A temperature distribution optimization system and method for an extrusion molding device

Abstract

The present application relates to the technical field of temperature control of polymer extrusion molding process, and particularly relates to a temperature distribution optimization system and method for an extrusion molding device, which performs phase synchronization on a wall temperature detection signal based on a screw phase, corrects a wall temperature signal of a current period by using a phase response template formed in a previous rotation period, and updates the template after the period ends for subsequent use. Amplitude and change rate are extracted from the phase-dependent thermal response and normalized by a heat transfer driven temperature difference, and a heat transfer correction coefficient and reliability are determined by a parameter calibration model in combination with operating condition information; the obtained equivalent heat transfer coefficient is written into a cylinder-melt heat transfer model and a temperature distribution prediction model at the same time, and melt temperature distribution estimation and multi-temperature control zone prediction control are performed accordingly. The present application can suppress the interference of screw phase-dependent thermal response on temperature inversion under non-invasive temperature measurement conditions, and improve the axial temperature distribution estimation accuracy and control adaptability under variable operating conditions.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology in polymer extrusion molding processes, and more particularly to a temperature distribution optimization system and method for extrusion molding equipment. Background Technology

[0002] During polymer extrusion, melt temperature reflects the plasticization state and thermal history of the material, and directly affects melt viscosity, conveying stability, and product quality. To avoid localized overheating, uneven plasticization, or thermal degradation of the melt, it is necessary to obtain temperature information that characterizes the actual thermal state of the melt during extrusion and coordinate the heat input of each temperature control zone in the barrel accordingly.

[0003] Existing extrusion equipment typically incorporates temperature sensors within the barrel wall to detect the barrel wall temperature at corresponding locations. This allows for estimation of the melt temperature within the screw channel based on the barrel wall's heat transfer characteristics. This non-invasive temperature measurement method eliminates the need for the sensing element to enter the melt flow path, avoiding direct interference with melt flow. However, as the screw rotates, the screw channel region and the screw clearance region periodically pass through the circumferential position of the barrel corresponding to the wall temperature sensor. Due to variations in the near-wall material layer thickness, shear state, and local heat transfer conditions in different regions, the thermal boundary on the inner wall side of the barrel at the sensor location undergoes periodic phase changes with screw rotation. This results in a periodic thermal response related to the screw phase superimposed on the wall temperature detection signal.

[0004] When the temperature to be determined is the melt temperature of the main body of the screw channel or the average melt temperature within one screw rotation cycle, the periodic thermal response mainly characterizes the change in the local heat transfer boundary at the measurement location with the screw phase, and is not equivalent to a change in the melt temperature of the main body of the melt. If the wall temperature detection signal containing the periodic thermal response is directly used for heat conduction inversion, the melt temperature estimation result is prone to being superimposed with phase correlation bias.

[0005] Existing signal processing methods typically employ periodic averaging or low-pass filtering to suppress the periodic thermal response. However, these methods primarily separate signals based on time frequency, failing to establish a correlation between wall temperature response and screw angle position. As screw speed changes, the frequency of the periodic thermal response in the time domain also changes, making it difficult to consider different speed conditions with fixed filtering parameters. Furthermore, when the effective dynamic change in melt temperature is close to the periodic thermal response in the frequency domain, enhanced filtering may weaken the effective temperature information and increase estimation delay. On the other hand, the equivalent heat transfer coefficient on the inner wall of the barrel varies with material conveying conditions, screw operating conditions, and heat input conditions; using fixed heat transfer parameters can also cause a mismatch between the melt temperature inversion model and the control prediction model.

[0006] Therefore, how to separate the repetitive thermal response in the wall temperature detection signal based on the screw phase while maintaining the non-invasive setting of the wall temperature sensor, and how to use the near-wall heat transfer state reflected by the response to consistently correct the heat transfer boundary of the melt temperature inversion model and the temperature distribution prediction model, remains a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of this invention is to provide a temperature distribution optimization system and method for an extrusion molding apparatus, so as to reduce the influence of the phase-related thermal response caused by screw rotation on melt temperature inversion under the condition that the wall temperature sensor does not enter the material flow channel, and enable the heat transfer boundary on the inner wall of the barrel used for melt temperature inversion and temperature distribution prediction to be adaptively updated according to the operating conditions.

[0008] To address the aforementioned technical problems, the present invention provides the following technical solutions.

[0009] A temperature distribution optimization system for an extrusion molding apparatus, the extrusion apparatus including a barrel, a screw rotatably disposed within the barrel, and a plurality of temperature control zones disposed along the axial direction of the barrel, the system comprising:

[0010] The wall temperature detection assembly includes wall temperature sensors respectively disposed at multiple axial temperature measurement positions of the barrel. The temperature sensing end of the wall temperature sensor is embedded in the barrel wall and separated from the material flow channel, and is used to acquire the wall temperature detection signal at the corresponding axial temperature measurement position.

[0011] A screw phase detection component is used to acquire screw phase information characterizing the screw angular position;

[0012] The operating parameter acquisition component is used to acquire the actual heat input parameters of each temperature control zone, the operating parameters characterizing the material conveying state, and the operating parameters characterizing the screw operating state. The actual heat input parameters include heating input parameters and equivalent cooling input parameters characterizing the cooling effect.

[0013] A data acquisition component, connected to the wall temperature detection component, the screw phase detection component, and the operating parameter acquisition component, is used to acquire the wall temperature detection signal, the screw phase information, and the operating parameters, and to ensure that the wall temperature detection signal, the screw phase information, and the operating parameters have a unified time reference; and

[0014] A controller, connected to the data acquisition component and the heating and / or cooling devices of each of the temperature control zones, is configured to:

[0015] Based on the screw phase information, the wall temperature detection signals are phase synchronized so that the wall temperature detection values ​​corresponding to the same screw phase interval within different screw rotation cycles are aligned with each other.

[0016] For each axial temperature measurement position, the trend baseline and detrending residual within the current screw rotation cycle are determined based on the wall temperature detection value within the current screw rotation cycle. The phase response template formed before the start of the current screw rotation cycle is called, and the phase-related thermal response component of the current screw rotation cycle is determined based on the phase response template. The phase-related thermal response component is subtracted from the wall temperature detection signal to obtain a wall temperature signal corrected by the phase response. The phase response template is updated based on the detrending residual after the end of the current screw rotation cycle.

[0017] The response amplitude and response rate of change are extracted from the phase-correlated thermal response components. The response amplitude and response rate of change are normalized according to the heat transfer driven temperature difference. The normalized response amplitude, the normalized response rate of change, and the operating parameters constitute the current input. Neighborhood samples are selected based on the standardized distance between the current input and each pre-established calibration sample. The heat transfer correction coefficient is obtained through local weighted interpolation. The confidence value is determined based on the distance between the current input and the nearest calibration sample and the dispersion of the heat transfer correction coefficient in the neighborhood samples.

[0018] The current effective equivalent heat transfer coefficient corresponding to each of the axial temperature measurement positions is determined based on the confidence value, and the current effective equivalent heat transfer coefficient corresponding to each of the axial temperature measurement positions is written into the corresponding barrel inner wall side heat transfer boundary parameters in the barrel-melt heat transfer model and the temperature distribution prediction model with the same value.

[0019] Based on the updated barrel-melt heat transfer model, the phase-response corrected wall temperature signal, and the operating parameters, the axial melt temperature distribution of the extrusion unit is determined; and

[0020] The updated temperature distribution prediction model is used to predict the influence of the control quantity of each temperature control zone on the axial melt temperature distribution. Based on the deviation between the target melt temperature distribution and the predicted axial melt temperature distribution, at least one of the heating control quantity and cooling control quantity of each temperature control zone is determined.

[0021] Furthermore, the screw phase detection assembly includes an angle encoder, a rotary transformer, or a motor position detector associated with the screw or screw drive mechanism;

[0022] The data acquisition component is configured to use the screw phase information as a sampling trigger reference to acquire the wall temperature detection signal at a preset screw rotation angle interval, or to resample the wall temperature detection value acquired at a preset time interval to multiple preset phase intervals.

[0023] The controller is configured to update the phase response template according to the following formula:

[0024]

[0025] And adopt Correction of the first The wall temperature detection signal during the screw rotation cycle, in the... After the screw completes one rotation cycle, it generates a supply for the first... Used per screw rotation cycle .

[0026] Furthermore, the controller is also configured to normalize the response amplitude and the response rate of change according to the following formula:

[0027]

[0028]

[0029] The heat transfer correction coefficient and the confidence value are determined based on the normalized response amplitude, the normalized response change rate, and the operating parameters.

[0030] When the confidence value is not lower than the preset confidence threshold, the equivalent heat transfer coefficient corresponding to the current operating condition is determined according to the currently obtained heat transfer correction coefficient. When the confidence value is lower than the preset confidence threshold, the previous effective equivalent heat transfer coefficient or the nominal equivalent heat transfer coefficient is adopted.

[0031] The equivalent heat transfer coefficients corresponding to each of the axial temperature measurement positions are written into the corresponding barrel inner wall heat transfer boundary parameters in the barrel-melt heat transfer model and the temperature distribution prediction model, respectively, with the same value; and

[0032] Under the conditions of satisfying the upper and lower limits of the control quantity, the limit of the rate of change of the control quantity, the allowable temperature range of the barrel and the allowable temperature range of the melt in each temperature control zone, the control objective function including the target temperature tracking term, the axial temperature uniformity term and the control quantity change term is solved, and the obtained control quantity is output to the heating device and / or cooling device of the corresponding temperature control zone.

[0033] On the other hand, the present invention also provides a method for optimizing temperature distribution in an extrusion molding apparatus, the extrusion apparatus including a barrel, a screw rotatably disposed within the barrel, and a plurality of temperature control zones disposed along the axial direction of the barrel, the method comprising:

[0034] The system acquires wall temperature detection signals corresponding to multiple axial temperature measurement positions of the barrel, screw phase information characterizing the screw angle position, actual heat input parameters of each temperature control zone, operating parameters characterizing the material conveying state, and operating parameters characterizing the screw operating state. The actual heat input parameters include heating input parameters and equivalent cooling input parameters characterizing the cooling effect. The wall temperature detection signals are obtained by a wall temperature sensor whose sensing end is embedded in the barrel wall and separated from the material flow channel.

[0035] The wall temperature detection signals are phase synchronized according to the screw phase information so that the wall temperature detection values ​​corresponding to the same screw phase interval within different screw rotation cycles are aligned with each other.

