Width control method for finish rolled material and manufacturing method for hot rolled steel sheet

Abstract

Provided is a width prediction method for a finish rolled material according to the present invention, which predicts the width of a finish rolled material in a hot rolling line that includes: a heating furnace which heats a slab; a rough rolling mill which roughly rolls the heated slab and manufactures a rough rolled material; and a finish rolling mill which finish-rolls the rough rolled material and manufactures the finish rolled material, wherein the width prediction method comprises a prediction step for predicting the statistical information about the width of the finish rolled material by using a width prediction model that includes, as input data, set values or actual measurement values of the width of the rough rolled material and one or more operation parameters selected from operation parameters of the finish rolling mill, and, as output data, statistical information about the width of the finish rolled material and is trained through a Gaussian process regression method.

Description

[Technical field]

[0001] The present invention relates to a width prediction method for a finish rolled material in a hot rolling line, a width control method for a finish rolled material, a manufacturing method for a hot rolled steel sheet, and a method for generating a width prediction model for a finish rolled material. [Background technology]

[0002] In a hot rolling line, first, a slab, which is a steel billet material, is heated in a heating furnace, the width of the slab is adjusted by a width reduction press (sizing press), and one or more rough rolling mills are used for rough rolling to produce a semi-finished steel plate called a rough bar with a thickness of about 30 to 50 mm (hereinafter referred to as a rough rolled material). Next, the head and tail ends of the rough rolled material are cut off by a crop shear, and the rough rolled material is finish-rolled by a finishing mill consisting of 5 to 7 rolling stands capable of continuous rolling to produce a steel plate with a thickness of about 1.0 to 25.0 mm (hereinafter referred to as a finish rolled material). Finally, the finish rolled material in a high temperature state is cooled by a cooling device on the run-out table, and then wound by a coiler (winder) to become a hot-rolled steel plate. In a hot rolling line, the width of a steel plate varies in a complex manner during the manufacturing process of a hot rolled steel plate, since the steel plate is plastically deformed in the thickness direction and width direction in the width reduction press, rough rolling mill, and finish rolling mill. On the other hand, the width accuracy of a hot rolled steel plate has a direct impact on the product yield. For this reason, in a hot rolling line, the width of the rough rolled material is controlled (rough width control) at the stage after rough rolling and before it is charged into the finish rolling mill, and the width of the steel plate is controlled (finish width control) during the process of passing through the finish rolling mill.

[0003] In a hot rolling line, the width of a steel sheet changes due to various causes, and therefore various techniques have been proposed to improve the width accuracy of a hot rolled steel sheet. For example, Patent Document 1 describes a method for controlling the width of a finish rolled material (hereinafter referred to as the finish exit width). Specifically, the method described in Patent Document 1 calculates the width change amount of a steel sheet in a finish rolling mill, and controls the opening degree of an edger installed on the entry side of a rough rolling mill and / or a finish rolling mill based on the calculated value and a target value of the finish exit width, thereby controlling the finish exit width. Patent Document 1 also describes predicting the width change amount of a steel sheet in a finish rolling mill using a prediction formula with the thickness of the steel sheet in each rolling stand, the reduction ratio, the tension between rolling stands, the crown ratio change, the deformation resistance and temperature of the steel sheet, and the passing time between rolling stands as parameters. Furthermore, the method described in Patent Document 1 calculates the prediction formula separately for three regions: near the entrance of the roll bite, in the roll bite, and between the rolling stands.

[0004] Patent Document 2 also describes a method for controlling the finish exit width. Patent Document 2 also describes that when using a prediction formula for predicting the width change of a steel plate in a finish rolling mill, a different formula is applied as a prediction formula for the width change in the vicinity of the roll bite depending on whether the crown ratio change in each rolling stand is positive or negative. Patent Document 3 also describes a method for correcting a target value of the width of a steel plate based on the deviation of an actual value of the width of the steel plate acquired at the beginning of rolling from a target value when controlling the finish exit width. That is, the method described in Patent Document 3 acquires actual data of the width change of the steel plate during finish rolling at the beginning of rolling, and corrects the target value of the width of the steel plate based on the data. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 5-285516 [Patent Document 2] Japanese Patent Application Publication No. 8-112609 [Patent Document 3] JP 2006-51512 A [Non-patent literature]

[0006] [Non-Patent Document 1] "Gaussian Processes and Machine Learning", Daichi Mochihashi and Shigeyuki Ohba, published 2019 / 03 / 07 ISBN978-4-06-152926-7, Kodansha Summary of the Invention [Problem to be solved by the invention]

[0007] However, the prediction formula used in the method described in Patent Document 1 calculates a representative value of the width change of the steel plate in the finishing rolling mill, but does not predict the variation in the width change of the steel plate. Therefore, according to the method described in Patent Document 1, the width change of the steel plate varies due to factors such as an error in estimating the temperature of the steel plate during finish rolling, and it is inevitable that the finish exit width will be too small or too large. Similarly, the method described in Patent Document 2 calculates a representative value of the width change of the steel plate in the finishing rolling mill, but does not predict the variation in the width change of the steel plate. Therefore, it is inevitable that the finish exit width will be too small or too large. In contrast, the method described in Patent Document 3 attempts to identify the variation in the width change of the steel plate in the finishing rolling mill based on actual measurement data obtained at the beginning of rolling. However, since the width change of the steel plate in the finishing rolling mill varies due to changes in operating conditions, there is room for improvement in terms of predicting the variation in the width change of the steel plate with high accuracy.

[0008] As described above, conventional finishing width control attempts to improve the accuracy of the finishing delivery width by predicting the width change of the steel plate using a strict physical model or by identifying the variation in the width change of the steel plate obtained from the actual value. However, it is inevitable that the width change of the steel plate in the finishing rolling mill will vary due to various causes. For this reason, the width of the steel plate may be too small or too large in some cases, which inevitably leads to poor width accuracy of the hot-rolled steel plate and a decrease in product yield.

[0009] The present invention has been made to solve the above problems, and an object of the present invention is to provide a width prediction method for finish rolled material capable of predicting statistical information including the width variation of the finish rolled material. Another object of the present invention is to provide a width control method for finish rolled material capable of accurately controlling the longitudinal width of the finish rolled material taking into account the width variation of the finish rolled material. Another object of the present invention is to provide a manufacturing method for hot rolled steel sheet capable of improving the product yield of the hot rolled steel sheet. Another object of the present invention is to provide a method for generating a width prediction model for finish rolled material capable of generating a width prediction model that predicts statistical information including the width variation of the finish rolled material. [Means for solving the problem]

[0010] The width prediction method of the present invention is a method for predicting the width of a finish rolled material in a hot rolling line including a heating furnace for heating a slab, a rough rolling mill for rough rolling the heated slab to produce a rough rolled material, and a finishing rolling mill for finish rolling the rough rolled material to produce a finish rolled material, and includes a prediction step of predicting statistical information of the width of the finish rolled material using a width prediction model learned by a Gaussian process regression technique, which includes as input data a set value or an actual measured value of the width of the rough rolled material and one or more operation parameters selected from the operation parameters of the finishing rolling mill, and outputs statistical information of the width of the finish rolled material.

[0011] The width prediction model may include one or more parameters selected from attribute information of the slab as input data.

[0012] The width control method for finish rolled material of the present invention includes a setting step of predicting statistical information of the width of the finish rolled material using the width prediction method for finish rolled material of the present invention, and setting one or more operational parameters selected from the operational parameters of the finishing rolling mill based on the predicted statistical information so as to reduce the probability that the width of the finish rolled material will fall below the target width.

[0013] The statistical information of the width of the finish rolled material is the average value W mand standard deviation W σ The setting step includes a target width W of the finish rolled material. t It is preferable to include a step of setting one or more operational parameters selected from the operational parameters of the finishing mill so that the relationship shown in the following formula (1) is satisfied.

[0014]

number

[0015] The method for producing a hot-rolled steel sheet according to the present invention includes a step of producing a hot-rolled steel sheet using the method for controlling the width of a finish rolled material according to the present invention.

