Method for predicting the shape of unequal leg angle steel, manufacturing method for unequal leg angle steel, rolling equipment, and method for generating a shape prediction model

Abstract

To provide a shape prediction method for unequal angle steel that can accurately predict a bending amount of unequal angle steel.SOLUTION: In a shape prediction method for unequal angle steel, which predicts a bending amount of unequal angle steel that is manufactured by a rolling facility which has a plurality of rolling machines that hot-rolls heated steel billets to unequal angle steel, a cooling device that cools the unequal angle steel and a shape measurer that measures a bending amount of the cooled unequal angle steel, input data, which includes rolling-operation parameters of the rolling machines, a cooling-operation parameter of the cooling device and an attribute parameter of the unequal angle steel, is inputted to a shape prediction model and the bending amount is outputted, so as to predict the bending amount of the unequal angle steel.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a shape prediction method for unequal leg angle steel that predicts the amount of bending of unequal leg angle steel, a manufacturing method for unequal leg angle steel, rolling equipment, and a method for generating a shape prediction model. [Background technology]

[0002] The background art will be explained using an example of a type of unequal angle steel (NAB), which is used as a reinforcing steel material for structures such as ship hulls, bridges, and tanks. NABs are manufactured by hot-rolling a billet of steel. Figure 1 is a schematic diagram showing the cross-sectional shape of an NAB. As shown in Figure 1, an NAB 10 consists of a relatively thin, wide long side 12 and a relatively thick, narrow short side 14. There are approximately a dozen combinations of thickness and width dimensions for the long side 12 and the short side 14. Furthermore, the mechanical properties that NAB must satisfy are determined by its intended use and classification society classification, and the chemical composition of the NAB is adjusted accordingly. When NAB is used as a steel material for a structure, the dimensions, thickness, and chemical composition of the long side 12 and the short side 14 are selected based on the required performance of the structure.

[0003] The production of such NABs generally involves rough rolling, intermediate rolling, and finish rolling of a steel billet, which is a material preheated in a heating furnace, using multiple rolling mills to form it into a cross-sectional shape with the desired dimensions. For example, in the rough rolling and intermediate rolling processes, the steel billet is rolled multiple times using a rough rolling mill and an intermediate rolling mill, each having a pair of upper and lower rolls with multiple grooves called calibers, to roughly form it into a shape close to the desired dimensions. Finally, a single pass of finish rolling is performed using a finishing rolling mill, which has a pair of upper and lower rolls with one caliber, to form it into an NAB with the desired cross-sectional shape.

[0004] The Japanese Industrial Standard JIS G3192:2021 (Shape, Dimensions, Mass, and Tolerances of Hot-Rolled Section Steel) and corresponding foreign and international standards specify tolerances for the cross-sectional dimensions of section steel. For example, the NAB specifies typical values ​​and tolerances for each dimension, such as long side width A, short side width B, long side thickness t1, and short side thickness t2, and requires that all of these dimensions be controlled to fall within the tolerance range from the typical value. Therefore, rolling conditions such as roll gaps for each rolling mill and each pass are adjusted and modified to ensure that each NAB dimension falls within the tolerance range.

[0005] As shown in Figure 1, NAB rolls a steel sheet in a V-shaped position to produce a cross-sectional shape that is asymmetric in the vertical direction, resulting in different reduction ratios for the long and short sides in one rolling pass. In other words, the amount of elongation in the rolling direction differs between the long and short sides 12 and 14 in one rolling pass, which can result in bending along the longitudinal direction. In addition, if the rolling conditions, such as the gap between the upper and lower rolls, are changed in each pass for the purpose of dimensional adjustment, the amount of bending in the longitudinal direction due to rolling will differ each time.

[0006] Furthermore, because the narrow sides of NAB are thicker than the long sides, the narrow sides are less likely to cool than the long sides during the cooling process during rolling, resulting in higher temperatures. Thermal stress caused by this temperature difference between the long and short sides, as well as stress caused by phase transformation, can also cause bending along the longitudinal direction during or after cooling. Therefore, for example, a cooling device can be installed before the finishing mill to cool the long or short sides of the NAB during the hot rolling process, thereby eliminating the temperature difference between the long and short sides and reducing the amount of bending. Furthermore, to produce higher-strength NAB, an accelerated cooling device can be installed on the exit side of the finishing mill to water-cool the long or short sides of the NAB in order to control the steel's structure.

[0007] As described above, in the manufacture of NAB, bending occurs due to the complex overlap of the effects of rolling and cooling, making it difficult to control the amount of NAB bending, and it has been difficult to keep the amount of NAB bending within the tolerance range until now. Furthermore, if the amount of NAB bending is large at the product stage, additional processes such as pressing are required, which increases manufacturing costs and reduces productivity.

[0008] As a technique for reducing the amount of bending of the NAB, Patent Document 1 discloses a cooling method in which the long and short sides are cooled so that the temperature of the long sides is equal to or higher than the temperature of the short sides and the temperature difference is within 50° C. According to Patent Document 1, by controlling the cooling of the long and short sides of the NAB so that the temperature difference is achieved, bending of the NAB can be suppressed.

[0009] Patent Document 2 discloses a control method in which predicted values ​​are calculated using a rolling deformation prediction model based on various setting data and measurement data related to rolling conditions, the amount of NAB bending is determined from these predicted values, and the rolling conditions are set based on this amount of bending. According to Patent Document 2, it is possible to reduce warpage and bending of the rolled material by setting and adjusting the optimum positions of the roll gap, entry guide, and exit guide based on the amount of NAB bending. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Japanese Patent Application Publication No. 188726 / 1983 [Patent Document 2] Japanese Patent Application Laid-Open No. 2003-39107 Summary of the Invention [Problem to be solved by the invention]

[0011] The method disclosed in Patent Document 1 is not particularly limited to the dimensions or size of the NAB, the steel type, or the rolling conditions, and claims that bending can be suppressed as long as the temperature of the long sides is equal to or higher than the temperature of the short sides and the temperature difference is within 50° C. However, there has been a problem that, depending on the dimensions or sizes of some of the dozens of NABs, the amount of NAB bending is not sufficiently reduced even when cooling is performed under the above cooling conditions.

[0012] Furthermore, the bending of the NAB is also affected by stresses caused by phase transformation during cooling. There are several types of steel for NAB, and different steel types cause different stresses due to phase transformation even when the thermal history and temperature are the same. For this reason, there was a problem that, depending on the steel type, the amount of bending of the NAB cannot be sufficiently reduced even when cooling using the cooling method described in Patent Document 1.

[0013] Furthermore, the amount of NAB bending may be affected not only by temperature but also by rolling conditions such as the roll gap of each rolling mill used for dimensional adjustment, i.e., by plastic deformation due to rolling. The method described in Patent Document 1 does not take into consideration the effects of rolling conditions, and therefore has the problem that the amount of NAB bending cannot be sufficiently reduced depending on the rolling conditions.

[0014] Patent Document 2 discloses, as an example of a shape prediction model, a formula for predicting NAB bending using a linear sum of each parameter of the rolling conditions. However, since the amount of NAB bending is affected not only by the rolling conditions but also by temperature, it is difficult to accurately predict the amount of bending using such a formula. Furthermore, to accurately predict the amount of NAB bending taking into account not only the temperature but also the effects of rolling, two-dimensional or three-dimensional structural analysis is required, which imposes an extremely high computational load on online prediction and is difficult to achieve with the current capabilities of computers.

