Rough rolled material width prediction method, rough rolled material width control method, hot rolled steel sheet manufacturing method, and rough rolled material width prediction model generation method

Abstract

The present invention provides a method for predicting the width of a rough rolled material in a hot-rolling line which includes a heating furnace that heats a slab, a width reduction press device that intermittently reduces the width of the heated slab, a rough rolling device that performs rough rolling on the width-reduced slab to produce a rough rolled material, and a finishing rolling device that performs finishing rolling on the rough rolled material to produce a hot-rolled steel sheet, said method comprising a step for predicting a width distribution in the lengthwise direction of the rough rolled material using a width prediction model which is trained by machine learning, which includes, as input data, one or more operation parameters selected from operation parameters of the width reduction press device and one or more operation parameters selected from operation parameters of the rough rolling device, and which outputs, as output data, information pertaining to the width distribution in the lengthwise direction of the rough rolled material.

Description

[Technical field]

[0001] The present invention relates to a method for predicting the width of a rough rolled material in a hot rolling line, a method for controlling the width of a rough rolled material, a method for manufacturing a hot rolled steel sheet, and a method for generating a width prediction model for a rough 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 a semi-finished steel plate called a rough bar (roughly rolled material) having a thickness of about 30 to 50 mm is produced by rough rolling with one or more rough rolling mills. Next, the head and tail ends of the rough bar are cut by a crop shear, and the rough bar is finish-rolled by a 5-7 stand finishing rolling mill capable of continuous rolling to produce a steel strip having a thickness of about 1.0 to 25.0 mm. Finally, the steel strip 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 strip. In a hot rolling line, the width reduction press, rough rolling mill, and finishing rolling mill give plastic deformation to the steel plate in the thickness direction and width direction, so that the width of the steel plate varies in a complex manner during the manufacturing process of the hot-rolled steel strip. On the other hand, the width accuracy of the hot-rolled steel strip has a direct effect on the product yield. For this reason, in a hot rolling line, the width of the steel plate is controlled (coarse width control) at the stage after rough rolling and before it is loaded into the finishing mill, and the width of the steel plate is controlled (finishing width control) while it passes through the finishing mill.

[0003] In a hot rolling line, the width of a steel plate changes due to various causes, and therefore various techniques have been proposed to improve the width accuracy of a hot rolled steel strip. For example, Patent Document 1 describes a method for predicting the amount of width change in finish rolling at a skid mark portion from the measurement results of the rolling direction temperature distribution of the rolled material or the fluctuation of the rolling load of a roughing mill. Patent Document 2 describes a method for forming multiple steps from the front and rear ends of a steel plate toward a steady portion by a width reduction press device in order to suppress width fluctuations at the front and rear ends of the steel plate. Patent Document 3 describes a method for predicting the width fluctuation of a steel plate after horizontal rolling by learning the relationship between the actual values ​​of the operating conditions of a vertical rolling mill (edger) and a horizontal rolling mill constituting a roughing mill and the actual values ​​of the width of the steel plate after horizontal rolling, and for setting the aperture of the vertical rolling mill so that the difference between the predicted value and the actual value of the width of the steel plate after horizontal rolling becomes zero. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 2968647 [Patent Document 2] Japanese Patent Application Publication No. 5-200411 [Patent Document 3] Patent No. 3260616 Summary of the Invention [Problem to be solved by the invention]

[0005] The method described in Patent Document 1 predicts the amount of width change in a finishing rolling mill caused by the temperature distribution in the rolling direction of the rolled material generated in the heating furnace, but the length and width of the rolled material change due to width reduction, rough rolling, etc. For this reason, it is necessary to identify which position of the rolled material corresponds to the temperature distribution generated in the heating furnace. However, since the length of the rolled material is extended by about 5 to 10 times before the slab becomes the rough rolled material, it is difficult to accurately track the temperature distribution generated in the heating furnace. For this reason, it is difficult to accurately predict the amount of width change and suppress the occurrence of width defects in hot-rolled steel sheets.

[0006] On the other hand, the method described in Patent Document 2 uses a width reduction press device to suppress width fluctuations at the front and rear ends of the steel plate, but it is difficult to accurately predict the complex metal flow at the front and rear ends of the steel plate and form corresponding steps. This may cause variations in the width of the steel plate, resulting in width defects in the hot-rolled steel plate. Patent Document 3 also describes predicting the width fluctuation of the steel plate after horizontal rolling by learning using the actual values ​​of the operating conditions of the vertical rolling mill and the horizontal rolling mill. However, the longitudinal width distribution of the steel plate is affected not only by the operating conditions in rough rolling, but also by the temperature distribution of the slab generated in the heating furnace and the width fluctuation caused by the width reduction press device. For this reason, it is difficult to accurately predict the width fluctuation of the steel plate after horizontal rolling.

[0007] The present invention has been made to solve the above problems, and an object of the present invention is to provide a rough rolled material width prediction method capable of accurately predicting the longitudinal width distribution of the rough rolled material. Another object of the present invention is to provide a rough rolled material width control method capable of accurately controlling the longitudinal width of the rough rolled material. Another object of the present invention is to provide a hot rolled steel sheet manufacturing method capable of manufacturing a hot rolled steel sheet having excellent longitudinal width accuracy. Another object of the present invention is to provide a rough rolled material width prediction model generation method capable of generating a width prediction model that accurately predicts the longitudinal width distribution of the rough rolled material. [Means for solving the problem]

[0008] The method for predicting the width of rough rolled material according to the present invention is a method for predicting the width of rough rolled material in a hot rolling line including a heating furnace for heating a slab, a width reduction press device for intermittently reducing the width of the heated slab, a rough rolling mill for rough rolling the slab after width reduction to produce a rough rolled material, and a finishing rolling mill for finish rolling the rough rolled material to produce a hot rolled steel plate. The method includes, as input data, one or more operation parameters selected from the operation parameters of the width reduction press device and one or more operation parameters selected from the operation parameters of the rough rolling mill, and includes a step of predicting the longitudinal width distribution of the rough rolled material using a width prediction model learned by machine learning, which includes, as output data, information on the longitudinal width distribution of the rough rolled material.

[0009] The operation parameters of the width reduction press device and the operation parameters of the rough rolling mill may be operation parameters identified as representative values ​​in the longitudinal direction of the rough rolled material.

[0010] The width prediction model may include as input data one or more operational parameters selected from operational parameters of the furnace.

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

[0012] The rough rolled material width control method of the present invention predicts the longitudinal width distribution of the rough rolled material using the rough rolled material width prediction method of the present invention, and sets the operating parameters of the rough rolling mill based on the predicted width distribution.

[0013] The method for producing a hot-rolled steel sheet according to the present invention produces a hot-rolled steel sheet by performing finish rolling, by the finishing mill, on a rough-rolled material whose width has been controlled using the method for controlling the width of the rough-rolled material according to the present invention.

