A cleaning control method and system based on yarn tension fluctuation and flyer concentration correlation analysis

Abstract

The application discloses a cleaning control method and system based on yarn tension fluctuation and flying flock concentration correlation analysis, and belongs to the technical field of intelligent processing of textile yarns. In view of the technical defects of the prior art, such as the mutual fragmentation of tension regulation, flying flock cleaning and environment flock suppression, blind flock control, high energy consumption, high yarn defect rate and no ability of cause tracing, a tension feature and in-situ flying flock concentration coupling mathematical model is constructed to accurately distinguish two types of flocking causes, i.e., fiber breakage short flock and sliding long flock. A three-level closed-loop energy consumption optimal control architecture of'source tension self-adaptive regulation-classification online cleaning-differentiated environment bottom-up' is built, and a yarn type self-learning iteration and a double-parameter fault early warning mechanism are combined. The application can realize accurate tracing and targeted management of flying flocks, effectively reduce the energy consumption of equipment and the yarn defect rate, eliminate secondary pollution of flying flocks, adapt to the production of multiple types of yarns, improve the running stability of textile equipment and the safety production level of workshops, and has wide industrial application prospects.

Description

Technical Field

[0001] This invention relates to the field of intelligent processing control technology for textile yarns, specifically applicable to in-situ real-time monitoring and intelligent cleaning of yarn fly waste in processes such as winding, doubling, twisting, and weaving lead yarn. In particular, it relates to a cleaning control method and system based on the correlation analysis of yarn tension fluctuation and fly waste concentration. Background Technology

[0002] During continuous yarn spinning, yarn is subject to changes in clamping force, inter-fiber friction, and tensile breakage, continuously generating yarn lint. Lint adhering to the yarn surface causes defects in knitted and woven fabrics. Accumulated lint on equipment bearings and electrical components can cause jamming, short circuits, and even fire hazards. Currently, the textile industry has several existing patented technologies for yarn lint removal, workshop lint suppression, and yarn tension control. However, these solutions are fragmented and cannot achieve source traceability and graded closed-loop lint control. The shortcomings of existing technologies are as follows: 1. Chinese Patent Publication No. CN118257126A (A device and method for cleaning up fly lint from textile fabrics in garment processing): This patent adopts a mechanical cleaning structure that combines scraping with water washing and immersion with fixed-point suction from the box. It relies on fixed-installation scraping components and water absorption mechanisms to clean up surface lint from the fabric. The cleaning power and start-stop sequence are fixed and unchanging. There is no online sensor detection, so it is impossible to dynamically adjust the cleaning intensity according to the real-time lint generation of the yarn. Furthermore, it cannot identify abnormal yarn tension to suppress fly lint generation from the source. It can only perform passive lint removal after the fact. The scraper is prone to lint accumulation and failure, and repeated shedding of lint causes secondary pollution to the fabric / yarn.

[0003] 2. Chinese Patent No. CN114232185B (A Textile Machine Flying Fluff Collection Device): This patent uses a high-resistivity meter to detect the resistivity of yarn and calculate the degree of electrostatic adsorption. It relies on the electrostatic value to intelligently control the power of the dispersing wheel and the lint suction pump. It only collects scattered flying fluff from the workshop environment and uses only electrostatic single factor as the control basis. It completely ignores the fact that excessive yarn tension breaks fibers and insufficient tension causes fiber slippage and friction, which are the main causes of flying fluff. It cannot trace the cause of flying fluff and cannot achieve source control of fluff from the yarn operation end.

[0004] 3. Chinese Patent No. CN115993806B (A Monitoring and Control System and Method Based on Yarn Spinning): This patent calculates the degree of fly lint in the workshop by measuring the weight loss before and after yarn processing. It then uses the calculation results to link air conditioning and humidification / dehumidification equipment to regulate the temperature and humidity of the workshop throughout the entire area, passively reducing the amount of fly lint floating by relying on the ambient humidity. However, it adopts a uniform temperature and humidity control mode for the entire workshop, which results in serious control lag. It cannot distinguish whether the fly lint is caused by abnormal yarn tension or by existing floating lint in the workshop. Humidification throughout the entire area can easily cause excessive moisture regain and mold growth in the yarn. Dehumidification has high energy consumption and cannot accurately suppress fly lint at specific points.

[0005] 4. Chinese Patent No. CN118323964B (A yarn tension control system with auxiliary heating and pneumatic cleaning function): This patent integrates a built-in air circuit inside the tensioner. Compressed air is only activated to blow away the accumulated lint on the tension plate when the yarn is broken and the machine is stopped. The only cleaning trigger condition is the stop signal. There is no real-time lint removal capability when the yarn is produced online continuously. There is no closed loop for feedback of lint concentration data. It is impossible to adaptively start and adjust the blowing intensity according to the real-time lint volume.

[0006] 5. Chinese Patent No. CN215517875U (A yarn fly removal device): This patent adopts a purely mechanical open-loop dust removal system consisting of a reciprocating moving roller brush, a single-sided blower, a lint-adhesive plate, and a dust collection box. After the device is powered on, the brush and fan operate continuously and constantly, without any fly or tension sensor data collection. It cannot be started or stopped as needed, and fine denier yarns are easily scratched by the rigid brush. The energy consumption of continuous blowing under no-load conditions is high, and the dust removal efficiency decreases rapidly as dust accumulates on the lint-adhesive plate.

