Uniform carding method based on dynamic tension adjustment of carding machine

Abstract

The present invention discloses a uniform combing method based on dynamic tension adjustment of a cotton carding machine, which relates to the technical field of dynamic tension adjustment and comprises the following steps: obtaining historical monitoring data of the tension of the cotton carding machine and setting an interval to evenly divide the tension gears based on the maximum and minimum values ​​of the historical tension data of the cotton carding machine; obtaining fiber influence data and equipment influence data of the tension gears; inputting the fiber influence data and equipment influence data of the tension gears into a preset uniform evaluation model of the cotton carding machine to obtain a uniform evaluation coefficient of the cotton carding machine; sorting the uniform evaluation coefficients of the cotton carding machine to obtain a uniform evaluation sorting table, and performing dynamic tension adjustment based on the uniform evaluation sorting table, so as to solve the problem that the traditional method lacks refined tension adjustment means and the tension of the cotton carding machine often shows large fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic tension regulation, and more particularly to a uniform carding method based on dynamic tension regulation of a carding machine. Background Art

[0002] The uniform combing method based on dynamic tension adjustment of the carding machine is an important research direction in the textile industry, which aims to improve the uniformity of fibers, reduce the impurity content and improve production efficiency by accurately controlling the tension of the carding machine. In recent years, with the continuous development of textile technology, more and more research has begun to focus on how to optimize the tension adjustment in the carding process through intelligent and automated technology to improve the quality and processing efficiency of fibers.

[0003] Since traditional methods lack sophisticated tension adjustment methods, the tension of the carding machine often fluctuates greatly. This fluctuation will lead to uneven stretching of the fibers, affect the carding effect, and may even damage the fibers, reducing the quality of the textiles.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a uniform combing method based on dynamic tension adjustment of a cotton carding machine. The uniform combing method based on dynamic tension adjustment of a cotton carding machine solves the problem that the traditional method lacks refined tension adjustment means and the tension of the cotton carding machine often shows large fluctuations.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The uniform carding method based on dynamic tension adjustment of the carding machine comprises the following steps:

[0008] Obtain historical monitoring data of the tension of a cotton carding machine and set an interval based on the maximum and minimum values ​​of the historical tension data of the cotton carding machine to evenly divide the interval into several tension gears; obtain fiber influence data and equipment influence data of the several tension gears, the fiber influence data include a fiber uniformity influence coefficient and an impurity removal efficiency influence coefficient, and the equipment influence data include an equipment stress and deformation influence coefficient; input the fiber influence data and equipment influence data of the several tension gears into a preset cotton carding machine uniformity evaluation model to obtain a cotton carding machine uniformity evaluation coefficient; sort the cotton carding machine uniformity evaluation coefficients to obtain a uniformity evaluation sorting table, and perform dynamic tension adjustment based on the uniformity evaluation sorting table.

[0009] In a preferred embodiment, the interval is set based on the maximum and minimum values ​​of the historical tension data of the cotton carding machine to evenly divide into several tension gears, specifically: the maximum and minimum values ​​of the historical tension data of the cotton carding machine are obtained, the maximum value is the maximum tension value, and the minimum value is the minimum tension value; based on the maximum and minimum values ​​of the historical tension data of the cotton carding machine, the interval is divided into several tension gears according to the preset number of tension gears that need to be divided, and the preset number of tension gears that need to be divided can be set according to the process requirements and the historical fluctuation range of the tension.

[0010] In a preferred embodiment, the specific method for obtaining the fiber uniformity influence coefficient is as follows: select several rectangular areas according to the preset length and width of the fiber web at the output end of the carding machine, and use the several rectangular areas as several sampling areas; count the number of fibers in the several sampling areas, and calculate the average number of fibers in the sampling areas and calculate the between-group variance, within-group variance, skewness and kurtosis of the fiber numbers in the several sampling areas; define a semivariogram function, and calculate the semivariogram values ​​between the several sampling areas based on the semivariogram function calculation formula; input the between-group variance, within-group variance, skewness, kurtosis and the semivariogram values ​​between the several sampling areas into the fiber uniformity influence coefficient calculation formula to obtain the fiber uniformity influence coefficient.

