Intelligent temperature control system and method for glasses flexible air bag nose pad forming process

Abstract

The invention relates to the technical field of intelligent temperature control, and discloses an intelligent temperature control system and method for a glasses flexible air bag nose pad forming process, and the method comprises the steps: preheating M temperature control subareas preset in a glasses flexible air bag nose pad mold, and obtaining a preheated mold; monitoring the rotating speed and the back pressure of the injection molding screw rod, calculating the shear temperature rise amount of the silica gel in the injection molding screw rod, and adjusting the temperature set value of the injection molding screw rod to obtain a stable melt of the silica gel; after a cavity of the preheating mold is filled with the stable melt of the silica gel, nitrogen is injected into an air bag cavity, model prediction optimization is executed, and an air bag formed part is obtained; by means of the method, accurate temperature control of the whole process from raw material pretreatment to final curing of the product is achieved. Compared with the traditional independent control and experience parameter adjustment mode of each process, the intelligent cooperative control method disclosed by the invention can systematically guarantee the wall thickness uniformity, the vulcanization quality and the batch consistency of the product.

Description

Technical Field

[0001] This invention relates to the field of intelligent temperature control technology, and in particular to an intelligent temperature control system and method for the molding process of flexible airbag nose pads for eyeglasses. Background Technology

[0002] As a novel comfort accessory, the molding process of flexible airbag nose pads for eyeglasses involves multiple continuous stages, including mold preheating, silicone injection molding, airbag cavity molding, and vulcanization curing. The temperature field distribution at each stage directly affects the product's wall thickness uniformity, airtightness, and vulcanization quality. Traditional temperature control methods typically employ single-point thermocouple temperature measurement combined with a single-loop PID control strategy, with each temperature control zone adjusted independently. This fails to consider the mutual influence between multiple areas of the mold through heat conduction. When the temperature of one zone fluctuates, heat is conducted through the mold steel to adjacent zones, causing passive temperature changes in other zones and creating coupling interference. For thin-walled precision structures like flexible airbag nose pads for eyeglasses, temperature fluctuations exceeding 0.5℃ can lead to localized wall thickness deviations and uneven vulcanization, resulting in poor airbag wall thickness uniformity and frequent localized overheating or underheating. Summary of the Invention

[0003] This invention provides an intelligent temperature control system and method for the molding process of flexible airbag nose pads for eyeglasses. This invention achieves precise temperature control throughout the entire process, from raw material pretreatment to final product curing. Compared to traditional methods of independent control of each process and experience-based parameter adjustment, the intelligent collaborative control method of this invention can systematically ensure product wall thickness uniformity, vulcanization quality, and batch consistency.

[0004] In a first aspect, the present invention provides an intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses, the intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses comprising: The M temperature control zones pre-set in the flexible airbag nose pad mold of the eyeglasses are preheated to obtain a preheated mold. The rotational speed and back pressure of the injection screw are monitored and the shear temperature rise of the silicone in the injection screw is calculated. Based on the shear temperature rise, the temperature setpoint of the injection screw is adjusted to obtain a stable melt of the silicone. After the stable melt of the silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity. The internal pressure and wall temperature of the airbag are collected simultaneously, and model prediction optimization is performed to obtain the airbag molded part. The airbag molding part is subjected to vulcanization treatment and the current vulcanization stage is obtained. The heat preservation time of the M temperature control zones is adjusted according to the current vulcanization stage to obtain the cured product.

[0005] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the preheating of the M pre-set temperature-controlled zones in the flexible airbag nose pad mold of the eyeglasses to obtain a preheated mold includes: Calculate the temperature deviation data of the M pre-set temperature control zones in the flexible airbag nose pad mold of the eyeglasses; The fuzzy PID control quantity for each temperature control zone is calculated based on the temperature deviation data. Obtain the injection pressure and flow rate values ​​for the silicone injection to be performed, and calculate the injection feedforward compensation for each temperature control zone based on the ratio of the injection pressure value to the rated injection pressure. Obtain the thermal coupling coefficient between each temperature control zone, and calculate the thermal coupling compensation amount for each temperature control zone based on the thermal coupling coefficient; The temperature controllers of each temperature control zone are driven by the fuzzy PID control quantity, the injection feedforward compensation quantity, and the thermal coupling compensation quantity to obtain the preheated mold.

[0006] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the step of monitoring the rotational speed and back pressure of the injection screw and calculating the shear temperature rise of the silicone within the injection screw, and adjusting the temperature setpoint of the injection screw based on the shear temperature rise to obtain a stable melt of the silicone, includes: The rotational speed and back pressure of the injection screw are obtained, and the shear temperature rise of the silicone in the injection screw is calculated based on the rotational speed and back pressure. The temperature setpoint of the injection screw is adjusted based on the shear temperature rise, and the heating device is driven by the temperature setpoint. Infrared temperature data of the injection screw is collected, and the estimated screw temperature is calculated based on the infrared temperature data. Adjust the heating power according to the estimated screw temperature and the set temperature until the temperature fluctuation at the nozzle converges to within the preset temperature range, thereby obtaining a stable melt of the silicone.

[0007] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the step of collecting infrared temperature measurement data of the injection molding screw and calculating an estimated screw temperature based on the infrared temperature measurement data includes: Infrared temperature measurement data of the injection screw in the feeding section, compression section and metering section are collected, and a temperature observation vector is constructed based on the infrared temperature measurement data; A state transition matrix is ​​constructed based on the thermal conductivity characteristics of the silicone melt within the injection screw, and the predicted temperature state and the predicted state covariance are calculated based on the state transition matrix. The Kalman gain matrix is ​​calculated based on the predicted state covariance, and the temperature observation vector and the predicted temperature state are fused based on the Kalman gain matrix to obtain the estimated screw temperature.

[0008] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present invention, after the stable melt of the silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity, and the internal pressure and wall temperature of the airbag are simultaneously collected and model prediction optimization is performed to obtain the airbag molded part, including: After the stable melt of the silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity; During the process of injecting nitrogen into the airbag cavity using an air needle, the internal pressure and wall temperature of the airbag are collected, and the current state estimate is calculated based on the internal pressure and wall temperature of the airbag. The current state estimate is recursively calculated forward to obtain the predicted state trajectory; Based on the state prediction trajectory, the optimal control sequence is solved under constraints of gas flow rate, heating power, pressure range, temperature range, and wall thickness range. Extract the gas flow control quantity and heating power control quantity from the optimal control sequence, send the gas flow control quantity to the proportional valve and the heating power control quantity to the electromagnetic induction heater, until the airbag wall thickness converges to a preset uniformity range, and obtain the airbag molded part.

[0009] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, the step of performing forward recursive calculation on the current state estimate to obtain the state prediction trajectory includes: Extract the intracavitary pressure, wall temperature, and airbag wall thickness from the current state estimate; The predicted value of the cavity pressure is calculated based on the cavity pressure and the wall temperature, and the predicted value of the wall temperature is calculated based on the wall temperature and the predicted value of the cavity pressure. The predicted value of the intracavitary pressure is converted into the wall thickness change rate, and the wall thickness change rate is integrated over time to obtain the predicted value of the airbag wall thickness. Based on the predicted values ​​of intracavitary pressure, wall temperature, and airbag wall thickness, repeated forward recursive calculations are performed until the entire prediction time domain is covered, thus obtaining the state prediction trajectory at each moment within the prediction time domain.