[0016] The method for generating a width prediction model of a finish rolled material according to the present invention is a method for generating a width prediction model for predicting the width of the finish rolled material in a hot rolling line including a heating furnace for heating a slab, a rough rolling mill for rough rolling the heated slab to produce a rough rolled material, and a finishing rolling mill for finish rolling the rough rolled material to produce a finish rolled material. The method includes a learning data acquisition step for acquiring a plurality of learning data including actual data of the set value or actual measurement value of the width of the rough rolled material, one or more operation actual data selected from the operation actual data of the finishing rolling mill, and actual data of the width of the finish rolled material, and a step of generating the width prediction model using a Gaussian process regression technique, which includes actual data of the set value or actual measurement value of the width of the rough rolled material and one or more operation actual data selected from the operation actual data of the finishing rolling mill as input actual data, and has statistical information of the width of the finish rolled material as output data. Effect of the Invention

[0017] According to the width prediction method of the finish rolled material of the present invention, it is possible to predict statistical information including the width variation of the finish rolled material. Moreover, according to the width control method of the finish rolled material of the present invention, it is possible to accurately control the longitudinal width of the finish rolled material taking into account the width variation of the finish rolled material. Moreover, according to the manufacturing method of the hot rolled steel sheet of the present invention, it is possible to improve the product yield of the hot rolled steel sheet. Moreover, according to the method of generating a width prediction model of the finish rolled material of the present invention, it is possible to generate a width prediction model that predicts statistical information including the width variation of the finish rolled material. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 is a schematic diagram showing an example of the configuration of a hot rolling line to which the present invention is applied. [Diagram 2] FIG. 2 is a schematic diagram showing a configuration example of the finishing rolling mill shown in FIG. [Diagram 3] FIG. 3 is a schematic diagram showing an example of the configuration of one rolling stand that constitutes the finishing mill shown in FIG. [Figure 4] FIG. 4 is a schematic diagram for explaining the operation of a looper disposed between rolling stands of the finishing mill shown in FIG. [Diagram 5] FIG. 5 is a schematic diagram for explaining an optical width measuring method. [Figure 6] FIG. 6 is a flowchart showing the flow of the model generation steps according to one embodiment of the present invention. [Figure 7] FIG. 7 is a flowchart showing the flow of the prediction step according to one embodiment of the present invention. [Figure 8] FIG. 8 is a block diagram showing a configuration of a width prediction model generating unit according to an embodiment of the present invention. [Figure 9] FIG. 9 is a diagram for explaining a method of setting a control target width in a conventional width control method. [Figure 10] FIG. 10 is a block diagram showing the configuration of a width prediction unit according to an embodiment of the present invention. [Figure 11]FIG. 11 is a diagram showing the relationship between the probability density distribution of the finish delivery width predicted by the width prediction model and the target width. [Figure 12] FIG. 12 is a diagram for explaining a method for setting a control target value for finishing width control in this embodiment. [Figure 13] FIG. 13 is a diagram showing the relationship between the probability density distribution of the finish delivery width predicted by the width prediction model and the target width. [Figure 14] FIG. 14 is a diagram showing the relationship between the deviation of the finish delivery width from the target width and the cut-off amount of the steel plate. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] Hereinafter, with reference to the drawings, a method for predicting the width of a finish rolled material, a method for controlling the width of a finish rolled material, a method for manufacturing a hot-rolled steel sheet, and a method for generating a width prediction model for a finish rolled material, which are embodiments of the present invention, will be described in detail.

[0020] [Hot rolling line] First, the configuration of a hot rolling line to which the present invention is applied will be described with reference to Figs. 1 to 5.

[0021] FIG. 1 is a schematic diagram showing a configuration example of a hot rolling line to which the present invention is applied. As shown in FIG. 1, the hot rolling line 1 to which the present invention is applied includes a heating furnace 2, a descaling device 3, a width reduction press device 4, a roughing mill 5, a finishing rolling mill 6, a cooling device 7, and a coiler (winder) 8. A cast slab (not shown) is charged into the heating furnace 2, heated to a predetermined set temperature, and extracted from the heating furnace 2 as a hot slab. The hot slab extracted from the heating furnace 2 has a primary scale formed on its surface removed by the descaling device 3, and then is width-reduced to a predetermined set width by the width reduction press device 4. The width-reduced slab is rolled to a predetermined thickness in the roughing mill 5 to become a rough rolled material, and is transported to the finishing rolling mill 6. In the finishing rolling mill 6, the rough rolled material is rolled to a product thickness by a continuous rolling mill consisting of 5 to 7 rolling stands to become a finish rolled material. Downstream of the finishing mill 6, a cooling device 7 is provided in a facility called a run-out table, and the finish-rolled material is cooled to a predetermined temperature and then wound into a coil by a coiler 8.

[0022] In addition, a plurality of width gauges are installed as width measuring means in the middle of the transport process of the hot rolling line 1. In the example shown in FIG. 1, a rough exit width gauge 11 is installed at the exit side of the rough rolling mill 5, and a finish exit width gauge 12 is installed at the exit side of the finish rolling mill 6. In addition, a pre-coiler width gauge (coiler entry width gauge) 13 is installed at the exit side of the cooling device 7 to measure the width of the finish rolled material before coiling. In addition, a finish entry width gauge 14 is installed at the entry side of the finish rolling mill 6 to measure the width of the rough rolled material. However, if there is no means for imparting plastic deformation to the rough rolled material between the rough rolling mill 5 and the finish rolling mill 6, the width of the rough rolled material measured by the rough exit width gauge 11 and the width of the rough rolled material measured by the finish entry width gauge 14 will be the same. In this embodiment, the width of the rough rolled material measured by the rough exit width meter 11 may be referred to as the rough exit width, the width of the finish rolled material measured by the finish exit width meter 12 as the finish exit width, and the width of the steel plate measured by the pre-coiler width meter 13 as the pre-coiler width. Also, the width of the rough rolled material measured by the finish entry width meter 14 may be referred to as the finish entry width, but this may be regarded as the same as the rough exit width.

[0023] The hot rolling line 1 includes a control controller (PLC) 90 that controls each device constituting the hot rolling line 1, a control computer (process computer) 91 that gives control commands to the control controller 90, and a host computer 92 that gives manufacturing instructions to the hot rolling line 1. The width control of the steel plate in the hot rolling line 1 is performed by the control computer 91 setting a control target value of the rough delivery side width (rough control target width), a control target value of the finish delivery side width (finish control target width), and a control target value of the pre-coiler width (pre-coiler control target width) based on the host computer 92 or a manufacturing instruction from the host computer 92, and setting the operating conditions of the rough rolling mill 5 and the finish rolling mill 6. Specifically, the host computer 92 or the control computer 91 sets a finish target width (hereinafter sometimes simply referred to as target width) which is a target width of a finish rolled material, taking into consideration the amount of width change of the steel sheet occurring between the exit side of the finishing mill 6 and the total width before the coiler 13, based on a pre-coiler target width (pre-coiler target width) determined from the product specifications of the hot-rolled steel sheet. Furthermore, the host computer 92 or the control computer 91 sets a target width (coarse target width) of a rough rolled material, taking into consideration the amount of width change of the steel sheet at the finishing mill 6, based on the set finish target width. In this case, the target values ​​of width control (coarse control target width, finish control target width, pre-coiler control target width) may be set by providing an extra width (margin) in advance for the rough target width, finish target width, and pre-coiler target width. Then, the host computer 92 or the control computer 91 sets the rolling conditions in each pass of the rough rolling so that the rough exit width coincides with the rough control target width. The host computer 92 or the control computer 91 also sets the rolling conditions in each rolling stand of the finish rolling so that the finish exit width coincides with the finish control target width. Furthermore, the host computer 92 or the control computer 91 may set the tension between the finish rolling mill 6 and the coiler 8 and the cooling conditions of the cooling device 7 so that the pre-coiler width coincides with the pre-coiler control target width. In this case, the finish rolling mill 6 may perform dynamic width control while referring to the actual measured values ​​of the rough exit width and the finish exit width. The control controller 90 has a function of collecting information acquired from various sensors (thickness gauge, thermometer, etc.) in addition to information acquired from a width gauge installed in the hot rolling line 1 at a predetermined sampling period and outputting them to the control computer 91.