[0011] In a preferred embodiment, the specific method for obtaining the impurity removal efficiency influence coefficient is as follows: use a high-resolution camera to capture the combed fiber image and perform preprocessing to obtain a preprocessed fiber image; input the preprocessed fiber image into a preset target detection model to identify impurities; place the combed fibers and impurities in a friction meter, and calculate the friction coefficients between several combed fibers and impurities by randomly applying several pressures of different sizes; based on the friction coefficients between the several combed fibers and impurities, calculate the average friction coefficient; input the average friction coefficient into a preset impurity removal efficiency influence coefficient calculation formula to obtain the impurity removal efficiency influence coefficient.

[0012] In a preferred embodiment, the specific method for obtaining the stress-deformation influence coefficient of the equipment is as follows: measuring the dimensions of each key component of the carding machine, the key components include but are not limited to a gear box, a transmission shaft, a comber, etc.; the dimensions include the diameter of the shaft, the number and size of teeth of the gear, and the geometric shape of the support, etc.; using CAD software to perform three-dimensional modeling on each key component of the carding machine, and importing it into finite element analysis software to obtain a carding machine geometric model; dividing the carding machine geometric model into a plurality of carding machine geometric units according to a preset division ratio, and the carding machine geometric units have specific stiffness, mass and other physical properties; obtaining the dynamic load size, stiffness value and load frequency applied to each geometric unit of the carding machine to calculate the local deformation of each geometric unit of the carding machine; calculating the average value and standard deviation of the local deformation of the geometric unit of the carding machine, and calculating the overall deformation coefficient of the carding machine based on the average value and standard deviation of the local deformation of the geometric unit; inputting the overall deformation coefficient of the carding machine into the equipment stress-deformation influence coefficient calculation formula to obtain the equipment stress-deformation influence coefficient.

[0013] In a preferred embodiment, the uniformity assessment coefficients of the cotton carding machine are sorted to obtain a uniformity assessment sorting table, and dynamic tension adjustment is performed based on the uniformity assessment sorting table, specifically: the uniformity assessment coefficients of the cotton carding machine for each tension gear are sorted to obtain a uniformity assessment sorting table; based on the uniformity assessment sorting table, when the uniformity assessment coefficient of the cotton carding machine is lower than a preset threshold, consider selecting a tension gear with a higher priority; when the uniformity assessment coefficient of the cotton carding machine is higher than the preset threshold, keep the tension gear unchanged.

[0014] The technical effects and advantages of the uniform carding method based on dynamic tension adjustment of the carding machine of the present invention are as follows:

[0015] 1. The present invention performs intelligent analysis based on the historical tension monitoring data of the carding machine, divides multiple tension gears according to the maximum and minimum values ​​of the data, and combines multi-dimensional data such as fiber uniformity, impurity removal efficiency, and equipment stress deformation to accurately adjust the tension in the carding process. This method can effectively optimize the fiber combing quality, improve the uniformity of the fiber web, and at the same time improve the impurity removal efficiency and reduce the loss and energy consumption of the equipment. By inputting the influence data of each tension gear into a preset uniform evaluation model, the system can intelligently evaluate and dynamically adjust the tension, thereby realizing an efficient and stable production process. This method not only improves the quality and consistency of fiber combing, but also reduces equipment wear and maintenance costs, reduces energy consumption, and reduces manual intervention through automatic adjustment, thereby improving the automation and intelligence level of the production process, and has significant economic benefits and technical advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1It is a structural schematic diagram of the uniform combing method based on dynamic tension adjustment of the carding machine of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1, Figure 1 The present invention provides a uniform combing method based on dynamic tension adjustment of a carding machine, comprising the following steps:

[0019] S1, obtaining the historical monitoring data of the tension of the carding machine and evenly dividing a number of tension gears into intervals based on the maximum and minimum values ​​of the historical tension data of the carding machine;

[0020] In this example, the historical monitoring data of the carding machine tension are as follows:

[0021] The carding machine tension history monitoring data includes obtaining the carding machine tension history data, carding machine operation status data, and environmental data.

