Multi-cutter shaft cooperative intelligent copper strip slitting machine set dynamic leveling system

Abstract

The present application relates to the technical field of unit dynamic leveling, in particular to a multi-cutter shaft coordinated intelligent copper strip slitting unit dynamic leveling system. For any observation time range, according to the change trend of torque data and rotation speed data of each cutter shaft at different time, the multi-cutter shaft coupling stress abnormality of each observation time range is obtained; according to the change trend of tension data and flatness data of each cutter shaft at different time, the tension-flatness change difference of each cutter shaft is obtained; then the coupling risk propagation coefficient of each observation time range is obtained; according to the change trend of rotation speed data, flatness data and tension data of each cutter shaft at different time in each observation time range, the motion-stress response delay coefficient of each cutter shaft in each observation time range is obtained; the dynamic leveling coefficient in the unit leveling system is obtained. Through self-adaptive adjustment of the dynamic leveling coefficient, the precision and stability of copper strip slitting are improved.

Description

Technical Field

[0001] This invention relates to the field of dynamic leveling technology for machine units, specifically to a dynamic leveling system for a multi-blade axis collaborative intelligent copper strip slitting machine unit. Background Technology

[0002] The multi-blade shaft collaborative slitting unit is a unit that arranges multiple adjustable blade shafts in the width direction of the copper strip to achieve multiple strips of copper strip at one time. By independently controlling each blade shaft, uniform tension distribution and precise positioning of wide copper strip can be achieved during the slitting process, thereby significantly improving the slitting quality of copper strip.

[0003] In existing technologies, dynamic leveling is performed based on fixed adjustment strategies or empirical parameters. However, in actual production, when copper strip is slit into high-width strips, the slight differences in thickness and hardness of the wide copper strip material itself can easily cause local thickness fluctuations and longitudinal ripples. At the same time, under the coordinated action of multiple cutter shafts, there may be slight deviations in the position, speed, and tension adjustment of each cutter shaft. The accumulation of these slight deviations will cause local stress concentration in the copper strip after slitting and tension stretching. There is a lack of real-time continuous compensation capability for the flatness and tension fluctuations of the copper strip, resulting in poor copper strip slitting accuracy. Summary of the Invention

[0004] To address the technical problem of slight deviations in the position, speed, and tension adjustment of each cutter shaft, which can lead to localized stress concentration and poor slitting accuracy of the copper strip after slitting and tensioning, this invention aims to provide a dynamic leveling system for a multi-cutter shaft collaborative intelligent copper strip slitting unit. The specific technical solution adopted is as follows:

[0005] This invention proposes a dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0006] Acquire torque, speed, tension, and flatness data of the cutting shaft at each moment within different observation time ranges during the copper strip slitting process;

[0007] For any given observation time range, the instability of each tool axis is obtained based on the changing trends of torque and rotation speed data at different times; and the multi-tool axis coupling stress anomaly is obtained based on the distribution of tool axis instability in different observation time ranges.

[0008] For any given observation time range, the tension-smoothness variation difference of each tool axis is obtained based on the changing trends of tension and smoothness data at different times; the coupling risk propagation coefficient of each observation time range is obtained based on the tension-smoothness variation difference of each tool axis and the anomaly of multi-tool axis coupling stress within each observation time range; and the motion-stress response delay coefficient of each tool axis within each observation time range is obtained based on the changing trends of rotation speed, smoothness, and tension data at different times within each observation time range.

[0009] The dynamic leveling coefficient in the unit leveling system is obtained based on the motion-stress response delay coefficient and coupling risk propagation coefficient of each cutter shaft within the real-time observation time range.

[0010] Furthermore, the method for obtaining the instability includes:

[0011] For torque or speed data, construct curves of the data at different times, and obtain the derivative of the curve at each time, which is used as the slope at each time.

[0012] The mean difference in slope between torque and speed data at different times is obtained and negatively correlated, which is used as the first instability coefficient; the extreme points in the curve formed by torque data at different times are obtained, and the mean difference in torque data between all adjacent extreme points is obtained, which is used as the second instability coefficient.