[0015] The present invention has been made in consideration of the above-mentioned problems, and its purpose is to provide a shape prediction method for unequal-leg angle steel that can accurately predict the amount of bending of unequal-leg angle steel, a manufacturing method for unequal-leg angle steel using the shape prediction method for unequal-leg angle steel, rolling equipment, and a method for generating a shape prediction model. [Means for solving the problem]

[0016] The means for solving the above problems are as follows. [1] A shape prediction method for unequal leg angle steels that predicts the amount of bending of unequal leg angle steels manufactured in rolling equipment having a plurality of rolling mills that hot-roll heated steel billets into unequal leg angle steels, a cooling device that cools the unequal leg angle steels, and a shape meter that measures the amount of bending of the unequal leg angle steels after cooling, wherein input data including rolling operation parameters of the rolling mills, cooling operation parameters of the cooling device, and attribute parameters of the unequal leg angle steels is input into a shape prediction model, and the amount of bending is output to predict the amount of bending. [2] Identifying the cooling operation parameters such that the amount of bending predicted using the shape prediction method for unequal leg angle steel described in [1] falls within a predetermined allowable value range; A method for manufacturing unequal angle steel, which manufactures unequal angle steel under manufacturing conditions including specified cooling operation parameters. [3] A rolling facility having a plurality of rolling mills that hot-roll heated steel billets into unequal leg angle steels, a cooling device that cools the unequal leg angle steels, a shape meter that measures the amount of bending of the unequal leg angle steels after cooling, and a shape prediction device for unequal leg angle steels that predicts the amount of bending, The shape prediction device includes a shape prediction unit that inputs input data including rolling operation parameters of the rolling mill, cooling operation parameters of the cooling device, and attribute parameters of the unequal leg angle iron into a shape prediction model and outputs the amount of bending. [4] A method for generating a shape prediction model that predicts the amount of bending of unequal leg angle steel produced in rolling equipment having a plurality of rolling mills that hot-roll heated steel billets into unequal leg angle steel, a cooling device that cools the unequal leg angle steel, and a shape meter that measures the amount of bending of the unequal leg angle steel after cooling, wherein a machine learning model is trained using multiple data sets as training data, each set consisting of actual values ​​of the rolling operation parameters of the rolling mills for unequal leg angle steel produced in the past, actual values ​​of the cooling operation parameters of the cooling device, actual values ​​of the attribute parameters of the unequal leg angle steel, and actual values ​​of the amount of bending, and a shape prediction model is generated that uses the rolling operation parameters, the cooling operation parameters, and the attribute parameters as inputs and the amount of bending. [5] The method for generating a shape prediction model described in [4], wherein the machine learning model is one of a neural network, decision tree learning, random forest, and support vector regression. [Effects of the Invention]

[0017] According to the present invention, the bending amount of unequal leg angle steel is predicted using a shape prediction model of unequal leg angle steel that takes into account the effects of not only cooling but also rolling, so the bending amount of unequal leg angle steel can be predicted with high accuracy. Furthermore, by using the predicted bending amount result to control the cooling conditions in the production of unequal leg angle steel, it becomes possible to consistently produce unequal leg angle steel with bending amounts within the allowable range. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 is a schematic diagram showing the cross-sectional shape of the NAB. [Figure 2] FIG. 2 is a schematic diagram showing the progression of the target cross-sectional shape in each manufacturing process of NAB. [Figure 3] FIG. 3 is a schematic diagram showing the cross-sectional shapes of the ABS and the BP. [Figure 4] FIG. 4 is a perspective view illustrating the amount of bending of the NAB. [Figure 5] FIG. 5 is a schematic diagram showing the general configuration of rolling equipment including an unequal leg angle steel shape prediction device capable of implementing the unequal leg angle steel shape prediction method according to this embodiment. [Figure 6] FIG. 6 is a schematic diagram showing the state in which the long and short sides of the NAB are cooled by the cooling device. [Figure 7] FIG. 7 is a schematic diagram showing the warpage of the NAB in the vertical direction. [Figure 8] FIG. 8 is a functional block diagram of a shape prediction device for unequal leg angle steel. [Figure 9] FIG. 9 is a flowchart showing the flow of the cooling operation parameter specification process performed by the cooling operation parameter specification unit. DETAILED DESCRIPTION OF THE INVENTION

[0019] An embodiment of the present invention will be described below with reference to the drawings. In the following embodiment, the present invention will be described using an example in which the present invention is applied to a NAB, which is a type of unequal leg angle iron. Note that the embodiment shown below exemplifies 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 following embodiment.

[0020] Figure 2 is a schematic diagram showing the progression of the target cross-sectional shape at each manufacturing process of NAB10. NAB10 is manufactured by heating the raw steel billet in a heating furnace, and then rolling the billet in each rolling mill in the rough rolling process, intermediate rolling process, and finish rolling process.

[0021] FIG. 3 is a schematic diagram showing the cross-sectional shapes of an unequal leg equal-thickness angle steel 16 and a spherical flat steel 20. FIG. 3(a) shows the cross-sectional shape of an unequal leg equal-thickness angle steel (hereinafter also referred to as "ABS") 16, in which the short sides 17 and long sides 18 are equal in thickness. FIG. 3(b) shows the cross-sectional shape of a spherical flat steel (hereinafter also referred to as "BP (valve plate)") 20, in which the short sides 21 are tapered. ABS 16 and BP 20 are each a type of unequal leg angle steel. Since ABS 16 and BP 20 can be manufactured using the same manufacturing method as NAB 10, the shape prediction method of unequal leg angle steel, manufacturing method of unequal leg angle steel, and rolling equipment according to this embodiment can also be applied to predicting the amount of bending of ABS and BP and manufacturing ABS and BP. Therefore, the unequal leg angle steels covered by this embodiment include NAB, ABS, and BP.

[0022] 4 is a perspective view illustrating the amount of bending of the NAB 10. In this embodiment, the amount of bending d of the NAB 10 is indicated by a sign "+" when the NAB 10 bends so that the long side 12 is convex with respect to a line connecting the bottom end of the short side 14 at the leading end and the bottom end of the short side 14 at the trailing end, and a sign "-" when the amount of bending d is opposite.

[0023] 5 is a schematic diagram showing the overall configuration of a rolling facility 100 including a shape prediction device 58 for unequal leg angles, which can implement the shape prediction method for unequal leg angles according to this embodiment. As shown in FIG. 5, the rolling facility 100 includes a heating furnace 30 for heating a steel billet to a predetermined temperature, a roughing mill 32 for rolling the steel billet heated in the heating furnace 30, intermediate rolling mills 34 and 36, a finishing rolling mill 38, a rolling control device 40 for controlling the operation of these rolling mills, cooling devices 42 and 44 for cooling the unequal leg angles during rolling, thermometers 46, 48, and 50 for measuring the temperatures of the unequal leg angles during rolling at the inlet and outlet sides of the cooling devices 42 and 44, a cooling control device 52 for controlling the operation of the cooling devices 42 and 44, a shape meter 54 for measuring the amount of bending of the unequal leg angles after cooling, a process computer 56, and a shape prediction device 58 for unequal leg angles.

[0024] Steel billets are charged into the heating furnace 30 and heated to a temperature above the austenite temperature range (for example, 1100 to 1300°C). The steel billets extracted from the heating furnace 30 are transported to the rolling mills by a plurality of table rollers installed on the outlet side of the heating furnace 30, and are hot rolled into NAB 10 by a roughing mill 32, intermediate rolling mills 34 and 36, and a finishing mill 38. Furthermore, before and after the rolling mills, the NAB 10 is cooled in cooling devices 42 and 44 while being transported by the table rollers.

[0025] In rough rolling, a roughing mill 32 performs multiple passes of rolling on the raw material, a rectangular cross-section steel billet, to adjust the size of the billet and form it into a rough billet. In intermediate rolling, intermediate mills 34 and 36 perform multiple passes of rolling on the rough billet, forming it into a shape with an upward convex shape near the joint between the long and short sides. In finish rolling, the rolled material is formed into the product shape by a finish rolling mill 38, which has a caliber for the final product shape. The number of calibers and the number of passes through which the rolled material passes vary depending on the size of the product.