[0024] A method for predicting a width of a finish rolled material according to one embodiment of the present invention is a method for predicting a finish exit width. A method for controlling a width of a finish rolled material according to one embodiment of the present invention is a method for controlling a width of a steel sheet such that a finish exit width satisfies a predetermined relationship with a finish target width.

[0025] [Finishing rolling mill] FIG. 2 is a schematic diagram showing a configuration example of the finishing mill 6 shown in FIG. 1. The rough rolled material is transported to the entrance of the finishing mill 6. As shown in FIG. 2, a crop shear 61 is arranged at the entrance of the finishing mill 6. The crop shear 61 is a device that cuts and removes crops (irregularly shaped parts at the leading and trailing ends of the rough rolled material) formed at the leading and trailing ends of the rough rolled material. As a result, the rough rolled material is shaped into a substantially rectangular planar shape that is easy to be smoothly bitten into the finishing mill 6. The rough rolled material SA charged into the finishing mill 6 is hereinafter simply referred to as a steel plate SB. The finishing mill 6 shown in FIG. 2 is composed of seven rolling stands F1 to F7, but the number of rolling stands is not limited to this. Generally, the number of rolling stands of the finishing mill 6 is 6 to 7, and it may be composed of five rolling stands. The finishing mill 6 takes the form of a hot tandem finishing mill that simultaneously rolls the steel plate SB, whose temperature has been lowered to a temperature within the range of 800 to 1100°C that is set according to the steel type, etc., in multiple rolling stands, but is simply referred to as a "finishing mill" for short.

[0026] 3(a) and (b) are schematic diagrams showing an example of the configuration of one rolling stand constituting the finishing rolling mill 6 shown in FIG. 2. As shown in FIG. 3(a) and (b), the rolling stand has a structure including a pair of work rolls 62a, 62b on the upper and lower sides sandwiching the pass line of the steel sheet SB. The work rolls 62a, 62b are supported by backup rolls 63a, 63b, respectively. The rolling load applied to the steel sheet SB is transmitted to a housing 65 via bearing parts (backup roll chocks) 64a, 64b of the backup rolls 63a, 63b. A load cell 66, which is a load detector, is disposed between the housing 65 and the backup roll chock 64b, and the rolling load applied to the steel sheet SB can be measured by the load cell 66. In addition, the upper backup roll chock 64a and the housing 65 are connected via a compression cylinder 67, and the compression cylinder 67 can adjust the opening (roll gap) between the pair of work rolls 62a, 62b by adjusting the vertical position of the pair of work rolls 62a, 62b.

[0027] The rolling stands F1 to F7 constituting the finishing rolling mill 6 are equipped with shape control actuators for controlling the profile (thickness distribution in the width direction of the steel sheet SB) and flatness of the steel sheet SB. The shape control actuator is a mechanism for adjusting the roll gap distribution between the pair of work rolls 62a, 62b. A representative example of the shape control actuator is a work roll bender. The work roll bender is equipped with a hydraulic device (not shown) that applies a force between the bearing boxes (work roll chocks) 68a, 68b at both ends of the pair of work rolls 62a, 62b. The hydraulic device applies a bending force to the pair of work rolls 62a, 62b by applying a force between the upper and lower work roll chocks 68a, 68b, and applies a bending deformation to the pair of work rolls 62a, 62b, thereby adjusting the roll gap distribution. At this time, the force applied between the upper and lower work roll chocks 68a, 68b is called a bender force. In addition to the work roll bender, the rolling stands F1 to F7 are often equipped with other shape control actuators as shape control actuators. For example, in the case of a pair cross mill, the upper work roll 62a and the upper backup roll 63a are set as a pair and inclined (crossed) in a horizontal plane relative to the lower work roll 62b and the lower backup roll 63b to adjust the roll gap distribution. In this case, the angle between the upper and lower rolls is called the cross angle, and the roll gap distribution changes by changing the cross angle. In addition, the shape control actuator may use a work roll shift in which a pair of work rolls 62a, 62b are shifted in opposite directions to each other with respect to the axial direction. Furthermore, in the case of a rolling mill with a six-high rolling stand, an intermediate roll shift is used in which upper and lower intermediate rolls are shifted in opposite directions to each other with respect to the axial direction of the rolls. The shape control actuator provided in the rolling stand can adjust the width of the steel sheet SB by changing the crown ratio of the steel sheet SB by adjusting the roll gap distribution.

[0028] FIG. 4 is a schematic diagram for explaining the operation of the looper arranged between the rolling stands F1 to F7 of the finishing mill 6 shown in FIG. 2. The looper 69 shown in FIG. 4 is a device for adjusting the balance between the conveying speed of the steel sheet SB discharged from the rolling stand Fi on the upstream side and the conveying speed of the steel sheet SB charged into the rolling stand Fi+1 on the downstream side. The looper 69 is equipped with a looper roll 69a whose tip contacts the steel sheet SB. The looper 69 controls the looper angle Lθ and the looper height LH so that the inter-stand tension applied to the steel sheet SB between the rolling stand Fi and the rolling stand Fi+1 is within a predetermined range. Also, the work roll peripheral speed of the upstream rolling stand Fi is controlled so that the looper height LH is within a predetermined range. In this case, the inter-stand tension may be measured using a load detector arranged on the looper roll 69a. Also, the inter-stand tension may be estimated from the torque loaded on the electric motor for driving the looper 69. In any case, since the width of the steel sheet SB changes depending on the inter-stand tension, the inter-stand tension applied to the steel sheet SB between the rolling stands of the finishing mill 6 is measured.

[0029] [Width meter] Return to FIG. 1. The width of the steel sheet in the hot rolling line 1 is measured by a rough-exit width gauge 11, a finishing-exit width gauge 12, a coiler-front width gauge 13, and a finishing-entry width gauge 14. An optical width measuring method is often used for these width gauges. In the optical width measuring method, a light source is disposed below the pass line along which the steel sheet is transported, and an image sensor is disposed above the pass line, and the width of the steel sheet is measured based on the shadow length in the width direction of the steel sheet of the light emitted from the light source while the steel sheet passes through. Some width gauges use a camera to identify the position of the width direction end of the steel sheet and measure the width. FIGS. 5(a) and (b) are schematic diagrams showing a configuration example of a rough-exit width gauge using a camera. In the example shown in FIG. 5(a), a pair of cameras 16a and 16b provided in the rough-exit width gauge capture an image of the steel sheet SB including the width direction end of the steel sheet SB. CMOS or CCD sensors are used for the cameras 16a and 16b. Then, an image processing unit provided in the rough-discharge side width gauge identifies the position of the width direction end of the steel sheet SB from the images captured by the cameras 16a, 16b, and calculates the width W of the steel sheet SB based on the installation interval of the cameras 16a, 16b. Reference numeral 15 in the figure indicates a pass line. However, since this width gauge captures an image of the width direction end of the steel sheet SB from an oblique direction, a width measurement error is likely to occur when the steel sheet SB floats up from the pass line 15. Therefore, the width gauge often has a function to correct the width measurement error in response to the floating of the steel sheet SB from the pass line. Specifically, as shown in FIG. 5(b), when the steel sheet SB is transported at a height of floating amount H from the pass line 15, the width measurement error is corrected as follows. That is, first, a pair of cameras 16a, 16b arranged in the width direction of the steel sheet SB identifies both width direction ends of the steel sheet SB, thereby determining the measured width value W1. Next, another pair of cameras 16c, 16d arranged in the width direction of the steel plate SB also identify both ends in the width direction of the steel plate SB, thereby identifying the measured width value W2. Then, based on the positional relationship between the two pairs of cameras (in the example shown in FIG. 5(b), the width direction interval D between the cameras 16a, 16b and the width direction interval L between the cameras 16a (16b) and 16c (16d)), the actual width W of the steel plate SB is calculated by the following formula (2), which becomes the measured width value of the steel plate SB.