[0013] The product of the first and second instability coefficients is obtained and normalized to determine the instability.

[0014] Furthermore, the method for obtaining the multi-axis coupled stress anomaly includes:

[0015] Obtain the instability sequence of each cutter axis corresponding to different observation time ranges within the historical range of each observation time range;

[0016] Based on the differences in instability sequences between different tool axes and the instability of different tool axes within each observation time range, the multi-tool axis coupling stress anomaly was obtained for each observation time range. The differences and instabilities were positively correlated with the multi-tool axis coupling stress anomaly.

[0017] Furthermore, the method for obtaining the multi-axis coupled stress anomaly includes:

[0018] The mean square error of the instability sequences between different cutter axes is obtained as a difference feature; for each observation time range, the mean instability of different cutter axes is obtained as the average instability level.

[0019] The product of the difference characteristics and the average instability level corresponding to each observation time range is obtained as the multi-axis coupled stress anomaly for each observation time range.

[0020] Furthermore, the method for obtaining the tension-smoothness variation difference includes:

[0021] For any observation time range, for tension data or smoothness data, obtain a curve composed of data from all times, obtain the derivative of the curve at each time, and use it as the slope at each time. The slope includes the tension slope or the smoothness slope.

[0022] For the tension slope or smoothness slope at different times, construct the slope increasing sequence in ascending order, obtain the difference sequence of the slope increasing sequence, obtain the position of the maximum value in the difference sequence as the dividing point, select the time when the slope of the slope increasing sequence corresponds to all values ​​to the right of the dividing point as the high change time.

[0023] Based on the slope difference between the tension slope and the flatness slope at the same high change time, and the degree of disorder of all tension slopes, the tension-flatness variation difference of each tool axis is obtained. Both the slope difference and the degree of disorder are positively correlated with the variation difference.

[0024] Furthermore, the method for obtaining the coupling risk propagation coefficient includes:

[0025] The product of the multi-axis coupling stress anomaly and the mean of the tension-smoothness variation difference of each cutter axis in each observation time range is obtained as the coupling risk propagation coefficient of each cutter axis in each observation time range.

[0026] Furthermore, the method for obtaining the motion-stress response delay coefficient includes:

[0027] For the rotational speed data, flatness data, or tension data of each cutter shaft at different times within each observation time range, the high change time of the corresponding data is obtained according to the method of obtaining the high change time. The high change time includes the high change time of tension, the high change time of flatness, and the high change time of rotational speed.

[0028] The difference between the tension slope at each high tension change moment and the smoothness slope at different high smoothness change moments is obtained. The high smoothness change moment corresponding to the smallest difference is selected as the matching moment for each high tension change moment.

[0029] Based on the first difference between different tension high change times and corresponding matching times, and the second difference between tension high change times and rotational speed high change times in the same order, the motion-stress response delay coefficient of each tool axis is obtained.

[0030] Furthermore, the method for obtaining the motion-stress response delay coefficient includes:

[0031] Obtain the first average difference between different tension high change times and corresponding matching times, obtain the second average difference between all tension high change times and rotation speed high change times in the same order, and calculate the product of the first average difference and the second average difference as the motion-stress response delay coefficient for each tool axis.

[0032] Furthermore, the method for obtaining the dynamic leveling coefficient includes:

[0033] The Euclidean norm between the coupling risk propagation index and the motion-stress response delay coefficient of each tool axis is obtained as a dynamic leveling coefficient.

[0034] Furthermore, the method for obtaining the degree of disorder includes:

[0035] Calculate the information entropy of all tension slopes as the degree of disorder.

