Method for controlling circular weft knitting machine based on multi-parameter automatic adjustment

Abstract

The invention relates to the technical field of knitting, in particular to a circular weft knitting machine control method based on multi-parameter automatic adjustment, and the method comprises the steps: obtaining environment humidity and instantaneous tension values of all yarn feeding paths; judging whether the overall tension reaches the standard or not based on the first tension fluctuation coefficient; if not, whether the hairiness adhesion risk exceeds the standard or not is judged based on the surface effective adhesion coefficient; if so, acquiring an image of the yarn guide device, calculating a hairiness coverage index, and judging the health state of the equipment; a yarn feeding performance attenuation coefficient is calculated, and compensation regulation and control are achieved by increasing the rotating speed of a yarn feeding servo motor and decreasing the current of an electromagnetic damper; calculating a fluctuation effective recovery coefficient after regulation and control, and evaluating regulation and control qualification; and if not, calculating a fine tuning factor or a strengthening factor according to the deviation degree of the recovery coefficient to optimize the gain coefficient. The yarn guide device solves the problem that yarn tension fluctuates due to the fact that yarn hairiness adheres to and accumulates on the yarn guide device due to environment humidity changes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of knitting, in particular to a knitting circular weft machine control method based on multi-parameter automatic adjustment. BACKGROUND

[0002] The yarn feeding tension stability of the knitting circular weft machine is a core factor determining the quality of the fabric surface; at present, the industry generally uses tension sensor feedback to adjust the yarn feeding servo motor or electromagnetic damper in real time to maintain constant tension.

[0003] In actual production, especially in high humidity seasons or specific raw material production processes, the free hair on the surface of the yarn will significantly increase its adhesion tendency on the surface of the metal parts such as the guide eye and the tension piece due to the influence of environmental humidity; this adhesion is a slow accumulation and continuous deterioration process, which can cause unpredictable nonlinear growth of the yarn feeding channel friction coefficient, and then cause time-varying tension fluctuations and drift that are difficult to eliminate by traditional control. The existing technology lacks the ability to perceive and diagnose this specific physical cause: the control system cannot distinguish whether the current tension anomaly is caused by adhesion accumulation or other transient disturbances; therefore, the operator can only rely on experience to regularly stop production for inspection and cleaning, or blindly increase the control gain, resulting in reduced production efficiency, increased energy consumption, and inability to fundamentally ensure long-term and self-adaptive stability of the tension.

[0004] Therefore, the existing technology has a deep-seated defect: in the face of chronic and hidden yarn feeding tension degradation caused by environmental humidity and accumulation of hair adhesion on the surface of mechanical parts, there is a lack of an intelligent control method that can automatically diagnose the cause, quantitatively evaluate, accurately compensate, and continuously optimize itself.

[0005] Chinese patent publication No. CN118007304A discloses a tension adjusting yarn feeding device in a circular weft knitting machine, belonging to the field of textile weaving technology, comprising a fixed frame, a yarn feeder, a first guide frame and a second guide frame, the first guide frame and the second guide frame are installed on the left and right sides of the fixed frame, and the height of the first guide frame is higher than that of the second guide frame, the yarn feeder is installed on the fixed frame, and the yarn feeder comprises a motor, a mounting seat, a guide rod and an electromagnetic inductor, the mounting seat is provided with a through hole for installing the electromagnetic inductor, and the both ends of the mounting seat are provided with mounting covers, the electromagnetic inductor comprises an induction coil, an iron core and a fixed conductive rod, both ends of the iron core are provided with fixed plates, and the fixed conductive rod is provided with two, the invention uses the magnetic field generated by the energized induction coil to act together with the iron core and generate an attractive force on the ferrous slider, so that the position of the guide rod changes, and the diameter of the yarn winding cylinder formed by all the guide rods also changes, thereby realizing tension adjustment of the yarn feeding.

[0006] As can be seen, the tension adjusting yarn feeding device in the circular weft knitting machine has the following problems: In the knitting production process, failure to consider the impact of ambient humidity on the adhesion of the yarn feeder's mechanical surface can easily lead to increased adhesion of yarn hairs to the feeder's mechanical surface due to fluctuations in ambient humidity. This results in a slow increase in the accumulation of hairs on the feeder's mechanical surface, affecting the friction between the yarn and the mechanical surface. Consequently, it affects the control of tension during the yarn feeding process. Furthermore, in actual control, it is impossible to make real-time dynamic adjustments based on real-time environmental or tension fluctuations, resulting in poor tension control performance. Summary of the Invention

[0007] Therefore, the present invention provides a control method for a circular knitting machine based on multi-parameter automatic adjustment, which overcomes the problem in the prior art that the yarn hairs will adhere and accumulate on the yarn guide device due to changes in the humidity of the production environment, thereby causing yarn tension fluctuations.

[0008] To achieve the above objectives, this invention provides a control method for a circular knitting machine based on multi-parameter automatic adjustment. It includes: Obtain the current ambient humidity parameters and the instantaneous tension values ​​of the yarns on all yarn feeding paths; Based on the instantaneous tension value, a first tension fluctuation coefficient is determined for the change of yarn tension within a preset time, so as to determine whether the tension fluctuation of yarn in all effective channels at the current moment meets the standard. Under the condition that the tension fluctuation of all effective yarn paths at the current moment is not up to standard, the effective adhesion coefficient of the surface, which characterizes the mechanical adhesion strength, is determined based on the environmental humidity parameter, so as to determine whether the mechanical adhesion force of hair on the effective yarn feeding path exceeds the standard. Image data of the yarn guide ceramic eye and tension plate surface on the yarn feeding path are acquired. Based on the image data, the hair coverage index, which characterizes the hair coverage of the mechanical surface, is determined to determine whether the health status of the mechanical equipment on the yarn feeding path meets the standard. Based on the hair coverage index, the yarn feeding performance attenuation coefficient, which characterizes the mechanical performance attenuation, is determined, so as to determine the speed reference value of the yarn feeding servo motor to be increased by a slight speed correction factor or an enhanced speed correction factor, and the current reference value of the electromagnetic damper to be decreased by a current correction factor. Obtain the second tension fluctuation coefficient, which characterizes the change in yarn tension within a preset stable time, and determine the effective recovery coefficient of the fluctuation, which characterizes the degree of recovery of yarn tension fluctuation after regulation, so as to determine whether the current regulation strategy is qualified. The gain coefficient is updated using a fine-tuning factor or an enhancement factor based on the deviation ratio between the effective recovery coefficient of the fluctuation and the preset effective recovery coefficient.

