Collaborative optimization method for plate shape control and plate thickness precision in rolling process of cold rolling mill

Abstract

The invention provides a plate shape control and plate thickness precision collaborative optimization method in the rolling process of a cold-rolling mill, which relates to the technical field of cold-rolling mill control, and comprises the following steps: acquiring contact parameters between a rolling working roll and a supporting roll, determining a contact stress distribution state, and acquiring the bending deformation of the working roll; constructing a coupling mapping relation between plate shape moment distribution and stress distribution; a compensation coefficient is adaptively adjusted according to the rolling state, and a dynamic collaborative optimization objective function is established; and working roll bending force and supporting roll displacement meeting the target are obtained through calculation, and rolling parameters are adjusted in real time. According to the method, cooperative control over the plate shape and the plate thickness is achieved, and the quality of cold-rolled products is improved.

Description

Technical Field

[0001] This invention relates to the field of cold rolling mill control technology, and in particular to a method for the coordinated optimization of plate shape control and plate thickness accuracy during the cold rolling process. Background Technology

[0002] In steel production, cold rolling is a crucial step in strip steel production, directly impacting the final product's shape and thickness accuracy. During the cold rolling process, uneven contact stress distribution between the work rolls and support rolls causes bending deformation of the work rolls, thus affecting the strip's shape and thickness accuracy. Traditional shape control techniques primarily rely on adjusting the bending force of the work rolls and the displacement of the support rolls to control the transverse thickness distribution of the strip, thereby improving shape quality.

[0003] Traditional shape control and thickness control are typically designed and implemented as two independent systems, neglecting their coupling relationship. This leads to a decrease in thickness accuracy while optimizing shape, or a deterioration in shape quality while controlling thickness, making it difficult to achieve coordinated optimization of shape and thickness. Existing control methods fail to fully consider the impact of the contact geometry between the work roll and the support roll, as well as the distribution of the lubricating oil film thickness, on the bending deformation of the work roll. This results in low accuracy of the established rolling model, affecting the precision and stability of shape control. Existing control strategies are mostly static control or simple feedback control, lacking the ability to adaptively adjust to different states of the rolling process. They cannot effectively cope with dynamic changes and disturbances that occur during rolling, making it difficult to meet the requirements of high-precision rolled products for coordinated control of shape and thickness. Summary of the Invention

[0004] This invention provides a method for the coordinated optimization of plate shape control and plate thickness accuracy during cold rolling, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for co-optimizing plate shape control and plate thickness accuracy during cold rolling, comprising: The contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll are collected to determine the contact stress distribution between the rolling work roll and the support roll. The bending deformation of the rolling work roll at different positions is obtained by combining the beam bending deformation equation. Based on the bending deformation of the rolling mill roll, and combined with the strip shape detection data and strip thickness detection data, a coupled mapping relationship between the strip shape moment distribution and stress distribution considering the deformation of the rolling mill roll is constructed. Based on the state judgment results of the strip rolling process, the compensation coefficients corresponding to the coupling mapping relationship are adaptively adjusted to establish a dynamic collaborative optimization objective function for strip shape and thickness. The bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function are obtained through iterative calculation. The strip shape and thickness data of each segment of the rolling process are input into the fuzzy rule base to obtain the initial control parameters. The state parameters of the roll system of each segment are collected to calculate the coupling influence coefficient and construct an adaptive collaborative function to correct the bending force of the rolling work roll and the displacement of the support roll. Based on the bending force of the rolling work rolls and the displacement of the support rolls, the rolling parameters of the cold rolling mill are adjusted in real time to achieve coordinated control of strip shape and thickness.

[0006] The contact geometry parameters and lubricating oil film thickness data between the rolling mill roll and the support roll are collected to determine the contact stress distribution between them. Combined with the beam bending deformation equation, the bending deformation of the rolling mill roll at different locations is obtained, including: Collect the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, and determine the unit width load and contact area coordinates based on the contact geometry parameters and lubricating oil film thickness data; Based on the unit width load and contact area coordinates, the contact stress distribution is calculated using Hertz contact theory, and the contact stress distribution is lubricated and corrected according to the lubricating oil film thickness data. The modified contact stress distribution state is used as the distributed load input into the beam bending deformation equation to establish the correspondence between the bending deformation of the rolling mill roll and the contact stress. The boundary constraint conditions of the rolling work roll are determined based on the position of the support roll. The boundary constraint conditions are combined with the effects of the rolling force and the bending roll force to solve the beam bending deformation equation. Based on the solution of the beam bending deformation equation, the bending deformation of the rolling work roll along the rolling direction is obtained, and the working position of the rolling work roll is adjusted based on the bending deformation.

[0007] Based on the bending deformation of the rolling mill rolls, and combined with the strip shape and thickness detection data, a coupled mapping relationship between the strip shape moment distribution and stress distribution, considering the deformation of the rolling mill rolls, is constructed, including: Collect actual plate thickness distribution data during the strip rolling process, compare the actual plate thickness distribution data with the preset target plate thickness data, calculate the relative plate shape deviation of the strip, and establish an initial plate shape-stress mapping function based on the correspondence between the relative plate shape deviation and the bending deformation of the rolling work roll. Obtain the actual roll gap value of the rolling work roll, calculate the distribution relationship between rolling force and contact arc length based on the actual roll gap value, substitute the distribution relationship into the initial shape-stress mapping function, and construct the coupled mapping relationship between the shape moment distribution and stress distribution considering the deformation of the rolling work roll.

