding temperature according to claim 1, characterized in that, The method for receiving the load feedback signal in step S104 is to call the oil pressure sensor data or in-mold pressure sensor data of the injection equipment in real time through the manufacturing execution system.