H-beam shape prediction method, H-beam manufacturing method, rolling equipment, and shape prediction model generation method

Abstract

To provide an H-shaped steel prediction method that can predict a flange warpage amount of an H-shaped steel with good accuracy.SOLUTION: An H-shaped steel prediction method predicts a flange warpage amount of an H-shaped steel manufactured by a rolling facility having a plurality of rolling machines which hot-roll a heated billet to the H-shaped steel, a cooling device for cooling the H-shaped steel, and a shape meter which mesures flange warpage of the H-shaped steel after cooling. The method predicts the flange warpage amount by inputting input data including a rolling operational parameter of the rolling machine, a cooling operational parameter of the cooling device and an attribute parameter of the H-shaped steel to a shape prediction model and outputting the flange warpage amount.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a shape prediction method for H-beam that predicts the amount of flange warpage of an H-beam, a manufacturing method for H-beam, rolling equipment, and a method for generating a shape prediction model. [Background technology]

[0002] H-shaped steel beams, which are used as beams and columns in buildings, are generally manufactured by rolling. Figure 1 is a schematic cross-sectional view showing the cross-sectional shape of an H-shaped steel beam 10. The relatively thick portions of the H-shaped steel beam 10 are the flanges 12, and the relatively thin portions where the pair of flanges 12 are joined are the webs 14. There are numerous combinations of dimensions for the H-shaped steel beam 10, such as the thickness tf and width B of the flanges 12 and the thickness tw and height H of the webs 14, ranging from several dozen for constant inside dimension H-shaped steel (JISH) to several hundred for constant outside dimension H-shaped steel. Here, the outside dimension refers to the web height H of the H-shaped steel beam 10, and the inside dimension refers to the outside dimension minus the flange thickness.

[0003] In addition, the strength classification of H-shaped steel 10 can be broadly divided into tensile strength of 400N / mm 2 class, 490N / mm 2 The chemical composition of the H-shaped steel 10 is adjusted appropriately for each steel type according to this strength classification. When the H-shaped steel 10 is actually used as a component, the dimensions and strength of the H-shaped steel 10 are selected according to the performance requirements of the building.

[0004] Such H-shaped steel 10 is mainly manufactured by hot rolling. H-shaped steel 10 manufactured by rolling is called rolled H-shaped steel. In the manufacture of rolled H-shaped steel, a material preheated in a heating furnace is subjected to rough rolling, intermediate rolling, and finish rolling by multiple rolling mills to be formed into an H-shaped steel 10 of the target dimensions.

[0005] For example, in rough rolling, the raw steel billet is rolled in multiple passes by a roughing mill having a pair of upper and lower rolls with multiple grooves, roughly shaping it to approach the target dimensions. Next, in intermediate rolling, multiple passes are performed by an intermediate universal rolling mill having a pair of upper and lower horizontal rolls and a pair of left and right hard rolls, and an edger rolling mill having a pair of upper and lower horizontal rolls, further shaping it to approach the target dimensions. Finally, in finish rolling, one pass is performed by a finish universal rolling mill having a pair of upper and lower horizontal rolls and a pair of left and right hard rolls, shaping it into an H-beam product of the target dimensions.

[0006] The Japanese Industrial Standard (JIS) G3192 (Shape, Dimensions, Mass, and Tolerances of Hot-Rolled Section Steel) and corresponding international standards specify the allowable cross-sectional dimensions of section steel. For example, for H-section steel, the typical values ​​and allowable values ​​for each dimension, such as web height H, flange width B, web thickness tw, and flange thickness tf, are specified, and all of these dimensions must be controlled to fall within the allowable range. Therefore, rolling conditions such as roll spacing for each rolling mill and each pass are adjusted and modified to ensure that each dimension falls within the allowable range.

[0007] Furthermore, since the flange 12 of the H-shaped steel 10 is thicker than the web 14, the flange 12 is slower to cool than the web 14 during the cooling process during rolling, resulting in a higher temperature. Residual stress caused by this temperature difference between the flange 12 and the web 14 can cause the web 14 to buckle and deform into a wavy shape after cooling. Therefore, for example, a cooling device may be installed before the finishing mill to cool the outer surface of the flange 12 of the H-shaped steel 10 during the hot rolling process. This can eliminate the temperature difference between the flange 12 and the web 14 and suppress buckling deformation of the web 14. Furthermore, when manufacturing higher-strength H-shaped steel, an accelerated cooling device may be installed on the outlet side of the finishing mill to cool the flange 12 in order to control the structure of the steel material.

[0008] On the other hand, if such cooling of the flange 12 causes a large temperature difference between the outer and inner surfaces of the flange 12, then thermal stress caused by this temperature difference or stress caused by phase transformation may cause flange warpage, in which the flange 12 warps outward or inward. Figure 2 is a cross-sectional schematic diagram of an H-shaped steel beam with flange warpage. Figure 2(a) shows an outwardly warped state in which the flange warps outward, while Figure 2(b) shows an inwardly warped state in which the flange warps inward. If the amount of flange warpage as shown in Figure 2 becomes large, additional processes such as pressing are required for the manufactured H-shaped steel beam 10, which increases manufacturing costs and reduces productivity.

[0009] As a technique for preventing flange warpage of H-shaped steel 10, Patent Document 1 discloses a flange shape control method for shaped steel in which a flange temperature in finish rolling that improves flange warpage is determined in advance through experiments according to the dimensions and type of H-shaped steel 10. According to Patent Document 1, the amount of flange warpage can be reduced by adjusting the flange cooling conditions so that the flange temperature is within ±25°C of the previously determined flange temperature.

[0010] Patent Document 2 discloses a manufacturing method for H-beams in which a temperature calculation model is used to predict the thermal history under certain cooling conditions, a shape calculation model is used to predict the shape from the predicted thermal history, and the cooling conditions are adjusted so that the predicted shape is favorable. According to Patent Document 2, by manufacturing H-beams based on cooling conditions found by repeatedly performing thermal history calculations and shape calculations, it is possible to prevent shape defects caused by cooling. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] Japanese Patent Application Publication No. 7-323320 [Patent Document 2] Japanese Patent Application Publication No. 8-323402 Summary of the Invention [Problem to be solved by the invention]

[0012] However, with the technology disclosed in Patent Document 1, it is necessary to determine the flange temperature during finish rolling that will improve flange warpage through experiments for a wide variety of H-beam dimensions, which requires a great deal of effort. Furthermore, flange warpage is also affected by stresses caused by phase transformation during cooling. Even if the thermal history and temperature distribution are the same, stresses caused by phase transformations vary depending on the steel type. Therefore, with the technology disclosed in Patent Document 1, it is necessary to determine the flange temperature during finish rolling that will improve flange warpage for each steel type, which requires a great deal of effort.

[0013] The technology disclosed in Patent Document 2 presents an example of a shape prediction model, which is an equation expressed as a linear sum using the dimensions of the H-beam and the flange temperature during finish rolling. However, it is difficult to accurately predict the shape of an H-beam with a two-dimensional cross-sectional shape using such an equation. Furthermore, flange warpage may change due to the influence of not only temperature but also rolling conditions such as the roll gap of each rolling mill used for dimension adjustment, i.e., the influence of plastic deformation due to rolling. Therefore, it is difficult to accurately predict flange warpage using a shape prediction model that does not take into account the influence of rolling. Accurately predicting the shape of an H-beam considering the influence of not only temperature but also rolling requires two-dimensional or three-dimensional structural analysis, which imposes a very high computational load for online prediction and is beyond the capabilities of current computers.