[0008] A dynamic collaborative optimization objective function for plate shape and thickness is established. Through iterative calculation, the bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function are obtained, including: Construct a comprehensive objective function that includes plate shape control objective, plate thickness control objective, and control cost, and assign corresponding weight coefficients to the plate shape control objective, plate thickness control objective, and control cost in the comprehensive objective function; Obtain the process parameters during the rolling process, establish process constraints based on the process parameters, and apply the process constraints to the comprehensive objective function; Real-time status data of the rolling process is collected, the gradient value of the comprehensive objective function is calculated based on the real-time status data, and the control variables are iteratively updated according to the gradient value; Calculate the difference between the control variables and the objective function in two adjacent iterations, and use the difference between the control variables and the objective function as the convergence criterion; The bending force of the rolling work roll is calculated based on the converged control variables. The displacement of the support roll is determined based on the relationship between the bending force and the rolling pressure. The coordinated control of the plate shape and plate thickness is achieved by adjusting the bending force and the displacement of the support roll.

[0009] The initial control parameters are obtained by inputting the plate shape and thickness data of each segment of the rolling process into a fuzzy rule base, collecting the state parameters of each segment of the roll system, calculating the coupling influence coefficient, and constructing an adaptive collaborative function to correct the bending force of the rolling work roll and the displacement of the support roll, including: The strip shape and thickness data of the entry, middle and exit sections of the strip rolling process are input into the corresponding fuzzy rule base. The initial control parameters of each section are obtained through fuzzy inference calculation. The initial control parameters include the bending force of the rolling work roll and the displacement of the support roll. The state parameters of each section of the rolling work roll and support roll are collected, and the coupling influence coefficient between each section is calculated based on the state parameters. An adaptive cooperative function is constructed based on the coupling influence coefficient. The initial control parameters of each section are input into the adaptive cooperative function, the cooperative compensation amount between each section is calculated, and the initial control parameters of each section are corrected based on the cooperative compensation amount. Adjust the bending force of each section of the rolling mill rolls and the displacement of the support rolls according to the revised control parameters.

[0010] An adaptive cooperative function is constructed based on the coupling influence coefficient. The initial control parameters of each segment are input into the adaptive cooperative function, the cooperative compensation amount between each segment is calculated, and the initial control parameters of each segment are corrected according to the cooperative compensation amount, including: Based on the coupling influence coefficient, a transfer function matrix is ​​constructed. The dynamic transfer relationship between each rolling segment is calculated according to the transfer function matrix, and the collaborative weight of each rolling segment is allocated according to the dynamic transfer relationship. The initial bending roll force and support roll displacement of each rolling segment are input into the transfer function matrix. The compensation coefficient of each rolling segment is calculated according to the collaborative weight, and an adaptive collaborative compensation function is constructed based on the compensation coefficient. The control deviations of each rolling section are collected, and the collaborative weights are dynamically updated based on the control deviations. The updated collaborative weights and the initial control parameters are substituted into the adaptive collaborative compensation function to obtain the bending roll force compensation amount and the support roll displacement compensation amount of each rolling section. The bending roll force compensation amount and the support roll displacement compensation amount are superimposed on the initial control parameters of each rolling section to obtain the corrected control parameters.

[0011] A second aspect of the present invention provides a system for the coordinated optimization of plate shape control and plate thickness accuracy during cold rolling, comprising: The first unit is used to collect the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, determine the contact stress distribution between the rolling work roll and the support roll, and obtain the bending deformation of the rolling work roll at different positions by combining the beam bending deformation equation. The second unit is used to construct a coupled mapping relationship between the strip shape moment distribution and stress distribution that takes into account the deformation of the rolling work roll, based on the bending deformation of the rolling work roll and the strip shape detection data and thickness detection data. The third unit is used to adaptively adjust the compensation coefficients corresponding to the coupling mapping relationship based on the state judgment results of the strip rolling process, establish a dynamic collaborative optimization objective function for strip shape and thickness, and obtain the bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function through iterative calculation; input the strip shape and thickness data of each segment of the rolling process into the fuzzy rule base to obtain the initial control parameters, collect the state parameters of each segment of the roll system to calculate the coupling influence coefficient to construct the adaptive collaborative function, and correct the bending force of the rolling work roll and the displacement of the support roll; The fourth unit is used to adjust the rolling parameters of the cold rolling mill in real time based on the bending force of the rolling work roll and the displacement of the support roll, so as to achieve coordinated control of strip shape and thickness.

[0012] A third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0013] Fourth aspect of the present invention, A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0014] The beneficial effects of this application are as follows: By collecting contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, the contact stress distribution state can be determined. Combined with the beam bending deformation equation, the bending deformation of the work roll at different positions can be accurately obtained, providing precise basic data for the coordinated control of plate shape and thickness, and improving the accuracy of plate shape control.

[0015] Based on the bending deformation of the work rolls, and combined with the coupled mapping relationship constructed from the shape detection data and the thickness detection data, the system can effectively capture the mutual influence between the shape moment distribution and the stress distribution. By adaptively adjusting the compensation coefficient, the dynamic synergistic optimization of the shape and thickness control targets is achieved, thereby improving the overall quality of cold-rolled strip steel.

[0016] Based on the coupling influence coefficients of the initial control parameters and the roll system state parameters of the fuzzy rule base, an adaptive collaborative function was constructed, which can correct the bending force of the work roll and the displacement of the support roll in real time. This makes the system more adaptable to dynamic changes in the rolling process, effectively reduces strip shape defects and thickness fluctuations, and improves the quality stability and rolling efficiency of strip steel products. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method for coordinating and optimizing plate shape control and plate thickness accuracy during the cold rolling process according to an embodiment of the present invention. Figure 2 A schematic diagram of the iterative calculation process for the objective function of dynamic collaborative optimization. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0020] Figure 1 This is a flowchart illustrating the method for co-optimizing plate shape control and plate thickness accuracy during the cold rolling process according to an embodiment of the present invention. Figure 1 As shown, the method includes: The contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll are collected to determine the contact stress distribution between the rolling work roll and the support roll. The bending deformation of the rolling work roll at different positions is obtained by combining the beam bending deformation equation. Based on the bending deformation of the rolling mill roll, and combined with the strip shape detection data and strip thickness detection data, a coupled mapping relationship between the strip shape moment distribution and stress distribution considering the deformation of the rolling mill roll is constructed. Based on the state judgment results of the strip rolling process, the compensation coefficients corresponding to the coupling mapping relationship are adaptively adjusted to establish a dynamic collaborative optimization objective function for strip shape and thickness. The bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function are obtained through iterative calculation. The strip shape and thickness data of each segment of the rolling process are input into the fuzzy rule base to obtain the initial control parameters. The state parameters of the roll system of each segment are collected to calculate the coupling influence coefficient and construct an adaptive collaborative function to correct the bending force of the rolling work roll and the displacement of the support roll. Based on the bending force of the rolling work rolls and the displacement of the support rolls, the rolling parameters of the cold rolling mill are adjusted in real time to achieve coordinated control of strip shape and thickness.

