A control system and method for spinneret forming of multi-leaf hollow fiber spinnerets

Abstract

This invention discloses a control system and method for spinneret forming of a multi-leaf hollow fiber spinneret, relating to the field of fiber spinneret technology. The system uses a set of sensors to collect dynamic aerodynamic data of the spinneret in real time. After preprocessing, it obtains a set of cavity airflow data, an environmental response data set, and a pressure distribution data set. The stability of the spinneret bundle is assessed by calculating the cavity transverse disturbance frequency Fvt. When an airflow anomaly is detected, the cone expansion angle θfz is calculated to determine the changes in expansion and contraction angles during fiber bundle formation. The disturbance symmetry offset Asy is calculated using the pressure distribution data set, and a width offset index Iwd is constructed by fitting it with the cavity transverse disturbance frequency Fvt and the cone expansion angle θfz to assess the width offset state. Process data is generated based on the assessment results and transmitted to the spinneret forming control system via a dedicated interface, automatically executing corresponding operation commands to ensure the stability of the spinneret forming process and the final quality of the fiber.

Description

Technical Field

[0001] This invention relates to the field of fiber spinneret technology, specifically to a control system and method for spinneret forming of a multi-leaf hollow fiber spinneret. Background Technology

[0002] Fiber spinneret technology, as one of the core technologies in textile, chemical fiber, and composite material production, is widely used in various fields, such as the manufacture of nonwoven fabrics, filter media, and medical textiles. Its basic principle is to extrude molten polymer or solution through a spinneret, which is then cooled by air to form long, thin fibers. However, traditional fiber spinneret technology faces a series of problems in practical applications, such as unstable spinneret bundles, uneven cooling, and uncontrolled take-up angles, affecting the quality of the final fibers. To solve these problems, multi-bladed hollow fiber spinnerets have been gradually developed. This technology uses multiple blades to control the airflow distribution during the spinneret process, thereby improving the stability and uniformity of the fibers. However, although multi-bladed hollow fiber spinnerets can improve the spinneret process to some extent, many challenges remain, especially in the precise adjustment of airflow disturbance, take-up angle control, and width offset.

[0003] Existing spinneret control methods typically rely on traditional parameter adjustments and manual intervention, primarily adjusting airflow and pressure based on fixed standards and empirical values. However, this approach is not suitable for real-time and precise responses to various dynamic disturbances generated during the spinneret process, leading to problems such as unstable airflow and uneven contraction angle changes. Furthermore, uneven pressure distribution on the spinneret can cause fiber width deviations, affecting the quality of the final product. Existing spinneret control methods fail to comprehensively consider various disturbance factors and their interactions, making it difficult to guarantee spinneret quality under complex operating conditions. Therefore, there is an urgent need for an intelligent, real-time feedback spinneret control system to cope with constantly changing production environments and improve production stability and product quality. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-leaf hollow fiber spinneret spinneret spinning control system and method, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: a multi-leaf hollow fiber spinneret spinneret forming control system, comprising a data acquisition module, a disturbance analysis module, a take-up angle analysis module, a width analysis module, and a control feedback module;

[0006] The data acquisition module is used to collect dynamic aerodynamic data of the spinneret in real time based on the set sensor group, and obtain cavity airflow data group, environmental response data group and pressure distribution data group after preprocessing.

[0007] The disturbance analysis module is used to perform airflow disturbance analysis based on the cavity airflow data set, and to evaluate the stability of the spinneret based on the analysis results. When the cavity airflow is evaluated as abnormal, the convergence angle analysis module is triggered.

[0008] The convergence angle analysis module is used to perform convergence angle evolution analysis on fiber spinnerets with abnormal airflow in the cavity based on the environmental response data set.

[0009] The width analysis module is used to analyze the perturbation symmetry offset of the pressure in the left and right chambers of the spinneret based on the pressure distribution data set, and fit the results with the airflow perturbation analysis and the convergence angle evolution analysis to analyze the width offset of the fiber, and evaluate the width offset state based on the analysis results.

[0010] The control feedback module is used to automatically generate process data pairs from the evaluation information and execute corresponding operation commands through the spinneret forming control system.

[0011] Preferably, the data acquisition module includes a data acquisition unit and a data processing unit;

[0012] The data acquisition unit is used to collect dynamic aerodynamic data of the spinneret in real time based on the sensor group set at each position of the spinneret.

[0013] The sensor group includes a wind speed sensor, a pressure sensor, and a tension sensor;

[0014] The wind speed sensors are respectively installed on the left and right sides of the spinneret cavity and at the outlet of the cooling air duct to collect the wind speed vl in the left channel, the wind speed vr in the right channel and the cooling wind speed wc in real time.

[0015] The pressure sensors are respectively installed at the ends of the left and right channels in the side holes of the spinneret cavity wall and the hollow cavity, and collect the spinneret cavity pressure ps, the left channel pressure pl of the spinneret cavity, and the right channel pressure pr of the spinneret cavity in real time.

[0016] The tension sensor is installed at the front end of the drawing roller inlet to collect the yarn tension tr in real time.

[0017] Preferably, the data processing unit is used to establish a communication connection between the sensor group and the spinneret forming control system based on the wireless network, transmit dynamic pneumatic data to the spinneret forming control system in real time for preprocessing, and output cavity airflow data group, environmental response data group and pressure distribution data group.

[0018] The preprocessing includes denoising, filling in missing values, and normalization.

[0019] The denoising process uses wavelet denoising technology to preserve the transient structural features of hydraulic operating condition data and remove high-frequency noise from the hydraulic operating condition data. Missing values ​​are filled in by missing value interpolation technology to complete the hydraulic operating condition data due to sampling interruption and communication packet loss. Normalization is performed by Max-Min method to remove the dimensional influence of hydraulic operating condition data.