[0022] It should be noted that the historical monitoring data of the carding machine tension mainly includes three parts: historical tension data, carding machine operating status data and environmental data. These data comprehensively reflect the tension changes in the carding process, the operation status of the equipment and the environmental conditions in which it is located. The historical tension data records the trend of tension changes over time, providing a basis for analyzing the uniformity of force on the fiber during the carding process; the operating status data can monitor whether the equipment is working properly and help identify faults; environmental data (such as temperature and humidity) reveals the impact of external conditions on tension. Combined with these data, more precise tension control can be achieved through dynamic tension adjustment, improving fiber carding uniformity, reducing the risk of yarn breakage or excessive tension, thereby improving product quality and production efficiency, and adapting to complex process requirements.

[0023] In this example, the interval is evenly divided into several tension gears based on the maximum and minimum values ​​of the historical data of the carding machine tension, specifically:

[0024] Obtain the maximum and minimum values ​​of the historical data of the carding machine tension, wherein the maximum value is the maximum tension value and the minimum value is the minimum tension value;

[0025] Based on the maximum and minimum values ​​of the historical tension data of the carding machine, the tension gears are divided into several tension gears according to the preset number of tension gears required. The preset number of tension gears required to be divided can be set according to process requirements and the historical fluctuation range of the tension.

[0026] It should be noted that the division of tension gears based on the maximum and minimum values ​​of the historical data of the carding machine tension is a scientific method for optimizing the dynamic adjustment of the carding process. First, by analyzing the historical data of the tension during the operation of the carding machine, its maximum value (maximum tension) and minimum value (minimum tension) are determined. These two values ​​reflect the boundary range of tension fluctuations. On this basis, according to the process requirements and the tension fluctuation range, the number of tension gears to be divided is preset, and the tension interval between the maximum and minimum values ​​is evenly divided into several gears. Each gear corresponds to a fixed tension range, such as 5 gears or 10 gears, and the specific number is determined by production requirements. The advantages of this division method are: on the one hand, through the precise definition of the gear interval, it can clearly reflect the regularity of tension distribution, which is convenient for analyzing tension uniformity and abnormal points; on the other hand, it provides a clear basis for dynamic tension adjustment, helps optimize tension control parameters, thereby reducing the risk of uneven fiber force and improving fiber combing quality. At the same time, this method can also improve the stability and efficiency of production and ensure the optimal operation of the equipment under different working conditions.

[0027] S2, obtaining fiber influence data and equipment influence data of the plurality of tension gears, wherein the fiber influence data includes a fiber uniformity influence coefficient and an impurity removal efficiency influence coefficient, and the equipment influence data includes an equipment stress deformation influence coefficient;

[0028] In this example, the fiber impact data includes the fiber uniformity impact coefficient and the impurity removal efficiency impact coefficient, and the equipment impact data includes the equipment stress deformation impact coefficient, specifically:

[0029] The fiber uniformity influence coefficient is a key indicator to measure the uniformity of fiber quality after combing. It indicates the consistency of fiber length, quality or distribution. It is usually evaluated by statistical data of fiber length or quality. The closer the fiber uniformity influence coefficient is to 1, the better the fiber uniformity.

[0030] Furthermore, the advantages of fiber uniformity influence coefficient in dynamic tension adjustment are as follows:

[0031] Improve combing quality: The fiber uniformity influence coefficient directly reflects the quality consistency of the fiber during the combing process. Dynamic tension adjustment can effectively disperse the fiber and avoid the formation of fiber aggregation areas during the combing process. By real-time monitoring and adjusting the tension, the occurrence of broken fibers and residual impurities can be reduced, and the cleanliness and uniformity of the fiber can be improved.

[0032] Improve equipment operation stability: The linkage between dynamic tension adjustment and fiber uniformity index helps to reduce stress concentration of equipment. Uniform tension reduces local pressure on the carding needle plate and carding needles, extending the service life of the equipment; tension control reduces the risk of jamming or overload caused by uneven fiber distribution;

[0033] Improve economic benefits: By optimizing fiber uniformity, dynamic tension adjustment can improve production efficiency and reduce costs. Improve fiber utilization and reduce waste fiber or defective products; evenly combed fiber products are of higher quality and can meet the needs of high-end textiles.

