Self-learning control method for fixed-width machine

Abstract

The present invention discloses a control method for self-learning of a width setting machine, which belongs to the field of hot rolling technology. It comprises: measuring the actual width of the slab and the actual width at the first roughing pass outlet; calculating the theoretical width at the first roughing pass outlet based on the relevant parameters of the width setting machine; determining the current value of the self-learning of the width setting machine according to the actual width at the first roughing pass outlet and the theoretical width at the first roughing pass outlet; verifying whether the current value of the self-learning of the width setting machine is qualified; when the verification is qualified, indexing the current width reduction layer and the self-learning old values ​​of the front and rear width reduction layers according to the theoretical hot value of the slab width and the width reduction amount of the width setting machine; determining the self-learning update values ​​of the current width reduction layer and the front and rear width reduction layers respectively according to the self-learning current value and the self-learning old value, and updating the self-learning parameters. This control method effectively improves the self-learning efficiency and setting accuracy of the width setting machine by solidifying the indexing method of the old self-learning value and adopting an update mode with different amplitudes for multiple width reduction layers in synchronization.

Description

Technical Field

[0001] The present invention relates to the field of hot rolling technology, and in particular to a self-learning control method for a width setting machine. Background Art

[0002] During hot rolling, strip width control accuracy reflects the level of product quality control. The width sizing machine, a major width reduction device preceding the vertical rollers, has a direct impact on subsequent strip width control accuracy. Factors such as slab width deviation and self-learning layer jumps during iterative width reduction calculations can lead to the use of different layer-specific self-learning parameters for the same strip specification. Large differences in self-learning parameters between adjacent layers can cause significant fluctuations in the width sizing machine's width reduction calculations, impacting the stability of strip width control accuracy and ultimately, product quality. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a control method for self-learning of a width-fixing machine that overcomes the above problems or at least partially solves the above problems. By solidifying the indexing method of the old self-learning value and adopting a multi-width reduction layer synchronous expansion update mode with different amplitudes, the self-learning efficiency and setting accuracy of the width-fixing machine can be effectively improved, and product quality can be guaranteed.

[0004] The present invention provides a control method for self-learning of a width-fixing machine, the control method comprising:

[0005] Measure the actual width of the slab and the actual width of the first rough rolling exit;

[0006] Obtaining parameters related to the width sizing machine, wherein the parameters related to the width sizing machine include at least an actual roll gap value, stiffness, pressure, and width reduction of the width sizing machine;

[0007] Calculating the theoretical width of the first rough rolling exit based on the relevant parameters of the width setting mill;

[0008] Determining a current value of the self-learning of the width setting machine according to the actual width at the rough rolling first pass exit and the theoretical width at the rough rolling first pass exit;

[0009] Verify whether the current value of the width-fixing machine self-learning is qualified;

[0010] When the verification is qualified, the self-learning old value of the current width reduction layer and the self-learning old values ​​of the previous and next width reduction layers are indexed according to the theoretical hot state value of the slab width and the width reduction amount of the width sizing machine;

[0011] Determine the self-learning update value of the current width reduction layer according to the self-learning old value of the current width reduction layer and the self-learning current value of the width constant machine, and update the self-learning parameters of the current width reduction layer;

[0012] According to the old self-learning values ​​of the front and rear width reduction levels and the current self-learning values ​​of the width-fixing machine, the self-learning update values ​​of the front and rear width reduction levels are determined, and the self-learning parameters of the front and rear width reduction levels are updated.

[0013] Optionally, the measuring of the actual width of the slab and the actual width at the first rough rolling pass outlet includes:

[0014] When the middle of the slab reaches the width gauge in front of the width gauge, measure the actual width of the slab;

[0015] When the middle of the slab reaches the width gauge at the first rough rolling pass exit, the actual width at the first rough rolling pass exit is measured.

[0016] Optionally, the calculating of the theoretical width at the first rough rolling pass outlet based on the relevant parameters of the width setting mill includes:

[0017] Calculating the width of the width setting machine outlet according to the actual roll gap value, rigidity and pressure of the width setting machine;

[0018] Determine the dog bone recovery amount and the first pass flat roller width;

[0019] The theoretical width of the rough rolling first pass outlet is calculated based on the width of the width setting machine, the dog bone recovery amount and the first pass flat roller width spread.

