Method for predicting abnormal vibration of roller grinding machine, method for grinding roller, method for rolling metal strip, device for predicting abnormal vibration of roller grinding machine, and roller grinding apparatus

Abstract

The invention provides an abnormal vibration prediction method of a roller grinding machine and an abnormal vibration prediction device of the roller grinding machine, which can predict the abnormal vibration of the roller grinding machine corresponding to the natural vibration frequency of the whole mechanical system including a grinding wheel and a workpiece. A method for predicting abnormal vibration of a roller grinding machine (10) that grinds the outer peripheral surface of a rotating roller (12) using a grinding wheel (14), the method comprising: an acquisition step for acquiring the outer peripheral surface of the rotating roller (12), and a prediction step for predicting the abnormal vibration of the roller grinding machine (10); acquiring a rigidity parameter related to the rigidity of the roller grinding machine (10) and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel (14); and a prediction step for predicting abnormal vibration in the grinding step of the roll (12) using the rigidity parameter and the grinding wheel rotation parameter.

Description

Technical Field

[0001] The present invention relates to a method for predicting abnormal vibration of a roll grinder, a method for grinding a roll, a method for rolling a metal strip, a device for predicting abnormal vibration of a roll grinder and a roll grinding device. Background Art

[0002] One of the surface defects generated in the cold rolling process is chatter mark. Chatter mark refers to a surface defect in which a linear pattern is generated in the width direction of the metal strip due to the change in plate thickness, and the pattern appears periodically in the length direction. It is known that chatter mark is mainly caused by rolling mill vibration (hereinafter referred to as chatter). If chatter occurs and chatter marks are formed on the metal strip, it becomes a product defect due to poor appearance. In addition, in the case of severe chatter, the metal strip may break due to the sudden change in tension, which becomes the main reason for a significant reduction in productivity.

[0003] Chatter is often caused by scratches on rotating parts such as bearings that constitute a rolling mill, poor lubrication of the rolls and metal strips, abnormal roll profiles, etc. Among them, if chatter is caused by an abnormal profile on the outer peripheral surface of the roll generated during the grinding process, the rolling mill vibration continues until the roll with the abnormal profile is replaced, which causes a large number of product defects, thus becoming a problem.

[0004] The abnormal profile of the roll is caused by abnormal vibration of the grinder generated in the grinding process using the roll as a workpiece. Regarding the vibration of the grinder generated in the grinding process of the roll, if the natural vibration frequency of the grinding wheel, the natural vibration frequency of the workpiece support system, or the natural vibration frequency of the entire mechanical system from the grinding wheel to the workpiece coincides with the excitation frequency, the vibration of the grinder increases due to resonance, thereby generating abnormal vibration in the roll grinder.

[0005] As a technique for preventing abnormal vibration of such a roller grinder, a method for preventing chatter in a roller grinder is disclosed in Patent Document 1. According to Patent Document 1, chatter can be predicted based on the correlation between a chatter waveform detected by a chatter detector using an optical sensor and a vibration waveform measured by a vibration meter provided on a grinding wheel head and a tail stock.

[0006] Prior art literature Patent Literature Patent Document 1: Japanese Patent Application Laid-Open No. 52-56468 Summary of the invention

[0007] Problems to be solved by the invention

[0008] The method disclosed in Patent Document 1 is a method for preventing chatter in a roller grinder. Chatter is generated at the contact point between the grinding wheel and the roller as a workpiece, that is, the grinding point. Therefore, in the method disclosed in Patent Document 1, the focus is on the vibration behavior of the grinding point as a vibration source and transmitted to the roller grinder. However, the abnormal vibration generated in the roller grinder is not necessarily caused by the vibration at the grinding point, and the abnormal vibration may also be generated corresponding to the natural vibration frequency of the entire mechanical system including the grinding wheel and the workpiece. Therefore, in the method disclosed in Patent Document 1, there is a problem that the generation of abnormal vibration corresponding to the natural vibration frequency of the entire mechanical system including the grinding wheel and the workpiece cannot be predicted.

[0009] The present invention is made to solve the problems of the prior art, and its purpose is to provide a method for predicting abnormal vibration of a roller grinder, a device for predicting abnormal vibration of a roller grinder, and a roller grinding equipment, which can predict the generation of abnormal vibration of the roller grinder corresponding to the natural vibration frequency of the entire mechanical system including the grinding wheel and the workpiece. In addition, its purpose is to provide a method for grinding a roll that suppresses the generation of abnormal vibration of the roller grinder during grinding with a grinding wheel, and a method for rolling a metal strip using a roll ground by the roll grinding method.