[0014] The method for generating a width prediction model of a rough rolled material according to the present invention is a method for generating a width prediction model for predicting the width of the rough rolled material in a hot rolling line including a heating furnace for heating a slab, a width reduction press device for intermittently reducing the width of the heated slab, a rough rolling mill for rough rolling the slab after width reduction to produce a rough rolled material, and a finishing rolling mill for finish rolling the rough rolled material to produce a hot rolled steel plate. The method includes the steps of acquiring a plurality of learning data, which includes, as input performance data, one or more operational performance data selected from the operation parameters of the width reduction press device and one or more operational performance data selected from the operation parameters of the rough rolling mill, and output performance data indicating the width distribution in the longitudinal direction of the rough rolled material, and generating the width prediction model by machine learning using the acquired plurality of learning data. Effect of the Invention

[0015] According to the width prediction method of the rough rolled material of the present invention, it is possible to predict the longitudinal width distribution of the rough rolled material with high accuracy. Moreover, according to the width control method of the rough rolled material of the present invention, it is possible to control the longitudinal width of the rough rolled material with high accuracy. Moreover, according to the manufacturing method of the hot rolled steel sheet of the present invention, it is possible to manufacture a hot rolled steel sheet with excellent longitudinal width accuracy. Moreover, according to the generation method of the width prediction model of the rough rolled material of the present invention, it is possible to generate a width prediction model that accurately predicts the longitudinal width distribution of the rough rolled material. [Brief description of the drawings]

[0016] [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 heating furnace shown in FIG. [Diagram 3] FIG. 3 is a schematic diagram of the inside of the heating furnace shown in FIG. 1 as viewed from above. [Figure 4] FIG. 4 is a schematic diagram showing a configuration example of the width reduction press device shown in FIG. [Diagram 5] FIG. 5 is a diagram showing the configuration of a stand constituting the roughing mill shown in FIG. [Figure 6] FIG. 6 is a schematic diagram for explaining an optical width measuring method. [Figure 7] FIG. 7 is a block diagram showing the configuration of a width prediction model generating unit according to an embodiment of the present invention. [Figure 8] FIG. 8 is a schematic diagram showing the configuration of a width prediction model using a neural network. [Figure 9] FIG. 9 is a diagram showing an example of the width distribution of a slab after width reduction by a width reduction press device. [Figure 10] FIG. 10 is a diagram showing width loss at the head and tail ends of a rolled material. [Figure 11] FIG. 11 is a diagram showing the width distribution formed when horizontal rolling is performed on a rolled material having a constant width in the longitudinal direction without performing width reduction using an edger. [Figure 12] FIG. 12 is a block diagram showing the configuration of a width prediction unit according to an embodiment of the present invention. [Figure 13] FIG. 13 is a diagram showing the width distribution of the roughly rolled material after width reduction of the slab using a width reduction press device, followed by one pass of width rolling using an edger and one pass of horizontal rolling using a horizontal rolling mill. [Figure 14] FIG. 14 is a diagram showing the predicted results of the width distribution in the longitudinal direction of the roughly rolled material in the examples. [Figure 15] FIG. 15 is a diagram showing the predicted results of the width distribution in the longitudinal direction of the roughly rolled material in the examples. [Figure 16] FIG. 16 is a diagram showing the predicted results of the width distribution in the longitudinal direction of the roughly rolled material in the examples. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, with reference to the drawings, a rough rolled material width prediction method, a rough rolled material width control method, a hot rolled steel sheet manufacturing method, and a rough rolled material width prediction model generation method, which are embodiments of the present invention, will be described in detail.

[0018] <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 6.

[0019] 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 a descaling device 3, and then is width-reduced to a predetermined set width by a width reduction press device 4. The width-reduced slab is then rolled to a predetermined thickness in a roughing rolling mill 5 to become a rough bar (roughly rolled material), and is transported to a finishing rolling mill 6. In the finishing rolling mill 6, the rough bar is rolled to a product thickness by a continuous rolling mill with 5 to 7 stands. A cooling device 7 is provided on a facility called a run-out table downstream of the finishing rolling mill 6, and the steel sheet is cooled to a predetermined temperature and then wound into a coil by a coiler 8. In addition, a plurality of width gauges are installed as width measuring means in the middle of the conveying process of the hot rolling line 1. In the example shown in FIG. 1, a rough exit width gauge 11 is installed on the exit side of the rough rolling mill 5, and a finish exit width gauge 12 is installed on the exit side of the finishing rolling mill 6. In addition, a pre-coiler width gauge (coiler entry width gauge) 13 is installed on the exit side of the cooling device 7 to measure the width of the steel sheet before coiling. Hereinafter, the width of the rough bar measured by the rough exit width gauge 11 may be referred to as the rough exit width, the width of the steel sheet measured by the finish exit width gauge 12 as the finish exit width, and the width of the steel sheet measured by the pre-coiler width gauge 13 as the pre-coiler width.

[0020] 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 host computer 92 or the control computer 91 setting a target value of the rough exit width (rough target width), a target value of the finish exit width (finish target width), and a target value of the width before the coiler (target width before the coiler) based on the manufacturing instructions from the host computer 92, and setting the operating conditions of the roughing mill 5 and the finishing mill 6. Specifically, the host computer 92 or the control computer 91 sets the finishing target width in consideration of the amount of change in width of the steel plate that occurs between the exit side of the finishing mill 6 and the width before the coiler 13, based on the target width before the coiler determined from the product specifications of the hot rolled steel plate. Furthermore, the host computer 92 or the control computer 91 sets the rough target width based on the set finish target width, taking into consideration the width change amount of the steel plate in the finish rolling mill 6. Then, the host computer 92 or the control computer 91 sets the rolling conditions in each pass of the rough rolling so that the width of the rough bar after the rough rolling is equal to the rough target width. The host computer 92 or the control computer 91 also sets the rolling conditions in each stand of the finish rolling so that the width of the steel plate after the finish rolling is equal to the finish 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 width of the steel plate before coiling is equal to the pre-coiler 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 the function of collecting information obtained from various sensors (thickness gauge, thermometer, etc.) at a predetermined sampling period, in addition to information obtained from a width gauge installed on the hot rolling line 1, and outputting them to the control computer 91.

[0021] The rough rolled material width prediction method according to one embodiment of the present invention is a method for predicting the longitudinal width distribution of a rough bar measured by a rough exit width meter 11 installed on the exit side of a rough rolling mill 5. The rough rolled material width control method according to one embodiment of the present invention is a method for controlling the longitudinal width of a rough bar so that the width of the rough bar after rough rolling is equal to a rough target width.

[0022] [Heating furnace] FIG. 2 is a schematic diagram showing a configuration example of the heating furnace 2 shown in FIG. 1. As shown in FIG. 2, in this embodiment, the slab SA is charged into the heating furnace 2 from the left side of FIG. 2. The temperature of the slab SA charged into the heating furnace 2 may be cooled to about room temperature in the slab yard after casting, or may be about 600 ° C during cooling. In addition, the slab SA may be charged at a temperature of about 600 to 800 ° C without passing through the slab yard after casting. The inside of the heating furnace 2 is divided into a plurality of zones, and generally, a heating zone divided into 2 to 8 zones and 1 to 3 soaking zones are provided on the upstream side. In the example shown in FIG. 2, five heating zones and one soaking zone are provided, and here, both are collectively referred to as "heating furnace zones". Each heating furnace zone is set to a different atmospheric temperature so that the average temperature of the slab SA charged into the heating furnace 2 gradually increases to a predetermined target heating temperature (the target value of the average temperature of the slab SA when it is extracted from the heating furnace 2). Further, a thermometer 21 for measuring the atmospheric temperature in each heating furnace zone is installed at the top of each heating furnace zone.

[0023] FIG. 3 is a schematic diagram of the inside of the heating furnace 2 shown in FIG. 1 viewed from above. As shown in FIG. 3, the slab SA charged in the heating furnace 2 passes through each heating furnace zone in sequence inside the heating furnace 2 by a conveying device called a walking beam 22. In addition, multiple slabs SA are charged simultaneously in the heating furnace 2, and are extracted from the extraction side outlet of the heating furnace 2 in the order in which they were charged into the heating furnace 2, and hot rolling is performed. The inside of the walking beam 22 is water-cooled, and a portion in which the temperature rise of the slab SA is locally inhibited is generated by a member called a skid that is in direct contact with the slab SA. The portion of the slab SA in contact with the skid is called a skid mark, and is a region with a lower temperature than the portion not in contact with the skid. As described in Patent Document 1, the width of the steel plate fluctuates due to the skid mark, which affects the width accuracy of the hot-rolled steel plate.