[0021] In one optional implementation, the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll are collected to determine the contact stress distribution between the rolling work roll and the support roll. The bending deformation of the rolling work roll at different positions is then obtained by combining this data with the beam bending deformation equation. Collect the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, and determine the unit width load and contact area coordinates based on the contact geometry parameters and lubricating oil film thickness data; Based on the unit width load and contact area coordinates, the contact stress distribution is calculated using Hertz contact theory, and the contact stress distribution is lubricated and corrected according to the lubricating oil film thickness data. The modified contact stress distribution state is used as the distributed load input into the beam bending deformation equation to establish the correspondence between the bending deformation of the rolling mill roll and the contact stress. The boundary constraint conditions of the rolling work roll are determined based on the position of the support roll. The boundary constraint conditions are combined with the effects of the rolling force and the bending roll force to solve the beam bending deformation equation. Based on the solution of the beam bending deformation equation, the bending deformation of the rolling work roll along the rolling direction is obtained, and the working position of the rolling work roll is adjusted based on the bending deformation.

[0022] High-precision displacement and pressure sensors are installed near the contact area between the work roll and the support roll to collect contact geometry parameters between the two rolls, including contact width, contact length, and contact radius of curvature. Simultaneously, an ultrasonic thickness gauge is used to measure the thickness of the lubricating oil film within the contact area; this thickness is typically in the range of 5-20 micrometers. For example, in one measurement, the contact width was 12 mm, the contact length was 1500 mm, the work roll radius of curvature was 300 mm, the support roll radius of curvature was 600 mm, and the average lubricating oil film thickness was 8 micrometers.

[0023] Based on the collected parameters, a coordinate system for the contact area is established by scanning the entire contact area, with the x-axis along the rolling direction and the y-axis along the roll width direction. The coordinate values ​​of each discrete point and the corresponding unit width load are determined. For example, when rolling a plate with a width of 1500 mm, the contact area is divided into 150 equal units, each with a width of 10 mm. The coordinates of the center point of each unit and the corresponding unit width load are recorded. The maximum unit width load is measured to be 12000 N / mm, which occurs at the center of the contact area.

[0024] Using the collected data, the contact stress distribution was calculated based on Hertz contact theory. The two rollers were treated as elastic bodies, taking into account the elastic modulus and Poisson's ratio of the materials (e.g., the elastic modulus of the work roller material is 210 GPa, and the Poisson's ratio is 0.3), and the contact stress at each point was calculated. Under typical operating conditions, the maximum contact stress can reach 900 MPa, located at the contact center, and gradually decreases to zero along the edge of the contact area.

[0025] Considering the effect of lubrication on contact stress, corrections are made based on the lubricating oil film thickness data. The presence of the oil film reduces the actual contact stress, and the correction factor is negatively correlated with the lubricating oil film thickness. For example, when the oil film thickness is 8 micrometers, the contact stress correction factor is approximately 0.92, reducing the corrected maximum contact stress to 828 MPa.

[0026] The modified contact stress distribution is substituted as a distributed load into the beam bending deformation equation. In this equation, the rolling mill roll is simplified as a beam with a specific moment of inertia. For example, a rolling mill roll with a diameter of 500 mm has a moment of inertia of approximately 3.07 × 10⁻⁶ mm. 9 A model was established to show the relationship between the bending deformation of the work roll and the contact stress, using square millimeters.

[0027] The boundary conditions are determined based on the actual arrangement of the support rolls. In a four-high rolling mill, there are usually two support rolls located on both sides of the work roll, providing support points for the work roll. These support points constitute the constraints of the work roll. For example, if the support rolls are located 400 mm from each end of the work roll, then the displacement and bending moment at these two locations become the boundary conditions for solving the equations.

[0028] At the same time, the effects of rolling force and bending force need to be included in the calculation. The rolling force usually acts on the middle of the work roll, vertically downward; the bending force acts on the roll end and is used to adjust the roll gap shape. For example, in a certain rolling process, the rolling force is 15,000 kN and the bending force is 500 kN. The distribution and magnitude of these forces directly affect the bending deformation of the work roll.

[0029] The beam bending deformation equation is solved using numerical integration or the finite difference method. During mesh generation, the roll body is typically divided into 300 uniform elements along its length, each element being approximately 5-10 mm in length, to ensure calculation accuracy. The calculation results provide the bending deformation of the work roll at various locations. For example, under the above conditions, the maximum bending deformation at the middle of the work roll is approximately 1.25 mm, while the deformation at each support point at both ends is close to zero.

[0030] Based on the calculated bending deformation, the axial and radial positions of the work rolls are adjusted via hydraulic cylinders or electric mechanisms to compensate for the effects of deformation. For example, when the deformation exceeds a set threshold (e.g., 1.5 mm), the roll gap at the corresponding position will be automatically adjusted to ensure the thickness accuracy and shape quality of the rolled product. The entire adjustment process is a closed-loop control system, ensuring the stability of the rolling process and the consistency of product quality.