[0030]

number

[0031] However, as another width measurement method, a method is sometimes used in which a laser beam is emitted in the width direction of the steel plate, the reflected light from the end faces of the steel plate is received, and the width of the steel plate is measured based on the distance to both end faces of the steel plate. Some width gauges are equipped with a thermal expansion correction function that converts the width of the steel plate after cooling based on the temperature of the steel plate. Since the width of the steel plate is measured using a width gauge during the steel plate conveyance process, the width measurement value of the steel plate obtained by the width gauge becomes time-series numerical information corresponding to the sampling pitch of the width gauge. In addition, the relationship between the longitudinal position of the steel plate and the actual width value of the steel plate is converted using information on the conveyance speed at which the steel plate passes the position of the width gauge. Then, in the control computer 91, a representative value of the width of the steel plate is calculated based on the acquired actual width measurement value of the steel plate. The representative value of the width of a steel plate may be, for example, the average value of the width of the steel plate in the longitudinal direction of the steel plate (average width), the measured value of the width of the steel plate in the steady portion excluding the leading and trailing ends of the steel plate (steady width), the measured value of the width of the steel plate at the leading end of the steel plate (leading end width), or the measured value of the width of the steel plate at the trailing end of the steel plate (trailing end width). In addition, the minimum value (minimum width) or maximum value (maximum width) of the width of the steel plate in the longitudinal direction of the steel plate may be calculated. The measured value of the width of the steel plate measured by a width gauge may be expressed as a deviation from a preset target width.

[0032] [Gaussian process regression] The finish rolled material width prediction method according to one embodiment of the present invention predicts the finish exit width in the hot rolling line 1. The finish rolled material width prediction method according to one embodiment of the present invention uses a width prediction model trained by a Gaussian process regression technique, which includes a set value or an actual measurement value of the rough rolled material width and one or more operation parameters selected from the operation parameters of the finishing rolling mill 6 as input data, and uses statistical information on the finish exit width as output data. The Gaussian process regression technique applied to the finish rolled material width prediction method according to one embodiment of the present invention will be described below.

[0033] Gaussian process regression, also known as Gaussian process regression or Gaussian process, is a type of nonlinear regression model that estimates a function from an input variable to an output variable. The output is a probability distribution, and the use of a Gaussian distribution specified by two parameters, the mean and variance, is called a Gaussian process, and the probability distribution can be obtained using a Bayesian estimation technique to express the reliability and uncertainty of the estimation. For example, when m variables are selected as inputs to a width prediction model, the input variables are represented by input vector x. In addition, the output variable associated with input vector x as learning data is represented by y. Hereinafter, n learning data x (1) ~x (n) ,y (1) ~y (n) Using Gaussian process regression, we apply the new input vector x * Statistical information on the finish output width for y * A method for obtaining the above will be specifically described. Since the m variables constituting the input vector x each represent a different physical quantity, the m variables may be standardized (normalized) in advance. Specifically, for each of the m variables, the average value and standard deviation may be calculated from the n pieces of learning data, and each variable may be standardized using the calculated average value and standard deviation. This is because standardizing the m variables in advance makes learning of hyperparameters, which will be described later, more efficient. In this case, to convert the m variables into physical quantities, an inverse conversion may be performed using the calculated average value and standard deviation.

[0034] In Gaussian process regression, a probability model is used in which a function f(x) that represents the output variable y from the input variable x is estimated and Gaussian noise is added to it. For example, the probability model is expressed as shown in the following formula (3). In this case, the function f(x) is assumed to follow a multivariate Gaussian distribution. In addition, the Gaussian noise ε (i) has a mean of zero and a variance of σ e (i)2 We assume that the noise is Gaussian-distributed, where ε (i) is n pieces of training data x (1) ~x (n) The n pieces of training data x (1) ~x (n)The noise may be constant regardless of the noise level.

[0035]

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[0036] The Gaussian distribution is a distribution in which the probability density N is expressed by the following formula (4). In formula (4), μ represents the mean value, and σ represents the standard deviation (σ 2 In other words, the Gaussian distribution has a mean μ and a standard deviation σ or variance σ 2 Gaussian process regression uses the multivariate normal distribution, which is a multidimensional extension of the Gaussian distribution.

[0037]

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[0038] In Gaussian process regression, the mean function (mean vector) representing the multivariate Gaussian distribution is set as a constant (e.g., zero), and the covariance matrix is ​​expressed by a kernel function. The kernel function is a function for calculating the similarity of data. The kernel function takes as an argument the input vector x (i) ,x (j) Using k(x (i) ,x (j) ) and the input vector x (i) and the input vector x (j) The similarity between the vector and the vector is output. As the kernel function, known kernel functions such as a White kernel, a linear kernel, a polynomial kernel, a Gaussian kernel, and a Matern kernel can be used. Some examples of kernel functions are expressed as shown in the following formulas (5) to (7) using a parameter θ. Formula (5) shows a linear kernel, formula (6) shows a quadratic polynomial kernel, and formula (7) shows a Gaussian kernel.

[0039]

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[0040]

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[0041]

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[0042] Based on the above assumptions, a function f(x) expressing an output variable y from an input variable x is expressed as the following equation (8) using a probability density N.

[0043]

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[0044] In this case, the Gaussian noise has a constant value σ e 2 If , the covariance matrix K specified by the kernel function n and the covariance matrix Σ with Gaussian noise n When is defined by the following formulas (9) and (10), formula (8) is expressed as formula (11) or formula (12) shown below, where I represents a unit matrix.

[0045]

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[0046]

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[0047]

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[0048]

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[0049] The covariance matrix K included in the right-hand side of equations (11) and (12) n ,Σ n The input learning data x (1) ~x (n) The left side of the formulas (11) and (12) is the training data y (1) ~y (n) For this reason, the hyperparameters (parameter θ and Gaussian noise σ) included in the kernel function are set so that the relationship shown in Equation (11) or Equation (12) holds. e 2 ) may be determined. The method for determining the hyperparameters may be selected from among known methods. For example, the hyperparameters may be calculated by calculating a likelihood function of the learning data and maximizing a log-likelihood represented by the logarithm of the calculated likelihood function. In this case, an optimization method such as the Monte Carlo method or the conjugate gradient method may be used as a calculation method for maximizing the log-likelihood. Alternatively, a method such as cross-validation or marginal likelihood maximization may be used.

[0050] Next, the function f(x) for which the hyperparameters of the kernel function have been determined is used to compute a new input vector x * Statistical information on the finish output width for y * We will explain how to estimate the unknown input vector x * The estimate for can be expressed as the following equation (13) by applying Bayesian estimation. In this case, the newly specified kernel function k * The vector is defined as shown in the following formula (14).

[0051]

number

[0052]

number

[0053] As a result, equation (13) can be expressed as equation (15) below. Then, the input vector x * The statistics for the mean value of W m , the variance is W σ These can be calculated by the following formulas (16) and (17). For details about the Gaussian process regression method, refer to known documents (for example, Non-Patent Document 1).

[0054]

number

[0055]

number

[0056]

number

[0057] In this embodiment, the step of identifying hyperparameters so as to express the relationship of the above formula (11) or formula (12) is called a model generation step. Specifically, in the model generation step, as shown in FIG. 6, first, n pieces of learning data x (1) ~x (n) ,y (1) ~y (n) (Step S1). Next, a kernel function to be used for machine learning is selected from, for example, equations (5) to (7) (Step S2). Next, Gaussian noise ε(i) with a mean value of zero and a variance of σ e 2 Next, the Gaussian distribution of the covariance matrix K n ,Σ n (Step S4). Then, using the formulas (11) and (12) that express the relationship between the input and output of the learning data, the parameter θ and the Gaussian noise σ are calculated as hyperparameters by a learning method using a likelihood function. e(Step S5). The thus determined hyperparameters specify a kernel function that represents the input / output relationship of the learning data. The determined hyperparameters may be stored in a storage device of the computer that executes the model generation step. On the other hand, the input vector x * As statistical information for the average value W m , standard deviation W σ The step of calculating x is called the prediction step. Specifically, in the prediction step, as shown in FIG. * Next, the hyperparameters stored in the storage device of the computer that executes the model generation step are obtained, and a new input vector x * Kernel function k for * (Step S12). Then, the input vector x * The predicted value for the average W m and standard deviation W σ (step S13).

[0058] [Method of generating width prediction model] Next, an embodiment in which the above-mentioned Gaussian process regression technique is applied as a method for generating a finish rolled material width prediction model according to one embodiment of the present invention will be described.