[0026] The rolling operation parameters in such a hot rolling process include the gap between the upper and lower rolls, the amount of leveling, and the amount of thrust. The gap between the upper and lower rolls is the gap between the upper and lower rolls provided in the roughing mill 32, the intermediate mills 34 and 36, and the finishing mill 38, and these devices are designed so that the gap between the upper and lower rolls can be adjusted as desired. By adjusting this gap between the rolls, the thickness of the rolled material, i.e., the reduction ratio, can be changed.

[0027] The leveling amount refers to the amount of tilt of the upper and lower rolls provided in the roughing mill 32, intermediate mills 34 and 36, and finishing mill 38. Normally, rolling is performed while maintaining the rolls in a horizontal position, but the structure allows for rolling by tilting the upper and lower rolls or one of the rolls from a horizontal position. By adjusting the leveling amount, the reduction ratios of the long sides 12 and the short sides 14 of the NAB 10 can be changed.

[0028] The thrust amount is the difference in the relative axial positions of the upper and lower rolls provided in the roughing mill 32, the intermediate mills 34 and 36, and the finishing mill 38. These devices are designed so that the axial positions of the upper and lower rolls can be adjusted as desired. By adjusting this thrust amount, the dimensions of the NAB 10 can be changed.

[0029] The rolling conditions, such as the gap between the upper and lower rolls, the leveling amount, and the thrust amount, are adjusted so that each dimension of the NAB 10 falls within the tolerance range. When the rolling conditions are changed to adjust each dimension, the reduction ratio of the long sides 12 and the short sides 14 of the NAB 10 changes. This means that the longitudinal elongation ratio of the long sides 12 and the short sides 14 changes, which may result in an increase in the amount of bending of the NAB 10. Since the amount of bending of the NAB 10 is affected not only by temperature but also by rolling conditions, the effects of the rolling conditions must be considered to accurately predict the amount of bending of the NAB 10. In particular, the finishing mill 38, which performs rolling last, has a particularly strong correlation with the amount of bending of the NAB 10.

[0030] When the widths of the long sides 12 and short sides 14 of the NAB 10 are the same, different manufacturing sizes can be produced by combining the distance between the upper and lower rolls, the thrust amount, and the leveling amount. Furthermore, when rolling NAB 10 of the same cross-sectional size continuously, the number of rolling passes in each rolling process of rough rolling, intermediate rolling, and finish rolling basically does not change.

[0031] The cooling devices 42, 44 cool the NAB 10 under predetermined cooling conditions. The cooling devices 42, 44 are divided into predetermined lengths (e.g., 10 m), and each division is called a bank. While the cooling devices 42, 44 shown in FIG. 5 each have six banks, the number of banks may be increased or decreased from six. The cooling operation parameters that define the cooling conditions for the cooling devices 42, 44 include the flow rate density of the cooling water and the cooling time. The cooling time can be calculated by multiplying the length per bank by the number of banks and dividing the resulting cooling length by the conveying speed.

[0032] Increasing the water flow density of the cooling water can increase the cooling rate and temperature drop of the NAB 10. Also, slowing the conveying speed and lengthening the cooling time can increase the temperature drop of the NAB 10. By adjusting the water flow density and cooling time, which are cooling operation parameters of the cooling devices 42 and 44, the surface temperatures of the long sides 12 and short sides 14 of the NAB 10 after cooling can be controlled.

[0033] The cooling devices 42, 44 are capable of individually cooling the long sides 12 and the short sides 14 of the NAB 10, and the density of the water sprayed onto each of the long sides 12 and the short sides 14 may be changed. Due to changes over time, such as equipment deterioration and changes in temperature, the temperature of the long sides 12 or the short sides 14 of NAB 10 with the same cross-sectional dimensions may vary depending on the manufacturing conditions, and may not always be the same temperature. Therefore, bending may not always be prevented with the same cooling conditions. In this way, by changing the cooling conditions for the long sides 12 and the short sides 14, the temperatures of the long sides 12 and the short sides 14 of the NAB 10 can be individually adjusted.

[0034] Furthermore, the cooling operation parameters of each of the cooling devices, such as the cooling devices 42 and 44, installed before and after the finishing mill 38, such as the water flow rate density and cooling time, may be changed independently. This is because the amount of bending of the NAB 10 is correlated with the temperature difference between the long sides 12 and the short sides 14 after finish rolling, and the mechanical properties of the steel material are correlated with the cooling stop temperature. Furthermore, the cooling device 44 may be an accelerated cooling device.

[0035] A typical flat spray nozzle, square spray nozzle, or multi-hole jet nozzle can be used for the cooling devices 42, 44. If it is difficult to cool the entire flange with a single flat spray or square spray nozzle, multiple nozzles may be arranged in the width direction of the long side 12 and short side 14 for cooling.

[0036] Fig. 6 is a schematic diagram showing the state in which the long sides 12 and short sides 14 of the NAB 10 are being cooled by a cooling device 44. Fig. 6 shows the state in which the long sides 12 and short sides 14 of the NAB 10 are being cooled by a flat spray 45, which is an example of the cooling device 44. As shown in Fig. 6, it is preferable to cool the long sides 12 and short sides 14 of the NAB 10 from both the top and bottom surfaces, respectively.

[0037] FIG. 7 is a schematic diagram showing the vertical warpage of the NAB 10. When either the top or bottom surface of the long and short sides 12 and 14 of the NAB 10 is cooled, a temperature difference occurs in the thickness direction, which may result in the vertical warpage y of the NAB 10, as shown in FIG. 7. Furthermore, after contacting the long and short sides 12 and 14 of the NAB 10, the cooling water sprayed from the bottom surface falls from the bottom surfaces of the long and short sides 12 and 14 due to gravity in a short time. Therefore, even if the top and bottom surfaces are cooled with the same amount of water, the cooling capacity of the bottom surface is lower than that of the top surface. For this reason, it is preferable to set the amount of cooling water on the bottom surface to between 1.1 and 1.5 times the amount of cooling water on the top surface. This allows the top and bottom surfaces of the long and short sides 12 and 14 of the NAB 10 to be uniformly cooled, thereby reducing the vertical warpage y of the NAB 10.

[0038] Referring again to Figure 5, thermometers 46, 48, and 50 are provided at the inlet and outlet sides of cooling devices 42 and 44 and measure the surface temperatures of long sides 12 and short sides 14 of NAB 10 before and after cooling by cooling devices 42 and 44. Thermometers 46, 48, and 50 are devices that measure the temperatures of long sides 12 and short sides 14 of NAB 10 using scanning thermometers that scan temperature measurement points across long sides 12 and short sides 14, or by using a method in which multiple spot thermometers are arranged across long sides 12 and short sides 14. When using the latter method, it is preferable to measure the temperatures by arranging spot thermometers at at least five different positions across long sides 12 and short sides 14 in order to grasp the temperature distribution across the width of long sides 12 and short sides 14.

[0039] The amount of bending of the NAB 10 correlates with the temperature difference between the long sides 12 and the narrow sides 14 after finish rolling. According to the inventors' investigations, the bending of the NAB 10 correlates particularly with the temperatures at the width center positions (A / 2 and B / 2 positions in FIG. 1 ) of the long sides 12 and the narrow sides 14. For this reason, it is preferable that the thermometer 48 measures the temperatures at least at the width center positions of the long sides 12 and the narrow sides 14. Furthermore, the mechanical properties of the NAB 10 depend on the structure of the steel material. When the cooling device 44 is an accelerated cooling device, the cooling device plays a role in controlling the structure of the steel material, i.e., controlling the mechanical properties, so the thermometer 50 measures the surface temperatures of the long sides 12 and the narrow sides 14 on the exit side of the cooling device 44.