[0031] In one optional implementation, based on the bending deformation of the rolling mill roll, and combined with the strip shape detection data and strip thickness detection data, a coupled mapping relationship between the strip shape moment distribution and stress distribution considering the deformation of the rolling mill roll is constructed, including: Collect actual plate thickness distribution data during the strip rolling process, compare the actual plate thickness distribution data with the preset target plate thickness data, calculate the relative plate shape deviation of the strip, and establish an initial plate shape-stress mapping function based on the correspondence between the relative plate shape deviation and the bending deformation of the rolling work roll. Obtain the actual roll gap value of the rolling work roll, calculate the distribution relationship between rolling force and contact arc length based on the actual roll gap value, substitute the distribution relationship into the initial shape-stress mapping function, and construct the coupled mapping relationship between the shape moment distribution and stress distribution considering the deformation of the rolling work roll.

[0032] During the strip steel rolling process, the work rolls undergo bending deformation under the rolling force. This bending deformation directly affects the final strip shape quality. To accurately describe this relationship, bending deformation data of the rolling work rolls is collected during actual production. Specifically, on a cold rolling production line, a displacement sensor array is uniformly arranged along the width of the work rolls to monitor the bending deformation at various points during the rolling process in real time. For example, on a work roll with a width of 1800 mm, a sensor is placed every 200 mm, acquiring bending deformation data at a total of 9 measuring points. Under typical operating conditions, the maximum bending deformation in the middle region of the work roll reaches 0.15 mm, while the deformation at both ends is approximately 0.03 mm.

[0033] The strip shape and thickness data are measured in real time using a shape gauge and a thickness gauge installed at the mill exit. The shape gauge reflects the straightness of the strip by measuring the tension distribution at various transverse positions, while the thickness gauge directly measures the actual thickness distribution. For example, for a cold-rolled strip with a width of 1500mm, the shape gauge sets 15 measurement areas in the transverse direction, each area being 100mm wide, and measures the tension value in each area; simultaneously, the thickness gauge measures the actual thickness of the strip at the same location. In one measurement, the tension value in the middle area of ​​the strip is 280MPa, and in the edge area it is 260MPa, indicating slight waviness in the middle of the strip; at the same time, the actual thickness in the middle area is 0.505mm, and in the edge area it is 0.508mm, which deviates from the target thickness of 0.5mm.

[0034] Based on the collected actual strip thickness distribution data, it is compared with the preset target thickness data to calculate the relative shape deviation. The relative shape deviation is calculated by subtracting the target thickness from the actual thickness and then dividing by the target thickness. Using the example data above, the relative shape deviation in the middle region is calculated to be 1%, and the relative shape deviation in the edge region is 1.6%. At this point, the correspondence between these relative shape deviations and the bending deformation of the rolling mill rolls is established, forming a preliminary mapping function. This correspondence can be established through data fitting, showing that when the bending deformation in the middle of the rolling mill rolls is 0.15 mm, the relative shape deviation in the middle of the strip is 1%; when the bending deformation at the edge of the rolling mill rolls is 0.03 mm, the relative shape deviation at the edge of the strip is 1.6%.

[0035] The actual roll gap value data is obtained through the rolling mill control system. For example, in the above rolling process, the roll gap value in the middle of the mill is 0.48 mm, and the roll gap value at the edge is 0.51 mm. Based on these roll gap values, the distribution relationship between rolling force and contact arc length is calculated: According to Hertz contact theory, there is a relationship between the contact arc length L and the roll gap value h, the work roll radius R, and the material deformation resistance k. Where Δh is the roll gap variation, the contact arc length at different transverse positions is calculated by substituting the roll gap values ​​of 0.48 mm at the center and 0.51 mm at the edge; the rolling force P can be obtained from the formula... The calculations show that b is the unit width of the strip, k is the deformation resistance, and Q is the influence coefficient, which is related to the friction coefficient and the degree of strip deformation. In actual calculations, the mill is uniformly divided into 9 calculation units in the transverse direction, and the rolling force and contact arc length at each unit are calculated separately to obtain the complete transverse distribution relationship. Specifically, factors such as strip deformation resistance and friction coefficient are considered to obtain the transverse distribution of the rolling force. For example, the rolling force is 800 kN in the central region and 700 kN in the edge region; the corresponding contact arc length is 5.2 mm in the central region and 4.8 mm at the edge.

[0036] Substituting the aforementioned relationship between rolling force and contact arc length distribution into the initial shape-stress mapping function, a coupled mapping relationship between the shape moment distribution and stress distribution considering the deformation of the rolling work roll is constructed. This coupled mapping relationship reflects how changes in the strip shape moment affect its internal stress distribution under specific work roll bending deformation conditions, and vice versa. For example, in this rolling condition, when the bending deformation in the middle of the work roll increases by 0.01 mm, the stress in the middle of the strip increases by approximately 5 MPa; while when the bending deformation at the edge decreases by 0.01 mm, the stress at the edge decreases by approximately 4 MPa.

[0037] This coupling mapping relationship enables precise prediction and control of strip shape during the rolling process. For example, when a waviness defect is detected in the strip, the bending deformation of the work rolls that need to be adjusted can be calculated based on the coupling mapping relationship, and the roll gap distribution of the mill can be adjusted accordingly to eliminate the strip shape defect. In a practical application, adjustments guided by this coupling mapping relationship improved the strip shape deviation value I5 from 35 to less than 15, significantly improving product quality.

[0038] In one optional implementation, a dynamic collaborative optimization objective function for plate shape and thickness is established. The bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function are obtained through iterative calculation, including: Construct a comprehensive objective function that includes plate shape control objective, plate thickness control objective, and control cost, and assign corresponding weight coefficients to the plate shape control objective, plate thickness control objective, and control cost in the comprehensive objective function; Obtain the process parameters during the rolling process, establish process constraints based on the process parameters, and apply the process constraints to the comprehensive objective function; Real-time status data of the rolling process is collected, the gradient value of the comprehensive objective function is calculated based on the real-time status data, and the control variables are iteratively updated according to the gradient value; Calculate the difference between the control variables and the objective function in two adjacent iterations, and use the difference between the control variables and the objective function as the convergence criterion; The bending force of the rolling work roll is calculated based on the converged control variables. The displacement of the support roll is determined based on the relationship between the bending force and the rolling pressure. The coordinated control of the plate shape and plate thickness is achieved by adjusting the bending force and the displacement of the support roll.

