Cloth cover tension control method and system for warp knitting fabric

Abstract

The invention discloses a cloth cover tension control method and system for a warp knitting fabric. The method comprises the following steps: creating a first relational expression of the tension of a target warp knitting fabric and a mesh entropy field; obtaining a real-time cloth cover image of the target warp knitting fabric, and according to the real-time cloth cover image, creating a spatial entropy used for representing the mesh distribution condition and a structural entropy used for representing the mesh form complexity; according to the spatial entropy and the structural entropy, calculating to obtain a real-time mesh entropy field, and substituting the real-time mesh entropy field into the first relational expression to obtain real-time expected tension of the target warp knitting fabric; and adjusting working parameters of the editing machine of the target warp knitting fabric according to the real-time expected tension. According to the method, the spatial distribution of the cloth surface meshes of the warp knitting fabric and the non-uniform degree of the mesh structure are reflected through the real-time mesh entropy field, and the working parameters of the editing machine of the target warp knitting fabric are adjusted according to the real-time expected tension, so that the cloth surface tension is finely adjusted, and a better knitting effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for controlling the surface tension of warp-knitted fabrics. Background Technology

[0002] In the process of weaving warp-knitted fabrics, the relationship between the warp feed and fabric tension varies for each warp knitting machine. Therefore, even when the warp feed is equal on all machines, the fabric texture still differs. To solve this problem, on-site personnel typically observe the size of the fabric mesh with the naked eye and then control the warp knitting machine based on the observation. However, warp-knitted fabrics often have many mesh openings of varying sizes, and their distribution on the fabric surface is uneven. Observing the size of the fabric mesh with the naked eye cannot achieve high precision and accuracy.

[0003] Furthermore, the most common existing method for counting mesh openings is the averaging method. This involves counting the number and size of each mesh opening in a photograph to obtain an average mesh opening value, and then establishing a functional relationship between the average mesh opening value and the fabric tension. However, the average mesh opening value cannot reflect the distribution of mesh openings on the fabric surface, nor can it reflect the different functional areas on the shoe upper. Due to usage requirements, the tension requirements for each part of the shoe upper are actually different; some areas need to be looser, and some areas need to be tighter. The mesh morphology has its own complexity and irregularity, especially when the aperture varies greatly and the fractal dimension is high, which can easily lead to stress concentration and yarn breakage. The "average aperture" method cannot perceive and prevent such localized unevenness related to the distribution pattern, which may cause equipment downtime and reduce production efficiency and utilization rate. Therefore, the "average aperture" method cannot reflect the uneven distribution of fabric tension, or rather, it cannot reflect the actual tension, and it cannot provide much help in early warning of failures in actual production. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for controlling the surface tension of warp-knitted fabrics, accurately analyze the uniformity of the mesh of the warp-knitted fabric, achieve fine adjustment of the surface tension, and achieve better weaving effect.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for controlling the surface tension of warp-knitted fabrics includes the following steps: Create the first expression relating the tension of the target warp-knitted fabric to the mesh entropy field; Acquire a real-time fabric image of the target warp-knitted fabric, and based on the real-time fabric image, create a spatial entropy to represent the mesh distribution and a structural entropy to represent the complexity of the mesh morphology. Based on the spatial entropy and the structural entropy, the real-time mesh entropy field is calculated. Substituting the real-time mesh entropy field into the first relational expression, the real-time expected tension of the target warp-knitted fabric is obtained. The operating parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension. To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A fabric tension control system for warp-knitted fabrics includes a main control device and a camera device, wherein the main control device is communicatively connected to the camera device; The main control device is used to connect to the controller of the warp knitting machine and execute the above-mentioned method for controlling the fabric tension of warp-knitted fabrics.

[0006] The beneficial effects of this invention are as follows: It provides a method and system for controlling the surface tension of warp-knitted fabrics. The method involves acquiring a real-time image of the target warp-knitted fabric surface, creating a spatial entropy to represent the mesh distribution and a structural entropy to represent the complexity of the mesh morphology based on the real-time image, calculating a real-time mesh entropy field based on the spatial entropy and the structural entropy, and using this real-time mesh entropy field to reflect the spatial distribution of the mesh and the degree of non-uniformity of the mesh structure, i.e., reflecting the stress distribution of the warp-knitted fabric surface. The real-time mesh entropy field is then substituted into a first relational expression to obtain the real-time desired tension of the target warp-knitted fabric. The working parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension to achieve fine-tuning of the surface tension and better weaving results. Attached Figure Description

[0007] Figure 1 This is a schematic diagram illustrating the steps of a method for controlling the surface tension of warp-knitted fabrics according to the present invention; Figure 2 This is a system block diagram of a warp-knitted fabric tension control system according to the present invention. Detailed Implementation

[0008] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0009] To better understand the technical content of this invention, the various terms involved in the solution are explained as follows: Warp-knitted fabrics: Fabrics woven using warp knitting technology, which involves bending one or more sets of parallel yarns into loops and interlocking them with knitting needles. They are characterized by stable longitudinal dimensions, good elasticity, and excellent breathability, and are widely used in clothing, home textiles, industrial textiles and other fields.