[0059] FIG. 8 is a block diagram showing a configuration of a width prediction model generating unit according to an embodiment of the present invention. As shown in FIG. 8, the width prediction model generating unit 100 according to this embodiment includes a database unit 101 and a machine learning unit 102. The database unit 101 stores a set value or a performance value of the rough exit side width, one or more pieces of operation performance data selected from the operation performance data of the finishing rolling mill 6, and performance data of the finish exit side width. The database unit 101 may store one or more pieces of performance data selected from the performance data of the attribute information of the slab, as necessary. Specific performance data stored in the database unit 101 will be described later.

[0060] The performance data stored in the database unit 101 can be appropriately acquired from the control controller 90, the control computer 91, or the host computer 92. In addition, a data acquisition unit 103 may be provided to collect these performance data, and the performance data may be temporarily stored in the data acquisition unit 103, and a data set in which multiple types of performance data are associated may be generated and then stored in the database unit 101. Since the data stored in the database unit 101 may be acquired at different times, it is easy to configure a data set in which the data sets correspond to each other by associating multiple types of performance data in the data acquisition unit 103. For the data set stored in the database unit 101, at least one performance data is acquired for one steel plate manufactured from one slab. For example, when the average width of the finish rolled material is used as the performance data of the finish delivery width, the representative value of the operation performance data of the finishing rolling mill 6 may be used as the performance data. In this case, the width data of the finish rolled material may be a representative value for the finish rolled material, or a set value or an actual measurement value of the width at multiple positions along the longitudinal direction of the finish rolled material may be used. Regarding the performance data of the attribute information of the slab, a representative value for one steel plate may be used as the performance data.

[0061] On the other hand, the data acquiring unit 103 may generate a plurality of data sets for one steel plate manufactured from one slab, and accumulate them in the database unit 101. For example, when acquiring performance data on the widths of three locations, the head, steady state, and tail end of the finish rolled material, as performance data on the finish exit width, the operation performance data of the finishing rolling mill 6 acquired at the head, steady state, and tail end of the finish rolled material may be associated with performance data on the finish exit width at the corresponding locations. However, for performance data specified regardless of the longitudinal position of the steel plate, such as performance data on the attribute information of the slab, performance data of the same attribute information is associated with the performance data on the finish exit width at the head, steady state, and tail end of the steel plate.

[0062] Furthermore, the data acquisition unit 103 may acquire the actual data of the finish exit width for each position divided in the longitudinal direction of one steel plate, and the operation actual data acquired for each position in the longitudinal direction of the steel plate may be associated with the actual data of the finish exit width measured at each position and stored in the database unit 101. That is, the number of divisions in the longitudinal direction of the steel plate is set to, for example, about 20 to 200, and the actual data of the finish exit width in each divided section is associated with the operation actual data corresponding to each position. In this case, although the length of the steel plate SB finish-rolled by the finishing rolling mill 6 changes for each rough rolling pass, if the operation actual data at the position corresponding to the division in the longitudinal direction of the steel plate SB is acquired, the data acquisition unit 103 can configure a data set corresponding to each divided section. When the database unit 101 accumulates data sets corresponding to multiple positions divided in the longitudinal direction of the steel plate, the machine learning unit 102 can also generate a different width prediction model for each position in the longitudinal direction of the steel plate.

[0063] The width prediction model generating unit 100 can be provided in a control computer 91 for controlling the production of steel sheets by the hot rolling line 1. The width prediction model generating unit 100 may be provided in a host computer 92 that gives a production instruction to the control computer 91, or may be provided in an independent computer that can communicate with other devices. The machine learning unit 102 may be configured in a device other than the database unit 101 using a device that can receive the data set stored in the database unit 101. The database unit 101 stores 100 or more data sets. Preferably, 10,000 or more data sets, more preferably 100,000 or more data sets, are stored in the database unit 101. Screening may be performed on the data stored in the database unit 101 as necessary.

[0064] The machine learning unit 102 generates a width prediction model M by machine learning using a Gaussian process regression technique using a data set stored in the database unit 101. The learning data used by the machine learning unit 102 are a plurality of data sets including performance data of the set value or performance value of the rough exit side width stored in the database unit 101, one or more operation performance data selected from the operation performance data of the finishing rolling mill 6, and performance data of the finish exit side width. The machine learning unit 102 uses the learning data to perform machine learning using a Gaussian process regression technique, which includes performance data of the set value or performance value of the rough exit side width and one or more operation performance data selected from the operation performance data of the finishing rolling mill 6 as input performance data, and uses statistical information of the finish exit side width as output data, to generate a width prediction model M. The machine learning unit 102 may also use a data set stored in the database unit 101 to perform machine learning using a Gaussian process regression technique using performance data of attribute information of the slab SA as input performance data, to generate a width prediction model M.

[0065] In this case, machine learning refers to identifying hyperparameters to be applied to Gaussian process regression by the steps shown in FIG. 6. The width prediction model M refers to the hyperparameters identified in this manner. By identifying the hyperparameters, the input / output relationship of the learning data is identified, and it becomes possible to predict statistical information on the finish exit width for an unknown input. In this embodiment, the statistical information on the finish exit width, which is the output of the width prediction model M trained by Gaussian process regression, includes the predicted average value of the width of the finish rolled material and the standard deviation or variance, which is an index representing the variation thereof. This makes it possible to predict the average value of the finish exit width as well as its variation.

[0066] On the other hand, in the conventional technology, as described in Patent Documents 1 and 2, for example, a prediction formula is used to predict the width change amount of a steel plate in finish rolling, and the average or representative value of the finish delivery width or the width change amount of a steel plate in a finish rolling mill is predicted, but information on the variation cannot be obtained. For this reason, a target width of a steel plate must be set by adding a preset extra width (margin). Specifically, in the conventional width control method, as shown in FIG. 9, a target width W t For this, a margin (margin) W is added in advance. r Set the target width W t and margin W r The sum of these is the target width W of the finished exit width. c Set as the margin W r The reason for setting is that there is a certain amount of variation in the finish exit width, so the finish exit width is set to the target width W t This is to ensure that the finishing exit width does not fall below the target width W t If the width is less than the target width, the width of the hot-rolled steel sheet may be smaller than the target product width, and the product cannot be produced due to the insufficient width. This may result in the hot-rolled steel sheet being scrapped or the product being shipped to a different destination, resulting in a decrease in product yield and delays in delivery. r If the width of the hot-rolled steel sheet is excessively large, the width of the hot-rolled steel sheet will be excessively large compared to the target product width, and it will be necessary to trim the steel sheet to obtain the product, which will result in a decrease in product yield. In other words, if an attempt is made to prevent the occurrence of insufficient width as a hot-rolled steel sheet, the finish exit width will be excessively large, and if an attempt is made to suppress the decrease in yield due to the edge trimming, insufficient width will easily occur. For this reason, in the conventional technology, actual data on the finish exit width and the width change amount in the finish rolling mill 6 are collected for each thickness and width category of the hot-rolled steel sheet, and the excess width is set in advance according to the variation, and the factory personnel regularly monitor whether the excess width setting is appropriate.

[0067] In contrast, according to the present embodiment, the average value and statistical variation of the finish exit width are predicted according to the input operation conditions of the hot rolling line, so that an appropriate surplus width can be set according to the operation conditions for each steel plate, not for each thickness or width category of the hot rolled steel plate as in the conventional technology. This makes it possible to suppress a decrease in product yield due to insufficient or surplus width of the hot rolled steel plate caused by the variation in the finish exit width.

[0068] [Slab attribute information] The attribute information of the slab that can be used as an input for the width prediction model M refers to information on the slab dimensions that affect the width change of the steel sheet in the finishing rolling mill 6 and information on the composition of the slab. The information on the slab dimensions is information on the thickness, width, length, and weight of the slab. The information on the composition of the slab is information on the content of the components contained in the slab, such as the C content, Si content, Mn content, P content, S content, Nb content, Ti content, Cu content, Ni content, Mo content, and B content of the slab. The information on the slab dimensions affects the temperature change of the steel sheet in the hot rolling line 1, and therefore affects the variation in the finish exit width. In addition, the information on the composition of the slab affects the deformation resistance of the steel sheet and the composition and thickness of the oxide film generated on the surface of the steel sheet. This affects the friction force at the interface between the rolling roll and the steel sheet, and changes the deformation state of the steel sheet, which affects the variation in the finish exit width. In addition, the information on the composition of the slab affects the behavior of short-term creep deformation between the rolling stands of the finishing rolling mill 6. This affects the width reduction in the finishing rolling mill 6, and affects the variation in the finish delivery width.