[0014] The present invention has been made to solve the above-mentioned problems, and its object is to provide a shape prediction method for H-shaped steel that can accurately predict the amount of flange warpage of H-shaped steel, a manufacturing method for H-shaped steel that uses the shape prediction method for H-shaped steel, rolling equipment, and a method for generating a shape prediction model. [Means for solving the problem]

[0015] The means for solving the above problems are as follows. [1] A shape prediction method for H-shaped steel that predicts the amount of flange warpage of H-shaped steel manufactured in a rolling facility having a plurality of rolling mills that hot-roll heated steel billets into H-shaped steel, a cooling device that cools the H-shaped steel, and a shape meter that measures the flange warpage of the H-shaped steel 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 H-shaped steel are input into a shape prediction model, and the flange warpage amount is output to predict the flange warpage amount. [2] A method for manufacturing H-shaped steel, comprising: specifying cooling operation parameters such that the amount of flange warpage predicted using the shape prediction method for H-shaped steel described in [1] falls within a predetermined allowable range; and manufacturing the H-shaped steel under manufacturing conditions including the specified cooling operation parameters. [3] A rolling facility having a plurality of rolling mills that hot-roll heated steel billets into H-shaped steel, a cooling device that cools the H-shaped steel, a shape meter that measures the amount of flange warpage of the H-shaped steel after cooling, and a shape prediction device for H-shaped steel that predicts the amount of flange warpage, wherein the shape prediction device has a shape prediction unit that inputs input data including rolling operation parameters of the rolling mills, cooling operation parameters of the cooling device, and attribute parameters of the H-shaped steel into a shape prediction model and outputs the amount of flange warpage. [4] A method for generating a shape prediction model that predicts the amount of flange warpage of H-shaped steel produced by rolling equipment having a plurality of rolling mills that hot-roll heated steel billets into H-shaped steel, a cooling device that cools the H-shaped steel, and a shape meter that measures the flange warpage of the H-shaped steel after cooling, the method comprising: training a machine learning model using multiple data sets, each set consisting of actual values ​​of rolling operation parameters of the rolling mills for H-shaped steel produced in the past, actual values ​​of cooling operation parameters of the cooling device, actual values ​​of attribute parameters of the H-shaped steel, and actual values ​​of the flange warpage, as training data; and generating a shape prediction model that uses the rolling operation parameters, the cooling operation parameters, and the attribute parameters as inputs and outputs the flange warpage amount. [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]

[0016] According to the present invention, the flange warpage of an H-shaped steel is predicted using a shape prediction model that takes into account the effects of not only cooling but also rolling, so that the flange warpage of an H-shaped steel can be predicted with high accuracy. Furthermore, by using the predicted flange warpage results to control the cooling in the production of the H-shaped steel, it becomes possible to stably produce H-shaped steel whose flange warpage falls within the allowable range. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a cross-sectional schematic diagram showing the cross-sectional shape of an H-beam. [Figure 2] Figure 2 is a cross-sectional schematic diagram of an H-beam with flange warpage. [Figure 3] FIG. 3 is a schematic diagram showing the general configuration of a rolling facility including an H-beam shape prediction device capable of implementing the H-beam shape prediction method according to this embodiment. [Figure 4] FIG. 4 is a schematic diagram showing an outline of the roll arrangement of the finishing rolling mill. [Figure 5] FIG. 5 is a functional block diagram of the shape prediction device for structural steel. [Figure 6] FIG. 6 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

[0018] An embodiment of the present invention will be described below with reference to the drawings. 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 in terms of the quality, shape, structure, arrangement, etc. of the components.

[0019] An embodiment of the present invention will be described with reference to Fig. 3 to Fig. 6. Fig. 3 is a schematic diagram showing the general configuration of a rolling facility 100 including an H-beam shape prediction device capable of implementing the H-beam shape prediction method according to this embodiment.

[0020] As shown in Figure 3, the rolling equipment 100 includes a heating furnace 20 that heats the steel billet to a predetermined temperature, a roughing mill 22, an intermediate mill 24, and a finishing mill 28 that roll the steel billet heated in the heating furnace 20, a rolling control device 30 that controls the operation of these multiple rolling mills, cooling devices 32 and 34 that cool the H-shaped steel 10 during rolling, thermometers 36, 38, and 40 that measure the temperature of the H-shaped steel 10 during rolling at the inlet and outlet sides of the cooling devices 32 and 34, a cooling control device 42 that controls the operation of the cooling devices 32 and 34, a shape meter 44 that measures the amount of flange warp of the H-shaped steel 10 after cooling, a process computer 46, and an H-shaped steel shape prediction device 48.

[0021] Steel billets are charged into the heating furnace 20 and heated to a temperature above the austenite temperature range (for example, 1100 to 1300°C). The steel billets extracted from the heating furnace 20 are transported to the rolling mill by a plurality of table rolls installed on the outlet side of the heating furnace 20, and are hot rolled into H-section steel 10 by a roughing mill 22, an intermediate rolling mill 24, and a finishing rolling mill 28. Furthermore, before and after the rolling mills, the H-section steel 10 is cooled by cooling devices 32, 34 while being transported by the table rolls.

[0022] In the rough rolling, the steel sheet is subjected to roughly 5 to 25 passes by a rough rolling mill 22 having a pair of upper and lower grooved rolls each having a plurality of grooves. Rough rolling is also called breakdown rolling.

[0023] In intermediate rolling, one or more intermediate universal rolling mills 25 and one or more edger rolling mills 26 are often used in combination as the intermediate rolling mills 24. In intermediate rolling, the intermediate universal rolling mills 25 and the edger rolling mills 26 are used to perform about 5 to 25 passes of reverse rolling until the product has roughly the cross-sectional dimensions.

[0024] The intermediate universal rolling mill 25 has a total of four rolls: a pair of upper and lower horizontal rolls driven to rotate about a horizontal axis, and a pair of left and right hard rolls that freely rotate about a vertical axis. The intermediate universal rolling mill 25 has the function of simultaneously reducing the web thickness tw and flange thickness tf of the H-section steel 10, and rolls the flange 12 so that, for example, the flange 12 is tilted outward by a maximum of approximately 10°. The horizontal rolls are selected to have a width corresponding to the height H of the web 14 of the H-section steel 10 and are installed in the intermediate universal rolling mill 25. The intermediate universal rolling mill 25 is designed to allow for arbitrary adjustment of the spacing between the upper and lower horizontal rolls, the relative axial positions of the horizontal rolls, and the spacing between the side surfaces of the horizontal rolls and the hard roll. Specifically, the spacing between the upper and lower horizontal rolls can be adjusted within a range of 0 to 200 mm, the relative axial positions of the horizontal rolls can be displaced within a range of -7 to +7 mm, and the spacing between the side surfaces of the horizontal rolls and the hard roll can be adjusted within a range of 0 to 300 mm.

