A winding machine nozzle tension intelligent control method and system

Abstract

A kind of winding machine nozzle tension intelligent control method and system, it is related to the control field of fiber winding equipment, solve the existing tension control lag, fluctuation, the problem that cannot accurately control into mould tension, the method includes the following steps: step 1, the yarn tension value of winding machine nozzle to product end is collected, and the actual sand yarn tension value of winding machine nozzle to product end is calculated;Step 2, the yarn tension value of winding machine nozzle to product end obtained by step 1 is compared with original set tension value, error signal is generated, and fuzzy-PID dual-mode control algorithm is used to calculate tension error and error change rate;Step 3, according to the adjustment amount calculated in step 2, servo motor or air cylinder is driven, yarn tension is adjusted in real time, for making tension stable in set range.The present application is also applicable to the manufacturing and other application fields of composite material products such as pressure vessel, pipeline, aerospace components, etc.

Description

Technical Field

[0001] This invention relates to the field of fiber winding equipment control technology, specifically to a method and system for intelligent control of the tension of the winding machine nozzle. Background Technology

[0002] Fiber winding technology is widely used in the manufacture of composite materials such as pressure vessels, pipelines, and aerospace components. During the winding process, fiber tension is a key process parameter that determines the quality of the product (such as adhesive content, porosity, and interlaminar shear strength).

[0003] In existing technologies, tension control systems are typically installed at the yarn frame end, i.e., the unwinding point. However, as the fiber travels a long distance from the yarn frame through guide rollers, a resin impregnation tank, and an extrusion roller to the yarn exit nozzle, the actual tension at the nozzle deviates significantly from the set tension at the yarn frame end due to frictional resistance along the way, changes in resin viscosity, and the high-speed movement of the winding machine head. This deviation is characterized by nonlinearity, time-varying nature, and hysteresis.

[0004] The specific issues are as follows: 1. Uncontrollable losses along the yarn path: As the yarn passes through multiple yarn guide points, friction causes tension to increase or fluctuate, and the control at the yarn frame end cannot compensate for the changes at the back end.

[0005] 2. Slow dynamic response: Traditional PID control is slow to respond to sudden tension changes when facing non-circular winding sections or high-speed reversal, which can easily cause "loose yarn" or "broken yarn".

[0006] 3. Open-loop or semi-closed-loop defects: Most systems do not provide closed-loop feedback at the "thread tip end" closest to the product, resulting in the tension accuracy of the final winding on the product being unreliable.

[0007] Therefore, developing a monitoring and adjustment system that can directly monitor the tension from the nozzle to the product end and has high precision and fast response capabilities is an urgent need to improve the quality of high-end composite material products. Summary of the Invention

[0008] This invention aims to overcome the problems of lagging tension control, large fluctuations, and inability to accurately control the tension entering the die in existing technologies. Therefore, this invention proposes an intelligent tension control method and system for winding machine nozzles.

[0009] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: Option 1: This invention proposes an intelligent tension control method for the nozzle of a winding machine, the method comprising the following steps: Step 1: Collect the yarn tension value from the winding machine nozzle to the product end, and calculate the actual yarn tension value from the winding machine nozzle to the product end; Step 2: Compare the yarn tension value from the winding machine nozzle to the product end obtained in Step 1 with the original set tension value to generate an error signal, and use the fuzzy-PID dual-mode control algorithm to calculate the tension error and the error change rate. Step 3: Drive the servo motor or cylinder according to the adjustment amount calculated in Step 2 to adjust the yarn tension in real time, so as to stabilize the tension within the set range.

[0010] Furthermore, a preferred embodiment is provided, wherein the method for calculating the actual tension value of the yarn from the winding machine nozzle to the product end in step 1 is as follows: Treal = Fm / 2sin(θ / 2) Where Treal is the actual yarn tension value from the nozzle to the product end, Fm is the normal resultant force of the yarn acting on the detection roller, and θ is the wrap angle of the yarn on the tension detection roller.

[0011] Furthermore, in a preferred embodiment, the method for determining the resultant normal force Fm exerted by the yarn on the detection roller is as follows: The analog electrical signal generated by the sensor is acquired at high speed through the A / D interface and converted into the current normal resultant force Fm according to the calibration coefficient.

[0012] Furthermore, a preferred embodiment is provided, wherein the method for calculating the tension error and the rate of change of error using the fuzzy-PID dual-mode control algorithm in step 2 is as follows: e(t) = Tset Treal(t) ec(t)=[ed(t) e(t 1)] / Δt Where t is the sampling period.