[0069] [Width data for rough rolled material] The width data of the rough rolled material (set value or actual measurement value of the width of the rough rolled material) used as the input of the width prediction model M is a set value or actual measurement value regarding the width of the rough rolled material charged into the finishing mill 6. The set value regarding the width of the rough rolled material may be a representative value regarding the width of the rough rolled material used in the setting calculation for the control computer 91 to set the operating conditions of the rough rolling mill 5 or the finishing mill 6. When performing rough rolling, if the control computer 91 sets the target width or the control target width of the rough rolled material at the exit side of the rough rolling mill 5, these can be used as the width data of the rough rolled material. Furthermore, the control computer 91 performs setting calculation using the width of the rough rolled material at the entry side of the finishing mill 6 before performing finish rolling, and the set value of the finish entry width used at this time may be used as the width data of the rough rolled material. On the other hand, the actual measurement value regarding the width of the rough rolled material may be the actual value of the rough exit side width measured by the rough exit side width meter 11. Also, it is possible to use the actual value of the finish entry width measured by the finish entry width meter 14. The actual width values ​​of the rough rolled material measured by the rough exit width meter 11 and the finish entry width meter 14 are sent from the control controller 90 to the control computer 91, so these values ​​can be obtained from the control computer 91 using the data acquisition unit 103.

[0070] [Finishing mill operating parameters] The operation parameters of the finishing mill 6 used to input the width prediction model M refer to the rolling operation conditions that affect the width of the steel sheet in any rolling stand of the finishing mill 6. The operation parameters of the finishing mill 6 may be operation parameters that affect the width of the steel sheet in three regions, namely, the vicinity of the roll bite entrance, the roll bite, and between the rolling stands corresponding to each rolling stand of the finishing mill 6. Specifically, the thickness of the steel sheet, the reduction ratio, the tension between rolling stands, the crown ratio change, the deformation resistance and the temperature of the steel sheet in each rolling stand may be used. In addition, the rolling load, the roll gap and the work roll diameter of each rolling stand, which indirectly affect the thickness and temperature of the steel sheet in each rolling stand, may be used as the operation parameters. Furthermore, the set value or actual value of the shape control actuator, which indirectly affects the crown ratio change of the steel sheet in each rolling stand, may be used. For example, the setting value or actual value of an actuator for controlling the profile of the steel sheet by changing the deflection of the work rolls, such as the bender force of the work roll bender, the cross angle in a pair cross mill, the shift amount of the intermediate roll in a six-high rolling mill, etc., can be used. In addition to these, the operation parameters of the finishing rolling mill 6 may also include operation parameters related to the speed of the steel sheet, such as the rolling speed, acceleration rate, rolling time, the peripheral speed of the work rolls of each rolling stand, and the passing time between rolling stands. This is because they affect the temperature change and deformation resistance of the steel sheet, which in turn affects the width change amount of the steel sheet. In addition, they affect the short-term creep deformation of the steel sheet, which in turn affects the width shrinkage between rolling stands. Furthermore, the rolling length and usage time after rearrangement of the work rolls used in each rolling stand may also be used as the operation parameters of the finishing rolling mill 6. This is because the friction state between the work rolls and the steel sheet changes as the surface condition of the work rolls changes over time, which affects the width spread during rolling.

[0071] [Method for predicting width of finish rolled material] The finish rolled material width prediction method according to one embodiment of the present invention includes a prediction step of predicting statistical information of the finish delivery width using the width prediction model M generated as described above. The width prediction unit that executes the prediction step can be provided in a control computer 91 for controlling the hot rolling line 1. The width prediction unit may be provided in a host computer 92 that gives manufacturing instructions to the control computer 91, or may be provided in an independent computer that can communicate with other devices. Hereinafter, the operation of the width prediction unit according to one embodiment of the present invention will be described with reference to FIG. 10.

[0072] The operation of the width prediction unit 110 shown in FIG. 10 is performed before the finish delivery width of the steel sheet manufactured in the hot rolling line 1 is measured by the finish delivery width meter 12. The operation of the width prediction unit 110 can be performed, for example, at the stage where the steel sheet to be predicted is charged as a slab into the heating furnace 2 or at the stage where it is extracted from the heating furnace 2. At the stage where the slab is charged into the heating furnace 2, information on the slab dimensions and the component composition of the slab are specified by the upper computer 92 as attribute information of the slab. In addition, at the stage where the slab is charged into the heating furnace 2, the manufacturing specifications of the hot rolled steel sheet are set in advance, and the corresponding standard operation parameters are specified. Therefore, the prediction step can be performed by inputting the actual data of the attribute information of the slab and the set values ​​of other operation parameters specified as standard operation parameters corresponding to the manufacturing specifications. In addition, the prediction step can also be performed, for example, during the rolling pass of the rough rolled material by the rough rolling mill 5. During rough rolling, rough width control is performed in the rough rolling mill 5, and a target width and a control target width for the rough exit width are set, so these set values ​​can be used as inputs to the width prediction model M.

[0073] Furthermore, the prediction step can be performed before the rough rolling is completed and the rough rolled material is charged into the finishing mill 6. By completing the rough rolling, the actual measurement value of the width of the rough rolled material can be obtained, and these actual values ​​can be used as inputs to the width prediction model M. In addition, the rough exit width is measured by the rough exit width meter 11 and the finishing entry width meter 14, and the control computer 91 performs setting calculations for the finishing mill 6 based on the actual measurement value of the rough exit width, and the operation conditions of the finishing mill 6 are set. Therefore, the operation parameters of the finishing mill 6 set by the setting calculations can be used as inputs to the width prediction model M. The prediction step can be performed at any time after the front end of the rough rolled material is charged into the finishing mill 6 and before the tail end of the rough rolled material passes through the finishing mill 6. In this case, the actual values ​​of the operation parameters of the finishing mill 6 at the current time can be obtained, so that the operation parameters of the finishing mill at the current time can be used as inputs to the width prediction model M. This makes it possible to predict the finish exit width of the rough rolled material charged into the rolling stand F1 of the finishing mill 6 when it is discharged from the rolling stand F7.

[0074] The input data acquisition unit 111 of the width prediction unit 110 shown in FIG. 10 acquires the actual values ​​or set values ​​of the operation parameters of the hot rolling line 1 held by the control computer 91 or the upper computer 92 as described above. Then, the width prediction unit 110 inputs the input data acquired by the input data acquisition unit 111 to the prediction unit 112. The prediction unit 112 acquires the hyperparameters determined by the width prediction model generation unit 100, calculates a kernel function for the input data (new input vector) as shown in FIG. 7, and calculates statistical information of the finish delivery width, which is output data. As described above, the operation of the width prediction unit 110 can be executed at any time until the tail end of the steel plate passes through the finish rolling mill 6, so that it may be executed multiple times in the process of manufacturing one steel plate. The statistical information of the finish delivery width output as described above may be displayed on a monitor connected to the width prediction unit 110. The operation parameters of the finish rolling mill 6 are reset based on the output display of the statistical information of the finish delivery width, thereby suppressing the occurrence of width defects in the hot rolled steel plate.

[0075] [Finishing Rolled Material Width Control Method] The width control method of the finish rolled material according to one embodiment of the present invention is a method for controlling the finish rolled material width by calculating the finish rolled material width target width (target width) W based on the statistical information of the finish rolled material width predicted as described above. t In the above-mentioned method for predicting the width of a finish rolled material, the statistical information of the finish exit width, which is the output of the width prediction model M, is, for example, the average value W m and standard deviation W σ In this case, the finish exit width W is predicted to follow the probability density distribution g(W) shown in the following formula (18).

