Saw cutting load estimation device, saw cutting load estimation method, saw cutting speed determination method, metal material manufacturing method, and method for generating a saw cutting load estimation model.

Abstract

To provide a sawing load estimation device which can estimate a sawing load in consideration of a hot rolling condition of a rolled metal material serving as an object to be sawn.SOLUTION: A sawing load estimation device estimates a sawing load generated when a rolled metal material rolled in a hot rolling step is sawn, wherein target cross-sectional shape sections of the rolled metal material for estimating the sawing load are the same, and the device has a sawing load estimation part for inputting an operational parameter and sawing speed in the hot rolling step to a sawing load estimation model, and outputting the sawing load of the rolled metal material.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a sawing load estimation device, a sawing load estimation method, a method for generating a sawing load estimation model used for the estimation, and a method for manufacturing a metal material, for estimating a sawing load when sawing a rolled metal material using a saw blade in a hot rolling line of the metal material.

Background Art

[0002] Section steel having webs and flanges such as H-shaped steel and steel sheet piles is manufactured by hot rolling a steel slab as a material for the section steel into a rolled material having a desired cross-sectional shape and sawing the rolled material into a predetermined length using a sawing device. In the sawing device for sawing this rolled material, a sawing load occurs on the motor that drives the saw blade when the rolled material is sawed. If this sawing load becomes large and exceeds the allowable value of the motor, fatigue damage accumulates in the motor that drives the saw blade, and the life of the motor is shortened. Therefore, in the sawing device, the sawing load is controlled so as to be within the allowable value range of the sawing device.

[0003] As a technique for controlling the sawing load in this way, Patent Document 1 discloses a sawing device that suppresses the bounce of the sawing load. According to Patent Document 1, the relationship between the position of the shift carriage that moves the saw blade and the load current value is obtained, and the speed of the shift carriage is made sufficiently slow from a position before the position where the load current value has a bounce, thereby suppressing the bounce of the load current value.

[0004] Further, Patent Document 2 discloses a machine learning device that can obtain a threshold value suitable for determining whether or not there is an abnormal load in a machine tool. According to Patent Document 2, a threshold value is obtained using a machine learning model that takes at least one of the information of the tool of the machine tool, the material of the workpiece, the traveling direction of the tool, the cutting speed, the spindle rotation speed, the depth of cut, and the coolant amount as an input and outputs the load current value.

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] Japanese Patent Publication No. 2008-296346 [Patent Document 2] Japanese Patent Publication No. 2017-220111 [Overview of the project] [Problems that the invention aims to solve]

[0006] Patent Document 1 discloses a technique for suppressing the surge in sawing load, but not a technique for estimating sawing load. Furthermore, Patent Document 2 estimates the load current value using a machine learning model that takes only information about the machine tool as input, and this estimation has the problem that the rolling conditions of the rolled material to be sawed are not taken into consideration. The present invention was made to address these problems, and its objective is to provide a sawing load estimation device and sawing load estimation method that can estimate the sawing load while taking into account the hot rolling conditions of the rolled metal material to be sawed, as well as a method for generating a sawing load estimation model used to estimate the sawing load, and a method for manufacturing a metal material. [Means for solving the problem]

[0007] The means to solve the above problems are as follows: [1] A sawing load estimation device for estimating the sawing load generated when sawing rolled metal material having the same cross-sectional shape rolled in a hot rolling process, wherein the target cross-sectional shape classification of the rolled metal material for which the sawing load is estimated is the same, and the sawing load estimation unit inputs the operating parameters of the hot rolling process and the sawing speed into a sawing load estimation model and outputs the sawing load of the rolled metal material. [2] The sawing load estimation device according to [1], wherein the sawing load estimation unit further inputs the sawing position to the sawing load estimation model and outputs the sawing load of the rolled metal material. [3] The sawing load estimation device according to [1] or [2], wherein the sawing load estimation unit further inputs at least one of the cooling parameters of the hot rolling process, the sawing condition parameters, and the attribute parameters of the rolled metal material to the sawing load estimation model and outputs the sawing load of the rolled metal material. [4] A saw cutting load estimation device according to any one of [1] to [3], comprising a saw cutting speed determination unit that determines a saw cutting speed such that the saw cutting load estimated by the saw cutting load estimation unit falls within a predetermined target load range. [5] A method for estimating the sawing load generated when sawing rolled metal materials with the same cross-sectional shape rolled in a hot rolling process, wherein the target cross-sectional shape classification of the rolled metal materials for which the sawing load is estimated is the same, and the operating parameters of the hot rolling process and the sawing speed are input to a sawing load estimation model, and the sawing load of the rolled metal material is output to estimate the sawing load. [6] The sawing load estimation method according to [5], further comprising inputting the sawing position into the sawing load estimation model and outputting the sawing load of the rolled metal material to estimate the sawing load. [7] The sawing load estimation method according to [5] or [6], further comprising inputting at least one of the cooling parameters of the hot rolling process, the sawing condition parameters, and the attribute parameters of the rolled metal material into the sawing load estimation model and outputting the sawing load of the rolled metal material to estimate the sawing load. [8] A method for estimating saw cutting load according to any one of [5] to [7], which determines a saw cutting speed such that the saw cutting load output from the saw cutting load estimation model falls within a predetermined target load range. A method for manufacturing a metal material, comprising cutting the rolled metal material at a cutting speed determined by the cutting load estimation method described in [9][8] to produce the metal material.