[0010] Fabric tension: The tensile force exerted on the fabric surface during weaving, finishing, and other processing is a key parameter affecting the quality of fabric weaving. Excessive tension can easily lead to fabric stretching deformation and yarn breakage; insufficient tension will cause the fabric surface to loosen and the mesh shape to become irregular, ultimately affecting the appearance and physical properties of the fabric.

[0011] Real-time expected tension: Based on the preset quality requirements of the target warp-knitted fabric and combined with the real-time fabric condition, the ideal fabric tension value that should be maintained in the current processing stage is determined, and it is the core basis for adjusting the working parameters of the warp knitting machine.

[0012] Mesh entropy field: A physical field used to quantitatively describe the overall state of the mesh openings on the surface of warp-knitted fabrics. It comprehensively reflects the uniformity of mesh distribution and the complexity of its shape, and is the core medium for establishing the relationship between tension and mesh state. Its numerical change can intuitively reflect whether the fabric mesh openings meet the process standards, and then, in turn, deduce the required fabric tension.

[0013] Spatial entropy: The entropy value characterizing the uniformity of the mesh openings in the spatial distribution of a warp-knitted fabric. A higher spatial entropy indicates a more uniform distribution of mesh openings across different areas of the fabric; conversely, a lower spatial entropy indicates the presence of obvious clusters or sparse areas in the mesh opening distribution, reflecting a possible local imbalance in fabric tension.

[0014] Structural entropy: An entropy value used to measure the complexity of the mesh shape of warp-knitted fabrics. It is calculated by combining parameters such as the difference in mesh size and fractal characteristics. The higher the structural entropy, the more irregular the mesh shape; the lower the structural entropy, the more uniform and regular the mesh shape. It is an important indicator for judging the processing precision of fabric.

[0015] Entropy gradient: The rate of change of the mesh entropy field in the fabric space, reflecting the degree of difference in mesh entropy values ​​in different regions. A large entropy gradient indicates significant differences in the mesh state in different parts of the fabric, suggesting local fluctuations in fabric tension, requiring targeted adjustment of warp knitting machine parameters to balance the tension.

[0016] Real-time fabric images: Visual information of the fabric surface during the processing of warp-knitted fabrics is acquired in real time through image acquisition equipment. It includes details such as mesh distribution, shape, and color. It is the original data source for calculating spatial entropy and structural entropy, and provides intuitive visual basis for tension control.

[0017] Mesh generation: This step involves regularly segmenting the real-time acquired fabric image into several small, uniformly sized grids. Mesh generation enables precise analysis of different regions of the fabric, avoiding the problem of overlooking local mesh anomalies due to overall analysis.

[0018] Functional Zones: Based on the process design requirements or fabric processing characteristics of warp-knitted fabrics, the divided grids are categorized into different zones according to preset classification rules. For example, the fabric edge area and the main body area can be divided into different functional zones, and spatial entropy is calculated separately to achieve zoned tension control.

[0019] Preset coefficients: These are constants set in advance based on the material, process requirements, and equipment characteristics of the target warp-knitted fabric when establishing the relationship between tension and mesh entropy field, and between mesh entropy field and spatial entropy / structural entropy. Their values ​​need to be determined through extensive experimental verification and process optimization to ensure the accuracy of the relational expressions.

[0020] Base tension: The initial tension value of the target warp-knitted fabric under basic weaving conditions without additional external interference. It serves as the benchmark for calculating the real-time expected tension. The base tension needs to be preset based on parameters such as yarn elasticity and knitting structure to ensure the stability of the weaving process.

[0021] Grid spatial location identification: Used to identify the spatial location of different grids in the distribution surface image. It can accurately locate the specific orientation of each grid and provide a location basis for analyzing the spatial characteristics of the mesh distribution.

[0022] Mesh Existence Probability: The probability that a mesh conforming to the manufacturing process exists within a single grid in a fabric image. This probability is calculated by comparing the actual number of mesh holes within the grid with the theoretically achievable number of mesh holes. This parameter directly reflects the density of mesh holes within the grid and is a core variable for calculating spatial entropy.