[0024] [Width reduction press device] FIG. 4 is a schematic diagram showing a configuration example of the width reduction press device 4 shown in FIG. 1. After the primary scale formed on the surface of the slab SA heated by the heating furnace 2 is removed by the descaling device 3, the slab SA is width-reduced to a predetermined set width by the width reduction press device 4. As shown in FIG. 4, the width reduction press device 4 is equipped with a pair of width reduction dies 41. The width reduction dies 41 reduce the slab SA in the width direction. The width reduction press device 4 drives the width reduction dies 41 by a drive device 42 while transporting the slab SA, and intermittently reduces the width of the slab SA from both sides in the width direction.

[0025] In the width reduction press device 4, the slab SA is transported using pinch rolls 43 and the like. The width reduction press device 4 can change the feed pitch of the slab SA between width reduction passes by changing the driving amount of the pinch rolls 43. The feed pitch means the transport distance of the slab SA for each width reduction pass in the width reduction press device 4. The driving amount of the pinch rolls 43 is controlled by a controller 90 that controls the width reduction press device 4. On the surface of the width reduction die 41 that contacts the slab SA, a parallel portion 41a parallel to the slab transport direction and an inclined portion 41b that widens in the width direction toward the opposite direction to the slab transport direction are formed in this order from the tip side in the slab transport direction. In the width reduction die 41, one or more parallel portions 41a may be provided between the inclined portions 41b in order to suppress the occurrence of slippage of the slab SA. The deformation state of the slab SA changes depending on the shape of the width reduction die 41, which affects the width accuracy of the steel plate in the rough rolling mill 5.

[0026] [Roughing mill] Return to FIG. 1. The roughing mill 5 is equipped with a reversible mill 5a capable of reverse rolling and a non-reversible mill 5b capable of rolling only in the downstream conveying direction. The arrows (solid lines) illustrated under the roughing mill 5 shown in FIG. 1 represent rolling passes (rolling passes for thinning the thickness). In the reversible mill 5a, usually, about 5 to 11 rolling passes are performed in the reversible direction (from the upstream side to the downstream side or from the downstream side to the upstream side). In the final rolling pass, rolling and conveying to the next rolling mill are performed simultaneously, so the number of rolling passes of the reversible mill is always an odd number, and the rough bar is conveyed to the downstream rolling mill while being rolled. At this time, the time from when the tail end of the rough bar leaves the rolling mill in the current rolling pass until the rolling direction is reversed and the rough bar is bitten into the rolling mill in the next rolling pass is called the interpass time of the reversible passes. In addition, in the final rolling pass of the reversing passes, the time from when the tail end of the rough bar leaves the reversing mill 5a until it is engaged in the non-reversing mill 5b is called the interpass time of successive passes. Furthermore, the interpass time of the reversing passes and the interpass time of the successive passes are collectively called the interpass air-cooling time. The interpass air-cooling time indicates the time during which the rough bar is air-cooled during transportation, and affects the temperature change of the rough bar.

[0027] FIG. 5 is a schematic diagram showing the configuration of a stand constituting the roughing mill 5 shown in FIG. 1. As shown in FIG. 5, the roughing mill 5 includes a horizontal rolling mill 51 that reduces the thickness of the steel sheet SB, and an edger (vertical rolling mill) 52 that reduces the width of the steel sheet SB. The edger 52 is a rolling mill in which a pair of rolls are arranged vertically, and is installed adjacent to the horizontal rolling mill 51. The width reduction (width rolling) of the steel sheet SB using the edger 52 is usually performed before the horizontal rolling in each rolling pass of the rough rolling. Therefore, when one edger 52 is arranged for the reversing rolling mill 5a, the width reduction by the edger 52 is performed only in the forward direction, i.e., the odd-numbered passes, and is not performed in the even-numbered passes, i.e., the reverse passes. However, when the edgers 52 are arranged on both sides of the reversing rolling mill 5a, the width reduction may be performed in either rolling pass. The roll opening (roll gap) of the horizontal rolling mill 51 and the opening of the edger 52 in each pass of rough rolling are set by the control computer 91. A descaling header for spraying descaling water toward the steel sheet SB is installed in the rough rolling mill 5, and descaling is performed at the inlet side of the horizontal rolling mill 51. However, descaling water is not necessarily sprayed in all rolling passes of rough rolling, and a predetermined descaling pattern is set according to the material of the steel sheet SB, for example, spraying only in the first pass or spraying only in odd-numbered passes. The steel sheet SB in a state in which all rolling passes preset by the rough rolling mill 5 have been completed is called a rough bar or sheet bar, and in the present invention, it is called a rough rolled material.

[0028] [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 finish exit width gauge 12, and a coiler front width gauge 13. These width gauges often use an optical width measurement method. In the optical width measurement method, a light source is placed below the pass line along which the steel sheet is transported, and an image sensor is placed above it, and the width 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 is passing. On the other hand, there is a width gauge that measures the width of the steel sheet by identifying the position of the width direction end of the steel sheet by image processing from an image of the steel sheet taken with a camera. The width gauge using a camera has a simple device configuration because it does not require a light source or an image sensor to be placed along the width direction of the steel sheet. However, since the width direction end of the steel sheet is photographed from an oblique direction, if the steel sheet floats up from the pass line, a width measurement error is likely to occur. Therefore, it is often equipped with a function to correct the width measurement error according to the amount of floating of the steel sheet from the pass line. Specifically, as shown in FIG. 6, when the steel sheet SB is transported at a height of a lift amount H from the pass line 14, the width measurement error is corrected as follows. First, a pair of cameras 15a, 15b arranged in the width direction of the steel sheet SB identify both ends in the width direction of the steel sheet SB and determine a width measurement value W1. Next, another pair of cameras 15c, 15d arranged in the width direction of the steel sheet SB identify both ends in the width direction of the steel sheet SB and determine a width measurement value W2. Then, based on the positional relationship between the cameras 15a, 15b and the cameras 15c, 15d (in the example shown in FIG. 6, the width direction interval D between the cameras 15a, 15b and the width direction interval L between the cameras 15a (15b) and 15c (15d)), the actual width W of the steel sheet SB is calculated by the following formula (1) and is set as the width measurement value of the steel sheet SB.

[0029]

number

[0030] 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 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 into a width 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, using information on the conveyance speed at which the steel plate passes the position of the width gauge, it is converted into a relationship between the position in the longitudinal direction of the steel plate and the actual width value. Then, in the control computer 91, a representative value of the width is calculated based on the acquired actual width measurement value. For the representative value of the width, the average value of the width in the longitudinal direction of the steel plate (average width), the actual measurement value of the width in the steady part excluding the leading and trailing ends of the steel plate (steady width), the actual measurement value of the width at the leading end of the steel plate (leading width), the actual measurement value of the width at the trailing end of the steel plate (tailing width), etc. are used. In addition, the minimum value (minimum width) and maximum value (maximum width) of the width in the longitudinal direction of the steel plate may be calculated. The actual width measured by a width gauge may be expressed as a deviation from a preset target width.

[0031] The information on the longitudinal width distribution of the rough rolled material in this embodiment refers to the width of the rough rolled material measured by the rough exit side width meter 11 and associated with the longitudinal position information. Usually, the measurement value by the rough exit side width meter 11 is acquired as discrete data for each position divided in the longitudinal direction from the tip of the rough rolled material, so that the distance from the tip of the rough rolled material and the width measurement value are generated as a plurality of data sets in which they are associated. The information on the longitudinal width distribution of the rough rolled material is a width measurement value associated with at least three position data points in the longitudinal direction of the rough rolled material. However, it is preferable that the information on the longitudinal width distribution of the rough rolled material is a width measurement value associated with 100 to 10,000 position data points in the longitudinal direction of the rough rolled material. When the position data is less than 100, it may be difficult to identify the longitudinal width fluctuation of the rough rolled material, and even if the position data exceeds 10,000, the accuracy of identifying the width fluctuation does not improve. The information on the width distribution may be obtained by normalizing the longitudinal position data using the length of the roughly rolled material, and the width measurement values ​​of the roughly rolled material may be associated with the normalized position data.