[10] A method for generating a sawing load estimation model for estimating the sawing load generated when a rolled metal material rolled in a hot rolling process is sawed, wherein the target cross-sectional shape classification of the rolled metal material for which the sawing load is estimated is the same, and a machine learning model is trained using multiple datasets, each of which consists of actual values ​​of the operating parameters of the hot rolling process, the sawing speed, and the sawing load of the rolled metal material that has been sawed in the past, as training data, and a sawing load estimation model is generated that takes the operating parameters and the sawing speed as input and outputs the sawing load. [Effects of the Invention]

[0008] According to the present invention, the sawing load is estimated using a sawing load estimation model that takes input data including the operating parameters of the hot rolling process as input and outputs the sawing load of the rolled metal material. Therefore, the sawing load can be estimated while taking into account the hot rolling conditions of the rolled metal material to be sawed. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a schematic cross-sectional view of a steel sheet pile. [Figure 2] Figure 2 is a schematic top view showing an example of a hot rolling mill used for manufacturing steel sheet piles. [Figure 3] Figure 3 is a schematic side view of the saw cutting device. [Figure 4] Figure 4 illustrates the sawing load when rolling steel sheet pile material is sawn at a constant sawing speed. [Figure 5] Figure 5 illustrates the sawing load when cutting rolled metal material of steel sheet piles at an increased sawing speed. [Figure 6] Figure 6 is a functional block diagram of the saw break load estimation device. [Figure 7] Figure 7 is a flowchart showing the process of determining the saw cutting speed by the saw cutting speed determination unit. [Figure 8] Figure 8 illustrates the sawing load when a rolled metal material of round steel is cut with a saw. [Figure 9]Figure 9 illustrates the sawing load when a rolled round steel is sawn at an increased sawing speed. [Figure 10] Figure 10 is a graph showing the correspondence between estimated current values ​​and measured current values. [Modes for carrying out the invention]

[0010] The present invention will be described below through embodiments of the present invention. In the sawing load estimation device and sawing load estimation method according to this embodiment, the sawing load is estimated when producing a metal material that is a structural steel by sawing a plurality of rolled metal materials that have the same target cross-sectional shape classification, which have been rolled in a hot rolling process, with a sawing device. The structural steel to be produced is, for example, an H-beam or a hat-shaped steel sheet pile (hereinafter referred to as "steel sheet pile"), but in the following embodiments, an example of sawing a rolled metal material of a steel sheet pile will be used for explanation. Furthermore, the material of the metal material is not limited to iron, but may also be aluminum or copper. Note that the plurality of materials that have the "same target cross-sectional shape classification" referred to here means a plurality of metal materials that have been produced using the same roll set in a rolling mill, as will be described later. In the case of a steel sheet pile, each type of steel sheet pile (for example, 10H, 3W, 4 type, etc.) will have the "same target cross-sectional shape classification". In the case of an H-beam, the series classified by web height and flange width will have the "same target cross-sectional shape classification".

[0011] Figure 1 is a schematic cross-sectional view of a steel sheet pile 10. The steel sheet pile 10 has a web 12, two flanges 14, two arms 16, and two joints 18. The two flanges 14 are provided at an angle so as to extend downward continuously from both sides of the web 12. The two arms 16 are provided so as to extend horizontally continuously from the flanges 14. The two joints 18 are provided at the ends of the two arms 16, respectively.

[0012] FIG. 2 is a top schematic view showing an example of a hot rolling facility 20 for manufacturing the steel sheet pile 10. The hot rolling facility 20 includes, for example, a heating furnace 22, a rough rolling mill 24, a transfer bed 26, an intermediate rolling mill 28, a finishing rolling mill 30, a cooling device 32, a table roller 34, a sawing device 36, a process computer 38, and a sawing load estimation device 40. Further, temperature sensors 25, 29, 31, 33 for measuring the surface temperature of the rolled metal material 11 are provided on the downstream side of the rough rolling mill 24, the intermediate rolling mill 28, and the finishing rolling mill 30 and on the upstream side of the sawing device 36, respectively. By these temperature sensors, the rolling temperature of each rolling mill and the temperature of the rolled metal material 11 before sawing are measured. Further, sensors (not shown) for measuring the length and mass of the rolled metal material 11 conveyed on the table roller 34 are provided respectively, and by these sensors, the mass and the length in the rolling direction of the rolled metal material 11 are measured.

[0013] The slab, bloom, or beam blank (hereinafter referred to as "slab etc.") that is the material for the steel sheet pile is heated to 1100 - 1350°C in the heating furnace 22. The slab etc. heated in the heating furnace 22 is roughly rolled by the rough rolling mill 24. The rough rolling mill 24 has a pair of upper and lower rolls engraved with a plurality of "passes", and by rolling with these rolls, the slab etc. is rolled into a rough-shaped steel piece with the approximate shape of the steel sheet pile. The rough-shaped steel piece is laterally moved from the rough rolling line to the intermediate / finishing rolling line by the transfer bed 26.

[0014] In the intermediate rolling mill 28, the rough-shaped steel piece is further intermediate-rolled so that the cross-sectional shape approaches that of the steel sheet pile. In the finishing rolling mill 30, it is finish-rolled so that the cross-sectional shape becomes that of the steel sheet pile. The rolled metal material 11 that has been rolled by the finishing rolling mill 30 and has a cross-sectional shape of the steel sheet pile is water-cooled by the cooling device 32 and then conveyed to the table roller 34. Then, the rolled metal material 11 conveyed on the table roller 34 is sawn by the sawing device 36, and a steel sheet pile 10 of a predetermined length is manufactured.

[0015] The process computer 38 is connected by wired or wireless means to each device constituting the hot rolling mill 20 and oversees the manufacturing process of the steel sheet piles 10. The process computer 38 sets the manufacturing parameters of each device constituting the hot rolling mill 20 based on the manufacturing conditions of the steel sheet piles 10, and also collects and stores manufacturing performance data from each device. The process computer 38 is also connected by wired or wireless means to the saw-cut load estimation device 40 and transmits and receives the setting values ​​of the manufacturing parameters and manufacturing performance data necessary for the saw-cut load estimation device 40 to estimate the saw-cut load. Each device constituting the hot rolling mill 20 may have a control device that controls it. In this case, the process computer 38 is connected to the control device to set the manufacturing parameters and collect manufacturing performance data.

