A yarn take-up adjustment system and method

Abstract

The application discloses a kind of yarn winding adjustment system and method, data platform and model platform are carried out data collection and modeling to winding shaft, guide device and transmission system on yarn winding device, and according to modeling, the parameter on yarn winding device is analyzed, and parameter adjustment is carried out on yarn winding device according to the analysis result of regulating unit, yarn winding process can be monitored and corrected in real time to the parameter of yarn winding device, can guarantee that yarn always keeps stable work, solve the defect that layering phenomenon occurs when yarn winding in prior art;When yarn winding device parameter is collected again and fed back to model unit, increase compensation adjustment amount, can guarantee that the parameter after data transmission is matched with real-time parameter, improve data precision, can reduce the hysteresis that data experiences after transmission, can make the data of transmission close to real-time data, enhance modeling accuracy, guarantee the accuracy of adjustment process.

Description

Technical Field

[0001] This invention relates to the field of yarn winding technology, and more particularly to a yarn winding adjustment system and method. Background Technology

[0002] A yarn winding system is a system used in the textile industry to collect and wind yarn from a spinning machine. This system typically includes components such as a yarn collection device, a yarn conveying device, and a yarn winding device. The main function of the yarn winding system is to collect and wind the yarn produced on the spinning machine according to specified requirements for use in subsequent processes or for sale. This system can improve production efficiency, ensure yarn quality, and reduce yarn waste.

[0003] The prior art CN 105926105 A discloses a yarn winding and warping system. The yarn winding and warping system includes: a first support and a second support, which provide support and respectively include a rear section and a front section; a shaping pressure roller mechanism erected between the rear section of the first support and the rear section of the second support; a reciprocating yarn laying mechanism clamped between the front section of the first support and the front section of the second support; and a warping shaft arranged along a horizontal axis. It can wind and warp yarns of various sizes. However, during the winding process, as the yarn winding diameter increases, the yarn will overlap, affecting the winding effect.

[0004] Therefore, it is necessary to improve the existing yarn winding system to solve the above problems. Summary of the Invention

[0005] This invention overcomes the shortcomings of the prior art and provides a yarn winding adjustment system and method, aiming to solve the defect of yarn stacking during winding in the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a yarn winding adjustment system and method, comprising:

[0007] A yarn winding device for winding yarn includes: a frame, a winding shaft mounted on the frame, a yarn guiding device and a transmission system respectively mounted on the frame; the transmission system controls the winding shaft to rotate, and the yarn guiding device guides the yarn into the winding shaft;

[0008] A data platform for collecting and preprocessing data during the yarn winding process includes a data collection unit and a data processing unit, wherein the data collection unit is connected to the winding shaft, the yarn guiding device, and the transmission system, respectively.

[0009] The model unit is used to build a model of the yarn winding process, connects to the data platform, builds a yarn winding model from the preprocessed data of the data platform, and analyzes the yarn winding situation based on the yarn winding model.

[0010] The control unit is used to control the working parameters of the yarn winding device. It is connected to both the yarn winding device and the model unit. The model unit formulates adjustment parameters based on the analysis and sends them to the control unit. The control unit adjusts the parameters of the yarn winding device according to the adjustment parameters.

[0011] In a preferred embodiment of the present invention, the data of the yarn winding process are the winding shaft rotation speed, yarn winding radius, winding shaft radius, and yarn tension on the winding shaft.

[0012] In a preferred embodiment of the present invention, the data collection unit includes, but is not limited to, a visual sensor, a tension sensor, and a photoelectric sensor.

[0013] In a preferred embodiment of the present invention, the data processing unit preprocesses the data, including but not limited to data cleaning, data transformation, and data dimensionality reduction.

[0014] In a preferred embodiment of the present invention, the model unit analyzes and fuses data based on machine learning algorithms and multimodal learning algorithms to construct a dynamic model based on a time axis.

[0015] In a preferred embodiment of the present invention, the control unit includes a central processing unit and a plurality of actuators. The central processing unit sends the adjustment parameters sent by the model unit to the plurality of actuators according to the data type. The plurality of actuators are respectively connected to the yarn winding device and adjust the parameters on the yarn winding device.

