Glass fiber yarn tension detection equipment and method

Abstract

The invention relates to the technical field of yarn force detection, in particular to glass fiber yarn tension detection equipment and method, and solves the problem that the detection result is low in accuracy due to the fact that glass fiber yarn tension detection only has an accurate measurement result on static tension, the equipment comprises a non-contact vibration measurement module and a dynamic compensation mechanism, a data acquisition and transmission device and a control processing system; the method comprises the steps that S100, vibration signals are extracted, and yarn transverse vibration displacement is measured; s200, establishing a correlation model of the tension T and the inherent frequency fn; s300, dynamic error compensation: introducing an additional tension error compensation item T caused in the movement of the guide wheel and the yarn, and compensating a tension error caused by a movement factor; and S400, tension calculation: calculating a yarn tension value by integrating the static tension and the dynamic error compensation amount. The device is used for detecting and measuring the tension of yarn guiding and winding in the production process of glass fiber yarn, dynamic compensation is achieved, and the result accuracy is higher.

Description

Technical Field

[0001] The invention relates to the technical field of yarn force detection, in particular to a glass fiber yarn tension detection device and method. Background Art

[0002] The testing of glass yarn is an important part of ensuring product quality and performance, involving a variety of technologies and methods. The testing items of glass yarn include: Linear density, an indicator that describes the thickness of the yarn, which affects textile processing and product quality. Breaking strength, which measures the ability of the yarn to withstand tension, is an important indicator of glass fiber quality. Curling performance, evaluates the degree of curling of the yarn, which affects the performance of the yarn. Heat resistance, tests the performance of the yarn in a high temperature environment to ensure its stability in high temperature applications. Resistivity, measures the resistivity of the yarn and evaluates its electrical properties. Moisture regain, determines the moisture content in the yarn, which affects its processing and use. Defects, detects defects on the surface or inside of the yarn, such as broken wires, stains, etc.

[0003] Yarn tension is an important control parameter that affects product quality. If the yarn tension is too high, it is easy to cause the yarn to be damaged or broken; if the yarn tension is too low, it is easy to cause the yarn to curl, affecting the smoothness and forming of the cloth. The main method used for glass yarn tension detection is contact tension sensor, which measures the tension through direct contact between the sensor and the yarn. Commonly used methods include resistance strain type, differential capacitance type, fiber Bragg grating type, surface acoustic wave type, etc.

[0004] In the production of glass fiber yarn, due to the particularity of the material, the temperature of the glass raw material drops sharply and quickly during the high-temperature cooling process, which makes it difficult to maintain the performance of the glass fiber yarn very stable in a short period of time. It is more difficult to maintain the overall state of the glass fiber yarn stable. There are also more technical difficulties in measuring tension under this condition.

[0005] The existing glass fiber yarn tension detection technology has the problem of large measurement errors. This is because the existing detection technology is usually contact measurement, which will cause certain damage to the yarn, and usually only has relatively accurate measurement results for static tension, but cannot well handle the disturbance in dynamic tension measurement, and thus cannot provide accurate measurement results in the dynamic process of yarn production. Summary of the invention

[0006] In order to solve the problem in the prior art that the glass fiber yarn tension detection can only accurately measure the static tension but cannot deal with the disturbance in the dynamic tension measurement, resulting in low accuracy of the detection result, the present invention provides a glass fiber yarn tension detection device and method.

[0007] The technical solution adopted by the present invention is:

[0008] A glass fiber yarn tension detection device comprises a non-contact vibration measurement module, the non-contact vibration measurement module is arranged on both sides of the glass fiber yarn movement direction, the non-contact vibration measurement module is connected with a data acquisition and transmission device, the data acquisition device is connected with a control processing system through signal transmission, the control processing system is connected with a dynamic compensation mechanism through signal transmission, and the dynamic compensation mechanism is arranged on the periphery of the glass fiber yarn;

[0009] The non-contact vibration measurement module is used to measure the vibration amount generated by the yarn during movement by setting a measurement light path without contacting the glass fiber yarn and using a photoelectric detector;

[0010] The dynamic compensation mechanism is used to monitor and compensate for the changing tension of the yarn during its movement;

[0011] The data acquisition and transmission device is used to convert and acquire the signal output by the photoelectric detector, and transmit the data to the control processing system;

[0012] The control processing system receives the signal from the data acquisition and transmission device, and sends the processing result to the motion compensation mechanism and the display device.

[0013] Furthermore, the non-contact vibration measurement module includes a laser transmitter, a laser interference optical path and a photodetector;

[0014] Among them, the laser transmitter is used to provide a laser light source for measurement; the laser interference optical path is used to cause the vibration of the yarn to cause changes in interference fringes through beam splitting and interference operations on the laser, and convert the vibration displacement of the yarn into a measurable change in light intensity; the photodetector is used to convert the light intensity change of the interference fringes into an electrical signal.

[0015] Further, the dynamic compensation mechanism includes a pneumatic suspension yarn guide wheel and an air pressure feedback system;

[0016] Wherein, the pneumatic suspension yarn guide wheel is used to provide stable guidance and support for the yarn;

[0017] The air pressure feedback system is used to monitor the pneumatic suspension state in real time, feed back information related to the air film thickness to the control processing system, and adjust the thickness of the suspended air film according to the yarn vibration information detected by the photoelectric detector.

[0018] Further, the data acquisition and transmission device includes a signal conditioning unit and a data acquisition mechanism;

[0019] The signal conditioning unit is connected to a photodetector and is used to amplify and filter the electrical signal output by the photodetector, and convert the electrical signal into a signal suitable for subsequent data acquisition mechanism processing;

[0020] The data acquisition mechanism is connected to the signal conditioning unit and is used to convert the analog signal processed by the signal conditioning unit into a digital signal, collect the signal at a certain sampling frequency, and transmit the collected digital signal to the data processing device.