[0040] The data measured by the thermometers 46, 48, and 50 are output as temperature information to the process computer 56. The temperature information is, for example, temperature measurements at representative points at the widthwise center of each of the long sides 12 and short sides 14 (five points equally spaced widthwise from positions A / 2 and B / 2 to the leading and trailing ends). Alternatively, the data measured by the thermometers 46, 48, and 50 may be approximated by a polynomial of degree two or higher, and parameters that can identify the function of the approximated polynomial may be output as temperature information. This temperature information is used to confirm that the temperature of the NAB 10 is within a predetermined range as a result of controlling the cooling devices 42 and 44.

[0041] The shape meter 54 measures the amount of bending of the NAB 10 after it has been cooled by the cooling device 44. The shape meter 54 may be any device capable of measuring the amount of bending of the NAB 10, and may, for example, be a two-dimensional laser displacement meter used to measure the bending profile along the longitudinal direction of the NAB 10. In this case, if the shape meter 54 is installed below the NAB 10, it becomes difficult to perform stable measurements over a long period of time due to factors such as the scale falling off. For this reason, it is preferable to install the shape meter 54 above the NAB 10. When performing measurements using a two-dimensional laser displacement meter, it is preferable to set the measurement pitch in the longitudinal direction to 1 to 50 mm in order to improve the accuracy of shape measurement.

[0042] Alternatively, multiple one-dimensional laser displacement meters may be provided along the length of the NAB 10, and the amount of bending of the NAB 10 may be measured using these multiple laser displacement meters. In this case, the shape of the NAB 10 at multiple different positions along its length can be measured without the need for scanning with the laser displacement meters as described above. In this case as well, it is preferable to provide the laser displacement meters at intervals of 20 to 50 mm along the length of the short side 14 to improve measurement accuracy.

[0043] The shape data measured by the shape meter 54 may be used as the actual value of the amount of curvature of the NAB 10. Alternatively, the shape data may be approximated by a polynomial of degree two or higher, and the actual value of the amount of curvature of the NAB 10 may be the distance between a line connecting the bottom ends of the short sides 14 (or long sides 12) at the leading and trailing ends of the NAB 10 and the bottom end of the curved short side 14 (or long side 12). Alternatively, the actual value of the amount of curvature of the NAB 10 may be a parameter that can identify a polynomial function that approximates the curved shape of the NAB 10. Furthermore, the shape meter 54 does not necessarily have to be positioned on an extension of the NAB 10's conveyance direction. The shape meter 54 may be installed above a cooling bed or the like so that the shape of the NAB 10 can be measured on the cooling device 44 after passing through the cooling device 44. The actual value of the amount of curvature of the NAB 10 measured by the shape meter 54 is output to the process computer 56.

[0044] The process computer 56 can be a general-purpose computer such as a workstation or a personal computer. The process computer 56 is connected to the rolling control device 40, the cooling control device 52, the thermometers 46, 48, 50, and the shapemeter 54 by wire or wirelessly, and controls the manufacturing process of the NAB 10. The process computer 56 also acquires attribute parameters of the NAB 10 from a higher-level computer. The attribute parameters include target dimensional values ​​of the NAB 10, information on the chemical composition (contents of C, Si, Mn, Cr, Mo, V, etc.), target values ​​of the mechanical properties of the NAB 10 (yield stress, tensile strength, elongation, toughness, hardness, etc.), and information on the steel type classification.

[0045] In addition to the attribute parameters, the process computer 56 stores information regarding the heating temperature of the NAB 10 and the cooling stop temperature at the outlet of the cooling devices 42, 44 required to obtain the desired material. The process computer 56 performs heat transfer calculations based on the internal model and identifies the cooling operation parameters of the cooling device, namely, the water flow rate density and the cooling time, required to achieve the cooling stop temperature.

[0046] The cooling operation parameters thus identified are sent to the cooling control device 52. Based on the cooling operation parameters, the cooling control device 52 controls the operating pressure and number of operating cooling water pumps in the cooling devices 42 and 44, the opening degree of the flow valves of the cooling nozzles, and the rotation speed of the motors that drive the table rollers.

[0047] The process computer 56 also sets rolling operation parameters, such as the number of passes and roll spacing, for each rolling mill in accordance with the attribute parameters of the NAB 10. Typical rolling operation parameter settings are set as table values ​​associated with the attribute parameters of the NAB 10 based on past rolling performance. However, when the rolls are rearranged, rolling the preceding and succeeding rolled materials using the same parameters may not produce the same rolling results. For example, even if the same set values ​​are used before and after the roll rearrangement, the reference state (e.g., the zero point setting state) on which the set values ​​are based actually changes, so the same cross-sectional dimensions may not be obtained if the rolling conditions are used as is. Furthermore, even if the rolls are not rearranged, changes in the roll spacing for NAB 10s with different cross-sectional dimensions may change the mill stretch behavior of the rolling mill and the friction characteristics between the housing and the roll chocks, resulting in nonlinear characteristics in the relationship between the roll spacing and the rolling load. In such cases, the cross-sectional dimensions of the NAB 10 may vary even if the roll spacing is the same. Therefore, in such cases, the operator may reset and change the values ​​in the table each time.

[0048] The process computer 56 sets the gap between the upper and lower rolls, the leveling amount, and the thrust amount for each pass in the roughing mill 32, the intermediate mills 34, 36, and the finishing mill 38 as rolling operation parameters for the rolling process. The process computer 56 outputs the set rolling operation parameters to the rolling control device 40. The rolling control device 40 controls the gap between the upper and lower rolls, the leveling amount, and the thrust amount for the roughing mill 32, the intermediate mills 34, 36, and the finishing mill 38 based on the rolling operation parameters.

[0049] The process computer 56 also collects and stores temperature information and bending information of the NAB 10 from the thermometers 46, 48, 50 and the shapemeter 54. The rolling operation parameters, cooling operation parameters, temperature information, actual values ​​of the bending amount of the NAB 10, and actual values ​​of the attribute parameters are stored in a database of the process computer 56 in association with the identification number of the NAB 10 being manufactured.

[0050] The unequal leg angle steel shape prediction device 58 obtains from the process computer 56 the rolling operation parameters, cooling operation parameters, and attribute parameters of the NAB10 manufactured by the rolling equipment 100, inputs input data including these into a shape prediction model, and outputs the amount of bending of the NAB10, thereby predicting the amount of bending of the NAB10. The unequal leg angle steel shape prediction device 58 also identifies cooling operation parameters that will bring the predicted amount of bending of the NAB10 within the allowable range, and outputs the identified cooling operation parameters to the process computer 56 to set them as cooling operation parameters for manufacturing the NAB10.

[0051] Next, a shape prediction device 58 for unequal leg angle steel that predicts the amount of bending of NAB10 will be described. FIG. 8 is a functional block diagram of the shape prediction device 58 for unequal leg angle steel. The shape prediction device 58 for unequal leg angle steel can be a general-purpose computer such as a workstation or a personal computer. The shape prediction device 58 for unequal leg angle steel has a control unit 60, an input unit 62, an output unit 64, and a storage unit 66. The control unit 60 is, for example, a CPU, and functions as a data acquisition unit 68, a shape prediction unit 70, a cooling operation parameter identification unit 72, and a shape prediction model generation unit 74 by executing a program stored in the storage unit 66.