[0032] <How to generate a width prediction model> Next, a method for generating a width prediction model according to one embodiment of the present invention will be described with reference to FIGS.

[0033] FIG. 7 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. 7, the width prediction model generating unit 100 according to an embodiment of the present invention includes a database unit 101 and a machine learning unit 102. The database unit 101 accumulates one or more operation performance data selected from the operation performance data of the width reduction press device 4, one or more operation performance data selected from the operation performance data of the rough rolling mill 5, and performance data of the width distribution in the longitudinal direction of the rough rolled material. The database unit 101 may accumulate one or more operation performance data selected from the operation performance data of the heating furnace and one or more performance data selected from the performance data of the attribute information of the slab, as necessary. Specific examples of the operation data accumulated in the database unit 101 will be described later.

[0034] The performance data accumulated in the database unit 101 is appropriately acquired from the control controller 90, the control computer 91, or the host computer 92. In this embodiment, the width prediction model generating unit 100 includes a data acquiring unit 103 for collecting these performance data. The data acquiring unit 103 temporarily stores the performance data, generates a data set in which multiple types of performance data are associated with each other, and then accumulates the data in the database unit 101. Since the data accumulated in the database unit 101 may be acquired at different times, the data acquiring unit 103 can associate multiple types of performance data with each other to configure a data set in a corresponding relationship with each other.

[0035] The operation performance data of the width reduction press device 4 and the operation performance data of the rough rolling mill 5 included in the data set accumulated by the database unit 101 may use performance data specified as a representative value in the longitudinal direction of one rough rolled material. The representative value in the longitudinal direction of the rough rolled material refers to one piece of data representative of the operation conditions in the longitudinal direction of the rough rolled material. For the representative value in the longitudinal direction of the rough rolled material, a representative position that specifies the position in the longitudinal direction of the rough rolled material may be set, and data related to the operation conditions at the set representative position may be used. In addition, the representative value in the longitudinal direction of the rough rolled material may use the average value or standard deviation of the operation data in the longitudinal direction of the rough rolled material. Furthermore, data related to the operation conditions that are fixed (unchanging) with respect to the longitudinal direction of the rough rolled material may be used for the representative value in the longitudinal direction of the rough rolled material. For example, when the width reduction is performed on the slab, a representative position may be set in the steady part of the slab, and the operation performance data set for the representative position may be used. Even when rough rolling is performed, a representative position may be set in the steady part of the rough rolled material, and operation performance data set for the representative position may be used. However, the representative position to be set is not limited to the steady part of the rough rolled material, and one representative position may be set from the end on the downstream side of the conveying direction of the hot rolling line to the 1 / 4 position and 3 / 4 position with respect to the entire length of the slab, or from the front end or tail end, and operation performance data may be acquired. In this case, the operation parameters of the width reduction press device and the operation parameters of the rough rolling mill are specified as operation parameters, with one representative value for each rough rolled material, and these are associated with the width distribution of the rough rolled material. That is, the width prediction model of this embodiment has the feature of outputting the width at a position in the longitudinal direction different from the longitudinal position of the rough rolled material associated with the input operation parameters as a prediction result.

[0036] 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 production instructions to the control computer 91, or may be provided in an independent computer that can communicate with other devices. The width prediction model generating unit 100 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.

[0037] The machine learning unit 102 uses a data set accumulated in the database unit 101 to generate a width prediction model M for predicting information on the longitudinal width distribution of the rough rolled material by machine learning using a plurality of learning data, including one or more operation performance data selected from the operation parameters of the width reduction press device 4 and one or more operation performance data selected from the operation parameters of the rough rolling mill 5 as input performance data, and output performance data on the longitudinal width distribution of the rough rolled material. The machine learning model for generating the width prediction model M may be any machine learning model as long as it can obtain a width prediction accuracy sufficient for practical use. For example, a commonly used neural network (including deep learning, convolutional neural network, etc.), decision tree learning, random forest, support vector regression, etc. may be used. An ensemble model combining a plurality of models may also be used. For example, the width prediction model M can be generated by machine learning using a general neural network as shown in FIG. 8. In particular, by using deep learning, the problem of multicollinearity is not taken into consideration, and other operational parameters that are correlated with the width distribution of rough rolled material can be freely selected as inputs, which can improve the prediction accuracy of the width distribution of rough rolled material. For example, a neural network with two intermediate layers, three nodes each, and a sigmoid function as the activation function can be used.

[0038] The machine learning unit 102 may improve the estimation accuracy of the width distribution of the rough rolled material by dividing the data set accumulated in the database unit 101 into training data and test data and performing learning. For example, the machine learning unit 102 may use the training data to learn the weight coefficients of the neural network, and generate the width prediction model M while appropriately changing the structure of the neural network (the number of intermediate layers and the number of nodes) so that the accuracy rate of the width distribution of the rough rolled material in the test data is increased. The weight coefficients can be updated using an error propagation method. The width prediction model M may be updated to a new model by re-learning, for example, every six months or every year. This is because the more data stored in the database unit 101, the more accurate the prediction of the width distribution of the rough rolled material becomes. By updating the width prediction model M based on the latest data, it is possible to generate a width prediction model M that reflects changes in the manufacturing conditions of the hot-rolled steel sheet manufactured using the hot rolling line. In this embodiment, when manufacturing a hot-rolled steel sheet using a hot rolling line, information on the width distribution in the longitudinal direction of the rough rolled material can be predicted from the operating conditions identified as representative values ​​in the longitudinal direction of the rolled material. In the conventional technique, the width prediction of the rough rolled material was performed by acquiring actual data on the operating conditions for each longitudinal position of the rolled material, and corresponding the actual data on the width acquired for each longitudinal position of the rough rolled material. As a result, when the slab extracted from the heating furnace 2 is plastically deformed by the width reduction press device 4 or the rough rolling mill 5 and significantly elongated as the rough rolled material, it was necessary to perform tracking to identify the longitudinal position of the rolled material and to associate the operating conditions of the width reduction press device 4 or the rough rolling mill 5 with the position information of the rough rolled material. In contrast, in the present embodiment, the representative value of the operating conditions in the longitudinal direction of the rolled material is directly associated with the width distribution of the rough rolled material, so that it is not necessary to track a specific position of the rolled material in rough rolling.

[0039] [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 rolled material in the width reduction press device 4 and the rough rolling mill 5 of the hot rolling line 1. The information on the slab dimensions is information on the thickness, width, length, and weight of the slab. The information on the slab dimensions affects the temperature change of the steel plate in the hot rolling line 1, and affects the deformation resistance of the rolled material during width reduction and rough rolling. In particular, the behavior of the temperature drop differs between the front and tail ends of the rolled material and the steady part, and as a result, a correlation is created with the longitudinal width distribution of the rough rolled material.

[0040] [Heating furnace operating parameters] The operation parameters of the heating furnace that can be used as inputs for the width prediction model M are parameters that represent the operation conditions when heating a slab in the heating furnace 2 of the hot rolling line 1, and refer to information that affects the width change of the rolled material in the width reduction press device 4 and rough rolling mill of the hot rolling line 1. The operation parameters of the heating furnace 2 can include the temperature of the slab when it is charged into the heating furnace 2, the residence time in a specific heating furnace zone in the heating furnace 2, the atmospheric temperature of the final heating furnace zone of the heating furnace 2, and the temperature of the slab extracted from the heating furnace 2. These parameters cause the behavior of the temperature drop to change between the head and tail ends of the rolled material and the steady part, creating a correlation with the longitudinal width distribution of the rough rolled material.