[0013] Furthermore, a preferred embodiment is provided, wherein the fuzzy-PID dual-mode control algorithm includes fuzzy control and fuzzy-PID control, and fuzzy control and fuzzy-PID control are completed by setting a segmentation threshold of Eth, that is, Step 5.1: Fuzzy control. When |e(t)| > Eth, output a large feedforward control quantity U(t). Step 5.2: Fuzzy-PID control. When |e(t)| ≤ Eth, switch to fuzzy-PID control and use fuzzy logic to correct the PID parameters in real time.

[0014] Furthermore, a preferred embodiment is provided, in which the method of real-time correction of PID parameters using fuzzy logic is as follows: Kp = Kp0 + ΔKp Ki = Ki0 + Δki Kd = Kd0 + ΔKd Wherein, Kp0, Ki0, Kd0 are the basic parameters; ΔKp, ΔKi, ΔKd are the adaptive adjustment values ​​output by e(t) and ec(t) after fuzzy inference and defuzzification.

[0015] Furthermore, a preferred embodiment is provided, wherein the absolute value of the tension error of the switching threshold Eth of the fuzzy-PID dual-mode control algorithm is 0.3N, and the fuzzy-PID control adopts a two-dimensional input, wherein the two-dimensional input includes the error and the error change rate.

[0016] Option 2: An intelligent control system for the tension of the thread nozzle of a winding machine, the system comprising: The tension monitoring module is used to collect the yarn tension value from the winding machine nozzle to the product end, and to calculate the actual yarn tension value from the winding machine nozzle to the product end. The control module is used to compare the yarn tension value from the winding machine nozzle to the product end collected by the tension monitoring module with the original set tension value, generate an error signal, and use a fuzzy-PID dual-mode control algorithm to calculate the tension error and the error change rate. The execution module is used to drive a servo motor or cylinder to adjust the yarn tension in real time according to the adjustment amount calculated by the control module, so as to stabilize the tension within the set range.

[0017] Furthermore, a preferred embodiment is provided in which the tension monitoring module includes a strain gauge tension sensor or an angle sensor, which is installed at the nozzle outlet or on the product winding path to convert the tension signal into an electrical signal.

[0018] Furthermore, a preferred embodiment is provided, wherein the control module includes a programmable logic controller (PLC) or an industrial control computer (IPC), and has a built-in fuzzy-PID dual-mode control algorithm. The fuzzy-PID dual-mode control algorithm adopts fuzzy control when the tension error is greater than a threshold, and switches to fuzzy-PID control when the tension error is less than the threshold.

[0019] The advantages of this invention are: The intelligent tension control method and system for winding machine nozzles described in this invention can form a high-precision closed loop, directly monitor the tension in the last meter, eliminate friction interference along the winding process, and control the tension fluctuation rate within ±3%. Furthermore, it can respond quickly: combining the adaptive capability of fuzzy algorithms with the high-frequency response characteristics of servo / pneumatic systems, the system response time is less than 0.2 seconds, effectively coping with acceleration and deceleration impacts during high-speed winding. The dual-mode control described in this invention solves the problem of rapid following under large deviations, while PID solves the steady-state accuracy under small deviations, achieving smooth control without overshoot. By integrating real-time monitoring and intelligent control, this invention significantly improves the uniformity and reliability of the wound products.

[0020] This invention is also applicable to applications such as the manufacture of composite material products, including pressure vessels, pipelines, and aerospace components. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the intelligent control system for the tension of the winding machine nozzle as described in Embodiment 1.

[0022] Among them, yarn ball 1, impregnation tank 2, fixed roller A 3, active adjustment floating roller 4, fixed roller B 5, tension detection roller 6, winding core mold 7, AC servo motor 8.

[0023] Figure 2 This is a schematic diagram of the tension adjustment mechanism driven by the servo motor described in Embodiment 1.

[0024] Figure 3 This is a block diagram of the fuzzy-PID dual-mode control logic described in Implementation Method 1. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0026] Implementation Method 1, see [link] Figures 1 to 3 This embodiment describes a method and system for intelligent control of the tension of the nozzle in a winding machine. The method specifically includes the following steps: The tension sensor is installed below or behind the tension detection roller. Assuming the wrap angle of the yarn on the tension detection roller is θ (usually designed as a fixed value), the tension sensor measures the resultant normal force Fm exerted by the yarn on the detection roller in real time. According to the force analysis, the actual tension value Treal from the nozzle to the product end is calculated as: Treal = Fm / 2sin(θ / 2); The control module acquires the analog electrical signal generated by the sensor at high speed through the A / D interface, converts it into the current normal resultant force Fm according to the calibration coefficient, and calculates the current actual tension value Treal in real time using the above formula. An improved fuzzy-PID control model is given based on the tension calculation method, and the target tension value is set to Tset internally within the control module. In each sampling period t, the current tension error e(t) = Tset is calculated. Treal(t), and the rate of change of error ec(t) = [ed(t)] e(t 1)] / Δt. The system adopts a dual-mode control strategy, and the control model is as follows: Let the segmentation threshold be Eth. (1) Fuzzy control (when |e(t)|>Eth): The algorithm directly outputs a large feedforward control quantity U(t) based on the fuzzy rule table, driving the actuator to move at full speed to reduce the error as quickly as possible, ignoring the integral effect of PID to prevent system overshoot.

