A continuous carbon fiber aspect ratio online regulation system based on tension and temperature feedback

Abstract

The application discloses a continuous carbon fiber aspect ratio online regulation and control system based on tension and temperature feedback, and relates to the technical field of fiber manufacturing process control. The system comprises a sensing and detecting module, a decision decoupling module and a monitoring and protection module. The sensing and detecting module is used for collecting and acquiring fiber tension and heat treatment temperature. The decision decoupling module is used for obtaining temperature pre-compensation instructions and PID feedback control instructions through a tension-temperature coupling model and a controller PID algorithm, superimposing the two instructions and generating composite control instructions. The monitoring and protection module is used for issuing driving instructions according to the composite control instructions, obtaining a comprehensive stability coefficient in a driving process, and adjusting the composite control instructions according to a comparison result between the comprehensive stability coefficient and a preset stability threshold. Thus, effective decoupling and dynamic coordination of two key variables can be realized, the system can stably and quickly respond to various disturbances, and the problem of oscillation caused by single parameter adjustment can be avoided, so that the stability of the production process is fundamentally improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fiber manufacturing process control, in particular to a continuous carbon fiber aspect ratio online regulation system based on tension and temperature feedback. BACKGROUND

[0002] Carbon fibers are widely used in aerospace, rail transportation, new energy vehicles and high-end sports equipment due to their high specific strength, high specific modulus, corrosion resistance and excellent thermal stability. In the preparation process of continuous carbon fiber reinforced thermoplastic composite (CFRTP), the aspect ratio of the fiber (i.e. the ratio of the fiber length to its diameter) is one of the key parameters affecting the mechanical properties of the final product. Higher aspect ratio helps to improve the tensile strength, impact toughness and fatigue life of the composite, while fiber breakage or excessive shear will significantly reduce its reinforcing effect.

[0003] Currently, continuous carbon fibers are usually compounded with thermoplastic resins through melt impregnation, solution impregnation or powder blending, etc. In this process, the fiber needs to go through multiple processes such as traction, heating, cooling and winding. However, in high-speed continuous production, the fiber bundle is easily affected by uneven tension, local overheating or uneven cooling, etc., leading to micro-fracture, hairiness or even breakage of the fiber, thus causing the actual aspect ratio to deviate from the design value, seriously affecting the product quality consistency.

[0004] Chinese patent application with publication number CN120353121A discloses a cross-linked cable production control method based on an improved PID control algorithm, which includes establishing a coordinated control architecture of temperature, pressure and traction speed, taking the vulcanization pipe temperature as the main control variable, the extruder pressure and the traction speed as the slave control variables, and constructing an optimal reference model. The main controller tracks the temperature set value in real time, dynamically adjusts the PID parameters according to the insulation layer thickness deviation and material cross-linking degree, etc. process parameters, and realizes precise control through the improved PID controller of three-ring coordinated coupling: the temperature control loop makes the steady-state error of the actual temperature and the set value converge to zero; the pressure and speed control loop makes the fluctuation amplitude converge to the process allowable range. The invention can improve the temperature stability, pressure uniformity and traction synchronicity of cross-linked cable production, reduce process fluctuations and defect rates, and improve product quality and production efficiency.

[0005] However, the above and similar technical solutions still have the following shortcomings: in the running process of the continuous carbon fiber production line, in order to improve the orientation degree and strength of the carbon fiber, the speed of the drafting roller is usually increased to increase the fiber tension. However, due to the coupling relationship between fiber tension and heat treatment temperature, the heat treatment temperature will be out of adjustment during the adjustment of fiber tension, which will cause the system to be unstable. SUMMARY

[0006] The present application aims to provide a continuous carbon fiber aspect ratio online regulation system based on tension and temperature feedback to solve the problems raised in the background.