[0020] The cavity airflow data set includes the instantaneous wind speed vl in the left spinneret cavity channel and the instantaneous wind speed vr in the right spinneret cavity channel;

[0021] The environmental response data set includes cooling wind speed wc, spinneret chamber pressure ps, and filament tension tr;

[0022] The pressure distribution data set includes the pressure pl in the left channel and the pressure pr in the right channel of the spinneret hollow cavity.

[0023] Preferably, the disturbance analysis module includes a disturbance frequency analysis unit and an airflow stability assessment unit;

[0024] The disturbance frequency analysis unit is used to perform airflow disturbance frequency analysis on the airflow guide channels on the left and right sides of the spinneret cavity based on the cavity airflow data group, and calculate the cavity lateral disturbance frequency Fvt. This is used to identify the lateral airflow disturbance characteristics formed by the wind speed difference between the left and right channels in the spinneret cavity, and to reflect the influence of airflow disturbance on the fiber bundle swing amplitude.

[0025] Preferably, the airflow stability assessment unit is used to calculate the average value of the cavity lateral disturbance frequency Fvt when the spinneret is stable in history according to the statistical method, and preset the airflow balance threshold Qf based on the average value, and perform spinneret stability assessment based on the real-time acquired cavity lateral disturbance frequency Fvt. The specific assessment scheme is as follows.

[0026] When the cavity lateral disturbance frequency Fvt < airflow balance threshold Qf, it indicates that the cavity airflow is stable and the fiber swing is normal. At this time, airflow stability information is generated.

[0027] When the lateral disturbance frequency Fvt of the cavity is greater than or equal to the airflow balance threshold Qf, it indicates that the airflow in the cavity is abnormal and the fiber spinneret is deviated. At this time, a deviation warning signal is sent to the controller and the convergence angle evolution analysis is performed.

[0028] Preferably, the contraction angle analysis module is used to perform a contraction angle evolution analysis on the fiber spinneret with abnormal cavity airflow based on the environmental response data group when the spinneret stability assessment indicates abnormal cavity airflow, and calculate the cone expansion angle θfz of the fiber spinneret exit path, which represents the contraction cone angle formed by the fiber during the cooling and shrinkage process, reflecting the expansion and contraction angle of the fiber bundle from the spinneret to the landing point.

[0029] Preferably, the width analysis module includes an offset analysis unit and an offset evaluation unit;

[0030] The offset analysis unit calculates the symmetry offset of the pressure disturbance in the left and right cavities, Asy, based on the pressure distribution data set. This reflects the symmetry of the airflow inside the spinneret. The offset is obtained from the pressure difference between the left and right cavities and the pressure ratio. It is then fitted with the lateral disturbance frequency Fvt of the cavity and the cone expansion angle θfz to analyze the fiber width offset. A width offset index Iwd is constructed to analyze the relative difference between the final fiber width offset and the target design width.

[0031] Preferably, the offset evaluation unit is used to calculate the average width offset index Iwd when the historical width offset meets the standard according to the statistical method, and preset the width offset standard threshold Qi based on the average value, and evaluate the width offset status based on the real-time acquired width offset index Iwd. The specific evaluation scheme is as follows.

[0032] When the width offset index Iwd < width offset standard threshold Qi, it means that the width offset meets the standard, the abnormal airflow in the cavity does not affect the bundle angle during fiber cooling, the fiber forming is stable, and quality qualified information is generated at this time.

[0033] When the width offset index Iwd is greater than or equal to the width offset standard threshold Qi, it indicates that the width offset does not meet the standard, the abnormal airflow in the cavity affects the bundle angle during cooling, and the fibers diverge. At this time, fiber divergence information is generated.

[0034] Preferably, the control feedback module is used to automatically form process data pairs from all the evaluation information generated by the evaluation, and transmit them to the spinneret control system through a dedicated API interface to execute the corresponding operation instructions, as follows;

[0035] Airflow stability information: Maintain normal operation of the current program;

[0036] Quality pass information: Keep the current program running normally, archive any abnormalities in the cavity lateral disturbance frequency Fvt, and mark it as an abnormality in the lateral disturbance but without affecting the final width;

[0037] Fiber dispersion information: Reduce the spinneret frequency by 10%, increase the cooling air velocity by 5%, and increase the winding tension by 3%. Perform iterative analysis through the disturbance analysis module. If the standard is not met three times in a row, immediately stop the machine and notify maintenance personnel for inspection.

[0038] A method for controlling the spinning formation of a multi-leaf hollow fiber spinneret includes the following steps:

[0039] S1. Based on the set sensor group, collect the dynamic aerodynamic data of the spinneret in real time, and obtain the cavity airflow data group, environmental response data group and pressure distribution data group after preprocessing.

[0040] S2. Perform airflow disturbance analysis based on the cavity airflow data set, and evaluate the stability of the spinneret based on the analysis results. Trigger S3 when the cavity airflow is assessed as abnormal.

[0041] S3. Based on the environmental response data set, perform a convergence angle evolution analysis on the fiber spinneret with abnormal airflow in the cavity;

[0042] S4. Based on the pressure distribution data, analyze the symmetry of pressure disturbance in the left and right chambers of the spinneret, and fit the results with the airflow disturbance analysis and the convergence angle evolution analysis to analyze the fiber width deviation and evaluate the width deviation state based on the analysis results.

[0043] S5. The evaluation information is automatically generated into process data pairs, and the corresponding operation instructions are executed through the spinneret forming control system.