[0016] The sawing load estimation device 40 estimates the sawing load when the rolled metal material 11 is sawed by the sawing device 36. The sawing load estimation device 40 also obtains the setting values ​​of the manufacturing parameters used for estimating the sawing load and actual manufacturing data from the process computer 38.

[0017] When estimating the sawing load, the sawing load estimation device 40 obtains from the process computer 38 the operating parameters of the hot rolling process of the rolled metal material 11 for which the sawing load is to be estimated, and the set value of the sawing speed at a specific sawing position, and estimates the sawing load. Here, the operating parameters of the hot rolling process are at least one of the following: the heating temperature in the heating furnace 22, the rolling temperature and rolling time of the roughing mill 24, the rolling temperature and rolling time of the intermediate rolling mill 28, and the rolling temperature and rolling time of the finishing rolling mill 30.

[0018] Figure 3 is a schematic side view of the saw cutting device 36. The saw cutting device 36 comprises a drive unit 42, a trolley 44, an electric motor 46, a drive transmission belt 48, and a saw blade 50. The electric motor 46 rotates the saw blade 50 by transmitting rotational driving force to the saw blade 50 via the drive transmission belt 48. The electric motor 46, drive transmission belt 48, and saw blade 50 are mounted on the trolley 44. The drive unit 42 causes the trolley 44 to reciprocate toward the rolled metal material 11. As a result, the rolled metal material 11 is cut by the saw blade 50.

[0019] Furthermore, the drive unit 42 outputs the position of the trolley 44 to the process computer 38, associating it with time. In addition, the electric motor 46 outputs the load current value of the electric motor 46 to the process computer 38, associating it with time. When the process computer 38 obtains the position of the trolley 44, it refers to the time associated with that position to determine the saw cutting position and the saw cutting speed at that saw cutting position. Furthermore, when the process computer 38 obtains the load current value, it refers to the time associated with that load current value and the information obtained from the drive unit 42 to determine the saw cutting load at each saw cutting position.

[0020] Next, the sawing load when the rolled metal material 11 is sawed by the sawing device 36 will be explained. Figure 4 is a diagram illustrating the sawing load when the rolled metal material 11 of the steel sheet pile is sawed at a constant sawing speed. Figure 4(a) is a diagram showing the sawing position of the rolled metal material 11 of the steel sheet pile. The dashed line in Figure 4(a) shows the outer shape of the saw blade 50. Figure 4(b) is a graph showing the relationship between the sawing position and the sawing speed, and Figure 4(c) is a graph showing the relationship between the sawing position and the sawing load. The sawing positions (1) to (6) in Figure 4(a) correspond to the sawing positions (1) to (6) in Figures 4(b) and (c).

[0021] As shown in Figure 4(a), the saw blade 50 moves from left to right, approaching the rolled metal material 11 of the steel sheet pile. The saw blade 50 contacts the rolled material of the steel sheet pile at position (1) in Figure 4 and begins cutting. The saw blade 50 moves further to the right and cuts the steel sheet pile, and when it reaches position (6), the rolled metal material 11 of the steel sheet pile is cut and the steel sheet pile 10 is manufactured.

[0022] Figure 4(b) shows the sawing speed at each sawing position. As shown in Figure 4(b), the saw blade 50 moves at high speed until just before it contacts the rolled metal material 11. Just before the saw blade 50 contacts the rolled metal material 11 at position (1), the sawing speed is reduced to the reference speed. After that, the saw blade 50 moves at a constant speed at the reference speed to position (6), and the rolled metal material 11 is sawed.

[0023] Figure 4(c) shows the sawing load (current value) at each sawing position. As shown in Figure 4(c), the sawing load increased when the saw blade 50 contacted the rolled metal material 11 at position (1), and increased further when it moved to position (2) and the saw blade 50 contacted the flange 14. The sawing load decreased when the saw blade 50 passed position (2) and moved to position (3), and decreased even further when it moved to position (4). Subsequently, the sawing load increased slightly at positions (5) and (6), and decreased significantly when the sawing of the rolled metal material 11 was completed.

[0024] From the sawing load profile shown in Figure 4(c), it can be seen that the sawing load increases as the contact length between the saw blade 50 and the rolled metal material 11 increases, and decreases as the contact length decreases. That is, as shown in Figure 4(a), at sawing position (2), the contact length between the saw blade 50 and the flange 14 is the longest, so the largest sawing load occurred at sawing position (2). Also, at sawing positions (1), (5), and (6), the contact length with the saw blade 50 is the next longest, and the contact lengths are similar on both sides, so sawing loads of roughly the same magnitude occurred immediately after the start of sawing and near the end of sawing. On the other hand, at sawing positions (3) and (4), the contact length between the saw blade 50 and the rolled metal material 11 is shorter, so the sawing load was smaller at sawing positions (3) and (4).

[0025] Furthermore, it is known that in the saw cutting device 36, increasing the saw cutting speed increases the saw cutting load, and decreasing the saw cutting speed decreases the saw cutting load. As shown in Figure 4(c), the saw cutting load changes depending on the contact length between the saw blade 50 and the rolled metal material 11. Therefore, at saw cutting positions (3) and (4), where the saw cutting load is small, it may be possible to increase the saw cutting speed to improve the productivity of saw cutting.

[0026] Figure 5 illustrates the sawing load when the rolled metal material 11 of the steel sheet pile is sawn at an increased sawing speed. Figure 5(a) shows the sawing position of the rolled metal material 11 of the steel sheet pile, and is the same figure as Figure 4(a). Figure 5(b) is a graph showing the relationship between the sawing position and the sawing speed, and Figure 5(c) is a graph showing the relationship between the sawing position and the sawing load. In Figure 5, the sawing positions (1) to (6) in Figure 5(a) correspond to the sawing positions (1) to (6) in Figures 5(b) and (c).

