Method for predicting cross-sectional dimension of shaped steel, manufacturing method of shaped steel, device for predicting cross-sectional dimension of shaped steel, and method for generating cross-sectional dimension prediction model

Abstract

The present invention provides a method for predicting the cross-sectional dimensions of shaped steel that can be applied to shaped steel of various cross-sectional dimensions, not limited to preceding rolled material and following rolled material that are continuously hot rolled, as long as the shaped steel is produced by hot rolling with the same roll set. A method for predicting the cross-sectional dimensions of structural steel to be manufactured by hot rolling, comprising: a cross-sectional dimension prediction model that takes as input the difference in rolling operation parameters for two structural steels manufactured by hot rolling with the same roll set; and outputs the difference in the cross-sectional dimension deviation of the two structural steels; the difference between the rolling operation parameters of a structural steel to be a predecessor material manufactured by hot rolling with the same roll set and the rolling operation parameters of a structural steel to be manufactured by hot rolling with the same roll set is input into the model; the difference in the cross-sectional dimension deviation is the deviation between the target dimension and actual dimension of the structural steel; and the cross-sectional dimension of the structural steel to be manufactured is predicted using the output difference in cross-sectional dimension deviation and the cross-sectional dimension of the structural steel to be the predecessor material.

Description

[Technical field]

[0001] The present invention relates to a method for predicting the cross-sectional dimension of a sectional steel having a web and a flange, a manufacturing method for a sectional steel, a device for predicting the cross-sectional dimension of a sectional steel, and a method for generating a cross-sectional dimension prediction model. [Background technology]

[0002] Section steel with webs and flanges is mainly manufactured by hot rolling, where heated raw steel billets are rolled to the target product dimensions using multiple rolling mills. In the rough rolling process, multiple passes are performed using a rough rolling mill with multiple grooves to roughly shape the raw steel billets to approximate the shape of the product. In the subsequent intermediate rolling process, multiple passes are performed using one or more intermediate universal rolling mills and one or more edging rolling mills to bring the outer dimensions and thickness closer to the target product dimensions. In the final finishing rolling process, a finishing universal rolling mill usually performs one pass of rolling to roll the billets to the target dimensions.

[0003] The cross-sectional dimensions of H-beams, a type of shaped steel, have representative values ​​and dimensional tolerance ranges for dimensions such as web height H, flange width B, web thickness tw, and flange thickness tf, and all cross-sectional dimensions must be within the dimensional tolerance range (within the target dimension range). Therefore, the rolling conditions are adjusted to ensure that the cross-sectional dimensions after hot rolling are within the dimensional tolerance range. Specifically, each dimension of the cross-section of the rolled shaped steel is measured, and if any dimension deviates from the dimensional tolerance, the roll gap of each rolling mill is adjusted so that the dimension falls within the dimensional tolerance range.

[0004] As a method for controlling the dimensions of shaped steel, Patent Document 1 discloses a method for controlling the thickness of H-shaped steel, in which the dimensions of hot-rolled shaped steel are measured, and the amount of roll correction of the horizontal roll and the vertical roll is made substantially the same between successive passes including the final pass of rough universal rolling. According to Patent Document 1, the thickness of the H-shaped steel can be controlled by predetermining the correspondence between the web thickness and flange thickness and the amount of roll correction, measuring the web thickness and flange thickness to determine the deviation from the target value, and adding the amount of roll correction to the setup calculation for the next material. Patent Document 2 describes a method for controlling the thickness of H-shaped steel, which specifically specifies the method of adjusting the roll position to eliminate the variation in flange thickness at four points.

[0005] Patent Document 3 discloses the following technology for continuously manufacturing shaped steel with the same cross-sectional dimensions. According to Patent Document 3, rolling operation parameters are acquired for each of the preceding rolled material and the following rolled material of the shaped steel, and their cross-sectional dimensions are measured. Machine learning is performed using multiple data sets including the relationship between the difference in rolling operation parameters and the difference in cross-sectional dimensions of the preceding rolled material and the following rolled material. Then, a method for generating a prediction model for the cross-sectional dimension change amount of shaped steel, which generates a prediction model for predicting the change amount of the cross-sectional dimension corresponding to the correction amount of the rolling operation parameters, and a rolling control method using this model are disclosed. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 62-151214 [Patent Document 2] Japanese Patent Application Publication No. 10-296311 [Patent Document 3] Patent Publication No. 2021-194701 Summary of the Invention [Problem to be solved by the invention]

[0007] According to the techniques disclosed in Patent Documents 1 and 2, the dimensions of the shaped steel can be controlled to some extent by measuring the dimensions of the shaped steel during or after hot rolling and changing the settings of each roll to eliminate the difference from the target dimensions. However, there are a great many dimensions of the shaped steel, and it requires a great deal of effort to predict the dimensions of all of them with high accuracy. In addition, the influence of the change in material temperature during rolling due to the number of passes and the weight of the material, and the influence of the steel type and chemical components on the rolling load are not taken into consideration, so there is a problem with the accuracy of the dimensional control. In addition, the technique disclosed in Patent Document 3 is effective only when shaped steel with the same cross-sectional dimensions are continuously rolled, and there is a problem that it cannot handle the first cross-sectional change when rolling shaped steel with various cross-sections with the same roll set. In addition, there is also a problem that it takes a long time to accumulate actual data that serves as learning data for generating a prediction model.

[0008] The present invention has been made in consideration of the problems of the conventional technology, and has an object to provide a method and an apparatus for predicting the cross-sectional dimension of a shaped steel that can be applied to shaped steel having various cross-sectional dimensions, so long as the shaped steel is manufactured by hot rolling with the same roll set. Another object of the present invention is to provide a method for manufacturing shaped steel using the method for predicting the cross-sectional dimension of a shaped steel, and a method for generating a cross-sectional dimension prediction model used in the method for predicting the cross-sectional dimension of a shaped steel. [Means for solving the problem]