[0076]

number

[0077] FIG. 11 shows the probability density distribution g(W) of the finish exit width predicted by the width prediction model M, as a function of the target width W t FIG. 11 shows a schematic example of the target width W t The average value W of the finished exit width W predicted by the width prediction model M m Because of the small size and the variation in the finish exit width W, there is a high probability that the finish exit width W is the target width W t In this case, it is predicted that the finish exit width W will be insufficient with a high probability based on the operating conditions of the hot rolling line 1 currently set. Therefore, in this embodiment, the finish exit width W is set to the target width W t One or more operation parameters selected from the operation parameters of the finishing rolling mill 6 are reset so as to reduce the probability of the finish exit width W falling below the target width W. Specifically, the operation parameters of the finishing rolling mill 6 are corrected so that the probability density distribution g(W) shown by the solid line in FIG. 11 becomes the probability density distribution shown by the dashed line. In this case, a prediction step for predicting statistical information on the finish exit width W is executed before the rough rolled material is loaded into the finishing rolling mill 6, so that the control computer 91 executes the setting calculation again using the corrected operation parameters of the finishing rolling mill 6. As a result, the finish exit width W becomes equal to the target width W. tThe operation parameters of the finishing mill 6 are set so that the probability of the width W falling below the target width W becomes small. The reset operation parameters are preferably selected from the operation parameters used as the input of the width prediction model M. In FIG. 11, the reset operation parameters of the finishing mill 6 are input again to the width prediction model M to output statistical information on the finish exit width W, and the finish exit width W becomes equal to the target width W as shown by the dashed line in FIG. t This is because by checking whether the probability of the temperature falling below 0.1 becomes smaller, it is possible to determine whether the operating conditions have been reset appropriately.

[0078] On the other hand, as shown in FIG. 12, the probability density distribution g(W) of the finish exit width predicted by the width prediction model M shows that the finish exit width W is smaller than the target width W t If it is predicted that there is a high probability that the finished exit width W will be smaller than the target width W t The target value W of the finishing width control is set so that the probability of falling below c This allows the set control target value W c The finishing width control of the hot rolling line 1 is performed using the above, and even if there is a variation in the finishing exit width W, the target width W t It is possible to reduce the probability of the width being insufficient for the target width W t It is preferable to reset one or more operation parameters selected from the operation parameters of the finishing mill 6 so that satisfies the following mathematical formula (1).

[0079]

number

[0080] FIG. 13 shows the probability density distribution g(W) of the finish exit width W predicted by the width prediction model M and the target width W t In the above formula (1), the target width W t is the average width W of the finished exit width W output from the width prediction model M. m and standard deviation W σ W, specified by m -2.5W σ and W m -1.5W σIn this case, the target width W t One or more operation parameters selected from the operation parameters of the finishing mill 6 should be reset so that the probability density distribution g(W) of the finish exit width W output from the width prediction model M satisfies the above formula (1) with . t 14 is a diagram showing the relationship between the deviation from the target width Wt and the amount of cut-off of the steel sheet. As can be seen from FIG. 14, even if the finish exit width W is too large or too small with respect to the target width Wt, the amount of cut-off of the steel sheet increases and the product yield of the hot-rolled steel sheet decreases. t If the width W is too large, the hot-rolled steel sheet must be trimmed in a post-process to meet the width specification. t If the width W is too small, the steel plate cannot be used as a product and some of it must be scrapped. t Therefore, the above formula (1) is expressed as the target width W t The operation parameters of the finishing mill 6 are set so that the probability of the finishing exit width W being too small is reduced even if there is a variation in the finishing exit width W. That is, the target width W in formula (1) t The lower limit for is to reduce the probability that the finishing exit width W will be too small, and the target width W t The upper limit for the finishing exit width W is the target width W t This is to prevent it from becoming too large compared to the

[0081] In the width control method of the finish rolled material in this embodiment, statistical information on the finish exit width is output in accordance with different operating conditions for each hot rolled steel plate, in addition to the classification of the steel type and size of the slab and the product dimensions of the hot rolled steel plate, so that the variation in the finish exit width for each hot rolled steel plate can be predicted. As a result, unlike the conventional technology, it is not necessary to set the extra width in advance according to the steel type and size classification of the steel plate, and an appropriate extra width can be given to each hot rolled steel plate manufactured in the hot rolling line, thereby improving the product yield of the hot rolled steel plate. Furthermore, in the width control method of the finish rolled material in this embodiment, the prediction step of predicting the statistical information on the finish exit width may be performed at any time from when the front end of the rough rolled material is charged into the finish rolling mill 6 until the tail end passes through the finish rolling mill 6. This allows the so-called dynamic finish width control to be realized. Specifically, the actual values ​​of the operational parameters of the finishing mill 6 at the current time point can be acquired during the finish rolling, and the finish exit width when the steel plate charged into the rolling stand F1 of the finishing mill 6 is carried out from the rolling stand F7 can be predicted. Then, statistical information on the finish exit width is predicted as needed, and the finish exit width W is adjusted to the target width W based on the predicted statistical information. t One or more operation parameters selected from the operation parameters of the finishing rolling mill 6 are set as needed so as to reduce the probability of the width falling below the threshold value. As a result, even if width defects occur in a part of the front end of the hot-rolled steel sheet, width defects can be prevented in other areas, and a decrease in product yield can be suppressed. In this case, it is preferable to use actual width values ​​of the rough rolled material measured at each longitudinal position of the rough rolled material as the width data of the rough rolled material. This is because the accuracy of width prediction for each longitudinal position of the hot-rolled steel sheet is improved, and the accuracy of width control of the hot-rolled steel sheet is also improved. EXAMPLES

[0082] Example 1 In this embodiment, the steel plate width prediction method and the width control method of the present invention were applied to a hot rolling line 1 including a width reduction press device 4 arranged downstream of a heating furnace 2, a roughing mill 5 consisting of four reversible rolling mills and one non-reversible rolling mill, and a finishing mill 6 consisting of seven rolling stands. In this embodiment, in the hot rolling line 1, a slab having a slab thickness of 250 to 270 mm and a slab width of 600 to 1600 mm was heated by a heating furnace 2, and a hot rolled steel plate having a plate thickness of 30 to 35 mm on the delivery side of the roughing mill 5 and a plate thickness of 2 to 3 mm on the delivery side of the finishing mill 6 was produced. In addition, the hot rolling line 1 is equipped with a roughing delivery side width meter 11, a finishing delivery side width meter 12, and a coiler front width meter 13. In addition, the control computer 91 and the host computer 92 of the hot rolling line 1 collect the set value and the actual measurement value of the rough exit width and the set value and the actual value of the operation parameters of the finishing rolling mill 6, and the data acquisition unit 103 acquires these actual data. The data acquisition unit 103 acquires the actual measurement value of the width of the rough rolled material, and the set value of the entry thickness, the reduction amount, the work roll peripheral speed, the work roll diameter, and the actual values ​​of the temperature of the front end and the tail end of the steel plate measured at the entry side of the finishing rolling mill 6 in all rolling stands as the operation actual data of the finishing rolling mill 6. On the other hand, the data acquisition unit 103 calculates the average width of the steady part of the steel plate from the actual value of the total finish exit width 12, and this is used as the actual data of the finish exit width. The actual data of the finish exit width is associated with the above operation actual data by the data acquisition unit 103 to generate one data set for one finish rolled material, and is stored in the database unit 101. Then, when 30,000 data sets were accumulated in the database unit 101, these data sets were divided into 20,000 pieces of training data and 10,000 pieces of test data, and a width prediction model M was generated by the machine learning unit 102 using the training data. In this embodiment, a radial basis function (RBF) kernel was used as the kernel function of the Gaussian process regression. In addition, a constant variance σ was used as Gaussian noise regardless of the training data. e 2 The noise having the following structure was used. The kernel function used in this embodiment is expressed by the following formula (19). However, ||x (i) -x (j) || represents the Euclidean distance between the input vectors.