[0010] Technical solutions to solve problems

[0011] The technical means for solving the above-mentioned problems are as follows.

[0012] [1] A method for predicting abnormal vibration of a roller grinder, wherein the roller grinder grinds the outer peripheral surface of a rotating roller using a grinding wheel, wherein the method for predicting abnormal vibration of the roller grinder comprises: an acquisition step of acquiring a rigidity parameter related to the rigidity of the roller grinder and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel; and a prediction step of using the rigidity parameter and the grinding wheel rotation parameter to predict the occurrence of abnormal vibration in the grinding process of the roller.

[0013] [2] The abnormal vibration prediction method of a roller grinder according to [1], wherein the rigidity parameter includes the distance between the rotation center of the roller and the rotation center of the grinding wheel or the total value of the diameter of the roller and the diameter of the grinding wheel.

[0014] [3] According to the abnormal vibration prediction method of the roller grinder described in [1], wherein in the acquisition step, a load parameter related to the load acting on the grinding wheel is also acquired, and in the prediction step, the occurrence of abnormal vibration in the grinding process of the rolling mill is predicted using the rigidity parameter, the grinding wheel rotation parameter and the load parameter.

[0015] [4] The abnormal vibration prediction method of a roller grinder according to [3], wherein the load parameter includes a load current value of a motor that rotates the grinding wheel.

[0016] [5] A method for predicting abnormal vibration of a roller grinder, wherein the roller grinder grinds the outer peripheral surface of a rotating roller using a grinding wheel, wherein the method for predicting abnormal vibration of the roller grinder comprises: an acquisition step of acquiring input data, wherein the input data comprises a rigidity parameter related to the rigidity of the roller grinder and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel; and a prediction step of predicting the occurrence of abnormal vibration in the grinding process of the roller using an abnormal vibration prediction model, wherein the abnormal vibration prediction model takes the input data as input and takes abnormal vibration information in the grinding process of the roller as output.

[0017] [6] The abnormal vibration prediction method of a roller grinder according to [5], wherein the rigidity parameter includes the distance between the rotation center of the roller and the rotation center of the grinding wheel or the total value of the diameter of the roller and the diameter of the grinding wheel.

[0018] [7] The abnormal vibration prediction method of a roller grinder according to [5], wherein input data is acquired in the acquisition step, and the input data includes the rigidity parameter, the grinding wheel rotation parameter and a load parameter related to the load acting on the grinding wheel.

[0019] [8] The abnormal vibration prediction method of a roller grinder according to [7], wherein the load parameter includes a load current value of a motor that rotates the grinding wheel.

[0020] [9] A method for grinding a rolling mill roll, using the abnormal vibration prediction method for a roll grinder described in any one of [1] to [8], wherein, when the abnormal vibration is predicted to occur through the prediction step, the grinding conditions of the roll grinder are changed to grinding conditions in which the abnormal vibration is predicted not to occur.

[0021]

[10] A method for rolling a metal strip, comprising: inserting a roll ground by the roll grinding method described in [9] into a rolling mill to roll the metal strip.

[0022]

[11] An abnormal vibration prediction device for a roller grinder, wherein the roller grinder grinds the outer peripheral surface of a rotating roller using a grinding wheel, wherein the abnormal vibration prediction device for the roller grinder comprises: a data acquisition unit, which acquires input data, the input data including a rigidity parameter related to the rigidity of the roller grinder and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel; and an abnormal vibration prediction unit, which predicts the occurrence of abnormal vibration of the roller grinder using an abnormal vibration prediction model, wherein the abnormal vibration prediction model takes the input data as input and takes abnormal vibration information as output.

[0023]

[12] The abnormal vibration prediction device for a roller grinder according to

[11] , wherein the data acquisition unit acquires input data including the rigidity parameter, the grinding wheel rotation parameter, and a load parameter related to a load acting on the grinding wheel.

[0024]

[13] The abnormal vibration prediction device for a roller grinder according to

[11] or

[12] , wherein the abnormal vibration prediction device for the roller grinder also has a guidance information acquisition unit, which uses the abnormal vibration prediction model to determine the input data in which the abnormal vibration will not occur.