[0027] Table 1. Fuzzy rule control table

[0028] (2) Fuzzy-PID control (when |e(t)|≤Eth): The algorithm switches to PID regulation and uses fuzzy logic to correct the PID parameters in real time. The real-time PID parameter calculation model is as follows: Kp = Kp0 + ΔKp Ki = Ki0 + Δki Kd = Kd0 + ΔKd Where Kp0, Ki0, and Kd0 are the basic parameters; ΔKp, ΔKi, and ΔKd are the adaptive adjustment quantities output from e(t) and ec(t) after fuzzy inference and defuzzification. The discrete formula for the final control output U(t) is:

[0029] Based on the above method, the present invention also discloses a method for real-time tension monitoring and adjustment, comprising the following steps: 1. Set the target tension value Tset and the allowable error range; 2. The tension sensor collects the yarn tension (Treal) at the nozzle at a high frequency (≥500Hz); 3. Calculate the tension deviation e=Tset Treale and its rate of change ec; 4. Utilize fuzzy logic rules to adjust PID parameters (Kp, Ki, Kd) in real time based on the magnitudes of e and ec, or switch between fuzzy control and PID control; 5. Drive the servo motor or cylinder to move, causing the floating roller to move quickly and compensate for tension fluctuations.

[0030] This embodiment discloses an intelligent control system for the tension of the thread nozzle of a winding machine, the system specifically comprising: The tension monitoring module is used to collect the yarn tension value from the winding machine nozzle to the product end, and to calculate the actual yarn tension value from the winding machine nozzle to the product end. The control module is used to compare the yarn tension value from the winding machine nozzle to the product end collected by the tension monitoring module with the original set tension value, generate an error signal, and use a fuzzy-PID dual-mode control algorithm to calculate the tension error and the error change rate. The execution module is used to drive a servo motor or cylinder to adjust the yarn tension in real time according to the adjustment amount calculated by the control module, so as to stabilize the tension within the set range.

[0031] The tension monitoring module is a miniature tension sensor installed at the front end of the yarn nozzle, used to collect the actual tension value of the yarn before it enters the winding mandrel in real time. The execution module is located upstream of the tension detection module and includes a floating roller assembly and an actuator (servo motor or precision cylinder) that drives the assembly. It fine-tunes the tension by changing the yarn path length or applying a direct force. The control module has a built-in fuzzy-PID dual-mode control algorithm controller that receives the signal from the tension sensor, compares it with the set value, and outputs a control signal to the brake.

[0032] Example 1: Tension fine-tuning system based on servo motor, see [link / reference] Figure 1 and Figure 2 This embodiment is described below. Figure 1 This is a schematic diagram of the intelligent tension control system for a winding machine provided by the present invention. Figure 2 This invention provides a flowchart for tension control of a winding machine. The system is integrated into the yarn exit carriage of the winding machine.

[0033] 1. Hardware Components The winding machine yarn feeding device includes a yarn ball 1, an impregnation tank 2, an A fixed roller 3, an active adjustment floating roller 4, a B fixed roller 5, a tension detection roller 6, a winding mandrel 7, and an AC servo motor 8. Yarn path: Impregnated yarn - A fixed roller 3 - active adjustment floating roller 4 - B fixed roller 5 - tension detection roller 6 - yarn outlet nozzle - winding mandrel 7.

[0034] Tension sensor: A resistance strain gauge type three-wheel tension sensor with a range of 0-100N is selected and installed 50mm-100mm in front of the thread nozzle to ensure that the detected data represents the true entry tension of the die. The sensor outputs a 0-10V analog signal.

[0035] Actuator: A low-inertia AC servo motor (e.g., 400W, 3000rpm) is used, which is connected to an eccentric swing arm through a reducer. An active adjustment floating roller is installed at the end of the swing arm.

[0036] Controller: Uses a PLC or embedded motion controller (such as STM32 series), with a high-speed A / D acquisition interface and PWM output interface.