[0009] Furthermore, the process of determining whether the tension fluctuation of the yarn in all valid pathways at the current moment meets the standard includes, Obtain the instantaneous tension values ​​of yarns along all valid yarn feeding paths within a preset time period; Calculate the average tension value and the overall standard deviation of all instantaneous tension values ​​within the preset time period; The first tension fluctuation coefficient is obtained by the ratio of the overall standard deviation to the average tension value; Compare the first tension fluctuation coefficient with the preset tension fluctuation coefficient; Based on the fact that the first tension fluctuation coefficient is greater than the preset tension fluctuation coefficient, it is determined that the tension of the yarn in all effective channels is not up to standard at the current moment.

[0010] Furthermore, given that the tension of the yarn in all effective paths is below standard at the current moment, the process of determining whether the mechanical adhesion force of hairiness on the effective yarn feeding path exceeds the standard includes: Set the adhesion growth factor; Obtain the initial adhesion humidity of the current yarn raw material; The effective adhesion coefficient of the surface is determined based on the ambient humidity parameter, the adhesion growth coefficient, and the adhesion initiation humidity. The effective adhesion coefficient of the surface is compared with the preset adhesion coefficient; Based on the fact that the effective adhesion coefficient of the surface is greater than the preset adhesion coefficient, it is determined that the mechanical adhesion force to the hair on the effective yarn feeding path exceeds the standard.

[0011] Furthermore, given that the mechanical adhesion force of yarn hair on the effective yarn feeding path exceeds the standard, the process of determining whether the health status of the mechanical equipment on the yarn feeding path meets the standard includes: Acquire color images of the yarn guide eye and the surface of the tension sheet, and convert the color images into grayscale images; Extract the observation region from the grayscale image; The total number of white pixels within the observation area is calculated based on color features; Count the total number of pixels within the observation area; The feather coverage index is calculated based on the total number of white pixels and the total number of pixels; The feather coverage index is compared with a preset coverage index; Based on the fact that the hair coverage index is greater than the preset coverage index, it is determined that the health status of the mechanical equipment on the yarn feeding path is substandard.

[0012] Furthermore, the process of determining to increase the speed reference value of the yarn feeding servo motor with a slight speed correction factor or an enhanced speed correction factor, and to decrease the current reference value of the electromagnetic damper with a current correction factor includes, The yarn feeding performance attenuation coefficient is calculated and determined based on the hair coverage index and the preset coverage index. The yarn feeding performance attenuation coefficient is compared with the preset attenuation coefficient; Based on the fact that the yarn feeding performance attenuation coefficient is less than or equal to the preset performance attenuation coefficient, the product of the mild speed correction factor and the current speed reference value of the yarn feeding servo motor is determined as the first upward adjustment correction amount for the speed reference value of the yarn feeding servo motor.

[0013] Furthermore, the process of determining to increase the speed reference value of the yarn feeding servo motor with a slight speed correction factor or an enhanced speed correction factor, and to decrease the current reference value of the electromagnetic damper with a current correction factor, also includes... Based on the fact that the yarn feeding performance attenuation coefficient is greater than the preset performance attenuation coefficient, it is determined that the product of the enhanced speed correction factor and the current speed reference value of the yarn feeding servo motor is used as the second upward adjustment correction amount for the speed reference value of the yarn feeding servo motor, and the product of the current correction factor and the current reference value of the electromagnetic damper is used as the downward adjustment correction amount for the current reference value of the electromagnetic damper.

[0014] Furthermore, the process of determining whether the currently implemented control strategy is qualified includes, The effective recovery coefficient of the fluctuation is calculated and determined based on the first tension fluctuation coefficient and the second tension fluctuation coefficient; The effective recovery coefficient of the fluctuation is compared with the preset effective recovery coefficient; Based on the fact that the effective recovery coefficient of the fluctuation is less than the preset effective recovery coefficient, it is determined that the currently implemented control strategy is unqualified.

[0015] Furthermore, the process of determining the update of the gain coefficient with a fine-tuning factor or an enhancement factor based on the deviation ratio between the effective recovery coefficient of the fluctuation and the preset effective recovery coefficient includes, The deviation ratio is compared with a preset deviation ratio. Based on the deviation ratio being greater than or equal to a preset deviation ratio, the gain coefficient is determined to be updated with a fine-tuning factor. Based on the fact that the deviation ratio is less than the preset deviation ratio, the gain coefficient is determined to be updated with an enhancement factor.

[0016] Furthermore, the process of determining the update of the gain coefficient with the fine-tuning factor includes, Based on the current control strategy being the first control strategy, the mild compensation gain coefficient is updated to the product of the current mild compensation gain coefficient and the fine-tuning factor; Based on the current control strategy being the second control strategy, the enhancement compensation gain coefficient is updated to the product of the current enhancement compensation gain coefficient and the fine-tuning factor, and the current correction gain coefficient is updated to the product of the current current correction gain coefficient and the fine-tuning factor, and the correction amount is recalculated.