[0025]

[14] A roller grinding device comprising: a roller grinder that grinds the outer peripheral surface of a rotating roller using a grinding wheel; and an abnormal vibration prediction device for the roller grinder described in

[11] or

[12] .

[0026]

[15] A roller grinding device comprising: a roller grinder that grinds the outer peripheral surface of a rotating roller using a grinding wheel; and an abnormal vibration prediction device for the roller grinder described in

[13] .

[0027] Effects of the Invention

[0028] According to the present invention, since the abnormal vibration of the roller grinder is predicted using the rigidity parameter and the grinding wheel rotation parameter, the occurrence of the abnormal vibration of the roller grinder corresponding to the natural frequency of the entire mechanical system including the grinding wheel and the workpiece can be predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram showing the structure of a roll grinding facility capable of implementing the abnormal vibration prediction method of the roll grinding machine according to the first embodiment. Figure 2 A front view of a roller grinder with grinding wheels of different diameters. Figure 3 This is a graph showing the relationship between the total value of the roll diameter and the grinding wheel diameter and the rigidity of the grinding machine. Figure 4It is a graph showing the relationship between vibration frequency and vibration intensity. Figure 5 This is a graph showing the relationship between the grinding wheel rotation frequency and the vibration intensity. Figure 6 is the abnormal vibration map associated with the stiffness parameters. Figure 7 is the abnormal vibration map associated with the stiffness parameters. Figure 8 This is a graph showing the relationship between the grinding wheel load current value and the grinding machine rigidity. Fig. 9 It is a map of abnormal vibrations related to load parameters. Fig.10 1 is a schematic diagram showing a roll grinding facility 110 capable of implementing the abnormal vibration prediction method of a roll grinding machine according to the second embodiment. Fig.11 1 is a schematic diagram showing a configuration example of the abnormal vibration prediction device 44 . Fig.12 This is a diagram showing an example of using a neural network as an abnormal vibration prediction model. Fig.13 This is a graph showing the measurement results of vibration intensity. Fig.14 It is a graph showing the measurement results of vibration intensity. DETAILED DESCRIPTION

[0030] [First embodiment]

[0031] Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. Figure 1 1 is a schematic diagram showing the configuration of a roll grinding facility 100 capable of implementing the abnormal vibration prediction method of the roll grinding machine according to the first embodiment. Figure 1 (a) shows a top view of the roller grinding apparatus 100 . Figure 1 (b) shows a front view of the roller grinder 10 . Figure 1 The roll grinder 10 shown is a roll grinder that grinds the outer peripheral surface of a roll 12 using a cylindrical grinding wheel. The roll 12 to be ground is used in a rolling mill and then transported to a roll shop using a crane or the like. Then, the roll 12 is pulled out of the bearing box, cooled to room temperature by natural cooling, and then installed in the roll grinder 10 one by one.

[0032] The roller grinding machine 10 includes a grinding head 16 supporting a grinding wheel 14, a support table 18 supporting the grinding head 16, guides 26, 28, a roller support table 68, and a vibrator 32. The grinding head 16 supports the grinding wheel 14 and a grinding wheel rotating motor 30 driving the grinding wheel 14. A pulley and a belt for power transmission are arranged between the grinding wheel 14 and the grinding wheel rotating motor 30. In addition, there is also a case where the grinding wheel 14 is directly driven to rotate by the grinding wheel rotating motor 30.

[0033] The support table 18 is guided by the guide 26 and moves in parallel with the axial direction of the roll 12. The movement of the support table 18 on the guide 26 is performed by position control using a servo motor, thereby controlling the relative position of the grinding wheel 14 and the roll 12 in the axial direction. The grinding head 16 is guided by the guide 28 and moves in a direction perpendicular to the axis of the roll 12. The movement of the grinding head 16 on the guide 28 is also performed by position control using a servo motor, thereby controlling the amount of cut-in by the grinding wheel 14. However, the support table 18 may be composed of a double-axis table that moves on the guide 26 and the guide 28. In the case of using a double-axis table, the support table 18 moves along the guide 26 arranged parallel to the axial direction of the roll 12, and the grinding wheel 14 is also moved along the guide 28 in a direction perpendicular to the axis of the roll 12. In the following description, a structure including the grinding head 16 that directly or indirectly supports the grinding wheel 14 , the support table 18 , the guide 28 , and the grinding wheel rotating motor 30 is referred to as a grinding wheel supporting portion 66 .