[0021] Furthermore, the control processing system includes a microcontroller and a host computer module;

[0022] The microcontroller is used to receive signals from the data acquisition card, send the processing results to the actuator and the display device, control the dynamic compensation mechanism, trigger the data acquisition and transmission device, and communicate with external devices;

[0023] The host computer module is connected to the data acquisition and transmission device through a communication interface, and is used to analyze and process the collected data, send control instructions to the microcontroller, output real-time tension measurement results, and store and manage data.

[0024] A method for detecting tension of a glass fiber yarn comprises the following steps:

[0025] S100, extracting vibration signals, measuring the transverse vibration displacement of the yarn by means of the laser Doppler effect, obtaining a digital signal obtained from a data acquisition and transmission device, and obtaining an expression for the transverse vibration displacement of the yarn;

[0026] S200, establish a model to represent the relationship between tension and frequency, and establish the relationship between tension T and natural frequency f according to string vibration theory n The association model of

[0027] S300, dynamic error compensation, introducing an additional tension error compensation term △T caused by the guide wheel and the yarn movement, obtaining the physical properties of the yarn and the measured parameters to calculate the tension error compensation value, and compensating for the tension error caused by the movement factor;

[0028] S400, tension calculation, calculating the yarn tension value by integrating the static tension and the dynamic error compensation amount.

[0029] Furthermore, the vibration signal extraction in S100 is specifically to measure the lateral vibration displacement y(t) of the yarn by laser Doppler effect:

[0030] ;

[0031] in:

[0032] A: steady-state amplitude;

[0033] f n : natural frequency of yarn;

[0034] B i : initial amplitude of the i-th order damped vibration;

[0035] t: time variable;

[0036] e -ζit : Damping attenuation factor of the i-th order vibration mode;

[0037] ζ i : i-th order damping ratio;

[0038] f d,i : i-th order damped vibration frequency;

[0039] The tension-frequency relationship modeling in S200 is specifically to measure and obtain the physical characteristic parameters of the yarn, and establish the tension T and the natural frequency f of the yarn according to the string vibration theory. n Quantitative relationship:

[0040] ;

[0041] in:

[0042] ρ: linear density;

[0043] L: effective length of vibration section;

[0044] E: elastic modulus of yarn;

[0045] A: yarn cross-sectional area;

[0046] D: guide wheel diameter;

[0047] F: overall force of the yarn;

[0048] The dynamic error compensation in S300 is specifically to introduce an additional tension error compensation item caused by the motion of the guide wheel:

[0049] ;

[0050] Among them, m, c, k are the equivalent mass of the yarn vibration section, air damping coefficient and yarn stiffness coefficient respectively; y is the yarn deformation; t is the time variable;

[0051] The tension calculation in S400 is specifically to calculate the actual yarn tension value by combining the static tension and the dynamic compensation amount:

[0052] ;

[0053] Among them, T measuredTo measure the tension statically, the tension is calculated by inputting the natural frequency of the yarn into the tension-frequency relationship model in S200;

[0054] Finally, the tension value of the glass fiber yarn after dynamic error compensation is measured.

[0055] Furthermore, the method also includes dynamically adjusting the weight of the yarn tension value and the tension dynamic compensation amount by a multi-sensor data fusion method, which specifically includes the following steps:

[0056] S500, decomposing the signal in the frequency domain, performing Fourier transform on the vibration signal, and extracting the main frequency component;

[0057] S600, fusing multi-sensor data, establishing a state space model to predict and update the state of the system, and fusing the data of multiple sensors to correct the measurement results;

[0058] S700, adaptive weight allocation, obtain the signal-to-noise ratio of each sensor, dynamically obtain the data weight of each sensor according to the signal-to-noise ratio, and calculate and output the final tension according to the sensor data weight:

[0059] ;

[0060] in, To measure the tension weight, is the tension dynamic error compensation weight.

[0061] Furthermore, it also includes measuring the yarn temperature and the temperature field around it, obtaining the quantified value of the thermal shock characteristics under the dynamic temperature gradient, and establishing a tension compensation model for the influence of the tension on the glass fiber yarn under thermal stress, and calculating the final yarn tension under the influence of thermal stress, specifically:

[0062] S800, dynamic temperature gradient calculation, collects the temperature characteristics of glass fiber yarn and its surrounding temperature field through infrared thermal imager to form the original temperature matrix, measures and obtains the surface emissivity of glass fiber, corrects the original temperature matrix by using the surface emissivity of glass fiber, and then extracts the temperature gradient characteristics generated by cooling and frictional heat in the direction of yarn movement; calculates the transient temperature change rate through the temperature gradient characteristics of glass fiber yarn;

[0063] S900, establishing a glass fiber yarn thermal stress-tension compensation model, measuring the material thermal characteristic parameters of the glass fiber yarn, and calculating the thermally induced tension change value, including equivalent temperature difference calculation and thermal stress compensation amount;

[0064] S1000, multi-source data fusion weight allocation, determines the temperature change rate-tension sensitivity coefficient of glass fiber yarn, introduces the confidence of tension change value under the influence of thermal stress and vibration respectively, and further calculates the dynamic compensation weight and thermal stress compensation weight of glass fiber yarn in combination with vibration and thermal stress, and obtains the final actual glass fiber yarn tension value under the influence of vibration and thermal force.