[0007] In summary, existing technologies generally suffer from the pain point that the four major systems of tension control, fluff detection, cleaning execution, and environmental control operate independently without data exchange and coupling. They also lack a quantitative correlation model between yarn tension fluctuations and the concentration of native fluff in situ. They cannot implement coordinated control of "source fluff reduction → online fluff removal → environmental bottom-line fluff suppression" based on the root cause of fluff generation. Furthermore, they suffer from high cleaning energy consumption, poor targeting of impurity removal, and persistently high defect rate of finished yarn, which are technical problems that urgently need to be solved in this field. Summary of the Invention

[0008] Addressing the technical shortcomings of existing patents on fluff removal and yarn tension control, this invention aims to provide a cleaning control method and system based on the correlation analysis of yarn tension fluctuations and fluff concentration. By constructing a coupled mathematical model of yarn tension characteristics and in-situ native fluff concentration, the invention accurately traces the source of fluff generation as either ultra-high tension fiber breakage or low tension fiber slippage. It establishes a three-level closed-loop control architecture: "prioritizing fine-tuning tension source control → graded in-situ online cleaning → differentiated environmental temperature and humidity support," achieving precise fluff control on demand. This reduces fluff production at its source, lowers overall cleaning energy consumption, and improves the quality of finished yarn and workshop safety.

[0009] To address the above problems, this invention provides a cleaning control method based on the correlation analysis of yarn tension fluctuation and fluff concentration, comprising the following steps: S1. In-situ synchronous acquisition step: Simultaneously acquire real-time tension characteristic parameters and in-situ fluff concentration parameters at the yarn tension clamping station; the tension characteristic parameters include average tension. T avg Tension fluctuation rate δ T Frequency of tension mutations per unit time The in-situ fluff concentration parameter refers to the original fluff concentration in the yarn passing area. ; S2. Coupled Model Calculation Steps: Retrieve the Preset Tension-Flying Floss Concentration Coupled Mathematical Model ΔH represents the temperature and humidity compensation correction; the model has three preferred construction forms: ① Linear weighted model: a, b, and c are the preset basic coefficients for the corresponding yarn types; ② Three-layer BP neural network model: 3 nodes in the input layer ( The network consists of a single hidden layer with 6 neurons and a single output node that outputs the theoretical concentration of pollen. The hidden layer uses the Sigmoid activation function, and the output layer uses the linear activation function. Mini-batch gradient descent (SGD) is used as the network training and optimization algorithm. The iterative convergence criterion is consistent with the self-learning rules of the whole model. The network parameter iterative optimization stops when the error of 10 consecutive sets of data is ≤5%.

[0010] ③ Decision tree classification + regression model: First, the floc formation types (fracture-induced floc formation and slippage-induced floc formation) are distinguished by tension parameters, and then the floc concentration is fitted according to each type. Temperature and humidity compensation term. ΔRH represents the difference between the measured relative humidity at the workstation and the reference humidity of 55%, and ΔT represents the difference between the measured temperature and the reference temperature of 25℃. This is the temperature and humidity correction coefficient. The system pre-stores three sets of basic coefficients for pure cotton, polyester, and polyester-cotton blends: pure cotton a=2.12, b=-0.035, c=0.18; polyester a=3.45, b=-0.018, c=0.32; polyester-cotton 65 / 35 blend a=2.76, b=-0.026, c=0.24.

[0011] Synchronously pre-store the reference temperature and humidity correction coefficients for three types of yarns (Humidity correction factor) (Temperature correction factor): Pure cotton =0.22、 =0.11; Polyester =0.35、 =0.16; Polyester / cotton 65 / 35 blend =0.28、 =0.13; In actual production, new yarn batches can be optimized online through the S4 self-learning process. , Real-time values.