[0020] Optionally, the step of calculating the width of the width sizing machine outlet according to the actual roll gap value, stiffness, and pressure of the width sizing machine includes:

[0021] The outlet width of the width setting machine is calculated according to the following formula:

[0022] ΔD=D+P / E;

[0023] Wherein, ΔD represents the outlet width of the width sizing machine, D represents the actual roll gap value of the width sizing machine, P represents the pressure of the width sizing machine, and E represents the rigidity of the width sizing machine.

[0024] Optionally, the calculating of the theoretical width at the first roughing pass outlet according to the width setting mill outlet width, the dog bone recovery amount, and the first flat roll width spread includes:

[0025] The sum of the width of the width setting machine outlet, the dog bone recovery amount and the width expansion of the first pass flat roller is calculated as the theoretical width of the first pass outlet.

[0026] Optionally, determining the current value of the self-learning of the width setting machine according to the actual width at the rough rolling first pass outlet and the theoretical width at the rough rolling first pass outlet includes:

[0027] The difference between the actual width at the rough rolling first pass outlet and the theoretical width at the rough rolling first pass outlet is determined as the current value of the width setting machine self-learning.

[0028] Optionally, the checking whether the current value of the self-learning of the width-fixing machine is qualified includes:

[0029] Determine whether the self-learning current value of the width-fixing machine is within the preset upper and lower limits of the self-learning current value;

[0030] If the self-learning current value of the width-fixing machine is within the preset upper and lower limit values ​​of the self-learning current value, the self-learning current value of the width-fixing machine is verified to be qualified.

[0031] Optionally, determining the self-learning update value of the current width reduction layer according to the self-learning old value of the current width reduction layer and the self-learning current value of the width constant machine includes:

[0032] The self-learning update value of the current width reduction level is determined according to the following formula:

[0033] Zlsp ia =(Zlsp0-Zlsp ib )*G1+Zlsp ib

[0034] Among them, Zlsp ia Indicates the self-learning update value of the current i-th width reduction layer, Zlsp0 indicates the current self-learning value of the fixed width machine, Zlsp ib It represents the old self-learning value of the current i-th reduction layer, and G1 represents the preset first self-learning gain coefficient.

[0035] Optionally, determining the self-learning update values ​​of the front and rear width reduction layers according to the self-learning old values ​​of the front and rear width reduction layers and the self-learning current value of the width fixing machine includes:

[0036] The self-learning update value of the previous width reduction level of the current width reduction level is determined according to the following formula:

[0037] Zlsp (i-1)a =(Zlsp0-Zlsp (i-1)b )*G2+Zlsp (i-1)b

[0038] Among them, Zlsp (i-1)a Indicates the self-learning update value of the i-1th width reduction layer, Zlsp0 indicates the current value of the self-learning of the fixed width machine, and Zlsp (i-1)b represents the old self-learning value of the i-1th width reduction level, G2 represents the preset second self-learning gain coefficient, and the i-1th width reduction level is the previous width reduction level of the i-th width reduction level;

[0039] The self-learning update value of the next width reduction level after the current width reduction level is determined according to the following formula:

[0040] Zlsp (i+1)a =(Zlsp0-Zlsp (i+1)b )*G3+Zlsp (i+1)b

[0041] Among them, Zlsp (i+1)a Indicates the self-learning update value of the i+1th width reduction layer, Zlsp0 indicates the current value of the self-learning of the fixed width machine, and Zlsp (i+1)b It represents the old self-learning value of the i+1th width reduction level, G3 represents the preset third self-learning gain coefficient, and the i+1th width reduction level is the next width reduction level after the i-th width reduction level.