[0039] like Figure 2 As shown, the method includes: A comprehensive objective function is constructed, encompassing shape control, thickness control, and control costs. This function can be expressed as a weighted sum of three parts: the first part represents the sum of squares of shape deviations, characterizing the difference between the actual and target shape; the second part represents the sum of squares of thickness deviations, characterizing the difference between the actual and target thickness; and the third part represents the control costs, characterizing the adjustment magnitude of the control variables. A weight coefficient of 0.5 can be assigned to the shape control objective; a weight coefficient of 0.4 can be assigned to the thickness control objective; and a weight coefficient of 0.1 can be assigned to the control costs. These weight coefficients can be adjusted according to production needs. For example, when higher shape quality requirements are required, the weight coefficient for the shape control objective can be increased to 0.6, while correspondingly decreasing the weight coefficients of the other components.

[0040] Key parameters such as inlet plate thickness, outlet plate thickness, rolling speed, rolling force, and rolling temperature are needed to obtain the process parameters during rolling. Taking a cold rolling production line as an example, the inlet plate thickness is 2.5 mm, the target outlet plate thickness is 0.8 mm, the rolling speed is 800 m / min, and the rolling temperature is 60℃. Based on these process parameters, process constraints are established, including: the bending roll force range is 0-800 kN, the support roll displacement range is ±100 mm, and the rolling pressure does not exceed 2000 kN. These constraints are applied to the comprehensive objective function by adding penalty terms. When the control variables exceed the constraint range, the penalty terms will significantly increase the objective function value, guiding the optimization algorithm to search within the constraint region.

[0041] Plate shape and thickness data during the rolling process are collected using a shape gauge and a thickness gauge at a sampling frequency of 100Hz. Auxiliary data such as rolling force, rolling torque, and rolling speed are also collected. Based on the collected real-time data, the gradient of the comprehensive objective function with respect to the control variables is calculated. Specifically, the gradient can be approximated using the finite difference method, which estimates the gradient by considering the change in the objective function value before and after a small disturbance to the control variables. For example, when the bending roll force increases from 300kN to 301kN, if the objective function value decreases from 0.85 to 0.84, the estimated gradient value is -0.01 / 1 = -0.01, indicating that increasing the bending roll force is beneficial for reducing the objective function value.

[0042] Based on the calculated gradient value, the control variable is iteratively updated using the gradient descent method. That is, the new value of the control variable is equal to the current value minus the product of the learning rate and the gradient value. The learning rate can be set to 0.05 and gradually reduced to 0.01 as the number of iterations increases to ensure the convergence of the algorithm.

[0043] During the iteration process, the difference between the control variables and the objective function between two adjacent iterations needs to be calculated as convergence criteria. Specifically, the iteration is considered convergent when the Euclidean distance of the control variables is less than a preset threshold (e.g., 0.001) and the change in the objective function value is less than a preset threshold (e.g., 0.0001). In practical applications, in one iteration, the bending roller force changed from 299.5kN to 299.52kN, the Euclidean distance of the control variables was 0.0208, and the objective function value changed from 0.84 to 0.8399, with a difference of 0.0001, both of which met the convergence conditions, so the iteration terminated.

[0044] Based on the converged control variables, the bending force of the rolling mill roll is calculated. In the example above, the converged bending force is 299.52 kN. The displacement of the support roll is determined based on the relationship between the bending force and the rolling pressure. The relationship between the rolling pressure and the bending force can be obtained by fitting experimental data. For example, the relationship between the rolling pressure P and the bending force F can be expressed as P = 1500 - 0.5F (unit: kN). When the bending force is 299.52 kN, the rolling pressure is 1350.24 kN. The displacement of the support roll is related to the bending force and the rolling pressure. It can be calculated as: support roll displacement = 35 + 0.02 × bending force - 0.01 × rolling pressure. Substituting the values, the displacement of the support roll is 35 + 0.02 × 299.52 - 0.01 × 1350.24 = 27.488 mm.

[0045] By adjusting the bending roller force and the support roller displacement, the shape and thickness of the plate can be controlled in a coordinated manner. In actual production, when a wavy edge defect is detected in the plate shape, the bending roller force will be automatically increased and the support roller displacement will be adjusted according to the plate shape deviation. When the plate thickness is detected to be out of tolerance, the support roller displacement will be adjusted and the bending roller force will be adjusted accordingly according to the plate thickness deviation, thereby achieving coordinated and optimized control of the plate shape and thickness.

[0046] In one optional implementation, the shape and thickness data of each segment of the rolling process are input into a fuzzy rule base to obtain initial control parameters. The state parameters of each segment's roll system are collected to calculate the coupling influence coefficient and construct an adaptive collaborative function. The correction of the bending force of the rolling work roll and the displacement of the support roll includes: The strip shape and thickness data of the entry, middle and exit sections of the strip rolling process are input into the corresponding fuzzy rule base. The initial control parameters of each section are obtained through fuzzy inference calculation. The initial control parameters include the bending force of the rolling work roll and the displacement of the support roll. The state parameters of each section of the rolling work roll and support roll are collected, and the coupling influence coefficient between each section is calculated based on the state parameters. An adaptive cooperative function is constructed based on the coupling influence coefficient. The initial control parameters of each section are input into the adaptive cooperative function, the cooperative compensation amount between each section is calculated, and the initial control parameters of each section are corrected based on the cooperative compensation amount. Adjust the bending force of each section of the rolling mill rolls and the displacement of the support rolls according to the revised control parameters.