[0009] The means for solving the above problems are as follows. [1] A method for predicting the cross-sectional dimensions of structural steel to be manufactured by hot rolling, comprising: a cross-sectional dimension prediction model which takes as input the difference in rolling operation parameters for two structural steels manufactured by hot rolling with the same roll set; and outputs the difference in the cross-sectional dimension deviation of the two structural steels; the difference between the rolling operation parameters of a structural steel to be a precursor material manufactured by hot rolling with the same roll set and the rolling operation parameters of a structural steel to be manufactured by hot rolling with the same roll set is input to the model; and the difference in the cross-sectional dimension deviation is output, the cross-sectional dimension deviation being the deviation between the target dimension and actual dimension of the structural steel; and the cross-sectional dimension of the structural steel to be manufactured is predicted using the output difference in cross-sectional dimension deviation and the cross-sectional dimension of the structural steel to be the precursor material. [2] The method for predicting cross-sectional dimensions of structural steel as described in [1], wherein the structural steel is produced by hot rolling a steel slab using a roughing mill, an intermediate rolling mill and a finishing rolling mill, and the rolling operation parameters include the weight of the steel slab, a correction amount from a reference position of the rolling rolls of the intermediate rolling mill, the number of passes through the roughing mill and the intermediate rolling mill, and the rolling time of the roughing mill and the intermediate rolling mill. [3] A method for predicting the cross-sectional dimensions of structural steel as described in [1] or [2], wherein the input of the cross-sectional dimension prediction model includes an attribute parameter indicating the steel type classification of the structural steel. [4] A method for predicting the cross-sectional dimensions of a structural steel as described in any of [1] to [3], wherein the cross-sectional dimension is at least one of the cross-sectional dimensions of the web thickness of the structural steel, the four flange thicknesses at the top, bottom, left and right, the web height and the flange width. [5] A method for manufacturing structural steel, comprising: identifying differences in rolling operation parameters that result in the predicted cross-sectional dimensions of the structural steel being within the range of target dimensions using a method for predicting the cross-sectional dimensions of structural steel described in any one of [1] to [4]; and manufacturing the structural steel under manufacturing conditions that include rolling operation parameters determined from the identified differences in the rolling operation parameters. [6] A cross-sectional dimension prediction device for structural steel that predicts the cross-sectional dimensions of structural steel to be manufactured by hot rolling, comprising: a cross-sectional dimension prediction model that takes as input the difference in rolling operation parameters for two structural steels manufactured by hot rolling with the same roll set and outputs the difference in cross-sectional dimension deviation of the two structural steels; the cross-sectional dimension prediction model inputs the difference between the rolling operation parameters of a structural steel that is a precursor material manufactured by hot rolling with the same roll set and the rolling operation parameters of a structural steel that is manufactured by hot rolling with the same roll set, and outputs the difference in cross-sectional dimension deviation, the cross-sectional dimension deviation being the deviation between the target dimension and actual dimension of the structural steel; and the cross-sectional dimension prediction device has a cross-sectional dimension prediction unit that predicts the cross-sectional dimension of the structural steel to be manufactured using the output difference in cross-sectional dimension deviation and the actual value of the cross-sectional dimension of the structural steel that is the precursor material. [7] A method for generating a cross-sectional dimension prediction model, comprising: training a machine learning model using multiple data sets as training data, each set being a set of actual values ​​of the difference in rolling operation parameters for two structural steels manufactured by hot rolling with the same roll set and actual values ​​of the difference in cross-sectional dimension deviation of the two structural steels; generating a cross-sectional dimension prediction model that uses the difference in rolling operation parameters for the two structural steels as input and the difference in the cross-sectional dimension deviation of the two structural steels as output; and using the deviation between the target dimension and the actual dimension of the structural steel as the cross-sectional dimension deviation. Effect of the Invention

[0010] According to the present invention, by using the deviation between the target dimension and the actual dimension as the cross-sectional dimension deviation of the shaped steel, a cross-sectional dimension prediction model can be applied to shaped steel with various cross-sectional dimensions, so long as the shaped steel is manufactured by hot rolling. This makes it possible to apply the model to any two sets of shaped steel, not limited to the preceding rolled material and the following rolled material that are continuously hot rolled, so that the cross-sectional dimension of the shaped steel can be predicted even for the first shaped steel after the cross-section change. Furthermore, since the actual value of the deviation of the cross-sectional dimensions of any two sets of shaped steel hot rolled by the same roll set can be used as learning data, a large amount of learning data for generating the cross-sectional dimension prediction model can be accumulated in a short time. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic cross-sectional view showing the cross-sectional shape of an H-shaped steel. [Diagram 2] FIG. 2 is a schematic diagram showing an example of a hot rolling facility including a section dimension prediction device for section steel, in which the section dimension prediction method for section steel according to this embodiment can be implemented. [Diagram 3] FIG. 3 is a functional block diagram of a section steel cross-sectional dimension prediction device. [Figure 4] FIG. 4 is a flow diagram showing the flow of rolling operation parameter specification processing by the rolling operation parameter specification unit. [Diagram 5] FIG. 5 is a graph showing the relationship between the number of pieces of learning data and the average value of RMSE for each dimension of H-section steel. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the embodiment shown below is an example of an apparatus and method for embodying the technical concept of the present invention, and the technical concept of the present invention is not limited to the quality, shape, structure, arrangement, etc. of the components of the embodiment described below.

[0013] First, the cross-sectional shape of an H-shaped steel, which is an example of shaped steel, will be described with reference to FIG. 1. FIG. 1 is a schematic cross-sectional view showing the cross-sectional shape of an H-shaped steel 10. As shown in FIG. 1, the portion of the H-shaped steel 10 that is relatively thick is the flange 12, and the portion that is relatively thin and is connected to the pair of flanges 12 is the web 14. There are many combinations of cross-sectional dimensions of the H-shaped steel 10, such as the thickness tf and width B of the flange 12, and the thickness tw and height H of the web 14, ranging from several tens for constant inside dimension H-shaped steel (JISH) to several hundreds for constant outside dimension H-shaped steel. Here, the outside dimension is the web height H of the H-shaped steel 10, and the inside dimension is the dimension obtained by subtracting the flange thickness from the outside dimension.

[0014] In addition, the strength classification of H-shaped steel 10 can be broadly divided into tensile strength of 400N / mm 2 class, 490N / mm 2The H-shaped steel 10 is divided into three types: 40k steel, 50k steel, and above. The chemical composition of the H-shaped steel 10 is adjusted for each steel type classification (40k steel, 50k steel, and above) according to this strength classification. Such H-shaped steel 10 is mainly manufactured by hot rolling. H-shaped steel 10 manufactured by hot rolling is called rolled H-shaped steel.

[0015] Hereinafter, an example in which this embodiment is applied to an H-shaped steel, which is an example of shaped steel, will be described. Fig. 2 is a schematic diagram showing an example of a hot rolling facility 100 including a shaped steel cross-sectional dimension prediction device in which the shaped steel cross-sectional dimension prediction method according to this embodiment can be implemented. H-shaped steel is manufactured by rough rolling, intermediate rolling, and finish rolling using multiple rolling mills on a steel slab preheated in a heating furnace 20, and shaping it into shaped steel of a target dimension.

[0016] In the rough rolling process by the roughing mill 22, about 5 to 30 passes of reverse rolling are performed using grooved rolls. The intermediate rolling mill 24 is often a combination of one or more intermediate universal rolling mills 26 and edger rolling mills 28. In the intermediate rolling process, about 5 to 30 passes of reverse rolling are performed, and the material is rolled to a state that is roughly close to the cross-sectional shape of the final product. In the finish rolling process by the finish rolling mill 30, usually one pass of rolling is performed, and the material is formed into the target thickness and cross-sectional shape.