[0036] For each axial temperature measurement position, the trend baseline and detrending residual within the current screw rotation cycle are determined based on the wall temperature detection value within the current screw rotation cycle. The phase response template formed before the start of the current screw rotation cycle is called, and the phase-related thermal response component of the current screw rotation cycle is determined based on the phase response template. The phase-related thermal response component is subtracted from the wall temperature detection signal to obtain a wall temperature signal corrected by the phase response. After the current screw rotation cycle ends, the phase response template is updated based on the detrending residual so that the updated phase response template can be used for the next screw rotation cycle.

[0037] The response amplitude and response change rate are extracted from the phase-correlated thermal response components. The heat transfer driving temperature difference is determined based on the difference between the most recent effective melt temperature estimate and the periodic average value of the phase-response corrected wall temperature signal within the current screw rotation cycle. The response amplitude and response change rate are then normalized based on the heat transfer driving temperature difference.

[0038] Based on a pre-established calibration sample set, the normalized response amplitude, the normalized response change rate, the actual heat input parameters, the operating parameters characterizing the material conveying state, and the operating parameters characterizing the screw operating state constitute the current input. Neighborhood samples are selected according to the standardized distance between the current input and each calibration sample, and the heat transfer correction coefficient is obtained through local weighted interpolation. The confidence value is determined based on the distance between the current input and the nearest calibration sample and the dispersion of the heat transfer correction coefficient in the neighborhood samples.

[0039] When the confidence value is not lower than the preset confidence threshold, the current effective equivalent heat transfer coefficient corresponding to each of the axial temperature measurement positions is determined according to the currently obtained heat transfer correction coefficient and nominal equivalent heat transfer coefficient; when the confidence value is lower than the preset confidence threshold, the previous effective equivalent heat transfer coefficient or nominal equivalent heat transfer coefficient of the corresponding axial temperature measurement position is determined as the current effective equivalent heat transfer coefficient; and the current effective equivalent heat transfer coefficient corresponding to each of the axial temperature measurement positions is written into the corresponding barrel inner wall side heat transfer boundary parameters in the barrel-melt heat transfer model and temperature distribution prediction model with the same value.

[0040] Based on the updated barrel-melt heat transfer model, the phase-response corrected wall temperature signal, the actual heat input parameters, the operating parameters characterizing the material conveying state, and the operating parameters characterizing the screw operating state, the estimated melt temperature values ​​corresponding to each of the axial temperature measurement positions are determined to obtain the axial melt temperature distribution of the extrusion unit; and

[0041] The updated temperature distribution prediction model is used to predict the influence of the control quantity of each temperature control zone on the axial melt temperature distribution. Based on the deviation between the target melt temperature distribution and the predicted axial melt temperature distribution, at least one of the heating control quantity and cooling control quantity of each temperature control zone is determined.

[0042] It should be understood that the so-called operating condition information refers to information that can characterize the current thermal effects, material conveying status, and screw operating status of the extrusion molding unit, including at least one of the heating input parameters and cooling input parameters of each temperature control zone, material flow rate, and one of screw speed and screw drive torque. The operating condition information can be either direct measurement values ​​from the corresponding detection device or equivalent parameters converted from the direct measurement values.

[0043] The heat transfer state information refers to information obtained from the thermal response related to the screw phase that reflects the near-wall flow and heat transfer state at the corresponding axial position. It may include at least one of the following: response amplitude, response rate of change, and characteristics normalized by the heat transfer-driven temperature difference. This heat transfer state information is used to determine or correct the heat transfer boundary parameters between the barrel inner wall and the melt.

[0044] The heat transfer boundary parameter is used to characterize the heat transfer intensity between the inner wall of the barrel and the melt. In this embodiment, the equivalent heat transfer coefficient is used as the heat transfer boundary parameter. When using the equivalent thermal resistance per unit heat transfer area to characterize the same heat transfer boundary, the equivalent thermal resistance and the equivalent heat transfer coefficient can be converted according to a reciprocal relationship. The following explanation mainly uses the current effective equivalent heat transfer coefficient as an example.

[0045] Further, phase synchronization processing is performed on each of the wall temperature detection signals based on the screw phase information, including:

[0046] The screw phase information is obtained by an angle detection device associated with the screw or screw drive mechanism;

[0047] Using the screw phase information as a sampling trigger reference, the wall temperature detection signal is collected at preset screw rotation angle intervals to obtain wall temperature detection values ​​sampled at equal angles; or, the wall temperature detection signal is collected at preset time intervals, and the collected wall temperature detection values ​​are resampled to multiple preset phase intervals according to the screw phase corresponding to each sampling time; and

[0048] The wall temperature detection value is divided into periods based on the reference phase of each screw rotation cycle, so that the wall temperature detection values ​​corresponding to the same phase interval in different screw rotation cycles are aligned with each other.

[0049] Furthermore, for the first For each axial temperature measurement location, the phase response template is updated according to the following formula:

[0050]

[0051] in:

[0052] Indicates the first The first screw rotation cycle is formed before the start of the first screw rotation cycle. Phase response template values ​​corresponding to each phase interval;

[0053] Indicates the first Within the first screw rotation cycle, the first The detrended residuals corresponding to each phase interval;

[0054] Indicates the first Template update coefficient corresponding to each screw rotation cycle;

[0055] Indicates the first The first screw rotation cycle completed, forming the... Phase response template values ​​corresponding to each phase interval;

[0056] Among them, adopt Determine the first The phase-related thermal response components within each screw rotation cycle, and For the One screw rotation cycle.

[0057] Furthermore, the template update coefficient is determined based on the operating condition change index, which is determined by weighting the normalized information as follows:

[0058] The deviation between the detrended residual of the current screw rotation cycle and the previously formed phase response template;

[0059] The change in screw speed within a preset number of consecutive screw rotation cycles;

[0060] The change in material flow rate during the continuous screw rotation cycle; and

[0061] The amount of change in the actual heat input parameters of each temperature control zone during the continuous screw rotation cycle;

[0062] Among them, each change is normalized according to the allowable fluctuation range of the corresponding operating parameters under the predetermined stable operating conditions;

[0063] When the operating condition change index is not greater than a preset change threshold, the template update coefficient is determined as the first update coefficient; when the operating condition change index is greater than the preset change threshold, the template update coefficient is determined as the second update coefficient, so that the second update coefficient used when the operating condition changes is greater than the first update coefficient used when the operating condition is stable, wherein:

[0064] In the formula, This represents the first update coefficient. This represents the second update coefficient.

[0065] Furthermore, for the first The first axial temperature measurement position and the first For each screw rotation cycle, the heat transfer driving temperature difference is determined according to the following formula:

[0066]

[0067] in, This represents the estimated melt temperature at the time the previous temperature was determined. This represents the periodic average value of the phase-response corrected wall temperature signal within the current screw rotation cycle; the response amplitude is the difference between the peak and trough values ​​of the phase-correlated thermal response component within one screw rotation cycle; the response change rate is the temperature change rate of the phase-correlated thermal response component within a preset rising or falling range; the normalized response amplitude and the normalized response change rate are determined according to the following formulas:

[0068]

[0069]

[0070] in, This indicates the response amplitude. This indicates the rate of change of the response. This indicates the preset lower limit of the temperature difference.

[0071] Furthermore, the parameter calibration model includes calibration sample sets established for each of the axial temperature measurement positions; after standardizing the parameters of each dimension of the current input and each calibration sample, multiple calibration samples with the smallest standardized distance from the current input are selected as neighborhood samples, and the weight of each neighborhood sample is determined according to the standardized distance. The heat transfer correction coefficient and the confidence value are determined by local weighted interpolation.

[0072]

[0073] in, This represents the heat transfer correction coefficient. This represents the confidence value; a higher confidence value indicates a more reliable current heat transfer correction coefficient. This represents an operating parameter vector consisting of the actual heat input parameters, the operating parameters characterizing the material conveying state, and the operating parameters characterizing the screw operating state. This represents the parameter calibration model;

[0074] when When the current operating condition is met, the equivalent heat transfer coefficient is determined according to the following formula:

[0075]

[0076] in, This indicates a preset confidence threshold. This represents the equivalent heat transfer coefficient corresponding to the current operating conditions. Indicates the nominal equivalent heat transfer coefficient;

[0077] when When the heat transfer boundary parameters of the inner wall of the barrel are updated, the previous effective equivalent heat transfer coefficient or the nominal equivalent heat transfer coefficient is used.

[0078] Furthermore, in the Within each parameter update cycle, the heat transfer boundary on the inner wall side of the barrel in the barrel-melt heat transfer model satisfies:

[0079]

[0080] in, Indicates the first The heat flux density of the inner wall of the barrel corresponding to each axial temperature measurement position This indicates the melt temperature at the corresponding axial temperature measurement position. This indicates the temperature of the inner wall of the barrel at the corresponding axial temperature measurement position;

[0081] The temperature distribution prediction model uses the same equivalent heat transfer coefficient as the barrel-melt heat transfer model, and determines the control variables for each temperature control zone by solving the following control objective function:

[0082]

[0083]

[0084]

[0085] in, Indicates the prediction time domain, Indicates control of the time domain, Indicates the first The first control time predicted by the [number]th control time. Axial melt temperature distribution at each control moment Indicates the temperature distribution of the target melt. Represents the axial temperature difference matrix. This indicates the change in the control quantity for each of the aforementioned temperature control zones. , and The weights represent the control objectives; the solution of the control objective function satisfies the upper and lower limits of the control quantity, the limit of the rate of change of the control quantity, the allowable temperature range of the barrel, and the allowable temperature range of the melt for each of the temperature control zones.

[0086] Compared with the prior art, the present invention has at least the following beneficial effects:

[0087] This invention uses the screw phase, rather than a fixed time frequency, as the synchronization reference for the wall temperature detection signal, enabling wall temperature responses corresponding to the same screw angle position at different screw rotation speeds to be aligned within the phase domain. During the current screw rotation cycle, only the phase response template formed in the previous cycle is used for correction, and the template is updated again using the detrended residual after the current cycle ends. This avoids occasional non-phase-synchronous temperature changes within the current cycle from being immediately written into the template and subtracted from the wall temperature signal of the same cycle, ensuring that the corrected wall temperature signal retains effective dynamic information for melt temperature inversion.