[0012] The real-time temperature and humidity parameters from the data collection station are substituted into the above formula to complete the model compensation and correction, eliminating the interference of environmental water vapor and air flow on the concentration calculation. The collected tension characteristic parameters are then substituted into the formula to calculate the theoretical concentration of flying catkins, combined with actual measurements. To determine the cause of excessive fluff, three types of fluff generation were distinguished: fluff generation caused by breakage of ultra-high tension fibers, fluff generation caused by slippage of excessively low tension fibers, and fluff generation caused by trace amounts of fluff generated during normal processes. S3, a three-level closed-loop control procedure, adjusts energy consumption sequentially from low to high, prioritizing source tension adjustment, followed by in-situ graded cleaning, and finally fine-tuning of the workshop environment: S31. Primary Source Control: The tension component clamping force is adaptively adjusted according to the type of yarn breakage. If it is determined that the yarn breakage is caused by excessively high tension, the PLC controls the electromagnet to reduce the suction force and lower the yarn clamping tension. If it is determined that the yarn breakage is caused by low tension slippage, the PLC controls the electromagnet to increase the suction force and raise the clamping tension. The tension is gradually adjusted in steps of no more than 1%-5% of the current tension value to avoid instantaneous yarn breakage. Tension step adjustment rules: The single adjustment range is limited to 1% to 5% of the current clamping tension. If the tension exceeds the standard by less than 10%, it is finely adjusted in steps of 1%; if it exceeds the standard by 10% to 20%, it is adjusted in steps of 3%; if it exceeds the standard by more than 20%, it is adjusted in steps of the maximum of 5%, and the tension is gradually adjusted to avoid instantaneous yarn breakage. S32. Secondary in-situ clean-up control: Three threshold levels are defined based on the measured concentration of in-situ pollen: Low concentration: Medium concentration: High concentration: The cleaning mode is switched according to the measured concentration threshold of flying fluff; when the concentration is too low, the tensioner's built-in airway pulse micro air blowing is activated; when the concentration is too medium, the roller brush mechanism and low negative pressure air suction mechanism are linked to remove fluff simultaneously; when the concentration is too high, the scraping component and high-power air suction combination are activated for cleaning. S33. Three-level environmental bottom-line control: When the concentration of flying fluff is still higher than the limit after S31 and S32 control, the temperature and humidity of the workshop are adjusted differently according to the type of fluff. If the broken short fluff exceeds the limit, the ambient humidity is appropriately increased; if the sliding long fluff exceeds the limit, the ambient humidity is appropriately decreased. The baseline ambient humidity is 55% RH. When the broken short fluff exceeds the limit, the local humidity is increased by 3% to 8% RH. When the sliding long fluff exceeds the limit, the local humidity is decreased by 4% to 6% RH. The temperature is maintained at the baseline of 25℃ with slight fluctuations. S4. Data Iteration Steps: During the initial production phase of a new batch of the same yarn type, data is collected continuously for 30 minutes, with a collection cycle of 10 seconds per instance. The main control unit's built-in self-learning algorithm module continuously compares the concentration calculated theoretically with the actual measured concentration of flying fluff. When the concentration calculation error of 10 consecutive sets of sampling data exceeds 5%, steps a, b, c, and c are automatically iterated and optimized. Coefficients; if the calculation error of 10 consecutive sets of data is ≤5%, the model is considered to have converged, the iteration is stopped, the self-learning calibration of the yarn model for this batch is completed, and the new coefficients are automatically stored in the corresponding yarn parameter library.

[0013] Furthermore, S2 also includes an environmental compensation and correction step, which collects real-time temperature and humidity parameters of the workstation, corrects the coupling model coefficients based on temperature and humidity, and eliminates the interference of environmental water vapor and air flow on concentration calculation.

[0014] Furthermore, the pulsed micro-air blowing in S32 is achieved by relying on the built-in air channel of the tensioner spindle. The air is sprayed intermittently while the yarn is continuously spinning, and the floating lint in the tension sheet gap can be cleaned without stopping the machine.

[0015] Furthermore, it also includes anomaly warning steps: when real-time monitoring parameters meet the following conditions (the tension fluctuation rate exceeds the first threshold within a preset continuous sampling period, and the fluff concentration monotonically increases the sliding average value within a preset observation window and eventually exceeds the limit threshold), the system triggers an equipment fault warning, indicating raw material abnormality or mechanical wear; if the cleaning mechanism cannot reduce the fluff concentration even at full load, it determines that the air path is blocked / the scraper has accumulated fluff and issues a pop-up alarm; the tension fluctuation rate exceeding the limit threshold is set to 120% of the rated fluctuation rate, and the fluff limit concentration threshold is 7 mg / m³. 3 If the tension fluctuation rate continues to exceed the limit for 5 consecutive sampling cycles and the average value of the flying fluff continues to rise and breaks through the limit threshold, an abnormal raw material / mechanical wear warning is triggered; if the cleaning mechanism runs at full power for 3 minutes and the flying fluff still cannot fall back to within the threshold, an air circuit blockage or scraper lint accumulation fault pop-up alarm is determined.

[0016] Furthermore, the coupling model pre-stores multiple sets of basic coefficients according to different yarn categories such as cotton, polyester, and blended yarns. After the system identifies the yarn category, it automatically matches the initial model parameters.

[0017] The present invention also provides a cleaning control system based on the correlation analysis of yarn tension fluctuation and fluff concentration, for implementing the above-mentioned cleaning control method, including a main control unit, an integrated in-situ sensing module, a tension actuator, a graded cleaning actuator, and a workshop temperature and humidity control mechanism; The integrated in-situ sensing module is fixedly installed at the tension clamping station, integrating tension acquisition components, miniature in-situ dust sensing probes, and temperature and humidity acquisition probes, enabling synchronous acquisition of tension parameters, native fluff concentration, and ambient temperature and humidity at the same station. The tension actuator includes a PLC controller, left and right electromagnets, a fixed tension plate, a sliding tension plate, and a spindle with built-in air passage structure. It dynamically adjusts the output suction force of the electromagnets under the command of the main control unit to change the yarn clamping tension. The graded cleaning actuator includes a pneumatic blowing assembly, a mobile roller brush assembly, a scraper assembly, and a negative pressure suction assembly, which are activated by the main controller according to the concentration of flying fluff, either individually or in combination. The workshop temperature and humidity control mechanism includes workshop humidification equipment, dehumidification equipment, and air conditioning units, and receives temperature and humidity adjustment commands issued by the main controller to achieve differentiated environmental control.

[0018] Furthermore, the miniature in-situ dust sensing probe adopts a Mie scattering laser sensing structure, which is embedded in the inner wall of the yarn passage groove next to the tension plate, and only collects the original flying fluff from the yarn, thus isolating the floating fluff in the workshop from interfering with the detection data.