[0017] The intermediate universal rolling mill 26, which is one of the intermediate rolling mills 24, is a rolling mill having a total of four rolls: upper and lower horizontal rolls that are driven to rotate about a horizontal axis, and left and right vertical rolls that rotate freely about a vertical axis.

[0018] The diameter of the upper and lower horizontal rolls is about 1000 to 1500 mm, and the width of the upper and lower horizontal rolls is appropriately adjusted according to the web height H of the H-shaped steel to be rolled, and is installed in the rolling mill. The diameter of the left and right vertical rolls is about 600 to 1000 mm. These upper and lower horizontal rolls and left and right vertical rolls are each structured so that their positions can be adjusted by a reduction device, and the distance between the upper and lower horizontal rolls and the distance between the side of the horizontal rolls and the vertical rolls can be set as desired. The intermediate universal rolling mill 26 has the function of simultaneously reducing the web thickness tw and the flange thickness tf, and is characterized by rolling with the flange 12 inclined outward by a maximum of about 10°.

[0019] The edger rolling machine 28, which is one of the intermediate rolling machines 24, has two horizontal rolls (hereinafter, these rolls will be referred to as E1 rolls) with grooves, one above the other, and adjusts the flange width B by pressing down the tip of the flange 12 from above and below with the E1 rolls. The diameter of the E1 rolls is about 800 to 1200 mm, and both of the E1 rolls are driven.

[0020] A finish universal rolling mill is used as the finishing rolling mill 30. In the finishing rolling mill 30, the intermediate rolled rolled material is finished into the product cross-sectional shape in one pass. The dimensions and configuration of the rolls of the finish universal rolling mill are the same as those of the intermediate universal rolling mill 26, but in the finish universal rolling mill, rolling is performed so that the flange 12 is perpendicular to the web 14.

[0021] The cross-sectional dimensions of the H-shaped steel formed and manufactured by the above-mentioned hot rolling are measured in the hot state by a hot dimensioner 32 provided downstream of the finishing mill 30. The hot dimensioner 32 measures the web height H of the H-shaped steel, the left and right flange widths B, the web thickness tw, and the flange thicknesses tf at four positions, top, bottom, left and right. It is preferable that the cross-sectional dimensions are measured at as small intervals as possible over the entire longitudinal length by the hot dimensioner 32. In addition, the measured cross-sectional dimensions are measured in the hot state, and when cooled to room temperature, the dimensions change due to thermal contraction. For this reason, it is preferable to measure the temperature of the H-shaped steel in or near the hot dimensioner 32, predict the thermal contraction due to cooling using the temperature and the linear expansion coefficient, and convert it into the product dimensions at room temperature. It is preferable that the actual dimensions used for predicting and controlling the cross-sectional dimensions are, for example, the average values ​​of the cross-sectional dimensions measured over the entire length. Furthermore, the actual dimensions may be the average value of the cross-sectional dimensions excluding a few meters from the ends, which are the unsteady parts of the tip and tail ends, or the average value of one or more cross-sectional dimensions at specific positions, or even the cross-sectional dimension at one specific position, such as the center of the length.

[0022] The process computer 36 can be a general-purpose computer such as a workstation or a personal computer. The process computer 36 is connected to the heating furnace 20, the roughing mill 22, the intermediate mill 24, the finishing mill 30, and the hot dimension gauge 32 by wire or wirelessly, and controls the manufacturing process of the H-shaped steel by the hot rolling equipment 100. The process computer 36 also acquires attribute parameters of the H-shaped steel to be manufactured from a higher-level computer. The attribute parameters include information on the target dimensions of the H-shaped steel (web height H, left and right flange widths B, web thickness tw, and flange thicknesses tf at four locations (top, bottom, left, and right)), steel type classification (40k steel, 50k steel, and three types higher than this), chemical composition (contents of C, Si, Mn, Cr, Mo, V, etc.), and target values ​​of mechanical properties (yield stress, tensile strength, elongation, toughness, hardness, etc.).

[0023] The process computer 36 sets rolling operation parameters such as the number of passes and roll intervals for each rolling mill in accordance with the attribute information of the H-beam. The set values ​​of normal rolling operation parameters are set as table values ​​associated with the attribute parameters of the H-beam based on past rolling results.

[0024] The process computer 36 sets the correction value of the horizontal roll gap from the reference condition in each pass in the roughing mill 22 and the correction value of the axial relative position of the horizontal roll as the rolling operation parameters of the rolling process. In addition, the process computer 36 sets the correction amount of the horizontal roll gap from the reference condition in the intermediate universal rolling mill 26, the correction amount of the thrust position of the horizontal roll, the correction amount of the horizontal roll height, the correction amount of the vertical roll gap from the reference condition, and the center correction amount of the vertical roll, as well as the correction amount of the E1 roll gap from the reference condition in the edger rolling mill 28, the leveling correction amount of the E1 roll, and the correction amount of the thrust relative position of the E1 roll as the rolling operation parameters of the rolling process. In addition, the process computer 36 sets the correction amount of the horizontal roll gap from the reference condition in the finishing mill 30, the correction amount of the thrust position of the horizontal roll, the correction amount of the horizontal roll height, the correction amount of the vertical roll gap from the reference condition, and the center correction amount of the vertical roll.

[0025] The process computer 36 outputs the set rolling operation parameters to the roughing mill 22, the intermediate universal mill 26, the edger mill 28, and the finishing mill 30. Each rolling mill controls the roll gap, roll position, etc. of each rolling mill in accordance with the acquired rolling operation parameters.

[0026] The process computer 36 also collects the actual dimensions of the H-beam measured by the hot dimension gauge 32. The process computer 36 stores in a database of the process computer 36 the target dimensions of the manufactured H-beam, the steel type classification, the chemical composition, the target characteristics, the cross-sectional dimensions of the billet, the weight of the billet, the rolling operation parameters, the actual dimensions, and an identification number for identifying the roll set used, in association with the production lot number of the H-beam.

[0027] The section dimension prediction device 38 acquires the rolling operation parameters of the H-shaped steel, which will be the precursor material that has been hot rolled with the same roll set as the H-shaped steel to be manufactured, from the process computer 36. Then, the difference between the rolling operation parameters of the H-shaped steel to be manufactured and the rolling operation parameters of the precursor material is input to the section dimension prediction model, and the difference in the section dimension deviation of the H-shaped steel is output to predict the section dimension of the H-shaped steel to be manufactured.