[0036] The present invention has the following beneficial effects:

[0037] This invention, for any given observation time range, obtains the multi-cutter shaft coupling stress anomaly based on the changing trends of torque and speed data for each cutter shaft at different times, assessing the interaction anomalies of the entire system. For any given observation time range, it obtains the tension-flatness variation difference for each cutter shaft based on the changing trends of tension and flatness data at different times, quantifying the system deviation between driving tension and response rate of change. Based on the tension-flatness variation difference and multi-cutter shaft coupling stress anomaly for each cutter shaft within each observation time range, it obtains the coupling risk propagation coefficient for each observation time range, quantifying the global propagation potential of the risk. Based on the changing trends of speed, flatness, and tension data for each cutter shaft at different times within each observation time range, it obtains the motion-stress response delay coefficient for each cutter shaft within each observation time range, providing a clearer understanding of the system's dynamic inertial characteristics and enabling control timing compensation. Finally, it obtains the dynamic leveling coefficient in the unit leveling system. This invention improves the accuracy and stability of copper strip slitting by adaptively adjusting the dynamic leveling coefficient. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the implementation method of a dynamic leveling system for a multi-blade axis collaborative intelligent copper strip slitting unit, as provided in one embodiment of the present invention;

[0040] Figure 2 A flowchart illustrating a method for obtaining instability according to an embodiment of the present invention;

[0041] Figure 3 This is a flowchart illustrating a method for obtaining tension-flatness variation differences according to an embodiment of the present invention. Detailed Implementation

[0042] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a multi-axis collaborative intelligent copper strip slitting machine dynamic leveling system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0044] The following description, in conjunction with the accompanying drawings, details the specific scheme of the dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine provided by the present invention.

[0045] Please see Figure 1 The diagram illustrates a flowchart of an implementation method for a dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to an embodiment of the present invention. The method specifically includes:

[0046] Step S1: Obtain the torque data, speed data, tension data, and flatness data of the cutting shaft at each moment within different observation time ranges during the copper strip slitting process.

[0047] In the embodiments of the present invention, considering that under the coordinated action of multiple cutter shafts, there may be slight deviations in the position, speed, and tension adjustment of each cutter shaft, resulting in local stress concentration of the copper strip after slitting and tension stretching, but relying on fixed adjustment strategies or empirical parameters, there is a lack of real-time continuous compensation capability for the flatness and tension fluctuations of the copper strip. Therefore, relevant data is collected to analyze the changing trends and make adjustments. First, within any observation time range, a torque sensor is installed at the end of the cutter shaft to obtain the torque data of all cutter shafts at each moment. The cutter shaft speed data is obtained based on a high-precision rotary encoder. The flatness data of the copper strip surface of the cutter shaft is obtained by non-contact optical scanning or image flatness detection. The tension data of the cutter shaft is obtained by a tension sensor. The timing axis of the data acquisition is kept synchronized.

[0048] It should be noted that, in one embodiment of the present invention, the observation time range is 3 minutes. In other embodiments of the present invention, the observation time range can be set according to specific circumstances, and will not be limited or described in detail here. In addition, all sensor-collected signals are digitized in real time through the altitude data acquisition module to obtain sensor data at each moment. The specific means are well known to those skilled in the art and will not be described in detail here.

[0049] Step S2: For any observation time range, obtain the instability of each tool shaft based on the changing trends of torque and rotation speed data of each tool shaft at different times; obtain the multi-tool shaft coupling stress anomaly in each observation time range based on the distribution of tool shaft instability in different observation time ranges.

[0050] Under normal operating conditions of the slitting machine cutter shaft, the changes in torque and speed data conform to physical laws. That is, under non-rigid compensation, an increase in torque is accompanied by a decrease in speed. Therefore, by analyzing the changing trends of torque and speed data, the correlation between the changing trends of torque and speed data can be reflected, and the stability of the cutter shaft can be quantified. For any observation time range, the instability of each cutter shaft can be obtained based on the changing trends of torque and speed data of each cutter shaft at different times.

[0051] Preferably, in one embodiment of the present invention, the method for obtaining instability is described in [reference needed]. Figure 2 It illustrates a flowchart of a method for obtaining instability, including:

[0052] Step S201: For torque data or speed data, construct curves of the data at different times, and obtain the derivative of the curve at each time as the slope at each time.

[0053] It should be noted that the slope reflects the trend of data change; the larger the slope, the greater the change in the data.