[0034] Furthermore, the specific method of obtaining the fiber uniformity influence coefficient is as follows:

[0035] Selecting a plurality of rectangular areas of the fiber web at the output end of the carding machine according to a preset length and width, and using the plurality of rectangular areas as a plurality of sampling areas;

[0036] Counting the number of fibers in the plurality of sampling areas, finding the average number of fibers in the sampling areas, and calculating the inter-group variance, intra-group variance, skewness, and kurtosis of the number of fibers in the plurality of sampling areas;

[0037] Define the semivariogram function, and calculate the semivariogram values ​​between several sampling areas based on the semivariogram function calculation formula;

[0038] The inter-group variance, intra-group variance, skewness, kurtosis and semi-variance values ​​between several sampling areas are input into a formula for calculating the fiber uniformity influence coefficient to obtain the fiber uniformity influence coefficient.

[0039] Among them, the specific calculation formula of the fiber uniformity influence coefficient is as follows:

[0040] The semi-variance calculation formula is as follows:

[0041]

[0042] in, is the semivariate value, E is the expected value, X i is the number of fibers in the i-th sampling area, X i+h is the number of fibers in the (i+h)th sampling area, and h is the distance from sampling area i to sampling area (i+h).

[0043] The calculation formulas for the skewness and kurtosis are as follows:

[0044]

[0045] in, For biased attitude, is the kurtosis, Xi is the number of fibers in the i-th sampling area, is the average fiber number in all sampling areas, σ X is the standard deviation of all sampling areas, and N is the number of sampling areas.

[0046] The calculation formula of the fiber uniformity influence coefficient is as follows:

[0047]

[0048] Among them, C f is the fiber uniformity influence coefficient, is the between-group variance, is the within-group variance, For biased attitude, is the kurtosis, is the semivariance value, λ 1 and λ 2 is the weight coefficient.

[0049] Furthermore, the impurity removal efficiency influence coefficient is an indicator used to evaluate the contribution of various influencing factors to the removal efficiency of fiber processing equipment (such as carding machines) during the impurity removal process. It measures the comprehensive influence of equipment operating conditions, fiber characteristics and process parameters on the impurity removal efficiency. By calculating this coefficient, the equipment settings and processing technology can be optimized to improve the fiber purity and processing efficiency.

[0050] It should be noted that the advantages of the impurity removal efficiency influence coefficient in dynamic tension adjustment are as follows:

[0051] Improve the accuracy of impurity removal: The impurity removal efficiency coefficient quantifies the contribution of tension to the impurity removal process. In dynamic tension adjustment, by real-time monitoring of this coefficient, the best balance between fiber stretching and impurity separation can be found to avoid incomplete impurity removal due to insufficient or excessive tension, thereby improving fiber purity;

[0052] Dynamic adjustment has greater adaptability: The types and contents of impurities in fiber raw materials may vary from batch to batch, and traditional fixed tension settings are difficult to adapt to changes. By introducing the impurity removal efficiency influence coefficient, dynamic tension adjustment can automatically adjust the tension to adapt to different fiber raw material characteristics based on the real-time monitoring of impurity removal efficiency. This adaptability ensures high efficiency of impurity removal;

[0053] Improve overall process efficiency: Dynamic tension adjustment that introduces the impurity removal efficiency coefficient can quickly reach the optimal tension parameters, reducing adjustment time and the need for multiple tests. This not only improves processing efficiency, but also significantly saves production time and resource costs.

[0054] Furthermore, the specific method for obtaining the impurity removal efficiency influence coefficient is as follows:

[0055] Using a high-resolution camera to capture the combed fiber image and pre-process it to obtain a pre-processed fiber image;

[0056] Inputting the preprocessed fiber image into a preset target detection model to perform impurity identification;

[0057] The combed fibers and impurities are placed in a friction meter, and the friction coefficients between the combed fibers and impurities are calculated by randomly applying a number of pressures of different magnitudes;

[0058] Calculating an average friction coefficient based on the friction coefficients between the plurality of combed fibers and impurities;

[0059] The average friction coefficient is input into a preset impurity removal efficiency influence coefficient calculation formula to obtain the impurity removal efficiency influence coefficient.

