Method for controlling thickness of rolling mill and method for monitoring rigidity

Abstract

The present disclosure provides a plate thickness control method for a rolling mill whereby it is possible to calculate an appropriate gap correction amount for the rolling mill on the basis of the amount of change in a rolling load between a previous material and a material in question, and to thus improve plate thickness precision. The plate thickness control method for a rolling mill includes: a step in which a rolled coil rolled immediately beforehand by a rolling mill is designated as a previous material, a rolled coil rolled after the previous material is designated as a material in question, the relationship between the amount of change in a rolling load between the previous material and the material in question and the amount of change in a gap error regarding the rolling mill is approximated sequentially by means of a regression model, and a gap error in the rolling of the material in question is predicted on the basis of the regression model; a step in which a gap setting amount in rolling of the material in question is corrected on the basis of the predicted value of the gap error; and a step in which a regression coefficient of the regression model is updated on the basis of the actual rolling load obtained by rolling the material in question and the amount of change in the actual gap error.

Description

[Technical field]

[0001] The present disclosure relates to a method for controlling thickness and a method for monitoring stiffness of a rolling mill. [Background technology]

[0002] In the rolling process, in order to control the thickness of the rolled material with high precision, it is necessary to set the gap between the rolls of the rolling mill appropriately. When setting this gap, the gap setting value is determined by calculation using a model (gauge meter model) for calculating the gap of the rolling mill, which takes into account the elastic deformation (mill elongation) of the rolling mill due to the reaction force (rolling load) from the rolled material and gap fluctuations due to various factors. Therefore, improving the accuracy of the gauge meter model is important for achieving high precision in the thickness of the rolled material.

[0003] The amount of mill elongation depends on the strength of the rolling mill against elastic deformation (rolling mill rigidity). The rolling mill rigidity is estimated from the results of a measurement test called mill curve measurement. A mill curve is a characteristic curve obtained as the relationship between the amount of mill elongation and the rolling load applied to the rolling mill, that is, the gradient of the mill curve indicates the rigidity of the rolling mill. In the gauge meter model, the gap is predicted based on the amount of mill elongation predicted based on the mill curve for the predicted rolling load, and the gap setting value is determined. A typical mill curve measurement method is the tightening method. In the tightening method, the upper and lower work rolls of the rolling mill are in direct contact with each other without sandwiching the plate (kiss roll state), and the rolling mill rigidity is estimated from the relationship between the measured tightening amount and the rolling load. Since such mill curve measurement requires stopping the operation, it is often not performed frequently, and as a result, the estimated results based on past measurement results are used for a long time.

[0004] In predictions using the above-mentioned gauge meter model, the change in the stiffness of the rolling mill over time may affect the calculation accuracy of the gap setting value. Both Fig. 6 and Fig. 7 are graphs in which the horizontal axis shows the change in rolling load (the difference between the rolling load of the coil (previous material) that was rolled immediately before and the rolling load of the coil (relevant material) to be rolled next) and the vertical axis shows the change in gap error (the difference between the gap error of the previous material and the gap error of the relevant material). The tendency shown in Fig. 6 is a tendency seen when the prediction error of the mill elongation is small, and no correlation is seen between the two variables, the change in rolling load and the change in gap error. On the other hand, the tendency shown in Fig. 7 is a tendency seen when the prediction error of the mill elongation is large, and a correlation is seen between the two variables. That is, in Fig. 7, the gap error change amount caused by the change in rolling load occurs.

[0005] As mentioned above, the prediction error of the mill elongation due to the change in the rolling mill stiffness over time can be a cause of the gap error. However, since the mill stiffness can only be obtained directly by measuring the mill curve, it was difficult to take this error into account in the rolling model.