[0010] In conjunction with the first aspect, in a sixth implementation of the first aspect of the present invention, the step of solving for the optimal control sequence based on the state prediction trajectory under constraints of gas flow rate, heating power, pressure range, temperature range, and wall thickness range includes: Based on the state prediction trajectory, a multi-objective optimization function is constructed, which includes a state deviation cost term and a control increment penalty term. Set the upper and lower limit boundary values ​​of the gas flow control quantity to obtain the gas flow constraint; set the upper and lower limit boundary values ​​of the heating power control quantity to obtain the heating power constraint; set the allowable range boundary value of the cavity pressure to obtain the pressure range constraint; set the allowable range boundary value of the wall temperature to obtain the temperature range constraint; set the allowable range boundary value of the airbag wall thickness to obtain the wall thickness range constraint. A linear inequality constraint matrix and constraint vector are formed based on the gas flow rate constraint, the heating power constraint, the pressure range constraint, the temperature range constraint, and the wall thickness range constraint. The coefficients of the quadratic terms in the multi-objective optimization function are extracted as Hessian matrices, and the coefficients of the linear terms are extracted as gradient vectors. The Hessian matrix, the gradient vector, the linear inequality constraint matrix, and the constraint vector are input into the quadratic programming solver. The iteration terminates when the multi-objective optimization function converges to the minimum value, and the optimal control sequence is output.

[0011] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, the step of performing vulcanization treatment on the airbag molded part and obtaining the current vulcanization stage, and adjusting the heat preservation time of the M temperature control zones for the current vulcanization stage to obtain a cured product includes: During the vulcanization heating process of the airbag molding part, the capacitance value and loss tangent of the silicone are measured, and the real-time degree of vulcanization is calculated based on the capacitance value and the loss tangent. When the real-time vulcanization degree is less than the gel stage threshold, the current vulcanization stage is determined to be the gel stage; when the real-time vulcanization degree is between the gel stage threshold and the curing stage threshold, the current vulcanization stage is determined to be the curing stage; when the real-time vulcanization degree is greater than the curing stage threshold, the current vulcanization stage is determined to be the post-curing stage. Based on the current vulcanization stage, query the target temperature values ​​of M temperature control zones and calculate the holding time required to reach the target degree of vulcanization; The target temperature values ​​of the M temperature control zones are sent to the temperature controller to execute heating control. A heat preservation timer is set according to the heat preservation time. When the real-time degree of vulcanization reaches the target degree of vulcanization, the vulcanization ends and a cured product is obtained.

[0012] In conjunction with the first aspect, in the eighth implementation of the first aspect of the present invention, the step of querying the temperature target values ​​of M temperature control zones based on the current vulcanization stage and calculating the holding time required to reach the target degree of vulcanization includes: Based on the current vulcanization stage, query the target temperature values ​​of M temperature control zones and calculate the average temperature value of the airbag molding part; The average temperature value, the preset frequency factor, and the preset activation energy parameter are substituted into the Arrhenius exponential function to calculate the sulfidation reaction rate constant, and the sulfidation rate value is calculated based on the sulfidation reaction rate constant and the real-time sulfidation degree. The difference between the real-time degree of sulfidation and the target degree of sulfidation is used to obtain the increment of the degree of sulfidation to be achieved. The increment of the degree of sulfidation to be achieved is divided by the sulfidation rate value and logarithmic operation is performed to obtain the holding time required to achieve the target degree of sulfidation.

[0013] Secondly, the present invention provides an intelligent temperature control system for the molding process of flexible airbag nose pads for eyeglasses, the intelligent temperature control system for the molding process of flexible airbag nose pads for eyeglasses comprising: The preheating module is used to preheat the M temperature-controlled zones pre-set in the flexible airbag nose pad mold of the glasses to obtain a preheated mold; The temperature adjustment module is used to monitor the rotational speed and back pressure of the injection screw and calculate the shear temperature rise of the silicone in the injection screw. Based on the shear temperature rise, the temperature setpoint of the injection screw is adjusted to obtain a stable melt of the silicone. The airbag molding module is used to inject nitrogen into the airbag cavity after the stable melt of the silicone fills the cavity of the preheated mold, and simultaneously collect the internal pressure and wall temperature of the airbag and perform model prediction optimization to obtain the airbag molded part. The vulcanization module is used to vulcanize the airbag molding part and obtain the current vulcanization stage. It adjusts the heat preservation time of the M temperature control zones according to the current vulcanization stage to obtain the cured product.

[0014] The technical solution provided by this invention quantitatively describes the heat conduction influence relationship of each temperature control zone through a pre-stored interval thermal coupling weight coefficient matrix. Thermal coupling compensation is superimposed on the control output of each zone. When the temperature of an adjacent zone deviates from the target value, feedforward compensation adjustment is automatically performed on the current zone, achieving overall collaborative optimization of the temperature field across multiple regions. This technical feature enables the control system to actively suppress thermal coupling interference between zones, avoiding temperature oscillations and response lag problems caused by mutual influence between zones in traditional single-loop PID control. Based on the real-time speed and back pressure parameters of the injection screw, the temperature rise of silicone in the screw is quantitatively predicted using a shear heat generation calculation formula, and the three-segment temperature setpoints of the screw are dynamically adjusted accordingly to achieve real-time compensation for shear heat effects. This technical feature overcomes the shortcomings of traditional fixed temperature settings that cannot adapt to changes in process parameters, ensuring the consistency of melt temperature under different production batches and operating conditions. A state-space model including pressure rise equation, temperature change equation, and wall thickness expansion equation is established for the gas bladder cavity molding process. For the first time, the dynamic coupling relationship between pressure change caused by gas injection, temperature rise caused by compression heat generation, and wall thickness expansion under pressure is incorporated into a unified predictive control framework. The current degree of vulcanization is calculated by measuring the capacitance and loss tangent of the silicone material in real time using a dielectric sensor. Based on the degree of vulcanization, the stage of gelation, curing, or post-curing is determined, and the temperature of each temperature control zone is dynamically adjusted by querying the corresponding stage temperature setting rules. By substituting the real-time degree of vulcanization, average temperature, and target degree of vulcanization into the calculation, the required holding time parameter to reach the target degree of vulcanization is quantitatively predicted, thereby achieving adaptive optimization of the vulcanization process.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an embodiment of the intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses in this invention. Figure 2 This is a schematic diagram of an embodiment of the intelligent temperature control system for the molding process of flexible airbag nose pads for eyeglasses in this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0020] To facilitate understanding of this embodiment, a detailed description of the intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses, as disclosed in this embodiment of the invention, will be provided first. For example... Figure 1 As shown, this method includes the following steps: 101. Preheat the M temperature control zones pre-set in the flexible airbag nose pad mold of the glasses to obtain a preheated mold; Specifically, the actual temperature of each temperature-controlled zone is collected in real time and compared with the target set temperature. Temperature deviation data is generated based on the difference between the two. The temperature deviation value and its rate of change are input into the fuzzy logic unit. The deviation is mapped to a seven-level fuzzy hierarchy using a triangular membership function. The proportional, integral, and derivative coefficients are dynamically generated using a preset table of 49 fuzzy rules. The fuzzy PID control quantity for each temperature-controlled zone is then calculated. The fuzzy PID control quantity has adaptive characteristics and can automatically adjust the control strength according to different deviation amplitudes. Considering the impending thermal interference during injection molding, the pressure and flow rate signals of the injection section are detected in advance. The normalized ratio of the detected injection pressure value to the rated injection pressure is calculated, and a feedforward compensation factor is constructed using the square root mapping function. Based on this, the injection feedforward compensation amount for each temperature control zone is derived. The injection feedforward compensation amount is injected into the controller in advance to effectively suppress the cooling abrupt change caused by silicone injection. At the same time, considering the non-negligible thermal conduction coupling effect between the M temperature control zones of the mold, based on the established thermal coupling weight matrix, the difference between the actual temperature and the target temperature of each zone is used as the disturbance source. The conduction interference to other zones is estimated based on the thermal coupling coefficient, thereby deriving the thermal coupling compensation amount in reverse. The fuzzy PID control amount, injection feedforward compensation amount, and thermal coupling compensation amount are accumulated for each temperature control zone to form the final composite control signal. This signal drives the temperature control actuator, such as the electromagnetic induction heater, to work in conjunction with the heat transfer oil circulation pipeline. The heating power is finely controlled through PWM modulation to ensure that the temperature of all zones converges stably to the set target, achieving thermal equilibrium in a short time and obtaining a high-precision preheated mold.