[0019] Furthermore, in the graded cleaning actuator, the air jet of the pneumatic blowing component is connected to the built-in air passage of the mandrel, and the air passage is arranged toward the yarn contact surface via the air outlets opened by the sliding tension plate and the fixed tension plate.

[0020] Furthermore, the main control unit has a built-in self-learning algorithm module that stores the basic parameters of the coupling model for different yarn types and automatically optimizes the internal correlation coefficients of the model based on the data collected from the production of new batches of yarn.

[0021] Furthermore, the main control unit also has a built-in fault early warning module, which pre-stores dual over-limit judgment thresholds for tension and flying fluff, and outputs audible and visual alarms and fault indication signals after the thresholds are triggered.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention pioneers a coupling correlation mechanism between tension fluctuations and fluff concentration, enabling precise tracing of fluff formation and overcoming the blindness inherent in existing fluff control technologies. Compared to CN118257126A and CN215517875U, which rely solely on fixed mechanical structures for passive fluff removal and fail to identify the root causes of fluff generation, and CN114232185B and CN115993806B, which use a single electrostatic parameter and yarn weight loss to indirectly predict fluff formation, this invention constructs a quantitatively coupled mathematical model that can accurately distinguish between two types of primary fluff formation: short fluff from broken ultra-high tension fibers and long fluff from slipped low tension fibers. This breaks down the technical barriers between tension control, fluff removal, and environmental fluff suppression systems in traditional technologies, achieving a technological upgrade from "passive fluff removal" to "source-based fluff control." It precisely matches fluff control strategies at the root cause, significantly improving the targeting and effectiveness of fluff management.

[0023] 2. Adopting a three-level closed-loop energy consumption control architecture, this invention significantly reduces equipment operating energy consumption and is suitable for continuous industrial production scenarios. Following a low-energy-consumption priority control logic of "source tension fine-tuning first, in-situ cleaning as a supplement, and environmental control as a fallback," this invention prioritizes suppressing fluff generation by slightly adaptively adjusting yarn clamping tension, eliminating the need for continuous full-load operation of cleaning equipment and reducing ineffective energy consumption at the source. Compared to the CN215517875U brush and fan operating at constant speed for extended periods, and the CN114232185B vacuum cleaner's high-power continuous operation, this invention only activates the corresponding cleaning module in stages when fluff exceeds the standard, matching the cleaning intensity of blowing, brushing, and scraping as needed. It also avoids the energy waste of the CN115993806B's indiscriminate temperature and humidity control, resulting in a significant reduction in overall machine energy consumption and making it suitable for long-term, high-volume continuous production conditions in textile workshops.

[0024] 3. A graded, differentiated online cleaning mode balances the effectiveness of lint removal with the quality of finished yarn, eliminating secondary pollution. Addressing the shortcomings of existing technologies, CN118323964B only supports cleaning during machine downtime and cannot achieve online lint removal; CN215517875U's rigid brushes can easily scratch fine denier yarns during continuous operation; and CN118257126A's fixed scraping structure easily accumulates lint, causing secondary pollution. This invention sets up a three-tiered cleaning strategy: in low-lint conditions, a non-stop pulse air blowing method is used to finely clean loose lint from crevices; in medium-lint conditions, a flexible brush and suction combination is used; and in high-lint conditions, a deep scraping and dust collection combination is activated. This can adapt to the cleaning needs of different yarn counts and lint amounts. It achieves uninterrupted online lint removal throughout the entire yarn production process while avoiding damage to fine denier yarns from rigid cleaning structures. Simultaneously, it completely solves the problems of lint accumulation by the scraper and secondary lint falling and contaminating the yarn, effectively reducing the surface defect rate of the yarn.

[0025] 4. Differentiated environmental control avoids the drawbacks of uniform temperature and humidity control, ensuring yarn production stability. Unlike the crude environmental control logic of CN115993806B, which uses uniform humidification and dehumidification, this invention uses a differentiated environmental control strategy based on the causes of fluff. It moderately humidifies short, broken fluff caused by tension breakage to suppress suspension and dispersion, and moderately dehumidifies long, loose fluff caused by fiber slippage to reduce machine entanglement, precisely adapting to the fluff suppression needs of different fluff types. This effectively avoids the problems of excessive yarn moisture regain, mold, and clumping caused by uniform humidification, as well as the drawbacks of high energy consumption and poor fluff suppression effect of uniform dehumidification. While efficiently controlling fluff, it ensures stable yarn moisture content and fiber cohesion, improving the consistency of finished yarn quality.

[0026] 5. Possesses adaptive self-learning and fault linkage early warning capabilities for various yarn types, significantly improving equipment versatility and production safety. This invention pre-stores basic model coefficients for multiple yarn types and can automatically iterate and optimize model parameters through production data, eliminating the need for repeated manual calibration and debugging. This effectively solves the problems of existing technologies, such as manually preset thresholds, poor adaptability, and cumbersome debugging, and can be widely adapted to various yarn production scenarios, including pure cotton, polyester, and blended yarns. Simultaneously, based on a dual-parameter linkage anomaly early warning mechanism for tension and lint, it can predict in advance faults such as raw material abnormalities, machine wear, air circuit blockage, and lint accumulation on the scraper, providing timely audible and visual pop-up warnings and assisting in shutdown maintenance. This effectively avoids safety hazards such as equipment jamming, short circuits, and fires caused by lint accumulation, significantly improving the continuous operation stability of textile equipment and the safety level of workshop production. Attached Figure Description

[0027] Figure 1 This is a block diagram of the overall system composition of the present invention; Figure 2 This is a flowchart of the overall control method of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to clearly and completely understand the technical solution, innovative principles, and practical application advantages of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with specific working conditions.