[0088] This invention further uses the phase-correlated thermal response separated from the wall temperature detection signal as observation information of the near-wall heat transfer state, rather than discarding it directly as interference. The amplitude and rate of change of the phase response are normalized by the heat transfer-driven temperature difference, and a heat transfer correction coefficient is determined from calibration samples established for a specific extrusion unit, combined with heat input, material conveying status, and screw operating status. Simultaneously, the reliability of the current identification results is evaluated using the distance to the nearest calibration sample and the dispersion of neighboring samples. When the current operating condition deviates from the calibration range or sample consistency is insufficient, previously valid parameters are used, thereby reducing heat transfer boundary distortion caused by abnormal data or unfounded extrapolation.

[0089] This invention updates the barrel-melt heat transfer model and temperature distribution prediction model using the same currently effective equivalent heat transfer coefficient, ensuring that the melt temperature state estimation and temperature control prediction adopt the same heat transfer boundary on the inner wall of the barrel. Therefore, the phase-response corrected wall temperature information, the online determined heat transfer state, and the control variables for multiple temperature control zones are correlated within the same thermal model framework. This reduces parameter mismatch between the state estimation model and the control prediction model, improving the adaptability of axial melt temperature distribution estimation and control to changes in operating conditions.

[0090] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0091] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0092] Figure 1 Schematic diagram of the control system structure for optimizing temperature distribution in an extrusion unit;

[0093] Figure 2 Flowchart of a method for optimizing temperature distribution control in an extrusion unit;

[0094] Figure 3 Schematic diagram of the control function architecture for temperature distribution optimization;

[0095] Figure 4 A schematic diagram illustrating phase synchronization, trend baseline determination, and phase response template formation;

[0096] Figure 5 This is a schematic diagram of the heat transfer model of the barrel melt. Detailed Implementation

[0097] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0098] Example 1

[0099] like Figure 1 As shown, this embodiment provides a temperature distribution optimization system for an extrusion molding apparatus, which performs... Figure 2The temperature distribution optimization method is shown. The extrusion molding apparatus includes a barrel, a screw disposed within the barrel, a drive mechanism for rotating the screw, and multiple temperature control zones distributed along the barrel axis. Each temperature control zone is equipped with a heating unit and a cooling unit to regulate the thermal state of the corresponding barrel section. An electric heater is used to input heat to the corresponding barrel section; the cooling mechanism is used to remove heat from the corresponding barrel section. When using an air-cooled mechanism, the cooling intensity can be adjusted by the fan's duty cycle; when using a liquid-cooled mechanism, the cooling intensity can be adjusted by the cooling medium flow rate or the control valve opening.

[0100] Figure 2 The method described includes, in sequence, data acquisition, phase response processing, heat transfer parameter determination and updating, melt temperature inversion, temperature distribution prediction, and control execution. Condition monitoring and anomaly handling are integrated throughout these stages, used to limit parameter updates and switch control strategies when the screw phase signal, wall temperature detection signal, or model condition becomes abnormal.

[0101] Multiple axial temperature measurement positions are set along the barrel axis, each located within the main temperature control area of ​​its corresponding temperature control zone. Each axial temperature measurement position is equipped with a wall temperature sensor embedded in the barrel wall. The sensing end of the wall temperature sensor maintains a metal layer between itself and the inner wall of the barrel and does not enter the material flow channel. In this embodiment, a 0.5mm outer diameter armored thermocouple is used, with the sensing end 1.5mm from the inner wall of the barrel. These dimensions represent a specific implementation configuration; in actual applications, the configuration can be determined based on the barrel wall thickness, sensor dynamic response, and allowable installation strength. This ensures the sensor can detect dynamic temperature changes near the inner wall while avoiding direct exposure to melt pressure and screw shearing action.

[0102] In this embodiment, the axial temperature measurement positions and temperature control zones are established in correspondence according to the axial position of the barrel. One temperature control zone can correspond to one or more axial temperature measurement positions, and adjacent temperature control zones can also share the axial temperature measurement position located in their boundary area. The controller stores the mapping relationship between the axial temperature measurement positions, hot zones, and temperature control zones to uniformly map the wall temperature detection signal, melt temperature estimate, and temperature control zone control quantity to the barrel axial coordinates.

[0103] Each of the wall temperature sensors has the same angular offset relative to a preset circumferential reference at its mounting position in the barrel circumferential direction, ensuring a consistent screw phase reference for wall temperature signals at different axial positions. After installation, static temperature calibration and dynamic response calibration are performed on each sensor: static temperature calibration corrects zero-point deviation and sensitivity deviation, while dynamic response calibration obtains the equivalent time constant formed by the sensor and its protective layer. The controller performs uniform dynamic compensation on the wall temperature detection signal based on the equivalent time constant; the compensation bandwidth and gain are limited by the sensor noise level and the barrel wall thermal response bandwidth to avoid amplifying measurement noise and misinterpreting it as a phase response difference.

[0104] An angle encoder is mounted on the screw drive shaft. The angle encoder maintains a defined angular position relationship with the screw and outputs a reference pulse per revolution and an angle increment pulse. The reference pulse per revolution is used to determine the starting phase of each screw rotation cycle, and the angle increment pulse is used to determine the screw angular position at any sampling moment. This embodiment uses an angle encoder that outputs 2048 increment pulses per revolution.

[0105] The data acquisition components are connected to each wall temperature sensor, angle encoder, power detection circuit of each temperature control zone, cooling mechanism, feeding metering device, and screw driver. The wall temperature detection signal, screw phase information, and operating parameters are all time-stamped by the same hardware clock. The sampling frequency of the wall temperature detection signal is set to 1kHz, the angle encoder signal is acquired by the high-speed counting channel, and the sampling frequency of the operating parameters is set to 100Hz.

[0106] The actual heat input parameters for each temperature control zone are determined based on the actual voltage, current, and duty cycle of the heating circuit. For ease of explanation, this embodiment uses both the actual heating power and the equivalent cooling input as heat effect parameters, where the equivalent cooling input is taken as a non-negative value and is included as a subtraction term in the energy balance relationship. The actual heating power of the j-th temperature control zone at time t is calculated according to the following formula:

[0107]

[0108] in, Indicates the first The actual voltage of each heating circuit Indicates the actual current. This indicates the duty cycle of the heating circuit within the corresponding statistical period. The actual cooling effect of the cooling mechanism is converted into an equivalent cooling input based on the fan's duty cycle, cooling medium flow rate, or control valve opening. The operating parameters characterizing the material conveying status are material mass flow rates. The operating parameters characterizing the screw's operating state are the screw speed. and screw drive torque The controller combines the above parameters into an operating parameter vector:

[0109]

[0110] like Figure 3 As shown, the controller functionally includes a data acquisition unit, a phase response processing unit, a heat transfer parameter determination unit, a melt temperature inversion unit, a temperature prediction control unit, an execution feedback unit, and a status monitoring and anomaly handling unit. These functional units can be implemented by software modules executed by the same processor, or they can be implemented collaboratively by multiple processors or control modules; the status monitoring and anomaly handling unit exchanges status information with the other functional units.

[0111] In this embodiment, the thermal parameters of each temperature control zone, material mass flow rate, screw speed, and screw drive torque received by the controller together constitute the operating condition information; the response features extracted from the phase-correlated thermal response components and normalized by the heat transfer-driven temperature difference constitute the heat transfer state information. The controller determines the heat transfer correction coefficient based on the heat transfer state information and the operating condition information, and forms the current effective equivalent heat transfer coefficient accordingly.

[0112] The equivalent heat transfer coefficient is used to characterize the overall heat transfer intensity between the barrel inner wall and the melt. It is equivalent to the heat transfer boundary on the barrel inner wall side, including near-wall material renewal, local shear, contact state, and other factors that are difficult to measure online separately. Therefore, parameter update is not a pure mathematical fitting of the wall temperature signal, but rather the conversion of the near-wall heat transfer information contained in the phase-correlated thermal response into physical boundary parameters in the dynamic heat transfer model.

[0113] During extruder operation, the screw channel region and the screw clearance region alternately pass through the same wall temperature measurement position as the screw rotates. Different regions correspond to different near-wall material layer thicknesses, local shear states, material turnover rates, and heat transfer conditions. Therefore, the wall temperature detection signal will generate a repetitive thermal response that corresponds to the screw angle position. This thermal response does not require direct contact between the screw edges and the inner wall of the barrel; rather, it reflects the changes in the near-wall flow boundary and heat transfer boundary caused by screw rotation.

[0114] The repetitive thermal response is superimposed on the wall temperature change, which is jointly formed by the overall melt temperature, barrel thermal inertia, heat input in the temperature control zone, and material conveying status. When the screw speed changes, the frequency of the repetitive thermal response in the time domain also changes. To avoid the fixed-time filtering band failing to adapt to speed changes, this embodiment performs phase synchronization processing on the wall temperature signal according to the screw angle position.

[0115] like Figure 4 As shown, the original wall temperature signal is first resampled to the phase domain according to the screw phase, and then the trend baseline within the period is removed from the phase-synchronized wall temperature signal to obtain the detrended residual. The phase response template formed in the previous screw rotation cycle represents the repeatable thermal response that can be predicted by the screw phase, and the phase response template is subtracted from the current phase-synchronized wall temperature signal to obtain the phase-response corrected wall temperature signal.

[0116] The first The rotation cycle of each screw is divided into: A phase interval, which is taken in this embodiment as:

[0117]

[0118] No. The center phase of each phase interval is:

[0119]

[0120] The controller determines the screw phase based on the encoder count value corresponding to each wall temperature sampling moment, and resamples the wall temperature detection values ​​collected at fixed time intervals to each phase interval. For cases where the center of a phase interval does not coincide with the actual sampling moment, linear interpolation is performed between two adjacent sampled values. After phase synchronization processing, the... The axial temperature measurement position is at the first Within the first screw rotation cycle, the first The wall temperature detection values ​​corresponding to each phase interval are denoted as:

[0121]

[0122] After the phase synchronization process described above, the wall temperature detection values ​​corresponding to the same screw angle position within different screw rotation cycles are aligned with each other. Even if the durations of adjacent rotation cycles are different, the temperature response patterns in the phase domain can still be compared.

[0123] Within each screw rotation cycle, the wall temperature signal may also exhibit slow changes due to overall barrel heating, overall melt cooling, or changes in heat input to the temperature control zone. If the wall temperature detection values ​​at the same phase position within different rotation cycles are directly averaged, these slow changes will be included in the phase response template. Therefore, the controller determines a trend baseline for each screw rotation cycle.