[0060] Among them, the calculation formula of the impurity removal efficiency influence coefficient is as follows:

[0061] The friction coefficient calculation formula is as follows:

[0062]

[0063] Among them, μ i is the friction coefficient after the i-th pressure is applied, F friction (i) is the friction force between the fiber and impurities after the i-th pressure is applied, F normal (i) is the pressure magnitude of the i-th application of pressure.

[0064] The impurity removal efficiency influence coefficient calculation formula is as follows:

[0065]

[0066] Among them, C m is the impurity removal efficiency influence coefficient, is a constant coefficient, n is the total number of times different pressures are applied, μ i is the friction coefficient after the i-th pressure is applied.

[0067] The equipment stress deformation influence coefficient is specifically:

[0068] The equipment stress-deformation influence coefficient is used to quantify the comprehensive influence of deformation caused by force (tension, pressure or other mechanical force) on equipment performance and process effect during the operation of textile equipment such as carding machines. This coefficient reflects the contribution of structural strength, material properties, operating parameters and working conditions of textile equipment such as carding machines to deformation behavior. For textile equipment such as carding machines, the equipment stress-deformation influence coefficient can be used to evaluate the influence of tension on the deformation of equipment parts and the degree of interference with fiber processing quality.

[0069] It should be noted that the advantages of equipment stress deformation influence coefficient in dynamic tension adjustment are as follows:

[0070] Improve the accuracy of tension adjustment: The equipment stress deformation influence coefficient can quantify the degree of deformation of the equipment under different tensions, providing a scientific basis for dynamic tension adjustment. By real-time monitoring of this coefficient, the system can accurately adjust the tension parameters according to the actual stress and deformation state of the equipment, avoid fiber processing errors caused by equipment deformation, and improve the overall adjustment accuracy;

[0071] Reduce fiber processing errors: Stress deformation during operation may cause fiber path deviation and processing errors. By introducing the equipment stress deformation influence coefficient, dynamic tension adjustment can respond to the deformation state of the equipment in real time, correct the tension setting in time, and reduce the negative impact of deformation on fiber uniformity and processing quality. This is of great significance to ensure uniform combing of fibers;

[0072] Reduce energy consumption and resource waste: Excessive tension may increase the power consumption of the equipment, while unreasonable adjustment may lead to fiber loss. By introducing the equipment stress deformation influence coefficient, tension resources can be allocated more efficiently, reducing energy consumption and material waste caused by equipment deformation, thereby achieving the goal of green production.

[0073] The application of equipment stress-deformation influence coefficient in dynamic tension adjustment reflects the deep integration of equipment performance and process requirements, and provides strong support for improving production quality and equipment reliability.

[0074] Furthermore, the specific method for obtaining the equipment stress deformation influence coefficient is as follows:

[0075] Measure the dimensions of key components of the carding machine, including but not limited to gearbox, transmission shaft, carder, etc.; the dimensions include shaft diameter, number and size of gear teeth, and support geometry, etc.;

[0076] Using CAD software to perform three-dimensional modeling on each key component of the carding machine, and importing it into finite element analysis software to obtain a geometric model of the carding machine;

[0077] The carding machine geometric model is divided into a plurality of carding machine geometric units according to a preset division ratio, and each of the carding machine geometric units has specific stiffness, mass and other physical properties;

[0078] The dynamic load magnitude, stiffness value and load frequency applied to each geometric unit of the carding machine are obtained to calculate the local deformation of each geometric unit of the carding machine;

[0079] Calculating the average value and standard deviation of the local deformation of the geometric unit of the carding machine, and calculating the overall deformation coefficient of the carding machine based on the average value and standard deviation of the local deformation of the geometric unit;

[0080] The overall deformation coefficient of the carding machine is input into the equipment stress deformation influence coefficient calculation formula to obtain the equipment stress deformation influence coefficient.

[0081] Among them, the specific formula of the equipment stress deformation influence coefficient is:

[0082] The calculation formula of the overall deformation coefficient of the carding machine is as follows:

[0083]

[0084] Among them, ρ is the overall deformation coefficient of the carding machine, β is the constant coefficient, N is the number of geometric units, F i is the dynamic load applied to the i-th geometric unit, k i is the stiffness value of the i-th geometric unit.