[0028] In hot rolling of H-shaped steel, a plurality of H-shaped steels having a target cross-sectional dimension, in which the web height H or the web inner width and the flange width B are almost equal, are rolled continuously with the same roll set. For this reason, there are many cases where H-shaped steels having the same web height H and flange width B but changed web thickness tw and flange thickness tf are manufactured with the same roll set, and when changing the cross-sectional dimension, the dimensional tolerance may be removed. When the dimension is close to the upper or lower limit of the dimensional tolerance, the rolling operation parameters are changed, not only when the dimensional tolerance is removed, but also when the dimension is within the dimensional tolerance. In this way, the cross-sectional dimension prediction device 38 of the shaped steel according to this embodiment is used to predict the cross-sectional dimension of the H-shaped steel when the rolling operation parameters are changed while rolling with the same roll set. Note that "hot rolling with the same roll set" means that all rolls used in hot rolling (rough rolling, intermediate rolling, and finish rolling) are rolled without rearrangement or replacement.

[0029] In the section dimension prediction device 38 of this embodiment, the deviation between the target dimension and the actual dimension in the cross section of the H-shaped steel is used as the predicted section dimension. This makes the section dimension prediction model a model that can predict the influence of changes in rolling operation parameters on the deviation from the target dimension of the H-shaped steel, and therefore the model can be applied to various types of hot-rolled steel with different section dimensions.

[0030] In this way, the cross-sectional dimension prediction model used in the section dimension prediction device 38 of the section steel according to this embodiment can be applied to various section steels with different cross-sectional dimensions. This makes it possible to apply it to any two H-shaped steels hot rolled by the same roll set, not limited to the preceding rolled material and the following rolled material that are hot rolled consecutively. As a result, it becomes possible to predict the cross-sectional dimension of the first H-shaped steel produced by hot rolling after changing the cross-sectional dimension. Furthermore, since the actual values ​​of the difference between the rolling operation parameters of any two H-shaped steels and the actual values ​​of the difference between the cross-sectional dimension deviations can be used as learning data for the cross-sectional dimension prediction model, a large amount of learning data for generating the cross-sectional dimension prediction model can be accumulated in a short time.

[0031] For example, when a cross-sectional dimension prediction model is generated using operational performance data of 100 H-shaped steels manufactured by hot rolling with the same roll set, the maximum number of sets of learning data that can be obtained from two H-shaped steels that are continuously rolled as disclosed in Patent Document 3 is 99 sets. In contrast, when any two H-shaped steels are selected from the operational performance data of 100 H-shaped steels, a maximum of 4950 sets of learning data can be obtained, and the number of learning data obtained from the operational performance data is significantly increased. When a cross-sectional dimension prediction model is generated using operational performance data of 100 H-shaped steels manufactured by hot rolling with two different roll sets, the difference in the operational performance data and the difference in the cross-sectional dimension deviation are limited to data from the same roll set. However, these can be used as learning data for the same cross-sectional dimension prediction model, so the number of learning data is 4950 sets x 2.

[0032] Next, a section dimension prediction device 38 for predicting the section dimension of a section steel will be described. FIG. 3 is a functional block diagram of the section dimension prediction device 38 for a section steel. The section dimension prediction device 38 for a section steel can be a general-purpose computer such as a workstation or a personal computer. The section dimension prediction device 38 for a section steel has a control unit 40, an input unit 42, an output unit 44, and a storage unit 46. The control unit 40 is, for example, a CPU, and functions as a data acquisition unit 50, a difference calculation unit 52, a section dimension prediction unit 54, a rolling operation parameter identification unit 56, and a section dimension prediction model generation unit 58 by executing a program stored in the storage unit 46.

[0033] The input unit 42 is, for example, a keyboard, a touch panel integrally provided with a display, or the like. The output unit 44 is, for example, an LCD or CRT display, or the like. The storage unit 46 is, for example, an information recording medium such as an updatable flash memory, a built-in hard disk or a memory card connected via a data communication terminal, and a read / write device thereof. The storage unit 46 stores programs and data for realizing each function of the section dimension prediction device 38 of section steel. The storage unit 46 further stores a database 60 and a section dimension prediction model 62. The database 60 stores 10,000 or more sets of learning data sets each consisting of a difference in rolling operation parameters of H-beam steel and a difference in section dimension deviation for each roll set of the hot rolling equipment 100. The number of learning data sets stored in the database 60 is preferably 20,000 or more sets, and more preferably 50,000 or more sets.

[0034] The cross-sectional dimension prediction model 62 is a trained machine learning model that has been trained using the learning data stored in the database 60 as teacher data. The cross-sectional dimension prediction model 62 according to this embodiment is a trained machine learning model that receives the difference in the rolling operation parameters as input and outputs the difference in the cross-sectional dimension deviation of the H-beam 10, and uses the deviation between the target dimension and the actual dimension as the cross-sectional dimension deviation. The difference in the cross-sectional dimension deviation that is the output of the cross-sectional dimension prediction model 62 is the difference in at least one cross-sectional dimension deviation selected from seven cross-sectional dimensions of the H-beam, namely the web height H, flange width B, web thickness tw, and flange thicknesses tf at four locations on the top, bottom, left, and right.

[0035] Next, the processing executed by the data acquisition unit 50, difference calculation unit 52, and cross-sectional dimension prediction unit 54 when predicting the cross-sectional dimension of the H-shaped steel 10 to be manufactured will be described. The data acquisition unit 50 acquires from the process computer 36 the rolling operation parameters of the H-shaped steel to be manufactured, the target dimensions of the cross-sectional dimensions of the H-shaped steel to be manufactured, and the rolling operation parameters, target dimensions, and actual dimensions of a preceding steel beam manufactured in the past with the same roll set. The data acquisition unit 50 outputs the acquired rolling operation parameters to the difference calculation unit 52, and outputs the acquired target dimensions and actual dimensions to the cross-sectional dimension prediction unit.

[0036] The difference calculation unit 52 calculates the difference between the rolling operation parameters of the H-shaped steel to be manufactured and the rolling operation parameters of a preceding H-shaped steel manufactured in the past with the same roll set. The difference calculation unit 52 outputs the calculated difference in the rolling operation parameters to the cross-sectional dimension prediction unit 54. It is preferable to select a preceding steel section that is as close in time as possible to the H-shaped steel to be manufactured. This makes it possible to reduce the influence of parameters that change over time, such as roll wear, and that are not included in the input.