[0041] Also, information such as the charging position of the slab in the heating furnace 2 and the positional relationship between the slab and other slabs in the heating furnace 2 may be used. The charging position of the slab in the heating furnace 2 may be information on the furnace entry position P, which represents the distance between the walking beam 22 located at one end of the heating furnace 2 and the longitudinal end of the slab SA, as shown in FIG. 3. This is because the position of the skid mark in the longitudinal direction of the slab SA changes depending on the furnace entry position P, which affects the position where the width fluctuation occurs in the longitudinal direction of the steel plate. The charging position of the slab SA may be the distance D1 between the longitudinal end of the slab SA and the furnace wall of the heating furnace 2, or a parameter representing the interval between the walking beam 22 (fixed skid) or the moving skid 23 of the heating furnace 2 into which the slab SA is charged.

[0042] As information on the positional relationship between the slab and other slabs in the heating furnace 2, the distance (charging interval) D2 between adjacent slabs in the heating furnace 2 can be used, as shown in Fig. 3. When the charging interval D2 changes in the heating furnace 2, the temperature at the end face of the slab changes. This affects the temperature distribution of the rolled material in the hot rolling line 1 and affects the width distribution of the roughly rolled material. As information on the positional relationship between the slab and other slabs in the heating furnace 2, the length or thickness of the other slabs, the difference in distance between the front end of the slab to be predicted and the front end of the other slab to the furnace wall of the heating furnace, etc. may be used.

[0043] [Operation parameters of width reduction press device] The operation parameters of the width reduction press device 4 that can be used as input for the width prediction model M refer to the operation conditions when performing width reduction on a heated slab. The operation parameters of the width reduction press device 4 can include operation parameters related to the width reduction amount of the slab. The operation parameters related to the width reduction amount of the slab include the width reduction amount of the slab at a representative position in the longitudinal direction of the slab and the feed pitch of the slab between width reduction passes. The operation parameters related to the width reduction amount of the slab cause a difference between the dog-bone (thickness distribution in the width direction) shape of the steady part formed in the slab after width reduction and the dog-bone shape of the front and rear ends, which affects the longitudinal width distribution of the rough rolled material. In other words, even if the width reduction amount, which is a representative value in the longitudinal direction of the slab, is constant in the longitudinal direction of the slab, the dog-bone shapes are different at the steady part, front end, and rear end of the slab, which affects the longitudinal width distribution of the rough rolled material. Even when the feed pitch of the slab between width reduction passes is constant, the dogbone shape is different between the steady portion and the leading and trailing ends, which affects the longitudinal width distribution of the rough rolled material. Different width reduction amounts may be set for the leading portion, steady portion, and trailing end of the slab. In this case, any of the width reduction amounts set for the leading portion, steady portion, and trailing end of the slab may be used as a representative value of the operation parameters of the width reduction press device 4. Different feed pitches may be set for the leading portion, steady portion, and trailing end of the slab. In this case, any of the feed pitches set for the leading portion, steady portion, and trailing end of the slab may be used as a representative value of the operation parameters of the width reduction press device 4.

[0044] Furthermore, the operation parameters of the width reduction press device 4 can be operation parameters related to the die shape applied to the width reduction press device 4. The operation parameters related to the die shape are representative values ​​fixed with respect to the longitudinal direction of the slab. For example, the length of the parallel part 41a and the angle of the inclined part 41b formed in the width reduction die 41 may be used. This is because the shape of the width reduction die 41 causes a difference in the deformation state between the stationary part and the leading and trailing ends of the slab, which affects the longitudinal width distribution of the roughly rolled material. FIG. 9 shows an example of the width distribution after width reduction by the width reduction press device 4 with the width reduction amount and feed pitch constant in the longitudinal direction of the slab. As shown in FIG. 9, it can be seen that periodic width fluctuations (also called press marks) occur along the leading and trailing ends of the slab after width reduction. Since width reduction using the width reduction press device 4 is an intermittent process, wide and narrow parts appear at predetermined intervals. This affects the longitudinal width distribution of the roughly rolled material.

[0045] As described above, by identifying the operational parameters of the width reduction press device 4 as representative values ​​in the longitudinal direction of one slab, the pattern of longitudinal width fluctuation of the slab after width reduction can be characterized. In other words, it is possible to identify the distribution characteristics of the longitudinal width fluctuation of the slab after width reduction, without identifying the operational parameters at each position along the longitudinal direction of the slab.

[0046] [Roughing mill operating parameters] The operation parameters of the roughing mill 5 used to input the width prediction model M refer to rolling operation conditions that affect the longitudinal width distribution of the rolled material in any rolling pass of rough rolling by the roughing mill 5. The operation parameters of the roughing mill 5 preferably include the rolling conditions of the horizontal rolling mill 51 and the edger 52 that constitute the roughing mill 5. The rolling conditions of the horizontal rolling mill 51 may be the roll gap, work roll diameter, entry thickness, exit thickness, reduction ratio, rough target width, rolling load, and steel plate temperature in any rolling pass. Even if the operation conditions in horizontal rolling are constant in the longitudinal direction of the rolled material, the behavior of the width spread is different between the steady part and the leading and trailing ends of the rolled material, and therefore affect the longitudinal width distribution of the rough rolled material.

[0047] The rolling conditions of the edger 52 may include the edger opening, edger roll diameter, plate thickness, width reduction rate, and width reduction load in any rolling pass. These rolling conditions affect the width spread behavior in horizontal rolling of the rolled material, and also affect the difference in width spread between the steady part and the leading and tailing ends of the rolled material. Even if the operating conditions of width rolling are constant in the longitudinal direction of the rolled material, the dog bone formation behavior differs between the steady part and the leading and tailing ends of the rolled material, which affects the longitudinal width distribution of the rough rolled material. The rolling conditions, which are the operating parameters of the rough rolling mill, have a significant effect on the width distribution of the rough rolled material, so it is preferable to include the rolling conditions corresponding to all rolling passes of the rough rolling in the operating parameters of the rough rolling mill. Regarding the edger opening, different edger openings may be set at the leading part, the steady part, and the tailing end of the rolled material. In this case, any of the edger openings set for the head end, steady state end, and tail end of the rolled material may be used as the representative value of the operation parameters of the roughing mill 5.

[0048] In addition, the operation parameters of the roughing mill 5 may include a set value or an actual value of the outlet thickness from the first pass to the final pass of roughing. This is the so-called roughing rolling pass schedule. If the pass schedule is different, the thickness of the rolled material when transported between the rolling passes changes, which changes the temperature distribution during air cooling, causing a difference in the deformation behavior in the width direction depending on the longitudinal position of the rolled material. Furthermore, the operation parameters of the roughing mill 5 may include information on the air cooling time between rolling passes and whether or not descaling water is injected in any rolling pass. Although these are operation conditions that do not change in the longitudinal direction of the rolled material, they affect the difference in the temperature drop amount between the steady part and the front and rear ends of the rolled material, causing a difference in the deformation behavior in the width direction depending on the longitudinal position of the rolled material. Regarding the roll opening of the horizontal rolling mill 51, different roll openings may be set at the front end, the steady part, and the rear end of the rolled material. In this case, any of the roll gaps set for the head end, steady state end, and tail end of the rolled material may be used as the representative value of the operation parameters of the roughing mill 5.