[0085] The calculation formula of the equipment stress deformation influence coefficient is as follows:

[0086]

[0087] Among them, C S is the equipment stress deformation influence coefficient, γ is the weight coefficient, and ρ is the overall deformation coefficient of the carding machine.

[0088] S3, inputting the fiber influence data and equipment influence data of the plurality of tension gears into a preset carding machine uniformity evaluation model to obtain a carding machine uniformity evaluation coefficient;

[0089] In this example, the calculation formula of the carding machine uniformity evaluation coefficient is as follows:

[0090]

[0091] Where k is the uniformity evaluation coefficient of the carding machine, C f , C S and C m are the fiber uniformity influence coefficient, equipment stress deformation influence coefficient and impurity removal efficiency influence coefficient, η1 is the preset proportional coefficient of the fiber uniformity influence coefficient, η 2 is the preset proportional coefficient of the impurity removal efficiency influence coefficient, η 3 It is the preset proportional coefficient of the equipment stress deformation influence coefficient.

[0092] It should be noted that the fiber influence data and equipment influence data of several tension gears are input into the preset carding machine uniformity evaluation model to obtain the carding machine uniformity evaluation coefficient, which has several significant advantages. First of all, this method can achieve more accurate tension adjustment. By real-time monitoring and inputting fiber influence data and equipment influence data under different tension gears, the model can comprehensively evaluate the performance of the carding machine in actual work. This evaluation method not only focuses on the uniformity of the fiber, but also takes into account factors such as equipment stress and deformation, and can comprehensively reflect the carding effect under different working conditions.

[0093] Secondly, dynamic adjustment based on uniformity assessment coefficient can help adjust the tension setting of the carding machine, thereby effectively avoiding fiber unevenness caused by uneven tension or equipment stress problems. By quantitatively analyzing the impact of equipment and fiber, the model can predict and optimize the working state of the carding machine, ensuring that the final output of textiles has higher quality and consistency.

[0094] In addition, the uniform evaluation coefficient model is highly adaptable and scalable, and can be adjusted according to different equipment models, fiber types, and working environment parameters to provide personalized optimization solutions. This flexibility allows carding machine users to make fine adjustments based on actual needs, improve production efficiency, and reduce energy consumption and material waste.

[0095] Finally, relying on this evaluation model, carding machine operators can obtain more intuitive, data-driven operation suggestions, reduce errors caused by human intervention, and improve the level of automation. This data-based optimization decision can greatly improve the stability and repeatability of the production process, and enhance the overall efficiency and product quality of the production line.

[0096] S4, sorting the uniformity evaluation coefficients of the carding machine to obtain a uniformity evaluation sorting table, and performing dynamic tension adjustment based on the uniformity evaluation sorting table.

[0097] Sorting the uniformity evaluation coefficients of the carding machines at each tension gear to obtain a uniformity evaluation ranking table;

[0098] Based on the uniformity evaluation ranking table, when the uniformity evaluation coefficient of the carding machine is lower than a preset threshold, consider selecting a tension gear with a higher priority;

[0099] When the carding machine uniformity evaluation coefficient is higher than the preset threshold, the tension gear is kept unchanged.

[0100] It should be noted that the sorting and dynamic tension adjustment method based on the uniformity evaluation coefficient of the carding machine has significant advantages and can improve the accuracy and efficiency of the carding process. First, using the uniformity evaluation coefficient sorting can fully quantify the impact of each tension gear on fiber uniformity, impurity removal efficiency and equipment stability. This sorting method makes the adjustment of the carding machine more scientific, no longer relying on manual experience, but through a data-driven approach to ensure that the selection of each gear is the optimal decision based on actual performance.

[0101] Secondly, when the carding machine uniformity assessment coefficient is lower than the preset threshold, the system will automatically select a higher priority tension gear, which means that when encountering fiber unevenness or other problems, the system can respond quickly and automatically adjust the tension to avoid affecting product quality. Compared with traditional manual adjustment or fixed settings, dynamic adjustment can respond to unforeseen changes in the production process more quickly, greatly improving the stability of the production line and the consistency of the product.