[0042] Optionally, before updating the self-learning parameters of the current width reduction layer and the previous and next width reduction layers, the control method further includes:

[0043] Verifying whether the self-learning update of the current width reduction layer and the self-learning update values ​​of the previous and next width reduction layers are qualified;

[0044] When the verification is qualified, the self-learning parameters of the current width reduction layer and the front and back width reduction layers are synchronously updated according to the self-learning update value of the current width reduction layer and the self-learning update values ​​of the front and back width reduction layers.

[0045] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0046] An embodiment of the present invention provides a method for controlling the self-learning of a width setting machine. The method determines the current self-learning value of the width setting machine by comparing the actual width at the first roughing pass exit with the theoretical width at the first roughing pass exit. The method then verifies whether the current self-learning value of the width setting machine is qualified. This prevents factors such as slab width deviation from causing the current self-learning value of the width setting machine to fail, thereby affecting the strip width control accuracy. Once the verification is qualified, the old self-learning value of the current width reduction layer, as well as the old self-learning values ​​of the previous and next width reduction layers, is indexed based on the theoretical hot-state value of the slab width and the width reduction amount of the width setting machine, thereby solidifying the indexing method of the old self-learning values. Finally, based on the self-learning current value and the self-learning old value of the index, the self-learning update values ​​of the current width reduction layer, and the previous and next width reduction layers are determined respectively. The self-learning parameters of the current width reduction layer, and the previous and next width reduction layers are updated to achieve synchronous expansion updates of multiple width reduction layers with different amplitudes. This can improve the problem of width machine setting fluctuations caused by factors such as slab incoming width deviation and self-learning layer jumps during width reduction iterative calculation, improve the setting accuracy of the width machine, and provide a guarantee for product quality.

[0047] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be construed as limiting the present invention. The same reference numerals are used throughout the accompanying drawings to denote the same components.

[0049] In the attached figure:

[0050] Figure 1 This is a flow chart of a control method for self-learning of a fixed-width machine provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present disclosure will be described below in more detail with reference to the accompanying drawings.

[0052] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments of the present disclosure. These figures are not drawn to scale, and for the purpose of clarity, certain details are exaggerated and certain details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0053] In the context of this disclosure, when a layer / element is referred to as being "on" another layer / element, the layer / element may be directly on the other layer / element or an intervening layer / element may exist therebetween. Additionally, if a layer / element is "on" another layer / element in one orientation, the layer / element may be "below" the other layer / element when the orientation is reversed. In the context of this disclosure, similar or identical components may be denoted by the same or similar reference numerals.

[0054] In order to better understand the above technical solution, the above technical solution will be described in detail below in combination with specific implementation methods. It should be understood that the embodiments of the present disclosure and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0055] Figure 1This is a flow chart of a control method for self-learning of a fixed width machine provided by an embodiment of the present invention. Figure 1 As shown, the control method includes:

[0056] Step S101: measuring the actual width of the slab and the actual width at the outlet of the first rough rolling pass.

[0057] Optionally, step S101 may include:

[0058] When the middle of the slab reaches the width gauge in front of the width gauge, measure the actual width of the slab;

[0059] When the middle of the slab reaches the width gauge at the first rough rolling pass exit, the actual width at the first rough rolling pass exit is measured.

[0060] It should be noted that the theoretical value (cold value) of the slab width is issued in the rolling plan. Theoretically, the slab width changes when heated. The theoretical hot value of the slab width can be obtained by multiplying the theoretical value (cold value) of the slab width issued in the rolling plan by its thermal expansion coefficient. The actual slab width measured in step S101 is the actual width of the slab after heating.

[0061] Step S102: Obtain relevant parameters of the width-fixing machine.

[0062] In this embodiment, the relevant parameters of the width sizing machine include at least: the actual roll gap value, rigidity, pressure and width reduction of the width sizing machine.

[0063] Step S103: Calculate the theoretical width of the first rough rolling exit based on the relevant parameters of the width setting mill.

[0064] Optionally, step S103 may include:

[0065] The first step is to calculate the outlet width of the width setting machine based on the actual roller gap value, stiffness and pressure of the width setting machine.