[0083]

number

[0084] In this embodiment, the hyperparameters of the width prediction model M were specified by a Gaussian process regression method using the learning data. Then, the operation performance data of the test data was input to the prediction section 112 of the width prediction unit 110, and the average finish exit width W m and standard deviation W σ The actual data W of the finishing exit width W, which is the test data, was obtained. a and the average finish exit width W m The RMSE (root mean square error) was calculated from the deviation from the actual data W a But, W m ±W σ Range and W m ±1.96W σ The number of test data that fell within each range was calculated, and the ratio of the number of test data that fell within each range to the total number of test data was calculated. a and the average finish exit width W m The RMSE calculated from the deviation from the standard deviation was 0.1 mm, which was good. m ±W σ The probability of falling within the range of W is 67.3%. m ±1.96W σ The probability that the width W falls within the range of 1000 to 1200 mm was 95.3%. This means that if the variation in the finish exit width W is assumed to follow a normal distribution, the respective probabilities would be 68.3% and 95.0%, and it was confirmed that the width prediction model M of this embodiment can accurately predict the variation in the finish exit width W. Meanwhile, the width prediction model M generated as described above was stored in the prediction section 112 of the width prediction unit 110, and width control was performed on steel plates with slab widths in the range of 1000 to 1200 mm. In this case, when rough rolling was completed for each rough rolled material and the actual measurement value of the rough exit width was acquired by the rough exit width meter 11, statistical information on the finish exit width was calculated using the width prediction model M, and the target width W of the finish exit width W set for each steel plate was calculated. tThe operation parameters of the finishing mill 6 were reset so that the relationship shown in the above formula (1) was satisfied. The tension between rolling stands in the finishing mill 6 was selected as the operation parameter of the finishing mill 6 to be reset. The steel sheet that was finish-rolled in this way was cooled on the run-out table to produce a hot-rolled steel sheet. The hot-rolled steel sheet produced was 400 coils. As a result, the ratio of coils whose width was smaller than the target width before the coiler was reduced by 35% in this embodiment compared to the conventional example in which the extra width was set in advance. In addition, the edge trimming allowance that was cut off because the width was excessively larger than the target width before the coiler was reduced by 0.8 mm on average in this embodiment. From the above, it was confirmed that the statistical information of the width of the finish-rolled material can be predicted by this embodiment, and that by applying it to the width control of the finish-rolled material, the width defects of the hot-rolled steel sheet can be reduced and the product yield can be improved.

[0085] Example 2 Another example of the width prediction method for finish rolled material will be described as an embodiment of the present invention. In the above embodiment 1, when the actual data is stored in the database unit 101, the data acquisition unit 103 acquires actual data of the slab thickness, slab width, and slab length, which are information related to the slab dimensions, as the attribute information of the slab. In addition, the data acquisition unit 103 acquires actual data of the contents of C, Si, Mn, P, S, Nb, Ti, Cu, Ni, Mo, and B, which are information related to the component composition of the slab, as the attribute information of the slab. Then, the actual data of the attribute information of the slab is stored in the database unit 101 as a data set associated with the actual data acquired in embodiment 1 by the data acquisition unit 103.

[0086] In this embodiment, when 30,000 data sets were accumulated in the database unit 101, these data sets were divided into 20,000 pieces of training data and 10,000 pieces of test data, and the training data were used to generate a width prediction model M by the machine learning unit 102. In the machine learning, a radial basis function (RBF) kernel was used as a kernel function for Gaussian process regression.

[0087] The width prediction model M generated in this embodiment is a width prediction model learned by a Gaussian process regression technique, in which the measured width of the rough rolled material, the operation parameters of the finishing rolling mill, and parameters selected from the attribute information of the slab are used as input data from the data set accumulated in the database unit 101, and statistical information on the width of the finish rolled material is used as output data. The operation parameters of the finishing rolling mill used as input data are the entry thickness, reduction amount, work roll peripheral speed, work roll diameter, and temperature of the front and tail ends of the steel sheet at the entry side of the finishing rolling mill in all rolling stands of the finishing rolling mill 6, as in Example 1. In addition, the parameters of the attribute information of the slab used as input data are the thickness, width, and length of the slab, which are information on the dimensions of the slab, and the contents of C, Si, Mn, P, S, Nb, Ti, Cu, Ni, Mo, and B of the slab, which are information on the composition of the slab.

[0088] In this embodiment, the hyperparameters of the width prediction model M were identified by a Gaussian process regression method using the learning data. Then, the operation performance data of the test data was input to the prediction section 112 of the width prediction unit 110, and the average value Wm and standard deviation Wσ of the finish exit width W, which is the output of the width prediction model, were obtained. In addition, the RMSE (root mean square error) was calculated from the deviation between the performance data Wa of the finish exit width W, which is the test data, and the average value Wm. Furthermore, the number of test data in which the performance data Wa of the finish exit width W falls within the range of Wm±Wσ was obtained, and the ratio of the number of test data falling within these ranges to all the test data was calculated.

[0089] As a result, according to the generated width prediction model M, the RMSE calculated from the deviation between the actual data Wa of the finish exit width W and the average value Wm was 0.0 mm. In addition, the probability that the finish exit width W falls within the range of Wm±Wσ was 65.3%, which is close to the probability of 68.3% when it is assumed that the variation of the finish exit width W follows a normal distribution. From the above, it was confirmed that according to the present invention, the average value Wm and standard deviation Wσ of the finish exit width, which are statistical information of the finish exit width, can be accurately predicted.

[0090] Although the embodiments of the present invention have been described above, the present invention is not limited by the descriptions and drawings that form part of the disclosure of the present invention according to the present embodiments. In other words, other embodiments, examples, and operation techniques, etc., made by those skilled in the art based on the present embodiments are all included in the scope of the present invention. [Industrial Applicability]

[0091] According to the present invention, it is possible to provide a width prediction method for finish rolled material capable of predicting statistical information including the width variation of the finish rolled material. Also, according to the present invention, it is possible to provide a width control method for finish rolled material capable of accurately controlling the longitudinal width of the finish rolled material taking into account the width variation of the finish rolled material. Also, according to the present invention, it is possible to provide a manufacturing method for hot rolled steel sheet capable of improving the product yield of the hot rolled steel sheet. Also, according to the present invention, it is possible to provide a method for generating a width prediction model for finish rolled material capable of generating a width prediction model that predicts statistical information including the width variation of the finish rolled material. [Explanation of symbols]

[0092] 1 Hot rolling line 2 Furnace 3 Descaling device 4. Width reduction press device 5 Roughing mill 6 Finishing rolling mill 7 Cooling device 8 Coiler (winding machine) 11 Rough width gauge 12 Finishing exit width gauge 13 Coiler front width gauge (coiler entry width gauge) 14 Finishing width gauge 15 Pass Line 16a, 16b, 16c, 16d Camera 61 Cropshire 62a, 62b Work rolls 63a, 63b Backup roll 64a, 64b Bearing section (backup roll chock) 65 Housing 66 Load Cell 67 Pressing cylinder 68a, 68b Bearing box (work roll chock) 69 Looper 69a Looper Roll 90 Control Controller 91 Control computer 92 Upper computer 100 Width prediction model generation unit 101 Database Department 102 Machine Learning Department 103 Data Acquisition Section 110 Width Prediction Unit 111 Input data acquisition unit M Width Prediction Model SA rough rolled material SB steel plate

Claims

1. A method for predicting the width of a finish rolled material in a hot rolling line including a heating furnace for heating a slab, a rough rolling mill for rough rolling the heated slab to produce a rough rolled material, and a finish rolling mill for finish rolling the rough rolled material to produce a finish rolled material, the method including a prediction step of predicting statistical information of the width of the finish rolled material using a width prediction model learned by a Gaussian process regression technique, the input data being a set value or an actual value of the width of the rough rolled material and one or more operation parameters selected from the operation parameters of the finish rolling mill, and output data being statistical information of the width of the finish rolled material, the method including a setting step of predicting statistical information of the width of the finish rolled material using the width prediction method for the finish rolled material, and setting one or more operation parameters selected from the operation parameters of the finish rolling mill based on the predicted statistical information so as to reduce the probability that the width of the finish rolled material falls below the target width, The statistical information on the width of the finish rolled material includes an average value W m and a standard deviation W σ of the width of the finish rolled material, and the setting step includes a step of setting one or more operational parameters selected from operational parameters of the finish rolling mill so that the target width W t of the finish rolled material satisfies the relationship shown in the following mathematical formula (1). [0010]

2. The method of claim 1 , wherein the width prediction model includes one or more parameters selected from attribute information of the slab as input data.

3. A method for producing a hot-rolled steel sheet, comprising the step of producing a hot-rolled steel sheet using the method for controlling width of finish rolled material according to claim 1 or 2.

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