[0037] When the cross-sectional dimension prediction unit 54 acquires the difference in the rolling operation parameters, it reads out the cross-sectional dimension prediction model 62 from the storage unit 46. The cross-sectional dimension prediction unit 54 inputs the difference in the rolling operation parameters to the cross-sectional dimension prediction model 62, and outputs the difference in the cross-sectional dimension deviation between the H-shaped steel to be manufactured and the H-shaped steel to be the preceding material manufactured in the past by the same roll set. In the cross-sectional dimension prediction device 38 of the shaped steel according to the present embodiment, the deviation between the target dimension and the actual dimension is used as the cross-sectional dimension. Therefore, the difference in the cross-sectional dimension deviation to be output is the sum of the difference in the target dimension between the H-shaped steel to be the preceding material manufactured in the past and the H-shaped steel to be manufactured, and the difference in the actual dimension between the H-shaped steel to be manufactured and the H-shaped steel to be the preceding material manufactured in the past. Of the difference in the target dimension and the difference in the actual dimension, the target dimension, the actual dimension, and the target dimension of the H-shaped steel to be manufactured of the preceding material manufactured in the past are acquired from the data acquisition unit 50. Therefore, the cross-sectional dimension prediction unit 54 can predict the cross-sectional dimensions of the H-shaped steel to be manufactured by using the difference between the predicted cross-sectional dimensions and the above-mentioned known dimensions. The cross-sectional dimension prediction unit 54 may output the predicted cross-sectional dimensions of the H-shaped steel to be manufactured to the output unit 44, and display the predicted cross-sectional dimensions of the steel on the output unit 44. This allows the operator to confirm the predicted cross-sectional dimensions of the H-shaped steel by visually checking the output unit 44.

[0038] It is preferable that the data acquisition unit 50 acquires from the process computer 36 the rolling operation parameters to be input into the cross-sectional dimension prediction model 62, such as the weight of the steel slab, the correction amount from the standard conditions of the rolling rolls of the intermediate universal rolling mill 26 and the edger rolling mill 28 that constitute the intermediate rolling mill, and the number of passes and rolling time in the roughing mill 22 and the intermediate rolling mill 24.

[0039] Here, the correction amounts from the standard conditions of the rolling rolls of the intermediate universal rolling mill 26 refer to the correction amount from the standard conditions of the horizontal roll gap of the intermediate universal rolling mill 26, the correction amount of the thrust position of the horizontal roll, the correction amount of the horizontal roll height, the correction amount from the standard conditions of the vertical roll gap, and the difference in the gap between the left and right vertical rolls. Also, the correction amount from the standard conditions of the rolling rolls of the edger rolling mill 28 refers to the correction amount from the standard conditions of the E1 roll gap in the edger rolling mill 28.

[0040] In intermediate rolling, the roughly rolled material is rolled in multiple passes until it roughly assumes the cross-sectional shape of the product. Therefore, the amount of correction of the roll gap and position in intermediate rolling greatly affects the cross-sectional dimensions of the H-beam. For this reason, it is preferable to include a correction amount from the reference conditions of the rolling rolls of the intermediate universal rolling mill 26 and the edger rolling mill 28 that constitute the intermediate rolling mill in the rolling operation parameters that are input to the cross-sectional dimension prediction model 62.

[0041] Furthermore, since the number of passes and rolling time in the roughing mill 22 and the intermediate rolling mill 24 also have a significant effect on the cross-sectional dimensions of the H-shaped steel, it is preferable to include the number of passes and rolling time in the roughing mill 22 and the intermediate rolling mill 24 in the rolling operation parameters that are input to the cross-sectional dimension prediction model 62.

[0042] Furthermore, since the weight of the billet, which is the raw material, also greatly affects the cross-sectional dimensions of the H-beam, it is preferable to include the weight of the billet in the rolling operation parameters that are input to the cross-sectional dimension prediction model 62. If the weight of the billet is heavy, the rolling length and rolling time will be longer, which will result in different cross-sectional temperature distributions and deformation resistance. Therefore, if the weight of the billet is different, the reduction ratio will be different even if the billet is rolled with the same roll gap, and as a result, the cross-sectional dimensions of the H-beam will change. Note that instead of the weight of the billet, the length of the billet may be included in the rolling operation parameters.

[0043] In addition, the data acquisition unit 50 preferably acquires at least one of the following rolling operation parameters from the process computer 36 as inputs to the cross-sectional dimension prediction model 62: the cross-sectional dimension of the billet, the rolling time in the finish rolling process, the transport time from the heating furnace to the roughing mill, the transport time from the roughing mill to the intermediate mill, the transport time from the intermediate mill to the finishing mill, the ambient temperature of the heating furnace 20, the time spent in the furnace, the gas flow rate in the furnace, the extraction temperature, the correction amount of the horizontal roll gap of the finishing mill 30 from the reference position, the correction amount of the vertical roll gap from the reference position, and the correction amount of the thrust position of the horizontal roll. These parameters also directly or indirectly affect the cross-sectional dimension of the H-beam, so by including these parameters in the rolling operation parameters as inputs to the cross-sectional dimension prediction model 62, it becomes possible to predict the cross-sectional dimension of the H-beam with higher accuracy.

[0044] Next, a description will be given of the processing of the rolling operation parameter specifying unit 56. The rolling operation parameter specifying unit 56 specifies rolling operation parameters that make the cross-sectional dimensions predicted by the cross-sectional dimension predicting unit 54 fall within the range of the target dimensions (within the tolerance range), and outputs the rolling operation parameters to the process computer 36 to reflect them in the manufacturing conditions of the H-beam.

[0045] Fig. 4 is a flow diagram showing the flow of the rolling operation parameter specification process by the rolling operation parameter specification unit 56. The flow shown in Fig. 4 is started, for example, by receiving an input from an operator to start the process.

[0046] First, the data acquisition unit 50 acquires performance data of H-beams rolled with the same roll set from the process computer 36 (step S101). The rolling operation parameter identification unit 56 sets rolling operation parameters for the H-beam to be manufactured (step S102). The rolling operation parameters may be set based on attribute parameters such as the target dimensions of the H-beam to be manufactured and the steel type classification. The data acquisition unit 50 and the rolling operation parameter identification unit 56 output these rolling operation parameters to the difference calculation unit 52.

[0047] The difference calculation unit 52 calculates the difference of the acquired rolling operation parameters (step S103). The difference calculation unit 52 outputs the difference of the rolling operation parameters to the cross-sectional dimension prediction unit 54. The cross-sectional dimension prediction unit 54 reads the cross-sectional dimension prediction model 62 from the storage unit 46. The cross-sectional dimension prediction unit 54 inputs the difference of the acquired rolling operation parameters to the cross-sectional dimension prediction model 62 and outputs the difference of the cross-sectional dimension deviation of the H-shaped steel, thereby predicting the cross-sectional dimension of the H-shaped steel to be manufactured (step S104). The cross-sectional dimension prediction unit 54 obtains a predicted value of the cross-sectional dimension of the H-shaped steel to be manufactured using the difference of the cross-sectional dimension deviation output from the cross-sectional dimension prediction model 62, the target dimension of the H-shaped steel rolled by the same roll, the actual dimension, and the target dimension of the H-shaped steel to be manufactured. The cross-sectional dimension prediction unit 54 outputs the cross-sectional dimension of the H-shaped steel to the rolling operation parameter identification unit 56.