[0124] This embodiment uses robust weighted linear fitting to determine the trend baseline within the period. The first... The normalized position of each phase interval in the current rotation cycle is denoted as:

[0125]

[0126] No. The axial temperature measurement position is at the first The trend baseline within each rotation cycle is represented as follows:

[0127]

[0128] in, The baseline temperature indicating the starting position of the current cycle. This represents the overall temperature change within the current period. Parameter and Determined by solving the following formula:

[0129]

[0130] During the initial fitting, all sampled values ​​are given the same weight. After the initial fitting, robust weights are calculated based on the fitting residuals. The greater the deviation of a local peak or trough from the initial baseline, the smaller its corresponding weight. The controller then performs fitting again using the updated weights, ensuring that the trend baseline primarily reflects the overall temperature change within one rotation cycle, rather than following local peaks and troughs related to the screw phase.

[0131] Robust weights can be determined based on standardized residuals using the Huber weight function or the Tukey double weight function: higher weights are assigned when the standardized residuals do not exceed a set threshold, and lower weights are assigned as the residuals increase when they exceed the set threshold. The robust weight function used is to suppress the pull of local peaks and troughs on the trend baseline without changing the principle of determining the trend baseline based on the overall temperature change within the period.

[0132] No. The axial temperature measurement position is at the first The first rotation cycle, the first The detrended residuals for each phase interval are:

[0133]

[0134] Detrending processing does not aim to separate fixed-frequency components, but rather to eliminate differences in overall temperature levels and intra-cycle temperature slopes between rotational cycles, allowing residuals within the same phase interval across different rotational cycles to be compared under a consistent benchmark. After the extruder starts, the controller first initializes the phase response template. Once material delivery enters a continuous state and screw speed fluctuations do not exceed the set range, continuous data acquisition begins. One complete screw rotation cycle. In this embodiment, we take:

[0135]

[0136] The initial phase response template is determined according to the following formula:

[0137]

[0138] To prevent the non-zero mean value generated by the trend baseline fitting error from entering the template, the initial template is subjected to mean removal processing:

[0139]

[0140] The resulting phase response template primarily preserves the wall temperature changes that recur steadily at the same screw phase within multiple screw rotation cycles. The template's periodic average is zero; therefore, subtracting the template from the wall temperature detection signal does not directly alter the average wall temperature level for the corresponding rotation cycle.

[0141] After template initialization is complete, in the first... At the start of the screw rotation cycle, the controller calls the... The phase response template formed after one screw rotation cycle The phase-dependent thermal response components of the current rotation period are:

[0142]

[0143] The phase-response corrected wall temperature value is determined according to the following formula:

[0144]

[0145] The phase-response corrected wall temperature signal only subtracts the repetitive response predicted based on the template formed in the previous rotation cycle. Wall temperature changes caused by variations in material flow rate, melt temperature, heat input, and other disturbances not synchronized with the screw phase are still retained. Therefore, this signal can be used as a measurement basis for melt temperature feedback. (Currently the...) After each screw rotation cycle, the controller updates the template using the detrended residual of the current cycle:

[0146]

[0147] Updated For the Used for one screw rotation cycle, not for recalibrating the sampled part that has already been sampled. One screw rotation cycle. By placing the template call and template update in adjacent rotation cycles, it is possible to prevent occasional temperature changes within the current cycle from being immediately absorbed into the template and then subtracted from the wall temperature signal of the same cycle.

[0148] After each template update, the mean removal process is re-executed on the updated results:

[0149]

[0150] The template update factor is determined based on the degree of change in extrusion conditions. The controller first calculates the template deviation:

[0151]

[0152] in, This represents the detrended residual vector for the current period. This represents the phase response template vector before the start of the current cycle. The values ​​are positive. The changes in screw speed, material flow rate, and actual heat input are respectively:

[0153]

[0154]

[0155]

[0156] Based on the above changes, construct the following operating condition change indicators:

[0157]

[0158] in, to The weights are non-negative and satisfy:

[0159]

[0160] when:

[0161]

[0162] When using the first update coefficient:

[0163]

[0164] when:

[0165]

[0166] When the second update coefficient is used:

[0167]

[0168] And satisfy:

[0169]

[0170] This embodiment uses:

[0171] Under steady-state conditions, a smaller update coefficient is used to ensure the template primarily retains the stable, repetitive responses across multiple rotation cycles while suppressing random noise. When conditions change, a larger update coefficient is used to allow the template to adapt to changes in the near-wall material state and local heat transfer status. The phase response template is also used to extract response features characterizing the near-wall heat transfer state. The axial temperature measurement position is at the first The response amplitude within one rotation cycle is:

[0172]

[0173] The controller identifies consecutive rising intervals of the phase response within one screw rotation cycle and determines the interval with the largest cumulative temperature rise as the main rising interval. When multiple intervals have the same or similar cumulative temperature rise, the interval with the smaller phase span and stable position across cycles is selected first. Within the main rising interval, the controller determines the screw phase when the response reaches 10% and 90% of the peak-to-valley amplitude, respectively, through interpolation. The difference between the two phases is converted into a time difference based on the current screw speed.

[0174]

[0175] in, The unit is The rate of change of the response is:

[0176]

[0177] When the main characteristic of the phase response is a decreasing process, the controller uses the main decreasing interval to determine the change time and uses the absolute value of the change rate as... .

[0178] The phase response amplitude and the rate of change of the response are simultaneously affected by the near-wall heat transfer state and the average heat transfer temperature difference. In order to reduce the direct impact of the change in the average heat transfer temperature difference on the characteristic quantities, the controller uses the melt temperature estimate obtained at the previous temperature estimation time and the corrected wall temperature average value of the current rotation cycle to determine the heat transfer driving temperature difference.

[0179] No. The axial temperature measurement position is at the first The average corrected wall temperature within each rotation cycle is:

[0180]

[0181] The heat transfer driven temperature difference is:

[0182]

[0183] in, This represents the melt temperature estimate obtained from the most recent effective state estimation before the current heat transfer correction coefficient is determined. Using the most recent effective estimate avoids the algebraic closed loop formed by the interdependence between the current heat transfer correction coefficient and the current melt temperature estimate, which cannot be directly solved. The normalized response amplitude and response rate of change are as follows:

[0184]

[0185]

[0186] This embodiment will Set as .when Less than At this time, the current phase response characteristics are used only for condition monitoring and not for updating the heat transfer correction coefficient. This processing is used to prevent the eigenvalues ​​from being abnormally amplified due to an excessively small normalization denominator.

[0187] The parameter calibration model is established before the extrusion unit is put into normal production. Calibration tests cover screw speed, material flow rate, barrel temperature, actual heat input, and screw drive torque within a predetermined production range. For each calibration condition, operation is maintained until the wall temperature, material flow rate, and screw torque enter the allowable fluctuation range. At least 30 complete screw rotation cycles are continuously collected, and the normalized response amplitude and normalized response change rate are obtained according to the above method. The parameter calibration model is established for a specific extrusion unit, sensor mounting structure, and predetermined material system; when changes occur in the barrel structure, screw configuration, sensor mounting location, or main material system sufficient to alter the near-wall heat transfer characteristics, recalibration is performed.

[0188] During the calibration phase, a reference melt temperature sensor is temporarily installed at the corresponding axial position. Based on the known barrel wall thermal parameters and actual heat input, the reference equivalent heat transfer coefficient is obtained through offline reverse heat conduction calculation. Subscript Indicates the first The calibration samples are grouped together. The corresponding heat transfer correction coefficient calibration values ​​are:

[0189] The reference melt temperature sensor is only used for parameter calibration or independent verification. It can be removed during the online production control phase and does not constitute a necessary component for implementing the non-invasive wall temperature detection and temperature distribution optimization method.

[0190]

[0191] in, Indicates the first The nominal equivalent heat transfer coefficient at each axial position. The input vector for the group of calibration samples is:

[0192]

[0193] To avoid uneven impacts on distance calculations due to differences in the units and numerical ranges of the input parameters, the mean and standard deviation of the calibration data are used to standardize the input for each dimension. The current input vector generated during online runtime is denoted as... This embodiment uses local weighted interpolation to construct the parameter calibration model. The controller calculates the standardized distance between the current input vector and each calibration sample, and selects the sample with the smallest distance. The group of calibrated samples forms a neighborhood sample set. . No. The weights of the neighboring samples are:

[0194]

[0195] in, Indicates the relationship between the current input vector and the first... Standardized distance between the group of calibrated samples It is a positive number. The heat transfer correction factor is determined according to the following formula:

[0196]

[0197] The weighted dispersion of the heat transfer correction coefficient in the neighborhood samples is:

[0198]

[0199] The confidence value is determined based on the distance to the most recently calibrated sample and the dispersion of neighboring samples:

[0200]

[0201] in, This represents the standardized distance between the current input and the most recently calibrated sample. and These represent the distance scale parameter and the discrete scale parameter, respectively, and are determined based on the cross-validation results of the calibration data. The closer the current input is to the calibration sample, and the more concentrated the heat transfer correction coefficients corresponding to the neighborhood samples, the higher the confidence value.

[0202] It should be understood that the confidence value is constructed using an exponential decay form, such that the confidence value monotonically decays in the interval [0,1] as the distance between the current input and the nearest calibrated sample increases and the dispersion of the heat transfer correction coefficient in the neighboring samples increases; when the distance approaches zero and the dispersion approaches zero, the confidence value approaches 1; when the distance or dispersion approaches infinity, the confidence value approaches 0.

[0203] After calibration, the parameter calibration model is verified using a validation condition that does not participate in the establishment of the locally weighted interpolation model. Under this validation condition, a reference melt temperature is obtained using a temporarily set reference melt temperature sensor, and this reference melt temperature is compared with the estimated melt temperature output by the barrel-melt heat transfer model. When the validation error exceeds a preset allowable range, additional calibration samples are added near the corresponding screw speed, material flow rate, heat input, or material conditions, and the number of neighborhood samples, distance weight parameters, and confidence thresholds are redefined. The data from this validation condition is not used as interpolation samples in the same verification to avoid using the same data for both model fitting and performance evaluation.

[0204] when At that time, the currently obtained heat transfer correction coefficient is used to determine the candidate equivalent heat transfer coefficient:

[0205]

[0206] This embodiment will Set as To avoid abrupt changes in the equivalent heat transfer coefficient between adjacent update times, range and rate of change restrictions are imposed on the candidate equivalent heat transfer coefficients. The effective equivalent heat transfer coefficient after restrictions satisfies:

[0207]

[0208] as well as:

[0209]

[0210] When the credibility value is lower than If the current interpolation result is not used, the previous effective equivalent heat transfer coefficient will continue to be used. If the previous effective value has not yet been formed after system startup, the nominal equivalent heat transfer coefficient will be used. .