[0021] 102. Monitor the rotational speed and back pressure of the injection screw and calculate the shear temperature rise of the silicone in the injection screw. Adjust the temperature setpoint of the injection screw based on the shear temperature rise to obtain a stable melt of silicone. Specifically, the actual rotational speed and back pressure parameters of the injection screw are acquired through a high-frequency data acquisition module. Combined with the current process settings and material properties, the rotational speed and back pressure are input into a shear heat analysis model to calculate the instantaneous temperature rise of the silicone inside the injection screw due to high-speed rotation and pressure shearing. This reflects the temperature disturbance caused by the rheological heating process of the silicone melt in the compression and metering sections. Based on the shear temperature rise, the target temperatures of the feeding, compression, and metering sections of the injection screw are dynamically corrected to form new temperature setpoints. These setpoints are then sent to the corresponding heating devices, enabling the system to pre-adjust the local thermal field to match the heat load changes caused by shearing and improve the thermal stability of the melt. Simultaneously, a multi-channel infrared thermometer deployed on the outer wall of the screw synchronously collects the surface temperature of each section. Through specific filtering and spatial mapping algorithms, the estimated temperature of the internal melt is derived, and a state estimation model based on thermal diffusion and shear distribution characteristics is constructed to keep the error between the measured value and the actual melt temperature within an acceptable range. Deviation analysis is performed between the estimated temperature and the set temperature. If the deviation exceeds a preset threshold, the heating power is automatically adjusted through a proportional-integral control mechanism to achieve closed-loop control of heat output, so that the actual melt temperature quickly approaches the target thermal field state. As the adjustment process continues to iterate, the melt temperature fluctuation at the nozzle tip gradually converges to the set stable range (e.g., within ±2℃), forming a silicone melt flow.

[0022] 103. After the stable melt of silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity. The internal pressure and wall temperature of the airbag are collected simultaneously, and model prediction optimization is performed to obtain the airbag molded part. Specifically, after the stable melt of silicone fills the cavity of the preheated mold, nitrogen is injected through micro-needles located on the airbag cavity of the mold. This allows the inert gas to gradually fill the airbag cavity while maintaining a constant flow rate, thereby pushing the cavity wall outward to form a basic contour structure. Simultaneously, a micro-pressure sensor and a K-type thermocouple array embedded within the airbag cavity are used to collect real-time data on the current airbag internal pressure and wall temperature. These two sets of high-frequency sensor data are input into a state estimation module, and a fusion filter is used to calculate an estimated value of the current system state, including the actual internal air pressure, cavity wall temperature, and the current wall thickness evolution trend. Based on the state estimate, a forward recursive calculation is performed using a nonlinear state-space model. A state prediction trajectory for the future airbag wall thickness evolution is generated using a 2-second prediction step time window. Based on this state prediction trajectory, considering process constraints such as changes in gas injection flow rate, local heating power adjustment, upper limit of cavity pressure, upper limit of wall temperature, and target wall thickness uniformity range, an objective function is constructed. A quadratic programming solver is then used to solve for the optimal control sequence, which includes the optimal adjustment paths for multiple control variables. The gas flow control and heating power control quantities are extracted from the optimal control sequence. The gas flow control quantity is sent to the proportional valve to dynamically adjust the nitrogen injection rate, ensuring a uniform expansion force field while avoiding wall overshoot. The heating power control quantity is sent to the electromagnetic induction heater, which fine-tunes the heating of local areas of the mold by adjusting the coil output power, keeping the wall temperature within the optimal molding range. These two control commands are continuously iterated in a high-frequency closed-loop manner within the MPC rolling control framework until sensor monitoring data indicates that the airbag wall thickness fluctuation has converged to a preset uniformity threshold (e.g., within ±0.05 mm). Under the multi-objective conditions of satisfying wall thickness consistency, cavity pressure stability, and thermal field uniformity, the fabrication of a high-quality airbag molded part is completed.

[0023] 104. Perform vulcanization treatment on the airbag molded parts and obtain the current vulcanization stage. Adjust the heat preservation time of M temperature control zones according to the current vulcanization stage to obtain the cured product.

[0024] Specifically, while the mold temperature control system maintains a constant thermal field, the dielectric property sensing module deployed in the key area of ​​the airbag molding part measures the capacitance and loss tangent of the silicone material at high frequency, reflecting the evolution of the degree of molecular cross-linking within the material. Based on a preset electrochemical-curvature mapping model, the capacitance change trend and loss angle amplitude are analyzed together to calculate the curvature value corresponding to the current moment in real time and continuously track its growth curve to determine the current reaction stage. When the real-time curvature is detected to be lower than the set gel stage threshold (e.g., 0.3), the current curing stage is determined to be the gel stage. At this time, the target temperature of the core temperature zone of the mold is increased to a slightly higher level (e.g., 168–170℃) to accelerate the reaction activation. If the real-time curvature is between 0.3 and 0.8, the curing stage is entered. According to the characteristic that the crosslinking rate tends to be stable in the curing stage, the temperature distribution gradient of the central temperature zone is maintained at 165–168℃ and the edge temperature zone at 155–160℃. At the same time, the curing rate is monitored to dynamically adjust the subsequent heat preservation time. When the real-time curvature reaches or exceeds 0.8, the post-curing stage is entered. At this time, it is recognized that the main reaction process has been completed, and the overall temperature zone setting is lowered to 160–162℃ to prevent over-curing from causing thermal degradation or a decrease in mechanical properties. Based on the judgment results, and using the vulcanization stage-temperature target value mapping table, the target temperature for each temperature control zone at the current stage is matched. Combined with the Arrhenius vulcanization kinetic model, the required holding time is calculated based on the current average temperature, real-time vulcanization degree, and the preset target vulcanization degree (e.g., 0.95). This time value is then input into the holding timer module. The temperature target values ​​for the M temperature control zones are synchronously sent to the corresponding temperature controllers, which control the heating actuators to precisely maintain the required temperature field conditions and start timing. During the holding process, dielectric parameters are measured and vulcanization degree is updated at a fixed sampling period. It is also determined in real-time whether the current real-time vulcanization degree has reached the target threshold. When the measured vulcanization degree reaches the target value, the holding timer is automatically terminated and the heating circuit is shut down, completing the entire vulcanization and curing process and obtaining the vulcanized and cured finished product.