[0052] The input unit 62 is, for example, a keyboard, a touch panel integrated with a display, or the like. The output unit 64 is, for example, an LCD or CRT display, or the like. The storage unit 66 is, for example, an updatable flash memory, a built-in hard disk or a memory card connected via a data communication terminal, or an information recording medium and a read / write device therefor. The storage unit 66 stores programs and data for realizing each function of the unequal leg angle steel shape prediction device 58. The storage unit 66 further stores a database 76 and a shape prediction model 78. The database 76 stores 500 or more, preferably 2000 or more, data sets, each set including rolling operation parameters, cooling operation parameters, attribute parameters, and actual values ​​of flange warpage of NAB 10 previously manufactured using the same rolling equipment 100 (with the same roll sets in the roughing mill 32, intermediate rolling mills 34 and 36, and finishing mill 38).

[0053] The shape prediction model 78 is a trained machine learning model that has been trained using training data that is a data set stored in the database 76. The shape prediction model 78 according to this embodiment is a trained machine learning model that receives input data including rolling operation parameters, cooling operation parameters, and attribute parameters as input, and outputs the amount of bending of the NAB 10.

[0054] Next, a description will be given of the processing executed by the data acquisition unit 68 and the shape prediction unit 70. The data acquisition unit 68 acquires rolling operation parameters, cooling operation parameters, and attribute parameters from the process computer 56 as input data.

[0055] The data acquisition unit 68 acquires, from the process computer 56, rolling operation parameters such as the gap between the upper and lower rolls of the finishing mill 38 and the leveling amount. The finishing mill 38 is the final rolling mill that finishes the product to the target dimensions, and the gap between the upper and lower rolls and the leveling amount of this rolling directly affect the cross-sectional shape of the product. For this reason, the gap between the upper and lower rolls and the leveling amount of the finishing mill 38 have a particularly strong correlation with the amount of bending of the NAB 10. Therefore, it is preferable to use the gap between the upper and lower rolls of the finishing mill 38 and the leveling amount as rolling operation parameters, which improves the accuracy of predicting the amount of bending of the NAB 10.

[0056] The data acquisition unit 68 may further acquire the thrust amounts of the upper and lower rolls of the finishing mill 38 from the process computer 56 as rolling operation parameters. As described above, the finishing mill 38 is the final rolling mill that finishes the product to the target dimensions, and therefore the thrust amounts of the upper and lower rolls in this rolling also have a significant effect on the cross-sectional shape of the product. For this reason, the thrust amounts of the upper and lower rolls of the finishing mill 38 are also closely correlated with the amount of bending of the NAB 10. Therefore, it is preferable to use the thrust amounts of the upper and lower rolls of the finishing mill 38 as rolling operation parameters, which further improves the accuracy of predicting the amount of bending of the NAB 10.

[0057] The data acquisition unit 68 may further acquire, as rolling operation parameters from the process computer 56, the spacing between the upper and lower rolls, the leveling amount, and the thrust amount in each pass of the roughing mill 32. The data acquisition unit 68 may further acquire, as rolling operation parameters from the process computer 56, the spacing between the upper and lower rolls, the leveling amount, and the thrust amount in each pass of the intermediate rolling mills 34, 36. These values ​​also affect the amount of bending of the NAB 10, so by including the spacing between the upper and lower rolls, the leveling amount, and the thrust amount in each pass of the roughing mill 32 and the intermediate rolling mills 34, 36 in the rolling operation parameters, the accuracy of predicting the amount of bending of the NAB 10 is further improved.

[0058] The data acquisition unit 68 acquires the water flow rate and cooling time of the cooling devices 42, 44 from the process computer 56 as cooling operation parameters. In the cooling devices 42, 44, the higher the water flow rate and the longer the cooling time, the greater the temperature difference between the inner and outer surfaces of the long sides 12 and short sides 14 of the NAB 10, and the greater the amount of bending. Therefore, by using the water flow rate and cooling time of the cooling devices 42, 44 as cooling operation parameters, the accuracy of predicting the amount of bending of the NAB 10 can be further improved.

[0059] Furthermore, the data acquisition unit 68 acquires the target dimensions, chemical composition, and mechanical property values ​​of NAB10 as attribute parameters from the process computer 56. By including the target dimensions, chemical composition, and mechanical property values ​​of NAB10 in the input data for the shape prediction model, the shape prediction model takes into account the target dimensions, chemical composition, and mechanical properties. This allows the amount of bending to be predicted using the same shape prediction model for NAB10 with different target dimensions, chemical composition, and mechanical properties. The data acquisition unit 68 may acquire the steel type classification of NAB10 instead of the chemical composition of NAB10. The data acquisition unit 68 outputs the acquired input data to the shape prediction unit 70.

[0060] When the shape prediction unit 70 acquires input data from the data acquisition unit 68, it reads the shape prediction model 78 from the storage unit 66, inputs the input data into the shape prediction model 78, and causes it to output the amount of bending of the NAB 10. In this way, by causing the shape prediction model 78 to output the amount of bending of the NAB 10, the shape prediction unit 70 predicts the amount of bending of the NAB 10 to be manufactured. Note that the shape prediction unit 70 may output the output amount of bending to the output unit 64 and display the amount of bending of the NAB 10 on the output unit 64. This allows the operator to visually check the output unit 64 and confirm the predicted value of the amount of bending of the NAB 10.

[0061] Next, we will explain the processing of the cooling operation parameter specifying unit 72 and the shape prediction model generating unit 74. The cooling operation parameter specifying unit 72 specifies cooling operation parameters that will bring the amount of bending of the NAB 10 predicted by the shape prediction unit 70 within the allowable range, and outputs the cooling operation parameters to the process computer 56 to set them as cooling conditions.

[0062] Fig. 9 is a flow diagram showing the flow of the cooling operation parameter specification process by the cooling operation parameter specification unit 72. The flow shown in Fig. 9 is started, for example, by receiving an input from the operator to start the process.

[0063] First, the data acquisition unit 68 acquires rolling operation parameters of the NAB 10 to be manufactured from the process computer 56 (step S101). The data acquisition unit 68 also acquires attribute parameters of the NAB 10 to be manufactured from the process computer 56 (step S102). The data acquisition unit 68 outputs the acquired rolling operation parameters and attribute parameters to the shape prediction unit 70.

[0064] The cooling operation parameter specifying unit 72 sets arbitrary cooling operation parameters (step S103). The cooling operation parameter specifying unit 72 outputs the set cooling operation parameters to the shape prediction unit 70. The shape prediction unit 70 reads out the shape prediction model 78 from the storage unit 66 and inputs the acquired rolling operation parameters, cooling operation parameters, and attribute parameters into the shape prediction model 78 to output the amount of bending of the NAB 10, thereby predicting the amount of bending of the NAB 10 to be manufactured (step S104). The shape prediction unit 70 outputs the output predicted value of the amount of bending of the NAB 10 to the cooling operation parameter specifying unit 72.

[0065] The range of the allowable value of the amount of bending of the NAB 10 may be predetermined and stored in the storage unit 66, or may be input by the operator via the input unit 62. After acquiring the predicted value of the amount of bending of the NAB 10 and the range of the allowable value of the amount of bending of the NAB 10, the cooling operation parameter specifying unit 72 determines whether the predicted value of the amount of bending of the NAB 10 is within the allowable value range (step S105). If the cooling operation parameter specifying unit 72 determines that the predicted value of the amount of bending of the NAB 10 is outside the allowable value range (step S105: No), it changes the cooling operation parameters in accordance with predetermined conditions (step S106), returns the process to step S104, and repeatedly executes the processes of steps S104 to S106 until the predicted amount of bending of the NAB 10 is within the allowable value range.

[0066] On the other hand, if it is determined in step S105 that the predicted value of the amount of bending of the NAB 10 is within the allowable range (step S105: Yes), the cooling operation parameter specifying unit 72 specifies that the cooling operation parameters used to predict the amount of bending of the NAB 10 are cooling operation parameters that can bring the amount of bending of the NAB 10 within the allowable range (step S107), and the flow of the cooling operation parameter specifying process shown in Fig. 8 ends. The cooling operation parameter specifying unit 72 outputs the specified cooling operation parameters to the process computer 56, and sets the specified cooling operation parameters as the cooling operation parameters of the cooling devices 42, 44.