[0007] To achieve the above object, the present application provides the following technical solution: a continuous carbon fiber aspect ratio online regulation system based on tension and temperature feedback, comprising:

[0008] The perception detection module: through the fiber tension detection unit and the heat treatment temperature detection unit, the fiber tension and the heat treatment temperature are collected and acquired;

[0009] The decision decoupling module: through the tension-temperature coupling model and the controller PID algorithm, the fiber tension and the heat treatment temperature are processed to obtain the temperature pre-compensation instruction and the PID feedback control instruction, and the temperature pre-compensation instruction and the PID feedback control instruction are superimposed to obtain the composite control instruction, comprising:

[0010] SB1: Constructing an internal model: according to the fiber tension and the heat treatment temperature, a tension-temperature coupling model is constructed;

[0011] SB2: Feedforward compensation: according to the tension difference between the preset tension target value and the current fiber tension value, the tension adjustment instruction is determined, and the tension adjustment instruction is taken as the input of the tension-temperature coupling model to output the corresponding temperature pre-compensation instruction;

[0012] SB3: Feedback control: according to the tension adjustment amount between the current fiber tension value and the preset tension target value, and the temperature adjustment amount between the current temperature value and the preset temperature target value, the PID feedback control instruction is determined through the controller PID algorithm;

[0013] SB4: Cooperative adjustment: the temperature pre-compensation instruction and the PID feedback control instruction are algebraically superimposed to determine the composite control instruction;

[0014] The monitoring and protection module: according to the composite control instruction, the driving instruction is issued, and the comprehensive stability coefficient in the driving process is acquired, and according to the comparison result between the comprehensive stability coefficient and the preset stability threshold, the composite control instruction is adjusted.

[0015] Further, the fiber tension and the heat treatment temperature are collected and acquired, comprising:

[0016] SA1: Tension detection setting: a non-contact laser diffraction tension meter is arranged at the fiber free section between the drafting rollers, and is rigidly connected with the production line through the installation base;

[0017] SA2: Temperature detection setting: a thermocouple is arranged at different process sections in the furnace and is fixedly installed with the furnace through a flange or a thread, and a measurement end of the thermocouple is arranged inside the furnace;

[0018] SA3: Real-time monitoring: a fiber tension value is acquired through the non-contact laser diffraction tension meter, and a heat treatment temperature is acquired through the thermocouple.

[0019] Further, the measurement end of the thermocouple and the outer surface of the protective sleeve are provided with a high-temperature oxidation-resistant and carbonization-resistant coating, and a compensation lead of the thermocouple is extended to a control cabinet through a metal hose or a wire slot.

[0020] Further, a tension-temperature coupling model is constructed through physical simplification of a digital twin model, including:

[0021] WA1: Decomposition definition: a heat treatment furnace is divided into multiple temperature zones, and input data and output data of each temperature zone are acquired;

[0022] WA2: Unit processing: according to the input data and the output data of each temperature zone, a thermodynamic equation and a mechanical equation of each temperature zone are constructed;

[0023] WA3: Model construction: the thermodynamic equation and the mechanical equation are converted from an ordinary differential form into a difference form to construct a tension-temperature coupling model.

[0024] Further, when the thermodynamic equation and the mechanical equation are converted into the ordinary differential form, the temperature in each temperature zone is arranged to be uniformly distributed.

[0025] Further, a tension-temperature coupling model is constructed through a data machine learning model, including:

[0026] WB1: Data acquisition: process parameters and performance parameters are acquired through a distributed control system or a data acquisition and monitoring control system, the process parameters including a linear speed of a drafting roller, a fiber tension value, a heating zone power and a temperature value, and the performance parameters including a tensile strength, an elastic modulus and an elongation at break of the fiber;

[0027] WB2: Data processing: the process parameters and the performance parameters are processed through a preset time window to acquire data features;

[0028] WB3: Model training: a neural network model is trained according to the data features and a set label to construct a tension-temperature coupling model.

[0029] Further, the label includes a tensile strength value and an elastic modulus value.

[0030] Further, the temperature pre-compensation instruction comprises a power adjustment amount, the PID feedback control instruction comprises a speed adjustment amount and a power adjustment amount, and the composite control instruction comprises a sum of the speed adjustment amount and the power adjustment amount.

