A winding control method in an aluminum foil slitting and winding process

Abstract

The application provides a winding control method in an aluminum foil slitting and winding process, comprising: extracting a thickness extreme value according to a boundary of each thickness region in a thickness distribution matrix, determining a local stress threshold value through a mapping relationship between the thickness extreme value and a material allowable stress; determining width change data of a slitting band according to the slitting stress release amount and the elastic modulus, determining a retraction difference prediction value through the width change data; obtaining real-time rotating speed data of a winding device, determining a single-coil winding deviation according to a single-coil winding length corresponding to the real-time rotating speed data and the retraction difference prediction value; accumulating the single-coil winding deviation and a preset winding number to obtain a winding cumulative deviation; if the winding cumulative deviation exceeds a preset deviation range, generating a tension distribution instruction according to the corresponding relationship between the winding cumulative deviation and each thickness region; and driving a torque control unit of the tension device to adjust an output torque through the tension distribution instruction.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a winding control method in the aluminum foil slitting and winding process. Background Technology

[0002] Aluminum foil slitting is a crucial process in cutting master rolls into narrow strips, playing a vital role in high-end manufacturing fields such as battery current collectors. Maintaining the flatness and neatness of the aluminum foil edges is paramount in this process. Current tension control methods generally rely on reactive adjustment mechanisms, meaning the tension of the equipment is only changed after the sensor detects abnormal force. This delayed response can only handle deviations that have already occurred; it is often too slow to address initial physical defects inherent in the material itself, failing to prevent damage before it occurs.

[0003] The inherent initial defect of this material is mainly manifested in the uneven thickness of the aluminum foil in the width direction. Due to manufacturing limitations, a roll of aluminum foil often exhibits a thickness that is thicker in the middle and thinner at the edges. When the slitting machine applies overall tension, this slight thickness difference can trigger a severe chain reaction. Specifically, the thicker areas will bear tensile stress far exceeding the average level. Once this exceeds the material's tolerance limit, these thicker sections will be the first to be overstretched, producing irreversible wavy deformation. Subsequently, when this deformed roll of aluminum foil is cut into multiple narrow strips, the different thicknesses and stresses of these strips, after the stress is released at the moment of cutting, result in vastly different degrees of width shrinkage.

[0004] This inconsistent shrinkage of the various strips accumulates during subsequent winding processes. Narrow strips that should have been wound flat end up stacked on the roll with uneven edges due to slight variations in their width, severely compromising the usability of the finished product. Therefore, understanding the thickness differences in material width in advance and precisely distributing tension to different thickness areas to break the vicious cycle from localized stretching deformation to uneven end faces of the final roll becomes a key issue in improving the slitting quality of high-end aluminum foil. Summary of the Invention

[0005] This invention provides a winding control method in the aluminum foil slitting and winding process, mainly including:

[0006] Thickness data is collected by transverse scanning along the width of the aluminum foil using a thickness measuring device. A classification algorithm is then used to divide the thickness data into thickness intervals, resulting in a thickness distribution matrix. Thickness extrema are extracted based on the boundaries of each thickness region in the thickness distribution matrix. Local stress thresholds are determined by the mapping relationship between these thickness extrema and the allowable stress of the material. The overall tensile force applied to the aluminum foil by a tensioning device is obtained. Regional stress components are determined based on the overall tensile force and the thickness proportion of each region. A tensile deformation risk value is obtained by comparing the regional stress components with the local stress threshold. If the tensile deformation risk value exceeds a preset risk assessment threshold, a regression prediction algorithm is used to determine the tensile deformation risk. The risk value is used as an input feature for prediction to obtain the slitting stress release amount; the width variation data of the slitting strip is determined based on the slitting stress release amount and the elastic modulus, and the shrinkage difference prediction value is determined through the width variation data; the real-time rotation speed data of the winding equipment is acquired, and the single-turn winding deviation is determined based on the single-turn winding length corresponding to the real-time rotation speed data and the shrinkage difference prediction value; the cumulative winding deviation is calculated by accumulating the single-turn winding deviation with the preset number of winding turns; if the cumulative winding deviation exceeds the preset deviation range, a tension distribution command is generated based on the correspondence between the cumulative winding deviation and each thickness region; the torque control unit of the tension equipment is driven by the tension distribution command to adjust the output torque. Furthermore, the step of collecting thickness data by transverse scanning along the width direction of the aluminum foil using a thickness measuring device, and dividing the thickness data into thickness intervals using a classification algorithm to obtain a thickness distribution matrix, includes: acquiring thickness data with spatial coordinates; constructing a two-dimensional coordinate system with the width coordinate as the horizontal axis and the thickness value as the vertical axis based on the thickness data; extracting the cross-sectional profile from the two-dimensional coordinate system; dividing the cross-sectional profile into preset thickness intervals using the classification algorithm; counting the number of thickness data falling into each thickness interval to obtain the thickness distribution matrix; extracting frequency data for each thickness interval from the thickness distribution matrix; calculating the frequency difference between adjacent thickness intervals based on the frequency data to obtain fluctuation characteristics; if the fluctuation characteristics are greater than a preset fluctuation threshold, locating the deformation area; extracting the highest and lowest thickness points corresponding to the deformation area from the cross-sectional profile; calculating the deviation value between the highest and lowest thickness points; and multiplying the deviation value with a preset compensation coefficient to obtain compensation parameters.Furthermore, the step of extracting thickness extrema based on the boundaries of each thickness region in the thickness distribution matrix and determining local stress thresholds through the mapping relationship between the thickness extrema and the allowable stress of the material includes: obtaining the thickness distribution matrix; extracting the boundaries of each thickness interval in the thickness distribution matrix; determining the thickness extrema of each thickness interval based on the boundaries; obtaining a pre-established mapping relationship between the thickness extrema and the allowable stress of the material; determining the local stress threshold corresponding to each thickness interval through the mapping relationship; calculating the stress difference between the local stress threshold and a preset yield strength; constructing a stress gradient vector based on the stress difference; calculating the tension load value corresponding to each thickness interval through the stress gradient vector; and generating a tension adjustment command based on the tension load value if the tension load value is greater than a preset critical tension. Furthermore, the step of acquiring the overall tensile force applied to the aluminum foil by the tensioning device, determining the regional stress component based on the overall tensile force and the thickness ratio of each region, and obtaining the tensile deformation risk value based on the comparison result of the regional stress component and the local stress threshold includes: acquiring the overall tensile force applied to the aluminum foil by the tensioning device, multiplying the overall tensile force by a preset roller surface friction coefficient to obtain an effective tensile force; acquiring the thickness ratio of each region corresponding to the aluminum foil, and proportionally converting the effective tensile force value based on the thickness ratio of each region to obtain the regional stress component corresponding to each region; if the regional stress component is greater than the local stress threshold, calculating the difference between the regional stress component and the local stress threshold to obtain the tensile deformation risk value corresponding to each region; constructing a risk gradient matrix based on the tensile deformation risk value corresponding to each region, and obtaining the plastic elongation extreme value by feature extraction from the risk gradient matrix; and generating a roller speed compensation command based on the comparison result of the plastic elongation extreme value and the preset elongation safety threshold. Furthermore, if the tensile deformation risk value is greater than a preset risk judgment threshold, a regression prediction algorithm is used with the tensile deformation risk value as input feature to predict and obtain the cutting stress release amount. This includes: obtaining the tensile deformation risk value and the preset risk judgment threshold; if the tensile deformation risk value is greater than the risk judgment threshold, a support vector regression algorithm is used with the tensile deformation risk value as input feature to predict and output the corresponding cutting stress release amount; obtaining a preset tool entry depth benchmark value, and calculating the target entry depth by summing the cutting stress release amount and the tool entry depth benchmark value; obtaining the current winding radius during the aluminum foil winding process, and calculating the tension attenuation compensation amount by multiplying the target entry depth and the current winding radius; obtaining the initial winding torque, and calculating the target winding torque by subtracting the initial winding torque from the tension attenuation compensation amount.Furthermore, the step of determining the width variation data of the slitting strip based on the slitting stress release amount and the elastic modulus, and determining the shrinkage difference prediction value through the width variation data, includes: obtaining the slitting stress release amount and a preset elastic modulus; obtaining the slitting strain value by dividing the slitting stress release amount by the elastic modulus; obtaining a preset initial width of the slitting strip; calculating the width variation amount of the slitting strip by multiplying the slitting strain value and the initial width of the slitting strip; obtaining the edge thickness of each slitting strip; calculating the cross-sectional profile deformation value by multiplying the width variation amount of the slitting strip by the edge thickness; obtaining a preset guide roller spacing; calculating the lateral offset amount of each slitting strip by the ratio of the cross-sectional profile deformation value to the guide roller spacing; obtaining a preset offset threshold; and if the lateral offset amount is greater than the offset threshold, using a pre-trained linear regression model with the lateral offset amount as the input feature for prediction to obtain the corresponding correction compensation amount. Further, the step of calculating the cumulative winding deviation by accumulating the single-turn winding deviation and the preset number of winding turns includes: obtaining the single-turn winding deviation and the preset number of winding turns; calculating the cumulative winding deviation by accumulating the single-turn winding deviation and the preset number of winding turns; obtaining the elastic modulus of the winding material; determining the radial stress by multiplying the cumulative winding deviation by the elastic modulus of the winding material; obtaining the interlayer contact pressure; obtaining the stress attenuation factor by dividing the radial stress by the interlayer contact pressure; obtaining a preset attenuation threshold; if the stress attenuation factor is greater than the attenuation threshold, using a pre-trained random forest regression model with the stress attenuation factor as the input feature for regression prediction to obtain the tension compensation coefficient; obtaining a preset target winding tension; obtaining a tension correction command by multiplying the tension compensation coefficient and the target winding tension; and obtaining the current base drive torque; determining the final winding torque by adding the tension correction command to the base drive torque.Furthermore, if the cumulative winding deviation exceeds a preset deviation range, generating a tension distribution instruction based on the correspondence between the cumulative winding deviation and each thickness region includes: obtaining the cumulative winding deviation and the preset deviation range; if the cumulative winding deviation exceeds the preset deviation range, obtaining thickness gradient data of the winding material; determining multiple thickness regions based on the thickness gradient data; determining a distribution ratio based on the distribution state of the cumulative winding deviation in the multiple thickness regions; obtaining the allocation weight of each thickness region by multiplying the distribution ratio by a preset tension benchmark value; obtaining the current operating radius of each thickness region; obtaining a region tension adjustment value by dividing the allocation weight by the current operating radius; using a pre-trained support vector regression model, performing regression prediction with the region tension adjustment value as input features to obtain a smoothed tension value; generating a tension distribution instruction based on the correspondence between the smoothed tension value and each thickness region; obtaining roll diameter change data of each slitting strip; and correcting the tension distribution instruction based on the roll diameter change data to obtain a dynamic torque value.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention discloses a winding control method for aluminum foil slitting and winding. Addressing the issue of thickness variations in the width direction of aluminum foil, this invention acquires thickness distribution data through transverse scanning with a laser thickness gauge and constructs a thickness distribution matrix. The extreme thickness values ​​of each region are extracted and mapped to local stress thresholds. The overall tension applied by the tension roller is then converted into stress components for each region based on the thickness proportion. By comparing these components, the risk of tensile deformation is identified, and the stress release after slitting is predicted. Furthermore, the width variation of each narrow strip due to elastic recoil at the moment of slitting is calculated. The winding deviation is calculated by accumulating the rotational speed and winding length of each turn. When the accumulated deviation exceeds the limit, a tension distribution command is generated to drive the tension roller to perform differentiated torque adjustment. This ensures that the stress is evenly distributed among the narrow strips of different thicknesses during winding, thereby maintaining flush winding ends and effectively solving the winding quality problem caused by uneven thickness. Attached Figure Description

[0009] Figure 1 This is a flowchart of a winding control method in the aluminum foil slitting and winding process according to the present invention. Detailed Implementation

[0010] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0011] like Figure 1 , the winding control method in the aluminum foil slitting and winding process of this embodiment may specifically include:

[0012] S101. A laser thickness gauge is used to perform transverse scanning along the width direction of the aluminum foil to collect thickness data, and a threshold-based decision tree classification algorithm is adopted to divide the thickness data by thickness intervals to obtain a thickness distribution matrix. Specifically, the thickness thresholds T1 is set to 0.08 mm, T2 to 0.10 mm, and T3 to 0.12 mm, and the collected thickness data points are classified according to the thickness value h: when h<T1, they are classified into the thin zone A category; when T1≤h<T2, they are classified into the standard zone B category; when T2≤h<T3, they are classified into the thick zone C category; when h≥T3, they are classified into the extra-thick zone D category. The width direction of the aluminum foil is divided into n measurement points, the thickness value of each point is assigned a category code according to the above classification rules, the category codes A, B, C, D correspond to the values 1, 2, 3, 4 respectively, and finally a 1×n-dimensional thickness distribution matrix M=[m1, m2,..., mn] is formed, where mi represents the category code of the i-th measurement point.