[0023] Aperture standard deviation: A statistical measure used to describe the dispersion of aperture size in warp-knitted fabrics. The smaller the aperture standard deviation, the closer the aperture size is to the average aperture size, and the more uniform the mesh shape; conversely, a larger standard deviation indicates greater variation in aperture size and irregular mesh shape. It is an important parameter for calculating structural entropy.

[0024] Average aperture: The average aperture of all mesh openings on the surface of a warp-knitted fabric. It reflects the overall size of the mesh and is a fundamental parameter for assessing whether the mesh shape meets process requirements. The average aperture needs to be preset according to the intended use of the fabric.

[0025] Fractal dimension: A geometric parameter used to quantify the complexity of the mesh edge or overall shape of a warp-knitted fabric, calculated based on fractal geometry theory. A higher fractal dimension indicates a more complex mesh shape; conversely, a lower fractal dimension indicates a simpler and more regular shape. It is a key indicator for assessing the complexity of the mesh structure.

[0026] Fractal Dimension Coefficient: This coefficient corrects for the influence of fractal dimension on structural entropy. It is set according to the mesh design precision requirements of the target warp-knitted fabric. The larger the coefficient, the greater the contribution of fractal dimension to structural entropy, and the more emphasis is placed on the regularity of the mesh morphology.

[0027] First weighting coefficient and second weighting coefficient: These are weighting coefficients assigned to spatial entropy and structural entropy, respectively, when calculating the mesh entropy field using spatial entropy and structural entropy. If the fabric requires higher uniformity in mesh distribution, the first weighting coefficient is larger; if it requires higher regularity in mesh morphology, the second weighting coefficient is larger. These can be flexibly adjusted according to process requirements.

[0028] Warp feed rate: The length of warp yarns fed into the knitting area by the warp knitting machine per unit time or per knitting cycle is a key operating parameter for adjusting fabric tension. If the warp feed rate is too high, the fabric tension will decrease; if the warp feed rate is too low, the fabric tension will increase. It needs to be precisely matched with the desired tension in real time.

[0029] Expected warp feed: The length of warp yarn that the warp knitting machine should feed, calculated based on the real-time expected tension and the relationship between tension and warp feed, is the target value for adjusting the current warp feed to ensure that the warp feed matches the tension requirements.

[0030] Initial length: The original length of the warp yarns used to weave the target warp-knitted fabric in an unstretched (tension-free) state. It is the basic parameter for calculating the warp feed amount, and its value is determined by the purchase specifications of the warp yarns and the weaving width of the fabric.

[0031] Modulus of elasticity: A physical quantity that measures the ability of warp yarn material to resist elastic deformation, i.e., the ratio of stress to strain during the elastic deformation stage of the material. The larger the modulus of elasticity, the more difficult the warp yarn is to be stretched; conversely, the smaller the modulus of elasticity, the easier it is to stretch. It directly affects the relationship between tension and warp feed, and is a core material parameter for calculating warp feed.

[0032] Yarn cross-sectional area: The area of ​​the warp yarn's cross-section, reflecting the yarn's thickness. The larger the cross-sectional area, the stronger the warp yarn's ability to withstand tension, and the smaller the tensile deformation under the same tension. It is an important yarn physical parameter to consider when calculating warp feed.

[0033] Please refer to Figure 1 A method for controlling the surface tension of warp-knitted fabrics includes the following steps: Create the first expression relating the tension of the target warp-knitted fabric to the mesh entropy field; Acquire a real-time fabric image of the target warp-knitted fabric, and based on the real-time fabric image, create a spatial entropy to represent the mesh distribution and a structural entropy to represent the complexity of the mesh morphology. Based on the spatial entropy and the structural entropy, the real-time mesh entropy field is calculated. Substituting the real-time mesh entropy field into the first relational expression, the real-time expected tension of the target warp-knitted fabric is obtained. The operating parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension.

[0034] As can be seen from the above description, the beneficial effects of the present invention are as follows: A real-time fabric image of the target warp-knitted fabric is acquired; based on the real-time fabric image, a spatial entropy representing the mesh distribution and a structural entropy representing the complexity of the mesh morphology are created; based on the spatial entropy and the structural entropy, a real-time mesh entropy field is calculated; the real-time mesh entropy field reflects the spatial distribution of the mesh on the fabric surface of the warp-knitted fabric and the degree of non-uniformity of the mesh structure, i.e., it reflects the stress distribution on the fabric surface of the warp-knitted fabric; then, the real-time mesh entropy field is substituted into the first relational expression to obtain the real-time desired tension of the target warp-knitted fabric; the working parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension to achieve fine adjustment of the fabric tension and achieve a better weaving effect.