[0048] The rolling operation parameter specifying unit 56 judges whether the predicted cross-sectional dimension is within the range of the target dimension (within the range of the dimensional tolerance) (step S105). The range of the target dimension may be input by the operator from the input unit 42, or may be acquired from the process computer 36 via the data acquiring unit 50.

[0049] If the predicted cross-sectional dimension is not within the range of the target dimension (step S105: No), the rolling operation parameter specifying unit 56 changes the rolling operation parameters (step S106). The rolling operation parameter specifying unit 56 returns the process to step S103, and repeatedly executes the processes of steps S103 to S106 until the cross-sectional dimension predicted in step S105 falls within the range of the target dimension.

[0050] On the other hand, if the predicted cross-sectional dimension is within the range of the target dimension (step S105: Yes), the rolling operation parameter specification unit 56 specifies the difference of the rolling operation parameters used to predict the cross-sectional dimension as the difference of the rolling operation parameters that can bring the cross-sectional dimension within the range of the target dimension (step S107). The rolling operation parameter specification unit 56 calculates the rolling operation parameters to be manufactured using the rolling operation parameters of the H-beam rolled by the same roll set obtained in step S101 and the specified difference value (step S108). By executing this process, the flow of the rolling operation parameter specification process shown in FIG. 4 is ended. The rolling operation parameter specification unit 56 outputs the calculated rolling operation parameters to the process computer 36, thereby reflecting the rolling operation parameters in the manufacturing conditions of the hot rolling facility 100.

[0051] In this way, it is possible to calculate the rolling operation parameters that can bring the cross-sectional dimensions of the H-shaped steel into the target range. By manufacturing the H-shaped steel under manufacturing conditions that include the rolling operation parameters, it becomes possible to stably manufacture H-shaped steel whose cross-sectional dimensions fall within the target range.

[0052] Next, a method for generating the cross-sectional dimension prediction model 62 used to predict the cross-sectional dimension of the H-shaped steel will be described. The data acquisition unit 50 acquires the actual values ​​of the rolling operation parameters and the cross-sectional dimensions (deviation between the target dimension and the actual dimension) of two H-shaped steels manufactured in the past with the same roll set from the process computer 36. The data acquisition unit 50 outputs the acquired actual values ​​of the rolling operation parameters and the cross-sectional dimension deviation of the two H-shaped steels to the difference calculation unit 52. The difference calculation unit 52 calculates the difference between the two rolling operation parameters and the difference between the cross-sectional dimension deviation, and stores a data set including these differences as one set in the database 60 of the storage unit 46. The number of data sets stored in the database 60 is preferably 10,000 sets or more, more preferably 20,000 sets or more, and even more preferably 50,000 sets or more. The difference between the rolling operation parameters and the difference between the cross-sectional dimension deviation are calculated using the actual values ​​of the rolling operation parameters and the cross-sectional dimensions of the steels hot-rolled with the same roll set. However, the data set having the difference as one set is not limited to H-section steel hot rolled with the same roll set, and a data set of H-section steel hot rolled with different roll sets can be used.

[0053] The cross-sectional dimension prediction model generating unit 58 reads out the machine learning model stored in advance in the storage unit 46, and trains the machine learning model using the data set stored in the database 60 as training data to generate a trained machine learning model. This trained machine learning model becomes the cross-sectional dimension prediction model. The cross-sectional dimension prediction model generating unit 58 stores the generated cross-sectional dimension prediction model 62 in the storage unit 46. The machine learning model used in this embodiment may be any of the commonly used neural network, decision tree learning, random forest, and support vector regression.

[0054] In addition, the cross-sectional dimension prediction model may be updated to a new cross-sectional dimension prediction model by, for example, performing machine learning again every month or every year. The data acquisition unit 50 acquires performance data every time an H-shaped steel is manufactured and stores it in the database 60. Since the performance data of newly manufactured H-shaped steel is stored in the database 60, the number of stored performance data increases. As the number of performance data and the number of teacher data increase, the prediction accuracy of the cross-sectional dimension prediction model improves, so that more accurate cross-sectional dimension prediction is possible. Furthermore, by using new performance data, the latest state of the hot rolling equipment 100 is reflected in the cross-sectional dimension prediction model, so that by periodically performing machine learning to update the cross-sectional dimension prediction model, it becomes possible to predict the cross-sectional dimensions with even higher accuracy.

[0055] As described above, in the cross-sectional dimension prediction model used in the section dimension prediction device 38 of the shaped steel according to this embodiment, the deviation between the target dimension and the actual dimension in the section of the H-shaped steel is used as the predicted cross-sectional dimension. As a result, the cross-sectional dimension prediction model 62 becomes a model that can predict the influence on the deviation from the target dimension, and becomes a cross-sectional dimension prediction model that can be applied to various shaped steels having different cross-sectional dimensions that are the target dimensions, if the shaped steel is hot-rolled. By using this cross-sectional dimension prediction model 62, it becomes possible to predict the cross-sectional dimension of the shaped steel even if it is the first shaped steel manufactured by hot rolling after changing the cross-sectional dimension. Furthermore, it becomes possible to use the actual values ​​of the difference between the rolling operation parameters and the actual values ​​of the difference between the cross-sectional dimension deviations of any two shaped steels hot-rolled with the same roll set as learning data. As a result, it becomes possible to accumulate a lot of learning data for generating the cross-sectional dimension prediction model of the shaped steel in a short time, and it becomes possible to predict the cross-sectional dimension with high accuracy.

[0056] Although an example has been shown in which the difference between rolling operation parameters is used as the input for the cross-sectional dimension prediction model 62 in the cross-sectional dimension prediction device 38 for shaped steel according to this embodiment, the present invention is not limited to this. It is preferable to include the steel type classification, which is an attribute parameter of shaped steel, in addition to the rolling operation parameters as the input for the cross-sectional dimension prediction model. Since the steel type classification also affects the cross-sectional dimensions of shaped steel, by including the steel type classification as the input for the cross-sectional dimension prediction model 62, it becomes possible to predict the cross-sectional dimensions with high accuracy even for shaped steels of different steel type classifications.

[0057] It is preferable to further include the chemical composition of the shaped steel as an attribute parameter of the shaped steel used as an input for the cross-sectional dimension prediction model 62. Since the chemical composition of the shaped steel also affects the cross-sectional dimensions, by including the chemical composition as an input for the cross-sectional dimension prediction model 62, it becomes possible to predict the cross-sectional dimensions with high accuracy even for shaped steels with different chemical compositions.