[0016] To achieve the above objectives, the second technical solution adopted by the present invention is: a method for using a yarn winding adjustment system, based on a yarn winding adjustment system, comprising the following steps:

[0017] S1: The yarn winding device winds the yarn, and the data platform collects and preprocesses the data of the yarn winding device and sends it to the model unit.

[0018] S2: The model unit constructs a model based on the data, analyzes the yarn winding situation based on the constructed model, converts the analysis results into adjustment parameters, and sends them to the control unit;

[0019] S3: The control unit adjusts the parameters of the yarn winding device according to the adjustment parameters, thereby improving the yarn stacking phenomenon on the yarn winding device;

[0020] S4: The data platform collects the parameters of the yarn winding device again and feeds them back to the model unit. It compares the data with the constructed model and determines whether the parameters of the yarn winding device need to be adjusted again, and updates the constructed model.

[0021] In a preferred embodiment of the present invention, when the yarn winding device collects data in step S1, the data collection frequency gradually increases, and the data collection frequency range is 1 time / second to 3 times / second.

[0022] In a preferred embodiment of the present invention, when adjusting the parameters of the yarn winding device in step S3, the adjusted parameters include, but are not limited to, yarn spacing, winding tension, and winding speed.

[0023] In a preferred embodiment of the present invention, when the parameters of the yarn winding device are collected again and fed back to the model unit in step S4, a compensation adjustment amount is added.

[0024] This invention addresses the shortcomings of the prior art and has the following beneficial effects:

[0025] (1) This invention provides a yarn winding adjustment system and method. Data is collected and modeled on the winding shaft, yarn guide device and transmission system of the yarn winding device through a data platform and a model platform. The parameters of the yarn winding device are analyzed based on the modeling. The control unit adjusts the parameters of the yarn winding device according to the analysis results. The risk of overlapping phenomenon is reduced by data comparison. Compared with the yarn winding system in the prior art, the parameters of the yarn winding device can be monitored and corrected in real time during the yarn winding process, which can ensure that the yarn always keeps stable and solves the defect of overlapping phenomenon during yarn winding in the prior art.

[0026] (2) In this invention, the preprocessing includes, but is not limited to, data cleaning, data transformation and data dimensionality reduction. The model unit is based on machine learning algorithms and multimodal learning algorithms to simplify and clarify the data, ensuring that the data input to the model unit is of high quality and improving the quality of the generated model. Compared with the prior art, it can reduce the workload of generating the model, improve the accuracy and generation efficiency of the model, and reduce the data transmission time while ensuring the accuracy of the model.

[0027] (3) In this invention, after the model is generated, the data is collected again and compared with the model. Based on the comparison results, it is determined whether further adjustment is needed and the model is updated. This can increase the adjustment accuracy. Compared with the prior art, the yarn winding device can be continuously adjusted in multiple comparisons and the model can be updated based on the comparison data, thereby improving the model prediction accuracy.

[0028] (4) In this invention, when the parameters of the yarn winding device are collected again and fed back to the model unit, the compensation adjustment amount is increased, which can ensure that the parameters after data transmission match the real-time parameters, improve data accuracy, reduce the lag of data after transmission compared with the prior art, make the transmitted data close to the real-time data, enhance the modeling accuracy, and ensure the accuracy of the adjustment process. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a three-dimensional structural diagram of a preferred embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0033] like Figure 1 As shown, a yarn winding adjustment system and method include:

[0034] A yarn winding device for winding yarn includes: a frame, a winding shaft mounted on the frame, a yarn guiding device and a transmission system respectively mounted on the frame; the transmission system controls the rotation of the winding shaft, and the yarn guiding device guides the yarn into the winding shaft.

[0035] The data platform is used to collect and preprocess data during the yarn winding process. It includes a data collection unit and a data processing unit. The data collection unit is connected to the winding shaft, the yarn guiding device, and the transmission system, respectively.