[0066] Specifically, the width of the width setting machine outlet can be calculated according to the following formula:

[0067] ΔD=D+P / E;

[0068] Among them, ΔD represents the outlet width of the width sizing machine, D represents the actual roll gap value of the width sizing machine, P represents the pressure of the width sizing machine, and E represents the stiffness of the width sizing machine.

[0069] The second step is to determine the dog bone recovery amount and the width of the first flat roller.

[0070] In this embodiment, the dog bone recovery amount can be calculated according to the following formula:

[0071] ΔB DP =a1×ΔB SSP +a2×ΔBSSP ×(B SLB ―ΔB SSP )÷H SLB +a4×ΔB SSP 4 ÷H SLB 2 ÷(B SLB ―ΔB SSP )+a5×ΔB SSP 3 ÷H SLB 2 +a6*ΔB SSP 3 ÷H SLB ÷(B SLB ―ΔB SSP )+a7×ΔB SSP 2 ÷H SLB +zlsp

[0072] Where ΔB DP Indicates the dog bone recovery amount, ΔB SSP represents the width reduction of the fixed width machine, a1-a7 represent the model configuration parameters, B SLB Indicates the theoretical hot value of the slab width, H SLB Indicates the theoretical thermal state value of the slab thickness, zlsp indicates the self-learning value of the width setting machine (the self-learning value zlsp of the width setting machine here can be calculated based on the theoretical thermal state value B of the slab width). SLB and the width reduction of the width setting machine ΔB SSP It is obtained from the self-learning value of the corresponding level of the index in Table 2 below. For details, please refer to the relevant description in Table 2 below, which will not be repeated here).

[0073] In this embodiment, the width of the first flat roller can be calculated according to the following formula:

[0074]

[0075] Where ΔB Hi Indicates the width of the flat roller, B Ei Indicates the entrance width, h i―1 Indicates the inlet thickness; h i Indicates the outlet thickness, spread_mod indicates the correction coefficient, and R indicates the radius of the working roll.

[0076] The third step is to calculate the theoretical width of the first rough rolling exit according to the width setting machine exit width, dog bone recovery amount and the first pass flat roll width.

[0077] In this embodiment, the sum of the width of the width setting machine outlet, the dog bone recovery amount and the width expansion of the flat roller in the first pass can be calculated as the theoretical width of the first pass outlet.

[0078] Step S104: Determine the current value of the self-learning of the width setting machine according to the actual width at the first rough rolling pass outlet and the theoretical width at the first rough rolling pass outlet.

[0079] In this embodiment, the difference between the actual width at the rough rolling first pass outlet and the theoretical width at the rough rolling first pass outlet can be determined as the current value of the width determination machine self-learning.

[0080] Step S105: Check whether the current value of the width determination machine self-learning is qualified.

[0081] Optionally, step S105 may include:

[0082] Determine whether the current value of the width setting machine self-learning is within the preset upper and lower limits of the current self-learning value;

[0083] If the self-learning current value of the width-fixing machine is within the preset upper and lower limits of the self-learning current value, the self-learning current value of the width-fixing machine is verified to be qualified.

[0084] In this embodiment, the self-learning gain and the self-learning upper and lower limits can be set by obtaining the model table data. The self-learning upper and lower limits and gain table are shown in Table 1 below:

[0085] Table 1

[0086]

[0087] For example, as shown in Table 1 above, the upper and lower limits of the self-learning current value preset in this embodiment are 40 and -40 respectively. When the self-learning current value of the width calibrator is within the range of -40 to 40, the self-learning current value of the width calibrator is verified to be qualified.

[0088] In this embodiment, if the verification is qualified, step S106 can be further executed. If the verification is unqualified, it means that there is a problem with the current self-learning value of the width setting machine. At this time, it is necessary to check whether there is any abnormality in the actual width detection of the slab or the actual width detection of the first rough rolling exit.

[0089] Step S106: After the verification is qualified, the self-learning old value of the current width reduction layer and the self-learning old values ​​of the previous and next width reduction layers are indexed according to the theoretical hot value of the slab width and the width reduction amount of the width fixer.