[0058] In the description of the present embodiment, the hot rolling facility 100 shown in Fig. 2 has the process computer 36 and the section dimension prediction device 38, but is not limited to this. For example, the process computer 36 may have the function of the section dimension prediction device 38, and these may be configured as a single device.

[0059] In the description of this embodiment, in the section dimension prediction device 38 of the shaped steel shown in FIG. 3, an example was shown in which the control unit 40 has the data acquisition unit 50, the difference calculation unit 52, the section dimension prediction unit 54, the rolling operation parameter specification unit 56, and the section dimension prediction model generation unit 58, but this is not limited thereto. If the section dimension prediction device 38 of the shaped steel performs prediction of the section dimension, the control unit 40 does not need to have the rolling operation parameter specification unit 56. In addition, in the case where the section dimension prediction model 62 is generated externally and the generated section dimension prediction model 62 is stored in the storage unit 46 via the data acquisition unit 50, the section dimension prediction device 38 of the shaped steel does not need to have the section dimension prediction model generation unit 58. Furthermore, in the case where difference data such as the difference between the rolling operation parameters is stored in the database of the process computer 36 and the data acquisition unit 50 acquires the difference data, the control unit 40 does not need to have the difference calculation unit 52.

[0060] In the description of this embodiment, an example of predicting the cross-sectional dimensions of an H-shaped steel using the cross-sectional dimension prediction device 38 for shaped steel has been described, but the shaped steel for which the cross-sectional shape is predicted is not limited to H-shaped steel. The cross-sectional dimension prediction device 38 for shaped steel can also predict the cross-sectional shapes of other shaped steel manufactured by rolling with an intermediate universal rolling mill, such as channel steel, I-shaped steel, steel sheet piles, or rails. EXAMPLES

[0061] In order to verify the effects of the present invention, an example in which H-shaped steel was manufactured using the hot rolling equipment 100 shown in FIG. 2 will be described. In this example, H-shaped steel with a web height H of 600 mm and a flange width B of 300 mm was manufactured by hot rolling using the same roll set. There are two types of web thickness, 14 mm and 16 mm, and five types of flange thickness, 19 mm, 22 mm, 25 mm, 28 mm, and 32 mm, and there are ten types of cross-sectional dimensions as combinations of these. 100 pieces of each of these 10 types of H-shaped steel, for a total of 1000 pieces of H-shaped steel, were manufactured by changing the rolling operation parameters.

[0062] The cross-sectional dimensions of the H-beam were measured in hot using a hot dimension gauge installed downstream of the finishing universal rolling mill, measuring the web height H, left and right flange width B, web thickness tw, and top, bottom, left and right flange thicknesses tf. The measured cross-sectional dimensions were converted to dimensions at room temperature using the linear expansion coefficient.

[0063] The cross-sectional dimensions used in generating the cross-sectional dimension prediction model are the room temperature converted values ​​of the web height H, left and right flange width B, web thickness tw, and top, bottom, left and right flange thickness tf of the H-section steel measured by a hot dimension gauge, and in the example of the invention, the deviation between the target dimension and the actual dimension was used. The rolling operation parameters used in generating the cross-sectional dimension prediction model are the values ​​shown in (1) to (7) below.

[0064] (1) Correction amount from the standard condition of horizontal roll gap of intermediate universal rolling mill (2) Correction amount of horizontal roll height of intermediate universal rolling mill (3) Correction amount from standard condition of vertical roll gap of intermediate universal rolling mill (4) Center correction amount of vertical rolls of intermediate universal rolling mill (5) Correction amount from the standard condition of E1 roll gap of edger rolling mill (6) Weight of steel piece (7) Rolling time on intermediate universal rolling mill

[0065] In the invention example, 10 sets of H-shaped steel manufactured with the same roll set consisting of 100 pieces were prepared, and all combinations of two pieces extracted from the steel pieces manufactured with each roll set were selected. Since there were 4,950 sets of combinations of two pieces extracted from 100 pieces selected from the same roll set, a total of 49,500 sets of manufacturing performance data were used as learning data to generate a cross-sectional dimension prediction model.

[0066] First, we will explain the results of checking the number of learning data and the prediction accuracy of the cross-sectional dimension prediction model. Figure 5 is a graph showing the relationship between the number of learning data and the average RMSE when predicting each dimension of 100 H-shaped steel pieces that were not used for learning. In Figure 5, the horizontal axis is the number of learning data (sets), and the vertical axis is the average RMSE (root mean square error: mm) of each predicted dimension of the H-shaped steel (web height H, left and right flange width B, web thickness tw, and flange thickness at four locations top, bottom, left and right tf).

[0067] As shown in Fig. 5, the average RMSE value for each predicted dimension of H-beam decreased as the number of training data used to train the cross-sectional dimension prediction model increased. Considering the dimensional tolerances of the H-beam, it is preferable that the average RMSE value be 0.1 mm or less. In order to keep the average RMSE value at 0.1 mm or less, it is preferable to use 10,000 or more sets of training data to generate the cross-sectional dimension prediction model.

[0068] As described above, in the method for predicting the cross-sectional dimensions of shaped steel according to this embodiment, learning data can be accumulated in a short time, so that more than 10,000 sets of learning data can be obtained in a shorter time than in the past. It was confirmed that the dimensions of H-shaped steel can be predicted with high accuracy by using a cross-sectional dimension prediction model trained using a large amount of learning data acquired in this way.

[0069] Next, the results of confirming the manufacturing method of sectional steel using the sectional dimension prediction method of sectional steel will be described. Using the sectional dimension prediction model generated as described above, first, the sectional dimensions of the web height, left and right flange widths, web thickness, and top, bottom, left and right flange thicknesses were predicted. Next, rolling operation parameters were identified that would allow the predicted sectional dimensions to fall within the dimensional tolerances of these sectional dimensions. The tolerances of each dimension are as follows:

[0070] Web height: ±2.0mm Left and right flange width: ±2.0mm Web thickness: ±0.7mm Top, bottom, left and right flange thickness: -0.7~+2.3mm

[0071] 99 H-beams were manufactured by setting the rolling operation parameters as manufacturing conditions so that the predicted cross-sectional dimensions would fall within the above tolerances. The operation parameters for the second rolled piece were determined using the actual cross-sectional dimensions of the first rolled piece, the operation parameters for the first and second rolled pieces, and the target cross-sectional dimensions of the second rolled piece. The operation parameters for the third rolled piece were determined using the actual cross-sectional dimensions of the second rolled piece, the operation parameters for the second and third rolled pieces, and the target cross-sectional dimensions of the third rolled piece. Similarly, for subsequent H-beams, the operation parameters for the next H-beam to be rolled were set using the operation parameters of the preceding material and the H-beam to be next rolled. As a result, H-beams with good cross-sectional dimensions, all of which were within the above tolerances, were manufactured.