[0044] This invention provides a control system and method for spinneret spinning of multi-leaf hollow fiber spinnerets. It has the following beneficial effects:

[0045] (1) The data acquisition module of the system collects dynamic aerodynamic data of the spinneret in real time through wind speed sensors, pressure sensors and tension sensors set at key positions of the spinneret. The data processing unit completes the preprocessing and structural standardization of the data through wavelet denoising, missing value interpolation and Max-Min normalization, forming cavity airflow data group, environmental response data group and pressure distribution data group, ensuring that the data input to the disturbance analysis and deformation judgment module has high timeliness and structure, and meets the prerequisite for quantitative judgment of fiber stability under complex disturbance conditions.

[0046] (2) The system's disturbance analysis module constructs a transverse disturbance frequency Fvt based on the cavity airflow data set, identifies fiber swing amplitude changes caused by differences in wind speed inside the cavity, and sets an airflow equilibrium threshold Qf based on historical averages. When the cavity transverse disturbance frequency Fvt is greater than or equal to the airflow equilibrium threshold Qf, it is considered an abnormal airflow in the cavity, and the convergence angle analysis module is automatically triggered. The convergence angle analysis module constructs a cone expansion angle θfz based on the environmental response data set and uses arctangent and logarithmic functions to describe the divergence trend of the fiber bundle path during the contraction and cooling process. The width analysis module constructs a disturbance symmetry offset Asy based on the pressure distribution data set, and performs joint fitting with the cavity transverse disturbance frequency Fvt and the cone expansion angle θfz to obtain the width offset index Iwd. It is then compared with the width offset standard threshold Qi set by historical averages to accurately determine whether the current width has reached the expected control standard, forming the final evaluation of fiber forming.

[0047] (3) The system's control feedback module assembles all evaluation information into standardized process data pairs and transmits them to the spinneret control system via the API interface to execute specific operation instructions. This module establishes three feedback paths: when airflow stability information is generated, the current program continues to run; when quality qualified information is generated, abnormal situations of the cavity lateral disturbance frequency Fvt are marked and archived; when fiber divergence information is generated, the control instructions of "reducing the spinneret frequency by 10%, increasing the cooling wind speed by 5%, and increasing the winding tension by 3%" are triggered, and the iterative calculation mechanism of the disturbance analysis module is executed. If the deviation does not meet the standard three times in a row, the machine is directly stopped and reported for maintenance. Through the evaluation, instruction, and re-analysis closed-loop chain constructed by this control module, the disturbance identification results can be transformed into executable control actions, breaking the problems of insensitive boundary disturbance identification, imprecise width deviation control, and non-coordinated parameter adjustment mechanisms in previous spinneret control methods. Under the premise of unchanged structure and hardware, the dynamic steady-state control capability and process quality self-adaptation level of spinneret formation are systematically improved. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the spinning and forming control system for a multi-leaf hollow fiber spinneret according to the present invention;

[0049] Figure 2 This is a schematic diagram of the steps of a multi-leaf hollow fiber spinneret spinneret spinneret spinneret forming control method according to the present invention;

[0050] Figure 3 The present invention provides a flowchart illustrating the principle of a multi-leaf hollow fiber spinneret spinneret forming control system. Detailed Implementation

[0051] 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.

[0052] Example 1

[0053] Please see Figure 1 This invention provides a multi-leaf hollow fiber spinneret spinneret spinning forming control system. To achieve the above objectives, this invention is implemented through the following technical solutions: including a data acquisition module, a disturbance analysis module, a convergence angle analysis module, a width analysis module, and a control feedback module.

[0054] The data acquisition module is used to collect dynamic aerodynamic data of the spinneret in real time based on the set sensor group, and obtain cavity airflow data group, environmental response data group and pressure distribution data group after preprocessing.

[0055] The disturbance analysis module is used to perform airflow disturbance analysis based on the cavity airflow data set, and to evaluate the stability of the spinneret based on the analysis results. When the cavity airflow is evaluated as abnormal, the convergence angle analysis module is triggered.

[0056] The convergence angle analysis module is used to perform convergence angle evolution analysis on fiber spinnerets with abnormal airflow in the cavity based on the environmental response data set.

[0057] The width analysis module is used to analyze the perturbation symmetry offset of the pressure in the left and right chambers of the spinneret based on the pressure distribution data set, and fit the results with the airflow perturbation analysis and the convergence angle evolution analysis to analyze the width offset of the fiber, and evaluate the width offset state based on the analysis results.

[0058] The control feedback module is used to automatically generate process data pairs from the evaluation information and execute corresponding operation commands through the spinneret forming control system.

[0059] In this embodiment, the data acquisition module collects dynamic aerodynamic data of the spinneret in real time at different cavity locations and key fiber path nodes based on the deployed sensor group. After preprocessing, it outputs cavity airflow data, environmental response data, and pressure distribution data, providing the system with basic decision-making data for the entire process. The disturbance analysis module performs airflow disturbance analysis on the collected cavity airflow data and outputs the spinneret stability judgment result, establishing a logical judgment entry point for subsequent control processes. When the spinneret stability assessment result indicates abnormal cavity airflow, the system automatically links with the convergence angle analysis module to perform convergence angle evolution analysis based on the environmental response data. This quantifies the change in the cone angle of the fiber path during the cooling stage from a geometric evolution perspective, identifying potential forming divergence trends. The width analysis module constructs a disturbance symmetry offset model by analyzing the pressure distribution data and fits it with the results of the airflow disturbance analysis and convergence angle evolution analysis to analyze the fiber width offset. This enables quantitative tracking and composite judgment from airflow disturbance to width deformation, and performs width offset state assessment based on the analysis results. By integrating evaluation information into the control feedback module, structured process data pairs are formed and operation commands are directly issued to the spinneret control system, completing a full closed loop from data acquisition and status identification to feedback execution. Compared to existing spinneret control methods that rely on static parameter settings and manual experience intervention, this system achieves a systematic improvement in module settings, parameter usage, and feedback logic. By introducing a multi-dimensional data grouping, disturbance, geometric, and width-progressive analysis structure, as well as a closed-loop mechanism for automatically generating control commands, the system solves the problems of untimely disturbance identification, ambiguous width anomaly sources, and delayed control response in traditional methods. It enhances the ability to identify micro-disturbances and asymmetric airflow anomalies in the spinneret process, improving forming stability and automatic control accuracy while ensuring structural integrity.