[0067] In this way, by specifying the cooling operation parameters, it is possible to specify the cooling operation parameters that can control the amount of bending of the NAB 10 within the tolerance range. Then, by manufacturing the NAB 10 under manufacturing conditions that include the cooling operation parameters, it becomes possible to stably manufacture the NAB 10 whose amount of bending of the NAB 10 falls within the tolerance range, thereby suppressing an increase in the manufacturing cost of the NAB 10 and a decrease in productivity.

[0068] Next, we will explain how to generate a shape prediction model used to predict the amount of bending of the NAB 10. The data acquisition unit 68 acquires from the process computer 56 the actual values ​​of the rolling operation parameters, the actual values ​​of the cooling operation parameters, the actual values ​​of the attribute parameters, and the actual values ​​of the amount of bending of the NAB 10 for previously manufactured NABs 10, and stores these as one set of data in the database 76 of the storage unit 66. The number of data sets stored in the database 76 is preferably at least 500, and more preferably 2000 or more.

[0069] The shape prediction model generation unit 74 reads a pre-stored machine learning model from the storage unit 66, and performs machine learning on the machine learning model using the dataset stored in the database 76 as training data to generate a trained machine learning model. This trained machine learning model serves as the shape prediction model. The machine learning model used in the unequal leg angle steel shape prediction method and unequal leg angle steel shape prediction device 58 according to this embodiment may be any of commonly used neural networks, decision tree learning, random forests, and support vector regression. Instead of the amount of bending of the NAB 10, the output of the shape prediction model may be a machine learning model that outputs binary data, either pass or fail, indicating whether the amount of bending of the NAB 10 is within the tolerance range. In this case, a classification model such as k-nearest neighbor or logistic regression may be used.

[0070] The shape prediction model may be updated to a new one by re-training it every month or every year. The data acquisition unit 68 acquires performance data for each NAB 10 manufactured and stores it in the database 76. As performance data for newly manufactured NAB 10 is stored in the database 76, the amount of stored performance data increases. The greater the amount of performance data, the more accurate the shape prediction becomes. Therefore, by periodically performing machine learning to update the shape prediction model, the amount of bending of the NAB 10 can be predicted with even greater accuracy.

[0071] As described above, the shape prediction method for unequal leg angle steel and the shape prediction model used in the unequal leg angle steel shape prediction device 58 according to this embodiment include rolling operation parameters as input data. As described above, rolling operation parameters in the rolling process affect the amount of bending of unequal leg angle steel. Therefore, by including rolling operation parameters in the input data of the shape prediction model that predicts the amount of bending of unequal leg angle steel, the shape prediction model becomes a shape prediction model that takes the rolling process into account, and the prediction accuracy of the amount of bending of unequal leg angle steel is improved. Furthermore, if the amount of bending of unequal leg angle steel can be predicted with high accuracy in this way, it is possible to prevent the production of unequal leg angle steels with bending amounts exceeding the allowable range, thereby suppressing increases in the manufacturing costs of unequal leg angle steels and decreases in productivity.

[0072] Furthermore, by including attribute parameters of unequal leg angle steel in the input data of the shape prediction model, it becomes possible to predict the amount of bending of unequal leg angle steels with different target cross-sectional dimensions, chemical components, and / or mechanical properties using the same shape prediction model. Furthermore, by identifying cooling operation parameters that will bring the amount of bending of unequal leg angle steel predicted by the shape prediction model within an allowable range, and manufacturing unequal leg angle steels under manufacturing conditions that include these cooling operation parameters, it becomes possible to consistently manufacture unequal leg angle steels with an amount of bending within an allowable range.

[0073] Although the rolling equipment 100 shown in FIG. 5 includes a process computer 56 and an unequal-leg angle steel shape prediction device 58, the present invention is not limited to this. For example, the process computer 56 may have the functions of the unequal-leg angle steel shape prediction device 58, and these functions may be configured as a single device. Furthermore, in the unequal-leg angle steel shape prediction device 58 shown in FIG. 8, the control unit 60 includes a cooling operation parameter identification unit 72 and a shape prediction model generation unit 74, but the present invention is not limited to this. If the unequal-leg angle steel shape prediction device 58 predicts the amount of bending of the NAB 10, the control unit 60 does not need to include the cooling operation parameter identification unit 72. Furthermore, if the shape prediction model 78 is generated externally and stored in the storage unit 66 via the data acquisition unit 68, the unequal-leg angle steel shape prediction device 58 does not need to include the shape prediction model generation unit 74. [Example]

[0074] [Example 1] To verify the effects of the present invention, an NAB made of SS400 steel, with a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m was manufactured using NAB manufacturing equipment 100 shown in Fig. 5. The steel billet used as the raw material was rolled into an NAB with the desired cross section through five passes in rough rolling, a total of six passes in intermediate rolling, and one pass in finish rolling.

[0075] The cooling devices 42 and 44 before and after the finishing mill 38 were equipped with flat spray nozzles (see Figure 6) and cooled the long and short sides of the NAB from above and below. The spray widths of the flat spray nozzles on the long and short sides were 170 mm and 70 mm, respectively, and the nozzles were spaced 200 mm apart in the conveying direction. The total length of the cooling devices 42 and 44 was 18 m, and they were divided into six banks, each 3 m apart. A thermometer 46 was installed 5 m upstream from the inlet of the cooling device 42, a thermometer 48 was installed 5 m downstream from the finishing mill 38, a thermometer 50 was installed 20 m downstream from the outlet of the cooling device 44, and a shapemeter 54 was installed 25 m downstream from the outlet of the cooling device 44. The temperature and bending of the NAB were measured using the thermometers 46, 48, and 50 and the shapemeter 54. The temperatures measured by the thermometers 46, 48, 50 were used to verify that the temperatures of the NABs cooled by the cooling devices 42, 44 were within a predetermined range.

[0076] <Example 1> In Example 1 of the invention, approximately 5,000 data sets were prepared as learning data, each set consisting of the most recent past NAB rolling operation parameters, cooling operation parameters, attribute parameters, and actual values ​​of NAB bending amount, and a shape prediction model was used in which a neural network model was machine-learned using the data sets.

[0077] The rolling operation parameters used were the gap between the upper and lower rolls and the leveling amount in each pass of the roughing mill 32, the intermediate mills 34 and 36, and the finishing mill 38. The cooling operation parameters used were the flow rate density of the cooling water in each of the cooling devices 42 and 44 and the cooling time.

[0078] In Example 1 of the present invention, calculations were performed repeatedly using a shape prediction model according to the flowchart shown in Figure 9, and cooling operation parameters were identified that would result in the predicted value of the NAB bending amount being within the allowable range of less than ±0.003L mm (less than 0.3% of the longitudinal length L of the NAB, 60 mm in this case).

[0079] The cooling operation parameter to be reset is the cooling time, and the cooling operation parameter finally specified is to set the water flow density of the cooling water on the long side of the cooling device 42 to 1000 L / m 2 When the cooling time is 1.8 seconds, the cooling water flow density on the short side is 0. Also, when the cooling water flow density on the long side in the cooling device 44 is 1200 L / m 2 min, the cooling time is 4.4 s, and the water flow density on the short side is 1200 L / m 2 The cooling time was 2.9 s when the temperatures of the long sides measured by thermometers 46, 48, and 50 were 901°C, 825°C, and 701°C, respectively, and the temperatures of the short sides were 932°C, 827°C, and 680°C, respectively.