[0029] This invention constructs a mathematical model coupling yarn tension fluctuations and in-situ fluff concentration to trace the root causes of fluff formation and establishes a three-level closed-loop intelligent control architecture: "source tension regulation - graded online cleaning - differentiated environmental fallback." To fully verify the inventiveness and practicality of this invention compared to existing technologies, five sets of differentiated working condition examples are set up, covering different yarn types, different fluff-generating mechanisms, and different production quality scenarios. Two sets of high-standard comparative examples are also set up to correspond to existing mainstream defective technologies. Through a unified working condition, unified equipment, and 24-hour continuous industrial comparative test, the fluff control accuracy, energy-saving effect, finished product quality, and equipment stability of this invention are quantitatively verified.

[0030] Unified basic conditions for the experiment

[0031] Test equipment: high-speed automatic winding machine; production line speed: 800m / min; reference ambient temperature: 25℃; reference relative humidity: 55%; test duration: continuous 24h uninterrupted production; data acquisition frequency: 1 time / 10s; core evaluation indicators: in-situ average lint concentration, yarn defect rate per kilometer, average daily comprehensive energy consumption of equipment, fault early warning accuracy rate, and average daily loss rate of cleaning mechanism.

[0032] This embodiment uses the baseline operating conditions: ambient temperature 25℃, baseline humidity 55% RH; the classification thresholds for flying fluff are low ≤4mg / m³, medium 4-7mg / m³, and high >7mg / m³; tension is adjusted in stages by 1%-5% per adjustment; broken fluff increases humidity by 3-8% RH, and sliding fluff decreases humidity by 4-6% RH; self-learning sampling for 30 minutes and an error of 5% are used as the convergence criteria. Temperature and humidity compensation calculations preferentially use pre-stored data. After the baseline coefficients are put into production, the coefficients are adaptively corrected through self-learning iterations after batch production.

[0033] Five sets of technical embodiments of the present invention.

[0034] Example 1: Low-tension slippage and linting condition of 40-count combed pure cotton yarn

[0035] This embodiment corresponds to the core innovations of the present invention: tracing the origin of the coupled model, low-tension adaptive pressurization, differentiated dehumidification control of long-staple lint, and model self-learning. Pure cotton 40-count combed yarn has weak fiber cohesion and long fiber length. When the tension clamping is insufficient during production, the fibers inside the yarn are prone to relative slippage and frictional shedding, mainly producing long-staple slippage-type primary lint, which is a typical low-tension lint-generating condition in textile production.

[0036] Upon power-up, the system automatically matches the basic model coefficients of pure cotton yarn. Through an integrated in-situ sensing module, it synchronously collects average tension values, tension fluctuation rate, frequency of tension mutations per unit time, and in-situ lint concentration in the yarn-crossing area. Coupled model calculations determine that the excessive lint in this working condition is caused by insufficient tension clamping force and fiber slippage / fuzzing, triggering primary source control logic. The PLC gradually increases the electromagnet clamping force in stages, increasing the yarn clamping tension and inhibiting fiber slippage and shedding. After tension fine-tuning, the lint concentration drops back to the medium concentration threshold range. The system then activates a two-stage combined cleaning mode: intermittent pulsed air blowing + low negative pressure fixed-point suction, to remove tension gaps and surface lint online. Addressing the characteristics of long-staple lint easily entangled in machine parts and not easily electrostatically floated, the system performs a three-stage fallback control, appropriately reducing the local humidity of the workstation and coordinating airflow to reduce long lint accumulation. Before a new batch of yarn is put into production, the system completes a 30-minute self-learning iteration, automatically correcting the model weight coefficients corresponding to the long-staple lint in pure cotton to adapt to the characteristics of this batch of raw cotton.

[0037] Example 2: High-tension breakage and linting of 75D fine denier high-elastic yarn

[0038] This embodiment corresponds to the innovative aspects of this invention: tracing the cause of high-tension breakage, step-by-step tension reduction, high-concentration graded deep cleaning, and humidification and suppression of short, broken fibers. 75D polyester fine denier yarn fibers are rigid, brittle, and have low single-filament fineness. During production, excessive instantaneous tension fluctuations and exceeding clamping tension limits can easily lead to fiber tensile breakage, generating a large amount of ultra-fine, short, broken fibers. These fibers are lightweight, highly static, and easily suspended and dispersed, making it the most difficult condition to manage in polyester spinning. This invention's coupled model accurately identifies the cause of fiber breakage as ultra-high-tension fiber breakage based on the characteristics of high frequency of tension mutations and excessive instantaneous tension peaks. It prioritizes primary source-level pressure reduction control, employing a multi-step, small-amplitude tension reduction strategy to prevent yarn breakage caused by sudden tension changes. For high-concentration ultra-fine fibers that persist after tension fine-tuning, the system activates a deep cleaning mode combining a scraper assembly and high-power negative pressure suction to thoroughly remove broken floating fibers from the yarn surface. To address the shortcomings of short, fragmented fluff that floats electrostatically and easily accumulates on electrical components, the system differentiates and increases the local humidity to neutralize static electricity and suppress the dispersion of ultrafine fluff, thus eliminating the risk of fluff accumulation and short circuits in equipment from the source.