[0060] Example 2

[0061] Please refer to Figure 1 and Figure 3 Specifically: the data acquisition module includes a data acquisition unit and a data processing unit;

[0062] The data acquisition unit is used to collect dynamic aerodynamic data of the spinneret in real time based on the sensor group set at each position of the spinneret.

[0063] The sensor group includes a wind speed sensor, a pressure sensor, and a tension sensor;

[0064] The wind speed sensors are respectively installed on the left and right sides of the spinneret cavity and at the outlet of the cooling air duct to collect the wind speed vl in the left channel, the wind speed vr in the right channel and the cooling wind speed wc in real time.

[0065] The wind speeds vl in the left channel and vr in the right channel are used to reflect the intensity and symmetry of the disturbance of the airflow on the filament bundle in the left and right channels.

[0066] Cooling air velocity wc is used to analyze the stability and uniformity of cooling air.

[0067] The pressure sensors are respectively installed at the ends of the left and right channels in the side holes of the spinneret cavity wall and the hollow cavity, and collect the spinneret cavity pressure ps, the left channel pressure pl of the spinneret cavity, and the right channel pressure pr of the spinneret cavity in real time.

[0068] The spinneret pressure ps represents the overall air pressure change inside the spinneret, reflecting the macroscopic effect of airflow on the fiber outlet.

[0069] The pressure pl in the left channel of the spinneret hollow cavity and the pressure pr in the right channel of the spinneret hollow cavity are used to analyze the balance of airflow pressure distribution in the left and right channels inside the cavity.

[0070] The tension sensor is installed at the front end of the drawing roller inlet to collect the tension tr of the yarn bundle in real time, and to provide feedback on the stress state of the yarn bundle under airflow and pressure disturbances.

[0071] The data processing unit is used to establish a communication connection between the sensor group and the spinneret forming control system based on the wireless network, transmit dynamic pneumatic data to the spinneret forming control system in real time for preprocessing, and output cavity airflow data group, environmental response data group and pressure distribution data group.

[0072] The preprocessing includes denoising, filling in missing values, and normalization.

[0073] The denoising process uses wavelet denoising technology to preserve the transient structural features of hydraulic operating condition data and remove high-frequency noise from the hydraulic operating condition data. Missing values ​​are filled in by missing value interpolation technology to complete the hydraulic operating condition data due to sampling interruption and communication packet loss. Normalization is performed by Max-Min method to remove the dimensional influence of hydraulic operating condition data.

[0074] The cavity airflow data set includes the instantaneous wind speed vl in the left spinneret cavity channel and the instantaneous wind speed vr in the right spinneret cavity channel;

[0075] The environmental response data set includes cooling wind speed wc, spinneret chamber pressure ps, and filament tension tr;

[0076] The pressure distribution data set includes the pressure pl in the left channel and the pressure pr in the right channel of the spinneret hollow cavity.

[0077] In this embodiment, wind speed sensors are arranged on the left and right sides of the spinneret cavity and at the cooling air duct outlet to collect the wind speed vl in the left channel, the wind speed vr in the right channel, and the cooling wind speed wc, respectively, to identify the symmetry of the disturbance of the airflow to the filament bundle and the uniformity of cooling. Pressure sensors are set on the cavity wall and at the ends of the left and right channels of the hollow cavity to collect the spinneret cavity pressure ps, the pressure pl in the left channel of the hollow spinneret cavity, and the pressure pr in the right channel of the hollow spinneret cavity, to evaluate the equilibrium state of the internal air pressure distribution. A tension sensor is arranged at the front end of the drafting roller to obtain the filament bundle tension tr, reflecting the force response after disturbance. All sensor data are synchronously transmitted to the data processing unit of the spinneret forming control system via a wireless network for wavelet denoising, missing data interpolation, and normalization preprocessing. Finally, the data is divided into cavity airflow data group, environmental response data group, and pressure distribution data group to ensure the parameter basis for subsequent disturbance analysis, angle evolution, and offset assessment. This module not only opens up multi-point data flow paths in the spinning process, but also provides support for the system to build a stable and traceable disturbance identification chain and control logic through a standardized data structure output mechanism. Compared with the traditional method that relies on single-point measurement and manual identification, it has achieved an overall improvement in information accuracy, data continuity and control feedforward capability.

[0078] Example 3

[0079] Please refer to Figure 1 and Figure 3 Specifically: the disturbance analysis module includes a disturbance frequency analysis unit and an airflow stability assessment unit;

[0080] The disturbance frequency analysis unit is used to analyze the airflow disturbance frequency of the guide channels on both sides of the spinneret cavity based on the cavity airflow data set, and calculate the cavity lateral disturbance frequency Fvt. This is used to identify the lateral airflow disturbance characteristics formed by the wind speed difference between the left and right channels in the spinneret cavity, reflecting the influence of airflow disturbance on the fiber bundle swing amplitude. Specifically: In the formula, T represents the sampling period, vl(t) represents the instantaneous wind speed in the left spinneret channel at time t, vr(t) represents the instantaneous wind speed in the right spinneret channel at time t, and dt represents the time calculus symbol. This represents the rate of change of the wind speed difference between the left and right spinneret channels, i.e., the intensity of the lateral disturbance velocity over time.