[0102] When the uniformity evaluation coefficient is higher than the preset threshold, the system keeps the current tension gear unchanged, which helps to reduce unnecessary adjustments and avoid equipment wear or energy waste caused by frequent tension changes. This mechanism enables the equipment to maintain efficient operation under ideal working conditions, ensuring the continuity and economy of the production process.

[0103] In general, the sorting and dynamic tension adjustment method based on uniformity assessment coefficient not only improves fiber uniformity, but also improves production efficiency through precise adjustment, reduces energy consumption and equipment loss, enhances the intelligence level of the entire production system, and reduces the risk and cost of manual intervention while improving the quality of textiles.

[0104] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0105] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0106] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0107] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A uniform carding method based on dynamic tension adjustment of a carding machine, characterized in that: The following steps are involved: Obtain the historical monitoring data of the tension of the carding machine and evenly divide the interval into several tension gears based on the maximum and minimum values ​​of the historical tension data of the carding machine; Acquiring fiber influence data and equipment influence data of the plurality of tension gears, wherein the fiber influence data includes a fiber uniformity influence coefficient and an impurity removal efficiency influence coefficient, and the equipment influence data includes an equipment stress deformation influence coefficient; Inputting the fiber influence data and equipment influence data of the plurality of tension gears into a preset carding machine uniformity evaluation model to obtain a carding machine uniformity evaluation coefficient; The uniformity evaluation coefficients of the carding machine are sorted to obtain a uniformity evaluation sorting table, and dynamic tension adjustment is performed based on the uniformity evaluation sorting table.

2. The uniform combing method based on dynamic tension adjustment of the carding machine according to claim 1, characterized in that: The maximum and minimum value setting intervals based on the historical data of the carding machine tension are evenly divided into several tension gears, specifically: Obtain the maximum and minimum values ​​of the historical data of the carding machine tension, wherein the maximum value is the maximum tension value and the minimum value is the minimum tension value; Based on the maximum and minimum values ​​of the historical tension data of the carding machine, the tension gears are divided into several tension gears according to the preset number of tension gears required. The preset number of tension gears required to be divided can be set according to process requirements and the historical fluctuation range of the tension.

3. The uniform combing method based on dynamic tension adjustment of the carding machine according to claim 2, characterized in that: The specific method for obtaining the fiber uniformity influence coefficient is as follows: Selecting a plurality of rectangular areas of the fiber web at the output end of the carding machine according to a preset length and width, and using the plurality of rectangular areas as a plurality of sampling areas; Counting the number of fibers in the plurality of sampling areas, finding the average number of fibers in the sampling areas, and calculating the inter-group variance, intra-group variance, skewness, and kurtosis of the number of fibers in the plurality of sampling areas; Define the semivariogram function, and calculate the semivariogram values ​​between several sampling areas based on the semivariogram function calculation formula; The inter-group variance, intra-group variance, skewness, kurtosis and semi-variance values ​​between several sampling areas are input into a calculation formula for the fiber uniformity influence coefficient to obtain the fiber uniformity influence coefficient.

4. The uniform combing method based on dynamic tension adjustment of the carding machine according to claim 3, characterized in that: The specific method for obtaining the impurity removal efficiency influence coefficient is as follows: Using a high-resolution camera to capture the combed fiber image and pre-process it to obtain a pre-processed fiber image; Inputting the preprocessed fiber image into a preset target detection model to perform impurity identification; The combed fibers and impurities are placed in a friction meter, and the friction coefficients between the combed fibers and impurities are calculated by randomly applying a number of pressures of different magnitudes; Calculating an average friction coefficient based on the friction coefficients between the plurality of combed fibers and impurities; The average friction coefficient is input into a preset impurity removal efficiency influence coefficient calculation formula to obtain the impurity removal efficiency influence coefficient.