[0054] Step S202: Obtain the mean difference in slope between torque data and speed data at different times, and perform negative correlation mapping as the first instability coefficient; obtain the extreme points in the curve formed by torque data at different times, and obtain the mean difference in torque data between all adjacent extreme points as the second instability coefficient.

[0055] It should be noted that the difference represents the absolute value of the difference; the larger the absolute value of the slope difference, the more inconsistent the trend of data change, indicating a negative correlation. Considering that an increase in torque is accompanied by a decrease in speed, the larger the absolute value of the slope difference, the more matched the trends of torque and speed change, and the greater the stability. The smaller the absolute value of the slope difference, the more consistent the trend of data change, indicating a positive correlation. The more mismatched the trends of torque and speed change, the greater the instability.

[0056] It should be noted that, in one embodiment of the present invention, the extreme point is obtained by Newton's method. In other embodiments of the present invention, if the torque data at a certain moment is greater than or less than the torque data at the left and right adjacent moments, then the corresponding moment is the extreme point. The specific means are well known to those skilled in the art and will not be described in detail here. Adjacent extreme points are the maximum and minimum values. The greater the difference between extreme points, the greater the load fluctuation of the torque within the observation time range, and the higher the dynamic instability.

[0057] It should be noted that, in the embodiments of the present invention, this can be achieved by taking the reciprocal or an exponential function with the natural constant as the base. To perform negative correlation mapping, when calculating the reciprocal, in order to avoid the formula being meaningless with a denominator of 0, a manually set threshold, such as 0.01, needs to be added to the denominator. The specific means are well known to those skilled in the art and will not be limited or elaborated here.

[0058] Step S203: Obtain the product of the first instability coefficient and the second instability coefficient, and normalize it as the instability.

[0059] It should be noted that, in the embodiments of the present invention, normalization is performed by linear normalization or a normalization function. The specific means are well known to those skilled in the art and will not be described in detail here.

[0060] Instability reflects the consistency of load fluctuation amplitude and response of a single cutter shaft within the observation time range. The greater the instability, the smaller the consistency, and the more likely the coupling changes are to be abnormal. In a multi-axis slitting unit, multiple cutter shafts influence each other through the force, tension, and mechanical structure of the copper strip. Disturbances are well transmitted between multiple cutter shafts, and the more similar the cutter shaft response trends are, the more the correlation of multi-axis responses can be reflected by the instability distribution of different cutter shafts. Based on the instability distribution of different cutter shafts within each observation time range, the anomaly of multi-axis coupling stress within each observation time range can be obtained.

[0061] Preferably, in one embodiment of the present invention, the method for obtaining multi-axis coupled stress anomalies includes:

[0062] Obtain the instability sequence of each cutter axis corresponding to different observation time ranges within the historical range of each observation time range;

[0063] It should be noted that, in one embodiment of the present invention, the method for obtaining the historical range is to select the range formed by each observation time range as a reference and all historical observation time ranges; in other embodiments of the present invention, the implementers may set the specific settings according to the specific circumstances, which will not be limited or elaborated here.

[0064] Based on the differences in instability sequences between different tool axes and the instability of different tool axes within each observation time range, the multi-tool axis coupling stress anomaly was obtained for each observation time range. The differences and instabilities were positively correlated with the multi-tool axis coupling stress anomaly.

[0065] In one embodiment of the present invention, the method for obtaining multi-axis coupled stress anomalies includes:

[0066] The mean square error of the instability sequences between different cutter axes is obtained as a difference feature; for each observation time range, the mean instability of different cutter axes is obtained as the average instability level.

[0067] The product of the difference characteristics and the average instability level corresponding to each observation time range is obtained as the multi-axis coupled stress anomaly for each observation time range.

[0068] It should be noted that mean squared error is helpful in measuring the degree of difference in instability sequences among tool axes. By averaging, the instability level among all tool axes is quantified, reflecting the overall stability of the tool axes. The greater the anomaly of multi-tool axis coupling stress in each observation time range, the more complex the tool axis changes and the smaller the stability characteristics.