[0025] The edger rolling mill 26 has a pair of upper and lower horizontal rolls that are driven to rotate about a horizontal axis. The diameter of the horizontal rolls of the edger rolling mill 26 is, for example, about 800 to 1500 mm. Each horizontal roll is provided with a groove, and the front end of the flange 12 is pressed down from above and below to reduce the flange width B.

[0026] In the finish rolling, the intermediate-rolled material is rolled by the finish rolling mill 28, usually in one pass, to finish the cross-sectional dimensions, such as web thickness tw, flange thickness tf, web height H, and flange width B, to the target cross-sectional dimensions of the H-beam product. A finish universal rolling mill having a configuration substantially similar to that of the intermediate universal rolling mill 25 is used as the finish rolling mill 28. Like the intermediate universal rolling mill, the finish rolling mill 28 is capable of arbitrarily adjusting the spacing between the upper and lower horizontal rolls, the relative axial positions of the horizontal rolls, and the spacing between the side surfaces of the horizontal rolls and the hard rolls. Furthermore, in the finish rolling mill 28, the roll angle between the side surfaces of the horizontal rolls and the vertical rolls is set so that the flanges after finish rolling are inclined by a maximum of 1°.

[0027] 4 is a schematic diagram showing an outline of the roll arrangement of the finishing rolling mill 28. As shown in FIG. 4, the hard rolls are provided with a roll angle (θ) for tilting the flange 12. The roll spacing (1) of the web 14 and the roll spacings (2) to (5) of the four flange legs are adjusted by adjusting the position, spacing, and roll angle of each roll. The roll spacing (1) of the web 14 and the roll spacings (2) to (5) of the four flange legs are also adjusted in the intermediate universal rolling mill 25.

[0028] If the rolling conditions, such as the gap between the horizontal rolls and the hard rolls, are changed so that each dimension of the H-beam 10 falls within the tolerance range, the effect of plastic deformation on the cross section of the section steel changes, which in turn changes the final inclination of the flange 12, i.e., the magnitude of flange warpage. As described above, flange warpage is affected not only by temperature but also by rolling conditions, so the influence of the rolling conditions must be taken into account in order to accurately predict the amount of flange warpage. In particular, the finishing rolling mill 28 has a particularly strong correlation with flange warpage, as it performs rolling to incline the flange 12 at the end.

[0029] Referring again to Figure 3, the cooling devices 32, 34 cool the H-beam 10 under predetermined cooling conditions. The cooling devices 32, 34 are separated by a predetermined length (for example, 10 m), and each separation is called a bank. The cooling devices 32, 34 shown in Figure 3 each have four banks, but this is not limiting, and the number of banks may be increased or decreased from four. The cooling operation parameters that define the cooling conditions for the cooling devices 32, 34 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.

[0030] Increasing the water flow density of the cooling water can increase the cooling rate and temperature drop of the H-shaped steel 10. In addition, extending the cooling time can increase the temperature drop of the H-shaped steel 10. By adjusting the water flow density and cooling time, which are cooling operation parameters of the cooling devices 32 and 34, the surface temperature of the flange 12 after cooling can be controlled.

[0031] Furthermore, the water flow rate and cooling time may be changed independently for each of the cooling devices installed before and after finish rolling, such as the cooling devices 32 and 34. This is because flange warpage is correlated with the temperature of the flange 12 after finish rolling, and the mechanical properties of the steel material are correlated with the cooling stop temperature of the flange 12.

[0032] A typical flat spray nozzle, square spray nozzle, or multi-hole jet nozzle can be used for the cooling devices 32, 34. 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 flange 12 for cooling.

[0033] Thermometers 36, 38, 40 are provided on the inlet and outlet sides of the cooling devices 32, 34, and measure the surface temperature of the flange 12 of the H-shaped steel 10 before and after cooling by the cooling devices 32, 34. Thermometers 36, 38, 40 are devices that measure the temperature of the flange 12 of the H-shaped steel 10 by using scanning thermometers that scan temperature measurement points across the width of the flange 12, or by using a method in which multiple spot thermometers are arranged across the width of the flange 12. When the latter method is used, it is preferable to measure the temperature by arranging spot thermometers at at least five or more different positions across the width of the flange 12 in order to grasp the temperature distribution across the width of the flange 12.

[0034] In particular, flange warpage is correlated with the flange temperature after finish rolling. According to the inventors' investigations, flange warpage is particularly correlated with the temperature at the 1 / 2F position of the flange 12 (the center position in the flange width direction), so it is preferable to measure the temperature at least at the 1 / 2F position of the flange.

[0035] The temperature information measured by the thermometers 36, 38, 40 is output to the process computer 46. The temperature information output from the thermometers 36, 38, 40 may be temperature values ​​measured at representative points on the flange 12 (for example, five points equally spaced from the 1 / 2F position to the upper and lower ends of the flange in the width direction). Alternatively, the data obtained from the thermometers 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 H-beam 10 is within a predetermined range as a result of controlling the cooling devices 32, 34.

[0036] The shape meter 44 measures the amount of flange warpage of the H-shaped steel 10 after it has been cooled by the cooling device 34. As the shape meter 44, for example, a one-dimensional laser displacement meter capable of measuring the distance between one point can be used. The amount of warpage of the flange 12 is measured by scanning the flange 12 in the width direction with the laser displacement meter. When scanning the flange 12 in the width direction with the laser displacement meter in this way, it is preferable to set the measurement pitch to 20 to 50 mm in order to improve measurement accuracy.

[0037] Alternatively, a plurality of one-dimensional laser displacement meters may be provided in the flange width direction, and the flange warpage may be measured using these laser displacement meters. In this case, the shape of the flange 12 at a plurality of different positions in the width direction can be measured without scanning the laser displacement meters as described above. In this case as well, it is preferable to provide the laser displacement meters at a pitch of 20 to 50 mm in the flange width direction to improve measurement accuracy.

[0038] The shape data measured by the shape meter 44 is output to the process computer 46. The process computer 46 records the distance between the fillet portion and the flange width direction tip as the actual value of the flange warpage for the shape data acquired from the shape meter 44. The process computer 46 may also record parameters that can specify a polynomial function that approximates the warpage shape of the flange 12 as the actual value of the flange warpage.

[0039] The shape meter 44 does not necessarily have to be placed on an extension of the conveying direction of the H-shaped steel 10 as shown in Fig. 3. For example, it may be installed so that it can be measured on a cooling bed or the like that can measure the shape of the H-shaped steel 10 after it has passed through the cooling device 34. For example, if the flange warpage is measured when the average temperature within the flange cross section becomes 500°C or less, the flange warpage shape due to thermal strain will be stabilized, and the flange warpage can be measured with high accuracy.

[0040] The process computer 46 can be a general-purpose computer such as a workstation or a personal computer. The process computer 46 is connected to the rolling control device 30, the cooling control device 42, the thermometers 36-40, and the shapemeter 44 via wired or wireless connections, and controls the manufacturing process of the shaped steel. The process computer 46 also acquires attribute parameters of the H-shaped steel 10 from a host computer. The attribute parameters include target dimensional values ​​of the H-shaped steel 10 (web height H, flange width B, web thickness tw, flange thickness tf), steel type classification (40k steel, 50k steel, or three types higher), information on the chemical composition (contents of C, Si, Mn, Cr, Mo, V, etc.), and information on target mechanical properties of the H-shaped steel 10 (yield stress, tensile strength, elongation, toughness, hardness, etc.).