[0006] Meanwhile, the following Patent Documents 1 and 2 have proposed methods for improving the accuracy of gauge meter models. Patent Document 1 discloses a method for calculating a rolling load fluctuation value that satisfies a gauge meter equation while taking into account the dynamic characteristics of a rolling mill, and for determining a correction amount for a roll gap using the calculated dynamic characteristic rolling load fluctuation value. Patent Document 2 discloses a method for weighting each condition item based on the similarity with past data calculated from condition items (predetermined operational factors and mill elongation), calculating an influence coefficient of each condition item on the rolling load for a target rolled material, and using the influence coefficient (mill constant) of the mill elongation on the rolling load in the gauge meter equation. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent No. 5627553 [Patent Document 2] Japanese Patent Publication No. 2014-104491 Summary of the Invention [Problem to be solved by the invention]

[0008] However, in both Patent Documents 1 and 2, the rolling load change amount, i.e., Nobu The amount of change in the gap error caused by the change in the stiffness of the rolling mill over time cannot be directly taken into account, and the change in the stiffness of the rolling mill over time cannot be captured.

[0009] The present disclosure has been made to solve the above-mentioned problems, and has a first object to provide a method for controlling the thickness of a rolling mill that can calculate an appropriate gap correction amount for the rolling mill based on the amount of change in rolling load between the previous material and the current material, thereby improving the thickness accuracy. A second object of the present disclosure is to provide a method for monitoring the stiffness of a rolling mill that can capture changes that occur in the stiffness of the rolling mill. [Means for solving the problem]

[0010] The first aspect relates to a method for controlling the thickness of a rolling mill that rolls a material to be rolled to a target thickness. The method for controlling the thickness of a rolling mill includes the steps of: taking a rolled coil that has just been rolled by the rolling mill as a previous material, and a rolled coil that will be rolled after the previous material as the target material; sequentially approximating the relationship between the rolling load change amount of the previous material and the target material and the gap error change amount of the rolling mill using a regression model; predicting the gap error in rolling the target material based on the regression model; correcting the gap setting amount in rolling the target material based on the predicted gap error value; and updating the regression coefficients of the regression model based on the actual rolling load and actual gap error change amount obtained in rolling the target material.

[0011] The second aspect has the following features in addition to the first aspect: A difference between the gap error change amount predicted by the regression model using the actual rolling load and the actual gap error change amount is calculated, and if the calculated difference is larger than a reference value, the regression coefficient is not updated and the previous regression coefficient is maintained.

[0012] The third aspect relates to a method for monitoring the stiffness of a rolling mill, which includes the steps of: determining whether or not the stiffness of the rolling mill has changed over time based on a time-series transition of the regression coefficient updated for each rolling according to the first or second aspect; and reporting when it is determined that the stiffness of the rolling mill has changed over time. Effect of the Invention

[0013] According to the first aspect, by capturing the change in the stiffness of the rolling mill as a regression coefficient of a regression model that is sequentially identified, an appropriate gap correction amount is automatically calculated, and the gap set value is calculated based on the calculated gap correction amount. This makes it possible to reduce the influence of the change in stiffness of the rolling mill over time on the calculation accuracy of the gap set value, that is, the change in the plate thickness error caused by the change in the rolling load of the previous material and the material in question. Therefore, the plate thickness accuracy of the material being rolled can be improved.

[0014] According to the second aspect, excessive correction of the gap of the rolling mill can be prevented, thereby enabling more accurate plate thickness control.

[0015] According to the third viewpoint, the change in the regression coefficient over time is considered to represent a change in the stiffness of the rolling mill over time, and by monitoring the regression coefficient, the change occurring in the stiffness of the rolling mill can be captured. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a schematic diagram for explaining a system configuration of a rolling plant. [Diagram 2] 1 is a schematic diagram showing a configuration of a process control computer that implements a strip thickness control method for a rolling mill according to a first embodiment. [Diagram 3] FIG. 2 illustrates an example of a hardware configuration of a process control computer. [Figure 4] FIG. 11 is a schematic diagram showing the configuration of a process control computer that implements a method for monitoring stiffness of a rolling mill according to a third embodiment. [Diagram 5]FIG. 11 is a schematic diagram illustrating an example of a transition of a regression coefficient. [Figure 6] 1 is a graph showing an example in which there is a correlation between a rolling load change amount and a gap error change amount. [Figure 7] 1 is a graph showing an example in which there is a correlation between a rolling load change amount and a gap error change amount. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Note that elements common to the various drawings are designated by the same reference numerals and duplicated explanations will be omitted.