[0013] A laser thickness gauge is used to scan the aluminum foil laterally along its width to obtain thickness data with spatial coordinates. A two-dimensional coordinate system is constructed based on this thickness data, with the width coordinate as the horizontal axis and the thickness value as the vertical axis. The cross-sectional profile is extracted from this coordinate system, and a classification algorithm is used to divide the profile into preset thickness intervals. The number of thickness data points falling into each thickness interval is counted to obtain a thickness distribution matrix. Frequency data for each thickness interval is extracted from the thickness distribution matrix and arranged in ascending order. The frequency of the i-th thickness interval is denoted as f_i. The frequency difference between adjacent thickness intervals is d_i = f_i + 1 - f_i. The maximum absolute value of all d_i values ​​is taken as the fluctuation feature W, i.e., W = max|d_i|, where i ranges from 1 to n-1, and n is the total number of thickness intervals. A preset fluctuation threshold of 50 is set. If W is greater than 50, roll deformation is determined to exist. When locating the deformation region, first, the adjacent thickness interval pairs that generate the maximum frequency difference are locked. Then, the width coordinate index saved when the thickness distribution matrix was established is traced back, and the width coordinates corresponding to all thickness data points falling within this thickness interval pair are extracted. Adjacent coordinate points are then clustered and merged according to their width coordinates. Clusters with a spacing of less than 5 mm are grouped into the same cluster. The start and end range of the width coordinates covered by each cluster constitutes a deformation region, thus completing the physical location of the deformation region in the width direction of the aluminum foil. The highest and lowest thickness points corresponding to the deformation region are extracted from the cross-sectional contour, and the deviation value ΔH between them is calculated. ΔH is multiplied by a preset compensation coefficient k to obtain the compensation parameter C, i.e., C = k * ΔH. The compensation coefficient k is determined based on the target thickness of the aluminum foil, the rolling speed, and the material of the rolls, and its value ranges from 0.6 to 1.2. The thinner the target thickness of the aluminum foil, the larger the value of k, and the higher the rolling speed, the smaller the value of k. For example, when the target thickness is 0.02 mm and the rolling speed is 600 m / min, k is 0.95, and when the target thickness is 0.1 mm and the rolling speed is 300 m / min, k is 0.75. The specific value is determined by looking up a reference table obtained by fitting historical rolling data.

[0014] For example, on an aluminum foil rolling production line, a laser thickness gauge positioned at the exit is used to monitor the moving aluminum foil in real time. The probe of the laser thickness gauge performs a reciprocating transverse scan along the width direction of the aluminum foil between its two edges, that is, it moves from one edge of the aluminum foil to the other edge and then moves in the opposite direction, repeating this cycle, and acquires continuous thickness measurements at a fixed sampling frequency, thereby forming a raw thickness data sequence containing transverse position coordinates and corresponding thickness values.

[0015] Specifically, multiple continuous thickness ranges are pre-defined according to the aluminum foil production process standards. For example, if the target thickness is set to 0.08 mm, 0.075 to 0.078 mm is classified as the thinner range, 0.078 to 0.082 mm as the acceptable range, and 0.082 to 0.085 mm as the thicker range. The classification algorithm uses a threshold-based decision tree. It receives the input thickness value and compares it step by step with the preset boundary thresholds of each range, mapping each thickness data point to the corresponding thickness range label. After the range division is completed, the distribution density of data points in each range is statistically analyzed. When the proportion of data points in the thinner or thicker range exceeds 15%, the fluctuation amplitude Δh of the thickness value in that range is extracted as a fluctuation feature. The degree of roll deformation is determined based on the fluctuation feature. If Δh is greater than 0.003 mm and appears continuously within 5 consecutive sampling periods, it is determined that the roll has deformation caused by local wear. The compensation parameter λ is calculated based on the judgment result, where λ = k × Δh, and k is the compensation coefficient, which is 1.2 for the thinner range and 0.8 for the thicker range. This compensation parameter is used to adjust the rolling pressure or roll gap in the subsequent process.

[0016] To improve classification accuracy, the raw thickness data needs to be filtered and smoothed before being input into the classification algorithm. This removes anomalous jumps caused by equipment vibration or oil contamination on the aluminum foil surface, ensuring that the data used in the classification accurately reflects the actual thickness of the aluminum foil. Specifically, a combination of sliding window midpoint filtering and mean smoothing is used: a window of 5 sampling points is used, and the median thickness value within the window is first taken to suppress impulsive jumps. Then, a second smoothing is performed using mean filtering with a window length of 3. Sampling points where the deviation before and after filtering exceeds three times the standard deviation of the local mean are identified as outliers and replaced with neighborhood interpolation results. After completing the above filtering and smoothing, a thickness distribution matrix is ​​constructed based on the classification results.

[0017] The rows of this matrix represent the scanning time series along the length of the aluminum foil, and the columns represent different scanning positions along the width of the aluminum foil. Each element in the matrix is ​​the interval label obtained after the thickness data at the corresponding position has been processed by a classification algorithm. In this way, massive and disordered continuous thickness values ​​are transformed into a structured state distribution matrix, which can intuitively reflect the thickness uniformity distribution characteristics of the aluminum foil across the entire sheet, providing a precise data foundation for automatic adjustment of rolling mill roll gaps or sheet shape control.

[0018] S102. Extract the thickness extrema from the boundaries of each thickness interval in the thickness distribution matrix, and determine the local stress threshold through the mapping relationship between the thickness extrema and the allowable stress of the material. Specifically, the local stress threshold σ_th is calculated based on the allowable stress σ_allow and the thickness extrema t_max and t_min. For metallic materials, the allowable stress is usually taken as 0.6 to 0.8 times the yield strength of the material as a benchmark value. The calculation of the local stress threshold takes into account the stress concentration effect caused by thickness variation, and is corrected using a thickness ratio coefficient k, where k = t_max ÷ t_min. When the thickness ratio coefficient k is greater than 1.5, the local stress threshold is σ_th = 0.7 × σ_allow ÷ k; when k is less than or equal to 1.5, the local stress threshold is σ_th = 0.8 × σ_allow. For example, for Q235 steel with a yield strength of 235 MPa, if the thickness ratio coefficient is 2.0, the local stress threshold is 82 MPa; if the thickness ratio coefficient is 1.2, the local stress threshold is 156 MPa.

[0019] Obtain the thickness distribution matrix, extract the boundaries of each thickness interval in the thickness distribution matrix, and take the upper and lower boundary values ​​of each thickness interval as the thickness extrema of that interval. Obtain a pre-established reference table of thickness extrema and allowable material stress, and look up the corresponding allowable material stress value in the reference table according to the thickness extrema as the local stress threshold corresponding to each thickness interval. The reference table records that the allowable stress range corresponding to the thickness range of 0.5 mm to 10 mm is 150 MPa to 450 MPa. Calculate the stress difference Δσ between the local stress threshold and the preset yield limit, and construct a stress gradient vector G according to the arrangement number of the thickness intervals. The i-th component of vector G is taken as the stress difference Δσi corresponding to the i-th thickness interval. Calculate the magnitude |G| of the stress gradient vector G, and multiply the magnitude |G| by the thickness coefficient k to obtain the tensile load value F corresponding to each thickness interval, where k is set to 0.8 to 1.2 according to the material type. If the tension load value F is greater than the preset critical tension of 50 kN, a tension adjustment command is generated based on the tension load value. When F is between 50 kN and 80 kN, the adjustment command sets the tension increment to 5 kN. When F exceeds 80 kN, the adjustment command sets the tension increment to 10 kN.

[0020] In one possible implementation, based on the aforementioned thickness distribution matrix, the thickness fluctuation characteristics on the aluminum foil sheet are further quantitatively analyzed. Specifically, the system traverses the thickness distribution matrix, identifying the locations where thinner and thicker interval labels alternate; these locations characterize the boundaries of each thickness region. Within each thickness region, the system extracts the thickness extrema and their corresponding lateral coordinates, forming a set of fluctuation characteristic parameters. When the minimum value in the thinner region is more than 0.015 mm below the target thickness, or the maximum value in the thicker region is more than 0.015 mm above the target thickness, it is determined that local deformation of the roll has occurred. The system calculates compensation parameters based on the deviation of the extrema, using 80% to 90% of the deviation as the adjustment range for rolling force or roll gap for subsequent process optimization. This quantitative analysis method transforms the thickness fluctuation characteristics from a qualitative description into a quantitative indicator that can be directly used for deformation determination and parameter compensation.

[0021] It should be noted that the boundaries of thickness regions are often where aluminum foil deformation is most severe and where the thickness gradient changes the greatest. During the aluminum foil rolling process, these boundary regions are prone to stress concentration. To accurately capture the physical state of these critical nodes, the system performs a local search within the identified boundary neighborhood to extract the thickness extrema.

[0022] For example, for the boundaries of regions marked as thin, the algorithm extracts the minimum thickness value within that local area; for the boundaries of regions marked as thick, it extracts the maximum thickness value. The region assignment of boundary points follows a forward assignment principle: when the thickness label of the i-th data point differs from that of the (i-1)-th data point, the boundary point is assigned to the region type of the (i-1)-th data point. The extraction process uses a fixed-size sliding window, set to 15 data points before and after the boundary point, covering a total of 31 consecutive data points in their neighborhood. For thinner region boundaries, the algorithm compares the actual thickness value h point by point within the 31-point window, recording the position with the smallest value as the minimum thickness point; for thicker region boundaries, it selects the position with the largest thickness value within the window as the maximum thickness point. When the boundary is less than 15 data points from the start or end of the aluminum foil, the window size is reduced accordingly to the number of usable data points. These thickness extremes directly reflect the worst dimensional state of the aluminum foil in a local area. Furthermore, after obtaining the thickness extremes at each boundary, these geometric dimensional features need to be converted into mechanical control indicators.