[0027] As shown in Figure 5(b), in the example shown in Figure 5, the sawing speed is increased at sawing positions (3) and (4), where the sawing load is small. As a result, as shown in Figure 5(c), the sawing load at positions (3) and (4) is high. However, since the sawing load at these positions is lower than the equipment upper limit of the sawing device 36, this increase in sawing load does not cause any problems. Keeping the sawing load below the equipment upper limit is one example of keeping the sawing load within a predetermined target load range.

[0028] Thus, at cutting positions where the sawing load is low, the sawing speed can be increased while keeping the sawing load within a predetermined target load range, thereby improving sawing productivity. Furthermore, if the sawing load when the sawing speed is increased can be estimated with high accuracy, the safety factor required to ensure that the upper limit is not exceeded can be reduced, allowing for even faster sawing speeds and further increasing sawing productivity.

[0029] Next, the saw-cutting load estimation device 40, which estimates the saw-cutting load, will be described. Figure 6 is a functional block diagram of the saw-cutting load estimation device. The saw-cutting load estimation device 40 is, for example, a general-purpose computer such as a workstation or personal computer. The saw-cutting load estimation device 40 has a control unit 52, an input unit 54, an output unit 56, and a storage unit 58. The control unit 52 is, for example, a CPU, and by executing a program stored in the storage unit 58, it functions as a data acquisition unit 60, a saw-cutting load estimation unit 62, a saw-cutting speed determination unit 64, and a saw-cutting load estimation model generation unit 66.

[0030] The input unit 54 is, for example, a keyboard, a touch panel integrated with a display, etc. The output unit 56 is, for example, an LCD or CRT display, etc. The storage unit 58 is, for example, an information recording medium such as a flash memory capable of updating data, a hard disk that is built-in or connected via a data communication terminal, a memory card, etc., and a device for reading and writing such a recording medium and a device for reading and writing such a recording medium. The storage unit 58 stores the database 68 and the sawing load estimation model 70. The database 68 stores more than 100 datasets, each consisting of operating parameters for the hot rolling process of rolled metal material 11 that has been rolled in the past using the same hot rolling equipment 20 (the same roll set for the roughing mill 24, intermediate rolling mill 28, and finishing mill 30) and sawn by the sawing device 36, with the same target cross-sectional shape classification, sawing speed at a specific sawing position, and sawing load. The specific sawing position may be, for example, the sawing position (3) or (4) in Figure 4(a).

[0031] The sawing load estimation model 70 is a pre-trained machine learning model that has been trained using the dataset stored in the database 68 as training data. The sawing load estimation model 70 according to this embodiment is a pre-trained machine learning model that takes input data including the operating parameters of the hot rolling process and the sawing speed at a specific sawing position as input, and outputs the sawing load.

[0032] Next, the processes performed by the data acquisition unit 60 and the saw cutting load estimation unit 62 will be described. The data acquisition unit 60 acquires the operating parameters of the hot rolling process and the set value of the saw cutting speed at a specific saw cutting position as input data from the process computer 38. The data acquisition unit 60 may also acquire the above data from the input unit 54. In this case, the data is input from the input unit 54 by the operator. The data acquisition unit 60 outputs the acquired data to the saw cutting load estimation unit 62.

[0033] When the sawing load estimation unit 62 acquires input data from the data acquisition unit 60, it reads the sawing load estimation model 70 from the storage unit 58, inputs the input data into the sawing load estimation model 70, and outputs the sawing load. In this way, by outputting the sawing load from the sawing load estimation model 70, the sawing load estimation unit 62 can estimate the sawing load at a specific sawing position. The sawing load estimation unit 62 may also output the output sawing load to the output unit 56 and display it on the output unit 56. This allows the operator to confirm the estimated sawing load value by visually checking the output unit 56.

[0034] Thus, in the sawing load estimation device and sawing load estimation method according to this embodiment, the sawing load is estimated using a sawing load estimation model 70 that takes input data including the operating parameters of the hot rolling process as input and outputs the sawing load. Therefore, the sawing load can be estimated while taking into account the hot rolling conditions of the rolled metal material to be sawed.

[0035] The heating temperature in the heating furnace 22, which is an operating parameter of the hot rolling process, and the rolling temperature and rolling time in the roughing mill 24, intermediate rolling mill 28, and finishing rolling mill 30, which are operating parameters of the rolling mill, affect the deformation resistance of the rolled metal material 11 due to its temperature. This deformation resistance affects the sawing load of the rolled metal material 11.

[0036] The rolling load changes due to the change in the deformation resistance of the rolled metal material 11. Therefore, the heating temperature in the heating furnace 22, which is an operating parameter of the hot rolling process, and the rolling temperature and rolling time in the roughing mill 24, intermediate rolling mill 28, and finishing rolling mill 30, which are operating parameters of the rolling mill, also affect the cross-sectional shape of the rolled metal material 11. The cross-sectional shape of the rolled metal material 11 affects the contact length between the rolled metal material 11 and the saw blade 50. As explained in Figures 4(a) and (c), the contact length with the saw blade 50 is correlated with the saw cutting load, so it can be seen that these parameters are correlated with the saw cutting load even through changes in the cross-sectional shape. Therefore, it can be seen that by using the saw cutting load estimation model 70, which takes input data including the operating parameters of the hot rolling process as input, the saw cutting load can be predicted with higher accuracy than when using a saw cutting load estimation model that does not include the operating parameters in the input data.

[0037] Furthermore, it is preferable that the input data for the sawing load estimation model 70 includes at least one of the following: sawing position, cooling parameters for the hot rolling process, sawing condition parameters, and attribute parameters of the rolled metal material. If these data are included in the input data, they will also be included in the dataset stored in the database 68.

[0038] It is preferable that the sawing position is included in the input data of the sawing load estimation model 70. As shown in Figure 4(c), the sawing position greatly affects the sawing load. By generating a sawing load estimation model 70 limited to specific sawing positions, it is possible to create a model that can estimate the sawing load even without including the sawing position in the input data. However, by including the sawing position in the input data, a sawing load estimation model is obtained that can estimate the sawing load without being limited to specific sawing positions. By estimating the sawing load using such a sawing load estimation model, estimated values ​​of the sawing load corresponding to each sawing position are obtained, as shown in Figure 4(c).