[0018] Embodiment 1 1 is a schematic diagram showing the configuration of a rolling plant 1. The rolling plant 1 takes steel or other metallic material as a material to be rolled M and hot rolls the material M into a plate shape.

[0019] The rolling plant 1 is equipped with, as main equipment, a heating furnace 2, a roughing mill 3, a crop shear 4, a finishing mill 5 as a hot rolling mill, a cooling device 6, and a winder 7. In this embodiment, a case will be described in which the plate thickness at the delivery side of the finishing mill 5 as a hot rolling mill is controlled to a very thin product target plate thickness (for example, 1.0 mm or less).

[0020] The heating furnace 2 is configured to heat the slab, which is the material M to be rolled before rolling, to a predetermined temperature. The heating temperature is, for example, 1200°C. La The tube is, for example, a rectangular parallelepiped having a thickness of 200 to 250 mm, a width of 800 to 2000 mm, and a length of 5 to 12 m.

[0021] The roughing mill 3 has at least one, usually one to three, rolling stands, and is configured to roll the material M heated in the heating furnace 2 in a forward direction (from upstream to downstream of the rolling line) and a reverse direction (from downstream to upstream of the rolling line) in multiple passes. The roughing mill 3 may be provided with a width adjustment device called an edger (not shown).

[0022] The crop shear 4 is configured to cut off any defective shape portions at the leading or trailing end of the rolled material M using upper and lower blades, based on the shape measured by a shape detector 81 described later.

[0023] The finishing mill 5 corresponds to the rolling mill of this embodiment. The finishing mill 5 is a tandem rolling mill equipped with a plurality of rolling stands Fi (1≦i≦N) arranged side by side in the conveying direction of the material M to be rolled. In this embodiment, a case where seven rolling stands F1 to F7 are arranged side by side will be described as an example. Each of the rolling stands F1 to F7 is equipped with two upper and lower work rolls 51, two upper and lower backup rolls 52, and an electric motor 53 for rotating the rolls. The backup rolls 52 are provided with a reduction device 54, which is configured to be able to adjust the gap between the upper and lower work rolls 51 (hereinafter simply referred to as "gap"). The rolling load of each of the rolling stands F1 to F7 is measured by a rolling load sensor 55 described later.

[0024] The cooling device 6 is configured to be able to cool the material M by injecting water onto the material M using a cooling bank. The cooled material M is wound into a coil by a winder 7.

[0025] In this embodiment, the material M to be rolled after rolling and wound into a coil shape may be referred to as a coil. Also, the material M or coil that has just been rolled may be referred to as the "previous material" or the "n-1th coil", and the material M or coil to be rolled may be referred to as the "material" or the "nth coil".

[0026] Various sensors are installed as measuring instruments at key points in the rolling plant 1. Key points in the rolling plant 1 include, for example, the exit side of the heating furnace 2, the exit side of the roughing mill 3, the exit side of the finishing mill 5, and the entry side of the winder 7. Various sensors may also be installed between the rolling stands F1 to F7 of the finishing mill 5. The various sensors include a shape detector 81 capable of measuring the shape of the rolled material M at the exit side of the roughing mill 3, a thermometer 82 that measures the surface temperature of the rolled material M at the entry side of the finishing mill 5, a speed detector 83 that measures the speed Va of the rolled material M at the exit side of the finishing mill 5, a thickness gauge 84 that measures the thickness Ta of the rolled material M at the exit side of the finishing mill 5, a thermometer 85 that measures the surface temperature of the rolled material M at the entry side of the winder 7, and a temperature sensor 86 that measures the temperature of the rolled material M at the entry side of the winder 7. 7 The rolling load sensor 55 measures the rolling load at the rolling position. The various sensors successively measure the condition of the rolled material M and each device.