[0036] The model unit is used to build a model of the yarn winding process. It connects to the data platform, uses the preprocessed data from the data platform to build a yarn winding model, and analyzes the yarn winding situation based on the yarn winding model.

[0037] The control unit is used to control the working parameters of the yarn winding device. It is connected to both the yarn winding device and the model unit. The model unit formulates adjustment parameters based on the analysis and sends them to the control unit. The control unit then adjusts the parameters of the yarn winding device according to the adjustment parameters.

[0038] The transmission system is a device such as a motor that provides power, enabling the winding shaft to rotate periodically. The winding shaft supports the yarn during winding and provides fixed support points. During the winding process, the yarn needs to be wound around this shaft in an orderly manner. The position and stability of the winding shaft directly affect the quality and efficiency of yarn winding.

[0039] The yarn guide device ensures a smooth transfer of yarn from the yarn supply source to the winding shaft, reducing the possibility of stretching or breakage during the transfer process. The yarn guide device adjusts the position of the yarn during the guiding process, thereby regulating the tension stability of the yarn.

[0040] The data for the yarn winding process include the winding shaft speed, yarn winding radius, winding shaft radius, and yarn tension on the winding shaft. The winding shaft speed refers to the number of rotations the winding shaft makes per unit time. The winding shaft speed determines the speed at which the yarn is wound onto the winding shaft. Too high a speed will lead to excessive yarn tension, making the yarn prone to breakage or uneven winding; while too low a speed will affect winding efficiency. The yarn winding radius refers to the radius of the winding layer formed by the yarn on the winding shaft. The yarn winding radius is the distance from the center of the winding shaft to the outermost edge of the yarn winding layer. Controlling the winding radius helps maintain yarn uniformity and prevent excessive tension. The size of the winding shaft radius directly affects the yarn arrangement density and yarn tension on the winding shaft. The yarn tension on the winding shaft refers to the tensile force borne by the yarn on the winding shaft.

[0041] The data collection unit includes, but is not limited to, vision sensors, tension sensors, and photoelectric sensors. Vision sensors refer to image acquisition devices that can capture visual information during the yarn winding process, such as the yarn's arrangement. Tension sensors are used to measure and monitor the yarn tension during winding; they are installed on the winding shaft and the yarn guiding device, respectively, and can detect yarn tension in a timely manner. Photoelectric sensors use light beams to detect the presence or passage of objects and can detect the rate of increase in the yarn winding radius. When the rate of increase in the yarn winding radius is abnormal, the sensor determines whether overlapping or yarn breakage has occurred based on the change in the rate of increase.

[0042] The data processing unit preprocesses the data, including but not limited to data cleaning, data transformation, and data dimensionality reduction. Data cleaning refers to correcting or deleting erroneous data points, filling in missing values, and removing duplicate records to improve the data. Data transformation refers to converting data from one format to another, or standardizing or normalizing the data. Data dimensionality reduction refers to reducing the dimensionality of the data through methods such as principal component analysis, while retaining as much important information as possible from the original data, in order to simplify the model and reduce computational complexity.

[0043] The model unit, based on machine learning and multimodal learning algorithms, analyzes and fuses data to construct a dynamic time-axis-based model. Machine learning algorithms are supervised learning algorithms used for data classification and regression analysis, while multimodal learning algorithms integrate and jointly model data from different data sources or of different types, improving the ability to combine multiple data types and make dynamic predictions, thereby enhancing the model's fit.

[0044] The control unit includes a central processing unit and several actuators. The central processing unit sends the adjustment parameters from the model unit to the actuators according to their data types. Each actuator is connected to the yarn winding device and adjusts the parameters on the yarn winding device. The central processing unit is a computer or other component with data processing capabilities. The actuators perform specific physical adjustment actions, such as adjusting the rotational speed of the winding shaft or the tension of the yarn guiding device. The actuators can be motors, cylinders, hydraulic cylinders, etc.

[0045] To achieve the above objectives, the second technical solution adopted by the present invention is: a method for using a yarn winding adjustment system, based on a yarn winding adjustment system, comprising the following steps:

[0046] S1: The yarn winding device winds the yarn, and the data platform collects and preprocesses the data from the yarn winding device and sends it to the model unit.