[0069] Step S3: For any observation time range, based on the changing trends of tension and flatness data of each tool shaft at different times, obtain the tension-flatness variation difference of each tool shaft; based on the tension-flatness variation difference of each tool shaft and the anomaly of multi-tool shaft coupling stress in each observation time range, obtain the coupling risk propagation coefficient of each observation time range; based on the changing trends of rotation speed data, flatness data and tension data of each tool shaft at different times in each observation time range, obtain the motion-stress response delay coefficient of each tool shaft in each observation time range.

[0070] Multi-axis coupled stress anomalies are reflected in the phenomenon of uneven local stress and load during the slitting process, which causes local tension increase or decrease in copper strip and flatness deviation. By analyzing the variation law of copper strip tension and flatness, the correlation between tension and flatness is reflected. Therefore, for any observation time range, the tension-flatness variation difference of each cutter axis within the observation time range can be obtained based on the variation trend of tension data and flatness data of each cutter axis at different times.

[0071] Preferably, in one embodiment of the present invention, the method for obtaining the tension-flatness variation difference is described in [reference needed]. Figure 3 It shows a flowchart of a method for obtaining the difference in tension-smoothness variation, including:

[0072] Step S301: For any observation time range, for tension data or smoothness data, obtain a curve composed of data from all times, obtain the derivative of the curve at each time moment as the slope at each time moment, the slope including tension slope or smoothness slope.

[0073] The slope can reflect the trend of data changes and help to understand more clearly when there is a sudden increase in data.

[0074] Step S302: For the tension slope or smoothness slope at different times, construct a slope increasing sequence in ascending order, obtain the difference sequence of the slope increasing sequence, obtain the position of the maximum value in the difference sequence as the dividing point, select the time when the slope of all values ​​to the right of the dividing point corresponds to the time when the slope of the slope increasing sequence is located as the time of high change.

[0075] It should be noted that the first-order difference method is used for difference calculation, and the specific means are well known to those skilled in the art, and will not be described in detail here.

[0076] To illustrate, for any data, there exist slopes of 1, 2, 3, 4, 5, and 6, with slopes of 1, 2, 5, 8, 6, and 5. The increasing slope sequence is 1, 2, 5, 5, 6, and 8, and the difference sequence is 1, 3, 0, 1, and 2. When the difference reaches its maximum of 3, the increasing slope sequence for all values ​​to the right is 5, 6, and 8, corresponding to times 3, 4, and 5, which are considered the times of high change.

[0077] Step S303: Based on the slope difference between the tension slope and the flatness slope at the same high change time in the same order, and the degree of disorder of all tension slopes, obtain the tension-flatness change difference of each tool axis. The slope difference and the degree of disorder are both positively correlated with the change difference.

[0078] To illustrate, if the high change times for tension slope screening are 3, 4, and 5, and the high change times for flatness slope screening are 4, 5, and 6, then calculate the slope difference between the corresponding high change times in the same order: the first time 3 and 4, the second time 4 and 5, and the third time 5 and 6.

[0079] It should be noted that the slope difference reflects the correlation between the changes in tension data and smoothness data. The smaller the slope difference, the greater the correlation and the smaller the difference. The information entropy of all tension slopes is calculated as the degree of disorder. Information entropy is a technique well-known to those skilled in the art, calculated by analyzing the proportion of times an upward or downward slope trend occurs out of all slopes. The formula is as follows: ,in, The slope variable X represents the tension slope. The probability of the nth category value, i.e., the probability of the nth category value occurring. The ratio of the number of slopes in each category to the total number of slopes. It represents the number of times the upward and downward trends are categorized; the greater the information entropy, the greater the degree of disorder and the greater the difference; therefore, both slope difference and information entropy are positively correlated with the difference in change.