[0023] It is understandable that the allowable stress of a material refers to the maximum working stress that aluminum foil can withstand during rolling or subsequent winding to ensure that the foil does not break and does not undergo plastic yielding. In actual operation, the allowable stress of aluminum foil is not a fixed constant, but is closely related to its local actual thickness. Based on the thickness extreme values ​​h_min and h_max extracted above, we substitute them into the allowable stress mapping relationship to obtain the allowable stress σ_p at the corresponding location, which is used as a control index for subsequent tension and rolling force. The specific mapping adopts a piecewise linear form: σ_p = (σ_0 - k * (h_0 - h)) where σ_0 is the reference allowable stress at the reference thickness h_0, h is the local actual thickness, and k is the thickness sensitivity coefficient. For conventional 1xxx series aluminum foil, h_0 = 0.1 mm, σ_0 = 120 MPa, and k = 400 MPa / mm; when h is less than 0.02 mm, it enters the thin specification zone, and k is adjusted to 600 MPa / mm to reflect the characteristic of accelerated strength attenuation in the thin zone. The conversion process is as follows: First, extract h_min and h_max from the thickness distribution curve; then substitute them into the above segmented mapping to obtain the lower limit σ_p,min and upper limit σ_p,max of the allowable stress along the width or length direction of the roll material; finally, take 0.7 times σ_p,min as the upper limit of the working stress, and calculate the allowable tension T_max=σ_p,min*0.7*h_min*B, where B is the width of the aluminum foil, which serves as the input index for the closed-loop control of the winding tension, thus completing the conversion from geometric dimensional characteristics to mechanical control indicators.

[0024] Specifically, the system pre-establishes and stores a mapping relationship between thickness extremes and allowable stress of the material. This mapping relationship is constructed based on the material properties of aluminum foil and historical tensile test data. The specific steps are as follows: collect tensile test data of aluminum foil from the same batch at different thicknesses, collecting at least 30 sets of valid data for each thickness point, covering a thickness range of 0.006 mm to 0.010 mm, with an interval of 0.0005 mm. Perform statistical analysis on the test data for each thickness point, removing outliers that deviate from the mean by more than twice the standard deviation, and take the average tensile strength of the remaining data as the allowable stress value corresponding to that thickness. Using thickness as the independent variable h and allowable stress as the dependent variable σ, a cubic polynomial fitting is used to establish the mapping relationship σ = a × h³ + b × h² + c × h + d, where a, b, c, and d are fitting coefficients. After fitting, calculate the correlation coefficient R², requiring R² to be greater than 0.95 to ensure that the fitting accuracy meets the requirements of engineering applications. Finally, store the fitting coefficients in the system database for real-time calculation of the allowable stress value corresponding to any thickness.

[0025] The allowable stress benchmark value refers to the engineering value, corrected based on the material's allowable stress and local cross-sectional area and thickness extremes, serving as a reference threshold for subsequent strength verification. A lookup table interpolation method is preferentially used to map the thickness extremes to the allowable stress benchmark value: a pre-established table mapping thickness ranges to benchmark values ​​is created. For example, a minimum thickness value between 8 and 12 mm corresponds to a benchmark value of 0.95 times the material's allowable stress; a minimum thickness value between 5 and 8 mm corresponds to 0.85 times; and a minimum thickness value below 5 mm corresponds to 0.70 times. Linear interpolation is used between adjacent nodes in the table to determine intermediate values. When sample data is sparse or the thickness distribution spans a large range, or when there are insufficient lookup table nodes, polynomial fitting is used as a supplement. The mapping is established in the form sigma = a0 + a1*t + a2*t^2, where t is the minimum thickness value, sigma is the corresponding allowable stress benchmark value, and a0, a1, and a2 are fitting coefficients obtained from regression based on measured data. The switching between the two methods is determined by whether the number of lookup nodes is no less than 5, thus ensuring that the reduction ratio of the baseline value can be quantitatively determined when the minimum thickness value is lower, and the overall reduction range is controlled within the range of 5% to 30%.

[0026] In one embodiment, the system samples the thickness data of the aluminum foil boundary region point by point in 0.5 mm increments, scans the 5 mm edge bands on both sides along the width direction, and takes the local minimum value within each sampling window as the minimum thickness value at that boundary. When the thickness difference between three adjacent sampling points is less than 0.001 mm, it is determined to be a stable minimum value and extracted. For the extracted minimum thickness value at the boundary, the system calculates the allowable stress reference value corresponding to the aluminum foil under the minimum value state by calling the above-mentioned thickness-stress mapping relationship, which serves as a reference for subsequent tensile stress control.

[0027] Preferably, the thickness anomaly boundary refers to a continuous chain of pixels in the aluminum foil thickness detection data where the thickness gradient between adjacent measuring points exceeds 0.3 micrometers per millimeter. This boundary is obtained from the thickness map after gradient extraction and connected component labeling. Each boundary corresponds to a local region to be evaluated. The system first reads the allowable stress reference value S0 for the corresponding aluminum foil grade, state, and temperature from the material property database. For industrial pure aluminum soft foil, the value typically falls between 80 and 120 MPa, while for semi-hard foil, it typically falls between 130 and 180 MPa. Then, the value is reduced according to the formula St = S0 * k1 * k2 * k3 to obtain the local stress threshold St specific to this boundary. Where k1 is the rolling speed coefficient, it is 0.95 when the speed is below 600 m / min, between 0.95 and 0.85 by linear interpolation when the speed is between 600 and 1200 m / min, and 0.80 when the speed is above 1200 m / min; k2 is the tension distribution coefficient, determined by the ratio r of the maximum and average tension of the current cross-section, it is 0.95 when r is not greater than 1.1, 0.90 when r is between 1.1 and 1.3, and 0.80 when r is greater than 1.3; k3 is the comprehensive safety factor, it is 0.85 for normal production, and 0.75 for continuous high-speed or large thickness fluctuation conditions. Taking a soft foil benchmark value of 100 MPa, a rolling speed of 900 m / min, r equal to 1.2, and normal working conditions as an example, St equals 100 * 0.90 * 0.90 * 0.85, which is approximately 68.9 MPa, and is used as the local stress threshold of this boundary for subsequent judgment.

[0028] S103. Obtain the overall tensile force value applied by the tension roller to the entire width of the aluminum foil, convert the overall tensile force value into regional stress components according to the thickness ratio of each region, and obtain the tensile deformation risk value based on the comparison result of the regional stress components and the local stress threshold.

[0029] Obtain the overall tensile force F applied by the tension roller to the entire width of the aluminum foil, in Newtons. Obtain the contact area A between the tension roller and the aluminum foil and the coefficient of friction μ of the roller surface. The coefficient of friction is used as a correction factor for subsequent calculation of the transverse stress distribution, with a value ranging from 0.15 to 0.35. Calculate the average normal stress σ0 by dividing F by A. Obtain the thickness percentage of each region corresponding to the entire width of the aluminum foil, which is the ratio of the thickness of that region to the average thickness of the entire width. Calculate the average normal stress σ0 inversely based on the thickness percentage of each region. Regions with a larger thickness percentage experience a smaller stress component, and regions with a smaller thickness percentage experience a larger stress component, thus obtaining the corresponding regional stress component σi for each region. Obtain a preset local stress threshold σth, which is determined based on the yield strength of the aluminum foil material, and is taken as 0.6 to 0.8 times the material's yield strength. For 3003 aluminum foil, it is taken as 110 to 145 MPa. If the regional stress component σi is greater than the local stress threshold σth, the difference Δσi = σi - σth is calculated as the tensile deformation risk value for that region. If σi is less than or equal to σth, the risk value for that region is recorded as 0. The regions are arranged in order of position along the width of the aluminum foil, constructing a one-dimensional risk gradient vector with a length equal to the number of regions. The maximum value is extracted from the risk gradient vector, and the resulting maximum risk value is defined as the plastic elongation extreme value Rmax. A preset elongation safety threshold Rsafe is obtained, set according to production process requirements, with a value ranging from 20 to 35 MPa. If Rmax is greater than Rsafe, the over-limit ratio k = Rmax ÷ Rsafe is calculated, and a roll speed reduction command is generated based on the ratio k, with a reduction range of 5% × k to 10% × k of the current roll speed. If Rmax is less than or equal to Rsafe, the current roll speed remains unchanged.

[0030] In one possible implementation, the system collects the total longitudinal tension F of the entire aluminum foil width using a force sensor at the end of the tension roll shaft, with a sampling frequency of 1000Hz. A sliding window is applied to F, with a window length of 200ms and a step size of 50ms. The standard deviation σ and peak-to-peak value ΔF within the window are calculated as fluctuation characteristics. Abnormal tension fluctuations are determined when σ exceeds 1.5% of the rated tension, or when ΔF exceeds 5% of the rated value for three consecutive windows. The fluctuation characteristics, along with synchronously collected radial displacement d and axial temperature rise ΔT of the roll, are fed into a pre-trained XGBoost classification model. The output roll deformation type includes three categories: thermal expansion protrusion, mechanical deflection, and roll surface wear. The model input is a 7-dimensional feature vector [σ, ΔF, mean d, peak d, ΔT, mean F, rolling speed v], and the output is the category label and confidence level. Compensation parameters are generated based on the deformation type: For thermal expansion bulges, the roll gap is reduced by 0.8 μm and the coolant flow rate is increased by 5 L / min for every 10°C increase in ΔT; for mechanical deflection, the bending roll force is adjusted by 0.3 kN for every 1 μm increase in d; for roll surface wear, the target tension is reduced by 2% and a roll change warning is triggered. After the compensation parameters are sent to the rolling mill PLC, the next sampling cycle checks whether σ and ΔF have fallen back below the threshold. If not, an iterative adjustment is performed.

[0031] Specifically, obtaining the overall tensile force value typically relies on a tension sensor deployed below the tension roller bearing housing. In actual operation, the sensor collects the radial resultant force signal acting on the tension roller in real time. After receiving these raw electrical signals, the system performs analog-to-digital conversion and low-pass filtering to eliminate high-frequency noise caused by equipment vibration or electromagnetic interference. The signal-conditioned data, combined with the wrap angle parameters of the tension roller, undergoes trigonometric function calculation to ultimately reconstruct the actual overall tensile force value acting on the entire cross-section of the aluminum foil. Furthermore, after obtaining the macroscopic overall tensile force value, it needs to be refined to the local microscopic level, that is, the overall tensile force value is converted into regional stress components according to the thickness ratio of each region.

[0032] It should be noted that due to the inherent thickness variations of aluminum foil in the transverse direction, the entire strip is not an ideally uniformly stressed body. Under macroscopic tensile force, the actual tensile force distributed across different thickness regions and the true internal stress differ significantly.

[0033] For example, to achieve accurate mechanical conversion, the system first needs to calculate the cross-sectional area ratio of each region. This calculation relies on a thickness distribution matrix constructed by a pre-existing online laser thickness gauge scanning the transverse surface of the aluminum foil at equal intervals. Each column in the matrix corresponds to the instantaneous thickness value at a sampling location, each row corresponds to one scanning cycle, the scanning interval is 5 mm, and the scanning cycle is 200 milliseconds. Based on this matrix, the system divides the transverse surface of the aluminum foil into n consecutive micro-regions, where n ranges from 20 to 50, and takes the arithmetic mean of the column thickness values ​​covered by each micro-region to obtain the average thickness hi of that region. Considering that the actual width wi of each micro-region may differ after cutting or edge trimming, the system further collects the width value corresponding to each micro-region, using hi*wi to represent the cross-sectional area si of that region, and then uses the ratio of si to the sum of the cross-sectional areas of all micro-regions as the proportion ki of that region, i.e., ki = si / Σsj, where j ranges from 1 to n. This proportion accurately reflects the weight of the cross-sectional area of ​​each local region in the total cross-sectional area of ​​the entire aluminum foil. When a certain region is thicker or wider, its cross-sectional area is larger, and the corresponding proportion increases accordingly, and vice versa. When the width of each micro-element region is consistent, the calculation naturally degenerates into a linear thickness proportion, thus ensuring the consistency of the calculation logic under different cutting conditions.