[0039] Furthermore, it is preferable to include at least one of the following in the input data of the sawing load estimation model 70: a cooling parameter for the hot rolling process, a sawing condition parameter, and an attribute parameter of the rolled metal material. Here, the cooling parameter for the hot rolling process is at least one of the cooling water volume and cooling time of the cooling device 32. The sawing condition parameter is at least one of the number of sawings of the rolled metal material 11, the cumulative number of sawings of the saw blade 50, and the temperature of the rolled metal material 11 before sawing. The number of sawings represents the number of sawings for a given rolled metal material 11. The attribute parameter of the rolled metal material is at least one of the length of the rolled metal material 11 in the rolling direction and the mass of the rolled metal material 11.

[0040] The amount of cooling water and the cooling time of the cooling device 32, which are cooling parameters in the hot rolling process, affect the deformation resistance caused by the temperature of the rolled metal material 11, as described above. Therefore, these parameters also affect the sawing load.

[0041] Furthermore, as the number of cuts on the rolled metal material 11 increases, the temperature of the rolled metal material 11 decreases, so the sawing load tends to increase with an increase in the number of cuts. The cumulative number of cuts on the saw blade 50 correlates with the wear of the saw blade 50, and as this wear progresses, the sawing load tends to increase. When the temperature of the rolled metal material 11 before sawing is high, the sawing load tends to decrease. Therefore, the sawing condition parameters also affect the sawing load.

[0042] Furthermore, since the length of the rolled metal material 11 in the rolling direction and the mass of the rolled metal material 11 also affect the sawing load, the attribute parameters of the rolled metal material also affect the sawing load. Therefore, by including at least one of the cooling parameters of the hot rolling process, the sawing condition parameters, and the attribute parameters of the rolled metal material in the input data of the sawing load estimation model 70, the accuracy of the sawing load estimation is improved.

[0043] The chemical composition, steel grade classification, and target strength (strength classification) of the rolled metal material 11 may also be added as attribute parameters for the rolled metal material. By adding these to the attribute parameters of the rolled metal material, it becomes possible to estimate the saw cutting load of rolled metal materials that have different chemical compositions, steel grade classifications, and strength classifications but are rolled using the same roll set.

[0044] Next, the processes performed by the saw cutting speed determination unit 64 and the saw cutting load estimation model generation unit 66 will be described. The saw cutting speed determination unit 64 identifies a saw cutting speed at which the saw cutting load estimated by the saw cutting load estimation unit 62 falls within a predetermined target load range. The process of identifying the saw cutting speed by the saw cutting speed determination unit 64 will be explained using Figure 7.

[0045] Figure 7 is a flowchart showing the process of determining the cutting speed by the cutting speed determination unit 64. The flow shown in Figure 7 is initiated, for example, by an instruction from the operator to start the process and input of a target cutting speed.

[0046] First, the data acquisition unit 60 acquires the target saw cutting speed set as a reference upper limit, or the target saw cutting speed input by the operator from the input unit 54 (step S101). The data acquisition unit 60 also acquires other input data, such as the operating parameters of the hot rolling process, from the process computer 38 (step S102). The data acquisition unit 60 outputs this data to the saw cutting load estimation unit 62.

[0047] The sawing load estimation unit 62 reads the sawing load estimation model 70 from the storage unit 58, inputs the sawing speed and hot rolling temperature parameters to the sawing load estimation model 70, outputs the sawing load, and estimates the sawing load (step S103). The sawing load estimation unit 62 outputs the outputted sawing load to the sawing speed determination unit 64.

[0048] The saw cutting speed determination unit 64 reads the upper limit value of the saw cutting load of the saw cutting device 36, which is stored in the storage unit 58 in advance, and determines whether the saw cutting load obtained from the saw cutting load estimation unit 62 is less than or equal to the upper limit value (step S104). If the saw cutting load exceeds the upper limit value, the saw cutting speed determination unit 64 determines that the saw cutting load obtained from the saw cutting load estimation unit 62 is not within the range of the target load (step S104: No), and changes the saw cutting speed so that the saw cutting speed obtained in step S101 becomes slower (step S105). The saw cutting speed determination unit 64 outputs the changed saw cutting speed to the saw cutting load estimation unit 62 and causes the process in step S103 to be executed again. The processes in steps S105 and S103 are repeatedly executed until the saw cutting load estimated in step S103 is determined to be within the range of the target load in step S104. One unit of slowing down the saw cutting speed in step S105 is, for example, 20 mm / sec.

[0049] On the other hand, if the sawing load is less than or equal to the upper limit of the sawing load of the sawing device 36, the sawing speed determination unit 64 determines that the sawing load obtained from the sawing load estimation unit 62 is within the range of the target load (step S104: Yes), determines the sawing speed used for sawing load estimation as the sawing speed of the sawing device 36 (step S105), outputs the determined sawing speed to the process computer 38, and terminates the process of determining the sawing speed. When the process computer 38 obtains the sawing speed from the sawing speed determination unit 64, it controls the drive unit 42 of the sawing device 36 so that the speed of the trolley 44 becomes the determined sawing speed. In this way, by adjusting the sawing speed of the sawing device 36, the sawing speed can be increased while keeping the sawing load within the range of the target load, thereby improving the productivity of sawing. The sawing speed determination unit 64 may also display the determined sawing speed on the output unit 56. This allows the operator to visually check the output unit 56 and adjust the sawing speed of the sawing device 36.

[0050] The above describes a method for determining the saw cutting speed using the upper limit of the reference speed as the initial value, but this is not the only method. For example, the saw cutting speed may be determined by setting the lower limit of the reference speed as the initial value and increasing the saw cutting speed until the saw cutting load does not exceed the equipment's upper limit. Alternatively, the saw cutting speed may be determined by setting the standard saw cutting speed as the initial value and gradually increasing or decreasing the saw cutting speed so that the saw cutting load falls within the target range.