[0031] Further, the adjusting of the composite control instruction comprises:

[0032] SC1: driving execution: according to the composite control instruction, a driving instruction is set, and the drafting mechanism driving unit and the heating mechanism driving unit are regulated and controlled through the driving instruction;

[0033] SC2: index calculation: through time series analysis, the collected fiber tension data and heat treatment temperature data are analyzed and processed to obtain an oscillation amplitude and an oscillation frequency, and simultaneously according to the oscillation amplitude and the oscillation frequency, a comprehensive stability coefficient is determined;

[0034] SC3: adjustment control: the comprehensive stability coefficient is compared with a preset stability threshold value, and according to the comparison result, a corresponding control mode is determined, and specifically:

[0035] When the comprehensive stability coefficient is greater than the preset stability threshold value, the corresponding control mode is a high-performance mode; otherwise, the corresponding control mode is a safety mode, and the composite control instruction is adjusted according to the preset controller parameters and the target interval.

[0036] Further, through the time series analysis, a data waveform graph is obtained, and according to the data waveform graph, a peak number and a valley number are determined, a number difference between the peak and the valley is obtained, and according to a ratio between the number difference and a preset value, the oscillation amplitude is determined.

[0037] Through the time series analysis, a data waveform graph is obtained, an oscillation frequency in a preset time is determined, and according to a ratio between the oscillation frequency and the preset time, the oscillation frequency is determined.

[0038] Compared with the prior art, the beneficial effects of the present application are:

[0039] One: the present application calculates the temperature pre-compensation instruction required to offset the temperature influence caused by the tension change in advance according to the tension difference between the preset tension target value and the current actual tension value through the tension-temperature coupling model, simultaneously, the deviation between the real-time collected fiber tension and heat treatment temperature and their respective target values is accurately closed-loop corrected through the classic PID controller to generate the PID feedback control instruction, and the temperature pre-compensation instruction and the PID feedback control instruction are algebraically superimposed to generate the final composite control instruction, so that not only the effective decoupling and dynamic coordination of the two key variables can be realized, the system can respond to various disturbances smoothly and quickly, but also the oscillation problem caused by single parameter adjustment can be avoided, and the stability of the production process is fundamentally improved;

[0040] Secondly, the present application divides the complex heat treatment furnace physical space into multiple temperature zones, establishes equations based on thermodynamics and mechanics principles for each region, and converts them into difference forms suitable for real-time calculation to build a digital twin model based on physical mechanism, and a machine learning model based on data driving can also be built by training a neural network model with historical production data, so that the system can more accurately predict the dynamic relationship between tension and temperature, and then more forward-looking and accurate compensation instructions can be issued to improve the intelligent level of control.

[0041] Thirdly, the present application calculates the oscillation amplitude and oscillation frequency of the fiber tension and temperature data in real time through time series analysis, and fuses them into a comprehensive stability coefficient, so that the real-time diagnosis of the system health status can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The system block diagram of the continuous carbon fiber aspect ratio online regulation and control system in the present application;

[0043] Figure 2 The process schematic diagram of the fiber tension detection unit collecting fiber tension in the present application;

[0044] Figure 3 The process schematic diagram of the heat treatment temperature detection unit collecting heat treatment temperature in the present application;

[0045] Figure 4 The process schematic diagram of the composite control instruction acquisition in the present application;

[0046] Figure 5 The process schematic diagram of the construction of the physical simplified digital twin model in the present application;

[0047] Figure 6 The process schematic diagram of the construction of the data machine learning model in the present application;