[0034] In one embodiment, based on the obtained regional thickness proportions, the system executes a conversion logic to regional stress components. Under the tensile force applied by the tension roller, due to the differences in thickness in various local areas, even if the overall tensile strain is relatively uniform, the stress levels corresponding to different thickness areas will be different. The system uses a simplified calculation method to multiply the overall tensile force value by the thickness proportion of a certain area to obtain an approximate value of the stress components in that area. This method assumes that the thickness proportion can approximately reflect the stress distribution characteristics and ignores the influence of complex factors such as edge effects and material heterogeneity. Through this conversion calculation based on thickness distribution, the system can reasonably estimate the stress component values ​​of each local area, thereby obtaining the stress distribution state on the aluminum foil surface. This conversion method considers the influence of uneven aluminum foil thickness on stress distribution and can better reflect the local stress difference characteristics compared with traditional mean calculation, but the calculation results are still within the scope of engineering approximation. This conversion method fully considers the mechanical nonlinear characteristics caused by uneven aluminum foil thickness and avoids the defect of traditional mean calculation that masks local stress concentration.

[0035] Preferably, after reconstructing the micro-stress field, the system obtains the tensile deformation risk value based on the comparison between the regional stress components and the local stress threshold. The local stress threshold is determined based on the material's yield strength σy, specifically taking a value of 0.6 to 0.8 times the material's yield strength. For common aluminum alloys, when the yield strength is 300 MPa, the local stress threshold is set to 180 to 240 MPa; for steel, when the yield strength is 500 MPa, the local stress threshold is set to 300 to 400 MPa. The system calculates the ratio R between the regional stress components and the corresponding local stress threshold. A value of R less than 0.7 indicates low risk, 0.7 ≤ R < 1.0 indicates medium risk, and R ≥ 1.0 indicates high risk, thus completing the assessment of the tensile deformation risk value.

[0036] The system iterates through each local region, extracting the regional stress component σ obtained from the finite element method at the current moment. This stress component includes longitudinal stress along the conveying direction and transverse stress perpendicular to the conveying direction. Simultaneously, it retrieves the corresponding local stress threshold σ_th for that region. This threshold is determined based on the tensile strength of the conveyor belt material, typically set to 0.7 to 0.85 times the material's tensile strength as a safety threshold. For example, for a rubber conveyor belt with a tensile strength of 15 MPa, the local stress threshold is set to 10.5 MPa to 12.75 MPa. The comparison process is achieved by constructing a dimensionless risk index R, calculated as R equal to σ divided by σ_th. When R is less than 0.6, it indicates that the current local stress is within the safety margin, and the system assigns a tensile deformation risk value V of 0 to 30. When R is between 0.6 and 0.85, the risk value V is set to 30 to 70, indicating a need for attention. When R is between 0.85 and 1, the risk value V is set to 70 to 90, indicating a warning state. If the R value is equal to or greater than 1, it means that the actual stress in the local area has approached or reached the plastic yield limit of the material, which is very easy to induce microcracks or directly lead to band breakage. At this time, the tensile deformation risk value V output by the system is set to 90 to 100, and the corresponding control intervention mechanism is triggered, including reducing the conveying speed or starting the emergency shutdown procedure.

[0037] S104. If the tensile deformation risk value is greater than the risk assessment threshold, a regression prediction algorithm is used with the tensile deformation risk value as the input feature to predict the stress release amount. The risk assessment threshold is set to a value between 0.65 and 0.75, preferably 0.7. The specific prediction process is as follows: the tensile deformation risk value R is input into a pre-trained regression prediction model. This model is constructed using a support vector regression algorithm or a random forest regression algorithm. After feature mapping and weight calculation, it outputs the stress release amount Q, in megapascals (MPa). This release amount Q represents the amount of stress that can be eliminated through the segmentation process, and its value range is typically between 50 and 200 MPa. When R = 0.8, the model predicts Q to be approximately 120 MPa, indicating that a larger stress release measure is required.

[0038] The tensile deformation risk value is obtained, and a preset risk judgment threshold of 0.65 is set. If the tensile deformation risk value is greater than the risk judgment threshold, a support vector regression algorithm is used for prediction. The training samples include 500 sets of tensile deformation risk values ​​and corresponding depth-of-cut adjustment coefficients collected in historical production. A radial basis function kernel is selected, with the penalty parameter C set to 10 and the kernel parameter γ set to 0.1. The tensile deformation risk value is used as the input feature, and the corresponding depth-of-cut adjustment coefficient K1 is output, with a value range of 1.0 to 1.5. A preset tool entry depth reference value d0 of 2 mm is obtained, and the target entry depth d = d0 × K1 is calculated. The current winding radius R during the aluminum foil winding process is obtained, and a preset tension coefficient K2 of 150 N / mm² is obtained. The tension attenuation compensation ΔT = d × R × K2 is calculated, where ΔT is in the dimension of torque. The initial winding torque T0 is obtained, and the target winding torque T = T0 - ΔT is calculated.

[0039] In one possible implementation, after acquiring the tensile deformation risk values ​​of each local area, the system executes a risk status determination logic, namely, determining whether the tensile deformation risk value is greater than a preset threshold of 0.75. Specifically, the system first extracts fluctuation features from the sampling sequences of the strip surface thickness sensor and tension sensor, including thickness fluctuation amplitude, fluctuation frequency, and tension abrupt change count, and normalizes them before inputting them into a classification algorithm, outputting the tensile deformation risk value of each local area, with a value range of 0 to 1. When the risk value of a certain local area is greater than 0.75, it is determined that the corresponding roll has a deformation trend, and the roll deformation determination process is entered. The deformation location is further confirmed by combining the roll radial displacement and roll gap deviation. When the risk value is between 0.4 and 0.75, it is marked as a warning state; when the risk value is less than 0.4, it is determined to be a normal state. For rolls determined to be deformed, the system generates compensation parameters based on the product of the risk value and the roll gap deviation. The adjustment range of the compensation parameters is controlled between -0.5 mm and +0.5 mm, and the parameters are sent to the mill hydraulic control unit to perform roll gap compensation.

[0040] It is understandable that this preset threshold is not an arbitrary empirical constant, but a critical safety level comprehensively calibrated based on the yield strength limit of the aluminum foil material and the tension control accuracy of the production line. The specific calibration rule is as follows: First, obtain the yield strength σy of the aluminum foil material, and take 70% to 85% of it as the safety margin coefficient α. Simultaneously, combine this with the measured accuracy δ of the production line's tension control system, where δ represents the tension fluctuation range between adjacent measuring points, typically between ±2% and ±5%. The preset threshold T is determined according to the following rule: T equals α multiplied by σy minus 3 times the δ value, where 3 times the δ value represents the abnormal fluctuation amount corresponding to three standard deviations. For example, when the yield strength of the aluminum foil is 150 MPa, the safety margin coefficient is 0.8, and the tension control accuracy δ is 3%, the preset threshold calibration is 120 MPa minus 13.5 MPa, ultimately taking a value of approximately 106 MPa. When the risk value of a certain micro-element region exceeds this threshold, it indicates that the internal lattice of that region is in a highly stretched metastable state. Conventional tension fine-tuning can no longer effectively resolve the potential mechanical crisis, and a targeted prediction mechanism must be activated to deal with the stress redistribution phenomenon in subsequent processes.

[0041] It should be noted that, for the aforementioned high-risk conditions, the system adopts the ridge regression prediction algorithm, using the tensile deformation risk value as the input feature, and combines it with the stress release samples collected under historical working conditions for model training. The regularization coefficient is set to 0.1, and the iteration convergence threshold is set to 0.001. After training, the current risk value is substituted into the fitted ridge regression model, and the corresponding stress release amount is output, in megapascals, with a value range generally between 5 and 25.

[0042] Specifically, the stress release during slitting is a key mechanical evolution indicator in the aluminum foil processing industry. In the continuous production process of aluminum foil, wide rolls of material ultimately need to be longitudinally cut into multiple narrow finished products using slitting equipment such as disc shears. Before slitting, the localized concentrated stress in high-risk areas is laterally restrained by adjacent low-stress areas, maintaining a fragile macroscopic mechanical equilibrium. However, once the slitting tool cuts off the lateral continuity of the material, this constraint boundary disappears instantly, and the elastic strain energy originally accumulated locally bursts forth in the form of stress release. The stress release during slitting is defined as the difference between the residual stress in the area before slitting and the measured stress after slitting, measured in megapascals (MPa). This parameter is obtained by X-ray diffraction or strain gauge method. Before slitting, the initial stress σ1 is measured at the location to be cut, and after slitting, the residual stress σ2 is measured at a distance of 5 mm from the cut edge. The difference between the two is the stress release during slitting, Δσ = σ1 - σ2. For aluminum foil with a thickness of 0.1 to 0.3 mm, the stress release is typically in the range of 15 to 80 MPa. When it exceeds 60 MPa, the narrow strip after slitting is prone to severe lateral bending deformation or edge wavy lines. Therefore, the stress release during slitting characterizes the mechanical equivalent of stress inside the high-risk area diffusing to the periphery and transforming into macroscopic deformation when the integrity of the original sheet is compromised.

[0043] For example, to quantify this mechanical evolution process, a polynomial regression prediction algorithm is deployed in the system. The algorithm relies on a historical production sample library, which records the tensile deformation risk value x of different batches of aluminum foil during the rolling stage, and the measured stress release y of the corresponding batches during the slitting process, with a sample size of no less than 500 groups. The system extracts paired xy data and fits them using a third-order polynomial y = a0 + a1*x + a2*x^2 + a3*x^3, where a0 to a3 are undetermined coefficients. The solution is obtained through gradient descent iteration, with a learning rate of 0.01 and an upper limit of 2000 iterations. The convergence criterion is that the sum of squared residuals is less than 0.05. During training, the training set and validation set are divided in a 7:3 ratio. The training is stopped early when the mean squared error of the validation set does not decrease for 20 consecutive rounds to avoid overfitting. The trained regression model can output the relationship between the strain energy accumulation and release of aluminum foil in the stage approaching the plastic limit, with the prediction error controlled within ±3%, which can be used for subsequent process parameter adjustment.

[0044] In one embodiment, when the real-time monitored tensile deformation risk value exceeds 0.85, the system triggers a prediction mechanism. The system uses this risk value R as the core input feature and feeds it into a pre-trained multiple linear regression model. This model constructs a high-dimensional hyperplane mapping relationship between the input features and the shear stress release amount. After receiving the dimensionless risk index, the model calculates the deviation ΔR of the risk value relative to the threshold 0.85, where ΔR = R - 0.85. The model performs forward computation along the pre-trained hyperplane, simultaneously considering both the absolute magnitude of the risk value R and the deviation ΔR as input parameters, performing continuous numerical mapping operations. This regression inference process is completed within 50 milliseconds, outputting a specific numerical value as the shear stress release amount.