[0034] The roller support stand 68 includes: a roller chuck 20 that supports the roll 12 from one end side in the axial direction; a roller rotating motor 22 that rotates and drives the roll 12 at a predetermined rotation speed; and a tailstock 24 that supports the roll 12 from the other end side in the axial direction. The tailstock 24 plays a role in aligning the axis of the roll 12 with the axis of the rotating shaft of the roller rotating motor 22. The contact portion of the tailstock 24 that contacts the roll 12 is in a conical shape, and the front end of the cone is pressed into the countersink opened at the center of the shaft end of the roll 12 and the countersink of the fixing fixture. As a result, the axis of the roll 12 is aligned with the axis of the rotating shaft of the roller rotating motor 22 and is fixed. The rotation speed of the roll 12 during grinding is controlled by the controller 42 for controlling the roll grinder 10.

[0035] The roller 12 is ground from one end to the other end in the axial direction of the roller 12, and then ground from the other end to one end. The unit in which the grinding wheel 14 reciprocates once in this way is called a traverse. The usual grinding process is divided into a rough grinding process in which the grinding amount is set to a larger value and a fine grinding process for finishing the surface of the roller 12. Usually, the number of traverses for rough grinding is about 80 to 150 times, and the number of traverses for fine grinding is about 5 to 15 times. Here, the rough grinding process is a grinding process for removing the surface of the roller 12 by cutting into it, thereby removing the fatigue layer and the microscopic cracks. The fine grinding process is a weak grinding process for adjusting the surface roughness of the roller to a specified range.

[0036] Figure 1 The roller grinding equipment 100 shown further includes a control computing device 40 and a control controller 42. The control computing device 40 sets grinding conditions for the roller 12 based on information on the size information of the roller 12 to be ground, the grinding amount, the target value of the surface finish roughness, and the like.

[0037] The grinding conditions include at least three setting conditions: the roller rotation speed during grinding, the rotation speed of the grinding wheel 14, and the cutting depth of the grinding wheel 14, and are set for each traverse from rough grinding to fine grinding. In addition, the set current value of the grinding wheel rotating motor 30 may be used instead of the grinding wheel cutting depth. In addition, the grinding conditions in the roller grinder 10 are sometimes appropriately corrected while the operator confirms the grinding state of the roll 12. In this case, the corrected grinding conditions of the roller grinder 10 are output to the control computing device 40.

[0038] When setting the grinding conditions of the roll grinder 10, a setting table is sometimes used that takes into account factors such as the diameter of the roll 12 to be ground, the hardness of the surface, and the surface roughness before grinding. In addition, as conditions for the grinding wheel 14, the grinding conditions are set in consideration of the grit number of the grinding wheel 14, the initial grinding wheel diameter, the current grinding wheel diameter, the cumulative grinding time of the grinding wheel 14, and the total grinding amount after dressing by the dressing device.

[0039] The so-called initial grinding wheel diameter is the diameter of the grinding wheel from the time the grinding wheel 14 is manufactured to the time it is first used in roller grinding. The so-called current grinding wheel diameter is the grinding wheel diameter measured before starting the grinding of the rolling roller 12 to be ground. Regarding the grinding wheel diameter, multiple locations on the outer periphery of the grinding wheel 14 are selected, and the multiple locations are measured using a micrometer. In addition, it is also possible to pre-apply marks with a spacing of 1mm to 5mm on the side of the grinding wheel 14 in the radial direction, and read the grinding wheel diameter based on the marks to determine the grinding wheel diameter. The initial grinding wheel diameter of the grinding wheel 14 is 850 to 950mm. The grinding wheel 14 is discarded after the grinding wheel diameter becomes about 450 to 600mm.

[0040] The control computing device 40 sets the control target value of the operating condition of the roller grinder 10. The control controller 42 controls each device so that the roller rotation speed, the rotation speed of the grinding wheel 14, and the cutting amount of the grinding wheel 14 during grinding for each traverse from the start of grinding to the end of grinding become the control target values. In addition, the control controller 42 obtains the actual value of the grinding wheel rotating motor 30 that drives the grinding wheel 14 during grinding. In addition, when the actual values ​​of the roller rotation speed, the rotation speed of the grinding wheel 14, and the cutting amount of the grinding wheel 14 during grinding can be measured, the control controller 42 obtains these actual values. The actual values ​​thus obtained are output to the control computing device 40 as data for analyzing the operating state of the roller grinding. The control computing device 40 and the control controller 42 are general-purpose computers such as workstations and personal computers. The control computing device 40 and the control controller 42 may also be composed of a single computer.