[0081] The airflow stability assessment unit is used to calculate the average value of the cavity lateral disturbance frequency Fvt when the spinneret is stable in history using statistical methods, and to preset the airflow balance threshold Qf based on the average value. The spinneret stability is assessed based on the real-time acquired cavity lateral disturbance frequency Fvt. The specific assessment scheme is as follows.

[0082] When the cavity lateral disturbance frequency Fvt < airflow balance threshold Qf, it indicates that the cavity airflow is stable and the fiber swing is normal. At this time, airflow stability information is generated.

[0083] When the lateral disturbance frequency Fvt of the cavity is greater than or equal to the airflow balance threshold Qf, it indicates that the airflow in the cavity is abnormal and the fiber spinneret is deviated. At this time, a deviation warning signal is sent to the controller and the convergence angle evolution analysis is performed.

[0084] In this embodiment, the disturbance frequency analysis unit calculates the time rate of change of the wind speed difference between the left and right spinneret channels based on the instantaneous wind speed vl and vr in the cavity airflow data set, constructs the cavity lateral disturbance frequency Fvt, quantitatively characterizes the lateral disturbance intensity caused by the channel wind speed difference, and reflects the influence of airflow asymmetry on the yarn swing amplitude. The formula derivation is based on the mean absolute derivative integral in signal processing and vibration analysis, and its basic form is: The standard practice for describing the average variation intensity of a time-varying signal f(t) within the interval [0,T] corresponds to the absolute value integral of the function's derivative in classical mathematical analysis, used to represent the drastic change in the signal. In fluid mechanics and turbulence theory, the intensity of airflow disturbance is often represented by the velocity difference and its rate of change over time. The wind speed difference vl(t)−vr(t) corresponds to a lateral asymmetric disturbance source, and the reciprocal of time... Corresponding to the rate of change of the transverse asymmetric disturbance source, the signal f(t) is specificized as the difference in wind speed between the left and right sides by combining it with the integral of the average absolute derivative, and given a physical meaning of the spinneret cavity, thus constructing the transverse disturbance frequency Fvt of the cavity. In the data processing stage, the instantaneous wind speeds vl and vr of the left and right spinneret cavity channels have been dimensionlessized, and the wind speed difference vl(t)−vr(t) is dimensionless. The reciprocal of the wind speed difference vl(t)−vr(t) is then calculated. Later formed ,Will After substituting the mean absolute derivative integral and canceling out the time units, the final cavity lateral disturbance frequency Fvt is a dimensionless value. The airflow stability assessment unit sets an airflow equilibrium threshold Qf based on the average cavity lateral disturbance frequency Fvt under historical stable spinneret conditions. It compares the real-time cavity lateral disturbance frequency Fvt with the airflow equilibrium threshold Qf. If the cavity lateral disturbance frequency Fvt is lower than the airflow equilibrium threshold Qf, the cavity airflow is determined to be stable, and airflow stability information is generated; if the cavity lateral disturbance frequency Fvt is greater than or equal to the airflow equilibrium threshold Qf, the cavity airflow is determined to be abnormal, triggering an offset warning and linking to the convergence angle evolution analysis. This module enables the active capture and early identification of airflow disturbance anomalies during the spinneret process, improving the system's sensitivity and responsiveness to changes in micro-disturbance states. It provides a preliminary judgment basis for downstream structural evolution analysis and control feedback, overcoming the untimely problem of traditional spinneret control systems that rely on end-point results to deduce airflow disturbances.

[0085] Example 4

[0086] Please refer to Figure 1 and Figure 3 Specifically: The take-up angle analysis module is used to perform take-up angle evolution analysis on the fiber spinneret with abnormal cavity airflow based on the environmental response data set when the spinneret stability assessment indicates abnormal cavity airflow. It also calculates the cone expansion angle θfz of the fiber spinneret's exit path, representing the take-up cone angle formed during the fiber's cooling and contraction process. This reflects the expansion and contraction angle of the fiber bundle from the spinneret nozzle to the landing point. In the formula, arctan represents the arctangent function, ln represents the logarithmic function, ε represents the minimum constant to avoid division by zero when the denominator is zero, and takes the value of 0.001. ln(1+Ps) is the logarithmic function of the spinneret pressure, which represents the nonlinear enhancement effect of the spinneret pressure on the fiber velocity.

[0087] In this embodiment, the take-off angle analysis module is triggered when the spinneret stability assessment result indicates abnormal airflow in the cavity. The system calls parameters such as cooling wind speed, spinneret cavity pressure, and fiber tension from the environmental response data set. It calculates the conical expansion angle θfz of the fiber spinneret exit path using a combination of arctangent and logarithmic functions. The nonlinear enhancement effect of cavity pressure on fiber velocity is characterized by ln(1+Ps), and a minimal constant ε is introduced to avoid division-by-zero errors. This angle reflects the expansion and contraction characteristics of the fiber bundle during cooling and contraction, revealing the impact of abnormal airflow in the cavity on fiber forming stability from a geometric evolution perspective. During spinning, the fiber forms a certain conical angle between the exit point and the landing point. This angle is influenced by the coupling effect of cooling wind speed, cavity pressure, and fiber tension. The formula derivation is based on the fundamental formula for angle calculation, and the fiber path angle adopts a geometric definition. Within this framework, the numerator of this formula represents the driving force enhancement effect, i.e., the cooling airflow and cavity pressure, while the denominator represents the fiber bundle resistance effect, i.e., the square of the tension. Based on Bernoulli's equation in fluid mechanics, the cavity pressure and flow velocity have a non-linear relationship; therefore, a logarithmic function ln(1+ps) is introduced to describe the enhancement effect of cavity pressure on fiber velocity. In the data processing stage, the cooling airflow velocity wc, the spinneret cavity pressure ps, and the fiber bundle tension tr have already been dimensionless. Since the value is dimensionless, the output unit of the arctangent function arctan is radians; therefore, the output of the cone expansion angle θfz is in radians (rad). This module can capture the evolution trend of the bundle convergence angle in real time during the spinning process, clarify the formation mechanism of filament divergence or deviation, and achieve the purpose of accurate quantification and dynamic monitoring of the forming state under abnormal airflow. Compared with the traditional method of relying on static empirical parameters for rough judgment, this method can more intuitively and in real time reveal the coupling effect between airflow and fiber, thereby improving the accuracy of abnormal identification and the timeliness of control adjustment in the spinning process, and effectively ensuring the stability of the fiber convergence process and the forming quality.