[0027] The rolling plant 1 is operated by a control system using a computer. The computer includes a host computer 10 and a process control computer 11, which are connected to each other via a network. An interface screen 12, which is an operation screen operated by an operator, is connected to the process control computer 11 via the network.

[0028] The process control computer 11 executes setting calculation and control of the control object in a series of rolling processes. The process control computer 11 also has a function of correcting a gap. The process control computer 11 receives input from the host computer 10 of slab information such as the thickness, width, length, steel type, etc. of the slab which is the material to be rolled M before rolling, and coil target information such as the target thickness, target width, target temperature, etc. of the coil which is the material to be rolled M after rolling.

[0029] 2 is a schematic diagram showing the configuration of a process control computer 11 that implements the method for controlling a thickness of a rolling mill according to embodiment 1. The process control computer 11 includes a gap correction amount calculation unit 111, a gap setting calculation unit 112, a gap error calculation unit 113, a rolling process database 114, and a rolling load calculation unit 115.

[0030] The gap correction amount calculation unit 111 has a function of calculating the gap correction amount. The gap correction amount calculation unit 111 calculates a gap correction amount based on the actual rolling load of the previous material and the actual gap error of the previous material from the rolling process database 114, the rolling load prediction value from the rolling load calculation unit 115, and the actual gap error of the material from the gap error calculation unit 113, and outputs the calculated gap correction amount to the gap setting calculation unit 112. A specific method for calculating the gap correction amount will be described later.

[0031] The gap setting calculation unit 112 has a function of calculating a gap setting value. The gap setting calculation unit 112 calculates the gap setting value of each rolling stand F1 to F7 based on the target plate thickness from the host computer 10 and the rolling load predicted value from the rolling load calculation unit 115, and outputs the calculated gap setting value to the finishing mill 5. The gap setting calculation unit 112 also corrects the gap setting value based on the gap correction amount from the gap correction amount calculation unit 111, and outputs the corrected gap setting value to the finishing mill 5.

[0032] The gap error calculation unit 113 has a function of calculating an actual gap error. The gap error calculation unit 113 calculates an actual gap error of the material based on the actual rolling information, and outputs the calculated actual gap error to the gap correction amount calculation unit 111.

[0033] The rolling process database 114 is adapted to sequentially store past rolling data. The past rolling data includes the actual rolling load of the previous material and the actual gap error of the previous material. The actual gap error is calculated as the difference between the mass flow thickness and the gauge meter thickness. The mass flow thickness is a thickness estimate value (actual thickness value) calculated based on the volume velocity (mass flow) constant law using the actual thickness Ta and actual speed Va measured at the exit side of the finishing mill 5. The gauge meter thickness is a thickness estimate value (actual thickness value) calculated by the gauge meter method using the actual rolling load, etc. The calculation methods of the mass flow thickness and the gauge meter thickness are well known, so a detailed explanation is omitted here.

[0034] The rolling load calculation unit 115 has a function of calculating a predicted rolling load value of the material. The rolling load calculation unit 115 calculates a rolling load based on a target plate thickness and the like input from the upper computer 10. Nobu A load prediction value is calculated and output to a gap correction amount calculation unit 111 and a gap setting calculation unit 112, respectively.

[0035] The specific structure of the process control computer 11 is not limited, and may be as follows, for example. FIG. 3 is a diagram showing an example of the hardware configuration of the process control computer 11. The functions of the process control computer 11 can be realized by a processing circuit shown in FIG. 3. This processing circuit may be dedicated hardware 20a. This processing circuit may include a processor 20b and a memory 20c. This processing circuit may be partially formed as dedicated hardware 20a, and further include a processor 20b and a memory 20c. In the example of FIG. 3, a part of the processing circuit is formed as dedicated hardware 20a, and the processing circuit also includes a processor 20b and a memory 20c.