[0051] Next, a method for generating a sawing load estimation model used to estimate sawing load will be described. The data acquisition unit 60 acquires actual values ​​of the operating parameters of the hot rolling process, actual values ​​of the sawing speed at a specific sawing position, and actual values ​​of the sawing load from the process computer 38, and stores a dataset of these as one set in the database 68 of the storage unit 58. The number of datasets stored in the database 68 is at least 100, preferably 500 or more, and more preferably 1000 or more.

[0052] The saw-cut load estimation model generation unit 66 reads a pre-stored machine learning model from the storage unit 58, trains the machine learning model using the dataset stored in the database 68 as training data, and generates a trained machine learning model. This trained machine learning model becomes the saw-cut load estimation model. In this embodiment, any of the following machine learning models may be used in the saw-cut load estimation method and saw-cut load estimation device: for example, a neural network, decision tree learning, random forest, support vector regression, Gaussian process, or k-nearest neighbor method.

[0053] As described above, the sawing load estimation model used in the sawing load estimation method and sawing load estimation device according to this embodiment includes the operating parameters of the hot rolling process as input. As mentioned above, the operating parameters of the hot rolling process are correlated with the sawing load. Therefore, by including the operating parameters of the hot rolling process that manufactures rolled metal materials as input data for the sawing load estimation model, the sawing load estimation model becomes an estimation model that takes into account the hot rolling conditions of the rolled metal material, and thus the accuracy of the sawing load estimation is increased. If the sawing load can be estimated with high accuracy in this way, the occurrence of overload exceeding the equipment limit during sawing can be suppressed, and the occurrence of "tripping" of the electric motor 46 due to overload and "sticking" of the saw blade 50 to the rolled metal material 11 can be suppressed.

[0054] Furthermore, by determining a sawing speed such that the sawing load estimated by the sawing load estimation model falls within a predetermined target load range, and then sawing the rolled metal material at that sawing speed to manufacture the metal material, it becomes possible to manufacture the metal material while maintaining the sawing load within the target range.

[0055] In Figure 2, the hot rolling mill 20 is shown as an example in which the hot rolling mill 20 has a process computer 38 and a sawing load estimation device 40, but it is not limited to this. For example, the process computer 38 may have the functions of the sawing load estimation device 40, and these may be configured as a single device. Also, in Figure 6, the sawing load estimation device 40 is shown as an example in which the control unit 52 has a sawing speed determination unit 64 and a sawing load estimation model generation unit 66, but it is not limited to this. If the sawing load estimation is performed by the sawing load estimation device 40, it is not necessary to have a sawing speed determination unit 64. Furthermore, if the sawing load estimation model 70 is generated externally and stored in the storage unit 58 via the input unit 54, the sawing load estimation device 40 does not need to have a sawing load estimation model generation unit 66.

[0056] Next, we will explain the sawing load when cutting round steel, which has a different cross-sectional shape from steel sheet piles. Figure 8 illustrates the sawing load when cutting rolled metal round steel. Figure 8(a) shows the sawing position of the round steel. Figure 8(b) is a graph showing the relationship between the sawing position and the sawing speed, and Figure 8(c) is a graph showing the relationship between the sawing position and the sawing load. Note that the sawing positions (1) to (3) in Figure 8(a) correspond to the sawing positions (1) to (3) in Figures 8(b) and (c).

[0057] As shown in Figure 8(a), the saw blade 50 moves from left to right, approaching the rolled metal material of the round steel. Then, at position (1) in Figure 8, the saw blade 50 contacts the rolled metal material and begins to cut. The saw blade 50 moves further to the right, cutting the rolled metal material, and when it reaches position (3), the rolled metal material is cut and a round steel is manufactured.

[0058] Figure 8(b) shows the sawing speed at each sawing position. As shown in Figure 8(b), in the production of round steel, the sawing speed is reduced just before contact with the rolled metal material at (1) to prevent a sudden increase in sawing load. After that, the sawing speed is increased to the reference speed. As the sawing is nearing its end at (3), the contact length with the saw blade 50 becomes shorter, so the sawing speed is increased above the reference speed to move to position (3), and the rolled metal material is sawed.

[0059] Figure 8(c) shows the sawing load (current value) at each sawing position. As shown in Figure 8(c), the sawing load increases when the saw blade 50 contacts the rolled round steel at position (1), but a sudden increase in the sawing load is prevented because the sawing speed has been reduced in advance. Subsequently, the sawing speed is controlled so that the sawing load remains constant, and when the sawing is completed at position (3), the sawing load decreases. As shown in Figure 8(c), the sawing load at position (2) is lower than the equipment's upper limit, so it may be possible to increase the sawing speed at position (2) to improve the productivity of sawing.

[0060] Figure 9 illustrates the sawing load when a rolled round steel is sawn at an increased sawing speed. Figure 9(a) shows the sawing position of the round steel, and is the same as Figure 8(a). Figure 9(b) is a graph showing the relationship between the sawing position and the sawing speed, and Figure 9(c) is a graph showing the relationship between the sawing position and the sawing load. In Figure 9, the sawing positions (1) to (3) in Figure 9(a) correspond to the sawing positions (1) to (3) in Figures 9(b) and (c).

[0061] As shown in Figure 9(b), in the example shown in Figure 9, the sawing speed is increased at position (2), where the sawing load is small. Therefore, as shown in Figure 9(c), the sawing load at position (2) is high, but it is lower than the upper limit of the sawing device 36, so this increase in sawing load does not cause any problems.

[0062] Thus, even with round steel, at cutting positions where the sawing load is low, the sawing speed can be increased while keeping the sawing load within a predetermined target load range, thereby improving the productivity of sawing. Furthermore, if the sawing load estimation device and sawing load estimation method according to this embodiment can estimate the sawing load when the sawing speed is increased with high accuracy, the sawing speed can be further increased while maintaining the target load range, thereby increasing productivity.