[0017] Furthermore, the process of determining the updated gain coefficients with the enhancement factor includes, If the currently implemented control strategy is the first control strategy, then the mild compensation gain coefficient will be updated to the product of the current mild compensation gain coefficient and the enhancement factor. Based on the current control strategy being the second control strategy, the enhancement compensation gain coefficient is updated to the product of the current enhancement compensation gain coefficient and the enhancement factor, and the current correction gain coefficient is updated to the product of the current current correction gain coefficient and the enhancement factor, and the correction amount is recalculated.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention detects the humidity of the environment during the production process of a circular knitting machine and obtains the instantaneous tension values ​​of the yarn on all effective paths of the yarn feeding path. This allows for the calculation of the tension fluctuation coefficient, characterizing the yarn tension within a preset time, effectively determining whether the current working state of the yarn feeding process meets the standards. If the conditions are not met, the effective surface adhesion coefficient, characterizing the adhesion strength of the yarn feeder's mechanical surface, is calculated based on the obtained environmental humidity of the circular knitting machine. This determines whether the mechanical adhesion force of the yarn feeder's mechanical surface to the fuzz generated during the yarn feeding process exceeds the standard. If it does exceed the standard, the fuzz coverage coefficient is determined based on image data of the yarn guide eye and tension plate surface on the yarn feeding path. This determines whether the health status of the mechanical equipment on the yarn feeding path meets the standards. Through this detection and determination step… This method can accurately quantify this hidden but difficult-to-quantify hidden danger, and calculate the yarn feeding performance attenuation coefficient, which characterizes the mechanical performance degradation, based on the deviation of the hairiness coverage index from the preset coverage index under standard conditions. This allows for the determination of tension fluctuations during the yarn feeding process, enabling targeted control of the hairiness coverage interference factor. This avoids the problem of traditional control methods that do not locate the interference factor and rely solely on manual experience to adjust the tension. The control results are detected within a preset stabilization time after the control is completed to determine whether the control strategy is qualified. If it is unqualified, the control strategy is optimized, ensuring the effectiveness of each control. This avoids insufficient or excessive tension caused by over-control, which can further affect the subsequent fabric forming, reduce the probability of defective fabrics, improve the production efficiency of qualified fabrics, and reduce the production costs of enterprises.

[0019] Furthermore, this invention simultaneously collects ambient humidity and instantaneous yarn tension values ​​along all effective yarn feeding paths, calculates a first tension fluctuation coefficient representing yarn tension changes within a preset time, and a surface effective adhesion coefficient representing mechanical adhesion strength. This allows the invention to determine whether the tension fluctuation of yarns in all effective paths meets standards and whether the mechanical adhesion strength to fuzz on the effective yarn feeding paths exceeds standards. By acquiring and analyzing image data of key components such as the yarn guide eye and tension plate, the invention transforms the traditionally vague cleanliness judgment, reliant on manual visual inspection, into a precisely quantified fuzz coverage index. This enables accurate diagnosis of the root cause of interference, significantly reducing reliance on operator experience and improving the accuracy and timeliness of fault diagnosis.

[0020] Furthermore, this invention transforms the interference of the yarn tension caused by the hairline coverage index on the surface of the yarn guide ceramic eye and tension plate into a yarn feeding performance attenuation coefficient. Based on the comparison between the yarn feeding performance attenuation coefficient and a preset performance attenuation coefficient, it executes a first control strategy and a second control strategy. This enables targeted compensation based on the severity of the fault, effectively avoiding over-adjustment: for mild attenuation, the speed reference value of the yarn feeding servo motor is increased proportionally to restore yarn tension stability; for severe attenuation, a larger increase in the speed of the yarn feeding servo motor and a decrease in the current reference value of the electromagnetic damper are implemented in synergy to overcome the significantly increased frictional resistance. This ensures that the compensation intensity accurately matches the actual mechanical performance loss under various working conditions corresponding to different levels of interference, thereby effectively suppressing tension fluctuations, ensuring fabric forming quality, and avoiding the energy waste, over-adjustment, and under-adjustment risks associated with a single fixed compensation strategy when facing different levels of interference. This significantly improves the adaptability and economy of tension adjustment.

[0021] Furthermore, this invention obtains a second tension fluctuation coefficient representing yarn tension changes within a preset stable time period and calculates the effective fluctuation recovery coefficient. The comparison between the effective fluctuation recovery coefficient and the preset effective recovery coefficient determines whether the currently executed control strategy is qualified. This effectively ensures that the effectiveness of each control strategy execution can be verified, and the control effect of the control strategy on the current control system can be detected in a timely manner, achieving instantaneous and quantitative feedback on the control effect. When the strategy is deemed unqualified, the control strategy is optimized based on the comparison between the deviation ratio of the effective fluctuation recovery coefficient and the preset effective recovery coefficient and the preset deviation ratio. For cases close to meeting the target, the current gain coefficient is fine-tuned based on a fine-tuning factor, ensuring that even small optimizations can achieve the target requirements of the control system. For cases with large discrepancies, the current gain coefficient is further optimized based on a strengthening factor to achieve more significant correction. This ensures that the actual optimization intensity is precisely matched to the severity of the problem, significantly improving the long-term robustness of the system in dealing with different disturbances and the consistency of control. It also reduces fabric production quality fluctuations caused by parameter fixation or improper experience-based adjustments in traditional tension regulation, thus improving the quality of the finished fabric. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the steps of the control method for a circular knitting machine based on multi-parameter automatic adjustment according to an embodiment of the present invention; Figure 2 This is a logic block diagram of an embodiment of the present invention for determining and controlling tension fluctuations during the yarn feeding process based on the yarn feeding performance attenuation coefficient; Figure 3 This is a logic block diagram illustrating how an embodiment of the present invention determines whether the currently executed control strategy is qualified based on the effective recovery coefficient of fluctuation. Figure 4 This is a logic block diagram of an embodiment of the present invention for determining the optimization of the control strategy based on the deviation ratio between the effective recovery coefficient of fluctuation and the preset effective recovery coefficient. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Please see Figure 1 As shown, it is a schematic diagram of the steps of the knitting circular knitting machine control method based on multi-parameter automatic adjustment in an embodiment of the present invention.