[0041] In addition to the attribute parameters, the process computer 46 stores information regarding the heating temperature of the H-beam 10 and the cooling stop temperature at the outlet of the cooling devices 32, 34 required to obtain the desired material properties. The process computer 46 performs heat transfer calculations based on the internal model and identifies the water flow rate density and cooling time, which are cooling operation parameters of the cooling device, to achieve the cooling stop temperature.

[0042] The cooling operation parameters determined in this manner are sent to the cooling control device 42. Based on the cooling operation parameters, the cooling control device 42 controls the operating pressure and number of operating cooling water pumps in the cooling devices 32 and 34, the opening degree of the flow valves of the cooling nozzles, and the rotation speed of the motors that drive the table rolls.

[0043] The process computer 46 also sets rolling operation parameters, such as the number of passes and roll spacing, for each rolling mill in accordance with the attribute information of the H-beam 10. Typical rolling operation parameter settings are set as table values ​​associated with the attribute information of the H-beam 10 based on past rolling performance. However, when the rolls are rearranged, the same rolling results may not be obtained even if the preceding and succeeding rolled materials are rolled using the same parameters. 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) that serves as the basis for the set values ​​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 H-beams 10 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, even if the roll opening setting value is the same, the cross-sectional dimensions of the H-beam may vary. Therefore, in such cases, the operator may reset and change the values ​​in the table each time.

[0044] The process computer 46 sets the gap between the horizontal rolls and the relative axial positions of the horizontal rolls in each pass in the roughing mill 22 as rolling operation parameters for the rolling process. The process computer 46 also sets the roll spacing between the web and the roll spacing between the four flange legs in the intermediate universal rolling mill 25, and the gap between the horizontal rolls, the horizontal inclination of the horizontal rolls, and the relative axial positions of the horizontal rolls in the edger rolling mill 26 as rolling operation parameters for the rolling process. The process computer 46 also sets the roll spacing between the web and the roll spacing between the four flange legs in the finishing universal rolling mill as rolling operation parameters for the rolling process.

[0045] The process computer 46 outputs the set rolling operation parameters to the rolling control device 30. The rolling control device 30 controls the roll gaps, roll positions, etc. of the roughing mill 22, the intermediate mill 24, and the finishing mill 28 based on the rolling operation parameters.

[0046] The process computer 46 also collects and stores temperature information and shape data of the H-beam 10 from the thermometers 36 to 40 and the shape meter 44. The actual values ​​of the rolling operation parameters, cooling operation parameters, temperature information, flange warpage data, and attribute parameters are stored in the database of the process computer 46 in association with the identification number of the H-beam 10 to be manufactured.

[0047] The H-beam shape prediction device 48 acquires from the process computer 46 the rolling operation parameters, cooling operation parameters, and attribute parameters of the H-beam 10 manufactured by the rolling facility 100, inputs input data including these into a shape prediction model, and outputs the amount of flange warpage of the H-beam 10, thereby predicting the amount of flange warpage of the H-beam 10. The H-beam shape prediction device 48 also identifies cooling operation parameters that will bring the predicted amount of flange warpage of the H-beam 10 within the allowable range, and outputs the identified cooling operation parameters to the process computer 46 to set them as cooling operation parameters in the manufacture of the H-beam 10.

[0048] Next, an H-beam shape prediction device 48 that predicts the amount of flange warpage of the H-beam 10 will be described. FIG. 5 is a functional block diagram of the H-beam shape prediction device 48. The H-beam shape prediction device 48 can be a general-purpose computer such as a workstation or a personal computer. The H-beam shape prediction device 48 has a control unit 50, an input unit 52, an output unit 54, and a storage unit 56. The control unit 50 is, for example, a CPU, and functions as a data acquisition unit 58, a shape prediction unit 60, a cooling operation parameter identification unit 62, and a shape prediction model generation unit 64 by executing a program stored in the storage unit 56.

[0049] The input unit 52 is, for example, a keyboard, a touch panel integrated with a display, or the like. The output unit 54 is, for example, an LCD or CRT display, or the like. The storage unit 56 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 56 stores programs and data for realizing each function of the H-beam shape prediction device 48. The storage unit 56 also stores a database 66 and a shape prediction model 68. The database 66 stores 500 or more, preferably 2000 or more, data sets, each set consisting of rolling operation parameters, cooling operation parameters, attribute parameters, and flange warpage amounts of H-beams 10 previously manufactured using the same rolling equipment 100 (the roll sets of the roughing mill 22, intermediate mill 24, and finishing mill 28 are the same).

[0050] The shape prediction model 68 is a trained machine learning model that has been trained using training data that are the data sets stored in the database 66. The shape prediction model 68 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 warpage of the flange.

[0051] Next, a description will be given of the processing executed by the data acquisition unit 58 and the shape prediction unit 60. The data acquisition unit 58 acquires rolling operation parameters, cooling operation parameters, and attribute parameters from the process computer 46 as input data.

[0052] The data acquisition unit 58 acquires the set values ​​of the web roll spacing and the four flange roll spacing of the finishing mill 28 from the process computer 46 as rolling operation parameters. The finishing mill 28 is the final rolling process that finishes the product to the target dimensions, and the web roll spacing and the four flange roll spacing of this rolling process directly affect the cross-sectional shape of the product. Furthermore, since this rolling process is the last of the rolling processes that tilts the flange 12, there is a particularly strong correlation with flange warpage. Therefore, it is preferable to use the web roll spacing and the four flange roll spacing of the finishing mill 28 as rolling operation parameters, which improves the prediction accuracy of the flange warpage amount of the H-beam 10.

[0053] The data acquisition unit 58 may further acquire from the process computer 46 the gap between the horizontal rolls and the relative axial positions of the horizontal rolls in each pass of the roughing mill 22 as rolling operation parameters. The gap between the horizontal rolls in each pass of the roughing mill 22 directly affects the cross-sectional dimensions of the rolled material at the end of rough rolling. Furthermore, changing the relative axial positions of the horizontal rolls causes a horizontal shift in the vertical direction of the cross section of the rolled material, changing the cross-sectional dimensions of the rolled material. As described above, the gap between the horizontal rolls and the relative axial positions of the horizontal rolls in each pass of the roughing mill 22 affect flange warpage. Therefore, by including the gap between the horizontal rolls and the relative axial positions of the horizontal rolls in each pass of the roughing mill 22 as rolling operation parameters, the prediction accuracy of the flange warpage of the H-beam 10 can be further improved.

[0054] The data acquisition unit 58 may further acquire, from the process computer 46, as rolling operation parameters, the set values ​​of the web roll spacing and the flange four-leg roll spacing in each pass of the intermediate universal rolling mill 25, the gap between the horizontal rolls in each pass of the edger rolling mill 26, the horizontal inclination of the horizontal rolls, and the axial relative positions of the horizontal rolls.