[0025] In one specific embodiment, the process of performing step 101 may specifically include the following steps: Calculate the temperature deviation data of the M pre-set temperature control zones in the flexible airbag nose pad mold of the eyeglasses; Calculate the fuzzy PID control quantity for each temperature control zone based on the temperature deviation data; Obtain the injection pressure and flow rate values ​​for the silicone injection to be performed, and calculate the injection feedforward compensation for each temperature control zone based on the ratio of the injection pressure value to the rated injection pressure. Obtain the thermal coupling coefficient between each temperature control zone, and calculate the thermal coupling compensation amount of each temperature control zone based on the thermal coupling coefficient; The temperature controllers of each temperature control zone are driven by fuzzy PID control, injection feedforward compensation, and thermal coupling compensation to obtain the preheated mold.

[0026] Specifically, a real-time thermal state perception model for each temperature control zone is constructed based on a high-frequency temperature acquisition network. K-type thermocouple arrays and auxiliary distributed fiber optic temperature sensors arranged on the inner walls of M temperature control zones are used to continuously collect real-time temperature data for each zone. The measured temperature is compared with the preset target temperature to obtain temperature deviation data and deviation change rate as the basic input for the temperature control algorithm. For each temperature control zone, the temperature deviation and the rate of change of deviation are used as fuzzy inputs. These are mapped to seven levels of fuzzy hierarchy using a defined triangular membership function, forming a fuzzy subset. The corresponding seventh-order fuzzy control rule table is then searched, and the output rule combination corresponding to the current input state is extracted to determine the fuzzy PID control quantity required for the zone at the current moment. After a defuzzification process, the fuzzy PID control quantity generates specific proportional coefficients, integral coefficients, and derivative coefficients, dynamically driving the controller to output heating signals, achieving rapid response and adjustment to the temperature difference change trend of each zone. Simultaneously, the pre-start signal of the injection molding stage is monitored. When the pressure sensor data in the injection section at the front end of the injection screw indicates that the high-pressure injection stage is about to begin, the current injection pressure value and the corresponding injection flow rate are immediately read. A ratio model is constructed between the injection pressure value and the rated injection pressure. Based on a set nonlinear function structure, such as a square root mapping relationship, the feedforward compensation gain is calculated. This feedforward compensation gain is multiplied by the zone weight coefficient and distributed to the M temperature control zones to obtain the injection feedforward compensation amount for each zone. Based on the thermal coupling weight matrix, the thermal conduction influence coefficient between any two temperature control zones is quantified. By reading the difference between the real-time temperature and the target temperature of all adjacent zones, and combining it with the corresponding thermal coupling coefficient, a weighted sum is made to construct the thermal coupling compensation amount of the current zone, which is used to actively offset the coupled thermal offset caused by temperature disturbances in adjacent zones. The fuzzy PID control amount, injection feedforward compensation amount and thermal coupling compensation amount are weighted and summed to form a comprehensive drive signal, which is input to the temperature controller of the corresponding temperature control zone. The controller adjusts the heating power of the high-frequency electromagnetic induction heating coil or controls the flow rate and heat flow distribution of the heat transfer oil circulation system through PWM modulation, so that each zone can maintain the fine-tuning stability of the set target temperature under complex injection molding conditions, and achieve a preheating state with spatial uniformity, rapid response and accuracy of ±0.15℃ before injection molding starts.

[0027] In one specific embodiment, the process of performing step 102 may specifically include the following steps: Obtain the rotational speed and back pressure of the injection screw, and calculate the shear temperature rise of the silicone in the injection screw based on the rotational speed and back pressure; The temperature setpoint of the injection screw is adjusted based on the shear temperature rise, and the heating device is driven by the temperature setpoint. Infrared temperature data of the injection molding screw is collected, and the estimated screw temperature is calculated based on the infrared temperature data; Adjust the heating power according to the estimated screw temperature and the set temperature until the temperature fluctuation at the nozzle converges to within the preset temperature range, thus obtaining a stable melt of silicone.

[0028] Specifically, the high-frequency acquisition module extracts the rotational speed and back pressure of the injection molding screw during operation from the screw drive unit and the back pressure control system, respectively, and inputs them into the embedded shear heat modeling module. Within the module, based on preset silicone material properties, including viscosity, density, specific heat capacity, and shear heat generation coefficient, and combined with screw structural parameters such as diameter and screw channel depth, a nonlinear temperature rise estimation expression is established through a shear rate and power density relationship model. This dynamically calculates the shear temperature rise in different regions of the screw, especially the compression and metering sections, under the current rotational speed and back pressure, reflecting the instantaneous heat load added to the silicone melt due to high-pressure shearing. Using shear temperature rise as a correction factor, the original temperature setpoints of each section of the screw are dynamically adjusted, causing the target temperatures of the feeding section, compression section, and metering section to be adjusted upwards or downwards respectively to compensate for the non-uniform temperature field caused by shear heat. The updated temperature setpoints are then sent to the heating controller of the screw temperature control system. The heating controller drives the electric heating sleeve or induction heating ring to implement independent power adjustment in different temperature control zones, ensuring that the spatial distribution of temperature adjustment matches the shear heat distribution law. At the same time, multi-channel infrared temperature sensors deployed on the surface of the screw shell synchronously collect surface temperature data of each section. By constructing a heat diffusion and radial heat attenuation model, these surface temperatures are extrapolated to the melt temperature distribution inside the screw, forming dynamically updated temperature estimates. A Kalman filter algorithm is used to fuse multi-point infrared temperature data to suppress thermal disturbances and measurement noise, thereby improving estimation accuracy. The estimated actual screw temperature is compared and analyzed with the set target temperature to calculate the temperature control deviation. This deviation is then used as the input value for closed-loop control. A proportional-integral controller finely adjusts the heating power output of each segment to quickly narrow the gap between the actual and target temperatures until the melt temperature fluctuation range at the nozzle outlet converges to the set upper limit (e.g., within ±2℃). This signifies that the silicone melt has achieved a control state that combines temperature stability and rheological uniformity under dynamic shear conditions. In a specific embodiment, the process of collecting infrared temperature data from the injection screw and calculating the estimated screw temperature based on this data can specifically include the following steps: Infrared temperature measurement data of the injection screw in the feeding section, compression section and metering section are collected, and a temperature observation vector is constructed based on the infrared temperature measurement data; A state transition matrix is ​​constructed to consider the thermal conductivity characteristics of silicone melt in the injection screw, and the predicted values ​​of temperature state and state covariance are calculated based on the state transition matrix. The Kalman gain matrix is ​​calculated based on the state covariance prediction value, and the temperature observation vector and the temperature state prediction value are fused by deviation based on the Kalman gain matrix to obtain the screw temperature estimate.

[0029] Specifically, non-contact infrared temperature sensors are deployed in the screw feeding section, compression section, and metering section. Infrared temperature data from the outer surfaces of these three sections are simultaneously acquired with high temporal resolution. By establishing a spatial mapping relationship between the measurement points and the corresponding heat flow distribution within each section, the three infrared temperature values ​​obtained from each sampling are constructed into a set of observation vectors. A state transition model based on physical mechanisms is established. This model derives a state transition matrix by analyzing the heat conduction and local shear heating characteristics of the silicone melt along the screw axis. The structural design of the state transition matrix must simultaneously consider the axial heat diffusion term and the shear heat source input term. The heat diffusion coefficient is calculated from the material's thermal conductivity, density, and specific heat capacity, while the shear heating term is determined by the current rotational speed and viscosity model. Based on the state transition matrix A and the state estimation results from the previous moment, forward propagation is performed using the state transition equation to calculate the predicted temperature state value and the corresponding predicted state covariance value at the current time point. The Riccati equation is solved by jointly applying the predicted state covariance value and the preset measurement error covariance matrix to obtain the Kalman gain matrix at the current time. The Kalman gain matrix numerically measures the weighted distribution between the prediction reliability and the reliability of the observation data. Based on the Kalman gain matrix, the observation vector composed of infrared temperature measurement data is compared with the predicted temperature state, and the deviation between the two is extracted. The deviation is multiplied by the Kalman gain matrix to form a correction vector, which is then superimposed on the predicted state value to complete the state update process and obtain the estimated screw temperature at the current time.