[0090] In this embodiment, the self-learning old values ​​of the current layer and the front and rear width reduction layers can be obtained by indexing the self-learning index layer division table of the fixed width machine. The self-learning index layer division table is shown in Table 2 below:

[0091] Table 2

[0092]

[0093] In Table 2 above, Bp represents the width reduction amount of the sizing press, and Bs represents the theoretical hot state value of the slab width. The layer classification is set as follows: one layer for the theoretical hot state value of the slab width every 200 mm, and one layer for the width reduction amount every 50 mm. That is, through the theoretical hot state value of the slab width and the width reduction amount of the sizing press, the corresponding old self-learning value Zlsp of each layer can be indexed and found from Table 2. ib Among them, i represents the layer where the width reduction amount of the sizing press is located, and b represents the layer where the theoretical hot state value of the slab width is located. For example, Zlsp 14 means that the layer where the width reduction amount of the sizing press is located is 1, the layer where the theoretical hot state value of the slab width is located is 4, the corresponding width reduction amount of the sizing press is 50 < Bp ≤ 100, and the theoretical hot state value of the slab width is 1600 ≤ Bs < 1800.

[0094] When the old self-learning value of the current width reduction layer is indexed as Zlsp 14 from Table 2 above, the old self-learning value of the previous width reduction layer is Zlsp 04 , and the old self-learning value of the previous width reduction layer is Zlsp 24 .

[0095] It should be noted that the old self-learning value Zlsp corresponding to each layer in Table 2 above ib is defaulted to 0. During subsequent rolling, through gradual learning, the old self-learning value Zlsp corresponding to each layer ib will change continuously. The theoretical hot state value of the slab width in Table 2 above is usually obtained by multiplying the theoretical value (cold state value) of the slab width issued in the rolling plan by its thermal expansion coefficient. The width reduction amount of the sizing press can be obtained by calculating the difference between the inlet width of the sizing press and the theoretical width at the exit of the first rough rolling pass, plus the overpressure amount of the sizing press.

[0096] Step S107: Determine the self-learning update value of the current width reduction layer according to the old self-learning value of the current width reduction layer and the current self-learning value of the sizing press, and update the self-learning parameters of the current width reduction layer.

[0097] Optionally, step S107 includes:

[0098] Determine the self-learning update value of the current width reduction layer according to the following formula:

[0099] Zlsp ia = (Zlsp0 - Zlsp ib ) * G1 + Zlsp ib

[0100] Among them, Zlsp ia represents the self-learning update value of the current i-th width reduction layer, Zlsp0 represents the current self-learning value of the sizing press, Zlsp ibIt represents the old self-learning value of the current i-th reduction layer, and G1 represents the preset first self-learning gain coefficient.

[0101] In this embodiment, the first self-learning gain coefficient may be a preset constant. For example, referring to the current level self-learning gain set in Table 1 above, which is the first self-learning gain coefficient, ie, G1 = 0.35.

[0102] Step S108: Determine the self-learning update values ​​of the front and rear width reduction levels according to the old self-learning values ​​of the front and rear width reduction levels and the current self-learning values ​​of the width-fixing machine, and update the self-learning parameters of the front and rear width reduction levels.

[0103] Optionally, step S108 includes:

[0104] The self-learning update value of the previous width reduction level of the current width reduction level is determined according to the following formula:

[0105] Zlsp (i-1)a =(Zlsp0-Zlsp (i-1)b )*G2+Zlsp (i-1)b

[0106] Among them, Zlsp (i-1)a Indicates the self-learning update value of the i-1th width reduction layer, Zlsp0 indicates the current value of the fixed width machine self-learning, Zlsp (i-1)b represents the old self-learning value of the i-1th width reduction level, G2 represents the preset second self-learning gain coefficient, and the i-1th width reduction level is the previous width reduction level of the i-th width reduction level;

[0107] The self-learning update value of the next width reduction level after the current width reduction level is determined according to the following formula:

[0108] Zlsp (i+1)a =(Zlsp0-Zlsp (i+1)b )*G3+Zlsp (i+1)b

[0109] Among them, Zlsp (i+1)a Indicates the self-learning update value of the i+1th width reduction layer, Zlsp0 indicates the current value of the fixed width machine self-learning, Zlsp (i+1)b It represents the old self-learning value of the i+1th width reduction level, G3 represents the preset third self-learning gain coefficient, and the i+1th width reduction level is the next width reduction level after the i-th width reduction level.