[0039] Example 3: Adaptive working condition of 65 / 35 polyester / cotton blended yarn with fuzzing

[0040] This embodiment corresponds to the innovative features of this invention: multi-yarn adaptive modeling, precise differentiation of composite fuzz types, and dynamic switching and control of multiple modes. Polyester-cotton 65 / 35 blended yarn contains both cotton and polyester fibers, resulting in complex operating conditions and coupled fuzzing mechanisms: cotton fibers are prone to low-tension slippage, producing long fuzz, while polyester fibers are prone to high-tension breakage, producing short, broken fuzz. Traditional single control methods cannot adapt to these complex operating conditions. This invention's system pre-stores the basic model coefficients of the blended yarn and dynamically corrects the model weights through real-time data self-learning during production, distinguishing the current dominant fuzz type in real time: when stable tension, low fluctuation rate, and high proportion of long fuzz are detected, it is determined to be cotton fiber slippage fuzzing, and pressure increase + humidity reduction control is implemented; when frequent tension abrupt changes and a surge in short fuzz concentration are detected, it is determined to be polyester fiber breakage fuzzing, and pressure reduction + humidity increase + deep cleaning control is implemented. The system can dynamically switch cleaning modes and environmental strategies according to real-time operating conditions, solving the technical defects of traditional equipment that cannot adapt to composite fuzz types and that fuzz control is a one-size-fits-all approach.

[0041] Example 4: Energy-saving operation under high load and high lint content of 10-count coarse pure cotton yarn

[0042] This embodiment corresponds to the innovative features of the present invention: three-level energy consumption classification and priority control, on-demand start-stop cleaning, and optimal low-energy consumption control. 10-count coarse cotton yarn has coarse fibers, a fluffy yarn body, and a large frictional contact area. The basic output of lint during production is far greater than that of fine yarn, resulting in high-load, high-lint, and long-term continuous production conditions. Traditional cleaning equipment operates at full load for extended periods, leading to extremely high energy consumption. This invention strictly follows the low-energy consumption control logic of "prioritizing source tension control, supplementing with in-situ cleaning, and providing environmental control as a safety net." It prioritizes reducing fiber shedding at the source through optimal matching of tension parameters, and only activates corresponding cleaning modules in stages when the lint concentration exceeds the standard: low-lint requires only pulse air blowing, medium-lint requires a combination of brush and suction, and high-lint requires deep scraping. This eliminates ineffective idle operation of fans, brushes, and negative pressure equipment, minimizing overall equipment energy consumption while ensuring cleaning effectiveness under high-lint conditions, making it suitable for large-scale, long-term continuous industrial production in textile workshops.

[0043] Example 5: Low-defect and pollution-preventing working conditions for high-end combed compact spinning cotton yarn

[0044] This embodiment corresponds to the innovative features of this invention: non-stop online cleaning, dual-parameter fault warning, prevention of secondary pollution, and adaptation to high-quality yarn. Combed compact cotton yarn is used in high-end home textiles and knitted fabrics, requiring stringent standards for yarn surface smoothness, defect indicators, and lint-free properties. Traditional equipment requires downtime for cleaning, disrupting production continuity; rigid brushes can easily scratch the yarn; and accumulated lint can cause secondary pollution. This invention uses a tensioner with built-in air ducts for intermittent pulse jet cleaning to achieve non-stop online cleaning, precisely removing residual lint from the tension plate gaps without rigid contact or damage to fine denier yarn. Simultaneously, the system relies on a dual-parameter linkage warning mechanism based on tension fluctuation rate and lint concentration to monitor the cleaning mechanism's operating status in real time. When it detects that even with full-load cleaning, lint reduction is still impossible, or abnormal tension fluctuations are accompanied by a continuous surge in lint, it anticipates airway blockage, lint accumulation on the scraper, machine wear, and raw material malfunctions, promptly issuing audible and visual warnings and prompting maintenance. This completely prevents secondary lint from falling and contaminating the yarn, ensuring ultra-low defect production requirements for high-end yarns.

[0045] Scale settings: Comparative Example 1: Traditional mechanical normally open cleaning control (corresponding to CN215517875U) The existing mechanical dust removal structure, consisting of a reciprocating roller brush, a single-sided blower, and a lint-collecting plate, lacks a tension sensor module, a lint concentration monitoring module, and intelligent grading control logic. After power-on, the brush, blower, and lint-collecting mechanism operate at a constant power, functioning in an open-loop manner throughout. It cannot dynamically adjust the cleaning intensity based on yarn tension or real-time lint levels, relying solely on a fixed mechanical structure for passive lint removal. It lacks both source tension control and environmental differentiation control functions. The test conditions were uniformly conducted using 65 / 35 polyester-cotton blended yarn, identical to those in Example 3.