[0036] At least a portion of the processing circuitry may be at least one dedicated hardware 20a, for example a single circuit, a multiple circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

[0037] The processing circuit may include at least one processor 20b and at least one memory 20c. In this case, each function of the process control computer 11 is realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in the memory 20c. The processor 20b realizes the functions of each part by reading and executing the programs stored in the memory 20c.

[0038] The processor 20b is also called a CPU (Central Processing Unit), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, or a DSP. The memory 20c corresponds to, for example, a non-volatile or volatile semiconductor memory such as a RAM, a ROM, a flash memory, an EPROM, or an EEPROM. The memory 20c can also be configured to function as the database 13.

[0039] In this way, the processing circuit can realize each function of the process control computer 11 by hardware, software, firmware, or a combination of these.

[0040] Next, a method for controlling the thickness of the rolling mill 5, which is implemented by the process control computer 11, will be described.

[0041] When the process control computer 11 receives an input from the host computer 10, the rolling load calculation unit 115 calculates a rolling load prediction value for the material, and outputs it to the gap correction amount calculation unit 111 and the gap setting calculation unit 112. The gap correction amount calculation unit 111 calculates a gap correction amount, and outputs it to the gap setting calculation unit 112. The gap setting calculation unit 112 calculates a gap setting value based on the target plate thickness, the rolling load prediction value, etc., and outputs it to the finishing rolling mill 5. In the finishing rolling mill 5, the gap is controlled by the reduction device 54 of each rolling stand F1 to F7 according to the gap setting value. After rolling the material, the gap error calculation unit 113 calculates an actual gap error, and outputs it to the gap correction amount calculation unit 111.

[0042] The gap correction amount calculation unit 111 calculates the gap correction amount by the following formula (1).

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[0043] TIFF0007643639000002.tif45170

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[0044] TIFF0007643639000004.tif45170

[0045] The regression coefficient a[n] in the above formula (2) is updated each time a coil is rolled. The regression coefficient a[n] is calculated, for example, by the recursive least squares method. When the recursive least squares method is used, the regression coefficient a[n] is calculated and updated by the following formulas (3) and (4). In this way, the regression coefficient a[n] is learned by being updated.

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[0046] TIFF0007643639000007.tif17170

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[0047] TIFF0007643639000009.tif53170

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[0048] The initial value of a[n] can be determined, for example, by regression analysis of coils that have been rolled so far.

[0049] According to this embodiment, the change in the stiffness of the rolling mill 5 is captured as a regression coefficient of the regression model that is sequentially identified, so that an appropriate gap correction amount is automatically calculated. This makes it possible to reduce the influence of the change in the stiffness of the rolling mill 5 over time on the calculation accuracy of the gap setting value, that is, the change in the plate thickness error accompanying the change in the rolling load of the previous material and the material in question. In addition, the calculated gap correction amount is proportional to the magnitude of the regression coefficient a[n]. Therefore, when the regression coefficient is small, that is, when the correlation between the rolling load change amount and the gap error change amount of the previous material and the material in question is small, the absolute value of the calculated gap correction amount is inevitably small, and excessive correction of the gap of the finishing rolling mill 5 can be prevented. On the other hand, when the correlation between the rolling load change amount and the gap error change amount of the previous material and the material in question is large, the absolute value of the calculated gap correction amount is inevitably large, and insufficient correction of the gap of the finishing rolling mill 5 can be prevented.

[0050] Embodiment 2 The second embodiment differs from the first embodiment in that the difference between the gap error change calculated by the regression model using the actual rolling load and the actual gap error change is calculated, and when the difference is larger than a reference value, the regression coefficient a[n] is not updated. Note that the second embodiment does not mention the same points as the first embodiment.