[0045] After obtaining the stress release amount, the system extracts the current longitudinal length coordinates of the aluminum foil. These coordinates are derived from the real-time displacement data collected by the encoder on the production line. Simultaneously, it determines the transverse region number based on the layout of the slitting cutter, typically labeled 1 to N from left to right. The system binds and stores the stress release amount, longitudinal coordinates, and transverse numbers, forming a three-dimensional array-structured stress release topology map. The array is arranged in ascending order of timestamps and updated every second. The system transmits this topology map in real-time to the programmable controller of the slitting equipment via industrial Ethernet. The controller compares the stress release amount within a range of 0.5 to 2 meters in front of the current processing position with a preset threshold of 8 MPa. When an area exceeding the threshold is detected, an early warning signal is triggered, thus achieving early detection of high stress release areas.

[0046] S105. Calculate the narrow band width change at the instant of cutting based on the stress release amount and elastic modulus. Specifically, the width change of each narrow band is calculated using ΔW = σ × L / E, where ΔW is the narrow band width change, σ is the stress release amount, L is the original narrow band width, and E is the elastic modulus. After obtaining the width change of each narrow band, calculate the difference in width change between adjacent narrow bands to obtain a difference sequence. Taking the i-th narrow band and the (i+1)-th narrow band as an example, the difference is calculated as Di = ΔWi+1 - ΔWi. Summate the differences of all adjacent narrow bands to obtain the total difference Dtotal = D1 + D2 + ... + Dn-1, where n is the total number of narrow bands. Divide the total difference Dtotal by the total number of narrow bands n minus 1 to obtain the average difference Daverage = Dtotal / (n-1). This average difference is the predicted shrinkage difference value. For example, when there are 5 narrow bands, the width variations of each narrow band are 2.1 mm, 2.3 mm, 2.5 mm, 2.4 mm, and 2.6 mm, respectively. The adjacent differences are 0.2 mm, 0.2 mm, -0.1 mm, and 0.2 mm, respectively, with a total difference of 0.5 mm and a predicted shrinkage difference of 0.125 mm.

[0047] Obtain the elastic modulus E (in gigapascals) from the preset material parameter table. Divide the shear stress release by the elastic modulus to obtain the shear strain value ε. Narrow strips refer to sub-strips formed after longitudinally cutting a steel strip. Obtain the initial width W0 of each narrow strip. Multiply the shear strain value by the initial width to calculate the width change ΔW. The calculation formula is ΔW equal to ε multiplied by W0. Obtain the distance L between two adjacent guide rollers on the production line. Calculate the ratio of the width change to the guide roller distance to obtain the offset angle. Multiply this by the guide roller distance to obtain the lateral offset d of each narrow strip. The calculation formula is d equal to ΔW divided by 2. When the lateral offset is greater than 0.5 mm, correction is required. A pre-trained linear regression model is used for prediction. This model uses the shear stress release, lateral offset, and narrow strip width as input features and outputs a shrinkage difference prediction value. This prediction value represents the shrinkage deviation of the narrow strip relative to the standard size after cooling.

[0048] In one possible implementation, after acquiring the amount of stress released during the cutting process, the system first extracts the fluctuation characteristics of the narrow strip width, including the fluctuation amplitude A and the fluctuation frequency f. The fluctuation amplitude is calculated by the width range of continuous measurement points, and the fluctuation frequency is determined by the number of peaks per unit time. The system compares the fluctuation amplitude A with a preset threshold of 0.5 mm. When A is greater than 0.5 mm, it determines that the roll has deformed. Subsequently, the system introduces the key physical parameter of the elastic modulus E of the aluminum foil material and calculates the width change ΔW of the narrow strip at the moment of cutting, combined with the amount of stress released during the cutting process σ, where ΔW = σ × L ÷ E, and L is the effective length of the narrow strip. Based on the roll deformation determination result and the width change ΔW, the system generates compensation parameters, including the cutting position offset and the tension adjustment. The cutting position offset is taken as 0.8 times ΔW, and the tension adjustment is determined linearly according to the fluctuation frequency f. When f is in the range of 2 Hz to 5 Hz, the tension adjustment is 5% to 15% of the reference tension.

[0049] Understandably, the modulus of elasticity is an important mechanical indicator characterizing a material's resistance to elastic deformation, reflecting the direct proportional relationship between stress and strain in aluminum foil under stress. In the aluminum foil slitting process, aluminum foils of different alloy grades and rolling passes exhibit varying degrees of internal crystal density, resulting in different modulus values. The system pre-establishes a material property database based on tensile test data from historical production batches, recording the measured modulus values ​​of each alloy grade under different rolling passes. The database is organized using a three-level index structure: the first-level index is the alloy grade, such as 1060, 1070, 8011, etc.; the second-level index is the rolling pass, such as roughing, intermediate, and finishing rolling; and the third-level index is the thickness specification. When a new batch of aluminum foil enters the slitting process, the system automatically reads the material identification code of that batch, parsing out information such as alloy grade 8011, rolling pass being precision rolling, and thickness being 0.01 mm. Based on this, the system locates the corresponding elastic modulus benchmark value of 68,500 MPa in the database, which serves as the core conversion coefficient for subsequent geometric deformation quantification. If no perfect match is found in the database, the system retrieves the elastic modulus value of adjacent thickness specifications and calculates the reference value for the current batch using linear interpolation.

[0050] Specifically, calculating the narrow strip width change based on the stress release and elastic modulus is a process of converting the internal mechanical state at the moment of cutting into a measurable geometric quantity. After the wide aluminum foil is longitudinally cut by the disc shear, the stress originally constrained laterally by the adjacent strips is released at the cut, causing the narrow strip's lateral dimension to adjust. The stress-strain conversion module in the system takes the stress release and the elastic modulus of the region as input, calculates based on the linear relationship between lateral stress and lateral strain in the generalized Hooke's law, outputs the lateral strain ε of the region, and then multiplies ε by the theoretical width of the target narrow strip set by the cutting tool to obtain the width change Δw of the narrow strip. The conversion relationship is ε=σ / E, Δw=ε*w0, where σ is the stress release in Pascals, E is the lateral elastic modulus of the region in Pascals, ε is the dimensionless lateral strain, w0 is the theoretical width of the target narrow strip, and Δw is the narrow strip width change, with positive values ​​indicating expansion and negative values ​​indicating contraction. Taking aluminum foil as an example, when σ is 5 MPa, E is 70 GPa, and w0 is 100 mm, ε is approximately 7.14 x 10 to the power of -5, corresponding to Δw of approximately 7.14 micrometers. This value reflects the deviation of the narrow band from the theoretical width after it is freed from the constraints of adjacent materials.

[0051] For example, the width variation data calculated above is typically measured in micrometers and stored with high precision as floating-point numbers. The system arranges the width variation data of each narrow band at different longitudinal coordinate points in ascending order of longitudinal coordinates, forming an ordered one-dimensional data sequence. Then, it performs linear interpolation connecting the longitudinal coordinates as the horizontal axis and the width variation value as the vertical axis to construct a virtual contour curve reflecting the dynamic fluctuation of the narrow band width. This virtual contour curve serves as the input for defect identification in subsequent processing. The system slides a window with a length of 20 sampling points along the curve, statistically analyzing the range and standard deviation within the window. When the range exceeds 8 micrometers or the standard deviation exceeds 2.5 micrometers, the segment is marked as a suspected defect area. Simultaneously, a first-order difference operation is performed on the curve. If the absolute value of the width variation difference between adjacent sampling points exceeds 3 micrometers, it is determined to be a sudden change point, and the coordinates of the sudden change point and the suspected defect area are sent together to the subsequent defect classification and location module.

[0052] It should be noted that in actual continuous slitting production lines, wide aluminum foil is usually simultaneously cut into multiple parallel narrow strips. Due to the extremely uneven distribution of thickness tolerance, flatness, and residual stress left over from the previous rolling in the transverse direction of the master roll, the amount of cutting stress released at the moment of cutting varies significantly among the narrow strips at different locations. This inconsistency in mechanical state directly determines that the deformation of each narrow strip in the transverse direction cannot be kept synchronized. If the difference in deformation between adjacent narrow strips is too large, it can easily lead to serious production abnormalities such as uneven roll surface, local bulging, or even strip breakage in the subsequent synchronous winding process.

[0053] In one embodiment, to comprehensively assess the risk of deformation inconsistency in multi-band collaborative processing, the system determines the predicted shrinkage difference value by comparing the difference between the width change data of each narrow band. The data analysis module is deployed within the winding control unit and includes a data acquisition subunit, a vector construction subunit, and a difference aggregation subunit, which are respectively used to acquire the raw data of the online thickness and width measurement sensor, construct the lateral deformation vector, and perform difference comparison and weighted calculation. Specifically, the data acquisition subunit extracts the width change data w1, w2, ..., wn of all narrow bands on the same longitudinal section, and assembles them into a lateral deformation vector W by the vector construction subunit. The difference aggregation subunit automatically selects the best comparison strategy based on the width of the master roll and the number of narrow bands: when the total number of narrow bands n is less than or equal to 6 and the cutting is uniform, the adjacent comparison method is used to calculate di=|w(i+1)-wi| in sequence; when n is greater than 6 or there is a significant edge winding tension gradient, the benchmark comparison method is used, with the narrow band wc located at the center of the master roll as the mechanical benchmark, and di=|wi-wc| is calculated.

[0054] Subsequently, the difference aggregation sub-unit performs weighted aggregation on each difference. The weight ki is determined based on the relative position of the narrow strip from the center of the master roll: 0.6 for the central narrow strip, 0.8 for the second outermost narrow strip, and 1.0 for the outermost narrow strip, to reflect the higher sensitivity of edge deformation to winding tension. The predicted shrinkage difference value R is calculated as R=Σki*di / Σki, yielding an equivalent length quantity consistent with the width change. This reflects both the overall deformation dispersion of multiple narrow strips and can be used as a scalar physical quantity in the product calculation with the single-turn winding length in step S106. When R is less than 0.05 mm, it indicates that the deformation pace of each narrow strip is basically consistent; when R is greater than or equal to 0.15 mm, it is determined that there is a risk of extreme deformation misalignment and an early warning is triggered.

[0055] Preferably, after determining the predicted shrinkage difference value, the system maps it to the corresponding longitudinal mileage coordinates. This structured prediction data is pushed to the central control panel of the production line in real time, visualized in the form of multidimensional data tables or heat maps. This intuitive data representation allows on-site operators to clearly grasp the deformation evolution trend of the entire roll of aluminum foil during the slitting process. Furthermore, in a more refined implementation scenario, the system incorporates a temperature compensation mechanism when calculating the elastic modulus. Considering that the friction between the cutting tools generates a large amount of localized heat during high-speed slitting of the aluminum foil, causing a sharp rise in the material temperature of the cut area, typically from the ambient temperature of 25 degrees Celsius to the range of 80 to 150 degrees Celsius, the system uses infrared temperature probes positioned on both sides of the cutter to collect the real-time temperature T of the cut area every 10 milliseconds and compares it with a reference temperature T0 = 25 degrees Celsius to obtain the temperature rise ΔT = T - T0. The system then dynamically reduces the basic elastic modulus E0 using a preset thermodynamic correction function. The corrected elastic modulus E = E0 * (1 - α * ΔT), where α is the temperature decay coefficient, which is typically 0.0003 to 0.0005 degrees Celsius per degree Celsius for commonly used industrial pure aluminum foil. Specific values ​​were obtained from tensile tests conducted on this batch of aluminum foil in the 25-200 degree Celsius range and are stored in the system parameter library using a lookup table. When the temperature rise exceeds 180 degrees Celsius, the system switches to a piecewise correction mode to avoid linear extrapolation distortion. The width change data calculated based on the corrected elastic modulus more accurately reflects the material's physical response under complex working conditions, thereby further enhancing the engineering guidance value of the predicted shrinkage difference.