[0088] Example 5

[0089] Please refer to Figure 1 and Figure 3 Specifically: the width analysis module includes an offset analysis unit and an offset evaluation unit;

[0090] The offset analysis unit calculates the symmetry offset Asy of the pressure disturbance in the left and right cavities based on the pressure distribution data set. This reflects the symmetry of the airflow inside the spinneret and is obtained from the ratio of the pressure difference between the left and right cavities to the pressure sum. Then, the fiber width offset is analyzed by fitting the cavity lateral perturbation frequency Fvt and the cone expansion angle θfz, and a width offset index Iwd is constructed to analyze the relative difference between the final fiber width offset and the target design width. Specifically: In the formula, ln represents the logarithmic function, tan represents the tangent function, and d ref Lfz represents the target design width, and Lfz represents the vertical distance from the spinneret to the cooling and convergence point. This indicates the sway angle of the fiber bundle, representing the angle of sway of the fiber bundle relative to the vertical direction caused by the lateral disturbance of airflow inside the cavity. This indicates the actual width offset, representing the width offset of the filament bundle on the receiving surface.

[0091] The offset evaluation unit is used to calculate the average width offset index Iwd when the historical width offset meets the standard according to the statistical method, and preset the width offset standard threshold Qi based on the average value, and evaluate the width offset status based on the real-time acquired width offset index Iwd. The specific evaluation scheme is as follows.

[0092] When the width offset index Iwd < width offset standard threshold Qi, it means that the width offset meets the standard, the abnormal airflow in the cavity does not affect the bundle angle during fiber cooling, the fiber forming is stable, and quality qualified information is generated at this time.

[0093] When the width offset index Iwd is greater than or equal to the width offset standard threshold Qi, it indicates that the width offset does not meet the standard, the abnormal airflow in the cavity affects the bundle angle during cooling, and the fibers diverge. At this time, fiber divergence information is generated.

[0094] In this embodiment, the offset analysis unit calculates the perturbation symmetry offset Asy of the pressure in the left and right cavities based on the pressure distribution data set. It then fits the cavity lateral perturbation frequency Fvt with the cone expansion angle θfz to construct a width offset index Iwd, used to quantify the difference between the actual fiber width and the target designed width dref. During fiber spinning, the path of the fiber bundle from the spinneret to the cooling convergence point is a geometric model of the cone expansion angle. The vertical distance between the spinneret and the cooling convergence point is denoted as Lfz. When there is an angular offset θ at the fiber outlet, the actual width offset on the receiving surface can be expressed by the geometric primitive relationship Δd = Lfz * tan(θ). The fiber angle is not only controlled by the cone expansion angle θfz but also affected by the coupling influence of the airflow lateral perturbation frequency Fvt and the cavity pressure symmetry offset Asy. This is based on a fluid dynamics and trigonometric function superposition model. The actual width offset Δd is compared with the target design width d. ref In contrast, it is normalized using a logarithmic function, derived from the classic signal offset measurement model. The formula for the perturbation symmetry offset Asy is as follows: The value is defined as the ratio of the pressure difference between the left and right channels to the sum of the pressures. By eliminating the influence of units, it is converted into a dimensionless value. Secondly, the cavity transverse perturbation frequency Fvt output in the perturbation analysis module is a dimensionless value, and the cone expansion angle θfz output in the convergence angle analysis module is in radians. Since radians are defined as dimensionless in physics, and both the tangent and arctangent functions output in radians, therefore… The output is a dimensionless value. Finally, Lfz represents the vertical distance from the spinneret to the cooling termination point, and d... ref This indicates the target design width, so Since the width offset index Iwd is equal to the length ratio, it is dimensionless. The offset assessment unit then calculates the average width offset index Iwd for historically acceptable widths using statistical methods and sets a standard width offset threshold Qi. The real-time width offset index Iwd is compared to this threshold to determine the status: when Iwd is less than the standard threshold Qi, quality is considered acceptable, indicating stable forming; when Iwd is greater than or equal to the standard threshold Qi, fiber divergence is generated, indicating abnormal airflow in the cavity has affected the convergence angle and caused offset. Through this module, the system achieves quantitative monitoring and dynamic assessment of width offset, overcoming the shortcomings of traditional methods that lack linkage between airflow asymmetry disturbances and convergence angle evolution. This makes fiber forming quality assessment more accurate and real-time, improving the stability control and abnormal divergence suppression during the spinning process.

[0095] Example 6

[0096] Please refer to Figure 1 Specifically: the control feedback module is used to automatically form process data pairs from all the evaluation information generated by the evaluation, and transmit them to the spinneret forming control system through a dedicated API interface to execute the corresponding operation instructions, as follows;

[0097] Airflow stability information: Maintain normal operation of the current program;

[0098] Quality pass information: Keep the current program running normally, archive any abnormalities in the cavity lateral disturbance frequency Fvt, and mark it as an abnormality in the lateral disturbance but without affecting the final width;

[0099] Fiber dispersion information: Reduce the spinneret frequency by 10%, increase the cooling air velocity by 5%, and increase the winding tension by 3%. Perform iterative analysis through the disturbance analysis module. If the standard is not met three times in a row, immediately stop the machine and notify maintenance personnel for inspection.