[0211] like Figure 5 As shown, the barrel-melt heat transfer model is divided into six thermal zones according to the axial temperature measurement location. The barrel wall of each thermal zone is radially discretized into three thermal nodes, corresponding to the outer wall side, the depth of the wall temperature sensor, and the inner wall side, respectively. The heat capacity of each node and the thermal resistance between nodes are determined based on the barrel material density, specific heat capacity, thermal conductivity, and actual geometric dimensions. A measurement correspondence is established between the calculated temperature of the node where the wall temperature sensor is located and the corresponding calibrated wall temperature detection value.

[0212] Figure 5 In this model, the connection between the barrel inner wall node and the melt node represents the heat transfer boundary determined by the current effective equivalent heat transfer coefficient, rather than a pre-fixed interfacial thermal resistance. This heat transfer boundary is updated with the heat transfer correction coefficient and remains consistent in both the state estimation model and the temperature distribution prediction model.

[0213] For the For each hot zone, the discrete model of the cylinder wall is represented as follows:

[0214]

[0215] in, Indicates the state estimation period number. Indicates the state estimation period. This represents the temperature vector at three nodes on the barrel wall. Represents the heat capacity matrix. This represents the thermal conductivity and ambient heat dissipation matrix. , and These represent the distribution vectors for heating input, cooling input, and heat flux through the barrel's inner wall, respectively. The heat flux density through the barrel's inner wall is determined by the following formula:

[0216]

[0217] in, Indicates the first The melt temperature of each axial section, This indicates the corresponding barrel inner wall temperature. When the melt temperature is higher than the barrel inner wall temperature... A positive value indicates that heat is transferred from the melt to the barrel wall. The melt energy balance relationship for each axial section is as follows:

[0218]

[0219] in, Indicates the first Equivalent melt mass within each axial segment Indicates the specific heat capacity of the material. This indicates the heat exchange area of ​​the inner wall of the barrel in the corresponding section. This represents the heat input generated by the mechanical work of the screw within this section. This indicates heat loss not described by the barrel wall heat exchange item and the material axial transport item.

[0220] The mechanical work and heat input of the screw is determined by the following formula:

[0221]

[0222] in, Indicates the screw angular velocity. Indicates the distribution of mechanical work to the first The coefficients for converting each axial segment into melt heat. The coefficients corresponding to each axial segment. Determined through steady-state operating data and energy balance experiments.

[0223] The temperature of each barrel wall node and the melt temperature of each axial section are combined to form a state vector:

[0224]

[0225] The dynamic heat transfer model is written as:

[0226]

[0227] in, This indicates the heating and cooling control values ​​for each temperature control zone. This represents a vector composed of the effective equivalent heat transfer coefficients at each axial position. This indicates an unmodeled disturbance.

[0228] The controller calculates the phase-response corrected wall temperature values ​​obtained during each screw rotation cycle according to the state estimation cycle. Resampling to form a measurement vector:

[0229]

[0230] The measurement equation is:

[0231]

[0232] matrix Used to extract the temperature of each radial node where the wall temperature sensor is located from the state vector. This represents measurement noise. This embodiment uses an extended Kalman filter for state estimation. The predicted state value is:

[0233]

[0234] Update the prediction covariance based on the linearized matrix of the state equation at the current estimation point, and calculate the Kalman gain:

[0235]

[0236] The status correction value is:

[0237]

[0238] The melt temperature estimates at each axial position are extracted from the corrected state vector to obtain the axial melt temperature distribution:

[0239]

[0240] This embodiment will define the state estimation period. Set as The phase response template is updated according to each complete screw rotation cycle, and the equivalent heat transfer coefficient is updated according to the newly formed effective phase response characteristics, remaining constant between two adjacent parameter update times. Therefore, the screw rotation cycle is numbered... With state estimation period number They are independent of each other, and the controller provides the latest effective equivalent heat transfer coefficient to the corresponding state estimation period based on the time stamp.

[0241] In other embodiments, the state estimation can employ unscented Kalman filtering, moving time-domain estimation, or a state observer constructed based on the dynamic heat transfer model, as long as the estimated melt temperature at each axial position can be determined using the phase-response corrected wall temperature signal and the current effective equivalent heat transfer coefficient.

[0242] The temperature distribution prediction model uses the same barrel wall thermal parameters, melt energy balance relationship, and equivalent heat transfer coefficient as the state estimation model. After the current effective equivalent heat transfer coefficient corresponding to each axial position is accepted, it is written into both the barrel-melt heat transfer model and the temperature distribution prediction model with the same value, to avoid the two models using different barrel inner wall side heat transfer boundaries at the same axial position.

[0243] Temperature distribution predictive control is numbered according to the control cycle. Execution. In this embodiment, the control cycle is set to 2 seconds. At the beginning of each control cycle, the controller reads the most recent state estimation result and uses it as the initial state of the prediction model:

[0244]

[0245] Based on the candidate temperature control zone control quantity sequence, predict the future axial melt temperature distribution:

[0246]

[0247] The control objective function is:

[0248]

[0249] in, Indicates the prediction time domain, Indicates control of the time domain, Indicates the temperature distribution of the target melt. Represents the axial temperature difference matrix. This indicates the change in the control quantity of each temperature control zone relative to the previous control cycle. , and This represents the weights of the control objectives. The first term of the objective function is used to reduce the deviation between the predicted melt temperature and the target melt temperature distribution; the second term is used to reduce the melt temperature difference between adjacent axial positions; and the third term is used to limit the rate of change of the heating and cooling control quantities. The control quantities satisfy:

[0250]

[0251]

[0252] The predicted barrel temperature and melt temperature satisfy the following conditions:

[0253]

[0254]

[0255] The controller solves the constrained optimization problem described above and outputs only the first set of control variables from the resulting control sequence to the heaters and cooling mechanisms of each temperature control zone. At the next control cycle, it re-acquires the latest state estimate and the latest effective equivalent heat transfer coefficient, and then performs temperature prediction and control variable solving again.

[0256] The constrained optimization problem can be solved using quadratic programming, sequential quadratic programming, or other numerical optimization methods suitable for online solutions. The controller can also adjust the prediction time domain, control time domain, and model order based on computational resources and the control cycle.

[0257] After the system starts up, the controller first uses the nominal equivalent heat transfer coefficient operating state estimation model and simultaneously collects wall temperature data to establish the initial phase response template. Before the initial template is formed, the phase response features are only used for state monitoring and not for updating heat transfer parameters; after the initial template is formed and the signal validity meets the requirements, the wall temperature phase response correction, phase response feature extraction, heat transfer correction coefficient determination, and online update of heat transfer boundary are initiated sequentially.

[0258] If no complete reference phase signal is detected within a screw rotation cycle, or if the number of missing phase sampling points exceeds the allowable value, the current cycle will not be used to update the phase response template. When the wall temperature sensor output exceeds the set temperature range, or the sensor's rate of change exceeds the allowable range, template updating and heat transfer coefficient updating will stop at the corresponding axial position. When the heat transfer drive temperature difference is less than... Or the confidence score is lower than At this time, the previous effective equivalent heat transfer coefficient is continued to be used. The controller also calculates the wall temperature innovation of the state estimation model:

[0259]

[0260] When the norm of the innovation quantity exceeds a set threshold for multiple consecutive state estimation cycles, it is determined that there is an anomaly in the current heat transfer parameters, sensor status, or operating parameters. The controller stops accepting new heat transfer correction coefficients, allowing the corresponding equivalent heat transfer coefficient to gradually recover to the nominal value according to a preset rate of change, and switches the temperature control to a safe control state with barrel wall temperature as feedback. After the screw phase signal, wall temperature detection signal, and model innovation quantity recover to the allowable range, the phase response template is re-established and the melt temperature distribution optimization control is restored.

[0261] In the above implementation process, the phase response template has two interrelated functions: First, it uses the template formed in the previous rotation cycle to subtract the repeatable thermal response that can be predicted by the screw phase from the current wall temperature detection signal, forming a phase response corrected wall temperature signal for melt temperature inversion; Second, it extracts the response amplitude and response change rate from the phase-related thermal response, and after normalization by the heat transfer driven temperature difference, it serves as the input for determining the current near-wall heat transfer state and heat transfer correction coefficient.

[0262] The current effective equivalent heat transfer coefficient, obtained based on the aforementioned heat transfer correction coefficient, is simultaneously incorporated into the barrel-melt heat transfer model used for state estimation and the temperature distribution prediction model used for temperature control. This ensures that the wall temperature signal correction, melt temperature inversion, and multi-temperature control zone predictive control all employ a consistent heat transfer boundary on the barrel's inner wall side. Therefore, the phase-correlated thermal response is not only a periodic component that needs to be separated from the melt temperature feedback signal, but is also transformed into heat transfer state information supported by samples and constrained by reliability.

[0263] Example 2

[0264] This embodiment is used to perform tiered verification of the technical effects of screw phase-related thermal response correction, online updating of near-wall heat transfer boundaries, and the use of consistent heat transfer boundaries between the state estimation model and the temperature distribution prediction model. To avoid unclear attribution of effects due to simultaneous changes in multiple technical aspects, separate control schemes are set up for wall temperature signal processing methods, heat transfer boundary update methods, and different consistency of heat transfer boundaries between the two models.

[0265] Except for the technical conditions explicitly changed in each test group, all groups used the same batch of polymer raw materials, the same extruder and screw structure, the same wall temperature sensor and reference melt temperature sensor, the same axial temperature measurement position, the same target temperature in the temperature control zone, the same data acquisition hardware and sampling frequency, and conducted the tests under the same environmental conditions. Algorithms used for evaluating the accuracy of melt temperature estimation preferentially used the same set of synchronously acquired raw data for offline replay; control schemes used for evaluating the performance of closed-loop temperature control were run independently, and the device was brought back to the same reference stable state before each test.