[0063] In the case of round bars, it is possible to produce products with diameters varying by several millimeters to more than ten millimeters using the same roll set by adjusting the rolling conditions. In such cases, by adding the target product diameter to the attribute parameters of the rolled metal material, it becomes possible to estimate the sawing load of round bars with different diameters using the same sawing load estimation model.

[0064] Furthermore, the sawing load estimation device and sawing load estimation method according to this embodiment can also be applied to H-shaped steel with an H-shaped cross-section, and to square steel bars and flat steel with a rectangular cross-section, all manufactured using the same roll set. When applied to H-shaped steel, by adding the target web thickness and target flange thickness to the attribute parameters of the rolled metal material, the sawing load of H-shaped steel with different target web thicknesses and target flange thicknesses can be estimated using the same sawing load estimation model. Furthermore, when applied to square steel bars and flat steel, by adding the target wall thickness and target width to the attribute parameters of the rolled metal material, the sawing load of square steel bars and flat steel with different target wall thicknesses and target widths can be estimated using the same sawing load estimation model. [Examples]

[0065] [Example 1] This section describes an example of generating a sawing load estimation model and estimating the sawing load. In this example, a sawing load estimation model was generated for each sawing position (3), (4), and (5) shown in Figure 4 to estimate the sawing load when sawing rolled metal material of steel sheet pile (type: 10H). The correlation between the estimated sawing load value estimated by each sawing load estimation model and the actual sawing load value was confirmed. The input data for the sawing load estimation model is shown in Table 1 below. The output of the sawing load estimation model is the maximum sawing load current generated in the motor when sawing was performed at each position (3) to (5).

[0066] [Table 1]

[0067] Using a dataset of 1100 data points, each consisting of actual input data and actual saw-break load current values, a machine learning model was trained to generate a pre-trained machine learning model. A decision tree model was used as the machine learning model. This pre-trained machine learning model was used as a saw-break load prediction model to predict saw-break loads, and the correlation with the actual measured saw-break load current values ​​generated by the motor was confirmed.

[0068] Figure 10 is a graph showing the correspondence between estimated and measured current values. In Figure 10, the horizontal axis represents the estimated current value (A), and the vertical axis represents the measured current value (A). As shown in Figure 10, the plot showing the correspondence between estimated and measured current values ​​lies on the 45° line, confirming that the saw break load can be estimated with high accuracy.

[0069] Furthermore, the standard deviation of the difference between the estimated current value and the measured current value (predicted current value - measured current value) shown in Figure 10 was 3.46A, and the standard deviation of the ratio of the predicted current value to the measured current value (predicted current value / measured current value) was 4.72%. In contrast, when the load was estimated using only the saw cutting condition parameters, the standard deviation of the difference between the estimated current value and the measured current value was 5.98A, and the standard deviation of the ratio of the predicted current value to the measured current value was 8.13%. From these results, it can be seen that the saw cutting load can be estimated with high accuracy by using a saw cutting load estimation model that includes the operating parameters of the hot rolling process as input data.

[0070] [Example 2] Next, we will describe Example 2, in which the sawing load at sawing positions (3) and (4) in Figure 5 was estimated, and the sawing speed was increased to a sawing speed where the estimated sawing load did not exceed the equipment's upper limit, and the rolled metal material was sawed. In Example 2, the sawing load estimation model created in Example 1 was used. In Invention Example 1, the sawing load was estimated at sawing positions (3) and (4) in Figure 5, and the rolled metal material was sawed by increasing the sawing speed (up to a maximum of 450 mm / second) within a range where the estimated sawing load did not exceed the equipment's upper limit. The sawed rolled metal material was steel sheet pile (model: 10H), and 100 steel sheet piles (80 of 295 steel and 20 of 390 steel) were sawed 12 times per steel sheet pile (including crop cuts).

[0071] In Comparative Example 1, the sawing load at cutting positions (3) and (4) in Figure 5 was not estimated, and the steel sheet piles were sawn at a cutting speed of 200 mm / second, which is the standard sawing speed. The type, number, and number of cuts per sheet pile were the same as in the Inventive Example. In Comparative Example 3, the sawing load at cutting positions (3) and (4) in Figure 5 was not estimated, and the steel sheet piles were sawn at a cutting speed of 400 mm / second for all cuts. The type, number, and number of cuts per sheet pile were the same as in the Inventive Example. The sawing results for the Inventive Example, Comparative Example 1, and Comparative Example 2 are shown in Table 2 below.

[0072] [Table 2]

[0073] As shown in Table 2, in the inventive example, there were no problems with the saw blade jamming, and the sawing time was shorter than in Comparative Examples 1 and 2, resulting in improved sawing productivity. In Comparative Example 1, the sawing speed was slower than in Inventive Example 1, so although there were no problems with the saw blade jamming, the sawing time was longer, and improved sawing productivity could not be achieved. In Comparative Example 2, because the sawing speed was increased, the saw blade jamming problem occurred twice, and the processing time for this took time, resulting in a longer total time required than in Comparative Example 1. From these results, it was confirmed that it is preferable to estimate the sawing load using the sawing load estimation device and sawing load estimation method according to this embodiment, and to increase the sawing speed so that the estimated sawing load does not exceed the equipment upper limit. This makes it possible to increase the sawing speed while suppressing problems such as jamming, and to improve sawing productivity. [Explanation of symbols]