[0047] The strip rolling process is typically divided into three main areas: the entry section, the middle section, and the exit section. Each area requires independent and coordinated control parameters to ensure the uniformity of the final strip shape and thickness. This requires the collection of strip shape and thickness data for each section through a sensor network. Strip shape data is usually expressed as crown values. For example, for a strip with a width of 1200mm, the crown of the entry section is approximately 0.03mm, the crown of the middle section is approximately 0.025mm, and the crown of the exit section is approximately 0.02mm. The thickness data includes the transverse thickness distribution, such as 3.2±0.05mm for the entry section, 2.8±0.04mm for the middle section, and 2.5±0.03mm for the exit section.

[0048] The collected data is input into a pre-established fuzzy rule base for processing. This fuzzy rule base, built upon expert experience and historical data, contains a set of rules in the form of "if-then". For example, fuzzy rules for the entry section include: "If the convexity is a large positive value and the thickness deviation is a small positive value, then the bending force of the work roll is set to medium-high and the displacement of the support roll is set to a small negative value." The fuzzy inference process involves three steps: fuzzification, rule matching, and defuzzification. Membership functions are used to convert precise convexity and thickness values ​​into fuzzy linguistic variables, such as "large," "medium," and "small." Then, fuzzy values ​​of the control parameters are calculated according to the rules. Finally, precise control quantities are obtained through defuzzification methods such as the center-of-gravity method.

[0049] Through this fuzzy reasoning, initial control parameters can be generated for each rolling zone. For example, for the inlet section, the bending force of the work roll is 800kN and the displacement of the support roll is -2.5mm; for the middle section, the bending force is 650kN and the displacement is -1.8mm; and for the outlet section, the bending force is 500kN and the displacement is -1.2mm.

[0050] The condition parameters of the rolling mill work rolls and support rolls are collected, including the wear condition, temperature distribution, and bending stiffness of the work rolls, as well as the positional accuracy and bearing condition of the support rolls. For example, the surface temperature of the work rolls is 85℃ at the inlet, 78℃ at the middle, and 70℃ at the outlet; the wear of the work rolls is 0.015mm at the inlet, 0.012mm at the middle, and 0.008mm at the outlet.

[0051] Based on these state parameters, the coupling influence coefficients between each segment are calculated. These coefficients reflect the degree to which changes in the control parameters of one segment affect other segments. The calculation method is based on a rolling mechanics model, considering roll deformation, contact stress distribution, and thermal expansion effects. The calculation comprehensively considers roll stiffness ratio, wear degree, temperature gradient, and stress distribution, combined with the geometric distances between segments, to form an influence coefficient matrix, which is then normalized and dynamically corrected. For example, the influence coefficient of the inlet segment on the middle segment is 0.65, the influence coefficient of the inlet segment on the outlet segment is 0.32, and the influence coefficient of the middle segment on the outlet segment is 0.78.

[0052] In one optional implementation, an adaptive cooperative function is constructed based on the coupling influence coefficient. The initial control parameters of each segment are input into the adaptive cooperative function, the cooperative compensation amount between each segment is calculated, and the initial control parameters of each segment are corrected according to the cooperative compensation amount, including: Based on the coupling influence coefficient, a transfer function matrix is ​​constructed. The dynamic transfer relationship between each rolling segment is calculated according to the transfer function matrix, and the collaborative weight of each rolling segment is allocated according to the dynamic transfer relationship. The initial bending roll force and support roll displacement of each rolling segment are input into the transfer function matrix. The compensation coefficient of each rolling segment is calculated according to the collaborative weight, and an adaptive collaborative compensation function is constructed based on the compensation coefficient. The control deviations of each rolling section are collected, and the collaborative weights are dynamically updated based on the control deviations. The updated collaborative weights and the initial control parameters are substituted into the adaptive collaborative compensation function to obtain the bending roll force compensation amount and the support roll displacement compensation amount of each rolling section. The bending roll force compensation amount and the support roll displacement compensation amount are superimposed on the initial control parameters of each rolling section to obtain the corrected control parameters.

[0053] The coupling effect coefficient reflects the degree of mutual influence between different rolling sections. Assuming a four-high rolling mill contains N rolling sections, each with two control variables: bending roll force and support roll displacement, a 2N×2N transfer function matrix T can be constructed. The element Tij in this matrix represents the degree of influence of the j-th control variable on the i-th control variable. The coupling relationship between bending roll force and support roll displacement can be obtained by fitting experimental data. For example, for a three-section cold rolling mill, experiments show that a 1000kN change in the bending roll force of the first section leads to a 0.02mm increase in the exit plate thickness of the second section; the corresponding transfer coefficient is 0.00002mm / kN.

[0054] By analyzing historical operating data from each rolling section, the correlation between changes in control parameters and the final rolling effect is extracted, and a dynamic transfer model is constructed. This model can automatically adjust the transfer coefficient according to the current rolling conditions. For example, in practical applications, when the strip thickness is reduced from 2.0 mm to 1.0 mm, the transfer coefficient increases from 0.00002 mm / kN to 0.00003 mm / kN, and the transfer function matrix is ​​automatically updated according to this change.

[0055] An importance assessment method was used to determine the weight coefficients of each rolling segment. Segments with a significant impact on downstream segments were assigned higher synergy weights, while segments significantly affected by upstream segments were assigned lower synergy weights. Taking a three-segment cold rolling mill as an example, assuming the first segment has a significant impact on downstream segments, it could be assigned a weight coefficient of 0.5; the second segment has a moderate impact, assigned a weight coefficient of 0.3; and the third segment has a relatively small impact, assigned a weight coefficient of 0.2.

[0056] When calculating the compensation coefficients for each rolling segment based on the collaborative weights, the importance and control sensitivity of each segment are comprehensively considered. A weighted average method is used for calculating the compensation coefficients. The bending roll force compensation coefficient CB and the support roll displacement compensation coefficient CS are calculated based on the collaborative weight W and the corresponding transmission coefficient T of each segment, respectively. For example, for the second segment, its bending roll force compensation coefficient can be expressed as W2×(T21×C1+T23×C3), where C1 and C3 are the control deviations of the first and third segments, respectively.