[0027] This invention relates to a control method for a circular knitting machine based on multi-parameter automatic adjustment, comprising: Step S1: Obtain the current ambient humidity parameters and the instantaneous tension values ​​of the yarns on all yarn feeding paths; Step S2: Calculate the first tension fluctuation coefficient of yarn tension change within a preset time based on the instantaneous tension value, and determine whether the tension fluctuation of yarn in all effective channels at the current moment meets the standard based on the first tension fluctuation coefficient. Step S3: Under the condition that the tension of the yarn in all effective paths is not up to standard at the current moment, calculate the surface effective adhesion coefficient, which characterizes the mechanical adhesion strength, based on the environmental humidity parameter, and determine whether the mechanical adhesion force to hair on the effective yarn feeding path exceeds the standard based on the surface effective adhesion coefficient. Step S4: If it is determined that the mechanical adhesion force of yarn hair on the effective yarn feeding path exceeds the standard, image data of the yarn guide ceramic eye and tension plate surface on the yarn feeding path are acquired. Based on the image data, the yarn hair coverage index, which characterizes the yarn hair covering the mechanical surface, is calculated. Based on the yarn hair coverage index, it is determined whether the health status of the mechanical equipment on the yarn feeding path meets the standard. Step S5: Under the condition that the health status of the mechanical equipment on the effective yarn feeding path is not up to standard, based on the degree of deviation of the hairiness coverage index from the preset coverage index under the standard state, calculate the yarn feeding performance decay coefficient, which characterizes the mechanical performance decay, and determine the tension fluctuation during the yarn feeding process according to the yarn feeding performance decay coefficient. Step S6: After the adjustment is completed, obtain the second tension fluctuation coefficient representing the change of yarn tension within a preset stable time. Calculate the effective recovery coefficient of fluctuation, which represents the degree of recovery of yarn tension fluctuation after adjustment, based on the second tension fluctuation coefficient and the first tension fluctuation coefficient. Determine whether the currently executed adjustment strategy is qualified based on the effective recovery coefficient of fluctuation. Step S7: After determining that the currently implemented control strategy is unqualified, optimize the control strategy based on the deviation ratio between the effective recovery coefficient of the fluctuation and the target recovery coefficient.

[0028] Specifically, this invention detects the humidity of the environment during the production process of a circular knitting machine and obtains the instantaneous tension values ​​of the yarn on all effective paths of the yarn feeding path. This allows for the calculation of a tension fluctuation coefficient representing the yarn within a preset time period, effectively determining whether the current working state of the yarn feeding process meets the standards. If the conditions are not met, the invention calculates an effective adhesion coefficient representing the adhesion strength of the yarn feeder's mechanical surface based on the obtained environmental humidity of the circular knitting machine. This determines whether the mechanical adhesion force of the yarn feeder's mechanical surface to the fuzz generated during the yarn feeding process exceeds the standard. If it does exceed the standard, the invention determines the fuzz coverage coefficient based on image data of the yarn guide eye and tension plate surface on the yarn feeding path, thereby determining whether the health status of the mechanical equipment on the yarn feeding path meets the standards. Through this detection and determination step, the working state of the yarn feeding machine can be accurately quantified. This method identifies hidden but difficult-to-quantify potential problems. Based on the deviation of the hairiness coverage index from the preset coverage index under standard conditions, a yarn feeding performance attenuation coefficient, representing mechanical performance degradation, is calculated. This allows for targeted control of tension fluctuations during the yarn feeding process. This addresses the interference factor of hairiness coverage, avoiding the limitations of traditional control methods that rely solely on manual experience to adjust tension without identifying the interfering factor. The control results are then monitored within a preset stabilization period after control to determine if the strategy is acceptable. If unacceptable, the strategy is optimized, ensuring the effectiveness of each control measure. This prevents over-control leading to insufficient or excessive tension, which could negatively impact subsequent fabric formation, reducing the probability of defective fabrics, increasing the production efficiency of acceptable fabrics, and lowering production costs for enterprises.

[0029] In this embodiment of the invention, in step S1, the environmental humidity parameter is the relative humidity value of the air in the production environment of the circular knitting machine, which is obtained by a digital humidity sensor installed near the yarn channel of the circular knitting machine; the instantaneous tension value is the real-time measurement value of the dynamic tension borne by the yarn at the outlet of each independently working yarn feeder of the circular knitting machine at the same time point, which is obtained by a high-response tension sensor installed between the outlet of each yarn feeder and the knitting inlet. It is worth noting that the measurement method preferably adopts non-contact measurement to reduce interference to the yarn.

[0030] Specifically, based on the instantaneous tension value, a first tension fluctuation coefficient is calculated to represent the change in yarn tension over a preset time period. Then, based on the comparison between the first tension fluctuation coefficient and the preset tension fluctuation coefficient, it is determined whether the tension of all effective yarn paths at the current moment meets the standard. If the first tension fluctuation coefficient is less than or equal to the preset tension fluctuation coefficient, then it is determined that the tension of the yarn in all effective channels meets the standard at the current moment. If the first tension fluctuation coefficient is greater than the preset tension fluctuation coefficient, then it is determined that the tension of the yarn in all effective channels at the current moment is not up to standard.