[0048] Figure 7 A flowchart for confirming a composite control instruction in the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0050] Reference Figure 1 The present embodiment provides a continuous carbon fiber aspect ratio online regulation system based on tension and temperature feedback, which comprises a sensing and detecting module, a decision decoupling module and a monitoring and protection module. The sensing and detecting module is used to set corresponding fiber tension detection units and heat treatment temperature detection units through a non-contact laser diffraction tension meter and a multi-point anti-coking thermocouple array embedded in the furnace interior, so as to collect and acquire corresponding fiber tension and heat treatment temperature. The decision decoupling module is used to process the fiber tension and heat treatment temperature collected and acquired by the sensing and detecting module through a set tension-temperature-microstructure correlation model, and generate corresponding temperature pre-compensation instructions. Meanwhile, the fiber tension and heat treatment temperature collected and acquired by the sensing and detecting module are compared with corresponding preset tension target values and preset temperature target values respectively, to generate corresponding PID feedback control instructions. The generated temperature pre-compensation instructions and PID feedback control instructions are superimposed to determine a final composite control instruction. The monitoring and protection module is used to drive corresponding physical devices according to the composite control instruction determined by the decision decoupling module, and at the same time, monitor the real-time data flow of the fiber tension and heat treatment temperature in the driving process, and quantify the corresponding comprehensive stability coefficient, and at the same time, according to the comparison between the comprehensive stability coefficient and the preset stability threshold, adjust the executed composite control instruction in real time.

[0051] In the present embodiment, the sensing and detecting module collects and acquires corresponding fiber tension and heat treatment temperature through the set fiber tension detection units and heat treatment temperature detection units. Reference Figure 2 and Figure 3 The present embodiment provides a sensing and detecting method for collecting and acquiring fiber tension and heat treatment temperature, which is specifically as follows:

[0052] Step SA1: tension detection setting. That is, a non-contact laser diffraction tension meter is installed at the free section of the fiber between the two drafting rollers. Notably, during installation, the position with minimal vibration and relatively stable fiber shaking, far away from the driving roller and the deflection roller, is selected for installation. Specifically, when installing the non-contact laser diffraction tension meter, it is rigidly connected to the production line foundation through the installed mounting base, such as a steel platform, to isolate external vibration interference on the non-contact laser diffraction tension meter measurement.

[0053] Step SA2: temperature detection setting. That is, according to the maximum temperature of the furnace, a corresponding suitable thermocouple type is selected, such as S-type thermocouple, R-type thermocouple and B-type thermocouple. Specifically, during installation of the thermocouple, according to the length direction of the furnace, corresponding installation measuring points are arranged at different process sections such as the preheating zone, reaction zone and heat preservation zone in the furnace. At the cross section of each process section, the furnace top, bottom and side wall are installed to measure and obtain the temperature gradient at each process section. At the same time, according to the installation measuring points arranged on the furnace, corresponding installation holes are opened at each installation measuring point to fix and install the thermocouple with the furnace through the connection between the flange or thread and the installation hole, and the measurement end of the thermocouple is arranged inside the furnace to enable it to sense and obtain the temperature of the fiber area.

[0054] Further, the measurement end of the thermocouple and the outer surface of the protective sleeve in the embodiment are provided with a high-temperature oxidation-resistant and carbonization-resistant coating, such as an alumina, boron nitride or other ceramic coating, to ensure the accuracy of long-term measurement. At the same time, the compensation lead of the thermocouple is arranged as a high-temperature resistant shield cable and is arranged in a metal hose or wire slot to extend from the furnace body to the control cabinet, so as to protect the compensation lead from physical damage and high-temperature radiation during use.

[0055] Step SA3: real-time monitoring. That is, the non-contact laser diffraction tension meter arranged in step SA1 is calibrated offline when the production line is stopped. Specifically, according to the position of the measuring point arranged at the free section of the fiber, a standard weight of known weight is hung by a pulley to obtain the signal value size collected by the PLC controller through the non-contact laser diffraction tension meter. That is, according to the weight of the standard weight and the corresponding collected signal value size, the corresponding relationship between the signal value and the weight of the standard weight (i.e. the fiber tension value, it is worth noting that the 50N weight in the embodiment corresponds to the fiber tension of 50N) is determined.

[0056] Further, according to the corresponding relationship between the signal value and the fiber tension value, the non-contact laser diffraction tension meter emits a laser beam to the running fiber, so as to determine the corresponding fiber tension value, that is, to detect and obtain the vibration frequency and morphological change of the fiber.

[0057] Meanwhile, in the present embodiment, according to the plurality of thermocouples arranged in step SA2, a multi-point anti-coking thermocouple array embedded inside the hearth can be constructed. That is, the longitudinal and transverse temperature distribution of the thermal field in the furnace can be measured by the multi-point anti-coking thermocouple array constructed, and the corresponding working temperature value can be obtained.