[0035] Furthermore, the first relational expression is as follows: ; Where T represents tension, and K, α, and γ all represent preset coefficients. T represents the entropy field of the mesh. b This indicates the basic tension of the target warp-knitted fabric. Represents the gradient of the entropy field. This represents the baseline entropy value for a fabric with zero porosity (0%). This represents the theoretical maximum entropy value when the porosity of the fabric is 100%.

[0036] As described above, this expression allows for the rapid and accurate calculation of the corresponding tension value based on the real-time changes in the mesh entropy field and the spatial trend of the mesh state reflected by the entropy field gradient. This enables real-time and precise control of fabric tension during the production of warp-knitted fabrics, based on the dynamic adjustment of the fabric mesh state, greatly improving the accuracy of tension control.

[0037] Furthermore, the step of creating spatial entropy to represent the mesh distribution and structural entropy to represent the mesh morphological complexity based on the real-time fabric image includes: The real-time fabric image is divided into grids; All grids are divided into at least two functional areas according to the preset classification rules, and the spatial entropy of each functional area is calculated.

[0038] As described above, dividing the real-time fabric image into a grid, decomposing the entire fabric surface into numerous small grid units, allows for a more detailed and accurate analysis of the fabric's mesh distribution. According to preset classification rules, all grids are divided into at least two functional zones. This partitioning method fully considers the differences in function, structure, and mesh distribution characteristics of different regions of the warp-knitted fabric. Calculating the spatial entropy of each functional zone allows for targeted analysis of the uniformity of mesh distribution in different areas, providing a foundation for a more accurate assessment of the overall fabric mesh distribution. This helps to promptly identify potential mesh distribution anomalies in different areas of the fabric, thus providing a more targeted basis for tension adjustment. This ensures that the mesh distribution in each functional zone meets design requirements, improving the overall consistency and quality stability of the fabric.

[0039] Further, obtaining the real-time mesh entropy field based on the spatial entropy and the structural entropy includes: The real-time mesh entropy field corresponding to the functional area is calculated based on the spatial entropy and structural entropy corresponding to the functional area.

[0040] As can be seen from the above description, by using this calculation method corresponding to functional areas, a refined analysis of the mesh state can be achieved for the unique needs of each functional area. This provides a tension control scheme that is more in line with the actual situation for each functional area, further improving the pertinence and effectiveness of tension control, and ensuring that the fabric can achieve the ideal weaving effect in different functional areas.

[0041] Furthermore, the spatial entropy H s The expression is as follows: ; Where, x i θ represents the spatial location identifier of the i-th grid. i p(x) represents the weight coefficient of the functional area to which the i-th grid belongs. i ) represents the probability of a mesh existing in the i-th grid, where n is an integer.

[0042] As described above, the ratio of the actual number of mesh openings within a grid to the theoretically achievable number of mesh openings directly reflects the mesh density within that grid. Calculating spatial entropy using this expression allows for a comprehensive and detailed consideration of factors such as the spatial location of mesh openings, the importance of functional areas, and the density of mesh openings. This provides a scientific and comprehensive quantitative method for accurately assessing the uniformity of mesh opening distribution, thus offering a more reliable foundation for tension control based on mesh opening distribution information.

[0043] Furthermore, the structural entropy H m The expression is as follows: ; in, Here, d represents the standard deviation of the aperture, d represents the average aperture, and FractalDim represents the fractal dimension. The coefficients represent the fractal dimension.

[0044] As can be seen from the above description, calculating the structural entropy using such an expression can comprehensively consider two key factors: mesh aperture and morphological complexity. This allows for precise quantification of the complexity of the mesh structure, providing an accurate quantitative indicator for judging the fabric processing precision. It also helps to make reasonable adjustments based on the actual situation of the mesh structure during tension control, ensuring that the mesh morphology meets the process standards.

[0045] Further, obtaining the real-time mesh entropy field based on the spatial entropy and the structural entropy includes: Preset first and second weighting coefficients. Based on the spatial entropy, the structural entropy, the first weighting coefficient, and the second weighting coefficient, the expression for the real-time mesh entropy field is obtained as follows: ; Among them, H s Represents spatial entropy, H represents the first weighting coefficient. m Represents structural entropy. This represents the second weighting coefficient.

[0046] As can be seen from the above description, by flexibly setting the weight coefficients, the real-time mesh entropy field can be calculated in a customized manner according to the special needs of different warp-knitted fabrics, thereby providing a more suitable quantitative assessment of the mesh state for various warp-knitted fabrics, further improving the versatility and effectiveness of the tension control method, and meeting diverse production needs.