[0031] In this embodiment of the invention, the specific calculation process of the first tension fluctuation coefficient is as follows: First, within the preset time period, the instantaneous tension values ​​of all yarns along all effective yarn feeding paths are analyzed as a whole; the overall standard deviation of all instantaneous tension values ​​within the preset time period is calculated to characterize the absolute dispersion of tension; simultaneously, the overall arithmetic mean of all instantaneous tension values ​​is calculated to characterize the central level of tension; finally, the ratio of the calculated overall standard deviation to the overall arithmetic mean is defined as the first tension fluctuation coefficient.

[0032] In this embodiment of the invention, the preset tension fluctuation coefficient is the upper limit of the overall relative fluctuation of yarn tension allowed to ensure the quality of the fabric, based on the target fabric quality grade, yarn type and production experience. The value range is 0.01 to 0.05, preferably 0.025. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0033] Specifically, given that the tension fluctuation of all effective yarn paths at the current moment is substandard, the effective surface adhesion coefficient, which characterizes the mechanical adhesion strength, is calculated based on the environmental humidity parameter. The comparison between the effective surface adhesion coefficient and a preset adhesion coefficient determines whether the mechanical adhesion strength against hairiness on the effective yarn feeding path exceeds the standard. If the effective adhesion coefficient of the surface is less than or equal to the preset adhesion coefficient, it is determined that the mechanical adhesion force of the yarn hair on the effective yarn feeding path does not exceed the standard. If the effective adhesion coefficient of the surface is greater than the preset adhesion coefficient, it is determined that the mechanical adhesion force to the hair on the effective yarn feeding path exceeds the standard.

[0034] In this embodiment of the invention, the effective adhesion coefficient of the surface is calculated according to the following formula: ; In the formula, The effective adhesion coefficient of the surface. It is a natural constant. The adhesion growth coefficient, The environmental humidity parameter is... The humidity is the adhesion initiation threshold.

[0035] The adhesion growth coefficient is used to adjust the rate at which the effective adhesion coefficient of the surface increases with humidity; the larger the adhesion growth coefficient, the more sensitive the yarn's fuzz adhesion force is to humidity; the adhesion growth coefficient is obtained by curve fitting of experimental data on the adhesion force of a specific yarn under different humidity conditions, for example, taking: .

[0036] The adhesion initiation humidity is the critical environmental humidity at which the yarn hairs begin to show significant adhesion. It is usually determined by the hydrophilicity of the yarn raw material. For example, for pure cotton yarn, a typical value is 60%RH.

[0037] In this embodiment of the invention, the preset adhesion coefficient is the upper limit of the risk of hair adhesion caused by environmental humidity that is allowed to maintain long-term stable and controllable tension. The preset risk judgment value is set according to the characteristics of yarn raw materials, historical production quality data and experience, and the value range is 0.4 to 0.6, preferably 0.5. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0038] Specifically, when it is determined that the mechanical adhesion force of yarn hair on the effective yarn feeding path exceeds the standard, image data of the yarn guide eye and tension plate surface on the yarn feeding path are acquired. Based on the image data, a hair coverage index characterizing the yarn hair covering the mechanical surface is calculated. The health status of the mechanical equipment on the yarn feeding path is determined based on the comparison result of the hair coverage index and a preset coverage index. If the hair coverage index is less than or equal to the preset coverage index, then the health status of the mechanical equipment on the yarn feeding path is determined to be up to standard. If the hair coverage index is greater than the preset coverage index, it is determined that the health status of the mechanical equipment on the yarn feeding path is substandard.

[0039] In this embodiment of the invention, the specific calculation process of the feather coverage index is as follows: First, a color image of the yarn guide ceramic eye and tension sheet surface is captured by an industrial camera. The color image is converted into a grayscale image, and then filtered to reduce noise and enhance the signal-to-noise ratio. Next, the observation area to be analyzed is accurately segmented from the image using an edge detection algorithm. Subsequently, since there are differences in texture and grayscale between the clean metal or ceramic surface and the feathers attached to it, the total number of white pixels in the observation area is counted based on color features. The total number of white pixels is the pixel area covered by the feathers. At the same time, the total number of pixels in the observation area is counted. Finally, the ratio of the total number of white pixels to the total number of pixels is calculated to obtain the feather coverage index. Specifically, for the yarn guide ceramic eye, the observation area is the annular area at the edge of the inner wall of the ceramic hole that guides the yarn; for the tension plate, the observation area is the rectangular working surface that directly rubs the yarn.

[0040] In this embodiment of the invention, the preset coverage index is the maximum hair coverage rate allowed on the surface of mechanical parts without causing abnormal fluctuations in yarn tension and a decrease in fabric quality. The preset value is set based on long-term production data, part cleaning standards, and tolerance for tension fluctuations. The value range is 0.05-0.15, preferably 0.1. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0041] Specifically, this invention simultaneously collects ambient humidity and instantaneous yarn tension values ​​along all effective yarn feeding paths, calculates a first tension fluctuation coefficient representing yarn tension changes within a preset time, and a surface effective adhesion coefficient representing mechanical adhesion strength. This allows the invention to determine whether the tension fluctuation of yarn in all effective paths meets standards and whether the mechanical adhesion strength to fuzz on the effective yarn feeding paths exceeds standards. Furthermore, by acquiring and analyzing image data of key components such as the yarn guide eye and tension plate, the invention transforms the traditionally vague cleanliness judgment, reliant on manual visual inspection, into a precisely quantified fuzz coverage index. This enables accurate diagnosis of the root cause of interference, significantly reducing reliance on operator experience and improving the accuracy and timeliness of fault diagnosis.

[0042] Please see Figure 2 As shown, it is a logic block diagram of the present invention for determining the tension fluctuation during the yarn feeding process based on the yarn feeding performance attenuation coefficient.