[0056] S106, Step S106a: Obtain the real-time rotational speed data of the take-up shaft. Calculate the single-turn take-up deviation by multiplying the single-turn winding length corresponding to the real-time rotational speed data with the predicted shrinkage difference value. The single-turn winding length L is calculated using the take-up shaft radius and rotational speed. The single-turn take-up deviation D = L × predicted shrinkage difference value, where L is the single-turn winding length in meters, and the predicted shrinkage difference value is dimensionless. When L is 1.2 meters and the predicted shrinkage difference value is 0.03, the single-turn take-up deviation is 0.036 meters. Step S106b: Calculate the tension fluctuation amplitude. Collect tension sensor data during the take-up process and calculate the difference between the maximum and minimum tension values ​​within 10 consecutive seconds as the fluctuation amplitude. A warning is triggered when the fluctuation amplitude exceeds 15% of the set tension value. Step S106c: Calculate the interlayer slippage distance. Detect the displacement difference between adjacent material layers using a displacement sensor, and the average displacement difference of 10 sampling points is used as the interlayer slippage distance. A compensation mechanism is activated when the slippage distance exceeds 0.5 millimeters. In step S106d, the single-turn winding deviation, tension fluctuation amplitude, and interlayer slip distance are used as input features, and a support vector machine regression model is employed to predict the winding quality score for the next turn. The score ranges from 0 to 100 points; if the score is below 70 points, the winding speed is reduced by 5% to 10%.

[0057] The real-time rotational speed data of the take-up shaft and the corresponding single-turn winding length are obtained. The single-turn winding length is multiplied by the obtained predicted shrinkage difference value to obtain the single-turn winding deviation ΔL. The preset winding tension reference T0 and the narrow band Young's modulus E are obtained, and the stress increment ΔF = E × ΔL / L0 is calculated, where L0 is the initial winding circumference, and the tension fluctuation coefficient k = ΔF / T0 is obtained. The friction coefficient distribution μ and contact width W of each narrow band surface are obtained, and the interlayer slip distance D = k × T0 × W / μ is calculated. The preset slip threshold Dth is set to 0.5 mm. If D is greater than Dth, a pre-trained support vector machine regression model is used. This model uses a radial basis function kernel function, and the training data comes from 2000 sets of samples collected in historical production. It includes four input features: interlayer slip distance D, current tension T, rotational speed n, and winding diameter R. The output is the winding torque command M, and the model cross-validation accuracy reaches over 95%.

[0058] In one implementation, the system first performs an operation to acquire real-time rotational speed data of the take-up shaft.

[0059] Specifically, a rotary encoder or Hall sensor installed on the take-up shaft motor can be used to collect the angular velocity information of the take-up shaft during operation in real time, thereby obtaining high-precision real-time rotational speed data. Based on the real-time rotational speed data obtained above, the system further needs to determine the single-turn winding length corresponding to the real-time rotational speed data.

[0060] Understandably, in film material winding scenarios, the diameter of the material roll on the take-up shaft continuously increases as the winding process progresses. In the entire winding system, the upstream traction roller assembly is responsible for tractioning the film material forward at a constant speed. This traction speed is obtained in real-time feedback from the encoder of the traction motor and is typically maintained between 50 and 200 meters per minute. Even if the rotational speed of the take-up shaft remains constant, the actual length of film wound per turn will increase with the increase in roll diameter. In actual operation, the system collects the linear velocity V of the traction roller assembly and the real-time rotational speed n of the take-up shaft. The current actual roll diameter D is calculated using the formula D = 60V ÷ πn, where V is in meters per minute and n is in revolutions per minute, thus calculating the single-turn winding length.

[0061] The system obtains the current winding circumference by dividing the traction line speed by the real-time rotational speed; this circumference is the single-turn winding length in the current state. The traction line speed is in meters per second (m / s), and the real-time rotational speed is in revolutions per second (rpm). Dividing the two yields the circumference value in meters per revolution (m / rpm). The calculation formula is L = v ÷ n, where L represents the single-turn winding length, v represents the traction line speed, and n represents the real-time rotational speed. This calculation process fully considers the non-linear characteristics of the dynamic changes in the winding diameter during the winding process, ensuring the accuracy of the length data. After obtaining the single-turn winding length, the system performs a product operation between the single-turn winding length and the predicted retraction difference value to obtain the single-turn winding deviation.

[0062] It should be noted that the shrinkage difference prediction value refers to the relative deviation rate between the actual length and the theoretical length of the film material after it has undergone front-end tension stretching and is wound onto the take-up shaft due to tension release. Because the elastic modulus and thickness uniformity of the material itself have slight fluctuations, this deviation rate is not constant. This prediction value is dimensionless, typically ranging from 0.001 to 0.015; a larger value indicates a higher degree of deformation recovery for that turn of film. By multiplying the single-turn winding length L, representing the current physical circumference, by the shrinkage difference prediction value δ, we obtain ΔL = L × δ. The system can accurately quantify the actual length shrinkage of this turn of film after winding. This shrinkage amount ΔL is the single-turn winding deviation, measured in millimeters.

[0063] The single-turn winding deviation calculation is based on two data points: real-time rotational speed detection and material shrinkage rate measurement. The system collects the current rotational speed n of the winding roller, in revolutions per minute (rpm), and simultaneously obtains the material shrinkage rate k under the current tension, typically ranging from 0.2% to 0.8%. The calculation process is as follows: multiply the winding roller circumference L by the rotational speed n to obtain the theoretical winding length, and then multiply by the shrinkage rate k to obtain the single-turn deviation δ. Taking a circumference of 1.2 meters, a rotational speed of 80 rpm, and a shrinkage rate of 0.5% as an example, the single-turn deviation is δ = 1.2 × 80 × 0.005 = 0.48 meters. This dynamic deviation calculation method based on real-time rotational speed and material shrinkage characteristics can accurately reflect the accumulation of microscopic deformation of the material during dynamic winding, providing precise data support for subsequent tension compensation control.

[0064] S107. The cumulative winding deviation is calculated by adding the single-turn winding deviation and the preset number of winding turns.

[0065] The preset target winding tension is obtained, and the corrected tension value F is obtained by multiplying the tension compensation coefficient with the target winding tension. The effective lever arm R corresponding to the current winding roll diameter is obtained, and F is multiplied by R to obtain the tension correction torque Tc, i.e., Tc = F * R, where F is in Newtons, R is in meters, and Tc is in Newton-meters. The current basic drive torque T0 is obtained, and the final winding torque Tout is determined by adding Tc to T0, i.e., Tout = T0 + Tc. The effective lever arm R is obtained from the real-time detected roll diameter sensor data. Initially, the roll radius is taken as 0.15 meters when empty, and it is updated cumulatively according to the layer thickness during winding to ensure that the torque synthesis is completed under the same dimension, avoiding the error in physical meaning caused by directly adding force and torque.

[0066] In one implementation, after obtaining the single-turn winding deviation, the system further performs an operation of cumulatively calculating the single-turn winding deviation and the preset number of winding turns.

[0067] Specifically, the preset number of winding turns refers to the theoretical total number of winding turns planned in advance by the system based on the target roll diameter of the film material and the initial roll diameter of the winding shaft in the current winding task. This preset number of winding turns serves as the upper limit of the cumulative calculation cycle. When the actual cumulative number of winding turns reaches this preset value, the system completes the tension control cycle of this winding task and stops the roll count accumulation. For example, when the target roll diameter is 800 mm, the initial roll diameter is 100 mm, and the film thickness is 0.05 mm, the system calculates a preset number of winding turns of 7000 turns, and subsequent tension compensation calculations use this 7000 turns as the cumulative boundary.

[0068] The per-turn cumulative calculation differs from traditional methods. Traditional methods calculate the cumulative deviation by multiplying a constant roll diameter by the total number of turns, without considering changes in roll diameter. Per-turn cumulative calculation, however, is a dynamic summation based on the actual state of each turn. In actual winding, the roll diameter increases from an initial value D0 to a final value Dn. The length L of each turn increases with the roll diameter, and the difference in internal stress distribution of the material at different roll diameters leads to fluctuations in shrinkage rate. The system calculates the deviation per turn by measuring the difference between the actual winding length Lactual and the theoretical length Ltheoretical for that turn, obtaining the single-turn winding deviation ΔL. The theoretical L is calculated based on the current roll diameter and material thickness, while the actual L is obtained by real-time acquisition of rotation speed and time by the encoder. The system sets up an accumulation register with an initial value of 0. Accumulation is triggered once every turn of the winding shaft, adding the current turn's ΔL to the register. For example, if ΔL is 0.8 mm for the first turn, 0.9 mm for the second turn, and 0.85 mm for the third turn, the register will store 0.8 mm, 1.7 mm, and 2.55 mm respectively. Accumulation stops when the preset number of winding turns is reached, and the register value at this point represents the total deviation. This method captures the cumulative effect of increasing roll diameter and minute changes in material shrinkage on the deviation.

[0069] For example, assume the preset number of winding turns is 100. The system clears the accumulator register at the start of a new calculation cycle. When the winding shaft completes its first rotation, the system calculates the single-turn winding deviation for that turn and stores it in the register. When the second rotation is completed, the system calculates the single-turn winding deviation for that turn again and adds it to the value of the first turn, and so on. The system continuously monitors the rotation of the winding shaft, strictly following the increasing order of the physical turns, and gradually adds the newly generated single-turn winding deviations to the total. Further, the final value obtained through this round-by-round accumulation calculation is the cumulative winding deviation, calculated as ΔLtotal = ΔL1 + ΔL2 + ... + ΔL100, where ΔLtotal represents the cumulative winding deviation, and ΔL1 to ΔL100 represent the single-turn winding deviation for each turn. This cumulative winding deviation, at the physical level, characterizes the total length shrinkage of the film material due to tension release and elastic deformation within 100 turns. This macro-level cumulative data provides a foundational data support for subsequent adjustments to the winding strategy.

[0070] S108. If the cumulative winding deviation exceeds a preset deviation range, a tension distribution command is generated based on the correspondence between the cumulative winding deviation and each thickness region. Specifically, this includes: S1081, acquiring thickness gradient data of the film in the winding direction, collecting a thickness measurement point every 10 millimeters along the winding width direction to form a thickness distribution curve; S1082, dividing the film width into multiple thickness regions based on the thickness gradient, grouping adjacent measurement points with a thickness difference of less than 0.5 micrometers into the same thickness region, and marking the starting position and average thickness value of each thickness region; S1083, calculating the deviation distribution of each thickness region, allocating the cumulative winding deviation according to the proportion of each thickness region on the film width, assigning a higher deviation weight to regions with larger thicknesses, with a weight value W, etc. S1084: Obtain the winding radius corresponding to each thickness region, and calculate the actual radius of each region based on the current winding layer number and the cumulative thickness of each region; S1085: Use the deviation distribution value, winding radius, and thickness value of each thickness region as input parameters, and calculate the target tension adjustment amount of each thickness region through the support vector regression (SVR) model. The SVR model outputs the tension value that needs to be increased or decreased for each region; S1086: Generate tension distribution instructions, which include the position coordinates of each thickness region, the current tension value, and the target tension adjustment amount. For example, for region A, the position is 0 to 200 mm, the current tension is 50 N, and the adjustment amount is -5 N; for region B, the position is 200 to 500 mm, the current tension is 50 N, and the adjustment amount is +3 N.