[0065] Furthermore, the S800 specifically includes:

[0066] Glass fiber surface emissivity correction:

[0067] ;

[0068] in:

[0069] T c [i,j] is the temperature matrix after calibration;

[0070] T r [i,j] is the original temperature matrix of the infrared thermal imager;

[0071] T amb is the ambient temperature;

[0072] ε is the surface emissivity of glass fiber;

[0073] Calculation of temperature gradient in the direction of motion:

[0074] ;

[0075] in, is the axial temperature difference of the i-th row of the matrix; N is the number of lateral pixels of the infrared thermal imager; i is the number of matrix rows, and j is the number of matrix columns;

[0076] Quantitative calculation of thermal shock characteristics:

[0077]

[0078] Among them, γ is the temperature change rate of glass fiber yarn, △t is the sampling time interval;

[0079] The S900 specifically includes:

[0080] Equivalent temperature difference calculation:

[0081]

[0082] in: is the equivalent comprehensive temperature difference; is the axial temperature difference; is the radial temperature difference, , is the surface temperature of the glass fiber yarn, is the core temperature of the glass fiber yarn;

[0083] Calculation of thermal stress compensation:

[0084]

[0085] in: is the tension compensation caused by thermal stress; E is the elastic modulus of the yarn; A is the cross-sectional area of ​​the yarn; is the axial thermal expansion coefficient of the glass fiber yarn; is the radial thermal contraction coefficient of the glass fiber yarn; ν is the Poisson's ratio of the glass fiber yarn;

[0086] The S1000 specifically includes:

[0087] Weight calculation:

[0088]

[0089]

[0090]

[0091] Final tension calculation:

[0092] ;

[0093] in, is the final fusion tension value; is the tension dynamic error compensation weight considering thermal stress compensation; is the thermal stress compensation weight; To measure confidence dynamically; Measuring confidence for thermal imagers; is the dynamic signal noise variance, is the temperature measurement variance; k is the glass fiber temperature change sensitivity coefficient; γ is the real-time temperature change rate; G is the weight denominator;

[0094] Finally, the final tension of the glass fiber yarn under vibration dynamic compensation and thermal stress compensation is measured and obtained.

[0095] The beneficial effects of the present invention are:

[0096] The glass fiber yarn tension detection device of the present invention can obtain the additional force influence caused by dynamic influencing factors such as vibration during the dynamic movement of the glass fiber yarn in production through a dynamic compensation mechanism and a control processing system, and calculate the real-time tension and tension error according to the measured dynamic data, and compensate the measured tension to obtain a more accurate dynamic tension detection value, so that the state of the glass fiber yarn can be judged more accurately, which is convenient for adjusting the production conditions during the production process. Compared with the prior art that can only measure static tension, or predict the actual tension after measuring dynamic tension, the tension value detected by the detection equipment of the present invention is more conducive to the control of the production process.

[0097] The glass fiber yarn tension detection method of the present invention extracts the vibration signal of the glass fiber yarn by measuring, establishes a relationship model between the tension and frequency of the yarn, calculates the dynamic error compensation, and finally calculates the dynamic tension value after the error is corrected, which can ensure that the dynamic change of the tension is fully covered and there is no measurement blind spot, that is, the method can obtain the complete dynamic tension value within the duration at any time and any object point. Compared with the detection method in the prior art that can only select some specified points to measure and obtain the single-point tension value of the corresponding time point, and then predict the change of the tension value according to multiple single-point tension values, the result obtained by the tension detection method of the present invention is more in line with the actual tension value, the measurement calculation process is more reasonable, and the detection result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 is a system configuration diagram of the tension detection device of the present invention;

[0099] Figure 2 A flow chart of a tension detection method for calculating a yarn tension value by combining static tension and dynamic error compensation according to the present invention;

[0100] Figure 3 A flow chart of a method for calculating a yarn tension value and a tension dynamic compensation amount using a multi-sensor data fusion method of the present invention;

[0101] Figure 4 The present invention is a flow chart of a method for detecting yarn tension value under the influence of thermal stress. DETAILED DESCRIPTION

[0102] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0103] The present invention relates to a glass fiber yarn tension detection device and method, which are mainly used for detecting the tension during the production process of the glass fiber yarn. For example, during the drawing and impregnation agent coating process of the glass fiber yarn produced by the pool kiln method and before the glass fiber yarn is wound, the tension applied to guide the yarn is detected to control the molding and processing process of the glass fiber yarn well, so as to avoid excessive yarn tension, which may cause yarn damage or breakage; or excessive yarn tension, which may easily cause yarn curling, affecting the smoothness and molding of the cloth.

[0104] As an optional implementation, the glass fiber yarn tension detection device and method can be applied to the following industrial production scenarios: In an automatic winding machine, the tension sensor can be used to detect the change in dynamic tension during yarn unwinding in real time, and the computer can be used to make corresponding adjustments in time to keep the winding tension constant. When used in an online detection device, it can be combined with a machine vision online detection device for real-time detection of glass fiber yarn numbers, linear density, broken wires and other indicators to improve detection speed and accuracy.

[0105] The specific embodiments of the present invention are described in detail below by way of examples:

[0106] Example 1

[0107] This embodiment is a specific implementation scheme of the glass fiber yarn tension detection device of the present invention, which is to solve the problem that the existing detection technology contact measurement equipment will cause certain damage to the yarn, and usually only has a relatively accurate measurement result for static tension, but cannot well handle the disturbance in dynamic tension measurement, and thus cannot provide accurate measurement results in the dynamic process of yarn production, resulting in a large measurement error in the glass fiber yarn tension detection technology, such as Figure 1 As shown, the tension detection device comprises:

[0108] The non-contact vibration measurement module is used to measure the vibration amount generated by the yarn during movement by setting a measurement optical path and using a photoelectric detector without contacting the glass fiber yarn; for example, a laser Doppler vibration measurement module is used, and a laser transmitter is set: wavelength 532nm, power 10mW, beam diameter 0.5mm; double-beam interference optical path: the beam splitter divides the laser into two beams with an angle θ=30° to form interference fringes; photoelectric detector: sampling frequency 50kHz, sensitivity 0.1mV / nm; wherein the laser transmitter is used to provide a laser light source for measurement; the laser interference optical path is used to cause the vibration of the yarn to cause changes in the interference fringes through beam splitting and interference operations on the laser, and convert the vibration displacement of the yarn into a measurable light intensity change; the photoelectric detector is used to convert the light intensity change of the interference fringes into an electrical signal.