[0037] 2. Fuzzy-PID dual-mode control logic, the controller internally runs the following algorithm: Inputs: tension error e and error change rate ec.

[0038] Fuzzification: divide e and ec into 7 fuzzy sets: {NB, NM, NS, ZO, PS, PM, PB} (negative large, negative medium, negative small, zero, positive small, positive medium, positive large).

[0039] Control strategy switching: When |e| > threshold A, or when |e| > threshold A (e.g., 10% of the set value), the system determines it as a large disturbance (such as during layer changing or yarn loading). In this case, fuzzy control is used, and a large adjustment amount is directly output according to the fuzzy rule table to make the servo motor rotate the swing arm quickly and take in and release the yarn significantly to reduce the error as quickly as possible.

[0040] When |e| ≤ threshold A, the system determines it to be a steady-state perturbation. At this point, it switches to PID control. Simultaneously, fuzzy inference is used to correct the three PID parameters Kp, Ki, and Kd in real time. For example, when the error e begins to decrease but ec is large, Kd is increased to suppress overshoot; when e is small but a steady-state error exists, Ki is increased.

[0041] For example, assuming the tension is set at 50N, when the winding machine accelerates, the tension instantly drops to 40N (error 10N, >10%). The controller, using a fuzzy algorithm, determines that a "significant increase" is needed and immediately instructs the servo motor to reverse, driving the floating roller to press down and tighten the yarn, with a response time of <0.1 seconds. When the tension recovers to 49N (error 1N), it switches to PID fine-tuning, the motor makes micro-movements to eliminate jitter, and finally stabilizes at 50N ± 1.5N (i.e., ±3% accuracy). Example 2: Tension buffer system based on precision cylinder 1. Hardware configuration: The difference from Embodiment 1 lies in the actuator.

[0042] Actuator: A low-friction cylinder connected to a floating roller. The cylinder's air circuit is connected to an electro-proportional valve (E / P converter).

[0043] Principle: The cylinder output force F = P × S (air pressure × area). The tension applied to the yarn can be directly controlled by controlling the air pressure.

[0044] 2. Control methods When the system sets the target tension value to 50N, the detailed steps of the fuzzy-PID algorithm in generating the tension adjustment command are as follows: Step 2.1 (Data Acquisition and Error Calculation): The tension sensor detects the current actual tension in real time (e.g., a sudden increase to 60N), and the controller calculates the current tension error e= 10Ne= 10N, and calculate the current error change rate ec.

[0045] Step 2.2 (Pattern Analysis and Fuzzy Inference): The controller determines |e|>Eth|e|>Eth (assuming a threshold of 5N), and then adopts fast fuzzy control; if the tension is pulled back to 52N, the deviation e= When 2N ≤ Eth, the controller automatically switches to fuzzy-PID mode. In this mode, the controller maps the continuous input variables e and ec to a fuzzy set {NB, NM, NS, ZO, PS, PM, PB}. Based on the built-in fuzzy rule matrix, if ee and ec are both positive, then ΔKp is negative to suppress excessive pressure, and the specific values ​​of ΔKp, ΔKi, and ΔKd are calculated.

[0046] Step 2.3 (Generate air pressure adjustment command): Substitute the adjusted real-time Kp, Ki, Kd into the PID calculation formula to calculate the current target cylinder driving force, and derive the target air pressure value Ptarget through the mapping formula.

[0047] Step 2.4 (Command Execution): The controller converts the target air pressure value Ptarget to 0 via D / A conversion. Analog voltage commands between 10V and 10V are sent to the electro-proportional valve (E / P converter). For example, when the detected tension is too high, the algorithm calculates that the tightening force needs to be reduced, thus outputting a corresponding lower voltage signal. The proportional valve then increases the exhaust volume to reduce the air pressure, causing the low-friction cylinder to retract, reducing the pushing force on the yarn, and allowing the yarn tension to drop rapidly until it stabilizes within the set range. Combined with the inherent mechanical "soft characteristics" of air pressure, this provides excellent mechanical filtering for minute high-frequency vibrations.

[0048] In summary, the intelligent tension control method and system for winding machine nozzles described in this embodiment can form a high-precision closed loop, directly monitor the tension in the last meter, eliminate friction interference along the winding process, and control the tension fluctuation rate within ±3%. Furthermore, it can respond quickly: combining the adaptive capability of fuzzy algorithms with the high-frequency response characteristics of servo / pneumatic systems, the system response time is less than 0.2 seconds, effectively coping with acceleration and deceleration impacts during high-speed winding. The dual-mode control described in this invention solves the problem of rapid following under large deviations, while PID solves the steady-state accuracy under small deviations, achieving smooth control without overshoot. This invention, by integrating real-time monitoring and intelligent control, significantly improves the uniformity and reliability of wound products. It is also applicable to applications such as the manufacturing of composite material products for pressure vessels, pipelines, and aerospace components.