[0041] In the roller grinding equipment 100 as described above, the roller 12 that has completed the fine grinding is moved to the grinding-completed roller storage area, and when it is the turn, it returns to the roller replacement device and is loaded into the rolling mill. In addition, the roller grinder 10 is equipped with a vibration meter 32. The vibration meter 32 obtains actual data related to abnormal vibrations in the grinding process of the roller. The vibration meter 32 is preferably set at either the grinding head 16 or the roller support table 68, and more preferably at a position close to the grinding wheel 14. By setting the vibration meter 32 at such a position, the vibration generated at the contact portion between the grinding wheel 14 and the roller 12 can be detected. In addition, the measurement direction of the vibration is not limited, but it is preferred to measure the vibration in the same direction as the direction in which the grinding wheel 14 gives the roller 12 a cut.

[0042] In the roller grinder abnormal vibration prediction method according to the first embodiment, abnormal vibration of the roller grinder 10 is predicted using a rigidity parameter related to the rigidity of the roller grinder 10 and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel 14. First, the rigidity parameter of the roller grinder 10 will be described.

[0043] The rigidity parameter related to the rigidity of the roll grinder 10 means a parameter that affects the rigidity of the roll grinder 10 when the roll 12 as a workpiece is supported on the roll support stand 68. The rigidity of the roll grinder 10 means the degree of influence of the external force acting on the roll grinder 10 on the displacement of the grinding wheel 14 at the contact portion with the roll 12.

[0044] Specifically, regarding the rigidity of the grinding wheel support 66 that directly or indirectly supports the grinding wheel 14 (hereinafter referred to as the grinder rigidity), a parameter related to the rigidity of the roller grinder 10 is set as a rigidity parameter. In addition, the mass and rigidity of the roll 12 and the roller support 68 as the workpiece are larger than the mass of the grinding wheel support 66, and the rigidity is also high. Therefore, the contribution of the roll 12 and the roller support 68 to the overall vibration behavior of the roller grinder 10 is lower than the grinder rigidity. Therefore, the rigidity parameter of the roller grinder 10 may not include the parameter related to the roll 12 and the roller support 68 as the workpiece.

[0045] As a rigidity parameter, it is preferable to use the distance between the rotation center of the roll 12 and the rotation center of the grinding wheel 14. This is because the relative position of the grinding head 16 with respect to the support table 18 in the grinding process changes according to the distance between the rotation center of the roll 12 and the rotation center of the grinding wheel 14, and thus the ease of vibration of the grinding head 16 changes. In addition, instead of the distance between the rotation center of the roll 12 and the rotation center of the grinding wheel 14, the sum of the diameter of the roll 12 and the diameter of the grinding wheel 14 may be used. In the following description, an example of using the sum of the diameter of the roll 12 and the diameter of the grinding wheel 14 as a rigidity parameter is described.

[0046] Figure 2 A front view of a roller grinder with grinding wheels of different diameters. Figure 2 (a) shows a roller grinder 70 having a grinding wheel with a large diameter. Figure 2 (b) shows a roller grinder 80 having a grinding wheel of a small diameter. If the diameter of the grinding wheel is small, the distance between the rotation center of the roll 12 and the rotation center of the grinding wheel becomes short, and in order to make the grinding wheel 14 contact the roll 12, the grinding head 16 needs to be further advanced along the guide 28 toward the roll 12. Therefore, compared with the roller grinder 70, the distance between the rotation center of the grinding wheel 14 and the center of gravity of the grinding wheel support 66 of the roller grinder 80 (hereinafter referred to as the arm length) becomes longer.

[0047] If the arm length is increased, even if the grinding force (tangential force in the rotation direction of the grinding wheel) acting between the roll 12 and the grinding wheel 14 is the same, the rotation moment about the center of gravity of the grinding wheel support 66 acts larger. As a result, the rigidity of the grinding wheel support 66 changes and the vibration behavior of the roll grinder changes. That is, when the distance between the rotation center of the roll and the rotation center of the grinding wheel 14 is short and the arm length is long, the rigidity of the roll grinder decreases.