[0030] In one specific embodiment, the process of performing step 103 may specifically include the following steps: After the stable melt of silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity. The internal pressure and wall temperature of the airbag are collected during the process of injecting nitrogen into the airbag cavity through the air needle, and the current state estimate is calculated based on the internal pressure and wall temperature of the airbag. The predicted trajectory of the state is obtained by forward recursion calculation based on the current state estimate. The optimal control sequence is solved based on state prediction trajectory under constraints of gas flow rate, heating power, pressure range, temperature range, and wall thickness range. Extract the gas flow control quantity and heating power control quantity from the optimal control sequence, send the gas flow control quantity to the proportional valve and the heating power control quantity to the electromagnetic induction heater, until the airbag wall thickness converges to the preset uniformity range, and obtain the airbag molded part.

[0031] Specifically, immediately after the silicone molten metal completes cavity filling, the system switches to the airbag forming control stage, initiating the inert gas nitrogen injection process. A high-frequency control signal drives the gas needle assembly to perform precise gas injection into the pre-set airbag cavity. Throughout the nitrogen injection process, dynamic signals from high-precision miniature pressure sensors and K-type thermocouple sensors deployed inside the airbag cavity are continuously collected, recording real-time intracavity pressure and mold wall temperature data. These two sets of data are input into the state estimation module via a state fusion mechanism. A Kalman filter structure is constructed by combining the prior model and the sensor measurement error covariance matrix to obtain the current state estimate. The state vector contains estimated information on the actual intracavity pressure, wall temperature, and current wall thickness change rate. Using the state estimate as initial conditions, a forward recursive calculation is performed through a nonlinear state-space model. This model incorporates the silicone elastic expansion response coefficient, gas compression heating effect parameters, and dynamic characteristics of heat conduction to predict the wall thickness change trend, temperature field distribution, and cavity pressure evolution trajectory over several future control cycles, forming a state prediction trajectory sequence covering the future control time domain. Using the predicted state trajectory as the optimization input, a control optimization problem with multiple physical boundary constraints is constructed. These constraints include upper and lower limits for gas flow rate, maximum output limit for heating power, safe control zone for cavity pressure (e.g., 0.5~8MPa), allowable temperature window for mold wall (e.g., 160~175℃), and target uniformity range for wall thickness (e.g., within ±0.05mm). Pressure deviation, temperature deviation, wall thickness deviation, and control increment penalty terms are comprehensively set in the objective function. A rolling optimization strategy is adopted to solve the optimal control sequence based on a quadratic programming algorithm. The optimal control sequence corresponds to the gas flow rate adjustment and heating power change at each sampling moment in the future control time domain. The two main control variables to be executed in the current control cycle are extracted from this sequence: gas flow rate control and heating power control. These variables are then sent to the electronic proportional valve and electromagnetic induction heater execution unit via the EtherCAT real-time bus, respectively. The electronic proportional valve adjusts the nitrogen injection rate in real time to stabilize the expansion rate and internal pressure distribution. The electromagnetic induction heater execution unit implements fine-grained power control on the local heating area of ​​the mold to maintain wall thermal balance. The cyclic process of continuously repeating state estimation, predictive trajectory generation, and optimal control solution forms a rolling model prediction closed-loop structure based on real-time sensor data feedback, until the wall thickness fluctuations fed back by the sensors converge within the set uniformity range. At this point, the molding process is judged to be stably completed, and the airbag molded part is obtained.

[0032] In one specific embodiment, the process of performing forward recursive calculation on the current state estimate to obtain the state prediction trajectory can specifically include the following steps: Extract the intracavitary pressure, wall temperature, and airbag wall thickness from the current state estimate; The predicted value of the cavity pressure is calculated based on the cavity pressure and the wall temperature, and the predicted value of the wall temperature is calculated based on the predicted value of the cavity pressure and the wall temperature. The predicted value of the intracavitary pressure is converted into the rate of change of wall thickness, and the rate of change of wall thickness is integrated over time to obtain the predicted value of the airbag wall thickness. Based on the predicted values ​​of intracavitary pressure, wall temperature, and airbag wall thickness, repeated forward recursive calculations are performed until the entire prediction time domain is covered, thus obtaining the state prediction trajectory at each moment within the prediction time domain.

[0033] Specifically, the cavity pressure, wall temperature, and airbag wall thickness are extracted from the current state estimation results to form the state baseline values ​​for dynamic modeling. These values ​​represent the current internal gas load of the molding cavity, the thermal boundary state of the mold wall, and the structural response degree after material expansion under pressure, respectively. Using the current cavity pressure and wall temperature as input conditions, the pressure evolution equation related to gas behavior in the state space model is invoked. This equation comprehensively considers multiple factors such as gas injection rate, gas compression heating effect, cavity leakage rate, and molding volume change. Through the ideal gas state equation at normal pressure and empirical calibration parameters, the predicted cavity pressure value for the next time step is calculated. Using the predicted wall temperature and cavity pressure values ​​as input, the predicted wall temperature value for the next time step is calculated through the heat energy budget equation set, combined with heat source factors such as mold heating power, cavity heat transfer coefficient, mold cavity radiation loss, and gas compression heat generation term. This establishes a pressure-temperature dual-variable linkage thermal state prediction channel. Based on the pressure prediction, and according to the thickness response model of the elastomer material under pressure, the predicted intracavity pressure is converted into the wall thickness change rate. This conversion is based on an experimentally fitted nonlinear function or derived from the elastic modulus and gas interaction area. The wall thickness expansion rate is estimated by combining the material compressibility coefficient and the mechanical damping parameters of the forming region. Time integration is performed on the wall thickness change rate, and the predicted wall thickness for the next time step is calculated by combining the current initial wall thickness value with the discrete integration time step, thereby updating the current state vector. After one prediction update, the new state vector is used as the input for the next time step, and the above three steps are executed: calculating the pressure for the next time step using the updated pressure and temperature; predicting the wall temperature for the next time step using the updated pressure and temperature; and predicting the wall thickness change rate using the updated pressure and integrating it to obtain the predicted wall thickness value. This process is repeated with a fixed time step until the entire prediction time domain is covered, for example, 20 control steps within the next 2 seconds, generating a state prediction trajectory containing intracavity pressure, wall temperature, and wall thickness change data for all future time points.

[0034] In one specific embodiment, the process of solving for the optimal control sequence based on the state prediction trajectory under constraints of gas flow rate, heating power, pressure range, temperature range, and wall thickness range can specifically include the following steps: Construct a multi-objective optimization function based on the state prediction trajectory, which includes a state deviation cost term and a control increment penalty term; Set the upper and lower limit boundary values ​​of the gas flow control quantity to obtain the gas flow constraint; set the upper and lower limit boundary values ​​of the heating power control quantity to obtain the heating power constraint; set the allowable range boundary value of the cavity pressure to obtain the pressure range constraint; set the allowable range boundary value of the wall temperature to obtain the temperature range constraint; set the allowable range boundary value of the airbag wall thickness to obtain the wall thickness range constraint. The constraints are based on gas flow rate constraints, heating power constraints, pressure range constraints, temperature range constraints, and wall thickness range constraints, forming a linear inequality constraint matrix and constraint vector. The coefficients of the quadratic terms in the multi-objective optimization function are extracted as Hessian matrices, and the coefficients of the linear terms are extracted as gradient vectors. The Hessian matrix, gradient vector, linear inequality constraint matrix, and constraint vector are then input into the quadratic programming solver. The iteration terminates when the multi-objective optimization function converges to the minimum value, and the optimal control sequence is output.