[0058] In the present embodiment, the decision decoupling module generates the final composite control instruction by superimposing the temperature pre-compensation instruction and the PID feedback control instruction. Referring to Figure 4 The present embodiment provides a decision decoupling method for obtaining a composite control instruction, which is specifically as follows:

[0059] Step SB1: Construct an internal model. That is, the fiber tension and heat treatment temperature collected by the perception detection module are used to construct a tension-temperature-microstructure correlation model, that is, a tension-temperature coupling model. Specifically, the tension-temperature coupling model in the present embodiment includes a physically simplified digital twin model or a data machine learning model. That is, the corresponding tension-temperature coupling model can be constructed by the physically simplified digital twin model. The corresponding tension-temperature coupling model can also be constructed according to the data machine learning model.

[0060] In the present embodiment, the corresponding tension-temperature coupling model is constructed by the physically simplified digital twin model, referring to Figure 5 The present embodiment provides a method for constructing a tension-temperature coupling model according to a physically simplified digital twin model, which is specifically as follows:

[0061] Step WA1: Decomposition definition. That is, according to the design drawings of the heat treatment furnace, the heat treatment furnace is divided into a plurality of continuous temperature zones, including but not limited to a preheating zone, a reaction zone and a holding zone. At the same time, according to the divided temperature zones, the corresponding input data and output data in each temperature zone are collected during the operation of the production line. Specifically, the input data corresponding to each temperature zone includes the temperature value, tension value and linear speed (i.e. mass flow) of the fiber entering the temperature zone and the heater power corresponding to the temperature zone. The output data corresponding to each temperature zone includes the temperature value and tension value of the fiber leaving the temperature zone.

[0062] Step WA2: Unit processing. That is, according to each temperature zone arranged in step WA1, each reaction unit is arranged. That is, each temperature zone is arranged as a corresponding reaction unit. At the same time, according to the input data and output data of each temperature zone, that is, the input data and output data of the reaction unit, the corresponding thermodynamic equation and mechanical equation of each reaction unit are determined, which are specifically as follows:

[0063]

[0064] wherein: is the mass flow rate of the fiber, is the specific heat capacity of the fiber at constant pressure, is the temperature of the fiber as it exits the temperature zone, is the temperature of the fiber as it enters the temperature zone, is the net heat transferred to the fiber by the heater, is the heat lost by the system to the ambient environment, is the tension experienced by the fiber, is the modulus of elasticity of the fiber, is the strain of the fiber, is the coefficient of viscosity of the fiber, is the strain rate of the fiber.

[0065] Further, in the embodiment, the strain of the fiber is determined according to the relative elongation of the fiber, specifically:

[0066]

[0067] wherein: is the strain of the fiber, is the current length of the fiber as it exits the temperature zone, is the original length of the fiber as it enters the temperature zone.

[0068] Step WA3: Model construction. That is, according to the thermodynamic equation and the mechanical equation set in each reaction unit in step WA2, the temperature in each reaction unit is set to be uniformly distributed in the process of setting the thermodynamic equation and the mechanical equation in each reaction unit. That is, by uniformly distributing the temperature in each reaction unit, the thermodynamic equation and the mechanical equation are simplified to an ordinary differential equation. At the same time, according to the preset time step, the ordinary differential equation is converted into a corresponding difference equation. That is, the difference form of the thermodynamic equation and the mechanical equation obtained is the tension-temperature coupling model constructed by the physical simplified digital twin model.

[0069] It is worth noting that in the embodiment, the thermodynamic equation and the mechanical equation are simplified to an ordinary differential equation, and the ordinary differential equation is converted into a corresponding difference equation, which are all conventional mathematical conversion techniques, so the embodiment is not specifically described.

[0070] In the embodiment, the corresponding tension-temperature coupling model is constructed by a data machine learning model, which is described in detail in the following. Figure 6 The embodiment provides a method for constructing a tension-temperature coupling model by a data machine learning model, which is specifically as follows:

[0071] Step WB1: data acquisition. That is, through the set distributed control system or data acquisition and monitoring control system, the corresponding process parameters and performance parameters are acquired. And according to the acquired process parameters and performance parameters, the corresponding database is constructed.