[0047] S2: The model unit constructs a model based on the data, analyzes the yarn winding situation based on the constructed model, converts the analysis results into adjustment parameters, and sends them to the control unit.

[0048] S3: The control unit adjusts the parameters of the yarn winding device according to the adjustment parameters, which improves the yarn stacking phenomenon on the yarn winding device.

[0049] S4: The data platform collects the parameters of the yarn winding device again and feeds them back to the model unit. It compares the data with the constructed model and determines whether the parameters of the yarn winding device need to be adjusted again, and then updates the constructed model.

[0050] In S1, when collecting data from the yarn winding device, the data collection frequency gradually increases, ranging from 1 to 3 times per second. As the data collection frequency increases, situations requiring adjustment can be identified more quickly. This allows the model unit to receive the latest data more rapidly and generate adjustment parameters in a timely manner. The control unit can also adjust the yarn winding device more quickly, thereby reducing quality problems caused by response delays. Furthermore, the initial low data collection frequency allows for the gradual generation and refinement of the model, coordinating the data collection process and preventing insufficient processing time due to excessively rapid data generation.

[0051] The models generated in S2 include, but are not limited to, regression models, support vector machines, and decision trees. Regression models are suitable for predicting continuous data, support vector machines perform well when handling high-dimensional data, and decision trees are easy to understand and interpret, making them suitable for classification problems. Using these models allows for the processing of large amounts of data and the identification and handling of nonlinear relationships within the data.

[0052] When adjusting the parameters of the yarn winding device in S3, the adjusted parameters include, but are not limited to, yarn spacing, winding tension, and winding speed. Yarn spacing refers to the distance between adjacent yarns during winding. A suitable yarn spacing effectively prevents overlapping caused by excessively small yarn spacing. Appropriate tension ensures the yarn maintains proper tightness during winding, avoiding overlapping due to excessive looseness or yarn damage due to excessive tightness. The winding speed affects the yarn arrangement and overlapping. Excessive speed may lead to uneven yarn arrangement, thus causing overlapping.

[0053] The following is a formula for the yarn adjustment process:

[0054] δ=(T s )%(T d )

[0055]

[0056]

[0057] Where δ is the modulo operation result of the yarn winding period and the drum winding period, and T s T is the yarn winding cycle. d R is the yarn winding period, r is the yarn winding radius, v is the yarn winding speed, and n is the yarn winding speed. s This refers to the drum rotation speed.

[0058] When the result of the modulus calculation is close to 0, it means that the ratio of the yarn winding period to the drum winding period is close to an integer. At this time, it is close to the condition for yarn stacking, and the parameters need to be adjusted to avoid stacking.

[0059]

[0060] Where α is the stacking factor, V t T is the winding speed that varies with time. t The tension is the time-varying value, ρ is the yarn density, and R is the yarn tension. t S is the yarn winding diameter that varies over time, S is the yarn pitch, and E is the yarn elastic modulus.

[0061] The overlay factor is an indicator used to describe the overlay phenomenon during yarn winding. The magnitude of the overlay factor can be used to evaluate the quality of yarn winding. A higher overlay factor usually indicates more severe overlay during yarn winding. The overlay factor can be used to determine the extent of overlay and to adjust the parameters of the yarn winding device.

[0062] In S4, when the parameters of the yarn winding device are collected again and fed back to the model unit, a compensation adjustment is added. Compensation adjustment is a mechanism that adjusts the system by predicting disturbances and using compensation. Using compensation adjustment can improve system performance and maintain system stability.

[0063]

[0064] β is the compensation amount, which is T p It is the yarn tension predicted in a short time, where T is the target yarn tension, ω is the yarn winding acceleration, K1, K2 and K3 are the compensation gain coefficients, and A, B and C are the coefficients for adjusting the compensation terms.

[0065] The control unit adjusts the parameters of the yarn winding device over time based on the compensation value calculated by the compensation formula, ensuring the stability and consistency of tension during yarn winding while reducing the lag problem of tension testing, thus finding a balance between system stability, speed and accuracy.