[0266] Table 1. Test equipment, materials and basic conditions

[0267] polymer materials Material type, grade and batch Polypropylene (PP), grade T30S, batch number 20251008A Raw material pretreatment Drying temperature, drying time and moisture content Drying temperature 80℃, drying time 2 h, moisture content ≤0.02% extruder Screw diameter, length-to-diameter ratio and screw structure The screw has a diameter of 30 mm, a length-to-diameter ratio of 25, and is a general-purpose three-stage tapered screw with a compression ratio of 2.5. Temperature-controlled area Number of temperature-controlled zones and target temperature distribution Six temperature control zones; the target temperatures from the feed inlet to the die head are 170℃ / 180℃ / 190℃ / 200℃ / 210℃ / 210℃ respectively. Wall temperature detection Sensor type, accuracy, distance of sensing end from the inner wall of the barrel Type K thermocouple, outer diameter 0.5 mm, accuracy ±0.5℃, sensing end 1.5 mm from the inner wall of the casing, circumferentially aligned with the same mounting reference. Reference melt temperature detection Sensor type, accuracy, insertion position and dynamic response Type K thermocouple, accuracy ±0.5℃, statically calibrated, dynamic time constant 0.1 s, inserted 3 mm from the inner wall of the melt flow channel. Screw phase detection Angle detection device and resolution per revolution Incremental angle encoder, 2048 pulses per revolution, including reference pulse per revolution. Data Acquisition and Control Wall temperature sampling frequency, operating parameter sampling frequency and control cycle Wall temperature is sampled at 1 kHz, operating parameters are sampled at 100 Hz, state estimation period is 0.5 s, and control period is 2 s. Baseline operating conditions <![CDATA[Reference rotational speed n0, reference mass flow rate G0 and target allowable deviation band ±δT]]> <![CDATA[n0=100 rpm, G0=10.0 kg / h, target allowable deviation band ±δT=±2°C]]> Number of repetitions Independent repetition count and effective data acquisition length for each working condition Each operating condition was repeated independently 5 times; each time for no less than 30 complete screw rotation cycles.

[0268] Six axial temperature measurement positions are selected along the barrel axis. The sensing end of each wall temperature sensor is embedded in the barrel wall and separated from the material flow channel. Each sensor adopts the same installation reference in the circumferential direction of the barrel. The screw phase is obtained by an angle encoder associated with the screw drive shaft. The wall temperature detection signal is resampled according to the screw phase, and a complete screw rotation cycle is used as the basic unit for phase response processing.

[0269] To obtain an independent verification benchmark, a reference melt temperature sensor was temporarily installed near each axial temperature measurement location. The reference sensor underwent static calibration using a traceable temperature standard before the test, and its dynamic time constant was determined based on the step temperature response; if necessary, the dynamic delay of different sensors was compensated for to ensure consistency. The reference sensor was only used to record the reference melt temperature and did not participate in model updates or online control for any test group; it could be removed after verification.

[0270] The comparative experiment was set up in four groups as follows: The first control group directly used the wall temperature detection signal without phase response correction, and adopted the nominal and fixed equivalent heat transfer coefficient in the state estimation model and the temperature distribution prediction model; the second control group used the wall temperature signal corrected by the phase response template, but both models still adopted the nominal and fixed equivalent heat transfer coefficient; the third control group implemented phase response template correction and online update of the heat transfer boundary, but only the currently effective equivalent heat transfer coefficient determined online was used in the barrel-melt heat transfer model, and the temperature distribution prediction model still adopted the nominal equivalent heat transfer coefficient; the experimental group of this invention simultaneously implemented phase response template correction and online update of the heat transfer boundary, and made the barrel-melt heat transfer model and the temperature distribution prediction model adopt the same currently effective equivalent heat transfer coefficient at the same axial position.

[0271] Table 2 Treatment methods and evaluation objectives for each experimental group

[0272] First control group Original wall temperature signal Fixed nominal value Fixed nominal value Evaluate the combined effects of phase response and fixed boundary conditions. Second control group Phase response correction Fixed nominal value Fixed nominal value Evaluate the independent role of phase response correction Third control group Phase response correction Online update value Fixed nominal value Evaluate the effect of online updates on temperature estimation and the impact of model boundary inconsistency. This invention test group Phase response correction Online update value The same online update values ​​are used with the state estimation model. The role of evaluating the complete scheme and the boundary consistency of the two models.

[0273] The test included four operating conditions: stable operation, screw speed variation, material flow rate variation, and heat input disturbance in the temperature control zone. Under stable operation, the screw speed, material flow rate, and actual heat input in each temperature control zone were maintained at the baseline setpoints. Under screw speed variation, the screw speed was stepped from the baseline speed of 100 rpm to 120 rpm. Under material flow rate variation, the material mass flow rate was stepped from the baseline flow rate of 10.0 kg / h to 11.5 kg / h. Under heat input disturbance, the heating input in the fourth temperature control zone (200℃ zone) was reduced to 85% of the pre-disturbance control value and maintained for 120 seconds before returning to normal. The step amplitude and disturbance duration were within the equipment's safe operating range and the material processing window.

[0274] Before each test, the barrel wall temperature sensor readings, material mass flow rate, and screw drive torque at each axial position are continuously kept within a preset allowable fluctuation range, and the rate of change of the reference melt temperature is kept below 0.1℃ / min. The barrel wall temperature sensor readings are only used to confirm that the device has entered a thermally stable state and are not used as an independent verification benchmark for the accuracy of melt temperature estimation. The test sequence for each control scheme is arranged in a randomized alternating manner to reduce systematic deviations caused by long-term equipment operation, material residence time, and slow changes in ambient temperature.

[0275] For each operating condition, at least 30 complete screw rotation cycles were continuously collected, and each condition was independently repeated five times. Wall temperature signal, screw phase, heating and cooling inputs, material mass flow rate, screw speed, screw drive torque, state estimation results, reliability, and current effective equivalent heat transfer coefficient were all recorded using a unified time reference. Only cycles with a complete reference phase signal, wall temperature sensor output within the effective range, and no reference sensor malfunction were included in the evaluation; data rejection rules were determined before the experiment, and data was not selectively deleted based on experimental results. The measured values ​​listed in the following tables are the statistical mean of five repeated experiments; for continuous indicators, the mean ± standard deviation is also given.

[0276] For each independent test, the maximum absolute error is the maximum error among all valid axial positions and valid sampling times in that test; the maximum absolute errors listed in Table 3 are the mean ± standard deviation of the maximum absolute errors obtained from five independent tests. The dynamic peak deviation and recovery time are also determined separately for each individual test, and then the five independent tests are statistically analyzed to avoid directly mixing different test processes and masking transient differences.

[0277] For the The first axial temperature measurement position and the first Each sampling time, among which , The estimated melt temperature output from the barrel-melt heat transfer model is denoted as... The measured value of the corresponding reference melt temperature sensor is recorded as Using mean absolute error Root mean square error and maximum absolute error To evaluate the accuracy of melt temperature estimation, the difference between the maximum and minimum estimated melt temperature values ​​at each axial position is used. The axial temperature uniformity is evaluated by the time required for the temperature at each axial position to enter and remain within the allowable deviation band of the target temperature after the disturbance occurs. Evaluate dynamic recovery performance. The above evaluation indicators are determined according to the following formulas:

[0278]

[0279]

[0280]

[0281]

[0282] Among them, the phase residual amplitude refers to the difference between the peak and valley values ​​of the residual temperature component that still corresponds to the screw phase repeating within a complete screw rotation cycle after phase response processing; the dynamic peak deviation refers to the maximum absolute deviation of the estimated melt temperature at each axial position relative to the target temperature after the disturbance occurs; the recovery time t s This refers to the time required for the melt temperature at all axial positions to enter the target allowable deviation zone ±δT after a disturbance occurs, and to remain continuously at least the number of preset control cycles.

[0283] To evaluate the reliability of heat transfer parameter updates, the following data were also recorded: the proportion of all parameter update cycles in which the current parameter update was accepted due to a confidence level not lower than the threshold; the number of times previous effective parameters were used due to insufficient confidence; the maximum single change in the current effective equivalent heat transfer coefficient; and the difference in heat transfer boundary parameters used by the state estimation model and the temperature distribution prediction model at the same axial position.

[0284] Table 3. Phase response correction and melt temperature estimation results under steady-state conditions

[0285] experimental group Phase residual amplitude / ℃ <![CDATA[E MAE / ℃]]> <![CDATA[E RMSE / ℃]]> <![CDATA[E max / ℃]]> <![CDATA[Average ΔT z / °C]]> First control group 2.82±0.15 1.75±0.11 2.08±0.13 3.21±0.22 3.45±0.18 Second control group 0.24±0.02 0.93±0.07 1.12±0.08 1.85±0.15 2.18±0.12 Third control group 0.23±0.02 0.62±0.05 0.78±0.06 1.24±0.12 1.76±0.10 This invention test group 0.21±0.02 0.51±0.04 0.64±0.05 0.98±0.10 1.53±0.09

[0286] As shown in Table 3, after introducing phase response correction, the residual phase amplitude of the second control group decreased from 2.82℃ in the first control group to 0.24℃, a decrease of approximately 91.5%; the mean absolute error E MAE The temperature decreased from 1.75℃ to 0.93℃, a reduction of approximately 46.9%. After further online updates to the heat transfer boundary of the barrel-melt heat transfer model, the E3 of the third control group... MAE The temperature was reduced to 0.62℃. When the state estimation model and the temperature distribution prediction model use the same online-updated heat transfer boundary, the E of the experimental group of this invention... MAE Further reduced to 0.51℃, E RMSE The temperature was reduced to 0.64℃, the average of the five tests with the maximum absolute error decreased to 0.98℃, and the average axial temperature difference decreased to 1.53℃. These results validate the effects of phase response correction, online updating of the heat transfer boundary, and consistency of the dual-model boundary on improving the temperature estimation and axial temperature uniformity under steady-state conditions. Table 4 shows the dynamic temperature estimation and control results under different disturbance conditions.