[0055] In the intermediate universal rolling mill 25, the set values ​​for the web roll spacing and the roll spacing between the four flange legs in each pass also affect the variation in flange thickness at four locations (top, bottom, left, and right). The gap between the upper and lower horizontal rolls in each pass of the edger rolling mill 26 affects the flange width. Furthermore, the horizontal inclination of the horizontal rolls of the edger rolling mill 26 (the difference in the height displacement of the roll axes on the working and driving sides of the rolling mill), also known as the leveling amount, creates a difference in the roll gap between the left and right at the positions where the left and right flanges are pressed down, thereby affecting the variation in the left and right flange widths. Furthermore, the relative axial position of the horizontal rolls can sometimes cause the left and right flange widths to be uneven. As described above, since the setting values ​​of each roll of the intermediate universal rolling mill 25 and the edger rolling mill 26 also affect the flange warpage, the accuracy of predicting the amount of flange warpage of the H-shaped steel 10 can be improved by including the average value of the roll gap dimensions of the four flange legs in each pass of the intermediate universal rolling mill 25, the opening between the horizontal rolls in each pass of the edger rolling mill 26, the horizontal inclination of the horizontal rolls, and the relative axial position of the horizontal rolls in the rolling operation parameters.

[0056] The data acquisition unit 58 acquires the water flow rate and cooling time of the cooling devices 32, 34 from the process computer 46 as cooling operation parameters. In the cooling devices 32, 34, the higher the water flow rate and the longer the cooling time, the larger the temperature difference between the inner and outer surfaces of the flange, resulting in greater flange warpage. Therefore, by using the water flow rate and cooling time of the cooling devices 32, 34 as cooling operation parameters, the prediction accuracy of the amount of flange warpage of the H-shaped steel 10 is improved.

[0057] Furthermore, the data acquisition unit 58 acquires the target dimension values ​​and steel type classification of the H-shaped steel 10 from the process computer 46 as attribute parameters. By including the target dimension values ​​and steel type classification of the H-shaped steel 10 in the input data for the shape prediction model, the shape prediction model takes into account the target dimension values ​​and steel type classification. Therefore, the flange warpage can be predicted using the same shape prediction model for H-shaped steel 10 with different target dimension values ​​and steel type classifications. The data acquisition unit 58 may also acquire, as attribute parameters, information on the chemical composition (e.g., the content of C, Si, Mn, Cr, Mo, and V) and target values ​​of the mechanical properties of the H-shaped steel 10 (e.g., yield stress, tensile strength, elongation, toughness, and hardness). By including information on the chemical composition and target values ​​of the mechanical properties, the shape prediction model takes into account the target values ​​of the chemical composition and mechanical properties. Therefore, the flange warpage can be predicted using the same shape prediction model for H-shaped steel 10 with different target values ​​of the chemical composition and mechanical properties. The data acquisition unit 58 outputs the acquired input data to the shape prediction unit 60 .

[0058] When the shape prediction unit 60 acquires input data from the data acquisition unit 58, it reads out the shape prediction model 68 from the storage unit 56, inputs the input data into the shape prediction model 68, and causes the shape prediction unit 60 to output the flange warpage amount. In this way, by causing the shape prediction model 68 to output the flange warpage amount, the shape prediction unit 60 predicts the flange warpage amount of the H-shaped steel 10 to be manufactured. The shape prediction unit 60 may output the output flange warpage amount to the output unit 54 and display the flange warpage amount on the output unit 54. This allows the operator to check the predicted value of the flange warpage amount by visually checking the output unit 54.

[0059] Next, we will explain the processing of the cooling operation parameter specifying unit 62 and the shape prediction model generating unit 64. The cooling operation parameter specifying unit 62 specifies cooling operation parameters that will bring the flange warpage amount predicted by the shape prediction unit 60 within the allowable range, and outputs the cooling operation parameters to the process computer 46 to set them as cooling conditions.

[0060] Fig. 6 is a flow diagram showing the flow of the cooling operation parameter specification process by the cooling operation parameter specification unit 62. The flow shown in Fig. 6 is started, for example, by receiving an input from the operator to start the process.

[0061] First, the data acquisition unit 58 acquires rolling operation parameters of the H-beam to be manufactured from the process computer 46 (step S101). The data acquisition unit 58 also acquires attribute parameters of the H-beam 10 to be manufactured from the process computer 46 (step S102). The data acquisition unit 58 outputs the acquired rolling operation parameters and attribute parameters to the shape prediction unit 60.

[0062] The cooling operation parameter specifying unit 62 sets arbitrary cooling operation parameters (step S103). The cooling operation parameter specifying unit 62 outputs the set cooling operation parameters to the shape prediction unit 60. The shape prediction unit 60 reads out the shape prediction model 68 from the storage unit 56 and inputs the acquired rolling operation parameters, cooling operation parameters, and attribute parameters into the shape prediction model 68 to output the flange warp amount of the H-shaped steel 10, thereby predicting the flange warp amount of the H-shaped steel 10 to be manufactured (step S104). The shape prediction unit 60 outputs the output predicted value of the flange warp amount of the H-shaped steel 10 to the cooling operation parameter specifying unit 62.

[0063] The range of allowable values ​​for the flange warpage may be predetermined and stored in the storage unit 56, or may be input by the operator via the input unit 52. After acquiring the predicted value of the flange warpage and the range of allowable values ​​for the flange warpage, the cooling operation parameter specifying unit 62 determines whether the predicted value of the flange warpage is within the allowable range for the flange warpage (step S105). If the cooling operation parameter specifying unit 62 determines that the predicted value of the flange warpage is outside the allowable 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 flange warpage falls within the allowable range.

[0064] On the other hand, if it is determined in step S105 that the predicted value of the flange warpage amount is within the allowable range (step S105: Yes), the cooling operation parameter specifying unit 62 specifies that the cooling operation parameters used to predict the flange warpage amount are cooling operation parameters that can bring the flange warpage amount within the allowable range (step S107), and the flow of the cooling operation parameter determination process shown in Fig. 6 ends. The cooling operation parameter specifying unit 62 outputs the specified cooling operation parameters to the process computer 46, and sets the specified cooling operation parameters as the cooling operation parameters of the cooling devices 32, 34.

[0065] In this way, by specifying the cooling operation parameters, it is possible to specify the cooling operation parameters that can control the amount of flange warpage within the allowable range. Then, by manufacturing the H-shaped steel 10 under manufacturing conditions that include the cooling operation parameters, it becomes possible to stably manufacture the H-shaped steel 10 whose amount of flange warpage falls within the allowable range, thereby suppressing an increase in the manufacturing cost of the H-shaped steel 10 and a decrease in productivity.

[0066] Next, a method for generating a shape prediction model used to predict the amount of flange warpage will be described. The data acquisition unit 58 acquires 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 flange warpage of the H-section steel 10 manufactured in the past from the process computer 46, and stores these as one set of data sets in the database 66 of the storage unit 56. The number of data sets stored in the database 66 is preferably at least 500, and more preferably 2000 or more.

[0067] The shape prediction model generation unit 64 reads out a pre-stored machine learning model from the storage unit 56, and performs machine learning on the machine learning model using the dataset stored in the database 66 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 H-beam shape prediction method and the H-beam shape prediction device 48 according to this embodiment may be any of commonly used neural networks, decision tree learning, random forests, and support vector regression. Furthermore, instead of the flange warpage amount, the output of the shape prediction model may be a machine learning model that outputs binary data, either pass or fail, indicating whether the flange warpage amount is within the allowable range. In this case, a classification model such as k-nearest neighbor method or logistic regression may be used.