[0109] The dynamic compensation mechanism is used to monitor and compensate for the changing tension of the yarn during movement. It can be started by suspending to a grinding wheel with a diameter of 20mm and a silicon carbide wear-resistant layer coated on the surface. Combined with the air pressure feedback system, the thickness of the suspended air film is adjusted through a piezoelectric valve to achieve dynamic compensation. Among them, the pneumatic suspension yarn guide wheel is used to provide stable guidance and support for the yarn; the air pressure feedback system is used to monitor the pneumatic suspension state in real time, feed back information related to the air film thickness to the control processing system, and adjust the thickness of the suspended air film according to the yarn vibration information detected by the photoelectric detector.

[0110] The data acquisition and transmission device is used to convert and collect the signal output by the photodetector, and transmit the data to the control processing system; the data acquisition and transmission device includes a signal conditioning unit and a data acquisition mechanism; wherein the signal conditioning unit is connected to the photodetector, and is used to amplify and filter the electrical signal output by the photodetector, and convert the electrical signal into a signal suitable for subsequent processing by the data acquisition mechanism; the data acquisition mechanism is connected to the signal conditioning unit, and is used to convert the analog signal processed by the signal conditioning unit into a digital signal, and then collect it at a certain sampling frequency, and transmit the collected digital signal to the data processing equipment, such as a high-speed data acquisition card with a sampling rate of 16-bit ADC, a synchronous sampling rate of 1MS / s, and an input channel of 4 differential inputs (for laser signals, temperature signals, etc.); the data acquisition mechanism can communicate with the host computer and transmit the data to the subsequent algorithm processing link;

[0111] The control processing system receives signals from the data acquisition and transmission device, and sends the processing results to the dynamic compensation mechanism and display device. The control processing system includes a microcontroller and a host computer module; the microcontroller is used to receive signals from the data acquisition card, and send the processing results to the actuator and display device, control the dynamic compensation mechanism, trigger the data acquisition and transmission device, and communicate with external devices; the microcontroller is connected to the data acquisition card, the air pressure feedback system and other external interfaces (such as display devices, alarm devices, etc.), and plays a central control role in the system.

[0112] The host computer module is connected to the data acquisition and transmission device through a communication interface, and is used to analyze and process the collected data, send control instructions to the microcontroller, output real-time tension measurement results, and store and manage data. The operation of the host computer module can be realized through software, connected to the data acquisition card through a communication interface (such as USB, Ethernet, etc.), receive the collected data and send control instructions to the lower computer (microcontroller).

[0113] Based on the above-mentioned glass fiber yarn tension detection equipment, this embodiment can obtain the additional force influence caused by dynamic influencing factors such as vibration during the dynamic movement of the glass fiber yarn in production through a dynamic compensation mechanism and a control processing system, and calculate the real-time tension and tension error according to the measured dynamic data, and compensate the measured tension to obtain a more accurate dynamic tension detection value, so that the state of the glass fiber yarn can be judged more accurately, which is convenient for adjusting the production conditions during the production process. Compared with the prior art that can only measure static tension, or predict the actual tension after measuring dynamic tension, the tension value detected by the detection equipment of the present invention is more conducive to the control of the production process.

[0114] Example 2

[0115] This embodiment is a specific implementation scheme of the glass fiber yarn tension detection method of the present invention. In order to solve the problem that the existing detection method cannot measure the continuous dynamic error in the dynamic measurement, and thus cannot accurately correct the tension value of the glass fiber yarn that changes dynamically during production, resulting in the inability to obtain continuous and accurate tension results through dynamic measurement, the present invention proposes a glass fiber yarn tension detection method that comprehensively considers the influence of dynamic vibration, such as Figure 2 As shown, the specific implementation is as follows:

[0116] S100, vibration signal extraction, measuring the lateral vibration displacement of the yarn through the laser Doppler effect, obtaining the digital signal obtained from the data acquisition and transmission device, and obtaining the expression of the lateral vibration displacement of the yarn; the present invention extracts the vibration signal of the glass fiber yarn based on the principle of the laser Doppler effect, which determines the motion state of the object (the lateral vibration of the yarn in the present invention) by measuring the change in laser frequency, and its mathematical relationship can be used to construct the expression of the lateral vibration displacement of the yarn. The laser Doppler effect means that when the laser is irradiated to the surface of a moving object, the frequency of the reflected light will change, and by detecting this frequency change, the motion state of the object surface and other information can be obtained. For this method, combined with the settings of the laser transmitter, interference optical path and other equipment of the non-contact vibration measurement module in the detection equipment, the digital signal can be obtained from the data acquisition and transmission device to obtain the expression of the lateral vibration displacement of the yarn.

[0117] Preferably, the vibration signal extraction is specifically to measure the lateral vibration displacement y(t) of the yarn by laser Doppler effect:

[0118] ;

[0119] in:

[0120] A: steady-state amplitude;

[0121] f n : natural frequency of yarn;

[0122] B i : initial amplitude of the i-th order damped vibration;

[0123] ζ i : i-th order damping ratio;

[0124] f d,i : i-th order damped vibration frequency;

[0125] S200, establish a model to represent the relationship between tension and frequency, and establish the relationship between tension T and natural frequency f according to string vibration theory n For glass fiber yarn, the tension of the yarn can be linked to the measurable vibration frequency by establishing a relationship model. After obtaining the vibration frequency of the yarn, the tension of the yarn can be calculated through this model. The string vibration theory describes the vibration law of the string under tension. For example, the vibration frequency of the string is related to factors such as the tension, length, and line density of the string.