[0043] Specifically, when the health status of the mechanical equipment on the effective yarn feeding path is determined to be substandard, a yarn feeding performance attenuation coefficient, characterizing the mechanical performance degradation, is calculated based on the deviation of the hairiness coverage index from the preset coverage index under standard conditions. The tension fluctuation during the yarn feeding process is then controlled based on the comparison between the yarn feeding performance attenuation coefficient and the preset performance attenuation coefficient. If the yarn feeding performance attenuation coefficient is less than or equal to the preset performance attenuation coefficient, then the tension fluctuation during the yarn feeding process is controlled by the first control strategy. If the yarn feeding performance attenuation coefficient is greater than the preset performance attenuation coefficient, then the tension fluctuation during the yarn feeding process is controlled by the second control strategy.

[0044] In this embodiment of the invention, the yarn feeding performance attenuation coefficient is calculated according to the following formula: ; In the formula, The yarn feeding performance attenuation coefficient, The currently calculated feather coverage index, This is the preset coverage index.

[0045] In this embodiment of the invention, the preset performance attenuation coefficient ranges from 0.3 to 0.6, preferably 0.5. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0046] In this embodiment of the invention, the first control strategy is a proportional correction mode, that is, the speed reference value of the yarn feeding servo motor is increased based on the yarn feeding performance attenuation coefficient. First, a slight compensation gain coefficient is set, then the first product of the slight compensation gain coefficient and the yarn feeding performance attenuation coefficient is calculated to obtain a slight speed correction factor, and finally the product of the current speed reference value of the yarn feeding servo motor and the slight speed correction factor is calculated as the first upward correction amount of the speed reference value of the yarn feeding servo motor.

[0047] The mild compensation gain coefficient is the relative compensation rate of the yarn feeding motor speed reference value corresponding to the unit performance attenuation coefficient, and its value ranges from 0.02 to 0.08, preferably 0.06.

[0048] In this embodiment of the invention, the second control strategy is an enhanced correction mode, that is, based on the yarn feeding performance attenuation coefficient, the speed reference value of the yarn feeding servo motor is increased, and the current reference value of the electromagnetic damper is decreased. First, an enhanced compensation gain coefficient is set, then the second product of the increased compensation gain coefficient and the yarn feeding performance attenuation coefficient is calculated to obtain an enhanced speed correction factor. Finally, the product of the current speed reference value of the yarn feeding servo motor and the enhanced speed correction factor is used as the second upward correction amount of the speed reference value of the yarn feeding servo motor. At the same time, a current correction gain coefficient is set, then the third product of the current correction gain coefficient and the yarn feeding performance attenuation coefficient is calculated to obtain a current correction factor. Finally, the product of the current reference value of the electromagnetic damper and the current correction factor is used as the downward correction amount of the current reference value of the electromagnetic damper.

[0049] The enhanced compensation gain coefficient is the relative compensation rate of the yarn feeding motor speed reference value corresponding to the unit performance attenuation coefficient, and its value ranges from 0.08 to 0.20, preferably 0.15.

[0050] The current correction gain coefficient is the relative down-adjustment rate of the electromagnetic damper current reference value corresponding to the unit performance attenuation coefficient, and its value ranges from -0.01 to -0.05, preferably -0.03.

[0051] Specifically, this invention converts the interference of the yarn tension caused by the hairline coverage index on the surface of the yarn guide ceramic eye and tension plate into a yarn feeding performance attenuation coefficient. Based on the comparison between the yarn feeding performance attenuation coefficient and a preset performance attenuation coefficient, it executes a first control strategy and a second control strategy. This enables targeted compensation based on the severity of the fault, effectively avoiding over-adjustment: for mild attenuation, the speed reference value of the yarn feeding servo motor is increased proportionally to restore yarn tension stability; for severe attenuation, a larger increase in the speed of the yarn feeding servo motor and a decrease in the current reference value of the electromagnetic damper are implemented to overcome the significantly increased frictional resistance. This ensures that the compensation intensity accurately matches the actual mechanical performance loss under various working conditions corresponding to different levels of interference, thereby effectively suppressing tension fluctuations, ensuring fabric forming quality, and avoiding the energy waste, over-adjustment, and under-adjustment risks associated with a single fixed compensation strategy when facing different levels of interference. This significantly improves the adaptability and economy of tension adjustment.

[0052] Please see Figure 3 As shown, it is a logic block diagram of an embodiment of the present invention for determining whether the currently executed control strategy is qualified based on the effective recovery coefficient of fluctuation.

[0053] Specifically, after the adjustment is completed, a second tension fluctuation coefficient representing the change in yarn tension within a preset stable time is obtained. Based on the second tension fluctuation coefficient and the first tension fluctuation coefficient, an effective recovery coefficient representing the degree of recovery of yarn tension fluctuation after adjustment is calculated. The suitability of the currently executed adjustment strategy is determined based on the comparison result between the effective recovery coefficient and the preset effective recovery coefficient. If the effective recovery coefficient of the fluctuation is less than the preset effective recovery coefficient, then the currently executed control strategy is determined to be unqualified; If the effective recovery coefficient of the fluctuation is greater than or equal to the preset effective recovery coefficient, then the currently executed control strategy is deemed qualified.

[0054] In this embodiment of the invention, the effective recovery coefficient of fluctuation is calculated according to the following formula: ; In the formula, The effective recovery coefficient for fluctuations, This is the second tension fluctuation coefficient. This is the first tension fluctuation coefficient.

[0055] In this embodiment of the invention, the preset effective recovery coefficient is set based on the dynamic response characteristics of the yarn feeding system and the acceptable residual fluctuation level of the production process. The value range is 0.25 to 0.45, preferably 0.3. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0056] Please seeFigure 4 As shown, it is a logic block diagram of an embodiment of the present invention for determining the optimization of the control strategy based on the deviation ratio between the effective recovery coefficient of fluctuation and the preset effective recovery coefficient.