[0057] When constructing an adaptive collaborative compensation function based on compensation coefficients using a nonlinear mapping method, the compensation coefficients are transformed into specific control adjustment quantities. This function can adaptively adjust the compensation intensity according to the current control deviation magnitude. For small deviations, a linear compensation strategy is adopted; for large deviations, a nonlinear suppression strategy is adopted to avoid overcompensation leading to system instability.

[0058] The actual rolling parameters are monitored in real time by sensors such as thickness gauges and tension gauges installed in each rolling section. Taking plate thickness control as an example, if the set thickness is 1.0 mm and the actual measured thickness is 1.02 mm, the control deviation is 0.02 mm. These deviation values ​​are fed back to the control algorithm in real time.

[0059] When an adaptive adjustment strategy is adopted to dynamically update the collaborative weights based on the control deviation, the collaborative weight of a certain rolling segment is increased when the control deviation of that segment continues to increase, and appropriately decreased when the deviation decreases. This dynamic adjustment mechanism ensures that the system can respond promptly to changes in the rolling process. For example, when the control deviation of the first segment increases from 0.01 mm to 0.03 mm, its collaborative weight is adjusted from 0.5 to 0.6, while the weights of other segments decrease accordingly.

[0060] The updated collaborative weights and initial control parameters are substituted into the adaptive collaborative compensation function. Taking into account the current control state and historical data, the optimal compensation amount is calculated. For the example of a three-section cold rolling mill, if a thickness deviation of 0.03mm occurs in the first section, according to the collaborative compensation function, a compensation of 200kN is needed to increase the bending force of the first section and a compensation of 0.1mm to decrease the support roll displacement; simultaneously, a compensation of 100kN is needed to increase the bending force of the second section and a compensation of 0.05mm to decrease the support roll displacement.

[0061] The calculated compensation is directly added to the original setpoint. Taking the first segment in the example above, the corrected bending roll force is 8000kN + 200kN = 8200kN, and the corrected support roll displacement is 5mm - 0.1mm = 4.9mm. These corrected control parameters will be sent to the rolling mill control system as new execution commands to achieve precise control.

[0062] The aforementioned adaptive collaborative control method effectively addresses the coupling effects between different sections during the rolling process, improving the control accuracy of the rolling mill and the stability of product quality. Practical applications show that, using this method, the plate thickness deviation can be reduced from ±0.03mm to ±0.01mm, and the flatness deviation can be reduced from ±5I units to ±2I units, significantly improving product quality.

[0063] This invention relates to a system for the coordinated optimization of plate shape control and plate thickness accuracy during cold rolling mill processes, the system comprising: The first unit is used to collect the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, determine the contact stress distribution between the rolling work roll and the support roll, and obtain the bending deformation of the rolling work roll at different positions by combining the beam bending deformation equation. The second unit is used to construct a coupled mapping relationship between the strip shape moment distribution and stress distribution that takes into account the deformation of the rolling work roll, based on the bending deformation of the rolling work roll and the strip shape detection data and thickness detection data. The third unit is used to adaptively adjust the compensation coefficients corresponding to the coupling mapping relationship based on the state judgment results of the strip rolling process, establish a dynamic collaborative optimization objective function for strip shape and thickness, and obtain the bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function through iterative calculation; input the strip shape and thickness data of each segment of the rolling process into the fuzzy rule base to obtain the initial control parameters, collect the state parameters of each segment of the roll system to calculate the coupling influence coefficient to construct the adaptive collaborative function, and correct the bending force of the rolling work roll and the displacement of the support roll; The fourth unit is used to adjust the rolling parameters of the cold rolling mill in real time based on the bending force of the rolling work roll and the displacement of the support roll, so as to achieve coordinated control of strip shape and thickness.

[0064] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0065] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0066] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for co-optimizing plate shape control and plate thickness accuracy during cold rolling, characterized in that, include: The contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll are collected to determine the contact stress distribution between the rolling work roll and the support roll. The bending deformation of the rolling work roll at different positions is obtained by combining the beam bending deformation equation. Based on the bending deformation of the rolling mill roll, and combined with the strip shape detection data and strip thickness detection data, a coupled mapping relationship between the strip shape moment distribution and stress distribution considering the deformation of the rolling mill roll is constructed. Based on the state judgment results of the strip rolling process, the compensation coefficients corresponding to the coupling mapping relationship are adaptively adjusted to establish a dynamic collaborative optimization objective function for strip shape and thickness. The bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function are obtained through iterative calculation. The strip shape and thickness data of each segment of the rolling process are input into the fuzzy rule base to obtain the initial control parameters. The state parameters of the roll system of each segment are collected to calculate the coupling influence coefficient and construct an adaptive collaborative function to correct the bending force of the rolling work roll and the displacement of the support roll. Based on the bending force of the rolling work rolls and the displacement of the support rolls, the rolling parameters of the cold rolling mill are adjusted in real time to achieve coordinated control of strip shape and thickness.

2. The method according to claim 1, characterized in that, The contact geometry parameters and lubricating oil film thickness data between the rolling mill roll and the support roll are collected to determine the contact stress distribution between them. Combined with the beam bending deformation equation, the bending deformation of the rolling mill roll at different locations is obtained, including: Collect the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, and determine the unit width load and contact area coordinates based on the contact geometry parameters and lubricating oil film thickness data; Based on the unit width load and contact area coordinates, the contact stress distribution is calculated using Hertz contact theory, and the contact stress distribution is lubricated and corrected according to the lubricating oil film thickness data. The modified contact stress distribution state is used as the distributed load input into the beam bending deformation equation to establish the correspondence between the bending deformation of the rolling mill roll and the contact stress. The boundary constraint conditions of the rolling work roll are determined based on the position of the support roll. The boundary constraint conditions are combined with the effects of the rolling force and the bending roll force to solve the beam bending deformation equation. Based on the solution of the beam bending deformation equation, the bending deformation of the rolling work roll along the rolling direction is obtained, and the working position of the rolling work roll is adjusted based on the bending deformation.