[0080] When an NAB with a material of SS400, a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m was manufactured under manufacturing conditions including the cooling operation parameters specified as described above, the predicted value of the amount of bending by machine learning was +12 mm, whereas the actual amount of bending measured by the shape meter 54 was +17 mm. From these results, it was confirmed that the amount of bending of the NAB can be predicted with high accuracy by using a shape prediction model including rolling operation parameters, and that by manufacturing an NAB with cooling operation parameters specified using the prediction results, it is possible to manufacture an NAB with an amount of bending of less than ±0.003L mm, which is within the allowable range.

[0081] <Comparative Example 1-1> In Comparative Example 1-1, a NAB was manufactured using the same material as in Invention Example 1, with a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m. The manufacturing equipment was the same as in Invention Example 1.

[0082] In Comparative Example 1-1, a data set of about 5,000 items, each of which was a set of the most recent past NAB cooling operation parameters and the actual values ​​of the NAB bending amount, was prepared as learning data, and a shape prediction model was used in which a neural network model was machine-learned using the data set. That is, in Comparative Example 1-1, a shape prediction model in which the input data did not include rolling operation parameters was used.

[0083] In Comparative Example 1-1, calculations were also performed repeatedly using the shape prediction model according to the flowchart shown in Figure 9, and cooling operation parameters were identified that satisfied the predicted value of the NAB bending amount of less than ±0.003 L mm. The cooling operation parameters finally identified were a water flow density of the cooling water on the long side in the cooling device 42 of 1000 L / m 2 When the cooling time is 1.5 seconds, the cooling water flow rate density on the short side is 0. Also, when the cooling water flow rate density on the long side in the cooling device 44 is 1200 L / m 2 min, the cooling time is 4.4 s, and the water flow density on the short side is 1200 L / m 2 The cooling time was 2.9 s when the temperatures of the long sides measured by thermometers 46, 48, and 50 were 902°C, 835°C, and 711°C, respectively, and the temperatures of the short sides were 933°C, 865°C, and 701°C, respectively.

[0084] When an NAB with a material of SS400, a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m was manufactured under manufacturing conditions including the cooling operation parameters identified as described above, the predicted value of the amount of bending by machine learning was +45 mm, whereas the actual amount of bending measured with the shape meter 54 was +62 mm. From these results, it was confirmed that when a shape prediction model that does not include rolling operation parameters is used, the amount of bending of the NAB cannot be predicted with high accuracy, and that even if an NAB is manufactured using cooling operation parameters identified using the prediction results, it is not possible to manufacture an NAB with an amount of bending of less than ±0.003L mm, which is within the allowable range.

[0085] <Comparative Example 1-2> In Comparative Example 1-2, a NAB was manufactured using the same material as in Invention Example 1, with a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m. The manufacturing equipment was the same as in Invention Example 1.

[0086] In Comparative Example 1-2, the amount of NAB bending was predicted using an equation expressed as a linear sum of each parameter of the rolling conditions disclosed in Patent Document 2. Furthermore, unlike Invention Example 1 and Comparative Example 1-1, the amount of bending was not repeatedly predicted, and cooling operation parameters were used that were set based on the results of heat transfer calculations that were previously performed offline using the finite element method. The cooling operation parameters used in Comparative Example 1-2 were a water flow density of the cooling water on the long side in the cooling device 42 of 1000 L / m 2 When the cooling time is 1.5 seconds, the cooling water flow rate density on the short side is 0. Also, when the cooling water flow rate density on the long side in the cooling device 44 is 1200 L / m 2 min, the cooling time is 4.4 s, and the water flow density on the short side is 1200 L / m 2 The cooling time was 2.9 s when the temperatures of the long sides measured by thermometers 46, 48, and 50 were 901°C, 833°C, and 713°C, respectively, and the temperatures of the short sides were 931°C, 867°C, and 699°C, respectively.

[0087] When an NAB having a material of SS400, a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m was manufactured under manufacturing conditions including these cooling operation parameters, the predicted value of the amount of bending was +50 mm, whereas the actual amount of bending measured by the shape meter 54 was +65 mm. From this result, it was confirmed that even if the formula expressed as a linear sum of each parameter of the rolling conditions disclosed in Patent Document 2 is used, the amount of bending of the NAB cannot be predicted with high accuracy, and that it is not possible to manufacture an NAB with an amount of bending of less than ±0.003L mm, which is within the allowable range.

[0088] As described above, in Example 1 of the present invention, by using a shape prediction model in which rolling operation parameters and cooling operation parameters are input data and the amount of NAB bending is output data to predict the amount of NAB bending after passing through the cooling device, it is possible to predict the amount of NAB bending with higher accuracy than in Comparative Examples 1-1 and 1-2.Furthermore, it was confirmed that by manufacturing an NAB using cooling operation parameters identified using the prediction results, it is possible to manufacture an NAB whose amount of bending is within the allowable range.

[0089] [Example 2] In Example 2, an NAB having a long side width of 200 mm, a long side thickness of 10 mm, a short side width of 90 mm, a short side thickness of 14 mm, and a length of 20 m was manufactured using the same rolling equipment as in Example 1. The differences between Example 1 and Example 2 are the dimensions and steel type of the NAB manufactured, and the spray widths of the flat spray nozzles for cooling the long sides and short sides in cooling devices 42 and 44, which were set to 370 mm and 75 mm, respectively.

[0090] <Example 2> In Example 2 of the invention, a data set of approximately 5,000 items was prepared as learning data, each set consisting of the most recent past NAB rolling operation parameters, cooling operation parameters, attribute parameters, and actual values ​​of NAB bending amount, and a shape prediction model was used in which a neural network model was machine-learned using the data set.

[0091] The rolling operation parameters used were the gap between the upper and lower rolls and the leveling amount in each pass of the roughing mill 32, the intermediate mills 34 and 36, and the finishing mill 38. The cooling operation parameters used were the flow rate density of the cooling water in each of the cooling devices 42 and 44 and the cooling time.

[0092] In Example 2 of the present invention, calculations were performed repeatedly using the shape prediction model according to the flowchart shown in Figure 9, and cooling operation parameters were identified that satisfied the tolerance range of the predicted value of the NAB bending amount, which was less than ±0.003L mm (less than 0.3% of the longitudinal length L of the NAB, 45 mm in this case).

[0093] The cooling operation parameter to be reset is the cooling time, and the cooling operation parameter finally specified is to set the water flow density of the cooling water on the long side of the cooling device 42 to 1000 L / m 2 When the cooling time is 2.3 seconds, the cooling water flow density on the short side is 0. Also, when the cooling water flow density on the long side in the cooling device 44 is 1200 L / m 2 min, the cooling time is 9.0 s, and the water flow density of the cooling water on the short side is 1200 L / m 2 The cooling time was 6.0 s. At this time, the temperatures of the long sides measured by thermometers 46, 48, and 50 were 920°C, 849°C, and 650°C, respectively, and the temperatures of the short sides were 944°C, 850°C, and 615°C, respectively.

[0094] An NAB having a long side width of 400 mm, a long side thickness of 13 mm, a short side width of 100 mm, a short side thickness of 16 mm, and a length of 15 m was manufactured under manufacturing conditions including the cooling operation parameters identified as described above. As a result, the predicted value of the amount of bending by machine learning was +9 mm, while the actual amount of bending measured by the shape meter 54 was +12 mm. From these results, it was confirmed that the amount of bending of the NAB can be predicted with high accuracy by using a shape prediction model including rolling operation parameters, and that by manufacturing an NAB with cooling operation parameters identified using the prediction results, it is possible to manufacture an NAB with an amount of bending of less than ±0.003L mm, which is within the allowable range.

[0095] <Comparative Example 2-1> In Comparative Example 2-1, a NAB was manufactured using the same material as in Invention Example 2, SM490A, with a long side width of 400 mm, a long side thickness of 13 mm, a short side width of 100 mm, a short side thickness of 16 mm, and a length of 15 m. The manufacturing equipment was the same as in Invention Example 1.