[0049] FIG. 10 shows a schematic diagram of width drop at the front and rear ends of the rolled material that occurs during rough rolling. Width drop refers to the behavior in which the width at the front or rear ends becomes smaller than that at the steady part due to rough rolling. When width rolling is performed on a rolled material using an edger, in the steady part, when the edger roll reduces the width direction, the materials at the front and rear in the longitudinal direction resist the reduction in the width direction, forming a widthwise thickness distribution called a dog bone, in which deformation is concentrated at the widthwise end of the rolled material. When a rolled material having a dog bone is horizontally rolled, the thickened part formed at the widthwise end promotes plastic flow in the width direction, and the width return becomes large. In contrast, when width rolling is performed on the front and rear ends using an edger, there is no material at the front or rear in the longitudinal direction, so resistance to the reduction in the width direction is weakened, and the thickness of the rolled material is likely to increase toward the center of the width direction of the rolled material. As a result, when horizontal rolling is performed, plastic flow in the width direction is suppressed, and the width return becomes smaller than that of the steady part. In this way, the thickness increase behavior of the rolled material by the edger differs between the steady portion and the head and tail ends of the rolled material, so the width expansion caused by horizontal rolling differs depending on the longitudinal position of the rolled material, resulting in width drop.

[0050] On the other hand, FIG. 11 shows a schematic of the width distribution formed when horizontal rolling is performed on a rolled material with a constant longitudinal width without width reduction using an edger. When horizontal rolling is performed, the material present at the front and rear in the longitudinal direction creates resistance (restraint) to the plastic flow in the width direction in the steady part, so the width spread of the rolled material is small. In contrast, at the head and tail ends, there is no material at either the front or rear in the longitudinal direction to restrain the plastic flow in the width direction, so the width spread of the rolled material is large. For this reason, when horizontal rolling is performed on a rolled material with a constant longitudinal width, the width spread at the head and tail ends of the rolled material is large, as shown in FIG. 11.

[0051] As described above, the width of the rolled material in the edger and horizontal rolling mill constituting the roughing mill 5 has a constant pattern in the longitudinal direction even if the operating conditions of the roughing mill 5 are constant in the longitudinal direction of the rolled material. For this reason, in this embodiment, the operating parameters of the roughing mill specified as representative values ​​in the longitudinal direction of the rolled material can be used to correspond to information on the longitudinal width distribution of the roughing rolled material.

[0052] <Width prediction method for rough rolled material> Next, a method for predicting the width of a roughly rolled material according to one embodiment of the present invention will be described with reference to Figs.

[0053] The method for predicting the width of a rough rolled material according to one embodiment of the present invention includes a step of predicting the width distribution in the longitudinal direction of the rough rolled material by inputting the operation parameters of the width reduction press device 4 and the operation parameters of the rough rolling mill 5 into the width prediction model M and outputting information on the width distribution in the longitudinal direction of the rough rolled material. The width prediction unit that executes the step of predicting the width distribution of the rough rolled material 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 a manufacturing instruction to the control computer 91, or in an independent computer that can communicate with other devices. The width prediction unit may be configured in a device separate from the width prediction model generation unit 100 using a device that can receive the width prediction model M generated by the width prediction model generation unit 100. Hereinafter, the operation of the width prediction unit according to one embodiment of the present invention will be described with reference to FIG. 12.

[0054] FIG. 12 is a block diagram showing the configuration of a width prediction unit according to an embodiment of the present invention. The operation of the width prediction unit 110 shown in FIG. 12 is executed before the rough rolling of the hot-rolled steel sheet manufactured in the hot rolling line 1 is completed. The operation of the width prediction unit 110 can be executed, for example, at the stage when the slab to be predicted in width is extracted from the heating furnace 2. At the stage when the slab is extracted from the heating furnace 2, the control computer 91 calculates the set values ​​of the operating conditions of at least the width reduction press device 4 and the rough rolling mill 5, and these set values ​​can be used as inputs to the width prediction model M. In this case, information regarding the dimensions of the slab charged into the heating furnace 2 is stored in the upper computer 92, and the actual data of the operating parameters of the heating furnace 2 can be obtained. In addition, the operation of the width prediction unit 110 can be executed, for example, at the stage after the steel sheet to be predicted in width is width reduced by the width reduction press device 4. For the slab SA after width reduction, actual data of the operating parameters of the width reduction press device 4 is acquired by the control controller 90 or the control computer 91, and the actual data is used as input for the width prediction model M, and the setting values ​​for the operating parameters of the roughing mill 5 are set in the control computer 91.

[0055] The operation of the width prediction unit 110 can be performed, for example, in the middle pass of rough rolling by the rough rolling mill 5. The actual data of the operation parameters of the width reduction press device 4 is specified, and the actual values ​​of the operation parameters up to the middle pass of rough rolling and the set values ​​for the next pass and after may be input to the width prediction model M. The input data acquisition unit 111 shown in FIG. 12 acquires the actual values ​​or set values ​​of the operation parameters in the hot rolling line 1 held by any of the control controller 90, the control computer 91, and the upper computer 92 in the above manner. Then, the width prediction unit 110 acquires information on the width distribution of the rough rolled material by inputting the input data acquired by the input data acquisition unit 111 into the width prediction model M. The operation of the width prediction unit 110 can be performed at each stage where the steel sheet passes through each process of the hot rolling line 1 as described above, so that it may be performed multiple times in the process of manufacturing one steel sheet.

[0056] The information on the width distribution of the rough rolled material output by the width prediction unit 110 may be displayed on a monitor connected to the width prediction unit 110. Based on the output display of the width distribution output by the width prediction unit 110, at least one of the rough target width, the finish target width, and the target width before the coiler can be reset, and the width defect of the hot-rolled steel sheet can be suppressed. The significance of using one or more operation parameters selected from the operation parameters of the width reduction press device 4 and one or more operation parameters selected from the operation parameters of the rough rolling mill 5 as inputs of the width prediction model M will be explained. FIG. 13 is a diagram showing the width distribution of the rough rolled material after performing one-pass width rolling using an edger and one-pass horizontal rolling using a horizontal rolling mill after performing width reduction of the slab using the width reduction press device 4. The vertical axis of FIG. 13 represents the difference between the width of the steady part and the width of the tip part of the rough rolled material, and is an index representing the width distribution of the rough rolled material. The horizontal axis represents the amount of width reduction of the steady part by the width reduction press device 4. In this case, the results are shown when the width reduction amount in the steady part of the width reduction press, which is an operation parameter of the width reduction press device 4, is changed in the range of 0 to 300 mm, and the width reduction amount by the edger in the steady part, which is an operation parameter of the rough rolling machine, is changed in the range of 5 to 20 mm. As can be seen from FIG. 13, the width difference between the steady part and the tip part changes so that a minimum value occurs with respect to the width reduction amount of the width reduction press device 4. It can also be seen that the width reduction amount of the width reduction press device 4 at which the width difference between the steady part and the tip part has a minimum value takes a different value depending on the width reduction amount by the edger of the rough rolling machine 5. In other words, the width reduction amount of the width reduction press device 4 for minimizing the width difference between the steady part and the tip part, which is an index of the width distribution in the longitudinal direction of the rough rolled material, changes depending on the width reduction amount by the edger of the rough rolling machine 5. Therefore, by using a combination of one or more operational parameters selected from the operational parameters of the width reduction press device 4 and one or more operational parameters selected from the operational parameters of the rough rolling mill 5 as inputs to the width prediction model M, the prediction accuracy of the longitudinal width distribution of the rough rolled material can be improved.

[0057] <Width control method for rough rolled material> Next, a method for controlling the width of a roughly rolled material according to one embodiment of the present invention will be described.