3. The method according to claim 1, characterized in that, Based on the bending deformation of the rolling mill rolls, and combined with the strip shape and thickness detection data, a coupled mapping relationship between the strip shape moment distribution and stress distribution, considering the deformation of the rolling mill rolls, is constructed, including: Collect actual plate thickness distribution data during the strip rolling process, compare the actual plate thickness distribution data with the preset target plate thickness data, calculate the relative plate shape deviation of the strip, and establish an initial plate shape-stress mapping function based on the correspondence between the relative plate shape deviation and the bending deformation of the rolling work roll. Obtain the actual roll gap value of the rolling work roll, calculate the distribution relationship between rolling force and contact arc length based on the actual roll gap value, substitute the distribution relationship into the initial shape-stress mapping function, and construct the coupled mapping relationship between the shape moment distribution and stress distribution considering the deformation of the rolling work roll.

4. The method according to claim 1, characterized in that, A dynamic collaborative optimization objective function for plate shape and thickness is established. Through iterative calculation, the bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function are obtained, including: Construct a comprehensive objective function that includes plate shape control objective, plate thickness control objective, and control cost, and assign corresponding weight coefficients to the plate shape control objective, plate thickness control objective, and control cost in the comprehensive objective function; Obtain the process parameters during the rolling process, establish process constraints based on the process parameters, and apply the process constraints to the comprehensive objective function; Real-time status data of the rolling process is collected, the gradient value of the comprehensive objective function is calculated based on the real-time status data, and the control variables are iteratively updated according to the gradient value; Calculate the difference between the control variables and the objective function in two adjacent iterations, and use the difference between the control variables and the objective function as the convergence criterion; The bending force of the rolling work roll is calculated based on the converged control variables. The displacement of the support roll is determined based on the relationship between the bending force and the rolling pressure. The coordinated control of the plate shape and plate thickness is achieved by adjusting the bending force and the displacement of the support roll.

5. The method according to claim 1, characterized in that, The initial control parameters are obtained by inputting the plate shape and thickness data of each segment of the rolling process into a fuzzy rule base, collecting the state parameters of each segment of the roll system, calculating the coupling influence coefficient, and constructing an adaptive collaborative function to correct the bending force of the rolling work roll and the displacement of the support roll, including: The strip shape and thickness data of the entry, middle and exit sections of the strip rolling process are input into the corresponding fuzzy rule base. The initial control parameters of each section are obtained through fuzzy inference calculation. The initial control parameters include the bending force of the rolling work roll and the displacement of the support roll. The state parameters of each section of the rolling work roll and support roll are collected, and the coupling influence coefficient between each section is calculated based on the state parameters. An adaptive cooperative function is constructed based on the coupling influence coefficient. The initial control parameters of each section are input into the adaptive cooperative function, the cooperative compensation amount between each section is calculated, and the initial control parameters of each section are corrected based on the cooperative compensation amount. Adjust the bending force of each section of the rolling mill rolls and the displacement of the support rolls according to the revised control parameters.

6. The method according to claim 5, characterized in that, An adaptive cooperative function is constructed based on the coupling influence coefficient. The initial control parameters of each segment are input into the adaptive cooperative function, the cooperative compensation amount between each segment is calculated, and the initial control parameters of each segment are corrected according to the cooperative compensation amount, including: Based on the coupling influence coefficient, a transfer function matrix is ​​constructed. The dynamic transfer relationship between each rolling segment is calculated according to the transfer function matrix, and the collaborative weight of each rolling segment is allocated according to the dynamic transfer relationship. The initial bending roll force and support roll displacement of each rolling segment are input into the transfer function matrix. The compensation coefficient of each rolling segment is calculated according to the collaborative weight, and an adaptive collaborative compensation function is constructed based on the compensation coefficient. The control deviations of each rolling section are collected, and the collaborative weights are dynamically updated based on the control deviations. The updated collaborative weights and the initial control parameters are substituted into the adaptive collaborative compensation function to obtain the bending roll force compensation amount and the support roll displacement compensation amount of each rolling section. The bending roll force compensation amount and the support roll displacement compensation amount are superimposed on the initial control parameters of each rolling section to obtain the corrected control parameters.

7. A system for coordinated optimization of plate shape control and plate thickness accuracy during cold rolling, used to implement the method as described in any one of claims 1-6, characterized in that, include: The first unit is used to collect the contact geometry parameters and lubricating oil film thickness data between the rolling work roll and the support roll, determine the contact stress distribution between the rolling work roll and the support roll, and obtain the bending deformation of the rolling work roll at different positions by combining the beam bending deformation equation. The second unit is used to construct a coupled mapping relationship between the strip shape moment distribution and stress distribution that takes into account the deformation of the rolling work roll, based on the bending deformation of the rolling work roll and the strip shape detection data and thickness detection data. The third unit is used to adaptively adjust the compensation coefficients corresponding to the coupling mapping relationship based on the state judgment results of the strip rolling process, establish a dynamic collaborative optimization objective function for strip shape and thickness, and obtain the bending force of the rolling work roll and the displacement of the support roll that satisfy the dynamic collaborative optimization objective function through iterative calculation; input the strip shape and thickness data of each segment of the rolling process into the fuzzy rule base to obtain the initial control parameters, collect the state parameters of each segment of the roll system to calculate the coupling influence coefficient to construct the adaptive collaborative function, and correct the bending force of the rolling work roll and the displacement of the support roll; The fourth unit is used to adjust the rolling parameters of the cold rolling mill in real time based on the bending force of the rolling work roll and the displacement of the support roll, so as to achieve coordinated control of strip shape and thickness.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

Images