[0126] Preferably, the tension-frequency relationship modeling is specifically to measure and obtain the physical characteristic parameters of the yarn, and establish the tension T and the yarn natural frequency f according to the string vibration theory. n Quantitative relationship:

[0127] ;

[0128] in:

[0129] ρ: linear density;

[0130] L: effective length of vibration section;

[0131] E: elastic modulus of yarn;

[0132] A: yarn cross-sectional area;

[0133] D: guide wheel diameter;

[0134] F: overall force of the yarn;

[0135] S300, dynamic error compensation, introduce the additional tension error compensation term △T caused by the guide wheel and yarn movement, obtain the physical characteristics of the yarn and the measured parameters to calculate the tension error compensation value, and compensate for the tension error caused by the motion factor; because under actual working conditions, the guide wheel and yarn movement will interfere with the measurement results, if no compensation is performed, the measured tension value will have a large deviation. This measurement compensation method is based on the analysis of the physical mechanism of the influence of the guide wheel and yarn movement on the tension. For example, by analyzing the physical process of the change of the tension distribution of the yarn under different motion states under the action of the guide wheel on the yarn, a calculation method for error compensation is established. This dynamic error compensation method is a key part of the dynamic detection of the present invention to improve the accuracy of tension measurement, which can make the final measured tension value have a higher degree of confidence.

[0136] Preferably, the dynamic error compensation is specifically to introduce an additional tension error compensation term caused by the motion of the guide wheel:

[0137] ;

[0138] Among them, m, c, k are the equivalent mass of the yarn vibration section, the air damping coefficient and the yarn stiffness coefficient respectively; t is the time variable;

[0139] S400, tension calculation, combining static tension and dynamic error compensation to calculate yarn tension value. Based on the tension-frequency correlation model established above and the calculation of dynamic error compensation, in this step, the measured frequency can be substituted into the model to obtain the initial tension value, and then combined with the above dynamic error compensation to obtain the final yarn tension value.

[0140] Preferably, the tension calculation is specifically to calculate the actual yarn tension value by combining the static tension and the dynamic compensation amount:

[0141] ;

[0142] Among them, T measured To measure the tension statically, the tension is calculated by inputting the natural frequency of the yarn into the tension-frequency relationship model in S200;

[0143] After the above detection steps, this embodiment finally achieves the measurement of the tension value of the glass fiber yarn after dynamic error compensation. The glass fiber yarn tension detection method extracts the vibration signal of the glass fiber yarn, establishes the relationship model between the tension and frequency of the yarn, calculates the dynamic error compensation, and finally calculates the dynamic tension value after the error correction, which can ensure that the dynamic change of the tension is fully covered and there is no measurement blind spot, that is, the method can obtain the complete dynamic tension value within the duration at any time and any object point. Compared with the prior art that can only select some specified points to measure and obtain the single-point tension value of the corresponding time point, and then predict the tension value according to the change of multiple single-point tension values, the tension detection method of the present invention obtains the result that is more in line with the actual tension value, the measurement and calculation process is more reasonable, and the detection result is more accurate.

[0144] Example 3

[0145] This embodiment is based on the embodiment 2. In addition, the embodiment 2 also includes dynamically adjusting the weights of the yarn tension value and the tension dynamic compensation amount by a multi-sensor data fusion method. This is because the data collected by multiple sensors may cause a large deviation in the measurement results due to a single sensor failure or performance degradation. The tension value data thus detected may also have a large deviation from the actual state even when the dynamic error is taken into account. This embodiment aims to reduce the influence of these interference components, such as Figure 3 As shown, the specific steps include:

[0146] S500, signal frequency domain decomposition, Fourier transform the vibration signal, and extract the main frequency component; the vibration signal is usually a time domain signal, containing the superposition of multiple frequency components. Fourier transform can convert the time domain signal into a frequency domain signal, clearly showing the energy distribution of the signal at different frequencies. Extracting the main frequency component can focus on the most important and representative frequency information in the vibration signal, remove other interfering frequency components, and make subsequent analysis more accurate and efficient. Through frequency domain decomposition, the essential characteristics of yarn vibration can be captured more accurately, avoiding the influence of complex and diverse interference factors in the time domain signal on the analysis results, and providing purer and more valuable data support for subsequent model establishment and analysis. The main frequency component is usually closely related to the key physical properties of the yarn (such as tension). Extracting the main frequency component helps to obtain key information related to tension more directly, simplify the subsequent analysis process, and improve the efficiency and accuracy of tension detection.

[0147] S600, fuse multi-sensor data, establish a state space model to predict and update the state of the system, and fuse the data of multiple sensors to correct the measurement results; a single sensor may have measurement errors, limitations or be affected by specific environmental factors. By fusing the data of multiple sensors, the advantages of different sensors can be comprehensively utilized to make up for the shortcomings of a single sensor. The state space model can comprehensively consider various state variables and influencing factors of the system, and make more comprehensive and accurate predictions and updates of the state of the system, thereby improving the reliability and accuracy of the measurement results. The data fusion of multiple sensors can reduce the risk of large deviations in the measurement results due to failure or performance degradation of a single sensor, so that the entire tension detection system can still maintain stable performance in a complex and changing environment, and improve the robustness and reliability of the system. Different sensors obtain the state information of the yarn from different angles. Through the fusion and correction of the state space model, the real tension state of the yarn can be more comprehensively and accurately reflected, effectively reducing measurement errors and improving the accuracy of tension detection.

[0148] S700, adaptive weight allocation. The signal-to-noise ratio of different sensors at different times or under different working conditions may be different, that is, the reliability and accuracy of the measured data will also vary. By obtaining the signal-to-noise ratio of the sensor, the weight of each sensor data can be dynamically adjusted to make the weight allocation more reasonable and scientific. By calculating the final tension according to the weight, the advantageous data of each sensor can be fully utilized, while reducing the impact of unreliable data, thereby obtaining a more accurate and reliable yarn tension value. Traditional weight allocation methods are often fixed and cannot be dynamically adjusted according to actual conditions. This method adjusts the weight in real time according to the signal-to-noise ratio of the sensor, and can adapt to different working environments and sensor states. By reasonably allocating weights, the contribution of sensor data with high signal-to-noise ratio is highlighted, and the impact of data with large noise and errors on the final result is reduced, making the final tension calculation result more accurate and reliable, further improving the performance of tension detection.