[0058] The method for controlling the width of a rough rolled material according to one embodiment of the present invention resets the operation parameters of the rough rolling mill 5 based on the information on the longitudinal width distribution of the rough rolled material predicted as described above. In the above-mentioned method for predicting the width of a rough rolled material, the information on the longitudinal width distribution of the rough rolled material, which is the output of the width prediction model M, represents the longitudinal width distribution of the rough rolled material from the leading end to the tail end. The width control in the hot rolling line is usually performed so that the minimum plate width in the longitudinal direction of the hot rolled steel sheet does not fall below the lower limit of the product width as the hot rolled steel sheet. If a region where the width is insufficient occurs in a part of the longitudinal direction of the hot rolled steel sheet, it becomes necessary to cut off that part, but if a part of the longitudinal direction is cut off, the unit weight of the coil will not satisfy the product specifications, so that the whole becomes a defective product and the yield is greatly reduced. Therefore, a lower limit is also set for the width of the rough rolled material, and the rough rolled material is controlled so that a part of the longitudinal direction of the rough rolled material does not fall below the lower limit of the rough target width.

[0059] According to this embodiment, the longitudinal width distribution from the front end to the tail end of the rough rolled material can be predicted before all passes of rough rolling are completed. Therefore, when it is predicted that a portion of the rough rolled material will fall below the lower limit of the rough target width in the longitudinal direction, the operation parameters of the rough rolling mill are set to control so that a portion of the rough rolled material will not fall below the lower limit of the rough target width in the entire longitudinal direction of the rough rolled material. Specifically, the edger opening of the edger 52 may be set to be large in the final pass of rough rolling. In this case, the edger opening of the edger 52 may be made larger than the set value only for the portion of the rough rolled material predicted to fall below the lower limit of the rough target width in the longitudinal direction. Since the front and rear ends of the rough rolled material may be cut off by a crop shear located upstream of the finishing mill 6, even if the rough rolled material has a portion that falls below the lower limit of the rough target width in a region of 0.2 to 0.5 m of the front or rear end of the rough rolled material in the longitudinal direction, it is not necessary to set the edger opening large. In this way, by controlling the rough rolled material so that it does not fall below the lower limit of the rough target width over the entire length, the finish rolling is performed by the finishing mill 6, and the occurrence of a width shortage in the hot rolled steel sheet wound by the coiler 8 is suppressed, and a hot rolled steel sheet with excellent width precision can be manufactured.

[0060] Example 1 In this embodiment, the present invention was applied to a hot rolling line including a roughing mill 5 consisting of four reversible rolling mills and one non-reversible rolling mill, and a finishing mill 6 with seven stands, which is equipped with a width reduction press device 4 downstream of a heating furnace 2. In this embodiment, a slab having a thickness of 250 to 270 mm and a width of 600 to 1600 mm was heated by the heating furnace 2, and a steel plate having a rough rolled material thickness (rough bar thickness) of 30 to 35 mm and a finishing mill outlet thickness of 2 to 3 mm was produced by the above-mentioned hot rolling line. The hot rolling line is equipped with a rough outlet width meter 11, a finishing outlet width meter 12, and a coiler front width meter 13. The hot rolling line control controller 90, control computer 91, and host computer 92 collect actual values ​​of operation parameters of the steel plate produced by the hot rolling line, and the data acquisition unit 103 acquires the actual data.

[0061] In this embodiment, the data acquisition unit 103 acquired the width reduction amount in the steady part of the slab and the slab feed pitch for each pass of the width reduction press as the operation performance data of the width reduction press device 4. As the operation performance data of the roughing mill 5, the set values ​​of the entry and exit widths of the edger in the steady part of the rolling material and the roll diameter of the edger roll were used, and these were acquired for all passes of the rough rolling pass. Furthermore, as the operation performance data of the roughing mill 5, the set values ​​of the entry and exit widths of the horizontal rolling in the steady part of the rolling material and the work roll diameter of the horizontal rolling mill were used, and these were acquired for all passes of the rough rolling pass. In this embodiment, in addition to the above, the plate thickness of the slab was acquired from the upper computer 92 as the performance data of the attribute information of the slab.

[0062] The data acquisition unit 103 acquired from the control computer 91 the actual data of the longitudinal width distribution of the rough rolled material measured using the rough output side width meter 11. The information on the width distribution of the rough rolled material was obtained by normalizing the longitudinal position data using the length of the rough rolled material, and dividing the rough rolled material into 1000 positions in the longitudinal direction, and the width was determined as the width at each position. Then, at the stage where 30,000 data sets were accumulated in the database unit 101, the machine learning unit 102 generated the width prediction model M. In this case, the machine learning unit 102 divided the data sets accumulated in the database unit 101 into 20,000 pieces of learning data and 10,000 pieces of test data, and evaluated the prediction accuracy for the test data using the width prediction model M generated using the learning data. The machine learning was performed using a neural network. The neural network had one intermediate layer, the number of nodes was 500, and a sigmoid function was used as the activation function. The number of nodes in the output layer was set to 200, and the widths of the rough rolled material were predicted at 200 positions divided in the longitudinal direction.

[0063] The longitudinal width distribution of the rough rolled material predicted in this embodiment is shown in Figures 14 and 15. Figures 14 and 15 show a comparison between the actual data of the longitudinal width distribution of the rough rolled material included in the test data and the information of the longitudinal width distribution of the rough rolled material predicted by the width prediction model M described above. The vertical axis of the graph indicates the deviation from the target value of the width set in the rough width control. The prediction result shown in Figure 14 and the prediction result shown in Figure 15 have different operating conditions of the width reduction press device 4 and the rough rolling mill 5. From both results, it was confirmed that the longitudinal width distribution of the rough rolled material can be accurately predicted, including the effects of width drops formed at the front or tail end of the rough rolled material and intermittent press marks formed in the normal part. From the 10,000 test data, 100 data sets in which the steel type of the slab and the dimensions of the hot-rolled steel sheet after finish rolling are in the same category were selected, and the actual data on the longitudinal width distribution of the rough rolled material contained therein was evaluated. The deviation from the target width to the total length of the rough rolled material was 5.5 mm on average, and the standard deviation was 5.0 mm. For rough rolled material having such width fluctuations, the width prediction model M generated in this embodiment was able to predict the deviation from the target width to the total length of the rough rolled material with an average accuracy of 0.0 mm and a standard deviation of 1.5 mm.

[0064] Example 2 In this example, a slab having a thickness of 230 to 235 mm and a width of 600 to 2000 mm was heated in the heating furnace 2 by the same hot rolling line as in Example 1 to produce a steel plate having a rough rolled material thickness (rough bar thickness) of 30 to 35 mm and a plate thickness of 2 to 3 mm at the exit of the finishing rolling mill. In this example, in addition to the operation parameters used in Example 1, the operation parameters of the heating furnace 2 were used as input data for the width prediction model M. As the operation parameters of the heating furnace 2, the slab temperature at the time of charging into the heating furnace, the residence time in the heating furnace 2, the charging position of the slab in the heating furnace 2, the charging interval between the slab and the preceding slab in the heating furnace 2, and the charging interval between the slab and the succeeding slab in the heating furnace 2 were selected. Then, similarly to Example 1, the data acquisition unit 103 acquires the operation performance data of the width reduction press device 4, the operation performance data of the rough rolling mill 5, and the performance data of the slab attribute information, as well as the operation performance data of the heating furnace 2, and accumulates the data set in the database unit 101. In this case, 30,000 data sets are accumulated in the database unit 101, and the accumulated data sets are divided into 20,000 pieces of learning data and 10,000 pieces of test data, and the width prediction model M is generated using the learning data. The machine learning method used in the machine learning unit 102 is a neural network, and its configuration is the same as that of Example 1. The longitudinal width distribution of the rough rolled material predicted in this example is shown in FIG. 16. As shown in FIG. 16, the characteristics of the width distribution of the rough rolled material caused by the skid marks formed in the heating furnace 2 were also predicted, and the actual measurement data could be predicted with high accuracy.