[0051] TIFF0007643639000011.tif20170

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[0052] TIFF0007643639000013.tif32170

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[0053] According to this embodiment, when the actual value of the gap error change amount of the material contains noise, the gap error at that time is regarded as an abnormal value and the regression coefficient a[n] is not updated, thereby preventing excessive correction of the gap of the finishing mill 5. As a result, the plate thickness control can be performed with higher accuracy.

[0054] Embodiment 3 FIG. 4 is a schematic diagram showing the configuration of a process control computer 11 that implements a method for monitoring stiffness of a rolling mill according to the third embodiment.

[0055] This embodiment differs from the first and second embodiments in that the process control computer 11 includes a monitoring unit 116. The following description will focus on this difference.

[0056] The regression coefficient a[n] calculated by the gap correction amount calculation unit 11 is stored sequentially in the rolling process database 14. The monitoring unit 116 monitors the time series data of the regression coefficient a[n]. The monitoring unit 116 detects and notifies the change in the stiffness of the finishing rolling mill 5 based on the trend change of the regression coefficient a[n]. The trend change of the regression coefficient a[n] can be determined, for example, by a change point detection method. The change point detection method is a method of determining a point where a trend change in data values ​​can be expected for the time series data of the regression coefficient a[n], and for example, a method such as Change Finder that detects a change point by two-stage learning using a time series model such as an autoregressive model is known. As a more robust method, the trend change may be determined based on a threshold value appropriately set for the regression coefficient a[n]. Alternatively, the trend change may be determined by using both of these methods. In addition, as a notification method, not only an alarm is issued as a warning sound, but also a display on the interface screen 12 can be used.

[0057] Fig. 5 is a schematic diagram for explaining an example of the change in the regression coefficient a[n]. In Fig. 5, the regression coefficient a[n] has an increasing tendency in the part surrounded by the dashed line. The monitoring unit 116 detects a change in the rolling mill stiffness based on such a change in the trend of the regression coefficient a[n] and notifies the operator.

[0058] According to this embodiment, the time series change in the regression coefficient a[n] is considered to be a change over time in the stiffness of the finishing mill 5, and by monitoring the regression coefficient a[n], it is possible to capture changes occurring in the stiffness of the finishing mill 5. This makes it possible to prompt adjustment of the parameters of the gauge meter model and implementation of mill curve measurements, and to avoid a gradual decrease in the control accuracy of the thickness of the rolled material M.

[0059] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-mentioned embodiments, and various modifications can be made without departing from the spirit of the present invention. In the above-mentioned embodiments, when the number, quantity, amount, range, etc. of each element is mentioned, the present invention is not limited to the mentioned numbers, unless otherwise specified or clearly specified in principle. Furthermore, the structures, etc. described in the above-mentioned embodiments are not necessarily essential to the present invention, unless otherwise specified or clearly specified in principle. [Explanation of symbols]

[0060] 5...Finishing rolling machine (rolling mill), M...Rolled material

Claims

1. A method for controlling thickness of a rolling mill for rolling a material to a target thickness, comprising: a step of sequentially approximating the relationship between the amount of change in rolling load of the previous material and the amount of change in gap error of the rolling mill using a regression model, and predicting the gap error in rolling the material based on the regression model; correcting a gap setting amount in rolling the material based on the predicted value of the gap error; A method for controlling the thickness of a rolling mill, comprising a step of updating the regression coefficients of a regression model based on the actual rolling load and the actual gap error change amount obtained in rolling the material.

2. 2. The method for controlling a thickness of a rolling mill according to claim 1, further comprising the steps of: calculating a difference between a gap error change amount predicted by the regression model using the actual rolling load and the actual gap error change amount; and, if the calculated difference is greater than a reference value, not updating the regression coefficients but maintaining the previous regression coefficients.

3. A step of determining whether or not there is a change over time in the rigidity of the rolling mill based on a time series transition of the regression coefficient updated for each rolling by the plate thickness control method according to claim 1 or 2; The method for monitoring the rigidity of a rolling mill includes a step of notifying when it is determined that a change in the rigidity of the rolling mill over time has occurred.

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