[0100] In this embodiment, the control feedback module converts all evaluation information generated by the assessment into process data pairs and sends them to the spinneret forming control system for execution via a dedicated API interface, forming an automated closed-loop control chain: when airflow stability information or quality qualification information is generated, the system maintains the current operating state and archives and marks any lateral disturbances that exist but do not affect the forming result, enabling traceability of anomalies; when fiber divergence information is generated, the system immediately executes a combined control of reducing the spinneret frequency by 10%, increasing the cooling air velocity by 5%, and increasing the winding tension by 3%, and iteratively verifies the results in conjunction with the disturbance analysis module. If the results fail to meet the standards three times consecutively, the system automatically shuts down and notifies maintenance personnel to intervene. Through the above implementation method, the system achieves full-process linkage control of the spinneret process from disturbance identification to width offset correction, ensuring the stability and consistency of the forming quality, and enhancing the adaptive adjustment and fault protection capabilities under abnormal conditions. Compared with existing technologies, it avoids the lag of relying on manual intervention and improves the automation level and operational reliability of the spinneret forming system.

[0101] Example 7

[0102] Please refer to Figure 2 A method for controlling the spinning formation of a multi-leaf hollow fiber spinneret includes the following steps:

[0103] S1. Based on the set sensor group, collect the dynamic aerodynamic data of the spinneret in real time, and obtain the cavity airflow data group, environmental response data group and pressure distribution data group after preprocessing.

[0104] S2. Perform airflow disturbance analysis based on the cavity airflow data set, and evaluate the stability of the spinneret based on the analysis results. Trigger S3 when the cavity airflow is assessed as abnormal.

[0105] S3. Based on the environmental response data set, perform a convergence angle evolution analysis on the fiber spinneret with abnormal airflow in the cavity;

[0106] S4. Based on the pressure distribution data, analyze the symmetry of pressure disturbance in the left and right chambers of the spinneret, and fit the results with the airflow disturbance analysis and the convergence angle evolution analysis to analyze the fiber width deviation and evaluate the width deviation state based on the analysis results.

[0107] S5. The evaluation information is automatically generated into process data pairs, and the corresponding operation instructions are executed through the spinneret forming control system.

[0108] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-leaf hollow fiber spinneret jetting forming control system, characterized by: The data acquisition module, the disturbance analysis module, the convergence angle analysis module, the width analysis module and the control feedback module are comprised. The data acquisition module is used for collecting dynamic aerodynamic data of the spinneret in real time according to the set sensor group, and obtaining the cavity airflow data group, the environmental response data group and the pressure distribution data group after preprocessing. The disturbance analysis module is used for performing wind flow disturbance analysis according to the cavity airflow data group, and performing fiber jet beam stability evaluation according to the analysis result, and triggering the convergence angle analysis module when the evaluation result is abnormal. The convergence angle analysis module is used for performing convergence angle evolution analysis on the fiber spinneret with abnormal cavity airflow according to the environmental response data group. The width analysis module is used for analyzing the disturbance symmetry deviation of the left and right cavity pressures of the spinneret according to the pressure distribution data group, fitting the results of the wind flow disturbance analysis and the convergence angle evolution analysis, analyzing the width deviation of the fiber, and performing width deviation state evaluation according to the analysis result. The control feedback module is used for automatically forming process data pairs with the evaluation information, and executing corresponding operation instructions through the jet forming control system. The control feedback module is used for automatically forming process data pairs with the evaluation information generated by all evaluations, and transmitting the process data pairs to the jet forming control system through a special API interface to execute corresponding operation instructions. Airflow smooth information: keep the current program running normally; Quality qualified information: keep the current program running normally, archive the abnormal situation of the cavity transverse disturbance frequency Fvt, and mark it as transverse disturbance abnormality but not affecting the final width; Fiber divergence information: reduce the jet frequency by 10%, increase the cooling air speed by 5%, and increase the winding tension by 3%, and perform iterative analysis through the disturbance analysis module, if it does not meet the standard for three times in a row, immediately stop and notify the maintenance personnel to check.

2. The multi-leaf hollow fiber spinneret jet forming control system of claim 1, wherein: The data acquisition module comprises a data acquisition unit and a data processing unit. The data acquisition unit is used for collecting dynamic aerodynamic data of the spinneret in real time according to the sensor group arranged at each position of the spinneret. The sensor group comprises a wind speed sensor, a pressure sensor and a tension sensor. The wind speed sensor is arranged at the left side, the right side of the spinneret cavity and the cooling air duct outlet position respectively, and is used for collecting the left side channel wind speed vl, the right side channel wind speed vr and the cooling wind speed wc in real time. The pressure sensor is arranged at the end of the left channel and the right channel inside the spinneret cavity wall side hole and the hollow cavity, and is used for collecting the spinneret cavity pressure ps, the spinneret cavity left channel pressure pl and the spinneret cavity right channel pressure pr in real time. The tension sensor is arranged at the front end of the drafting roller inlet, and is used for collecting the beam tension tr in real time.

3. The multi-leaf hollow fiber spinneret jetting forming control system according to claim 2, wherein: The data processing unit is used for establishing a communication connection between the sensor group and the jet forming control system through a wireless network, transmitting the dynamic aerodynamic data to the jet forming control system in real time for preprocessing, and outputting the cavity airflow data group, the environmental response data group and the pressure distribution data group. The preprocessing includes denoising, missing value filling and normalization processing. The denoising retains the transient structure characteristics of the hydraulic working condition data and removes the high-frequency noise in the hydraulic working condition data through the wavelet denoising technology, the missing value filling completes the hydraulic working condition data of sampling interruption and communication packet loss through the missing value interpolation technology, and the normalization processing removes the dimension influence of the hydraulic working condition data through the Max-Min maximum minimization method; The cavity airflow data set comprises a left-side spinneret cavity channel instantaneous wind speed vl and a right-side spinneret cavity channel instantaneous wind speed vr; The environmental response data set comprises a cooling wind speed wc, a spinneret cavity pressure ps and a fiber bundle tension tr; The pressure distribution data set comprises a spinneret plate hollow cavity left-side channel pressure pl and a right-side channel pressure pr.