[0110] In this embodiment, the second self-learning gain coefficient G2 and the third self-learning gain coefficient G3 may be pre-set constants. For example, referring to Table 1 above, the upper self-learning gain coefficient represents the second self-learning gain coefficient, and the lower self-learning gain coefficient represents the third self-learning gain coefficient, that is, G2 = G3 = 0.15.

[0111] Optionally, before updating the self-learning parameters of the current width reduction layer and the previous and next width reduction layers, the control method further includes:

[0112] Verify whether the self-learning update of the current width reduction layer, as well as the self-learning update values ​​of the previous and next width reduction layers are qualified;

[0113] When the verification is qualified, the self-learning parameters of the current width reduction layer and the previous and next width reduction layers are synchronously updated according to the self-learning update value of the current width reduction layer and the self-learning update values ​​of the previous and next width reduction layers.

[0114] In this embodiment, whether the self-learning update values ​​of the current width reduction level and the previous and next width reduction levels are qualified can be verified by determining whether the self-learning update values ​​of the current width reduction level and the previous and next width reduction levels are within the preset upper and lower limits of the self-learning update values.

[0115] For example, as shown in Table 1 above, the upper and lower limits of the self-learning update value preset in this embodiment are 30 and -30, respectively. When the self-learning update value of the width-fixing machine is within the range of -30 to 30, that is, the self-learning update values ​​of the current width reduction layer, as well as the previous and next width reduction layers, meet the update conditions and are verified as qualified.

[0116] In this embodiment, when the self-learning update values ​​of the current width reduction layer, and the previous and subsequent width reduction layers are determined according to step S107 and step S108, and the self-learning update values ​​of each width reduction layer are verified to be qualified, the self-learning update values ​​corresponding to each width reduction layer can be updated to the above Table 2 to complete the self-learning of the width fixing machine.

[0117] The following is a typical application example to further illustrate the technical solution of this embodiment:

[0118] If the self-learning method of the present invention is not adopted, when the theoretical width of a certain slab is 1175mm, the hot width is 1197.30mm. The actual measured value of the width gauge in front of the width setting machine is 1201.4mm. The self-learning update uses the measured actual width of the slab as the index, so the self-learning update layer is greater than 1200mm. If the self-learning parameters of the subsequent rolls of the same specification are less than 1200mm, the updated self-learning parameters will not work. Similarly, if the self-learning setting and update of the slabs of the same specification both use the actual width, the difference in the actual width of the front and rear rolls is likely to cause the self-learning parameters to jump to the layer when setting, affecting the stability of the setting. Therefore, in order to solve the above problem, the embodiment of the present invention adopts the index amount of the solidified self-learning old value (i.e., the theoretical hot value of the slab width and the width reduction of the width setting machine) when the width setting machine is self-learning updated, and indexes the self-learning old value of the current width reduction layer and the self-learning old value of the front and rear width reduction layers according to the theoretical hot value of the slab width and the width reduction of the width setting machine. The unified use of the theoretical hot width of the slab and the width reduction amount of the width setting machine can avoid the problem of inconsistent settings and updated layers and jumps in the width index layers of the front and rear rolls. At the same time, because the width of the rough rolling outlet is affected by the self-learning of the fine rolling outlet, there will be certain differences between the front and rear rolls, which can easily cause jumps in the self-learning width reduction index layers of the front and rear rolls, affecting the setting stability. Therefore, in order to solve the above problems, the update of the self-learning parameters in the embodiment of the present invention adopts a method of synchronously updating multiple width reduction layers with different amplitudes. After determining the current self-learning value, the self-learning update values ​​of the current width reduction layer and the front and rear width reduction layers are determined according to the current self-learning value and the old self-learning value of the index. The self-learning parameters of the current width reduction layer and the front and rear width reduction layers are updated to improve the setting accuracy of the width setting machine and provide a guarantee for product quality.