5. The uniform carding method based on dynamic tension adjustment of the carding machine according to claim 4, characterized in that: The specific method for obtaining the equipment stress deformation influence coefficient is as follows: Measure the dimensions of key components of the carding machine, including but not limited to gearbox, transmission shaft, carder, etc.; the dimensions include shaft diameter, number and size of gear teeth, and support geometry, etc.; Using CAD software to perform three-dimensional modeling on each key component of the carding machine, and importing it into finite element analysis software to obtain a geometric model of the carding machine; The carding machine geometric model is divided into a plurality of carding machine geometric units according to a preset division ratio, and each of the carding machine geometric units has specific stiffness, mass and other physical properties; The dynamic load magnitude, stiffness value and load frequency applied to each geometric unit of the carding machine are obtained to calculate the local deformation of each geometric unit of the carding machine; Calculating the average value and standard deviation of the local deformation of the geometric unit of the carding machine, and calculating the overall deformation coefficient of the carding machine based on the average value and standard deviation of the local deformation of the geometric unit; The overall deformation coefficient of the carding machine is input into the equipment stress deformation influence coefficient calculation formula to obtain the equipment stress deformation influence coefficient.

6. The uniform carding method based on dynamic tension adjustment of the carding machine according to claim 5, characterized in that: The uniformity evaluation coefficients of the carding machine are sorted to obtain a uniformity evaluation sorting table, and dynamic tension adjustment is performed based on the uniformity evaluation sorting table, specifically: Sorting the uniformity evaluation coefficients of the carding machines at each tension gear to obtain a uniformity evaluation ranking table; Based on the uniformity evaluation ranking table, when the uniformity evaluation coefficient of the carding machine is lower than a preset threshold, consider selecting a tension gear with a higher priority; When the carding machine uniformity evaluation coefficient is higher than the preset threshold, the tension gear is kept unchanged.

7. The uniform carding method based on dynamic tension adjustment of the carding machine according to claim 6, characterized in that: The calculation formula of the uniformity evaluation coefficient of the carding machine is as follows: Where k is the uniformity evaluation coefficient of the carding machine, C f , C S and C m They are respectively the fiber uniformity influence coefficient, the equipment stress deformation influence coefficient and the impurity removal efficiency influence coefficient, η1 is the preset proportional coefficient of the fiber uniformity influence coefficient, η2 is the preset proportional coefficient of the impurity removal efficiency influence coefficient, and η3 is the preset proportional coefficient of the equipment stress deformation influence coefficient.

8. The uniform carding method based on dynamic tension adjustment of the carding machine according to claim 7, characterized in that: The specific calculation formula of the fiber uniformity influence coefficient is as follows: The semi-variance calculation formula is as follows: The calculation formulas for the skewness and kurtosis are as follows: The calculation formula of the fiber uniformity influence coefficient is as follows: Among them, C f is the fiber uniformity influence coefficient, is the between-group variance, is the within-group variance, For biased attitude, is the kurtosis, is the semivariance value, λ1 and λ2 are weight coefficients, E is the expected value, X i is the number of fibers in the i-th sampling area, X i+h is the number of fibers in the (i+h)th sampling area, h is the distance between sampling area i and sampling area (i+h), is the average fiber number in all sampling areas, σ X is the standard deviation of all sampling areas, and N is the number of sampling areas.

9. The uniform carding method based on dynamic tension adjustment of the carding machine according to claim 8, characterized in that: The specific calculation formula of the impurity removal efficiency influence coefficient is as follows: The friction coefficient calculation formula is as follows: The impurity removal efficiency influence coefficient calculation formula is as follows: Among them, C m is the impurity removal efficiency influence coefficient, is a constant coefficient, n is the total number of times different pressures are applied, μ i is the friction coefficient after the i-th pressure is applied, F friction (i) is the friction force between the fiber and impurities after the i-th pressure is applied, F normal (i) is the pressure magnitude of the i-th application of pressure.

10. The uniform carding method based on dynamic tension adjustment of a carding machine according to claim 9, characterized in that: The specific formula of the equipment stress deformation influence coefficient is: The calculation formula of the overall deformation coefficient of the carding machine is as follows: The calculation formula of the equipment stress deformation influence coefficient is as follows: Among them, C S is the equipment stress deformation influence coefficient, γ is the weight coefficient, ρ is the overall deformation coefficient of the carding machine, β is the constant coefficient, N is the number of geometric units, F i is the dynamic load applied to the i-th geometric unit, k i is the stiffness value of the i-th geometric unit.

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