[0080] In one embodiment of the present invention, the average slope difference between the tension slope and the flatness slope at different high change times of the same order is obtained as the average slope difference; the product between the average slope difference and the information entropy is obtained as the tension-flatness change difference of each tool axis; therefore, based on the above basic mathematical operations, the correlation between slope difference, information entropy and change difference is constructed, that is, the greater the slope difference, the greater the information entropy, and the greater the change difference.

[0081] The anomaly of multi-axis coupled stress in each observation time range reflects the amplitude of disturbance and the degree of coupling anomaly. The greater the anomaly of multi-axis coupled stress in each observation time range, the greater the coupling risk. The variation difference reflects the difference between tension and flatness. The greater the difference, the greater the inconsistency and the greater the coupling risk. Based on the tension-flatness variation difference of each cutter axis and the anomaly of multi-axis coupled stress in each observation time range, the propagation coefficient of each coupling risk is obtained.

[0082] Preferably, in one embodiment of the present invention, the method for obtaining the coupling risk propagation coefficient includes:

[0083] The product of the multi-axis coupling stress anomaly and the mean of the tension-smoothness variation difference of each cutter axis in each observation time range is obtained as the coupling risk propagation coefficient of each cutter axis in each observation time range.

[0084] Based on this, the coupling risk propagation coefficient reflects the transient response of the tool shaft itself and the local system. The larger the coupling risk propagation coefficient, the more likely the local mismatch is to be affected and propagated by the system coupling mechanism, and the more likely there will be continuous longitudinal ripples, local thickness deviations or an increase in scrap rate.

[0085] The risk propagation coefficient does not take into account the propagation process of the disturbance copper-aluminum strip among multiple cutter shafts and the hysteresis effect of the stress response caused by the cutter shaft motion deviation. It is necessary to analyze the dynamic correlation characteristics between the cutter shaft motion deviation and the copper strip stress response. The correlation between tension data, rotation speed data and flatness data should be analyzed separately to quantify the hysteresis or synchronicity between tension change and flatness change, as well as the direct delay from the disturbance input to the copper strip tension. Therefore, based on the changing trends of the rotation speed data, flatness data and tension data of each cutter shaft at different times within each observation time range, the motion-stress response delay coefficient of each cutter shaft within each observation time range can be obtained.

[0086] Preferably, in one embodiment of the present invention, the method for obtaining the motion-stress response delay coefficient includes:

[0087] For the rotational speed data, flatness data, or tension data of each cutter shaft at different times within each observation time range, the high change time of the corresponding data is obtained according to the method of obtaining the high change time. The high change time includes the high change time of tension, the high change time of flatness, and the high change time of rotational speed.

[0088] It should be noted that by substituting any data into the method for obtaining the high change time corresponding to steps S301 and S302, the high change time of the corresponding data can be obtained.

[0089] The difference between the tension slope at each high tension change moment and the smoothness slope at different high smoothness change moments is obtained. The high smoothness change moment corresponding to the smallest difference is selected as the matching moment for each high tension change moment.

[0090] Based on the first difference between different tension high change times and corresponding matching times, and the second difference between tension high change times and rotational speed high change times in the same order, the motion-stress response delay coefficient of each tool axis is obtained.

[0091] It should be noted that the first difference between the moment of high tension change and the corresponding matching moment reflects the lag or synchronicity between the tension change and the flatness change. The larger the first difference, the greater the difference between the moment of high tension change and the matching flatness change moment, and the greater the lag. Considering that the stress response of the copper strip should usually occur after the sudden change of the cutter shaft speed, the second difference between the moments of high tension change and high speed change in the same order reflects the direct delay from the disturbance input from the cutter shaft motion to the generation of copper strip tension. The larger the second difference, the greater the direct delay and the greater the motion-stress response delay coefficient. Therefore, both the first and second differences are positively correlated with the motion-stress response delay coefficient.

[0092] In one embodiment of the present invention, a first average difference between different tension high change times and corresponding matching times is obtained, a second average difference between all tension high change times and rotational speed high change times in the same order is obtained, and the product of the first average difference and the second average difference is calculated as the motion-stress response delay coefficient of each tool axis.