[0035] Specifically, an optimization solution model is constructed based on the state prediction trajectory, which includes multi-objective control objectives and execution cost constraints. In the model predictive control framework, the predicted values ​​of cavity pressure, wall temperature, and wall thickness at each control step in the prediction time domain are respectively calculated with their target values ​​to form a state deviation vector. The weighted sum of squares of the state deviation vector is used as the first term of the optimization function, which is the state deviation cost term, to penalize the evolution trajectory of physical quantities that deviate from the target in the prediction path. At the same time, in order to suppress the drastic fluctuations of control variables between continuous time steps, the time change rates (i.e., control increments) of gas flow control and heating power control are calculated separately, and their squares are weighted and incorporated into the optimization function to form the control increment penalty term, forming the optimization objective function J. The optimization objective function is a quadratic objective, that is, a combination structure containing quadratic and linear terms of control variables. The constraint boundaries are constructed as follows: the upper and lower limits of the gas flow control quantity are set by the minimum and maximum allowable output flow rates of the gas source system, such as 1 L / min to 15 L / min, forming a gas flow constraint range; the upper and lower limits of the heating power control quantity are given based on the power module capacity of the electromagnetic induction heater, such as 0 to 2.5 kW, forming a heating power constraint; the allowable range of the cavity pressure is set based on the strength tolerance and airtightness requirements of the molded structure, such as 0.5 MPa to 8 MPa; the allowable range of the wall temperature is set based on the vulcanization characteristics and thermal stability of the material, such as 160℃ to 175℃; the allowable range of the wall thickness is set based on the product structure function and molding consistency index, such as 0.8 mm to 1.2 mm. The above five types of constraint conditions are respectively converted into linear boundary expressions containing upper and lower limit inequalities, and uniformly converted into linear inequality constraint matrix G and constraint vector h in standard mathematical form, where each row represents the range of single variable limits or state prediction values ​​on a control step. After constructing the optimization function and constraint boundaries, the coefficient matrix of the quadratic term in the optimization function is extracted as a Hessian matrix H. The Hessian matrix weights the squared terms of the control variables, reflecting the importance of control sensitivity and deviation. Simultaneously, the coefficients of the linear term in the optimization function are extracted as a gradient vector f, used to represent the target direction. The four sets of data H, f, G, and h are fed as standard inputs into the quadratic programming solver. An efficient interior-point method or sequential minimum optimization algorithm is used to iteratively solve this constrained optimization problem. In each iteration, the path of the control variables is gradually adjusted to minimize the objective function value, while ensuring that no boundary constraints are violated. The calculation terminates when the optimizer detects that the objective function has converged to a local minimum or the gradient norm is below a set threshold, and outputs the optimal control sequence in the corresponding time domain. The sequence contains the gas flow rate regulation command and heating power regulation command to be applied in each future control cycle.

[0036] In one specific embodiment, the process of performing step 104 may specifically include the following steps: During the vulcanization heating process of the airbag molding parts, the capacitance value and loss tangent of the silicone are measured, and the real-time degree of vulcanization is calculated based on the capacitance value and loss tangent. When the real-time vulcanization degree is less than the gel stage threshold, the current vulcanization stage is determined to be the gel stage. When the real-time vulcanization degree is between the gel stage threshold and the curing stage threshold, the current vulcanization stage is determined to be the curing stage. When the real-time vulcanization degree is greater than the curing stage threshold, the current vulcanization stage is determined to be the post-curing stage. Based on the current vulcanization stage, query the target temperature values ​​for M temperature control zones and calculate the holding time required to reach the target degree of vulcanization; The target temperature values ​​of M temperature control zones are sent to the temperature controller to execute heating control. The heat preservation timer is set according to the heat preservation time. When the real-time degree of vulcanization reaches the target degree of vulcanization, the vulcanization ends and the cured product is obtained.

[0037] Specifically, after entering the vulcanization stage, the dielectric property monitoring module is activated. An embedded dielectric sensor continuously and frequently measures the capacitance and loss tangent of the silicone material. The capacitance reflects the change in dielectric constant of the material's internal polar molecular structure during the cross-linking reaction, while the loss tangent captures the energy dissipation characteristics of molecular chain segment mobility. These two indicators exhibit significant nonlinear evolution trajectories during the vulcanization process. Based on the joint fitting model of capacitance-vulcanization degree and loss tangent-vulcanization degree, the real-time collected data is input into the fitting function for inversion calculation to obtain a quantitative value of the material's cross-linking degree at the current moment, i.e., the real-time vulcanization degree α(t), which is dynamically updated in each control cycle. To identify the current vulcanization stage, α(t) is compared with a preset stage threshold. When α(t) is less than the gel stage threshold (e.g., 0.3), the current stage is determined to be the initial gel stage, which is mainly characterized by low molecular weight aggregation and initial network structure formation. The temperature of the core area of ​​the mold needs to be increased to accelerate the reaction rate. When α(t) is between 0.3 and 0.8, the current stage is identified as the main curing stage, where the crosslinking rate is close to its maximum value. The temperature plateau needs to be maintained to ensure uniform curing of the structure. When α(t) is greater than 0.8, the current stage is identified as the post-curing stage, entering a high crosslinking density region. At this point, the temperature should be appropriately lowered to prevent over-vulcanization and material degradation. After determining the current vulcanization stage, the target temperature values ​​of M temperature control zones matching the current stage are automatically retrieved from the temperature control strategy library. Simultaneously, combined with the Arrhenius vulcanization kinetic model, the current measured degree of vulcanization, average temperature, and target degree of vulcanization (e.g., 0.95) are input. Based on formula derivation or numerical iteration, the required holding time to reach the target crosslinking degree is calculated and used as the initial countdown value for the holding timer. The retrieved zone temperature target values ​​are distributed to the temperature controllers of each zone, which drive the corresponding heating units to adjust the temperature field, enabling each area of ​​the mold to quickly enter the thermal boundary state suitable for the vulcanization stage. Simultaneously, a holding timer begins its countdown. During the holding period, capacitance and loss angle values ​​are continuously sampled, and the vulcanization degree curve is calculated and updated in real time. When the vulcanization degree reaches the target threshold, the vulcanization process is considered complete. The heating unit output is immediately terminated, and rapid cooling or holding-down temperature reduction measures are implemented. The current state of the airbag molded part is marked as vulcanization complete, and it proceeds to the downstream demolding and inspection processes.

[0038] In one specific embodiment, the process of querying the target temperature values ​​of M temperature control zones based on the current vulcanization stage and calculating the holding time required to reach the target degree of vulcanization can specifically include the following steps: Based on the current vulcanization stage, query the target temperature values ​​of M temperature control zones and calculate the average temperature value of the airbag molding part; The average temperature value, the preset frequency factor, and the preset activation energy parameter are substituted into the Arrhenius exponential function to calculate the sulfidation reaction rate constant, and the sulfidation rate value is calculated based on the sulfidation reaction rate constant and the real-time sulfidation degree. The difference between the real-time degree of sulfidation and the target degree of sulfidation is used to obtain the increment of the degree of sulfidation to be achieved. The increment of the degree of sulfidation to be achieved is divided by the sulfidation rate value and logarithmic operation is performed to obtain the holding time required to achieve the target degree of sulfidation.