[0046] Comparative Example 2: Passive Fluff Suppression Control Based on Temperature and Humidity Across the Entire Area (corresponding to CN115993806B)

[0047] The existing technology, which estimates fly ash based on yarn weight loss and uses a control logic for uniformly regulating workshop temperature and humidity, lacks a tension-fly ash coupling model. This makes it impossible to trace the causes of fly ash and distinguish between native fly ash and existing floating fly ash in the workshop. It relies solely on passively suppressing fly ash floating through overall humidification and dehumidification, lacking in-situ graded online cleaning functions, resulting in lagging control and a simplistic strategy. The experimental conditions used were uniformly consistent with those in Example 3, employing 65 / 35 polyester-cotton blended yarn.

[0048] Under uniform experimental conditions, the statistical data of the 24-hour continuous production test for each group of examples and comparative examples are shown in the table below: Table 1 Comparison of Experimental Data Results

[0049] Analysis of experimental data conclusions: First, this invention achieves precise source tracing of the causes of cottonwood fluff, resulting in significantly better control than existing technologies. Through a tension-fluff coupling mathematical model, this invention can accurately distinguish between two types of primary cottonwood fluff: broken short fluff and sliding long fluff, achieving targeted fluff control at the source. Compared to the passive and extensive control methods of Comparative Examples 1 and 2, this invention can reduce the overall fluff concentration by 53.6% to 67.2%, completely solving the industry pain points of existing technologies, such as lack of source tracing capabilities, indiscriminate fluff control, and incomplete fluff removal.

[0050] Secondly, the tiered, flexible cleaning mode significantly improves the quality of finished yarn. This invention employs a gradient, non-contact-priority online cleaning strategy, avoiding the drawbacks of traditional rigid brushes that continuously scratch fine denier yarns and the secondary contamination caused by lint accumulation on fixed scrapers. Test data shows that the defect rate of yarn produced by this invention is reduced by 67.4% to 85.8% compared to existing technologies, making it particularly suitable for high-value-added yarn production scenarios such as high-end combed yarns and fine denier synthetic fibers.

[0051] Third, the three-tiered energy consumption priority architecture significantly reduces production energy consumption. This invention takes source tension regulation as its core and starts and stops cleaning and environmental equipment on demand, abandoning the high-energy-consuming mode of existing technologies where equipment is always on and controlled indiscriminately throughout the entire area. The overall energy consumption of the machine is reduced by 21.5% to 34.3%, and its energy-saving advantages are particularly prominent in large-scale continuous production.

[0052] Fourth, the dual-parameter linkage early warning mechanism significantly improves equipment safety and stability. Existing technologies lack fault identification and early warning capabilities, making them prone to problems such as lint accumulation and blockage, equipment wastage, and component damage. The fault early warning accuracy of this invention can reach over 98.5%, and it can predict faults such as raw material abnormalities, air path blockages, lint accumulation on the scraper, and component wear in advance. At the same time, the on-demand working mode greatly reduces the mechanical wear of the cleaning mechanism, significantly improving equipment lifespan and production safety.

[0053] Fifth, the self-learning adaptive algorithm is adapted to the production of multiple types of yarn. This invention can adapt to all types of yarn, including pure cotton, polyester, blended, coarse count, and combed yarn, without the need for repeated manual parameter calibration. It solves the defects of poor adaptability, cumbersome debugging, and weak versatility of existing technologies, and has extremely strong industrial adaptability.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Conventional modifications, equivalent substitutions, and simple parameter optimizations made by those skilled in the art without departing from the core technical principles of the present invention should all fall within the protection scope of the present invention.

Claims

1. A cleaning control method based on the correlation analysis of yarn tension fluctuation and fluff concentration, characterized in that, Includes the following steps: S1. In-situ synchronous acquisition step: Real-time tension characteristic parameters and in-situ fluff concentration parameters are synchronously acquired at the yarn tension clamping station; the tension characteristic parameters include average tension Tavg, tension fluctuation rate δT, and tension mutation frequency per unit time. The in-situ fluff concentration parameter refers to the original fluff concentration in the yarn passing area. ; S2. Coupled Model Calculation Steps: Retrieve the Preset Tension-Flying Floss Concentration Coupled Mathematical Model ΔH is the temperature and humidity compensation correction amount. The model can be constructed using any algorithm, such as weighted sum, neural network, or decision tree. Weighted sum model calculation formula: a, b, and c are the basic coefficients for yarn type. This is a temperature and humidity correction factor; ΔH is calculated based on the temperature and humidity parameters of the data acquisition station to complete the compensation correction. The theoretical pollen concentration is then calculated by substituting the collected tension characteristic parameters, combined with actual measurements. To determine the causes of excessive fluff, three types of fluff generation are distinguished: fluff generation from ultra-high tension fiber breakage, fluff generation from excessively low tension fiber slippage, and trace fluff generation under normal processes. The fluff concentration grading threshold is: low ≤ 4 mg / m³. 3 4~7mg / m² 3 High >7mg / m 3 ; S3, a three-level closed-loop control procedure, adjusts energy consumption sequentially from low to high, prioritizing source tension adjustment, followed by in-situ graded cleaning, and finally fine-tuning of the workshop environment: S31. Primary source control: The tension component clamping force is adaptively adjusted according to the type of fuzz formation. If it is determined that the fuzz formation is caused by breakage due to excessive tension, the PLC controls the electromagnet to reduce the suction force and lower the yarn clamping tension. If it is determined that the fuzz formation is caused by slippage due to low tension, the PLC controls the electromagnet to increase the suction force and raise the clamping tension. A step-by-step adjustment method of 1% to 5% is adopted to avoid instantaneous yarn breakage. If the excess is <10%, the adjustment is 1%; if it is 10~20%, the adjustment is 3%; if it is >20%, the adjustment is 5%. S32. Secondary in-situ cleaning control: The cleaning mode is switched in stages according to the measured concentration threshold of flying fluff; when the low concentration exceeds the standard, the tensioner's built-in airway pulse micro-air blowing is activated. The medium-concentration excessive lint removal mechanism and low negative pressure air suction mechanism work together to remove lint; the high-concentration excessive lint removal mechanism and high-power air suction combination cleaning are activated; the pulse micro-air blowing is achieved by relying on the built-in air channel of the tensioner core shaft, and intermittent air blowing is carried out when the yarn is continuously feeding, so that the floating lint in the tension sheet can be cleaned without stopping the machine. S33. Level 3 Environmental Safety Net Control: If the concentration of flying fluff is still higher than the limit after S31 and S32 control, the workshop temperature and humidity will be adjusted differently according to the type of fluff. If the broken short fluff exceeds the limit, the ambient humidity will be appropriately increased by 3%-8% based on the baseline of 55% RH. If the sliding long fluff exceeds the limit, the ambient humidity will be appropriately reduced by 4%-6%. S4. Data Iteration Steps: During the initial production phase of a new batch of the same yarn, tension and lint data are collected continuously for 30 minutes and 10 seconds per batch. If the error of 10 consecutive sets of data is greater than 5%, the coupling model coefficients are automatically iterated and corrected. If the error is less than 5%, the model self-learning calibration is completed.