[0287] Screw speed variation First control group 2.12±0.18 2.85±0.23 68±5 2.54±0.20 Screw speed variation Second control group 1.53±0.12 2.05±0.16 52±4 1.85±0.15 Screw speed variation Third control group 1.02±0.09 1.48±0.13 39±3 1.52±0.12 Screw speed variation This invention test group 0.72±0.07 1.12±0.10 31±3 1.23±0.10 Material flow rate changes First control group 1.95±0.16 2.65±0.20 62±5 2.35±0.19 Material flow rate changes Second control group 1.38±0.11 1.89±0.15 47±4 1.72±0.14 Material flow rate changes Third control group 0.95±0.08 1.35±0.11 35±3 1.40±0.11 Material flow rate changes This invention test group 0.65±0.06 1.02±0.09 28±2 1.15±0.09 thermal input disturbance First control group 1.88±0.15 2.55±0.21 58±5 2.25±0.18 thermal input disturbance Second control group 1.30±0.10 1.78±0.14 44±4 1.65±0.13 thermal input disturbance Third control group 0.88±0.07 1.25±0.10 33±3 1.38±0.11 thermal input disturbance This invention test group 0.60±0.05 0.93±0.08 26±2 1.10±0.09

[0288] As shown in Table 4, under the three disturbance conditions of screw speed, material flow rate, and heat input, the experimental group of this invention achieved the lowest root mean square error, dynamic peak deviation, recovery time, and axial temperature difference after recovery. Taking the condition of screw speed increasing from 100 rpm to 120 rpm as an example, the recovery time of the experimental group of this invention was 31 s, which was shortened by about 54.4% compared with the first control group's 68 s and by about 20.5% compared with the third control group's 39 s; the axial temperature difference after recovery was 1.23℃. Under material flow rate disturbance and heat input disturbance, the recovery times of the experimental group of this invention were 28 s and 26 s, respectively, both significantly shorter than other experimental groups. This result indicates that using the same online heat transfer boundary between the state estimation model and the prediction model helps to keep the predicted control response consistent with the actual thermal state of the device, thereby reducing model mismatch in the closed-loop control process and improving disturbance recovery capability.

[0289] Table 5. Validity of heat transfer parameter updates and consistency results of dual-model boundaries.

[0290] First control group - - - 0 (fixed nominal value) Second control group - - - 0 (fixed nominal value) Third control group 91.5±1.2 12 16.2 2.4±0.3 This invention test group 94.8±0.9 5 9.5 <0.01 (zero within the calculation precision)

[0291] As shown in Table 5, both the third control group and the experimental group of this invention performed online updates of heat transfer parameters. However, due to the different heat transfer boundaries of the prediction model used in the closed-loop control process, the thermal state trajectories experienced by the two groups of devices and the distance of the current input relative to the calibration sample differed. The effective parameter update ratio of the experimental group of this invention was 94.8%, which was 3.3 percentage points higher than that of the third control group; the number of parameter rollbacks decreased from 12 to 5, and the maximum single change in the current effective equivalent heat transfer coefficient increased from 16.2 W / (m²) to 16.2 W / (m²). 2 •K) decreased to 9.5 W / (m 2 ·K). Meanwhile, the difference in boundary parameters between the two models at the same axial position in the experimental group of this invention is less than 0.01 W / (m). 2 K), within the range of computational accuracy, can be considered zero. The above results show that a unified heat transfer boundary not only eliminates the parameter differences between the two models, but also helps to reduce parameter regression and abrupt changes caused by closed-loop condition shifts.

[0292] Based on the above stratified comparison, the only difference between the first and second control groups is the wall temperature phase response correction method. When the phase residual amplitude and E of the second control group... MAE and E RMSE When the temperature is lower than that of the first control group, it can be seen that using the screw phase as a synchronization reference and the phase response template formed in the previous cycle helps to separate the thermal response that is repeated with the screw angle position, thereby reducing the influence of the thermal response on the melt temperature inversion.

[0293] The main difference between the second and third control groups lies in the change of the heat transfer boundary of the barrel-melt heat transfer model. When the temperature estimation error of the third control group under both steady-state and disturbed conditions is lower than that of the second control group, it indicates that the heat transfer state information obtained from the phase-correlated thermal response can be used to correct the heat transfer boundary on the inner wall of the barrel and reduce the temperature estimation deviation caused by changes in operating conditions when the fixed heat transfer parameters change.

[0294] The main difference between the third control group and the experimental group of this invention lies in the heat transfer boundary used in the temperature distribution prediction model. When the experimental group of this invention has a smaller peak deviation, recovery time, and axial temperature difference after recovery compared to the third control group, and the difference in heat transfer boundary parameters between the two models remains zero or within the allowable range of calculation accuracy, it can be shown that using the same current effective equivalent heat transfer coefficient for the state estimation model and the temperature distribution prediction model helps to reduce the heat transfer boundary mismatch between the two models and improve the adaptability of multi-temperature control zone coordinated control to changes in operating conditions.

[0295] The data in Tables 3 to 5 were calculated from traceable actual experimental records. All valid replicate experiments, except for pre-defined invalid periods, have been included in the statistics. The calculation procedures, parameter configurations, and original data files for each indicator have been preserved for result verification.

[0296] The comparative experiment in this embodiment is used to illustrate the verification method of each technical step of the present invention and does not constitute a necessary step for implementing online temperature distribution optimization control. The specific material system, equipment specifications, reference speed, reference flow rate, target temperature allowable deviation band, disturbance amplitude and duration can be determined according to the actual extrusion process.

[0297] In summary, this invention uses non-invasive wall temperature detection information for axial melt temperature distribution estimation through screw phase synchronization, pre-cycle template correction, phase response characteristic normalization, and reliability-constrained heat transfer parameter updates, and coordinates heating and cooling control of multiple temperature control zones under the same heat transfer boundary conditions.

[0298] The above are merely exemplary embodiments of the present invention. Those skilled in the art can make adjustments or equivalent substitutions to the specific structure, parameter values, model discretization method, state estimation algorithm, and control solution method without departing from the technical concept of the present invention. As long as the adjustments or substitutions still employ screw phase synchronization for wall temperature response correction and utilize the heat transfer state characterized by the phase response to consistently update the heat transfer boundaries of the melt temperature estimation model and the temperature distribution prediction model, they can all be considered embodiments of the present invention.

Claims

1. A temperature distribution optimization system for an extrusion molding apparatus, the extrusion molding apparatus comprising a barrel, a screw rotatably disposed within the barrel, and a plurality of temperature control zones disposed along the axial direction of the barrel, characterized in that, The temperature distribution optimization system includes a wall temperature detection component, a screw phase detection component, and at least one processor, wherein the at least one processor is programmed to: The wall temperature detection signals corresponding to multiple axial temperature measurement positions of the barrel, screw phase information characterizing the screw angle position, and operating condition information characterizing the operating status of the extrusion molding device are acquired. The wall temperature detection signals are obtained by the wall temperature detection component disposed on the barrel wall and separated from the material flow channel. Based on the screw phase information, each wall temperature detection signal is converted to the screw phase domain, so that the wall temperature detection values ​​corresponding to the same screw phase in different screw rotation cycles are aligned with each other; For each axial temperature measurement position, the thermal response related to the screw phase within the current screw rotation cycle is determined using a phase response template formed before the start of the current screw rotation cycle. The thermal response is then removed from the wall temperature detection signal after phase synchronization to obtain a corrected wall temperature signal. After the current screw rotation cycle ends, the phase response template is updated based on the phase response information obtained from the wall temperature detection value of the current screw rotation cycle, so that the updated phase response template can be used for the next screw rotation cycle. Based on the thermal response related to the screw phase, determine the heat transfer state information characterizing the near-wall heat transfer state at the corresponding axial temperature measurement position, and based on the heat transfer state information and the operating condition information, determine the heat transfer boundary parameters used to limit the heat transfer relationship between the inner wall of the barrel and the melt at the corresponding axial temperature measurement position. The axial melt temperature distribution of the extrusion molding apparatus is determined based on the corrected wall temperature signal and the barrel-melt heat transfer model using the heat transfer boundary parameters; and Based on the deviation between the axial melt temperature distribution and the target melt temperature distribution, at least one of the heating control quantity and cooling control quantity of each temperature control zone is determined and output.

2. The temperature distribution optimization system as described in claim 1, characterized in that, The wall temperature detection assembly includes wall temperature sensors respectively disposed at multiple axial temperature measurement positions. The sensing end of each wall temperature sensor is embedded in the barrel wall and separated from the material flow channel. The installation position of each wall temperature sensor in the circumferential direction of the barrel has the same angular offset relative to a preset circumferential reference. The screw phase detection assembly includes an angle encoder, a rotary transformer, or a motor position detector associated with the screw or screw drive mechanism; The operating condition information includes thermal parameters, material conveying parameters, and screw operating parameters for each of the temperature control zones. The thermal parameters include at least one of heating input parameters and cooling input parameters characterizing the cooling effect. The at least one processor is further programmed to give the wall temperature detection signal, the screw phase information, and the operating condition information a unified time reference, and to convert each of the wall temperature detection signals to the screw phase domain based on the screw phase information, including at least one of the following methods: acquiring the wall temperature detection signal at a preset screw rotation angle interval; or, resampling the wall temperature detection value acquired at a preset time interval to multiple preset phase intervals according to the screw phase corresponding to each sampling time.

3. The temperature distribution optimization system as described in claim 1, characterized in that, The at least one processor is also programmed to: The trend baseline within the cycle is determined based on the wall temperature detection value after phase synchronization within the current screw rotation cycle, and the detrending residual is determined based on the difference between the wall temperature detection value after phase synchronization and the trend baseline within the cycle. When the material conveying state and screw operation state meet the preset stability conditions, an initial phase response template is formed based on the statistical results of the detrended residuals of multiple consecutive complete screw rotation cycles at the same screw phase. For the j-th axial temperature measurement position, the k-th screw rotation cycle, and the i-th phase interval, within the k-th screw rotation cycle, the phase response template value formed before the start of that screw rotation cycle is used. Determine the thermal response related to the screw phase, and update the phase response template after the kth screw rotation cycle according to the following formula: in: This represents the phase response template value corresponding to the i-th phase interval, formed before the start of the k-th screw rotation cycle. This represents the detrended residual corresponding to the i-th phase interval within the k-th screw rotation cycle; This represents the template update coefficient corresponding to the kth screw rotation cycle; This represents the phase response template value corresponding to the i-th phase interval, which is formed after the k-th screw rotation cycle and used for the (k+1)-th screw rotation cycle. as well as Determine based on at least one of the following: the deviation between the detrended residual and the invoked phase response template, and the change in the operating condition information. This ensures that the template update coefficient used when operating conditions change is greater than the template update coefficient used when operating conditions are stable.