[0071] The cumulative winding deviation and a preset deviation range are obtained. If the cumulative winding deviation exceeds the preset deviation range, real-time thickness data of the winding material in the winding direction is collected using a thickness sensor. The thickness change rate of adjacent positions is calculated. When the thickness change rate exceeds 0.02 mm / m, a new thickness region is defined, thus dividing the winding material into multiple thickness regions. The absolute value of the thickness deviation of each thickness region is statistically analyzed, and the proportion of the thickness deviation of each thickness region to the total thickness deviation is calculated as a distribution ratio. This distribution ratio is multiplied by the rated winding tension of 800 N to obtain the initial distributed tension for each thickness region. The current roll diameter R of each thickness region is obtained in millimeters. The tension correction coefficient K is calculated as 1 divided by R and then multiplied by the reference radius value of 500. The initial distributed tension is multiplied by the tension correction coefficient K to obtain the region tension adjustment value. A pre-trained support vector regression model is used. The input features include four features: regional tension adjustment value, current roll diameter, thickness change rate, and historical deviation trend. The regression prediction outputs the final tension control value corresponding to each thickness region. Tension distribution instructions are generated based on the correspondence between the final tension control value and each thickness region.

[0072] In one implementation, after acquiring the cumulative winding deviation, the system compares it with a preset deviation range. Specifically, the preset deviation range refers to a tolerance interval set based on the physical properties of the film material and the current winding process requirements. The method for setting this deviation range is as follows: first, obtain the elastic modulus E and thickness δ of the film material; calculate the tensile sensitivity coefficient K = E × δ; then, combine this with the target accuracy P of the current winding process, and set the preset deviation range to ±K × P. For example, a polyester film has an elastic modulus of 4500 MPa, a thickness of 0.025 mm, and a tensile sensitivity coefficient of 112.5. If the required process accuracy is 0.02 mm, the preset deviation range is set to ±2.25 mm. For a biaxially oriented polypropylene film, the elastic modulus is 2800 MPa, the thickness is 0.020 mm, and the tensile sensitivity coefficient is 56. If the required process accuracy is 0.03 mm, the preset deviation range is set to ±1.68 mm. The system monitors in real time to determine whether the current cumulative winding deviation falls within the tolerance range, and uses this as the trigger condition for whether to initiate active intervention control.

[0073] It should be noted that in the actual production process, it is often difficult to maintain absolute thickness uniformity in the transverse width direction of thin film materials, and slight thickness fluctuations are usually present. In order to achieve fine control, the system pre-divides the transverse width of the film into multiple independent thickness regions.

[0074] It is understandable that the correspondence between cumulative winding deviation and thickness regions is essentially a multi-dimensional correspondence indexed by thickness distribution and measured by deviation percentage. During winding, thicker regions experience rapid local roll diameter growth due to faster material stacking, resulting in larger local winding deviations, while thinner regions exhibit the opposite. The system uses an online thickness gauge to collect real-time thickness data t_i at various lateral positions of the film using a 5 mm lateral sampling interval. The film is then divided into n thickness regions, and the mean thickness T_i within each region is normalized. Based on this, and combined with the material's elastic modulus E and Poisson's ratio μ, the proportion w_i of each region in the overall cumulative winding deviation is calculated using w_i = T_i^2 * E / [(1-μ^2) * ΣT_j^2], where ΣT_j^2 represents the sum of the squares of the mean thicknesses of all regions. For example, when a film is divided into three regions with average thicknesses of 20 micrometers, 22 micrometers, and 25 micrometers, the corresponding w_i values ​​are approximately 0.28, 0.34, and 0.38, respectively. This yields a quantitative correspondence between the overall cumulative winding deviation and the thickness regions. This correspondence details the specific distribution of macroscopic deviations in the microscopic physical space, providing precise data coordinates for subsequent differentiated control. Furthermore, when the system determines that the cumulative winding deviation exceeds a preset deviation range, it indicates that the current winding state has deviated from the theoretically planned trajectory. At this point, the system immediately invokes the constructed correspondence and enters the tension distribution instruction generation process.

[0075] Specifically, the process of generating tension distribution commands based on the correspondence between the cumulative winding deviation and each thickness region is a control quantity calculation process based on inverse dynamics. The correspondence between the cumulative winding deviation and each thickness region is established as follows: Based on online measurement data of the material thickness, the system divides the material into 3 to 8 thickness regions along the winding width direction, with each thickness region corresponding to an independent tension control unit. The system calculates the degree of deviation of each thickness region relative to the average thickness, normalizes this deviation degree, and uses it as a distribution weight. Regions with larger thickness deviations are assigned higher weights, with the sum of the weights for all regions being 1. After establishing this correspondence, the system first extracts the deviation overflow amount exceeding the preset deviation range, and then uses the distribution weights of each thickness region to discretize and decompose this deviation overflow amount into each thickness region. For each thickness region, the system calculates the compensation tension value required to offset the deviation using a proportional-integral control algorithm based on the local deviation amount allocated to that region. This compensation tension is superimposed on the reference tension of that region to form the final tension distribution command.

[0076] For example, for areas with a larger thickness and positive local deviation, the system calculates a smaller tension setpoint to reduce the winding tightness, while for areas with a smaller thickness and negative local deviation, it calculates a larger tension setpoint to increase the winding tightness. Subsequently, the system integrates and encodes the compensation tension values ​​for all thickness areas to generate a tension distribution command containing multi-channel control signals.

[0077] In one optional embodiment, considering the creep characteristics of the film material during long-term winding, the correspondence is not static. The system periodically and adaptively corrects the distribution weights in the mapping matrix based on the actual tension distribution execution data in historical winding cycles to ensure the continuous accuracy of the tension distribution commands. Specifically, the adaptive correction mechanism is implemented as follows: The system uses a single winding cycle as a statistical unit, collecting the actual output torque data of each roller section and the corresponding thickness deviation change of the film area. When the average thickness deviation of a certain thickness area exceeds 0.2 mm within three consecutive winding cycles, the correction process for the corresponding distribution weight of that area is triggered. The correction algorithm uses the gradient descent method, denoted as Wcurrent for the current weight value of the area, and denoted as Δh for the thickness deviation. With the learning rate α set to 0.05, the corrected weight Wcorrected equals Wcurrent minus α multiplied by Δh. The corrected weight value is immediately updated to the mapping matrix and applied to subsequent winding tasks. For example, if the initial weight of a certain region is 1.2, and the average thickness deviation over three consecutive cycles is +0.3 mm, then the corrected weight is 1.2 minus 0.05 multiplied by 0.3, which equals 1.185. The system normalizes the weight correction results for all thickness regions to ensure that the total weight remains constant. Furthermore, the generation logic of the tension distribution command will be adjusted for different film winding tasks.

[0078] For example, when handling highly ductile polyethylene films, the system introduces a nonlinear damping coefficient ζ when generating tension distribution commands. This coefficient is calculated using the formula ζ = ζ0 + k·Δε², where ζ0 is the baseline damping coefficient (0.3), k is a material-related constant (15 for polyethylene films), and Δε is the current tensile strain increment. This damping coefficient ranges from 0.3 to 0.8 to prevent excessive stretching of the material due to sudden changes in tension. When handling more rigid polyester films, the tension distribution command uses a response gain coefficient G set to 2.5 times the baseline value, while the command update cycle is shortened from 50 milliseconds to 20 milliseconds to achieve a rapid step response and quickly correct deviations. This material-characteristic-based command generation strategy further enriches the system's control dimensions.

[0079] S109. The torque control unit of the tension roller is driven by the tension distribution command to adjust the output torque, and the end faces of each narrow strip are kept flush during the winding process according to the differential adjustment of the output torque.

[0080] The tension distribution command is acquired and parsed to obtain the initial torque command T0 for each narrow strip. The roll diameter change data for each narrow strip is acquired, denoted as D for the current roll diameter and D0 for the initial roll diameter. The initial torque command is corrected based on the roll diameter change data to obtain the dynamic torque value, with the correction relationship being T1 = T0 × D / D0, where T1 is the corrected dynamic torque value. The friction coefficient μ between the tension roller and each narrow strip is acquired. This friction coefficient is obtained by pre-testing the static friction coefficient between different materials and the roller surface and is stored in the control system database. The friction coefficient of common film materials ranges from 0.2 to 0.5. Slip compensation is applied to the dynamic torque value based on the roller surface friction coefficient, calculated using the formula T2 = T1 × (1 + k × μ), where T2 is the compensated torque, and k is the slip compensation coefficient ranging from 0.1 to 0.3. This torque value is the torque distribution parameter. The torque control unit of the tension roller is driven to adjust the output torque difference based on the torque distribution parameter, and the flushing error data e of each narrow strip is acquired through a displacement sensor. The alignment error data is calculated using a proportional-integral-derivative (PID) algorithm. The proportional coefficient Kp is set to 1.5 to 3.0, the integral coefficient Ki to 0.5 to 1.5, and the derivative coefficient Kd to 0.1 to 0.5, to obtain the torque correction coefficient ΔT. Based on this torque correction coefficient, the torque distribution parameters are updated to T3 = T2 + ΔT. The updated torque distribution parameters then drive the torque control unit to achieve dynamic alignment control of each narrow band end face.

[0081] In one implementation, after the system generates a tension distribution command, it sends it to the actuator. Specifically, the torque control unit of the tension roller is driven by the tension distribution command to adjust the output torque. This process is a crucial step in converting digital control signals into physical mechanical forces.

[0082] Specifically, the torque control unit is the core power regulation component inside the tension roller. This unit typically includes a high-precision servo driver and a built-in current loop control module. When the torque control unit receives a tension distribution command, its internal microprocessor first decodes the command and extracts the torque setpoint for each independent control channel. Subsequently, the current loop control module calculates the magnitude and phase of the stator current required to drive each servo motor based on these setpoints.

[0083] It is understandable that the output torque of the motor is directly related to the electromagnetic thrust generated by the stator current in the magnetic field. By precisely adjusting the current waveform input to each motor winding, the torque control unit can convert the abstract tension distribution command into actual mechanical torque acting on each section of the tension roller with extremely high response speed. This control strategy based on the underlying current closed loop ensures the smoothness and accuracy of the torque output. Furthermore, based on the precise drive of the aforementioned torque control unit, the system can achieve differentiated adjustment according to the output torque. This differentiated adjustment is tailored to the physical state of different regions of the film in the transverse direction.

[0084] It should be noted that the specific operational process of differentiated adjustment is reflected in applying different amounts of traction force to each narrow strip. In the film winding operation, the film is logically divided into multiple narrow strips corresponding to the sections of the tension roller. Because thickness deviations in each narrow strip are detected in the early stage, the torque control unit controls the corresponding roller section to output different torques.