[0068] The shape prediction model may be updated to a new one by re-training it every month or every year. The data acquisition unit 58 acquires performance data every time an H-beam 10 is manufactured and stores it in the database 66. As performance data for newly manufactured H-beams 10 is stored in the database 66, the amount of performance data stored increases. The greater the amount of performance data, the more accurate the shape prediction becomes. Therefore, by periodically training the shape prediction model through machine learning, it becomes possible to predict the amount of flange warpage of the H-beam 10 with even greater accuracy.

[0069] As described above, the shape prediction method for H-beam according to this embodiment and the shape prediction model used in the shape prediction device 48 for H-beam include rolling operation parameters in their input data. As described above, the rolling operation parameters of the rolling process affect the amount of flange warpage. Therefore, by including the rolling operation parameters in the input data of the shape prediction model that predicts the amount of flange warpage, the shape prediction model takes the rolling process into consideration, and the prediction accuracy of the amount of flange warpage increases. Furthermore, if the amount of flange warpage can be predicted with high accuracy in this way, it is possible to prevent the production of H-beam 10 with an amount of flange warpage exceeding the allowable range, thereby suppressing an increase in the manufacturing cost of the H-beam 10 and a decrease in productivity.

[0070] Furthermore, by including attribute parameters of the H-shaped steel 10 in the input data of the shape prediction model, it becomes possible to predict the flange warpage of H-shaped steel 10 with different target cross-sectional dimensions and steel type classifications using the same shape prediction model. Furthermore, by specifying cooling operation parameters that bring the flange warpage predicted by the shape prediction model within the allowable range and manufacturing the H-shaped steel 10 under manufacturing conditions that include the cooling operation parameters, it becomes possible to stably manufacture H-shaped steel 10 with flange warpage within the allowable range.

[0071] Although the rolling equipment 100 shown in FIG. 3 includes an example in which the process computer 46 and the H-beam shape prediction device 48 are included, this is not limiting. For example, the process computer 46 may have the functions of the H-beam shape prediction device 48, and these functions may be configured as a single device. Furthermore, in the H-beam shape prediction device 48 shown in FIG. 5, the control unit 50 includes the cooling operation parameter identification unit 62 and the shape prediction model generation unit 64, but this is not limiting. If the flange warpage amount is predicted by the H-beam shape prediction device 48, the control unit 50 does not need to include the cooling operation parameter identification unit 62. Furthermore, if the shape prediction model 68 is generated externally and stored in the storage unit 56 via the data acquisition unit 58, the H-beam shape prediction device 48 does not need to include the shape prediction model generation unit 64. [Example]

[0072] [Example 1] To verify the effects of the present invention, constant outer dimension rolled H-beams made of SM400A steel with a web height of 900 mm, a flange width of 350 mm, a web thickness of 16 mm, and a flange thickness of 28 mm were manufactured using the H-beam manufacturing equipment 100 shown in Fig. 3. The raw steel material was rolled to the desired cross section through 17 passes in the rough rolling mill, 21 passes in the intermediate rolling mill, and one pass in the finishing rolling mill.

[0073] The cooling device 32 before the finishing rolling mill 28 uses water as a coolant and is composed of seven rows of diagonal spray nozzles arranged in the flange width direction, with six of these rows of nozzles being used to cool the outer flange surface of the H-beam 10. The nozzles are arranged at 50 mm intervals in the flange width direction, with the center of the lowest nozzle positioned 50 mm above the bottom edge of the flange. The nozzle spray pattern is a 50 mm x 50 mm square, and the nozzles are arranged at 100 mm intervals in the conveying direction. The total length of the cooling device 32 is 40 m, and it is divided into four banks, each 10 m long. The finishing rolling mill 28 is installed 30 m downstream from the outlet of this cooling device 32.

[0074] The cooling device 34 after the finishing mill 28 is installed 13 m downstream from the exit of the finishing mill, uses water as a coolant, and is composed of seven rows of multi-hole jet nozzles arranged in the flange width direction. Of these, the nozzles in the first to sixth rows from the bottom are used to cool the outer surface of the flange of the H-beam 10. The total length of the cooling device is 40 m, and is divided into a total of four banks, each 10 m long.

[0075] In addition, thermometer 36 was installed 5 m upstream from the inlet of cooling device 32, thermometer 38 was installed 5 m upstream from the inlet of cooling device 34 (8 m downstream from finishing rolling mill 28), thermometer 40 was installed 10 m downstream from the outlet of cooling device 34, and shapemeter 44 was installed at a position 35 m downstream from the outlet of cooling device 34, and each measured the temperature and flange warpage of H-beam 10. The temperatures measured by thermometers 36, 38, and 40 were used to confirm that the temperatures of the H-beams cooled by cooling devices 32 and 34 were within the specified range.

[0076] <Example 1> In Example 1 of the present invention, a shape prediction model was used that was machine-learned into a neural network model using approximately 5,000 recent historical H-beam rolling operation parameters, cooling operation parameters, attribute parameters, and actual values ​​of flange warpage as learning data.

[0077] The rolling operation parameters used were the roll spacing between the horizontal rolls and the relative axial position of the horizontal rolls in each pass of roughing, the web roll spacing and the roll spacing between the four flange legs in each pass of the intermediate universal rolling mill in intermediate rolling, the gap between the horizontal rolls in each pass of the edger rolling mill in intermediate rolling, the relative axial position of the horizontal rolls and their relative vertical position in relation to the vertical rolls, and the web roll spacing and the roll spacing between the four flange legs in the finish universal rolling mill in finish rolling. The cooling operation parameters used were the water flow rate density and cooling time of each cooling device 32, 34. The attribute parameter information used the target dimensions of the H-beam 10, the steel type classification, and the target values ​​of the mechanical properties of the H-beam 10 (yield stress, tensile strength, elongation, toughness).

[0078] In Example 1 of the present invention, calculations were performed repeatedly using the shape prediction model according to the flowchart shown in Figure 6, and cooling operation parameters were identified that would result in flange warpage falling within the allowable range of less than ±1.5 mm. Note that flange warpage in which the flange collapses toward the web (inward warpage) is indicated by a "-dimension," and flange collapses toward the outside (outward warpage) is indicated by a "+dimension."

[0079] The cooling operation parameter to be reset is the cooling time, and the finally specified cooling operation parameter is: 2 The cooling time was 6.7 seconds when the water flow density of the cooling water in the cooling device 34 was 1000 L / m 2 min, the cooling time was 8.0 s.

[0080] A constant outer dimension rolled H-section steel with material SM400, web height 900 mm, flange width 350 mm, web thickness 16 mm, and flange thickness 28 mm was manufactured under the manufacturing conditions including the cooling operation parameters specified as above. As a result, the flange warpage amount predicted by the shape prediction model was +0.4 mm, while the average flange warpage amount on the left and right at the longitudinal center measured with the shape meter 44 was +0.4 mm. From this result, it was confirmed that flange warpage can be predicted with high accuracy by using the shape prediction model including the rolling operation parameters, and that by manufacturing the H-section steel 10 with cooling operation parameters specified using the prediction results, rolled H-section steel with a flange warpage amount of less than ±1.5 mm, which is within the allowable range, can be manufactured.