4. The temperature distribution optimization system as described in claim 1, characterized in that, The heat exchange status information includes at least one of the response amplitude and response change rate obtained from the thermal response related to the screw phase; The at least one processor is further programmed to determine the heat transfer driven temperature difference based on the difference between the most recently valid melt temperature estimate and the periodic average value of the corrected wall temperature signal, and to normalize at least one of the response amplitude and the response change rate using the heat transfer driven temperature difference. The normalized response amplitude and the response rate of change, at least one of them, are combined with the operating condition information to form the current input; Based on the parameter calibration model established in advance for each of the axial temperature measurement positions, the heat transfer correction information used to correct the heat transfer boundary parameters is determined by the current input, and the reliability characterizing the applicability of the current input relative to the parameter calibration model is determined. as well as When the confidence level meets the preset confidence conditions, the heat transfer boundary parameters are corrected using the currently determined heat transfer correction information; when the confidence level does not meet the preset confidence conditions, the previous effective heat transfer boundary parameters are maintained or the preset heat transfer boundary parameters are used.

5. The temperature distribution optimization system as described in claim 4, characterized in that, The heat transfer correction information includes a heat transfer correction coefficient, the heat transfer boundary parameter includes the current effective equivalent heat transfer coefficient, and the parameter calibration model includes a neighborhood parameter model based on calibration samples. The neighborhood parameter model selects neighborhood samples based on the standardized distance between the current input and each of the calibration samples, and determines the heat transfer correction coefficient by locally weighting the neighborhood samples. The confidence level is determined based on the distance between the current input and the nearest calibrated sample and the degree of dispersion of the heat transfer correction coefficient corresponding to the neighboring samples; The reliability of the j-th axial temperature measurement position in the k-th parameter update cycle Not lower than the preset confidence threshold At that time, the at least one processor determines the candidate equivalent heat transfer coefficient according to the following formula: in: This represents the candidate equivalent heat transfer coefficient for the j-th axial temperature measurement position during the k-th parameter update cycle. This represents the currently obtained heat transfer correction factor; The nominal equivalent heat transfer coefficient represents the j-th axial temperature measurement position; when < When the aforementioned effective equivalent heat transfer coefficient has not yet been formed, the previous effective equivalent heat transfer coefficient shall be used; when the previous effective equivalent heat transfer coefficient has not yet been formed, the following shall be used. ; The candidate equivalent heat transfer coefficient is subject to a range restriction and a rate of change restriction between adjacent parameter update times to obtain the current effective equivalent heat transfer coefficient of the j-th axial temperature measurement position in the k-th parameter update cycle. ; The at least one processor is further programmed to use the current effective equivalent heat transfer coefficient corresponding to each of the axial temperature measurement positions with the same value for the barrel-melt heat transfer model and the temperature distribution prediction model at the barrel inner wall side heat transfer boundary at that axial temperature measurement position, to estimate the axial melt temperature distribution using the barrel-melt heat transfer model and the corrected wall temperature signal, and to predict the influence of the control quantity of each of the temperature control zones on the axial melt temperature distribution using the temperature distribution prediction model. The at least one processor is also programmed to stop updating at least one of the phase response template and the current effective equivalent heat transfer coefficient when the screw phase information is incomplete, the wall temperature detection signal is abnormal, or the state estimation result does not meet the preset effective conditions, and to switch the temperature control to a safe control state with the barrel wall temperature as feedback.

6. A method for optimizing temperature distribution in an extrusion molding apparatus, the extrusion molding apparatus comprising a barrel, a screw rotatably disposed within the barrel, and a plurality of temperature control zones disposed along the axial direction of the barrel, characterized in that, The method includes: The wall temperature detection signals corresponding to multiple axial temperature measurement positions of the barrel, screw phase information characterizing the screw angle position, and operating condition information characterizing the operating status of the extrusion molding device are acquired. The wall temperature detection signals are obtained by a wall temperature detection component disposed on the barrel wall and separated from the material flow channel. Based on the screw phase information, each wall temperature detection signal is converted to the screw phase domain, so that the wall temperature detection values ​​corresponding to the same screw phase in different screw rotation cycles are aligned with each other; For each axial temperature measurement position, the thermal response related to the screw phase within the current screw rotation cycle is determined using a phase response template formed before the start of the current screw rotation cycle. The thermal response is then removed from the wall temperature detection signal after phase synchronization to obtain a corrected wall temperature signal. After the current screw rotation cycle ends, the phase response template is updated based on the phase response information obtained from the wall temperature detection value of the current screw rotation cycle, so that the updated phase response template can be used for the next screw rotation cycle. Based on the thermal response related to the screw phase, determine the heat transfer state information characterizing the near-wall heat transfer state at the corresponding axial temperature measurement position, and based on the heat transfer state information and the operating condition information, determine the heat transfer boundary parameters used to limit the heat transfer relationship between the inner wall of the barrel and the melt at the corresponding axial temperature measurement position. The axial melt temperature distribution of the extrusion molding apparatus is determined based on the corrected wall temperature signal and the barrel-melt heat transfer model using the heat transfer boundary parameters; and Based on the deviation between the axial melt temperature distribution and the target melt temperature distribution, at least one of the heating control quantity and cooling control quantity of each temperature control zone is determined and output.

7. The temperature distribution optimization method as described in claim 6, characterized in that, The wall temperature detection signal is obtained by wall temperature sensors that are respectively set at multiple axial temperature measurement positions and whose sensing ends are embedded in the barrel wall. Each of the wall temperature sensors has the same angular offset relative to the preset circumferential reference at its installation position in the barrel circumferential direction. The screw phase information is obtained by an angle detection device associated with the screw or screw drive mechanism; The operating condition information includes the thermal parameters of each temperature control zone, material conveying parameters, and screw operating parameters; The wall temperature detection signal, the screw phase information, and the operating condition information are made to have a unified time reference, and the wall temperature detection signal is converted to the screw phase domain by at least one of the following methods: the wall temperature detection signal is collected at a preset screw rotation angle interval; or, the wall temperature detection value collected at a preset time interval is resampled to multiple preset phase intervals according to the screw phase corresponding to each sampling time.

8. The temperature distribution optimization method as described in claim 6, characterized in that, The trend baseline within the cycle is determined based on the wall temperature detection value after phase synchronization within the current screw rotation cycle, and the detrending residual is determined based on the difference between the wall temperature detection value after phase synchronization and the trend baseline within the cycle. When the material conveying state and screw operation state meet the preset stability conditions, an initial phase response template is formed based on the statistical results of the detrended residuals of multiple consecutive complete screw rotation cycles at the same screw phase. For the j-th axial temperature measurement position, the k-th screw rotation cycle, and the i-th phase interval, within the k-th screw rotation cycle, the phase response template value formed before the start of that screw rotation cycle is used. Determine the thermal response related to the screw phase, and update the phase response template after the kth screw rotation cycle according to the following formula: in: This represents the phase response template value corresponding to the i-th phase interval, formed before the start of the k-th screw rotation cycle. This represents the detrended residual corresponding to the i-th phase interval within the k-th screw rotation cycle; This represents the template update coefficient corresponding to the kth screw rotation cycle; This represents the phase response template value corresponding to the i-th phase interval, which is formed after the k-th screw rotation cycle and used for the (k+1)-th screw rotation cycle. as well as Determine based on at least one of the following: the deviation between the detrended residual and the invoked phase response template, and the change in the operating condition information. This ensures that the template update coefficient used when operating conditions change is greater than the template update coefficient used when operating conditions are stable.

9. The temperature distribution optimization method as described in claim 6, characterized in that, At least one of the response amplitude and the response change rate is obtained from the thermal response related to the screw phase, and the heat transfer drive temperature difference is determined based on the difference between the most recent effective melt temperature estimate and the periodic average value of the corrected wall temperature signal. The heat transfer drive temperature difference is used to normalize at least one of the response amplitude and the response change rate. The normalized response amplitude and the response rate of change, at least one of them, are combined with the operating condition information to form the current input; Based on the parameter calibration model established in advance for each of the axial temperature measurement positions, the heat transfer correction information used to correct the heat transfer boundary parameters is determined by the current input, and the reliability characterizing the applicability of the current input relative to the parameter calibration model is determined. as well as When the confidence level meets the preset confidence conditions, the currently determined heat transfer correction information is used; when the confidence level does not meet the preset confidence conditions, the previous effective heat transfer boundary parameters are maintained or the preset heat transfer boundary parameters are used.

10. The temperature distribution optimization method as described in claim 9, characterized in that, The heat transfer correction information includes a heat transfer correction coefficient, the heat transfer boundary parameter includes the current effective equivalent heat transfer coefficient, the parameter calibration model selects neighborhood samples based on the standardized distance between the current input and each calibration sample, determines the heat transfer correction coefficient by locally weighting the neighborhood samples, and determines the confidence level based on the distance between the current input and the nearest calibration sample and the dispersion of the heat transfer correction coefficient corresponding to the neighborhood sample. The reliability of the j-th axial temperature measurement position in the k-th parameter update cycle Not lower than the preset confidence threshold When the equivalent heat transfer coefficient is determined, the candidate equivalent heat transfer coefficient is determined according to the following formula: in: This represents the candidate equivalent heat transfer coefficient for the j-th axial temperature measurement position during the k-th parameter update cycle. This represents the currently obtained heat transfer correction factor; The nominal equivalent heat transfer coefficient represents the j-th axial temperature measurement position; when < When the aforementioned effective equivalent heat transfer coefficient has not yet been formed, the previous effective equivalent heat transfer coefficient shall be used; when the previous effective equivalent heat transfer coefficient has not yet been formed, the following shall be used. ; The candidate equivalent heat transfer coefficient is subject to a range restriction and a rate of change restriction between adjacent parameter update times to obtain the current effective equivalent heat transfer coefficient of the j-th axial temperature measurement position in the k-th parameter update cycle. ; The current effective equivalent heat transfer coefficient corresponding to each of the axial temperature measurement positions is used with the same value in the barrel-melt heat transfer model and the temperature distribution prediction model at the heat transfer boundary on the inner wall side of the barrel at that axial temperature measurement position. The axial melt temperature distribution is estimated using the barrel-melt heat transfer model and the corrected wall temperature signal. The influence of the control quantity of each temperature control zone on the axial melt temperature distribution is predicted using the temperature distribution prediction model. The control quantity for each temperature control zone is determined by solving a control objective function that includes at least one of the following: target melt temperature distribution tracking term, axial temperature uniformity term, and control quantity change term. When the screw phase information is incomplete, the wall temperature detection signal is abnormal, or the state estimation result does not meet the preset effective conditions, the update of at least one of the phase response template and the current effective equivalent heat transfer coefficient is stopped, and the temperature control is switched to a safe control state with barrel wall temperature as feedback.

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