[0149] Specifically, the signal-to-noise ratio of each sensor can be obtained first, and the data weight of each sensor can be dynamically obtained according to the signal-to-noise ratio. The final tension is calculated and output according to the sensor data weight:

[0150] ;

[0151] in, To measure the tension weight, is the tension dynamic error compensation weight.

[0152] Example 4

[0153] This embodiment is based on the implementation schemes of Example 2 and Example 3, and further implements the implementation scheme. Since the glass fiber yarn needs to melt the raw materials at high temperature during production, and then quickly cool the materials at the discharge end to achieve wire drawing, the temperature change of the glass fiber yarn in this process is rapid and drastic (for example, from thousands of degrees Celsius to below 50 degrees Celsius) to ensure that the fiber is quickly solidified and forms a stable amorphous structure. For example, the temperature gradient of glass fiber yarn produced by the pool kiln method is much higher than that of natural fibers (such as cotton and linen) or synthetic fibers (such as polyester and nylon); for example, the spinning temperature of polyester only needs 250-300℃, while the melting temperature of glass fiber is as high as 1500℃, and the cooling rate is faster, with a temperature difference of more than 1400℃. Glass fiber is easy to soften at high temperature (softening point is about 845℃) and is sensitive to microstructural changes (such as grain size and porosity), while synthetic fibers (such as carbon fiber) are usually more stable at high temperatures. This difference brings unique complexity to the control of the glass fiber yarn production process and the detection and measurement of tension:

[0154] During the high-temperature cooling process, the internal structure and physical properties of the glass fiber yarn are in a state of dynamic change. Due to the short time and large temperature difference of the rapid cooling process, the amorphous structure formation process of the glass fiber is unstable. This instability will cause the physical state of different parts of the glass fiber yarn to change during the cooling process, and the stress state will also change to a certain extent. For example, the thermal stress during the rapid cooling process will interfere with the tension measurement. Thermal stress is caused by the large temperature difference and inconsistent cooling speed of different parts of the glass fiber yarn. It will cause uneven forces inside the glass fiber yarn. This additional force will be superimposed on the measured tension, causing the measured tension value to deviate from the actual tension of the fiber in a stable state. Based on the particularity of glass fiber yarn production, the temperature drop during high-temperature cooling is large and fast, which makes it difficult to maintain the performance of the glass fiber yarn very stable in a short time. It is very difficult to control the overall temperature of the glass fiber yarn to be constant. When measuring tension under this condition, the thermal stress caused by the temperature difference will cause large fluctuations in the measurement results. Specifically, at different cooling rates, the elastic modulus of the fiber will change, and the elastic modulus is a key factor affecting the tension-strain relationship. If the elastic modulus is unstable, the tension measured under the same strain will be different, thus affecting the stability of the tension measurement. Also, due to the special sensitivity of glass fiber yarn materials, glass fiber is prone to internal stress during production and cooling. When measuring tension, the internal stress will overlap or interfere with the measured tension.

[0155] In order to solve the above problems caused by the special complexity of materials and processes in the production of glass fiber yarn, the specific implementation scheme of this embodiment includes measuring the yarn temperature and the temperature field around it, obtaining the quantitative value of the thermal shock characteristics under the dynamic temperature gradient, and establishing a tension compensation model for the influence of the tension on the glass fiber yarn under thermal stress, and calculating the final yarn tension under the influence of thermal stress, such as Figure 4 As shown, specifically:

[0156] S800, dynamic temperature gradient calculation, collects the temperature characteristics of glass fiber yarn and its surrounding temperature field through infrared thermal imager to form the original temperature matrix, measures and obtains the surface emissivity of glass fiber, corrects the original temperature matrix by using the surface emissivity of glass fiber, and then extracts the temperature gradient characteristics generated by cooling and frictional heat in the direction of yarn movement; calculates the transient temperature change rate through the temperature gradient characteristics of glass fiber yarn;

[0157] Specifically, S800 includes:

[0158] Glass fiber surface emissivity correction:

[0159] ;

[0160] in:

[0161] T c [i,j] is the temperature matrix after calibration;

[0162] T r [i,j] is the original temperature matrix of the infrared thermal imager;

[0163] T amb is the ambient temperature;

[0164] ε is the surface emissivity of glass fiber;

[0165] Calculation of temperature gradient in the direction of motion:

[0166] ;

[0167] in, is the axial temperature difference of the i-th row of the matrix; N is the number of lateral pixels of the infrared thermal imager;

[0168] Quantitative calculation of thermal shock characteristics:

[0169]

[0170] Among them, γ is the temperature change rate of glass fiber yarn, △t is the sampling time interval;

[0171] S900, establishing a glass fiber yarn thermal stress-tension compensation model, measuring the material thermal characteristic parameters of the glass fiber yarn, and calculating the thermally induced tension change value, including equivalent temperature difference calculation and thermal stress compensation amount;

[0172] S900 specifically includes:

[0173] Equivalent temperature difference calculation:

[0174]

[0175] in: is the equivalent comprehensive temperature difference; is the radial temperature difference, , is the surface temperature of the glass fiber yarn, is the core temperature of the glass fiber yarn;

[0176] Calculation of thermal stress compensation:

[0177]

[0178] in: is the tension compensation caused by thermal stress; E is the elastic modulus of the yarn; A is the cross-sectional area of ​​the yarn; is the axial thermal expansion coefficient of the glass fiber yarn; is the radial thermal contraction coefficient of the glass fiber yarn; ν is the Poisson's ratio of the glass fiber yarn;

[0179] S1000, multi-source data fusion weight allocation, determines the temperature change rate-tension sensitivity coefficient of glass fiber yarn, introduces the confidence of tension change value under the influence of thermal stress and vibration respectively, and further calculates the dynamic compensation weight and thermal stress compensation weight of glass fiber yarn in combination with vibration and thermal stress, and obtains the final actual glass fiber yarn tension value under the influence of vibration and thermal force.