[0119] The following example shows updated data from self-learning using the control method for width-sizing machines provided by the present invention. The measured actual slab width is 1450mm, the actual width at the exit of the first roughing pass is 1346.91mm, and the calculated theoretical width at the exit of the first roughing pass is 1351.21mm. Therefore, the current self-learning value for the width-sizing machine can be calculated as -4.3. Table 3 below shows the self-learning updates for the current and next width-sizing layers.

[0120] Table 3

[0121] Self-learning value category Self-learning old value Self-learning current value Self-learning update value Current width reduction level -5.73 -4.3 -5.25 The next width reduction level -10.78 -4.3 -9.81

[0122] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0123] An embodiment of the present invention provides a method for controlling the self-learning of a width setting machine. The method determines the current self-learning value of the width setting machine by comparing the actual width at the first roughing pass exit with the theoretical width at the first roughing pass exit. The method then verifies whether the current self-learning value of the width setting machine is qualified. This prevents factors such as slab width deviation from causing the current self-learning value of the width setting machine to fail, thereby affecting the strip width control accuracy. Once the verification is qualified, the old self-learning value of the current width reduction layer, as well as the old self-learning values ​​of the previous and next width reduction layers, is indexed based on the theoretical hot-state value of the slab width and the width reduction amount of the width setting machine, thereby solidifying the indexing method of the old self-learning values. Finally, based on the self-learning current value and the self-learning old value of the index, the self-learning update values ​​of the current width reduction layer, and the previous and next width reduction layers are determined respectively. The self-learning parameters of the current width reduction layer, and the previous and next width reduction layers are updated to achieve synchronous expansion updates of multiple width reduction layers with different amplitudes. This can improve the problem of width machine setting fluctuations caused by factors such as slab incoming width deviation and self-learning layer jumps during width reduction iterative calculation, improve the setting accuracy of the width machine, and provide a guarantee for product quality.

[0124] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0125] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0126] It should be noted that the above-mentioned embodiments illustrate rather than limit the invention and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims.

Claims

1. A control method for self-learning of a fixed width machine, characterized in that: The control method includes: Measure the actual width of the slab and the actual width of the first rough rolling exit; Obtaining parameters related to the width sizing machine, wherein the parameters related to the width sizing machine include at least an actual roll gap value, stiffness, pressure, and width reduction of the width sizing machine; Calculating the theoretical width of the first rough rolling exit based on the relevant parameters of the width setting mill; Determining a current value of the self-learning of the width setting machine according to the actual width at the rough rolling first pass exit and the theoretical width at the rough rolling first pass exit; Verify whether the current value of the width-fixing machine self-learning is qualified; When the verification is qualified, the self-learning old value of the current width reduction layer and the self-learning old values ​​of the previous and next width reduction layers are indexed according to the theoretical hot state value of the slab width and the width reduction amount of the width sizing machine; Determine the self-learning update value of the current width reduction layer according to the self-learning old value of the current width reduction layer and the self-learning current value of the width constant machine, and update the self-learning parameters of the current width reduction layer; Determine the self-learning update values ​​of the front and rear width reduction layers according to the self-learning old values ​​of the front and rear width reduction layers and the self-learning current values ​​of the width constant machine, and update the self-learning parameters of the front and rear width reduction layers; The step of determining the self-learning current value of the width setting machine according to the actual width at the rough rolling first pass exit and the theoretical width at the rough rolling first pass exit comprises: Determine the difference between the actual width at the rough rolling first pass exit and the theoretical width at the rough rolling first pass exit as the self-learning current value of the width setting machine; The step of determining the self-learning updated value of the current width reduction layer according to the self-learning old value of the current width reduction layer and the self-learning current value of the width fixing device includes: The self-learning update value of the current width reduction level is determined according to the following formula: Zlsp ia =(Zlsp0-Zlsp ib )*G1+Zlsp ib Among them, Zlsp ia Indicates the self-learning update value of the current i-th width reduction layer, Zlsp0 indicates the current self-learning value of the fixed width machine, Zlsp ib It represents the old self-learning value of the current i-th reduction layer, and G1 represents the preset first self-learning gain coefficient.