[0047] Furthermore, it also includes: Create a second expression relating the tension of the target warp-knitted fabric to the amount of warp feed. The process of adjusting the operating parameters of the editing machine for the target warp-knitted fabric according to the real-time desired tension includes: Substitute the real-time expected tension into the second relational expression to obtain the expected warp feed amount. Based on the expected warp feed amount, adjust the current warp feed amount of the editing machine for the target warp-knitted fabric.

[0048] As described above, by adjusting the current warp feed rate according to the desired warp feed rate, precise control of fabric tension can be achieved. When the desired tension changes in real time, the warp feed rate can be quickly adjusted to ensure that the fabric tension quickly reaches and is maintained at the desired level, avoiding problems of excessive or insufficient tension caused by improper warp feed rate, and ensuring that warp-knitted fabrics maintain a stable and appropriate tension state throughout the entire production process.

[0049] Furthermore, the second relational expression is as follows: ; Where L represents the amount of warp feed, L0 represents the initial length of the braided yarn used to make the target warp-knitted fabric, E represents the elastic modulus of the braided yarn, T represents the tension, and A represents the cross-sectional area of ​​the braided yarn.

[0050] As can be seen from the above description, the expression achieves dynamic matching between tension and warp feed. When the real-time expected tension changes with the mesh entropy field, the warp feed can be adjusted accordingly in real time, ensuring precise control of fabric tension from the equipment execution level, thereby stabilizing the mesh shape and fabric quality of warp-knitted fabrics.

[0051] Please refer to Figure 2 A fabric tension control system for warp-knitted fabrics includes a main control device and a camera device, wherein the main control device is communicatively connected to the camera device; The main control device is used to connect to the controller of the warp knitting machine and execute the above-mentioned method for controlling the fabric tension of warp-knitted fabrics.

[0052] As can be seen from the above description, the beneficial effects of the present invention are as follows: A real-time fabric image of the target warp-knitted fabric is acquired; based on the real-time fabric image, a spatial entropy representing the mesh distribution and a structural entropy representing the complexity of the mesh morphology are created; based on the spatial entropy and the structural entropy, a real-time mesh entropy field is calculated; the real-time mesh entropy field reflects the spatial distribution of the mesh on the fabric surface of the warp-knitted fabric and the degree of non-uniformity of the mesh structure, i.e., it reflects the stress distribution on the fabric surface of the warp-knitted fabric; then, the real-time mesh entropy field is substituted into the first relational expression to obtain the real-time desired tension of the target warp-knitted fabric; the working parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension to achieve fine adjustment of the fabric tension and achieve a better weaving effect.

[0053] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for controlling the surface tension of warp-knitted fabrics includes the following steps 102-108: In step 102, a first relationship expression between the tension of the target warp-knitted fabric and the mesh entropy field is created, as follows: ; Where T represents tension, and K, α, and γ all represent preset coefficients. T represents the mesh entropy field. b This indicates the basic tension of the target warp-knitted fabric, meaning that the fabric surface of the target warp-knitted fabric has tension even if there are no mesh openings; Represents the gradient of the entropy field. This represents the baseline entropy value for a fabric with zero porosity (0%). This represents the theoretical maximum entropy value when the porosity of the fabric is 100%.

[0054] In the first relational expression This represents the entropy field normalization. Normalization aims to eliminate the influence of the material itself, allowing different materials to be processed under the same control framework, with values ​​ranging from 0 to 1. The preset coefficients are determined using the method of undetermined coefficients. Here, α is a power-law fitting coefficient, used to calibrate the tension values ​​corresponding to different mesh entropy field values. The reason for using power-law fitting is briefly explained below: The nonlinear stress relaxation of textile materials can be expressed as: .

[0055] Where E is Young's modulus, and σ and ε are stress and strain, respectively. It is an exponentially decaying term. The greater the strain, the less stress is required, which represents nonlinear relaxation.

[0056] Strain and entropy are positively correlated. Therefore, the relationship between stress and entropy, expressed through Taylor expansion, should also exhibit some kind of exponential relationship: .

[0057] In step 104, a real-time fabric image of the target warp-knitted fabric is obtained. Based on the real-time fabric image, a spatial entropy is created to represent the mesh distribution and a structural entropy is created to represent the complexity of the mesh morphology. In this embodiment, the real-time fabric image is divided into grids; according to a preset classification rule, all grids are divided into at least two functional areas, and the spatial entropy of each functional area is calculated, as shown in the following expression: .