[0066] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method of using a yarn winding adjustment system, characterized in that, The system includes: A yarn winding device for winding yarn includes: a frame, a winding shaft mounted on the frame, a yarn guiding device and a transmission system respectively mounted on the frame; the transmission system controls the winding shaft to rotate, and the yarn guiding device guides the yarn into the winding shaft; A data platform for collecting and preprocessing data during the yarn winding process includes a data collection unit and a data processing unit. The data collection unit is connected to the winding shaft, the yarn guiding device, and the transmission system, respectively. The data during the yarn winding process includes the winding shaft rotation speed, the yarn winding radius, the winding shaft radius, and the yarn tension on the winding shaft. The model unit is used to build a model of the yarn winding process, connects to the data platform, builds a yarn winding model from the preprocessed data of the data platform, and analyzes the yarn winding situation based on the yarn winding model. The control unit is used to control the working parameters of the yarn winding device. It is connected to the yarn winding device and the model unit respectively. The model unit formulates adjustment parameters based on the analysis and sends them to the control unit. The control unit adjusts the parameters of the yarn winding device according to the adjustment parameters. The method of use includes the following steps: S1: The yarn winding device winds the yarn, and the data platform collects and preprocesses the data of the yarn winding device and sends it to the model unit. S2: The model unit constructs a model based on the data, analyzes the yarn winding situation based on the constructed model, converts the analysis results into adjustment parameters, and sends them to the control unit; S3: The control unit adjusts the parameters of the yarn winding device according to the adjustment parameters, thereby improving the yarn stacking phenomenon on the yarn winding device; when adjusting the parameters of the yarn winding device, the adjusted parameters include yarn spacing, winding tension, and winding speed; the formula for the yarn adjustment process is: , , Where δ is the modulo operation result of the yarn winding period and the spool winding period, and T s T is the yarn winding cycle. d R is the yarn winding period, r is the yarn winding radius, v is the yarn winding speed, and n is the yarn winding speed. s This refers to the drum rotation speed; When the result of the modulus operation is close to 0, it means that the ratio of the yarn winding period to the drum winding period is close to an integer. At this time, it is close to the condition for yarn stacking, and the parameters need to be adjusted to avoid stacking. Specifically Where α is the stacking factor, V t T is the winding speed that varies with time. t The tension is the time-varying value, ρ is the yarn density, and R is the yarn tension. t The yarn winding diameter varies with time, S is the yarn pitch, and E is the yarn elastic modulus. The layering coefficient can be used to determine the layering phenomenon and adjust the parameters of the yarn winding device. S4: The data platform collects the parameters of the yarn winding device again and feeds them back to the model unit. It compares the data with the constructed model and determines whether the parameters of the yarn winding device need to be adjusted again, and updates the constructed model.

2. The method of using the yarn winding adjustment system according to claim 1, characterized in that: The data collection unit includes a visual sensor, a tension sensor, and a photoelectric sensor.

3. The method of using the yarn winding adjustment system according to claim 1, characterized in that: The data processing unit preprocesses the data, including data cleaning, data transformation, and data dimensionality reduction.

4. The method of using the yarn winding adjustment system according to claim 1, characterized in that: The model unit uses machine learning and multimodal learning algorithms to analyze and fuse data, and constructs a dynamic model based on a time axis.

5. The method of using the yarn winding adjustment system according to claim 1, characterized in that: The control unit includes a central processing unit and several actuators. The central processing unit sends the adjustment parameters sent by the model unit to several actuators according to the data type. The several actuators are connected to the yarn winding device and adjust the parameters on the yarn winding device.

6. The method of using the yarn winding adjustment system according to claim 1, characterized in that: In S1, when collecting data from the yarn winding device, the data collection frequency gradually increases, ranging from 1 time / second to 3 times / second.

7. The method of using the yarn winding adjustment system according to claim 1, characterized in that: In step S4, when the parameters of the yarn winding device are collected again and fed back to the model unit, a compensation adjustment amount is added.

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