[0072] Further, the process parameters in the embodiment include the linear speed of the drafting roller, the fiber tension value, the heating zone power and the temperature value. It is worth noting that in the process of collecting each process parameter, the corresponding source of each process parameter is determined, that is, the corresponding acquired drafting roller ID, real-time fiber tension value, heating zone ID and temperature measurement point ID.

[0073] Further, the performance parameters in the embodiment include the tensile strength, elastic modulus and elongation at break of the fiber. It is worth noting that in the process of collecting the performance parameters, according to the production stage corresponding to the collected process parameters, the performance parameters corresponding to the production stage are collected.

[0074] Step WB2: data processing. That is, according to the process parameters and performance parameters collected in step WB1, each parameter is processed according to the preset time window to collect the corresponding data features. It is worth noting that in the process of processing each parameter, the corresponding feature data can be obtained according to the actual demand, so the acquisition of each data feature is not described in the embodiment.

[0075] Further, in the process of processing each parameter in the embodiment, the corresponding tension average value, tension standard deviation, tension slope, temperature curve integral area, temperature maximum value, time point of maximum temperature, maximum value of heating power and standard deviation of heating power can be collected according to the preset time window. That is, according to the collected data features, the corresponding feature vector is constructed.

[0076] Step WB3: model training. That is, according to the feature vector constructed in step WB2, the corresponding labels, i.e. tensile strength value and elastic modulus value, are set, and the set neural network model is trained according to the set feature vector and label to obtain the corresponding trained neural network model, i.e. the corresponding data machine learning model. That is, the tension-temperature coupling model constructed by the data machine learning model is obtained.

[0077] Step SB2: feedforward compensation. That is, according to the preset tension target value and the current fiber tension value, the tension difference value between the two is obtained, and according to the obtained tension difference value, the corresponding tension adjustment instruction is determined. At the same time, the determined tension adjustment instruction is taken as the input of the tension-temperature coupling model constructed in step SB1, and the output of the corresponding temperature pre-compensation instruction is obtained.

[0078] Step SB3: Feedback control. That is, according to the fiber tension and heat treatment temperature collected by the sensing detection module, the corresponding current fiber tension value and current temperature value are determined, and the current fiber tension value is compared with the preset tension target value, and the current temperature value is compared with the preset temperature target value, to determine the corresponding tension adjustment amount and temperature adjustment amount.

[0079] Further, according to the determined tension adjustment amount and temperature adjustment amount, the corresponding PID feedback control instruction is determined through the set controller PID algorithm. For example, the tension adjustment amount is -19N, and the corresponding PID feedback control instruction is "speed up 2%". The temperature adjustment amount is -5℃, and the corresponding PID feedback control instruction is "increase power 1.5%".

[0080] Step SB4: Cooperative adjustment. That is, according to the temperature pre-compensation instruction obtained in step SB2 and the PID feedback control instruction determined in step SB3, the temperature pre-compensation instruction and the PID feedback control instruction are algebraically superimposed to determine the corresponding composite control instruction.

[0081] It is worth noting that the temperature pre-compensation instruction generated in this embodiment is the corresponding power adjustment amount, and the PID feedback control instruction in this embodiment includes a speed adjustment amount and a power adjustment amount. That is, during the process of algebraic superposition, the speed adjustment amount remains unchanged, and the power adjustment amount is superimposed.

[0082] In this embodiment, the monitoring and protection module issues a driving instruction through the composite control instruction, and monitors the comprehensive stability coefficient during the driving process, and adjusts the composite control instruction in real time according to the monitoring result. Referring to Figure 7 , this embodiment provides a monitoring and protection method to adjust the composite control instruction in real time, as follows:

[0083] Step SC1: Driving execution. That is, according to the composite control instruction generated by the decision decoupling module, the corresponding driving instruction, i.e., the corresponding speed adjustment amount and power adjustment amount, is determined. That is, according to the determined speed adjustment amount and power adjustment amount, the drafting mechanism driving unit and the heating mechanism driving unit are regulated and controlled.