[0058] Where, x i This is represented as the spatial location identifier of the i-th grid. p(x) represents the weight coefficient of the functional area to which the i-th grid belongs. i Let p(x) represent the probability of a mesh existing in the i-th grid. Let log2 make the entropy value expressed in bits, i.e., 1 bit = the amount of information with / without a mesh. Then p(x) i log2p(x) i This represents the quantitative value of Shannon's information content.

[0059] The probability of a mesh existing is obtained by the following formula: .

[0060] Regarding the weighting coefficients for functional areas, since different functional areas have different requirements for tension values, the mesh distribution will have a weighting coefficient. Taking warp-knitted shoe materials as an example, the areas that currently need to be multiplied by the weighting coefficient are: the instep area, the logo area, and the ankle area. The instep area has high requirements for breathability; to meet ergonomics, a denser mesh distribution is beneficial for breathability, therefore... As for the logo area, too many mesh holes would disrupt the shoe's design, so the number of mesh holes is relatively small. Similarly, to enhance ankle protection, high tensile strength is required. A high number of mesh openings will reduce tensile strength; therefore, the required number of mesh openings should be relatively small. .

[0061] As can be seen from the above formula, spatial entropy reflects the uniformity of the spatial distribution of the mesh. Uniform spatial distribution means uniform stress distribution, while concentrated spatial distribution means stress concentration. In order to avoid potential risks such as breakage caused by stress concentration, the tension that needs to be controlled in different parts of the fabric is inconsistent, and the amount of warp feed is also inconsistent.

[0062] In this embodiment, the structural entropy H m The expression is as follows: .

[0063] in, Here, d represents the standard deviation of the aperture, d represents the average aperture, and FractalDim represents the fractal dimension. This represents the coefficient of the fractal dimension. The reason the formula subtracts the fractional expression from 1 is to reduce the structural entropy H. m The range of its value is restricted to [0, 1), that is... And when the fractal dimension is 0, H m A value of 0 indicates a completely ordered state; conversely, a value of 0 indicates an ordered state. m A tendency toward 1 represents an extremely disordered state.

[0064] The formula for calculating the standard deviation of pore size is: .

[0065] Where, d i It is the diameter of the i-th hole, and n is the total number of holes in this functional area.

[0066] As a preferred implementation, the fractal dimension FractalDim is achieved using box counting, as follows: First, select a set of gradually increasing box sizes s (in pixels), s=[1,2,4,8,16,32,64,...] which grow exponentially.

[0067] Secondly, for each box size s, the image is divided into an s×s square grid.

[0068] Next, count the number of boxes N(s) that contain holes.

[0069] Then, record N(s) corresponding to all dimensions s.

[0070] Finally, the fractal dimension is calculated.

[0071] Furthermore, the standard fractal dimension is defined as follows: .

[0072] The base of the logarithm does not affect the calculation result. It can be seen that there is a limit symbol in the above formula, but since s is discrete and there is no concept of a limit value, we replace the limit with the difference between the numerator and denominator, thus deriving the formula for calculating the fractal dimension: .

[0073] Using multiple log(s) and log(N(s)) data points, a straight line y=mx+b is obtained through linear fitting. The absolute value of the slope m of the fitted line is the fractal dimension FractalDim. The fractal dimension directly reflects the complexity of the mesh morphology; the more complex the morphology, the higher its structural entropy.

[0074] As can be seen from the above formula, structural entropy reflects the degree of non-uniformity in the mesh structure. To achieve a certain fabric style, meshes of varying sizes and shapes are designed, and these non-uniform meshes can lead to localized stress concentrations. To eliminate stress concentrations, it is necessary to control the tension in each region, which means controlling the warp feed in that region.

[0075] In step 106, the real-time mesh entropy field is calculated based on the spatial entropy and structural entropy. The real-time mesh entropy field is then substituted into the first relational expression to obtain the real-time expected tension of the target warp-knitted fabric.

[0076] In this embodiment, the real-time mesh entropy field corresponding to the functional area is calculated based on the spatial entropy and structural entropy of the functional area, with a first weight coefficient and a second weight coefficient preset.

[0077] Based on spatial entropy, structural entropy, the first weighting coefficient, and the second weighting coefficient, the expression for the real-time mesh entropy field is obtained as follows: .

[0078] Among them, H s Represents spatial entropy, H represents the first weighting coefficient. m Represents structural entropy. This represents the second weighting coefficient. The first and second weighting coefficients represent the relative importance of the corresponding entropy. For some fabrics, spatial entropy has a greater impact on tension; for others, structural entropy has a greater impact. Therefore, the first and second weighting coefficients should be determined based on the specific circumstances.

[0079] In step 108, the operating parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension.