[0093] The delay factor reflects the actual response time of each cutter shaft to the local tension and flatness of the copper strip, providing a basis for predicting the disturbance propagation path and response time, and helping to predict control strategies in a targeted manner.

[0094] Step S4: Based on the motion-stress response delay coefficient and coupling risk propagation coefficient of each cutter shaft within the real-time observation time range, obtain the dynamic leveling coefficient in the unit leveling system.

[0095] The coupling risk propagation coefficient reflects the degree of coupling risk, and the higher the coefficient, the more adjustments are needed. The response delay coefficient reflects the hysteresis characteristic from the cutter shaft motion input to the copper strip stress response, and the higher the coefficient, the more corrections are needed. Based on the motion-stress response delay coefficient and coupling risk propagation coefficient of each cutter shaft within the real-time observation time range, the dynamic leveling coefficient in the unit leveling system is obtained.

[0096] Preferably, in one embodiment of the present invention, the method for obtaining the dynamic leveling coefficient includes:

[0097] Obtain the Euclidean norm between the coupling risk propagation coefficient and the motion-stress response delay coefficient, and use it as the dynamic leveling coefficient.

[0098] It should be noted that the Euclidean norm reflects the overall dynamic characteristics of the coupling risk propagation coefficient and the motion-stress response delay coefficient. A larger norm indicates that the tool axis has both response hysteresis and a high coupling risk.

[0099] It should be noted that, in another embodiment of the present invention, the slitting unit is leveled based on the acquired dynamic leveling coefficient, including: acquiring the maximum speed adjustment value at each cutter shaft in the unit leveling system; multiplying the dynamic leveling coefficient by the maximum speed adjustment value to obtain the speed correction amount for each cutter shaft; adding the speed correction amount to the speed data of the corresponding cutter shaft during the slitting process to obtain the adjusted target speed; and correcting the target speed of the corresponding cutter shaft by an independent controller in the multi-cutter shaft; each cutter shaft is leveled according to the above analysis process.

[0100] After the adjustment action is executed, torque data, tension data, flatness data and speed data are reacquired, and new coupling risk propagation wave coefficient and motion-stress response delay coefficient are calculated. Adjustment is then carried out in combination with the new dynamic leveling coefficient obtained in the unit leveling system.

[0101] It should be noted that the maximum speed adjustment value can be obtained in advance by the implementers based on relevant professional technical data.

[0102] In summary, this invention, for any observation time range, obtains the multi-cutter shaft coupling stress anomaly based on the changing trends of torque and speed data for each cutter shaft at different times; obtains the tension-flatness variation difference for each cutter shaft based on the changing trends of tension and flatness data for each cutter shaft at different times; and further obtains the coupling risk propagation coefficient for each observation time range. Based on the changing trends of speed, flatness, and tension data for each cutter shaft at different times within each observation time range, it obtains the motion-stress response delay coefficient for each cutter shaft within each observation time range; and obtains the dynamic leveling coefficient in the unit leveling system. This invention improves the accuracy and stability of copper strip slitting by adaptively adjusting the dynamic leveling coefficient.

[0103] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: Acquire torque, speed, tension, and flatness data of the cutting shaft at each moment within different observation time ranges during the copper strip slitting process; For any given observation time range, the instability of each cutter shaft is obtained based on the changing trends of torque and speed data at different times. Based on the instability distribution of the cutter shaft within different observation time ranges, the multi-cutter shaft coupling stress anomaly is obtained for each observation time range; For any given observation time range, the tension-smoothness variation difference of each tool axis is obtained based on the changing trends of tension and smoothness data at different times; the coupling risk propagation coefficient of each observation time range is obtained based on the tension-smoothness variation difference of each tool axis and the anomaly of multi-tool axis coupling stress within each observation time range; and the motion-stress response delay coefficient of each tool axis within each observation time range is obtained based on the changing trends of rotation speed, smoothness, and tension data at different times within each observation time range. The dynamic leveling coefficient in the unit leveling system is obtained based on the motion-stress response delay coefficient and coupling risk propagation coefficient of each cutter shaft within the real-time observation time range.

2. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 1, characterized in that, The method for obtaining the instability includes: For torque or speed data, construct curves of the data at different times, and obtain the derivative of the curve at each time, which is used as the slope at each time. The mean difference in slope between torque and speed data at different times is obtained and negatively correlated, which is used as the first instability coefficient; the extreme points in the curve formed by torque data at different times are obtained, and the mean difference in torque data between all adjacent extreme points is obtained, which is used as the second instability coefficient. The product of the first and second instability coefficients is obtained and normalized to determine the instability.

3. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 1, characterized in that, The method for obtaining multi-axis coupled stress anomalies includes: Obtain the instability sequence of each cutter axis corresponding to different observation time ranges within the historical range of each observation time range; Based on the differences in instability sequences between different tool axes and the instability of different tool axes within each observation time range, the multi-tool axis coupling stress anomaly was obtained for each observation time range. The differences and instabilities were positively correlated with the multi-tool axis coupling stress anomaly.

4. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 3, characterized in that, The method for obtaining multi-axis coupled stress anomalies includes: The mean square error of the instability sequences between different cutter axes is obtained as a difference feature; for each observation time range, the mean instability of different cutter axes is obtained as the average instability level. The product of the difference characteristics and the average instability level corresponding to each observation time range is obtained as the multi-axis coupled stress anomaly for each observation time range.

5. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 1, characterized in that, The method for obtaining the tension-smoothness variation difference includes: For any observation time range, for tension data or smoothness data, obtain a curve composed of data from all times, obtain the derivative of the curve at each time, and use it as the slope at each time. The slope includes the tension slope or the smoothness slope. For the tension slope or smoothness slope at different times, construct the slope increasing sequence in ascending order, obtain the difference sequence of the slope increasing sequence, obtain the position of the maximum value in the difference sequence as the dividing point, select the time when the slope of the slope increasing sequence corresponds to all values ​​to the right of the dividing point as the high change time. Based on the slope difference between the tension slope and the flatness slope at the same high change time, and the degree of disorder of all tension slopes, the tension-flatness variation difference of each tool axis is obtained. Both the slope difference and the degree of disorder are positively correlated with the variation difference.

6. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 1, characterized in that, The method for obtaining the coupling risk propagation coefficient includes: The product of the multi-axis coupling stress anomaly and the mean of the tension-smoothness variation difference of each cutter axis in each observation time range is obtained as the coupling risk propagation coefficient of each cutter axis in each observation time range.

7. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 5, characterized in that, The method for obtaining the motion-stress response delay coefficient includes: For the rotational speed data, flatness data, or tension data of each cutter shaft at different times within each observation time range, the high change time of the corresponding data is obtained according to the method of obtaining the high change time. The high change time includes the high change time of tension, the high change time of flatness, and the high change time of rotational speed. The difference between the tension slope at each high tension change moment and the smoothness slope at different high smoothness change moments is obtained. The high smoothness change moment corresponding to the smallest difference is selected as the matching moment for each high tension change moment. Based on the first difference between different high tension change times and corresponding matching times, and the second difference between all high tension change times and high rotational speed change times in the same order, the motion-stress response delay coefficient of each tool axis is obtained.

8. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 7, characterized in that, The method for obtaining the motion-stress response delay coefficient includes: Obtain the first average difference between different tension high change times and corresponding matching times, obtain the second average difference between all tension high change times and rotation speed high change times in the same order, and calculate the product of the first average difference and the second average difference as the motion-stress response delay coefficient for each tool axis.

9. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting machine according to claim 1, characterized in that, The method for obtaining the dynamic leveling coefficient includes: The Euclidean norm between the coupling risk propagation index and the motion-stress response delay coefficient of each tool axis is obtained as a dynamic leveling coefficient.

10. The dynamic leveling system for a multi-axis collaborative intelligent copper strip slitting unit according to claim 5, characterized in that, The methods for obtaining the level of disorder include: Calculate the information entropy of all tension slopes as the degree of disorder.

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