[0065] Example 3 In this example, the prediction accuracy of the width distribution when the input data of the width prediction model M was changed was evaluated using the data set accumulated in the database unit 101 in Example 1. In addition to the operation record data of the width reduction press device 4 and the rough rolling mill 5 used in the input data of the width prediction model M in Example 1, the database unit 101 also accumulated operation record data of other width reduction press devices 4 and rough rolling mills 5. In this example, the 30,000 data sets accumulated in the database unit 101 were divided into 20,000 pieces of learning data and 10,000 pieces of test data, and the prediction accuracy for the test data was evaluated using the width prediction model M generated using the learning data. Machine learning was performed using a neural network. The intermediate layer of the neural network was set to one layer, the number of nodes was set to 500, and a sigmoid function was used as the activation function. The number of nodes in the output layer was set to 200, and the width was predicted at positions divided into 200 in the longitudinal direction of the rough rolled material. Table 1 below shows operational performance data used as input for the width prediction model M in this embodiment.

[0066] In Example 1, the width reduction amount and feed pitch in the steady state of the slab were obtained as representative values ​​as the operation performance data of the width reduction press device 4. As the operation performance data of the roughing mill 5, the set value of the edger opening and the edger roll diameter in the steady state of the rolled material in all rolling passes of rough rolling were used as representative values ​​as the operation performance data of the edger 52. In addition, as the operation performance data of the roughing mill 5, the set values ​​of the entry side plate width and the exit side plate width of the horizontal rolling mill in the steady state of the rolled material in all rolling passes of rough rolling and the work roll diameter of the horizontal rolling mill 51 were used as representative values ​​as the operation performance data of the horizontal rolling mill 5.

[0067] In Example 2, the width reduction amount and feed pitch at the leading end of the slab were obtained as representative values ​​for the operation performance data of the width reduction press device 4. The operation performance data of the roughing mill 5 was the same as that of Example 1.

[0068] In Example 3, the same operation performance data as in Example 1 was used for the width reduction press device 4. For the operation performance data of the roughing mill 5, the edger opening and edger roll diameter set for the tip of the rolled material in all rolling passes of rough rolling were used as representative values ​​for the edger 52. For the operation performance data of the roughing mill 5, the set values ​​of the entry and exit plate widths of the horizontal rolling mill at the steady part of the rolled material in all rolling passes of rough rolling and the work roll diameter of the horizontal rolling mill 51 were used as representative values ​​for the operation performance data of the horizontal rolling mill 51.

[0069] In this embodiment, using the width prediction model M generated as described above, 100 data sets in which the steel type of the slab and the dimensions of the hot-rolled steel sheet after finish rolling are in the same category were selected from 10,000 test data, and the prediction accuracy of the width distribution of those data sets was evaluated. Specifically, for the width at each of the positions divided into 200 in the longitudinal direction of the rough rolled material, the deviation between the predicted width by the width prediction model M and the actual width was calculated, and the average value and standard deviation of the calculated deviations were obtained. Table 1 shows the results of the prediction accuracy of the width distribution.

[0070] As shown in Table 1, in Example 1, the average value of the width deviation relative to the entire length of the rough rolled material was 0.0 mm, and the standard deviation was 1.7 mm, and it was confirmed that high prediction accuracy could be obtained for the entire length of the rough rolled material. In Example 2, the average value of the width deviation relative to the entire length of the rough rolled material was 0.0 mm, and the standard deviation was 2.3 mm. In Example 2, the width reduction amount and feed pitch at the front end of the slab, which are operation performance data of the width reduction press device 4, were used as inputs to the width prediction model M. In this case, it was confirmed that even if the width reduction amount and feed pitch in the width reduction press device are set differently at the front end and the steady part of the slab, it is possible to predict the width distribution with relatively high accuracy. In Example 3, the average value of the width deviation relative to the entire length of the rough rolled material was 0.0 mm, and the standard deviation was 2.6 mm. In Example 3, the edge opening set for the front end of the rolled material in all rolling passes of rough rolling was used as inputs to the width prediction model M. In this case, it was confirmed that even if the edger opening was set differently at the leading edge and the steady portion of the slab, it was possible to predict the width distribution with relatively high accuracy.

[0071] [Table 1]

[0072] 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]

[0073] According to the present invention, a rough rolled material width prediction method capable of accurately predicting the longitudinal width distribution of the rough rolled material can be provided. Also, according to the present invention, a rough rolled material width control method capable of accurately controlling the longitudinal width of the rough rolled material can be provided. Also, according to the present invention, a hot rolled steel sheet manufacturing method capable of manufacturing a hot rolled steel sheet with excellent longitudinal width accuracy can be provided. Also, according to the present invention, a rough rolled material width prediction model generation method capable of generating a width prediction model that accurately predicts the longitudinal width distribution of the rough rolled material can be provided. [Explanation of symbols]

[0074] 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 Pass Line 15a, 15b, 15c, 15d Camera 21 Thermometer 22 Walking beam (fixed skid) 23 Moving Skid 41 Width reduction die 41a Parallel part 41b Slope 42 Drive unit 43 Pinch 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 Slab SB steel plate

Claims

1. A method for predicting the width of a rough rolled material in a hot rolling line including a heating furnace for heating a slab, a width reduction press device for intermittently reducing the width of the heated slab, a rough rolling mill for rough rolling the slab after width reduction to produce a rough rolled material, and a finish rolling mill for finish rolling the rough rolled material to produce a hot rolled steel plate. The method includes a step of predicting a width distribution in the longitudinal direction of the rough rolled material using a width prediction model learned by machine learning, the input data including one or more operation parameters selected from the operation parameters of the width reduction press device and one or more operation parameters selected from the operation parameters of the rough rolling mill, and the output data including information on the width distribution in the longitudinal direction of the rough rolled material; The method for predicting the width of a rough rolled material, wherein the operation parameters of the width reduction press device and the operation parameters of the rough rolling mill are operation parameters at one representative position in the longitudinal direction of the rough rolled material, and the operation parameters of the width reduction press device include a width reduction amount and a feed pitch of the rough rolled material.

2. The method of claim 1 , wherein the width prediction model includes, as the input data, one or more operational parameters selected from operational parameters of the heating furnace.

3. The method for predicting width of a rough rolled material according to claim 1 , wherein the width prediction model includes, as the input data, one or more attribute parameters selected from attribute information of the slab.

4. A method for controlling the width of a rough rolled material, comprising: predicting a longitudinal width distribution of the rough rolled material using the width prediction method of the rough rolled material according to any one of claims 1 to 3; and setting operation parameters of the rough rolling mill based on the predicted width distribution.

5. A method for producing a hot-rolled steel sheet, comprising the steps of: performing finish rolling on a rough rolled material whose width has been controlled using the method for controlling the width of the rough rolled material according to claim 4, by using the finish rolling mill to produce a hot-rolled steel sheet.

6. A method for generating a width prediction model of a rough rolled material in a hot rolling line including a heating furnace for heating a slab, a width reduction press device for intermittently reducing the width of the heated slab, a rough rolling mill for rough rolling the slab after width reduction to produce a rough rolled material, and a finishing rolling mill for finish rolling the rough rolled material to produce a hot rolled steel plate. The method includes a step of acquiring a plurality of learning data, the input data including one or more operation performance data selected from the operation parameters of the width reduction press device and one or more operation performance data selected from the operation parameters of the rough rolling mill, and outputting information on the width distribution in the longitudinal direction of the rough rolled material as the output performance data, and generating the width prediction model by machine learning using the acquired plurality of learning data; A method for generating a width prediction model for rough rolled material, wherein the operational performance data of the width reduction press device and the operational performance data of the rough rolling mill are operational performance data at one representative position in the longitudinal direction of the rough rolled material, and the operational performance data of the width reduction press device includes performance data on the width reduction amount and feed pitch of the rough rolled material.

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