[0081] <Comparative Example 1-1> In Comparative Example 1-1, a constant outer dimension rolled H-section steel was manufactured using material SM400A, with a web height of 900 mm, a flange width of 350 mm, a web thickness of 16 mm, and a flange thickness of 28 mm, in the same manner as in Example 1. The manufacturing equipment was the same as that used in Invention Example 1.

[0082] In Comparative Example 1-1, a shape prediction model was used that was machine-learned into a neural network model using, as learning data, about 5,000 recent past cooling operation parameters, attribute parameters, and actual values ​​of flange warpage of H-shaped steel 10. That is, in Comparative Example 1-1, a shape prediction model in which rolling operation parameters were not included in the input data was used.

[0083] In Comparative Example 1-1, calculations were also performed repeatedly using the shape prediction model of the steel section according to the flowchart shown in Figure 6, and cooling operation parameters that satisfied the flange warpage of less than ±1.5 mm were identified. The cooling operation parameters that were finally identified were a water flow density of the cooling water in the cooling device 32 of 1600 L / m 2 The cooling time was 5.3 seconds when the water flow density of the cooling water in the cooling device 34 was 1000 L / m 2 min, the cooling time was 10.0 s.

[0084] When a constant outer dimension rolled H-section steel with material SM400, web height 900 mm, flange width 350 mm, web thickness 16 mm, and flange thickness 28 mm was manufactured under manufacturing conditions including the cooling operation parameters identified as described above, the flange warpage predicted by the shape prediction model was +0.5 mm, whereas the flange warpage at the longitudinal center actually measured with the shape meter 44 was +1.6 mm on average on the left and right. From these results, it was confirmed that when a shape prediction model that does not include rolling operation parameters is used, flange warpage cannot be predicted with high accuracy, and that even if H-section steel 10 is manufactured using cooling operation parameters identified using the prediction results, it is not possible to manufacture rolled H-section steel with a flange warpage of less than ±1.5 mm, which is within the allowable range.

[0085] <Comparative Example 1-2> In Comparative Example 1-2, constant outer dimension rolled H-beams were manufactured using the material SM400A, with a web height of 900 mm, a flange width of 350 mm, a web thickness of 16 mm, and a flange thickness of 28 mm, as in Example 1. The manufacturing equipment was the same as in Compliant Example 1. In Comparative Example 1-2, flange warpage was predicted using an equation expressed as a linear sum using the dimensions of the H-beam and the flange temperature during finish rolling, as disclosed in Patent Document 2, and cooling operation parameters that satisfied the requirement for a flange warpage of less than ±1.5 mm were identified by performing repeated calculations.

[0086] The cooling operation parameters finally specified were: a water flow density of the cooling water in the cooling device 32 of 1600 L / m 2 The cooling time was 3.3 seconds when the water flow density of the cooling water in the cooling device 34 was 1000 L / m 2 min, the cooling time was 20.0 s.

[0087] A constant outer dimension rolled H-beam having a material of SM400, a web height of 900 mm, a flange width of 350 mm, a web thickness of 16 mm, and a flange thickness of 28 mm was manufactured under manufacturing conditions including the cooling operation parameters identified as described above. As a result, the flange warpage predicted by the equation expressed as a linear sum was +0.6 mm, whereas the average flange warpage at the longitudinal center portion measured by the shape meter 44 was +1.8 mm on both sides. From these results, it was confirmed that the flange warpage cannot be predicted with high accuracy when the equation expressed as a linear sum disclosed in Patent Document 2 is used, and that even if the H-beam 10 is manufactured using the cooling operation parameters identified using the predicted results, it is not possible to manufacture a rolled H-beam having a flange warpage of less than ±1.5 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, cooling operation parameters, and attribute parameters are input data and the flange warpage amount is output data, the flange warpage amount of the H-shaped steel 10 after passing through the cooling device can be predicted with higher accuracy than in Comparative Examples 1-1 and 1-2.Furthermore, it was confirmed that by manufacturing the H-shaped steel 10 using cooling operation parameters specified using the prediction results, it is possible to manufacture rolled H-shaped steel in which the flange warpage amount is within the allowable range.

[0089] [Example 2] In Example 2, constant outer dimension rolled H-beams having a material of SM490A, a web height of 1000 mm, a flange width of 400 mm, a web thickness of 19 mm, and a flange thickness of 40 mm were manufactured using the same rolling equipment as in Example 1. The difference from Example 1 is the dimensions and material, i.e., the steel type, of the H-beams 10 to be manufactured. Furthermore, in Example 2, the flange thickness was as large as 40 mm, making offline correction of flange warpage difficult, so flange warpage was strictly controlled, and flange warpage of less than ±1.0 mm was deemed acceptable.

[0090] <Example 2> In Example 2, calculations were repeatedly performed using the same shape prediction model as in Example 1 according to the flowchart shown in Figure 6, and cooling operation parameters were identified that would result in a flange warpage of less than ±1.0 mm. The cooling operation parameters finally identified were a water flow density of the cooling water in the cooling device 32 of 1600 L / m 2 The cooling time was 6.1 seconds when the water flow density of the cooling water in the cooling device 34 was set to 1000 L / m 2 min, the cooling time was 10.0 s.

[0091] A constant outer dimension rolled H-section steel with material SM490, web height 1000 mm, flange width 400 mm, web thickness 19 mm, and flange thickness 40 mm was manufactured under the manufacturing conditions including the cooling operation parameters specified as described above. As a result, the flange warpage amount predicted by the shape prediction model was -0.6 mm, while the average flange warpage amount on the left and right at the longitudinal center portion measured with the shape meter 44 was -0.6 mm. From this result, it was confirmed that flange warpage can be predicted with high accuracy by using the shape prediction model including the rolling operation parameters, and that by manufacturing the H-section steel 10 with cooling operation parameters specified using the prediction results, rolled H-section steel with a flange warpage amount of less than ±1.0 mm, which is within the allowable range, can be manufactured.

[0092] <Comparative Example 2-1> In Comparative Example 2-1, as in Compliant Example 2, a constant outer dimension rolled H-section steel was manufactured using material SM490A, a web height of 1000 mm, a flange width of 400 mm, a web thickness of 19 mm, and a flange thickness of 40 mm.

[0093] In Comparative Example 2-1, as in Comparative Example 1-1, a shape prediction model was used in which the input data did not include rolling operation parameters. Following the flowchart shown in Fig. 6, repeated calculations were performed using the shape prediction model for section steel, and cooling operation parameters that satisfied the requirement that flange warpage be less than ±1.0 mm were identified. The cooling operation parameters that were finally identified were a water flow density of the cooling water in the cooling device 32 of 1600 L / m 2 The cooling time was 5.0 seconds when the water flow density of the cooling water in the cooling device 34 was set to 1000 L / m2 min, the cooling time was 10.7 s.