[0074] 10 Steel sheet piles 11 Rolled metal materials 12 Web 14 Flange 16 Arm 18 Joint section 20 Hot rolling equipment 22 Furnace 24 Roughing mill 25 Temperature Sensor 26 transfer beds 28 Intermediate Rolling Mill 29 Temperature sensor 30 Finishing Rolling Mill 31 Temperature sensor 32 Cooling device 33 Temperature Sensor 34 Table Rollers 36 Saw cutting device 38 Process Computers 40 Saw cutting load estimation device 42 Drive unit 44 bogies 46 Electric motor 48 Drive transmission belt 50 saw blade 52 Control Unit 54 Input section 56 Output section 58 Storage Unit 60 Acquisition Department 62 Saw cutting load estimation section 64 Saw cutting speed determination section 66. Saw breakage load estimation model generation unit 68 Databases 70. Saw Breaking Load Estimation Model 80 Round steel

Claims

1. A sawing load estimation device for estimating the sawing load generated when a rolled metal material, rolled in a hot rolling process, is sawn at a specific sawing position, The system includes a sawing load estimation unit that inputs the operating parameters of the hot rolling process, including at least one of the heating temperature in the heating furnace, the rolling temperature of the roughing mill, the rolling time of the roughing mill, the rolling temperature of the intermediate mill, the rolling time of the intermediate mill, the rolling temperature of the finishing mill, and the rolling time of the finishing mill, as well as the sawing speed, into a sawing load estimation model to output the sawing load of the rolled metal material. The sawing load estimation device is a pre-trained machine learning model that has been trained using multiple datasets, each dataset consisting of the operating parameters of the hot rolling process, the sawing speed, and the sawing load.

2. A sawing load estimation device for estimating the sawing load generated when sawing a rolled metal material that has been rolled in a hot rolling process, The system includes a sawing load estimation unit that inputs the operating parameters of the hot rolling process, including at least one of the heating temperature in the heating furnace, the rolling temperature of the roughing mill, the rolling time of the roughing mill, the rolling temperature of the intermediate mill, the rolling time of the intermediate mill, the rolling temperature of the finishing mill, and the rolling time of the finishing mill, as well as the sawing speed and sawing position, into a sawing load estimation model to output the sawing load of the rolled metal material. The sawing load estimation device is a pre-trained machine learning model that has been trained using multiple datasets, each dataset consisting of the operating parameters of the hot rolling process, the sawing speed, the sawing position, and the sawing load.

3. The sawing load estimation device according to claim 1 or 2, wherein the sawing load estimation unit further inputs at least one of the cooling parameters of the hot rolling process, the sawing condition parameters, and the attribute parameters of the rolled metal material to the sawing load estimation model to output the sawing load of the rolled metal material.

4. The saw cutting load estimation device according to claim 1 or claim 2, further comprising a saw cutting speed determination unit that determines a saw cutting speed such that the saw cutting load estimated by the saw cutting load estimation unit falls within a predetermined target load range.

5. The saw cutting load estimation device according to claim 3, further comprising a saw cutting speed determination unit that determines a saw cutting speed such that the saw cutting load estimated by the saw cutting load estimation unit falls within a predetermined target load range.

6. A method for estimating the sawing load generated when a rolled metal material, rolled in a hot rolling process, is sawn at a specific sawing position, The operating parameters of the hot rolling process, including at least one of the heating temperature in the heating furnace, the rolling temperature of the roughing mill, the rolling time of the roughing mill, the rolling temperature of the intermediate mill, the rolling time of the intermediate mill, the rolling temperature of the finish mill, and the rolling time of the finish mill, as well as the sawing speed, are input into a sawing load estimation model, and the sawing load of the rolled metal material is output to estimate the sawing load. The sawing load estimation method is a pre-trained machine learning model that has been machine-trained using multiple datasets, each dataset consisting of the operating parameters of the hot rolling process, the sawing speed, and the sawing load.

7. A method for estimating the sawing load generated when sawing a rolled metal material that has been rolled in a hot rolling process, The operating parameters of the hot rolling process, including at least one of the heating temperature in the heating furnace, the rolling temperature of the roughing mill, the rolling time of the roughing mill, the rolling temperature of the intermediate mill, the rolling time of the intermediate mill, the rolling temperature of the finishing mill, and the rolling time of the finishing mill, along with the sawing speed and sawing position, are input into a sawing load estimation model, and the sawing load of the rolled metal material is output to estimate the sawing load. The sawing load estimation method is a pre-trained machine learning model that has been machine-trained using multiple datasets, each dataset consisting of the operating parameters of the hot rolling process, the sawing speed, the sawing position, and the sawing load.

8. Furthermore, the sawing load estimation method according to claim 6 or 7, wherein at least one of the cooling parameters of the hot rolling process, the sawing condition parameters, and the attribute parameters of the rolled metal material is input to the sawing load estimation model, and the sawing load of the rolled metal material is output to estimate the sawing load.

9. A method for determining a saw cutting speed, wherein the saw cutting load output from the saw cutting load estimation model of the saw cutting load estimation method according to claim 6 or claim 7 is within a predetermined range of a target load.

10. A method for determining a saw cutting speed, wherein the saw cutting load output from the saw cutting load estimation model of the saw cutting load estimation method according to claim 8 is within a predetermined range of a target load.

11. A method for manufacturing a metal material, comprising cutting the rolled metal material with a saw at a saw speed determined by the saw cutting speed determination method described in claim 9.

12. A method for manufacturing a metal material, comprising cutting the rolled metal material with a saw at a saw speed determined by the saw cutting speed determination method described in claim 10.

13. A method for generating a sawing load estimation model for estimating the sawing load generated when a rolled metal material rolled in a hot rolling process is sawn at a specific sawing position, A machine learning model is trained using multiple datasets as training data, each dataset consisting of a set of operational parameters for a hot rolling process, including at least one of the following: heating temperature in the heating furnace of the previously sawed rolled metal material, rolling temperature of the roughing mill, rolling time of the roughing mill, rolling temperature of the intermediate rolling mill, rolling time of the intermediate rolling mill, rolling temperature of the finishing mill, and rolling time of the finishing mill, along with the sawing speed and sawing load. A method for generating a sawing load estimation model, which takes the operating parameters of the hot rolling process and the sawing speed as inputs and generates a sawing load estimation model that outputs the sawing load.

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