[0072] On the other hand, in the comparative example, a cross-sectional dimension prediction model was generated using the method disclosed in Patent Document 3. In the method disclosed in Patent Document 3, the difference between two shaped steels that were continuously hot rolled (a pre-rolled material and a post-rolled material) is used. However, since data between two pieces with different target cross-sectional dimensions is not used as learning data, 90 sets of data are extracted from 100 pieces selected from the same roll set. Therefore, in the comparative example, a total of 900 sets of manufacturing performance data were used as training data to generate a cross-sectional dimension prediction model.

[0073] Using this cross-sectional dimension prediction model, the cross-sectional dimensions of the web height, flange width, web thickness, and the four flange thicknesses at the top, bottom, left, and right were predicted, and the rolling operation parameters were identified that would allow the predicted cross-sectional dimensions to fall within the dimensional tolerances of these cross-sectional dimensions. The tolerances of each dimension are as shown above.

[0074] 99 H-beams were manufactured by setting the rolling operation parameters as manufacturing conditions so that the predicted cross-sectional dimensions would fall within the above tolerances. The rolling operation parameters for the next H-beam to be rolled were determined using the operation parameters for the preceding material and the next H-beam to be rolled. The rolling operation parameters for the H-beam to be rolled after the cross-section was changed were determined to be the standard conditions for that cross-section without using this model. As a result, some dimensions of 15 H-beams out of the second and subsequent rolled H-beams were outside the tolerances, and these H-beams required additional dimensional corrections and remanufacturing, and it was not possible to manufacture H-beams with good cross-sectional dimensions. [Explanation of symbols]

[0075] 10 H-beam 12 Flange 14. Web 20 Furnace 22 Roughing mill 24 Intermediate rolling mill 26 Intermediate universal rolling mill 28 Edger Rolling Mill 30 Finishing rolling mill 32 Hot dimension gauge 36 Process Computer 38 Sectional dimension prediction device for steel sections 40 Control section 42 Input section 44 Output section 46 Storage area 50 Data Acquisition Section 52 Difference calculation section 54 Sectional dimension prediction section 56 Rolling Operation Parameter Identification Section 58 Sectional dimension prediction model generation section 60 Databases 62 Cross-sectional dimension prediction model 100 Hot rolling equipment

Claims

1. A method for predicting a cross-sectional dimension of a shaped steel to be manufactured by hot rolling, comprising: a cross-sectional dimension prediction model which takes as input the difference in rolling operation parameters for two shaped steels produced by hot rolling with the same roll set and outputs the difference in cross-sectional dimension deviations of the two shaped steels, by inputting the difference between the rolling operation parameters of a shaped steel to be a precursor material produced by hot rolling with the roll set and the rolling operation parameters of a shaped steel to be produced by hot rolling with the roll set, and outputting the difference in cross-sectional dimension deviations; The cross-sectional dimension deviation is a deviation between a target dimension and an actual dimension of a shaped steel, A method for predicting the cross-sectional dimension of structural steel, which predicts the cross-sectional dimension of the structural steel to be manufactured using the output difference in cross-sectional dimension deviation and the cross-sectional dimension of the preceding structural steel material.

2. 2. The method for predicting cross-sectional dimensions of a structural steel according to claim 1, wherein the structural steel is produced by hot rolling a steel slab using a roughing mill, an intermediate rolling mill and a finishing rolling mill, and the rolling operation parameters include a weight of the steel slab, a correction amount from a reference position of the rolling rolls of the intermediate rolling mill, a number of passes through the roughing mill and the intermediate rolling mill, and a rolling time of the roughing mill and the intermediate rolling mill.

3. The method for predicting a cross-sectional dimension of a structural steel according to claim 1 , wherein an input of the cross-sectional dimension prediction model includes an attribute parameter indicating a steel type classification of the structural steel.

4. The method for predicting a cross-sectional dimension of a structural steel according to claim 2 , wherein an input of the cross-sectional dimension prediction model includes an attribute parameter indicating a steel type classification of the structural steel.

5. The method for predicting the cross-sectional dimensions of a structural steel according to any one of claims 1 to 4, wherein the cross-sectional dimensions are at least one of the cross-sectional dimensions of the web thickness of the structural steel, the thicknesses of the four flanges at the top, bottom, left and right, the web height and the flange width.

6. A method for manufacturing structural steel, comprising: identifying differences in rolling operation parameters that bring the cross-sectional dimensions of the structural steel predicted using the method for predicting the cross-sectional dimensions of the structural steel according to any one of claims 1 to 4 into the range of target dimensions; and manufacturing the structural steel under manufacturing conditions that include rolling operation parameters determined from the differences in the identified rolling operation parameters.

7. A method for manufacturing structural steel, comprising: identifying differences in rolling operation parameters that bring the cross-sectional dimensions of the structural steel predicted using the method for predicting cross-sectional dimensions of structural steel described in claim 5 into the range of target dimensions; and manufacturing the structural steel under manufacturing conditions that include rolling operation parameters determined from the identified differences in rolling operation parameters.

8. A section dimension prediction device for predicting a section dimension of a section steel manufactured by hot rolling, comprising: a cross-sectional dimension prediction model which takes as input the difference in rolling operation parameters for two shaped steels produced by hot rolling with the same roll set and outputs the difference in cross-sectional dimension deviations of the two shaped steels, by inputting the difference between the rolling operation parameters of a shaped steel to be a precursor material produced by hot rolling with the roll set and the rolling operation parameters of a shaped steel to be produced by hot rolling with the roll set, and outputting the difference in cross-sectional dimension deviations; The cross-sectional dimension deviation is a deviation between a target dimension and an actual dimension of a shaped steel, A cross-sectional dimension prediction device for structural steel having a cross-sectional dimension prediction unit that predicts the cross-sectional dimension of the structural steel to be manufactured using the difference in the output cross-sectional dimension deviations and the actual value of the cross-sectional dimension of the preceding structural steel material.

9. A machine learning model is trained using a plurality of data sets as training data, each set being a combination of actual values ​​of differences in rolling operation parameters for two shaped steels produced by hot rolling with the same roll set and actual values ​​of differences in cross-sectional dimensional deviations for the two shaped steels; A cross-sectional dimension prediction model is generated, in which the difference between the rolling operation parameters of the two shaped steels is input and the difference between the cross-sectional dimension deviations of the two shaped steels is output; A method for generating a cross-sectional dimension prediction model, in which the deviation between a target dimension and an actual dimension of a steel section is used as the cross-sectional dimension deviation.

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