[0049] Those skilled in the art will understand that the above description is merely a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. This is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0050] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for intelligent control of nozzle tension in a winding machine, characterized in that, The method includes the following steps: Step 1: Collect the yarn tension value from the winding machine nozzle to the product end, and calculate the actual yarn tension value from the winding machine nozzle to the product end; Step 2: Compare the yarn tension value from the winding machine nozzle to the product end obtained in Step 1 with the original set tension value to generate an error signal, and use the fuzzy-PID dual-mode control algorithm to calculate the tension error and the error change rate. Step 3: Drive the servo motor or cylinder according to the adjustment amount calculated in Step 2 to adjust the yarn tension in real time, so as to stabilize the tension within the set range.

2. The intelligent tension control method for the winding machine nozzle according to claim 1, characterized in that, The method for calculating the actual yarn tension from the winding machine nozzle to the product end in step 1 is as follows: Treal = Fm / 2sin(θ / 2) Where Treal is the actual yarn tension value from the nozzle to the product end, Fm is the normal resultant force of the yarn acting on the detection roller, and θ is the wrap angle of the yarn on the tension detection roller.

3. The intelligent tension control method for the winding machine nozzle according to claim 2, characterized in that, The method for determining the normal resultant force Fm of the yarn acting on the detection roller is as follows: The analog electrical signal generated by the sensor is acquired at high speed through the A / D interface and converted into the current normal resultant force Fm according to the calibration coefficient.

4. The intelligent tension control method for the winding machine nozzle according to claim 1, characterized in that, The method for calculating the tension error and the rate of change of error using the fuzzy-PID dual-mode control algorithm in step 2 is as follows: e(t)=Tset Treal(t) ec(t)=[ed(t) e(t 1)] / Δt Where t is the sampling period.

5. The intelligent tension control method for the winding machine nozzle according to claim 4, characterized in that, The fuzzy-PID dual-mode control algorithm includes fuzzy control and fuzzy-PID control. By setting the segmentation threshold Eth, fuzzy control and fuzzy-PID control are achieved. Step 5.1: Fuzzy control. When |e(t)| > Eth, output the feedforward control quantity U(t). Step 5.2: Fuzzy-PID control. When |e(t)| ≤ Eth, switch to fuzzy-PID control and use fuzzy logic to correct the PID parameters in real time.

6. The intelligent tension control method for the winding machine nozzle according to claim 5, characterized in that, The method of using fuzzy logic to correct PID parameters in real time is as follows: Kp = Kp0 + ΔKp Ki = Ki0 + Δki Kd = Kd0 + ΔKd Wherein, Kp0, Ki0, Kd0 are the basic parameters; ΔKp, ΔKi, ΔKd are the adaptive adjustment values ​​output by e(t) and ec(t) after fuzzy inference and defuzzification.

7. The intelligent tension control method for the winding machine nozzle according to claim 5, characterized in that, The absolute value of the tension error of the switching threshold Eth of the fuzzy-PID dual-mode control algorithm is 0.3N. The fuzzy-PID control adopts a two-dimensional input, which includes the error and the error change rate.

8. A smart control system for the tension of a winding machine nozzle, characterized in that, The system includes: The tension monitoring module is used to collect the yarn tension value from the winding machine nozzle to the product end, and to calculate the actual yarn tension value from the winding machine nozzle to the product end. The control module is used to compare the yarn tension value from the winding machine nozzle to the product end collected by the tension monitoring module with the original set tension value, generate an error signal, and use a fuzzy-PID dual-mode control algorithm to calculate the tension error and the error change rate. The execution module is used to drive a servo motor or cylinder to adjust the yarn tension in real time according to the adjustment amount calculated by the control module, so as to stabilize the tension within the set range.

9. The intelligent control system for the tension of the winding machine nozzle according to claim 8, characterized in that, The tension monitoring module includes a strain gauge tension sensor or an angle sensor, which is installed at the nozzle outlet or on the product winding path to convert the tension signal into an electrical signal.

10. The intelligent control system for the tension of the winding machine nozzle according to claim 8, characterized in that, The control module includes a programmable logic controller (PLC) or an industrial control computer (IPC), and has a built-in fuzzy-PID dual-mode control algorithm. The fuzzy-PID dual-mode control algorithm uses fuzzy control when the tension error is greater than a threshold, and switches to fuzzy-PID control when the tension error is less than the threshold.

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