2. The control method according to claim 1, characterized in that: The measuring of the actual width of the slab and the actual width at the first rough rolling pass outlet includes: When the middle of the slab reaches the width gauge in front of the width gauge, measure the actual width of the slab; When the middle of the slab reaches the width gauge at the first rough rolling pass exit, the actual width at the first rough rolling pass exit is measured.

3. The control method according to claim 1, wherein: The calculation of the theoretical width of the first rough rolling exit based on the relevant parameters of the width setting mill includes: Calculating the width of the width setting machine outlet according to the actual roll gap value, rigidity and pressure of the width setting machine; Determine the dog bone recovery amount and the first pass flat roller width; The theoretical width of the rough rolling first pass outlet is calculated based on the width of the width setting machine, the dog bone recovery amount and the first pass flat roller width spread.

4. The control method according to claim 3, characterized in that: The method of calculating the outlet width of the width setting machine according to the actual roll gap value, rigidity and pressure of the width setting machine includes: The outlet width of the width setting machine is calculated according to the following formula: ΔD=D+P / E; Wherein, ΔD represents the outlet width of the width sizing machine, D represents the actual roll gap value of the width sizing machine, P represents the pressure of the width sizing machine, and E represents the rigidity of the width sizing machine.

5. The control method according to claim 3, characterized in that: The calculating of the theoretical width of the first rough rolling exit according to the width setting mill exit width, the dog bone recovery amount and the first flat roll width spread includes: The sum of the width of the width setting machine outlet, the dog bone recovery amount and the width expansion of the first pass flat roller is calculated as the theoretical width of the first pass outlet.

6. The control method according to claim 1, characterized in that: The checking whether the current value of the width setting machine self-learning is qualified includes: Determine whether the self-learning current value of the width-fixing machine is within the preset upper and lower limits of the self-learning current value; If the self-learning current value of the width-fixing machine is within the preset upper and lower limit values ​​of the self-learning current value, the self-learning current value of the width-fixing machine is verified to be qualified.

7. The control method according to claim 1, characterized in that: The step of determining the self-learning update values ​​of the front and rear width reduction layers according to the self-learning old values ​​of the front and rear width reduction layers and the self-learning current values ​​of the width fixing machine includes: The self-learning update value of the previous width reduction level of the current width reduction level is determined according to the following formula: Zlsp (i-1)a =(Zlsp0-Zlsp (i-1)b )*G2+Zlsp (i-1)b Among them, Zlsp (i-1)a Indicates the self-learning update value of the i-1th width reduction layer, Zlsp0 indicates the current value of the self-learning of the fixed width machine, and Zlsp (i-1)b represents the old self-learning value of the i-1th width reduction level, G2 represents the preset second self-learning gain coefficient, and the i-1th width reduction level is the previous width reduction level of the i-th width reduction level; The self-learning update value of the next width reduction level after the current width reduction level is determined according to the following formula: Zlsp (i+1)a =(Zlsp0-Zlsp (i+1)b )*G3+Zlsp (i+1)b Among them, Zlsp (i+1)a Indicates the self-learning update value of the i+1th width reduction layer, Zlsp0 indicates the current value of the self-learning of the fixed width machine, and Zlsp (i+1)b It represents the old self-learning value of the i+1th width reduction level, G3 represents the preset third self-learning gain coefficient, and the i+1th width reduction level is the next width reduction level after the i-th width reduction level.

8. The control method according to claim 1, characterized in that: Before updating the self-learning parameters of the current width reduction layer and the previous and next width reduction layers, the control method further includes: Verifying whether the self-learning update of the current width reduction layer and the self-learning update values ​​of the previous and next width reduction layers are qualified; When the verification is qualified, the self-learning parameters of the current width reduction layer and the front and rear width reduction layers are synchronously updated according to the self-learning update value of the current width reduction layer and the self-learning update values ​​of the front and rear width reduction layers.

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