[0084] Specifically, in this embodiment, according to the determined speed adjustment amount, the operation of the servo motor and the servo driver in the drafting mechanism driving unit is adjusted to accelerate or decelerate the drafting roller motor, thereby linearly changing the tension on the fiber.

[0085] Further, in the embodiment, the operation of the thyristor power regulator or solid-state relay in the heating mechanism driving unit is specifically adjusted according to the determined power adjustment amount, so as to adjust the output power of the heating element by changing the current conduction angle or on-off ratio, thereby adjusting the furnace temperature in the furnace.

[0086] Step SC2: index calculation. That is, in the process of driving execution in step SC1, the corresponding fiber tension data and heat treatment temperature data are collected by presetting the sampling frequency. At the same time, the obtained fiber tension data and heat treatment temperature data are analyzed and processed by time series analysis to obtain the corresponding oscillation amplitude and oscillation frequency.

[0087] Specifically, in the embodiment, the number of wave crests and the number of wave troughs are determined according to the data waveform obtained by time series analysis. At the same time, the number difference between the wave crests and the wave troughs is determined according to the number of wave crests and the number of wave troughs. And the oscillation amplitude is determined according to the ratio between the number difference between the wave crests and the wave troughs and the preset value (which can be specifically set according to actual needs, so it is not specifically described in the embodiment).

[0088] Further, in the embodiment, the number of oscillations in the preset time is determined according to the data waveform obtained by time series analysis, and the oscillation frequency is determined according to the ratio between the number of oscillations and the preset time.

[0089] Further, the determined oscillation amplitude and oscillation frequency are combined and calculated by a weighting formula, so as to obtain the corresponding comprehensive stability coefficient, specifically:

[0090]

[0091] Wherein: the comprehensive stability coefficient, the amplitude weight factor, the normalized amplitude, the frequency weight factor, the normalized frequency.

[0092] Further, in the embodiment, the oscillation amplitude and the oscillation frequency are normalized to obtain the corresponding normalized amplitude and normalized frequency.

[0093] Step SC3: adjustment control. That is, the comprehensive stability coefficient obtained in step SC2 is compared with the preset stability threshold, and the corresponding control mode is determined according to the comparison result, specifically:

[0094] When the comprehensive stability coefficient is greater than the preset stability threshold, the corresponding control mode is a high-performance mode, that is, the specific operation is performed according to the composite control instruction generated by the decision decoupling module. Conversely, when the comprehensive stability coefficient is not greater than the preset stability threshold, the corresponding control mode is a safety mode, that is, the specific operation is performed according to the preset controller parameters and the target interval. That is, the composite control instruction is adjusted according to the preset controller parameters and the target interval.

[0095] While the embodiments of the application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

Claims

1. A continuous carbon fiber aspect ratio online control system based on tension and temperature feedback, characterized in that, Including: Sensing and detection module: It acquires fiber tension and heat treatment temperature through fiber tension detection unit and heat treatment temperature detection unit; Decision decoupling module: Processes the fiber tension and heat treatment temperature using a tension-temperature coupling model and a controller PID algorithm to obtain temperature pre-compensation commands and PID feedback control commands. Simultaneously, the temperature pre-compensation commands and PID feedback control commands are superimposed to obtain a composite control command, including: SB1: Constructing an internal model: Based on the fiber tension and heat treatment temperature, construct a tension-temperature coupling model; SB2: Feedforward compensation: Based on the tension difference between the preset tension target value and the current fiber tension value, a tension adjustment command is determined, and the tension adjustment command is used as the input of the tension-temperature coupling model to output the corresponding temperature pre-compensation command; SB3: Feedback Control: Based on the tension adjustment between the current fiber tension value and the preset tension target value, and the temperature adjustment between the current temperature value and the preset temperature target value, the PID feedback control command is determined through the controller's PID algorithm; SB4: Coordinated Adjustment: The temperature pre-compensation command and the PID feedback control command are algebraically superimposed to determine the composite control command; Monitoring and protection module: Issues drive commands according to the composite control commands, simultaneously acquires the comprehensive stability coefficient during the drive process, and adjusts the composite control commands based on the comparison between the comprehensive stability coefficient and a preset stability threshold, including: SC1: Drive execution: According to the composite control command, set the drive command, and regulate the stretching mechanism drive unit and the heating mechanism drive unit through the drive command; SC2: Index Calculation: Through time series analysis, the collected fiber tension data and heat treatment temperature data are analyzed and processed to obtain the oscillation amplitude and oscillation frequency. At the same time, the comprehensive stability coefficient is determined based on the oscillation amplitude and oscillation frequency. SC3: Adjustment Control: The comprehensive stability coefficient is compared with a preset stability threshold, and the corresponding control mode is determined based on the comparison result, specifically: When the overall stability coefficient is greater than the preset stability threshold, the corresponding control mode is high-performance mode; otherwise, the corresponding control mode is safety mode, and the composite control command is adjusted according to the preset controller parameters and target range.

2. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 1, characterized in that, The fiber tension and heat treatment temperature were collected, including: SA1: Tension detection setting: A non-contact laser diffraction tension meter is installed at the free section of the fiber between the drafting rollers and rigidly connected to the production line foundation through the mounting base; SA2: Temperature detection setup: Thermocouples are installed at different process sections inside the furnace and fixed to the furnace via flanges or threads, with the measuring end of the thermocouple located inside the furnace. SA3: Real-time monitoring: The fiber tension value is acquired by the non-contact laser diffraction tensiometer, and the heat treatment temperature is acquired by the thermocouple.

3. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 2, characterized in that, The measuring end of the thermocouple and the outer surface of the protective sheath are both coated with a high-temperature anti-oxidation and anti-carbonization coating. The compensating wire of the thermocouple is led into the control cabinet through a metal flexible tube or wire trough.

4. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 1, characterized in that, A tension-temperature coupling model is constructed using a physically simplified digital twin model, including: WA1: Decomposition Definition: Divide the heat treatment furnace into multiple temperature zones and collect input and output data for each temperature zone; WA2: Unit processing: Based on the input and output data of each temperature zone, construct the thermodynamic and mechanical equations for each temperature zone; WA3: Model Construction: The thermodynamic equations and mechanical equations are transformed into finite difference forms through ordinary differential form to construct the tension-temperature coupled model.

5. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 4, characterized in that, When the thermodynamic equations and mechanical equations are transformed into ordinary differential forms, the temperature in each temperature region is set to be uniformly distributed.

6. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 1, characterized in that, The tension-temperature coupling model is constructed using data-driven machine learning models, including: WB1: Data Acquisition: Process parameters and performance parameters are acquired through a distributed control system or a data acquisition and monitoring control system. The process parameters include the linear speed of the drafting roller, fiber tension value, heating zone power and temperature value. The performance parameters include the tensile strength, elastic modulus and elongation at break of the fiber. WB2: Data Processing: Processing the process parameters and performance parameters through a preset time window to collect and acquire data characteristics; WB3: Model Training: Based on the data features and the set labels, train the neural network model to construct a tension-temperature coupling model.

7. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 6, characterized in that, The label includes tensile strength and elastic modulus values.

8. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 1, characterized in that, The temperature pre-compensation command includes a power adjustment amount, the PID feedback control command includes a speed adjustment amount and a power adjustment amount, and the composite control command includes the sum of the speed adjustment amount and the power adjustment amount.

9. The continuous carbon fiber aspect ratio online control system based on tension and temperature feedback according to claim 1, characterized in that, The time series analysis is used to obtain a data waveform diagram, and based on the data waveform diagram, the number of peaks and troughs is determined, the difference between the number of peaks and troughs is obtained, and the oscillation amplitude is determined based on the ratio between the difference between the number of peaks and troughs and a preset value. The time series analysis is used to obtain the data waveform, determine the number of oscillations within a preset time period, and determine the oscillation frequency based on the ratio between the number of oscillations and the preset time period.

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