[0180] S1000 specifically includes:

[0181] Weight calculation:

[0182]

[0183]

[0184]

[0185] Final tension calculation:

[0186] ;

[0187] in, is the final fusion tension value; is the tension dynamic error compensation weight considering thermal stress compensation; is the thermal stress compensation weight; To measure confidence dynamically; Measuring confidence for thermal imagers; is the dynamic signal noise variance, is the temperature measurement variance; k is the glass fiber temperature change sensitivity coefficient; γ is the real-time temperature change rate;

[0188] The implementation scheme of this embodiment measures the temperature field of the glass fiber yarn and its surroundings, and calculates the dynamic temperature gradient change. After correcting the original temperature matrix using the surface emissivity of the glass fiber, the quantified value of the thermal shock feature is specifically calculated to obtain the temperature change rate of the glass fiber yarn. A thermal stress-tension compensation model for the glass fiber yarn is established through equivalent temperature difference calculation and thermal compensation calculation; the thermal stress-tension compensation model is the key part of this embodiment to exclude the thermal stress effect caused by temperature change from tension detection. The model obtained by detection and calculation can more scientifically and intuitively obtain the compensation required for tension detection caused by thermal stress from the properties and state of the glass fiber yarn, rather than predicting it based on a small amount of data and production experience. The model can ultimately reflect the actual tension value of the glass fiber yarn under rapid temperature changes in production, thereby significantly improving the accuracy of tension detection; this embodiment also introduces the confidence of the tension change value under the influence of thermal stress and vibration, respectively, and comprehensively analyzes the measurement of the influence values ​​of thermal stress and dynamic vibration and assigns weights, so that the comprehensive confidence of the measurement data is further improved, and finally the final tension of the glass fiber yarn under comprehensive vibration dynamic compensation and thermal stress compensation is detected.

[0189] In this embodiment, the conditions in the glass fiber yarn tension detection and the particularity of its material and product properties are taken into consideration, and the error influence problem specifically for the glass fiber yarn tension detection is solved; it is worth noting that this method is only applicable to the glass fiber yarn tension detection, and other types of yarns are not applicable to this method. For example, the surface emissivity of other yarns is too different from that of glass fibers, and they do not have the sensitivity to the amorphous structure inside the glass material. The properties of the materials are fundamentally different. The detection method described in this embodiment cannot effectively eliminate errors in the tension measurement of other types of yarns. Therefore, the tension detection method of the present invention only has the effect of significantly improving the measurement accuracy for glass fiber yarn tension detection.

[0190] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A glass fiber yarn tension detection device, characterized in that: It includes a non-contact vibration measurement module, which is arranged on both sides of the moving direction of the glass fiber yarn, and is connected to a data acquisition and transmission device, the data acquisition device is connected to a control processing system through signal transmission, the control processing system is connected to a dynamic compensation mechanism through signal transmission, and the dynamic compensation mechanism is arranged on the periphery of the glass fiber yarn; Among them, the non-contact vibration measurement module is used to measure the vibration amount generated by the yarn during movement by setting a measuring optical path without contacting the glass fiber yarn, using a photoelectric detector; the dynamic compensation mechanism is used to monitor and compensate for the changing tension of the yarn during movement; the data acquisition and transmission device is used to convert and collect the signal output by the photoelectric detector, and transmit the data to the control processing system; the control processing system receives the signal from the data acquisition and transmission device, and sends the processing result to the dynamic compensation mechanism and the display device.

2. A glass fiber yarn tension detection device according to claim 1, characterized in that: The non-contact vibration measurement module includes a laser transmitter, a laser interference optical path and a photoelectric detector; Among them, the laser transmitter is used to provide a laser light source for measurement; the laser interference optical path is used to cause the vibration of the yarn to cause changes in interference fringes through beam splitting and interference operations on the laser, and convert the vibration displacement of the yarn into a measurable change in light intensity; the photodetector is used to convert the light intensity change of the interference fringes into an electrical signal.

3. A glass fiber yarn tension detection device according to claim 1, characterized in that: The dynamic compensation mechanism includes a pneumatic suspension yarn guide wheel and an air pressure feedback system; Wherein, the pneumatic suspension yarn guide wheel is used to provide stable guidance and support for the yarn; The air pressure feedback system is used to monitor the pneumatic suspension state in real time, feed back information related to the air film thickness to the control processing system, and adjust the thickness of the suspended air film according to the yarn vibration information detected by the photoelectric detector.

4. A glass fiber yarn tension detection device according to claim 1, characterized in that: The data acquisition and transmission device includes a signal conditioning unit and a data acquisition mechanism; The signal conditioning unit is connected to a photodetector and is used to amplify and filter the electrical signal output by the photodetector, and convert the electrical signal into a signal suitable for subsequent data acquisition mechanism processing; The data acquisition mechanism is connected to the signal conditioning unit and is used to convert the analog signal processed by the signal conditioning unit into a digital signal, collect the signal at a certain sampling frequency, and transmit the collected digital signal to the data processing device.

5. A glass fiber yarn tension detection device according to claim 1, characterized in that: The control processing system includes a microcontroller and a host computer module; The microcontroller is used to receive signals from the data acquisition card, send the processing results to the actuator and the display device, control the dynamic compensation mechanism, trigger the data acquisition and transmission device, and communicate with external devices; The host computer module is connected to the data acquisition and transmission device through a communication interface, and is used to analyze and process the collected data, send control instructions to the microcontroller, output real-time tension measurement results, and store and manage data.

6. A method for detecting tension of a glass fiber yarn, according to the glass fiber yarn tension detection device according to any one of claims 1 to 5, characterized in that: The steps include: S100, extracting vibration signals, measuring the transverse vibration displacement of the yarn by means of the laser Doppler effect, obtaining a digital signal obtained from a data acquisition and transmission device, and obtaining an expression for the transverse vibration displacement of the yarn; S200, establish a model to represent the relationship between tension and frequency, and establish the relationship between tension T and natural frequency f according to string vibration theory n The association model of S300, dynamic error compensation, introducing an additional tension error compensation term △T caused by the guide wheel and the yarn movement, obtaining the physical properties of the yarn and the measured parameters to calculate the tension error compensation value, and compensating for the tension error caused by the movement factor; S400, tension calculation, calculating the yarn tension value by integrating the static tension and the dynamic error compensation amount.