[0039] Specifically, based on the current vulcanization stage, the temperature control strategy corresponding to the current stage is called from the temperature control parameter mapping table to determine the target temperature values ​​for M temperature control zones at the current stage. This reflects the specific requirements of different zones for thermal field control during vulcanization, including strategies such as enhanced heating in the central area and maintaining gradients in the edge area. A weighted average is applied to the M target temperature values, where the weights are based on the heat contribution ratio or actual geometric area distribution of each zone within the cavity. This calculates the average temperature value of the current airbag molding part within the entire mold, representing the effective thermal environment of the silicone body during the crosslinking reaction. The average temperature value and preset material reaction kinetic parameters are input into the Arrhenius exponential function, where the frequency factor k0 is an empirical constant reflecting the number of effective collisions that may occur per unit time at the material molecular level, and the activation energy E... a The energy barrier that the material needs to overcome to complete the monomer conversion is represented by these two parameters and the average temperature T, which are then substituted into the exponential term of the Arrhenius formula: exp(-E). a / RT), and multiplied by k0 to calculate the vulcanization reaction rate constant k under the current thermal field, reflecting the upper limit of the rate of increase in the degree of crosslinking of the material per unit time. Based on the rate constant k and the currently measured degree of vulcanization α. c Calculate the instantaneous vulcanization rate, i.e., the reaction rate dα / dt = k·(1-α) c ), where (1-α c The expression represents the proportion of the unreacted portion, reflecting the dynamic characteristic of a gradually decreasing rate as the unreacted portion decreases. The difference between the current degree of vulcanization and the target degree of vulcanization is calculated to obtain the remaining increment of vulcanization, representing the degree of crosslinking that the system needs to further advance. To determine the time required for the remaining vulcanization, Δα is divided by the vulcanization rate dα / dt, forming a theoretical estimate of the required reaction time: t = Δα / (k·(1-α)). cTo enhance stability and logarithmic scaling fit, the ratio is partially logarithmically calculated in the implementation to adapt to the linear fitting model under logarithmic transformation or to a pre-established fitting function of ln(1−α) and time, forming an estimated holding time value. This reflects the theoretical time required for the material to progress from the current vulcanization state to the target vulcanization degree under the premise that the current thermal conditions remain unchanged. The estimated holding time value is then input into the holding timer or closed-loop temperature control module to achieve dynamic duration control of the holding stage.

[0040] The above describes the intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses in embodiments of the present invention. The following describes the intelligent temperature control system for the molding process of flexible airbag nose pads for eyeglasses in embodiments of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the intelligent temperature control system for the molding process of flexible airbag nose pads for eyeglasses in this invention includes: Preheating module 201 is used to preheat the M temperature control zones pre-set in the flexible airbag nose pad mold of the glasses to obtain a preheated mold; The temperature adjustment module 202 is used to monitor the rotational speed and back pressure of the injection screw and calculate the shear temperature rise of the silicone in the injection screw. Based on the shear temperature rise, the temperature setpoint of the injection screw is adjusted to obtain a stable melt of silicone. The airbag molding module 203 is used to inject nitrogen into the airbag cavity after the stable melt of silicone fills the cavity of the preheated mold, and simultaneously collect the internal pressure and wall temperature of the airbag and perform model prediction optimization to obtain the airbag molded part. The vulcanization module 204 is used to vulcanize the airbag molding parts and obtain the current vulcanization stage. It adjusts the heat preservation time of M temperature control zones according to the current vulcanization stage to obtain the cured product.

[0041] Through the collaborative efforts of the aforementioned components, the thermal conduction influence of each temperature control zone is quantitatively described using a pre-stored interval thermal coupling weight coefficient matrix. Thermal coupling compensation is superimposed on the control output of each zone. When the temperature of an adjacent zone deviates from the target value, feedforward compensation adjustment is automatically performed on the current zone, achieving overall collaborative optimization of the temperature field across multiple regions. This technical feature enables the control system to actively suppress thermal coupling interference between zones, avoiding temperature oscillations and response lag caused by mutual influence between zones in traditional single-loop PID control. Based on the real-time speed and back pressure parameters of the injection screw, the temperature rise of silicone within the screw is quantitatively predicted using a shear heat generation calculation formula. The three-stage temperature setpoint of the screw is then dynamically adjusted accordingly to achieve real-time compensation for shear heat effects. This technical feature overcomes the shortcomings of traditional fixed temperature settings that cannot adapt to changes in process parameters, ensuring the consistency of melt temperature across different production batches and operating conditions. A state-space model including pressure rise equations, temperature change equations, and wall thickness expansion equations is established for the gas bladder cavity molding process. For the first time, the dynamic coupling relationship between pressure changes caused by gas injection, temperature rise caused by compression heat generation, and wall thickness expansion under pressure is incorporated into a unified predictive control framework. The current degree of vulcanization is calculated by measuring the capacitance and loss tangent of the silicone material in real time using a dielectric sensor. Based on the degree of vulcanization, the stage of gelation, curing, or post-curing is determined, and the temperature of each temperature control zone is dynamically adjusted by querying the corresponding stage temperature setting rules. By substituting the real-time degree of vulcanization, average temperature, and target degree of vulcanization into the calculation, the required holding time parameter to reach the target degree of vulcanization is quantitatively predicted, thereby achieving adaptive optimization of the vulcanization process.

[0042] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0043] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses, characterized in that, include: The M temperature control zones pre-set in the flexible airbag nose pad mold of the eyeglasses are preheated to obtain a preheated mold. The rotational speed and back pressure of the injection screw are monitored and the shear temperature rise of the silicone in the injection screw is calculated. Based on the shear temperature rise, the temperature setpoint of the injection screw is adjusted to obtain a stable melt of the silicone. After the stable melt of the silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity. The internal pressure and wall temperature of the airbag are collected simultaneously, and model prediction optimization is performed to obtain the airbag molded part. The airbag molding part is subjected to vulcanization treatment and the current vulcanization stage is obtained. The heat preservation time of the M temperature control zones is adjusted according to the current vulcanization stage to obtain the cured product.

2. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 1, characterized in that, The process of preheating the M pre-set temperature control zones in the flexible airbag nose pad mold of the eyeglasses to obtain a preheated mold includes: Calculate the temperature deviation data of the M pre-set temperature control zones in the flexible airbag nose pad mold of the eyeglasses; The fuzzy PID control quantity for each temperature control zone is calculated based on the temperature deviation data. Obtain the injection pressure and flow rate values ​​for the silicone injection to be performed, and calculate the injection feedforward compensation for each temperature control zone based on the ratio of the injection pressure value to the rated injection pressure. Obtain the thermal coupling coefficient between each temperature control zone, and calculate the thermal coupling compensation amount for each temperature control zone based on the thermal coupling coefficient; The temperature controllers of each temperature control zone are driven by the fuzzy PID control quantity, the injection feedforward compensation quantity, and the thermal coupling compensation quantity to obtain the preheated mold.

3. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 1, characterized in that, The process of monitoring the rotational speed and back pressure of the injection screw and calculating the shear temperature rise of the silicone within the injection screw, adjusting the temperature setpoint of the injection screw based on the shear temperature rise, and obtaining a stable melt of the silicone includes: The rotational speed and back pressure of the injection screw are obtained, and the shear temperature rise of the silicone in the injection screw is calculated based on the rotational speed and back pressure. The temperature setpoint of the injection screw is adjusted based on the shear temperature rise, and the heating device is driven by the temperature setpoint. Infrared temperature data of the injection screw is collected, and the estimated screw temperature is calculated based on the infrared temperature data. Adjust the heating power according to the estimated screw temperature and the set temperature until the temperature fluctuation at the nozzle converges to within the preset temperature range, thereby obtaining a stable melt of the silicone.

4. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 3, characterized in that, The process of collecting infrared temperature data of the injection molding screw and calculating an estimated screw temperature based on the infrared temperature data includes: Infrared temperature measurement data of the injection screw in the feeding section, compression section and metering section are collected, and a temperature observation vector is constructed based on the infrared temperature measurement data; A state transition matrix is ​​constructed based on the thermal conductivity characteristics of the silicone melt within the injection screw, and the predicted temperature state and the predicted state covariance are calculated based on the state transition matrix. The Kalman gain matrix is ​​calculated based on the predicted state covariance, and the temperature observation vector and the predicted temperature state are fused based on the Kalman gain matrix to obtain the estimated screw temperature.

5. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 1, characterized in that, After the stable melt of the silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity. Simultaneously, the internal pressure and wall temperature of the airbag are collected, and model prediction optimization is performed to obtain the airbag molded part, including: After the stable melt of the silicone fills the cavity of the preheated mold, nitrogen is injected into the airbag cavity; During the process of injecting nitrogen into the airbag cavity using an air needle, the internal pressure and wall temperature of the airbag are collected, and the current state estimate is calculated based on the internal pressure and wall temperature of the airbag. The current state estimate is recursively calculated forward to obtain the predicted state trajectory; Based on the state prediction trajectory, the optimal control sequence is solved under constraints of gas flow rate, heating power, pressure range, temperature range, and wall thickness range. Extract the gas flow control quantity and heating power control quantity from the optimal control sequence, send the gas flow control quantity to the proportional valve and the heating power control quantity to the electromagnetic induction heater, until the airbag wall thickness converges to a preset uniformity range, and obtain the airbag molded part.

6. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 5, characterized in that, The step of performing forward recursive calculation on the current state estimate to obtain the state prediction trajectory includes: Extract the intracavitary pressure, wall temperature, and airbag wall thickness from the current state estimate; The predicted value of the cavity pressure is calculated based on the cavity pressure and the wall temperature, and the predicted value of the wall temperature is calculated based on the wall temperature and the predicted value of the cavity pressure. The predicted value of the intracavitary pressure is converted into the wall thickness change rate, and the wall thickness change rate is integrated over time to obtain the predicted value of the airbag wall thickness. Based on the predicted values ​​of intracavitary pressure, wall temperature, and airbag wall thickness, repeated forward recursive calculations are performed until the entire prediction time domain is covered, thus obtaining the state prediction trajectory at each moment within the prediction time domain.

7. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 6, characterized in that, The process of solving for the optimal control sequence based on the state prediction trajectory under constraints of gas flow rate, heating power, pressure range, temperature range, and wall thickness range includes: Based on the state prediction trajectory, a multi-objective optimization function is constructed, which includes a state deviation cost term and a control increment penalty term. Set the upper and lower limit boundary values ​​of the gas flow control quantity to obtain the gas flow constraint; set the upper and lower limit boundary values ​​of the heating power control quantity to obtain the heating power constraint; set the allowable range boundary value of the cavity pressure to obtain the pressure range constraint; set the allowable range boundary value of the wall temperature to obtain the temperature range constraint; set the allowable range boundary value of the airbag wall thickness to obtain the wall thickness range constraint. A linear inequality constraint matrix and constraint vector are formed based on the gas flow rate constraint, the heating power constraint, the pressure range constraint, the temperature range constraint, and the wall thickness range constraint. The coefficients of the quadratic terms in the multi-objective optimization function are extracted as Hessian matrices, and the coefficients of the linear terms are extracted as gradient vectors. The Hessian matrix, the gradient vector, the linear inequality constraint matrix, and the constraint vector are input into the quadratic programming solver. The iteration terminates when the multi-objective optimization function converges to the minimum value, and the optimal control sequence is output.

8. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 1, characterized in that, The process of vulcanizing the airbag molding component and determining its current vulcanization stage, then adjusting the holding time of the M temperature-controlled zones based on the current vulcanization stage to obtain a cured product, includes: During the vulcanization heating process of the airbag molding part, the capacitance value and loss tangent of the silicone are measured, and the real-time degree of vulcanization is calculated based on the capacitance value and the loss tangent. When the real-time vulcanization degree is less than the gel stage threshold, the current vulcanization stage is determined to be the gel stage; when the real-time vulcanization degree is between the gel stage threshold and the curing stage threshold, the current vulcanization stage is determined to be the curing stage; when the real-time vulcanization degree is greater than the curing stage threshold, the current vulcanization stage is determined to be the post-curing stage. Based on the current vulcanization stage, query the target temperature values ​​of M temperature control zones and calculate the holding time required to reach the target degree of vulcanization; The target temperature values ​​of the M temperature control zones are sent to the temperature controller to execute heating control. A heat preservation timer is set according to the heat preservation time. When the real-time degree of vulcanization reaches the target degree of vulcanization, the vulcanization ends and a cured product is obtained.

9. The intelligent temperature control method for the molding process of flexible airbag nose pads for eyeglasses according to claim 8, characterized in that, The step of querying the target temperature values ​​of M temperature control zones based on the current vulcanization stage and calculating the holding time required to reach the target vulcanization degree includes: Based on the current vulcanization stage, query the target temperature values ​​of M temperature control zones and calculate the average temperature value of the airbag molding part; The average temperature value, the preset frequency factor, and the preset activation energy parameter are substituted into the Arrhenius exponential function to calculate the sulfidation reaction rate constant, and the sulfidation rate value is calculated based on the sulfidation reaction rate constant and the real-time sulfidation degree. The difference between the real-time degree of sulfidation and the target degree of sulfidation is used to obtain the increment of the degree of sulfidation to be achieved. The increment of the degree of sulfidation to be achieved is divided by the sulfidation rate value and logarithmic operation is performed to obtain the holding time required to achieve the target degree of sulfidation.

10. An intelligent temperature control system for the molding process of flexible airbag nose pads for eyeglasses, characterized in that, A smart temperature control method for performing the eyeglass flexible airbag nose pad molding process as described in any one of claims 1-9 includes: The preheating module is used to preheat the M temperature-controlled zones pre-set in the flexible airbag nose pad mold of the glasses to obtain a preheated mold; The temperature adjustment module is used to monitor the rotational speed and back pressure of the injection screw and calculate the shear temperature rise of the silicone in the injection screw. Based on the shear temperature rise, the temperature setpoint of the injection screw is adjusted to obtain a stable melt of the silicone. The airbag molding module is used to inject nitrogen into the airbag cavity after the stable melt of the silicone fills the cavity of the preheated mold, and simultaneously collect the internal pressure and wall temperature of the airbag and perform model prediction optimization to obtain the airbag molded part. The vulcanization module is used to vulcanize the airbag molding part and obtain the current vulcanization stage. It adjusts the heat preservation time of the M temperature control zones according to the current vulcanization stage to obtain the cured product.

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