[0085] For example, for narrow strips with excessive thickness leading to rapid local diameter growth, the corresponding roller section will output a smaller torque, thereby reducing the local tension on that narrow strip; conversely, for narrow strips with insufficient thickness, a larger torque will be output to increase the local tension. This change in tension directly causes microscopic deformation of the film material within its elastic range. Specifically, increased tension leads to an increase in the longitudinal elongation of the film, and according to the principle of incompressibility of the material's volume, its dimensions in the transverse and thickness directions will undergo slight shrinkage. By applying different torques to each narrow strip, the system artificially intervenes in the local tensile state of each narrow strip at a physical level.

[0086] For example, the ultimate business objective of the aforementioned differentiated adjustment is to ensure that the end faces of each narrow strip remain flush during the winding process. Flush end faces refer to the smooth, flat sides of the film roll after winding, without any obvious protrusions or depressions; this is one of the core indicators for measuring film winding quality. In a specific implementation scenario, the physical mechanism by which differentiated adjustment achieves flush end faces is a dynamic roll diameter compensation process. When the local tension of the thicker narrow strip is reduced, the longitudinal stretch of the film in that area decreases, the degree of plastic deformation decreases, and the internal stored elastic stress decreases accordingly. Although the winding tightness decreases, the moderate reduction in tension makes the film contact with the lower layer more gently during winding, and interlayer air is more easily released laterally under rolling action. Simultaneously, the reduced plastic deformation suppresses the stress release rebound effect during subsequent winding, effectively controlling the abnormal expansion of the local roll diameter. Meanwhile, the thinner narrow strip, under greater tension, experiences increased winding tightness, and the rate of increase in the local roll diameter is enhanced. As the winding process continues, this tension intervention based on torque differentiation continuously fine-tunes the roll diameter growth rate of each narrow strip. Ultimately, the local roll diameters of each narrow strip tend to be consistent through dynamic adjustment, thus macroscopically resulting in the end face of the entire membrane roll remaining highly flush, avoiding defects such as bulging or wrinkles caused by local stress concentration.

[0087] Preferably, when processing special films with low surface friction coefficients, the torque control unit also incorporates an anti-slip control strategy during differential adjustment. The system uses a non-contact laser speed sensor installed on the front side of the take-up roller to collect the film's linear velocity Vf in real time, and simultaneously acquires the actual rotational speed of each roller segment and converts it into linear velocity Vi. The torque control unit calculates the speed matching degree M for each roller segment, where M equals Vi divided by Vf and multiplied by 100%. When the M value of a roller segment corresponding to a narrow strip deviates from 100% by more than 0.3%, it is determined to be a micro-slip trend. At this time, the torque control unit immediately decreases the output torque of that channel at a rate of 0.5 Nm / s until the M value returns to within the range of 100% ± 0.2%, ensuring that differential adjustment is always performed within the allowable range of the material's static friction.

[0088] The above description is merely a specific implementation of this specification. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the scope of protection of this specification is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this specification, and these modifications or substitutions should all be covered within the scope of protection of this specification.

Claims

1. A method for controlling the winding process during aluminum foil slitting and winding, characterized in that, include: Thickness data is collected by transverse scanning along the width of the aluminum foil using a thickness measuring device. The thickness data is then divided into thickness intervals using a classification algorithm to obtain a thickness distribution matrix. Thickness extrema are extracted based on the boundaries of each thickness region in the thickness distribution matrix. Local stress thresholds are determined by the mapping relationship between the thickness extrema and the allowable stress of the material. The overall tensile force applied to the aluminum foil by the tensioning device is obtained. The regional stress component is determined based on the overall tensile force and the thickness ratio of each region. The tensile deformation risk value is obtained based on the comparison between the regional stress component and the local stress threshold. If the tensile deformation risk value is greater than the preset risk judgment threshold, a regression prediction algorithm is used to predict the stress release amount by using the tensile deformation risk value as the input feature. The width variation data of the slitting strip is determined based on the stress release amount and elastic modulus, and the shrinkage difference prediction value is determined through the width variation data; real-time rotation speed data of the winding equipment is acquired, and the single-turn winding deviation is determined based on the single-turn winding length corresponding to the real-time rotation speed data and the shrinkage difference prediction value; the cumulative winding deviation is calculated by accumulating the single-turn winding deviation with the preset number of winding turns; if the cumulative winding deviation exceeds the preset deviation range, a tension distribution command is generated based on the correspondence between the cumulative winding deviation and each thickness region; the torque control unit of the tension equipment is driven by the tension distribution command to adjust the output torque.

2. The winding control method in the aluminum foil slitting and winding process as described in claim 1, characterized in that, The process involves collecting thickness data by transverse scanning along the width of the aluminum foil using a thickness measuring device, and then dividing the thickness data into thickness intervals using a classification algorithm to obtain a thickness distribution matrix. This includes: acquiring thickness data with spatial coordinates; constructing a two-dimensional coordinate system with the width coordinate as the horizontal axis and the thickness value as the vertical axis based on the thickness data; extracting a cross-sectional profile from the two-dimensional coordinate system; dividing the cross-sectional profile into preset thickness intervals using the classification algorithm; counting the number of thickness data points falling into each thickness interval to obtain the thickness distribution matrix; extracting frequency data for each thickness interval from the thickness distribution matrix; calculating the frequency difference between adjacent thickness intervals based on the frequency data to obtain fluctuation characteristics; if the fluctuation characteristics are greater than a preset fluctuation threshold, locating the deformation region; extracting the highest and lowest thickness points corresponding to the deformation region from the cross-sectional profile; calculating the deviation between the highest and lowest thickness points; and multiplying the deviation value by a preset compensation coefficient to obtain compensation parameters.

3. The winding control method in the aluminum foil slitting and winding process as described in claim 1, characterized in that, The step of extracting thickness extrema based on the boundaries of each thickness region in the thickness distribution matrix and determining local stress thresholds through the mapping relationship between the thickness extrema and the allowable stress of the material includes: obtaining the thickness distribution matrix; extracting the boundaries of each thickness interval in the thickness distribution matrix; determining the thickness extrema of each thickness interval based on the boundaries; obtaining a pre-established mapping relationship between the thickness extrema and the allowable stress of the material; determining the local stress threshold corresponding to each thickness interval through the mapping relationship; calculating the stress difference between the local stress threshold and a preset yield strength; constructing a stress gradient vector based on the stress difference; calculating the tensile load value corresponding to each thickness interval through the stress gradient vector; and generating a tension adjustment command based on the tensile load value if the tensile load value is greater than a preset critical tension.

4. The winding control method in the aluminum foil slitting and winding process as described in claim 1, characterized in that, The process of acquiring the overall tensile force applied to the aluminum foil by the tensioning device, determining the regional stress component based on the overall tensile force and the thickness ratio of each region, and obtaining the tensile deformation risk value based on the comparison result of the regional stress component and the local stress threshold includes: acquiring the overall tensile force applied to the aluminum foil by the tensioning device, multiplying the overall tensile force by a preset roller surface friction coefficient to obtain an effective tensile force; acquiring the thickness ratio of each region corresponding to the aluminum foil, and proportionally converting the effective tensile force value based on the thickness ratio of each region to obtain the regional stress component corresponding to each region; if the regional stress component is greater than the local stress threshold, calculating the difference between the regional stress component and the local stress threshold to obtain the tensile deformation risk value corresponding to each region; constructing a risk gradient matrix based on the tensile deformation risk value corresponding to each region, and obtaining the plastic elongation extreme value by feature extraction from the risk gradient matrix; and generating a roller speed compensation command based on the comparison result of the plastic elongation extreme value and the preset elongation safety threshold.

5. A winding control method for aluminum foil slitting and winding process as described in any one of claims 1-4, characterized in that, If the tensile deformation risk value is greater than a preset risk judgment threshold, a regression prediction algorithm is used with the tensile deformation risk value as input feature to predict and obtain the cutting stress release amount. This includes: obtaining the tensile deformation risk value and the preset risk judgment threshold; if the tensile deformation risk value is greater than the risk judgment threshold, a support vector regression algorithm is used with the tensile deformation risk value as input feature to predict and output the corresponding cutting stress release amount; obtaining a preset tool entry depth reference value, and calculating the target entry depth by summing the cutting stress release amount and the tool entry depth reference value; obtaining the current winding radius during the aluminum foil winding process, and calculating the tension attenuation compensation amount by multiplying the target entry depth and the current winding radius; obtaining the initial winding torque, and calculating the target winding torque by subtracting the initial winding torque from the tension attenuation compensation amount.

6. A winding control method for aluminum foil slitting and winding process as described in any one of claims 1-4, characterized in that, The step of determining the width variation data of the slitting strip based on the slitting stress release amount and the elastic modulus, and determining the shrinkage difference prediction value through the width variation data, includes: obtaining the slitting stress release amount and a preset elastic modulus; obtaining the slitting strain value by dividing the slitting stress release amount by the elastic modulus; obtaining a preset initial width of the slitting strip; calculating the width variation amount of the slitting strip by multiplying the slitting strain value and the initial width of the slitting strip; obtaining the edge thickness of each slitting strip; calculating the cross-sectional profile deformation value by multiplying the width variation amount of the slitting strip by the edge thickness; obtaining a preset guide roller spacing; calculating the lateral offset amount of each slitting strip by the ratio of the cross-sectional profile deformation value to the guide roller spacing; obtaining a preset offset threshold; and if the lateral offset amount is greater than the offset threshold, using a pre-trained linear regression model with the lateral offset amount as the input feature for prediction to obtain the corresponding correction compensation amount.

7. A winding control method for aluminum foil slitting and winding process as described in any one of claims 1-4, characterized in that, The step of calculating the cumulative winding deviation by accumulating the single-turn winding deviation and the preset number of winding turns includes: obtaining the single-turn winding deviation and the preset number of winding turns; calculating the cumulative winding deviation by accumulating the single-turn winding deviation and the preset number of winding turns; obtaining the elastic modulus of the winding material; determining the radial stress by multiplying the cumulative winding deviation by the elastic modulus of the winding material; obtaining the interlayer contact pressure; obtaining the stress attenuation factor by dividing the radial stress by the interlayer contact pressure; obtaining a preset attenuation threshold; if the stress attenuation factor is greater than the attenuation threshold, using a pre-trained random forest regression model with the stress attenuation factor as input feature for regression prediction to obtain the tension compensation coefficient; obtaining a preset target winding tension; obtaining a tension correction command by multiplying the tension compensation coefficient and the target winding tension; and obtaining the current base drive torque; determining the final winding torque by adding the tension correction command to the base drive torque.

8. A winding control method for aluminum foil slitting and winding process as described in any one of claims 1-4, characterized in that, If the cumulative winding deviation exceeds a preset deviation range, a tension distribution instruction is generated based on the correspondence between the cumulative winding deviation and each thickness region. This includes: obtaining the cumulative winding deviation and the preset deviation range; if the cumulative winding deviation exceeds the preset deviation range, obtaining thickness gradient data of the winding material; determining multiple thickness regions based on the thickness gradient data; determining a distribution ratio based on the distribution state of the cumulative winding deviation in the multiple thickness regions; obtaining the allocation weight of each thickness region by multiplying the distribution ratio by a preset tension benchmark value; obtaining the current operating radius of each thickness region; obtaining a region tension adjustment value by dividing the allocation weight by the current operating radius; using a pre-trained support vector regression model, performing regression prediction with the region tension adjustment value as input features to obtain a smoothed tension value; generating a tension distribution instruction based on the correspondence between the smoothed tension value and each thickness region; obtaining roll diameter change data of each slitting strip; and correcting the tension distribution instruction based on the roll diameter change data to obtain a dynamic torque value.

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