[0096] In Comparative Example 2-1, a data set of about 5,000 items, each of which was a set of the most recent past NAB cooling operation parameters and the actual values ​​of the NAB bending amount, was prepared as learning data, and a shape prediction model was used in which a neural network model was machine-learned using the data set. That is, in Comparative Example 2-1, a shape prediction model in which the input data did not include rolling operation parameters was used.

[0097] In Comparative Example 2-1, calculations were also performed repeatedly using the shape prediction model according to the flowchart shown in Figure 9, and cooling operation parameters were identified that satisfied the predicted value of the NAB bending amount of less than ±0.003 L mm. The cooling operation parameters finally identified were a water flow density of the cooling water on the long side in the cooling device 42 of 1000 L / m 2 When the cooling time is 1.5 seconds, the cooling water flow rate density on the short side is 0. Also, when the cooling water flow rate density on the long side in the cooling device 44 is 1200 L / m 2 min, the cooling time is 9.0 s, and the water flow density of the cooling water on the short side is 1200 L / m 2 The cooling time was 6.0 s when the temperature of the long sides measured by thermometers 46, 48, and 50 was 921°C, 860°C, and 660°C, respectively, and the temperature of the short sides was 931°C, 880°C, and 640°C, respectively.

[0098] When an NAB with material SM490A, long side width 400 mm, long side thickness 13 mm, narrow side width 100 mm, narrow side thickness 16 mm, and length 15 m was manufactured under manufacturing conditions including the cooling operation parameters identified as described above, the predicted value of the amount of bending by machine learning was +36 mm, whereas the actual amount of bending measured with shape meter 54 was +47 mm. From these results, it was confirmed that when a shape prediction model that does not include rolling operation parameters is used, the amount of bending of the NAB cannot be predicted with high accuracy, and that even if an NAB is manufactured using cooling operation parameters identified using the prediction results, it is not possible to manufacture an NAB with an amount of bending of less than ±0.003L mm, which is within the allowable range.

[0099] <Comparative Example 2-2> In Comparative Example 2-2, a NAB was manufactured using the same material as in Invention Example 2, SM490A, with a long side width of 400 mm, a long side thickness of 13 mm, a short side width of 100 mm, a short side thickness of 16 mm, and a length of 15 m. The manufacturing equipment was the same as in Invention Example 1.

[0100] In Comparative Example 2-2, the amount of NAB bending was predicted using an equation expressed as a linear sum of each parameter of the rolling conditions disclosed in Patent Document 2. Furthermore, unlike Invention Example 2 and Comparative Example 2-1, the amount of bending was not repeatedly predicted for the cooling conditions, but cooling operation parameters were used that were set based on the results of heat transfer calculations that were performed offline in advance using the finite element method. The cooling operation parameters used in Comparative Example 2-2 were a water flow density of the cooling water on the long side in the cooling device 42 of 1000 L / m 2 When the cooling time is 1.5 seconds, the cooling water flow rate density on the short side is 0. Also, when the cooling water flow rate density on the long side in the cooling device 44 is 1200 L / m 2 min, the cooling time is 9.0 s, and the water flow density of the cooling water on the short side is 1200 L / m 2 The cooling time was 6.0 s. At this time, the temperatures of the long sides measured by thermometers 46, 48, and 50 were 920°C, 861°C, and 662°C, respectively, and the temperatures of the short sides were 930°C, 883°C, and 635°C, respectively.

[0101] When an NAB was manufactured under manufacturing conditions including these cooling operation parameters, using material SM490A, with a long side width of 400 mm, a long side thickness of 13 mm, a short side width of 100 mm, a short side thickness of 16 mm, and a length of 15 m, the predicted amount of bending was +30 mm, whereas the actual amount of bending measured by shape meter 54 was +49 mm. From this result, it was confirmed that even if the formula expressed as a linear sum of each parameter of the rolling conditions disclosed in Patent Document 2 is used, the amount of bending of the NAB cannot be predicted with high accuracy, and that it is not possible to manufacture an NAB with an amount of bending of less than ±0.003L mm, which is within the allowable range.

[0102] As described above, in Example 2 of the present invention, by using a shape prediction model in which rolling operation parameters and cooling operation parameters are input data and the amount of NAB bending is output data to predict the amount of NAB bending after passing through the cooling device, it is possible to predict the amount of NAB bending with higher accuracy than in Comparative Examples 2-1 and 2-2.Furthermore, it was confirmed that by manufacturing an NAB using cooling operation parameters identified using the prediction results, it is possible to manufacture an NAB with an amount of bending within the allowable range. [Explanation of symbols]

[0103] 10 NAB 12 Long Side 14 Short Side 16 ABS 17 Short Side 18 long side 20 BP 21 Short Side 30 Furnace 32 Roughing mill 34 Intermediate rolling mill 36 Intermediate rolling mill 38 Finishing Rolling Mill 40 Rolling control device 42 Cooling device 44 Cooling device 45 Flat Spray 46 Thermometer 48 Thermometer 50 thermometer 52 Cooling control device 54 Shape meter 56 Process Computer 58 Shape prediction device for unequal angle steel 60 Control Unit 62 Input section 64 Output section 66 Storage area 68 Data Acquisition Section 70 Shape Prediction Unit 72 Cooling operation parameter specification section 74 Shape prediction model generation unit 76 databases 78 Shape Prediction Model 100 Rolling Equipment

Claims

1. A method for predicting the amount of bending of unequal leg angle steel produced by rolling equipment having a plurality of rolling mills for hot rolling heated steel billets into unequal leg angle steel, a cooling device for cooling the unequal leg angle steel, and a shape meter for measuring the amount of bending of the unequal leg angle steel after cooling, comprising: A shape prediction method for unequal leg angle steel, which inputs input data including rolling operation parameters of the rolling mill, cooling operation parameters of the cooling device, and attribute parameters of the unequal leg angle steel into a shape prediction model, and outputs the amount of bending to predict the amount of bending.

2. Identifying the cooling operation parameters such that the amount of bending predicted using the shape prediction method for unequal leg angle steel according to claim 1 falls within a predetermined allowable value range; A method for manufacturing unequal angle steel, which manufactures unequal angle steel under manufacturing conditions including specified cooling operation parameters.

3. A rolling facility comprising a plurality of rolling mills that hot-roll heated steel billets into unequal leg angle steels, a cooling device that cools the unequal leg angle steels, a shape meter that measures the amount of bending of the unequal leg angle steels after cooling, and a shape prediction device for the unequal leg angle steels that predicts the amount of bending, The shape prediction device includes a shape prediction unit that inputs input data including rolling operation parameters of the rolling mill, cooling operation parameters of the cooling device, and attribute parameters of the unequal leg angle iron into a shape prediction model and outputs the amount of bending.

4. A method for generating a shape prediction model for predicting the amount of bending of unequal leg angle steel produced by rolling equipment having a plurality of rolling mills for hot rolling heated steel billets into unequal leg angle steel, a cooling device for cooling the unequal leg angle steel, and a shape meter for measuring the amount of bending of the unequal leg angle steel after cooling, comprising: a machine learning model is trained using a plurality of data sets as training data, each set being a set of actual values ​​of rolling operation parameters of the rolling mill for unequal leg angle steel produced in the past, actual values ​​of cooling operation parameters of the cooling device, actual values ​​of attribute parameters of the unequal leg angle steel, and actual values ​​of the amount of bending; A method for generating a shape prediction model, which generates a shape prediction model that receives the rolling operation parameters, the cooling operation parameters, and the attribute parameters as inputs and outputs the amount of bending.

5. The method for generating a shape prediction model according to claim 4 , wherein the machine learning model is one of a neural network, a decision tree learning model, a random forest model, and a support vector regression model.

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