4. The multi-leaf hollow fiber spinneret jetting forming control system according to claim 3, wherein: The disturbance analysis module comprises a disturbance frequency analysis unit and an airflow stability evaluation unit; The disturbance frequency analysis unit is used for analyzing the air flow disturbance frequency of the left and right guide channels of the spinning cavity according to the cavity air flow data set, and calculating the cavity transverse disturbance frequency Fvt, which is used for identifying the transverse air flow disturbance characteristics formed by the air speed difference between the left and right channels in the spinning cavity, reflecting the influence of air flow disturbance on the fiber tow swing, specifically as follows: , wherein T represents a sampling period, vl(t) represents the left spinning cavity channel instantaneous air speed at time t, vr(t) represents the right spinning cavity channel instantaneous air speed at time t, and dt represents a time differential symbol.

5. The multi-leaf hollow fiber spinneret jetting forming control system according to claim 4, wherein: The airflow stability evaluation unit is used for calculating the mean value of the cavity transverse disturbance frequency Fvt when the historical fiber bundle is stable according to the statistical method, and presetting an airflow balance threshold Qf based on the mean value, and performing fiber bundle stability evaluation on the real-time acquired cavity transverse disturbance frequency Fvt, and the specific evaluation scheme is as follows: When the cavity transverse disturbance frequency Fvt is less than the airflow balance threshold Qf, it indicates that the cavity airflow is stable, and the fiber swing is normal, and at this time, airflow stability information is generated; When the cavity transverse disturbance frequency Fvt is greater than or equal to the airflow balance threshold Qf, it indicates that the cavity airflow is abnormal, and the fiber spinneret bundle exists deviation, at this time, a deviation early warning signal is sent to the controller, and the convergence angle evolution analysis is performed.

6. A multi-leaf hollow fiber spinneret jet forming control system according to claim 5, wherein: The convergence angle analysis module is used for analyzing the convergence angle evolution of the fiber spinneret with the cavity gas flow anomaly according to the environmental response data set when the jet stability evaluation is the cavity gas flow anomaly, and calculating the cone expansion angle θfz of the fiber spinneret out-fiber path, representing the convergence cone angle formed in the cooling and shrinking process of the fiber, reflecting the expansion and contraction angle of the jet from the jet orifice to the landing point, specifically: , wherein arctan represents the inverse tangent function, ln represents the logarithmic function, and ε represents a very small constant to avoid division by zero error when the denominator is zero, and the value is 0.

001.

7. The multi-leaf hollow fiber spinneret jetting forming control system according to claim 6, wherein: The width analysis module comprises a deviation degree analysis unit and a deviation degree evaluation unit; The offset analysis unit calculates the disturbance symmetry offset Asy of the left and right cavity pressure based on the pressure distribution data set, reflects the symmetry of the internal airflow of the spinneret, obtains the pressure difference and the pressure ratio of the left and right cavities, and then fits the transverse disturbance frequency Fvt and the cone expansion angle θfz to analyze the width offset of the fiber and construct the width offset index Iwd, which is used to analyze the relative difference between the final fiber width offset and the target design width, specifically: , wherein ln represents the logarithmic function, tan represents the tangent function, d ref represents the target design width, and Lfz represents the vertical distance from the spinneret to the cooling collection point.

8. The multi-leaf hollow fiber spinneret jetting forming control system according to claim 7, wherein: The deviation degree evaluation unit is used for calculating the mean value of the width deviation index Iwd when the historical width deviation meets the standard according to the statistical method, and presetting a width deviation standard threshold Qi based on the mean value, and performing width deviation state evaluation on the real-time acquired width deviation index Iwd, and the specific evaluation scheme is as follows: When the width deviation index Iwd is less than the width deviation standard threshold Qi, it indicates that the width deviation meets the standard, the cavity airflow abnormality does not affect the convergence angle of the fiber bundle during cooling, and the fiber forming is stable, and at this time, quality qualified information is generated; When the width deviation index Iwd is greater than or equal to the width deviation standard threshold Qi, it indicates that the width deviation does not meet the standard, the cavity airflow abnormality affects the convergence angle of the fiber bundle during cooling, and the fiber exists divergence, and at this time, fiber divergence information is generated.

9. A method for controlling the spinning of a multi-leaf hollow fiber spinneret, applied to the multi-leaf hollow fiber spinneret spinning control system of any one of claims 1-8, characterized in that: The method comprises the following steps: S1, real-time acquisition of dynamic aerodynamic data of the spinneret plate according to the set sensor group, and acquisition of the cavity airflow data set, the environmental response data set and the pressure distribution data set after preprocessing; S2, airflow disturbance analysis according to the cavity airflow data set, and fiber bundle stability evaluation according to the analysis result, and triggering S3 when the evaluation is cavity airflow abnormality; S3, convergence angle evolution analysis of the fiber spinneret plate with cavity airflow abnormality according to the environmental response data set; S4, analysis of the disturbance symmetry deviation of the left and right cavity pressures of the spinneret plate according to the pressure distribution data set, fitting of the results of the airflow disturbance analysis and the convergence angle evolution analysis, analysis of the width deviation of the fiber, and width deviation state evaluation according to the analysis result; S5, automatically form the evaluation information into process data pairs, and execute corresponding operation instructions through the jet molding control system.

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