[0057] Specifically, after determining that the currently implemented control strategy is unqualified, the control strategy is optimized based on the comparison between the deviation ratio of the effective recovery coefficient of the fluctuation and the preset effective recovery coefficient and the preset deviation ratio. If the deviation ratio is greater than or equal to a preset deviation ratio, then the control strategy is optimized using the first optimization strategy. If the deviation ratio is less than the preset deviation ratio, then the control strategy is optimized using the second optimization strategy.

[0058] In this embodiment of the invention, the deviation ratio is the ratio of the effective recovery coefficient of the fluctuation to the preset effective recovery coefficient.

[0059] In this embodiment of the invention, the preset deviation ratio ranges from 0.5 to 0.8, preferably 0.7. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0060] In this embodiment of the invention, the first optimization strategy is a parameter fine-tuning optimization mode, that is, optimizing the currently executed control strategy based on a fine-tuning factor, wherein the fine-tuning factor is calculated according to the following formula: ; In the formula, For fine-tuning factors, As the first optimization coefficient, The actual deviation ratio is the value under the condition that the deviation ratio is greater than or equal to the preset deviation ratio.

[0061] Wherein, the first optimization coefficient is a normal number, and its value ranges from 0.1 to 0.3. The preferred value of the present invention is 0.2.

[0062] Specifically, given the fine-tuning factor, the currently executed control strategy is optimized. If the currently executed control strategy is the first control strategy, the mild compensation gain coefficient is updated to the product of the current mild compensation gain coefficient and the fine-tuning factor. If the currently executed control strategy is the second control strategy, the enhanced compensation gain coefficient is updated to the product of the current enhanced compensation gain coefficient and the fine-tuning factor, and the current correction gain coefficient is updated to the product of the current current correction gain coefficient and the fine-tuning factor. Then, the correction amount in the current adjustment strategy is recalculated using the fine-tuned gain coefficient.

[0063] In this embodiment of the invention, the second optimization strategy is a parameter enhancement adjustment optimization mode, that is, optimizing the currently executed control strategy based on a enhancement factor, wherein the enhancement factor is calculated according to the following formula: ; In the formula, As a reinforcing factor, This is the second optimization coefficient. This is the actual deviation ratio under the condition that the deviation ratio is less than the preset deviation ratio.

[0064] The second optimization coefficient is a constant, ranging from 0.3 to 0.6, and is preferably 0.5 in this invention.

[0065] Specifically, given the enhancement factor, the currently executed control strategy is enhanced and optimized. If the currently executed control strategy is the first control strategy, the mild compensation gain coefficient is updated to the product of the current mild compensation gain coefficient and the enhancement factor. If the currently executed control strategy is the second control strategy, the enhanced compensation gain coefficient is updated to the product of the current enhanced compensation gain coefficient and the enhancement factor, and the current correction gain coefficient is updated to the product of the current current correction gain coefficient and the enhancement factor. Then, the correction amount in the current adjustment strategy is recalculated using the enhanced and optimized gain coefficient.

[0066] Specifically, this invention obtains a second tension fluctuation coefficient representing yarn tension changes within a preset stable time period and calculates the effective fluctuation recovery coefficient. The comparison between the effective fluctuation recovery coefficient and the preset effective recovery coefficient determines whether the currently executed control strategy is qualified. This effectively ensures that the effectiveness of each control strategy execution can be verified, and the control effect of the control strategy on the current control system can be detected in a timely manner, achieving instantaneous and quantitative feedback on the control effect. When the strategy is deemed unqualified, the control strategy is optimized based on the comparison between the deviation ratio of the effective fluctuation recovery coefficient and the preset effective recovery coefficient and the preset deviation ratio. For cases close to meeting the target, the current gain coefficient is fine-tuned based on a fine-tuning factor, ensuring that even small optimizations can achieve the target requirements of the control system. For cases with large discrepancies, the current gain coefficient is further optimized based on a strengthening factor to achieve more significant correction. This ensures that the actual optimization intensity is precisely matched to the severity of the problem, significantly improving the long-term robustness of the system in dealing with different disturbances and the consistency of control. It also reduces fabric production quality fluctuations caused by parameter fixation or improper experience-based adjustments in traditional tension regulation, thus improving the quality of the finished fabric.

[0067] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A control method for a circular knitting machine based on multi-parameter automatic adjustment, characterized in that, include: Obtain the current ambient humidity parameters and the instantaneous tension values ​​of the yarns on all yarn feeding paths; Based on the instantaneous tension value, a first tension fluctuation coefficient is determined for the change of yarn tension within a preset time, so as to determine whether the tension fluctuation of yarn in all effective channels at the current moment meets the standard. Under the condition that the tension fluctuation of the yarn in all effective paths at the current moment is not up to standard, the effective adhesion coefficient of the surface, which characterizes the mechanical adhesion strength, is determined based on the environmental humidity parameter, so as to determine whether the mechanical adhesion force of hair on the effective yarn feeding path exceeds the standard. Image data of the yarn guide ceramic eye and tension plate surface on the yarn feeding path are acquired. Based on the image data, the hair coverage index, which characterizes the hair coverage of the mechanical surface, is determined to determine whether the health status of the mechanical equipment on the yarn feeding path meets the standard. Based on the hair coverage index, the yarn feeding performance attenuation coefficient, which characterizes the mechanical performance attenuation, is determined, so as to determine the speed reference value of the yarn feeding servo motor to be increased by a slight speed correction factor or an enhanced speed correction factor, and the current reference value of the electromagnetic damper to be decreased by a current correction factor. Obtain the second tension fluctuation coefficient, which characterizes the change in yarn tension within a preset stable time, and determine the effective recovery coefficient of the fluctuation, which characterizes the degree of recovery of yarn tension fluctuation after regulation, so as to determine whether the current regulation strategy is qualified. The gain coefficient is updated using a fine-tuning factor or an enhancement factor based on the deviation ratio between the effective recovery coefficient of the fluctuation and the preset effective recovery coefficient.

2. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 1, characterized in that, The process of determining whether the tension fluctuation of all effective yarn paths at the current moment meets the standard includes, Obtain the instantaneous tension values ​​of yarns along all valid yarn feeding paths within a preset time period; Calculate the average tension value and the overall standard deviation of all instantaneous tension values ​​within the preset time period; The first tension fluctuation coefficient is obtained by the ratio of the overall standard deviation to the average tension value; Compare the first tension fluctuation coefficient with the preset tension fluctuation coefficient; Based on the fact that the first tension fluctuation coefficient is greater than the preset tension fluctuation coefficient, it is determined that the tension of the yarn in all effective channels is not up to standard at the current moment.

3. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 2, characterized in that, Given that the tension of the yarn in all effective yarn paths is below standard at the current moment, the process of determining whether the mechanical adhesion force of hairiness on the effective yarn feeding path exceeds the standard includes: Set the adhesion growth factor; Obtain the initial adhesion humidity of the current yarn raw material; The effective adhesion coefficient of the surface is determined based on the ambient humidity parameter, the adhesion growth coefficient, and the adhesion initiation humidity. The effective adhesion coefficient of the surface is compared with the preset adhesion coefficient; Based on the fact that the effective adhesion coefficient of the surface is greater than the preset adhesion coefficient, it is determined that the mechanical adhesion force to the hair on the effective yarn feeding path exceeds the standard.

4. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 3, characterized in that, Given that the mechanical adhesion force of yarn hair on the effective yarn feeding path exceeds the standard, the process of determining whether the health status of the mechanical equipment on the yarn feeding path meets the standard includes: Acquire color images of the yarn guide eye and the surface of the tension sheet, and convert the color images into grayscale images; Extract the observation region from the grayscale image; The total number of white pixels within the observation area is calculated based on color features; Count the total number of pixels within the observation area; The feather coverage index is calculated based on the total number of white pixels and the total number of pixels; The feather coverage index is compared with a preset coverage index; Based on the fact that the hair coverage index is greater than the preset coverage index, it is determined that the health status of the mechanical equipment on the yarn feeding path is substandard.

5. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 4, characterized in that, The process of determining whether to increase the speed reference value of the yarn feeding servo motor with a slight speed correction factor or an enhanced speed correction factor, and to decrease the current reference value of the electromagnetic damper with a current correction factor, includes: The yarn feeding performance attenuation coefficient is calculated and determined based on the hair coverage index and the preset coverage index. The yarn feeding performance attenuation coefficient is compared with the preset attenuation coefficient; Based on the fact that the yarn feeding performance attenuation coefficient is less than or equal to the preset performance attenuation coefficient, the product of the mild speed correction factor and the current speed reference value of the yarn feeding servo motor is determined as the first upward adjustment correction amount for the speed reference value of the yarn feeding servo motor.

6. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 5, characterized in that, The process of determining whether to increase the speed reference value of the yarn feeding servo motor with a slight speed correction factor or an enhanced speed correction factor, and to decrease the current reference value of the electromagnetic damper with a current correction factor, also includes... Based on the fact that the yarn feeding performance attenuation coefficient is greater than the preset performance attenuation coefficient, it is determined that the product of the enhanced speed correction factor and the current speed reference value of the yarn feeding servo motor is used as the second upward adjustment correction amount for the speed reference value of the yarn feeding servo motor, and the product of the current correction factor and the current reference value of the electromagnetic damper is used as the downward adjustment correction amount for the current reference value of the electromagnetic damper.

7. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 6, characterized in that, The process of determining whether the currently implemented control strategy is qualified includes, The effective recovery coefficient of the fluctuation is calculated and determined based on the first tension fluctuation coefficient and the second tension fluctuation coefficient; The effective recovery coefficient of the fluctuation is compared with the preset effective recovery coefficient; Based on the fact that the effective recovery coefficient of the fluctuation is less than the preset effective recovery coefficient, it is determined that the currently implemented control strategy is unqualified.

8. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 7, characterized in that, The process of determining the update of the gain coefficient with a fine-tuning factor or an enhancement factor based on the deviation ratio between the effective recovery coefficient of the fluctuation and the preset effective recovery coefficient includes, The deviation ratio is compared with a preset deviation ratio. Based on the deviation ratio being greater than or equal to a preset deviation ratio, the gain coefficient is determined to be updated with a fine-tuning factor. Based on the fact that the deviation ratio is less than the preset deviation ratio, the gain coefficient is determined to be updated with an enhancement factor.

9. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 8, characterized in that, The process of determining the update of the gain coefficient with the fine-tuning factor includes: Based on the current control strategy being the first control strategy, the mild compensation gain coefficient is updated to the product of the current mild compensation gain coefficient and the fine-tuning factor; Based on the current control strategy being the second control strategy, the enhancement compensation gain coefficient is updated to the product of the current enhancement compensation gain coefficient and the fine-tuning factor, and the current correction gain coefficient is updated to the product of the current current correction gain coefficient and the fine-tuning factor, and the correction amount is recalculated.

10. The control method for a circular knitting machine based on multi-parameter automatic adjustment according to claim 9, characterized in that, The process of determining the updated gain coefficients with the enhancement factor includes, If the currently implemented control strategy is the first control strategy, then the mild compensation gain coefficient will be updated to the product of the current mild compensation gain coefficient and the enhancement factor. Based on the current control strategy being the second control strategy, the enhancement compensation gain coefficient is updated to the product of the current enhancement compensation gain coefficient and the enhancement factor, and the current correction gain coefficient is updated to the product of the current current correction gain coefficient and the enhancement factor, and the correction amount is recalculated.

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