[0094] A constant outer dimension rolled H-section steel with material SM490, web height of 1000 mm, flange width of 400 mm, web thickness of 19 mm, and flange thickness of 40 mm was manufactured under manufacturing conditions including the cooling operation parameters identified as described above. As a result, the flange warpage predicted by the shape prediction model was +-0.65 mm, while the flange warpage at the longitudinal center actually measured with the shape meter 44 was -1.2 mm on average on the left and right. From these results, it was confirmed that flange warpage cannot be predicted with high accuracy when a shape prediction model that does not include rolling operation parameters is used, and that even if H-section steel 10 is manufactured using cooling operation parameters identified using the prediction results, it is not possible to manufacture rolled H-section steel with a flange warpage of less than ±1.0 mm, which is within the allowable range.

[0095] <Comparative Example 2-2> In Comparative Example 2-2, a constant outside dimension rolled H-beam was manufactured using the material SM490A, with a web height of 1000 mm, a flange width of 400 mm, a web thickness of 19 mm, and a flange thickness of 40 mm, as in Compliant Example 2. The manufacturing equipment was the same as in Example 1. In Comparative Example 2-2, flange warpage was predicted using an equation expressed as a linear sum using the dimensions of the H-beam 10 and the flange temperature during finish rolling, as disclosed in Patent Document 2, and cooling operation parameters that satisfied the requirement for a flange warpage of less than ±1.0 mm were identified by performing repeated calculations.

[0096] The cooling operation parameters finally specified were: a water flow density of the cooling water in the cooling device 32 of 1600 L / m 2 The cooling time was 4.0 seconds when the water flow density of the cooling water in the cooling device 34 was 1000 L / m 2 min, the cooling time was 11.5 s.

[0097] A constant outer dimension rolled H-beam made of SM490A steel, with a web height of 1000 mm, a flange width of 400 mm, a web thickness of 19 mm, and a flange thickness of 40 mm, was manufactured under manufacturing conditions including the cooling operation parameters identified as described above. As a result, the flange warpage predicted by the equation expressed as a linear sum was -0.8 mm, whereas the average flange warpage at the longitudinal center portion measured by the shape meter 44 was -1.6 mm on both sides. From these results, it was confirmed that the flange warpage cannot be predicted with high accuracy when the equation expressed as a linear sum disclosed in Patent Document 2 is used, and that even if the H-beam 10 is manufactured using the cooling operation parameters identified using the predicted results, it is not possible to manufacture a rolled H-beam with a flange warpage of less than ±1.0 mm, which is within the allowable range.

[0098] As described above, in Example 2 of the invention, even if the dimensions and steel type of the H-shaped steel 10 change, the flange warpage of the H-shaped steel 10 after passing through the cooling device can be predicted with higher accuracy than in Comparative Examples 2-1 and 2-2 by using a shape prediction model in which rolling operation parameters, cooling operation parameters, and attribute parameters are input data and the flange warpage amount is output data.Furthermore, it was confirmed that by manufacturing the H-shaped steel 10 using cooling operation parameters specified using the prediction results, it is possible to manufacture rolled H-shaped steel in which the flange warpage amount is within the allowable range. [Explanation of symbols]

[0099] 10 H-beam 12 flange 14. Web 20 Furnace 22 Roughing mill 24 Intermediate rolling mill 25 Intermediate universal rolling mill 26 Edger Rolling Mill 28 Finishing Rolling Mill 30 Rolling control device 32 Cooling device 34 Cooling device 36 Thermometer 38 Thermometer 40 thermometer 42 Cooling control device 44 Shape meter 46 Process Computer 48 H-beam shape prediction device 50 control section 52 Input section 54 Output section 56 Storage area 58 Data Acquisition Section 60 Shape Prediction Unit 62 Cooling operation parameter specification section 64 Shape prediction model generation unit 100 Rolling Equipment

Claims

1. A method for predicting the amount of flange warpage of an H-shaped steel manufactured by a rolling facility having a plurality of rolling mills for hot-rolling heated steel billets into H-shaped steel, a cooling device for cooling the H-shaped steel, and a shape meter for measuring the flange warpage of the H-shaped steel after cooling, comprising: the plurality of rolling mills including a roughing mill, an intermediate universal mill, an edger mill, and a finishing mill; a setting value of the roll spacing of the four flange legs; an opening between the horizontal rolls in at least one pass of the roughing rolling mill, a horizontal inclination of the horizontal rolls, and a relative axial position of the horizontal rolls; a setting value of the roll spacing of the four flange legs; an opening between the horizontal rolls in at least one pass of the edger rolling mill, a horizontal inclination of the horizontal rolls, and a relative axial position of the horizontal rolls; cooling operation parameters of the cooling device; and attribute parameters of the H-beam, input data including: operation parameters of the rolling mill; cooling operation parameters of the cooling device; and attribute parameters of the H-beam;

2. Identifying the cooling operation parameters that cause the flange warpage amount predicted using the shape prediction method for H-section steel according to claim 1 to fall within a predetermined allowable value range, A method for manufacturing H-beam steel, which manufactures H-beam steel under manufacturing conditions including specified cooling operation parameters.

3. A rolling facility comprising: a plurality of rolling mills for hot rolling heated steel billets into H-shaped steel; a cooling device for cooling the H-shaped steel; a shape meter for measuring the amount of flange warpage of the H-shaped steel after cooling; and an H-shaped steel shape prediction device for predicting the amount of flange warpage, the plurality of rolling mills including a roughing mill, an intermediate universal mill, an edger mill, and a finishing mill; The shape prediction device has a shape prediction unit that inputs input data including rolling operation parameters of the rolling mill including at least one of the gap between the horizontal rolls in at least one pass of each pass of the roughing rolling mill, the axial relative position of the horizontal rolls, the roll spacing of the web in at least one pass of each pass of the intermediate universal rolling mill, the set value of the roll spacing of the four flange legs, the gap between the horizontal rolls in at least one pass of each pass of the edger rolling mill, the horizontal inclination of the horizontal rolls, and the axial relative position of the horizontal rolls, cooling operation parameters of the cooling device, and attribute parameters of the H-shaped steel into a shape prediction model and outputs the flange warpage amount.

4. A method for generating a shape prediction model for predicting the amount of flange warpage of H-shaped steel produced by rolling equipment having a plurality of rolling mills for hot-rolling heated steel billets into H-shaped steel, a cooling device for cooling the H-shaped steel, and a shape meter for measuring the flange warpage of the H-shaped steel after cooling, comprising: the plurality of rolling mills including a roughing mill, an intermediate universal mill, an edger mill, and a finishing mill; a machine learning model is trained using as training data a plurality of data sets each including actual values ​​of rolling operation parameters of the rolling mill, including at least one of the gap between the horizontal rolls in at least one pass of each pass of the roughing mill for H-section steel previously manufactured, the axial relative position of the horizontal rolls, the roll spacing of the web in at least one pass of each pass of the intermediate universal rolling mill, the set value of the roll spacing of the four flange legs, the gap between the horizontal rolls in at least one pass of each pass of the edger rolling mill, the horizontal inclination of the horizontal rolls, and the axial relative position of the horizontal rolls, actual values ​​of cooling operation parameters of the cooling device, actual values ​​of attribute parameters of the H-section steel, and actual values ​​of the flange warpage amount; a method for generating a shape prediction model, the method receiving the rolling operation parameters, the cooling operation parameters, and the attribute parameters as inputs and generating a shape prediction model having the flange warpage amount as output;

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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