[0080] In this embodiment, the relationship between the amount of warp feed and the tension is first modeled, and the specific process is as follows: Let L represent the feed rate, L0 represent the initial length of the yarn used to make the target warp-knitted fabric, E represent the elastic modulus of the yarn, T represent the tension, and A represent the cross-sectional area of ​​the yarn. According to Hooke's Law, we have: .

[0081] The actual elongation L of the braided yarn in the standard warp-knitted fabric m It should be the initial length L0 and the elastic elongation Δ L The sum: L m =L0+Δ L .

[0082] Eliminate Δ L ,get: .

[0083] To maintain tension T, the warp feeding system of the warp knitting machine must provide more yarn than the actual tension-free length required by the fabric, such that the feed amount L = the actual elongation L. m Thus, the second expression for the relationship between the amount of warp feed and the tension is obtained: .

[0084] Therefore, by substituting the real-time expected tension into the second relational expression, the expected warp feed amount is obtained. Based on the expected warp feed amount, the current warp feed amount of the editing machine for the target warp-knitted fabric is adjusted.

[0085] This embodiment illustrates the above method using a real-world scenario.

[0086] For example, during the production of sports shoe upper materials, when the product is being woven on the warp knitting machine, it was found that the mesh holes in the instep triangle area were stretched excessively, and a large number of micropores in the logo area were deformed, resulting in blurred patterns and a yarn breakage rate of 21.3%.

[0087] Through fabric scanning and entropy field modeling, the spatial entropy of the instep area was calculated to be 3.8, the structural entropy to be 0.4, the weighting coefficient of the spatial entropy to be 1.8, and the weighting coefficient of the structural entropy to be 0.4. Therefore, Ψ = 6.96, and the single yarn tension was calculated to be 18.2 cN. Since the mesh in this area is excessively stretched, the warp feed needs to be increased to reduce the yarn tension.

[0088] In the Logo area, there are a large number of dense micropores with a diameter between 0.1-0.3mm, a fractal dimension of 1.82, a spatial entropy of 0.7, a structural entropy of 0.82, a spatial entropy weighting coefficient of 0.6, and a structural entropy weighting coefficient of 0.4. Therefore, Ψ=0.808, and the single yarn tension is calculated to be 24.5cN.

[0089] The problem was further addressed by reducing tension in the instep area and increasing tension in the logo area. Through adjustments to the warp feed rate and tension, the yarn tension in the instep area was successfully reduced from 18.2 cN to 17.6 cN, while the yarn tension in the logo area was increased from 24.5 cN to 26.7 cN. After a period of operation, the yarn breakage rate decreased from 21.3% to 0.9%, and the logo pattern remained clear.

[0090] If the traditional "average aperture" method is used to calculate the warp feed, the tension will be uniformly increased or decreased across the entire fabric. This will either cause the mesh in the instep area to continue stretching, leading to a larger area of ​​yarn breakage, or the pattern in the logo area to become increasingly blurred, failing to solve the practical problem. However, this solution, considering the differences in local entropy fields, allows for fine-tuning of local tension, achieving better results.

[0091] Please refer to Figure 2 Embodiment two of the present invention is as follows: A warp-knitted fabric tension control system includes a main control device 202 and a camera device 204. The camera device 204 is used to acquire real-time fabric images, and the main control device 202 is communicatively connected to the camera device 204. The main control device 202 is used to connect to the controller 206 of the warp knitting machine and execute the warp-knitted fabric tension control method of Embodiment 1.

[0092] In summary, the present invention provides a method and system for controlling the surface tension of warp-knitted fabrics. This involves acquiring a real-time image of the target warp-knitted fabric, creating a spatial entropy representing the mesh distribution and a structural entropy representing the complexity of the mesh morphology based on the real-time image, and calculating a real-time mesh entropy field based on the spatial and structural entropies. This real-time mesh entropy field reflects the spatial distribution of the mesh and the non-uniformity of the mesh structure, thus reflecting the stress distribution on the fabric surface. The real-time mesh entropy field is then substituted into a first relational expression to obtain the desired real-time tension of the target warp-knitted fabric. The operating parameters of the editing machine for the target warp-knitted fabric are adjusted according to the desired real-time tension to achieve fine-tuning of the fabric tension and better weaving results. During image analysis, the real-time fabric image is divided into grids, decomposing the entire fabric surface into numerous small grid units, making the analysis of the mesh distribution more detailed and accurate. All grids are divided into at least two functional areas according to preset classification rules. This partitioning method fully considers the differences in function, structure, and mesh distribution characteristics of different regions of the warp-knitted fabric. Calculating the spatial entropy of each functional area separately allows for targeted analysis of the uniformity of mesh distribution in different regions. This provides a basis for more accurate subsequent assessment of the mesh distribution across the entire fabric surface, helps to promptly identify potential mesh distribution anomalies in different areas of the fabric, and provides a more targeted basis for adjusting tension. This ensures that the mesh distribution in each functional area meets design requirements, thereby improving the overall consistency and quality stability of the fabric.