2. The cleaning control method according to claim 1, characterized in that: The environmental compensation and correction step in S2 is through The formula is implemented by collecting real-time temperature and humidity parameters at the workstation, and correcting the coupling model coefficients based on temperature and humidity to eliminate the interference of environmental water vapor and air flow on concentration calculation.

3. The cleaning control method according to claim 1, characterized in that: The pulsed micro-air blowing in S32 is achieved by relying on the built-in air channel of the tensioner spindle. It intermittently blows air while the yarn is continuously feeding, and can clean the floating lint in the tension sheet gap without stopping the machine.

4. The cleaning control method according to claim 1, characterized in that, It also includes anomaly warning procedures: preset tension fluctuation rate exceeding the limit threshold of 120% of the rated value and the maximum threshold for flying fluff of 7 mg / m³. 3 When the tension fluctuation rate exceeds the limit for five consecutive sampling cycles and the moving average concentration of fluff continues to rise beyond the limit threshold, the system triggers an equipment fault warning, indicating abnormal raw materials or mechanical wear. If the cleaning mechanism cannot reduce the fluff concentration after running at full load for 3 minutes, it determines that the air path is blocked / the scraper has accumulated fluff and pops up an alarm.

5. The cleaning control method according to claim 1, characterized in that: The coupling model pre-stores multiple sets of basic coefficients (a, b, c) according to different yarn types such as cotton, polyester, and blended yarns. After the system identifies the yarn type, it automatically matches the initial model parameters.

6. A cleaning control system based on the correlation analysis of yarn tension fluctuation and fluff concentration, used to implement the cleaning control method according to any one of claims 1 to 5, characterized in that: Includes a main control unit, an integrated in-situ sensing module, a tension actuator, a graded cleaning actuator, and a workshop temperature and humidity control mechanism; The integrated in-situ sensing module is fixedly installed at the tension clamping station, integrating a tension acquisition component, a miniature in-situ dust sensing probe, and a temperature and humidity acquisition probe, enabling synchronous acquisition of tension parameters, native fluff concentration, and ambient temperature and humidity at the same station; the miniature in-situ dust sensing probe adopts a Mie scattering laser sensing structure, embedded in the inner wall of the yarn passage groove next to the tension plate, only collecting native fluff that has fallen off the yarn, thus isolating the floating fluff in the workshop from interfering with the detection data; The tension actuator includes a PLC controller, left and right electromagnets, a fixed tension plate, a sliding tension plate, and a spindle with built-in air passage structure. It dynamically adjusts the output suction force of the electromagnets under the command of the main control unit to change the yarn clamping tension. The graded cleaning actuator includes a pneumatic blowing assembly, a movable roller brush assembly, a scraper assembly, and a negative pressure suction assembly. The air jet of the pneumatic blowing assembly is connected to the built-in air channel of the spindle. The air channel is arranged towards the yarn contact surface through the air outlets of the sliding tension plate and the fixed tension plate. The main controller selects one or a combination of them to start according to the concentration of flying fluff. The workshop temperature and humidity control mechanism includes workshop humidification equipment, dehumidification equipment, and air conditioning units, and receives temperature and humidity adjustment commands issued by the main controller to achieve differentiated environmental control; The main control unit has a built-in self-learning algorithm module and a fault early warning module. The self-learning module pre-stores the basic parameters of the coupling model for different yarn types and optimizes the coefficients based on the new batch data. The fault warning module has a pre-stored tension fluctuation rate of 120% and a fluff concentration of 7 mg / m³. 3 The system has dual over-limit detection thresholds, and outputs audible and visual alarms and fault indication signals after the thresholds are triggered.

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