7. A method for detecting tension of a glass fiber yarn according to claim 6, characterized in that: The vibration signal extraction in S100 is specifically to measure the lateral vibration displacement y(t) of the yarn by the laser Doppler effect: ； in: A: steady-state amplitude; f n : natural frequency of yarn; B i : initial amplitude of the i-th order damped vibration; t: time variable; e -ζit : Damping attenuation factor of the i-th order vibration mode; ζ i : i-th order damping ratio; f d,i : i-th order damped vibration frequency; The model established in S200 to represent the relationship between tension and frequency is specifically to measure and obtain the physical characteristic parameters of the yarn, and to establish the relationship between tension T and yarn natural frequency f according to the string vibration theory. n Quantitative relationship: ； in: ρ: linear density; L: effective length of vibration section; E: elastic modulus of yarn; A: yarn cross-sectional area; D: guide wheel diameter; F: overall force of the yarn; The dynamic error compensation in S300 is specifically to introduce an additional tension error compensation term ΔT caused by the motion of the guide wheel: ； Among them, m, c, k are the equivalent mass of the yarn vibration section, air damping coefficient and yarn stiffness coefficient respectively; y is the yarn deformation; t is the time variable; The tension calculation in S400 is specifically to calculate the actual yarn tension value T by combining the static tension and the dynamic compensation amount. real Perform the calculation: ； Among them, T measured To measure the tension statically, the tension is calculated by inputting the natural frequency of the yarn into the tension-frequency relationship model in S200; Finally, the tension value of the glass fiber yarn after dynamic error compensation is measured.

8. A method for detecting tension of a glass fiber yarn according to claim 7, characterized in that: The method also includes dynamically adjusting the weight of the yarn tension value and the tension dynamic compensation amount by a multi-sensor data fusion method, which specifically includes the following steps: S500, decomposing the signal in the frequency domain, performing Fourier transform on the vibration signal, and extracting the main frequency component; S600, fusing multi-sensor data, establishing a state space model to predict and update the state of the system, and fusing the data of multiple sensors to correct the measurement results; S700, adaptive weight allocation, obtain the signal-to-noise ratio of each sensor, dynamically obtain the data weight of each sensor according to the signal-to-noise ratio, and calculate and output the final tension T according to the sensor data weight final : ； in, To measure the tension weight, is the tension dynamic error compensation weight.

9. A method for detecting tension of a glass fiber yarn according to claim 8, characterized in that: It also includes measuring the yarn temperature and the temperature field around it, obtaining the quantified value of the thermal shock characteristics under the dynamic temperature gradient, and establishing a tension compensation model for the influence of the tension on the glass fiber yarn under thermal stress, and calculating the final yarn tension under the influence of thermal stress, specifically: S800, dynamic temperature gradient calculation, collects the temperature characteristics of glass fiber yarn and its surrounding temperature field through infrared thermal imager to form the original temperature matrix, measures and obtains the surface emissivity of glass fiber, corrects the original temperature matrix by using the surface emissivity of glass fiber, and then extracts the temperature gradient characteristics generated by cooling and frictional heat in the direction of yarn movement; calculates the transient temperature change rate through the temperature gradient characteristics of glass fiber yarn; S900, establishing a glass fiber yarn thermal stress-tension compensation model, measuring the material thermal characteristic parameters of the glass fiber yarn, and calculating the thermally induced tension change value, including equivalent temperature difference calculation and thermal stress compensation amount; S1000, multi-source data fusion weight allocation, determines the temperature change rate-tension sensitivity coefficient of glass fiber yarn, introduces the confidence of tension change value under the influence of thermal stress and vibration respectively, and further calculates the dynamic compensation weight and thermal stress compensation weight of glass fiber yarn in combination with vibration and thermal stress, and obtains the final actual glass fiber yarn tension value under the influence of vibration and thermal force.

10. A method for detecting tension of a glass fiber yarn according to claim 9, characterized in that: The S800 specifically includes: Glass fiber surface emissivity correction: ； in: T c [i,j] is the temperature matrix after calibration; T r [i,j] is the original temperature matrix of the infrared thermal imager; T amb is the ambient temperature; ε is the surface emissivity of glass fiber; Calculation of temperature gradient in the direction of motion: ； in, is the axial temperature difference of the i-th row of the matrix; N is the number of lateral pixels of the infrared thermal imager; i is the number of matrix rows, and j is the number of matrix columns; Quantitative calculation of thermal shock characteristics: ； Among them, γ is the temperature change rate of glass fiber yarn, △t is the sampling time interval; The S900 specifically includes: Calculation of equivalent temperature difference: ； in: is the equivalent comprehensive temperature difference; is the axial temperature difference; is the radial temperature difference, , is the surface temperature of the glass fiber yarn, is the core temperature of the glass fiber yarn; Calculation of thermal stress compensation: ； in: is the tension compensation caused by thermal stress; E is the elastic modulus of the yarn; A is the cross-sectional area of ​​the yarn; is the axial thermal expansion coefficient of the glass fiber yarn; is the radial thermal contraction coefficient of the glass fiber yarn; ν is the Poisson's ratio of the glass fiber yarn; The S1000 specifically includes: Weight calculation: ； Final tension calculation: ； in, is the final fusion tension value; is the tension dynamic error compensation weight considering thermal stress compensation; is the thermal stress compensation weight; To measure confidence dynamically; Measuring confidence for thermal imagers; is the dynamic signal noise variance, is the temperature measurement variance; k is the glass fiber temperature change sensitivity coefficient; γ is the real-time temperature change rate; G is the weight denominator; Finally, the final tension of the glass fiber yarn under vibration dynamic compensation and thermal stress compensation is measured and obtained.

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