[0093] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for controlling the surface tension of warp-knitted fabrics, characterized in that, Includes the following steps: Create the first expression relating the tension of the target warp-knitted fabric to the mesh entropy field; Acquire a real-time fabric image of the target warp-knitted fabric, and based on the real-time fabric image, create a spatial entropy to represent the mesh distribution and a structural entropy to represent the complexity of the mesh morphology. Based on the spatial entropy and the structural entropy, the real-time mesh entropy field is calculated. Substituting the real-time mesh entropy field into the first relational expression, the real-time expected tension of the target warp-knitted fabric is obtained. The operating parameters of the editing machine for the target warp-knitted fabric are adjusted according to the real-time desired tension.

2. The method for controlling the surface tension of warp-knitted fabrics according to claim 1, characterized in that, The first relational expression is as follows: ； Where T represents tension, and K, α, and γ all represent preset coefficients. T represents the entropy field of the mesh. b This indicates the basic tension of the target warp-knitted fabric. Represents the gradient of the entropy field. This represents the baseline entropy value for a fabric with zero porosity (0%). This represents the theoretical maximum entropy value when the porosity of the fabric is 100%.

3. The method for controlling the surface tension of warp-knitted fabrics according to claim 1, characterized in that, The step of creating spatial entropy to represent the mesh distribution and structural entropy to represent the complexity of mesh morphology based on the real-time fabric image includes: The real-time fabric image is divided into grids; All grids are divided into at least two functional areas according to the preset classification rules, and the spatial entropy of each functional area is calculated.

4. The method for controlling the surface tension of warp-knitted fabrics according to claim 3, characterized in that, The step of obtaining the real-time mesh entropy field based on the spatial entropy and the structural entropy includes: The real-time mesh entropy field corresponding to the functional area is calculated based on the spatial entropy and structural entropy corresponding to the functional area.

5. The method for controlling the surface tension of warp-knitted fabrics according to claim 3, characterized in that, The spatial entropy H s The expression is as follows: ； Where, x i This is represented as the spatial location identifier of the i-th grid. p(x) represents the weight coefficient of the functional area to which the i-th grid belongs. i ) represents the probability of a mesh existing in the i-th grid, where n is an integer.

6. The method for controlling the surface tension of warp-knitted fabrics according to claim 1, characterized in that, The structural entropy H m The expression is as follows: ； in, Here, d represents the standard deviation of the aperture, d represents the average aperture, and FractalDim represents the fractal dimension. The coefficients represent the fractal dimension.

7. The method for controlling the surface tension of warp-knitted fabrics according to claim 1, characterized in that, The step of obtaining the real-time mesh entropy field based on the spatial entropy and the structural entropy includes: Preset first and second weighting coefficients. Based on the spatial entropy, the structural entropy, the first weighting coefficient, and the second weighting coefficient, the expression for the real-time mesh entropy field is obtained as follows: ； Among them, H s Represents spatial entropy, H represents the first weighting coefficient. m Represents structural entropy. This represents the second weighting coefficient.

8. The method for controlling the surface tension of warp-knitted fabrics according to claim 1, characterized in that, Also includes: Create a second expression relating the tension of the target warp-knitted fabric to the amount of warp feed. The process of adjusting the operating parameters of the editing machine for the target warp-knitted fabric according to the real-time desired tension includes: Substitute the real-time expected tension into the second relational expression to obtain the expected warp feed amount. Based on the expected warp feed amount, adjust the current warp feed amount of the editing machine for the target warp-knitted fabric.

9. The method for controlling the surface tension of warp-knitted fabrics according to claim 8, characterized in that, The second relational expression is as follows: ； Where L represents the amount of warp feed, L0 represents the initial length of the braided yarn used to make the target warp-knitted fabric, E represents the elastic modulus of the braided yarn, T represents the tension, and A represents the cross-sectional area of ​​the braided yarn.

10. A fabric tension control system for warp-knitted fabrics, characterized in that, It includes a main control device and a camera device, wherein the main control device is communicatively connected to the camera device; The main control device is used to connect to the controller of the warp knitting machine and execute the fabric tension control method for warp-knitted fabrics as described in any one of claims 1 to 9.

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