[0048] Figure 3 This is a graph showing the relationship between the total value of the diameter of the roll 12 and the diameter of the grinding wheel 14 and the rigidity of the grinding machine. Figure 3 The horizontal axis is the sum of the diameter of the roller 12 and the diameter of the grinding wheel 14 (mm), and the vertical axis is the grinding machine rigidity (N / mm). The grinding machine rigidity is obtained by approximating the grinding wheel support 66 using a vibration model of a mass spring damping system, and determining the parameters of the vibration model in such a way that the relationship between the vibration frequency and the vibration intensity measured by the vibration meter is consistent. In addition, the grinding machine rigidity can also be calculated based on the mass of the grinding wheel support 66 by measuring the natural vibration frequency through a hammer test on the grinding wheel support 66.

[0049] like Figure 3 As shown in the figure, if the sum of the diameter of the roller 12 and the diameter of the grinding wheel 14 increases, that is, the distance between the rotation center of the roller 12 and the rotation center of the grinding wheel 14 increases, the rigidity of the grinder increases. According to this result, it is confirmed that if the distance between the rotation center of the roller 12 and the rotation center of the grinding wheel 14 increases, the ease of vibration of the grinding wheel support 66 changes, and there is a tendency for the rigidity of the grinder to increase. This is because if the sum of the diameter of the roller 12 and the diameter of the grinding wheel 14 increases, the arm length becomes shorter, and the rotational moment around the center of gravity of the grinding wheel support 66 becomes smaller. According to this result, it is preferable to use the sum of the diameter of the roller 12 and the diameter of the grinding wheel 14 as the rigidity parameter.

[0050] Next, the grinding wheel rotation parameter related to the rotation speed of the grinding wheel 14 is described. The so-called grinding wheel rotation parameter is a parameter related to the rotation speed of the grinding wheel 14 among the parameters representing the grinding conditions for grinding the roll 12. Specifically, as the grinding wheel rotation parameter, any one of the rotation speed, rotation frequency and rotation angular velocity of the grinding wheel 14 can be used, and as their values, the rotation speed, rotation frequency and rotation angular velocity of the grinding wheel rotating motor 30 that drives the grinding wheel 14 can also be used. The grinding wheel rotation parameter becomes the frequency of the vibration source acting on the grinding wheel support part 66 from the outside, and affects the vibration behavior of the grinding wheel support part 66. That is, the vibration of the roller grinder 10 is determined by the rigidity of the grinding wheel support part 66 that supports the grinding wheel 14 and the frequency of the vibration source.

[0051] Figure 4 It is a graph showing the relationship between vibration frequency and vibration intensity. Figure 4 The frequency spectrum for a specific grinding wheel speed is shown, with the horizontal axis representing the vibration frequency (Hz) and the vertical axis representing the vibration intensity (m / sec 2 ). Figure 4 The graph shown shows the results of measuring the vibration intensity at a grinding wheel rotation speed of 8.5 rps (corresponding to a grinding wheel rotation frequency of 8.5 Hz) while keeping the total value of the diameter of the roll 12 and the diameter of the grinding wheel 14 and the grinding wheel load current value as a load parameter constant.

[0052] like Figure 4 As shown, it can be seen that the vibration intensity of the roller grinder 10 measured by the vibration meter 32 has a peak value at a vibration frequency equivalent to an integer multiple of the grinding wheel rotation speed. However, the vibration of the grinding wheel rotation frequency of 60 Hz is interference caused by the frequency of the power supply system, not the vibration intensity of the roller grinder 10. In this way, it can be seen that the peak value of the vibration intensity occurs when it becomes an integer multiple of a specific grinding wheel rotation speed. In the first embodiment, corresponding to a specific grinding wheel rotation speed N, the peak value corresponding to the frequency of the integer multiple of the grinding wheel rotation speed is set to PNi. Among them, i is a multiple of the grinding wheel rotation speed, and i is any integer greater than 1. In addition, the maximum multiple i of the grinding wheel rotation speed can be determined within the range of 6 to 12. This is because, when the maximum value of the multiple i of the grinding wheel rotation speed is less than 6, it is sometimes impossible to predict the occurrence of abnormal vibration, and when the maximum value of the multiple i of the grinding wheel rotation speed exceeds 12, a tendency for the peak value of the vibration to become smaller is observed.

[0053] Figure 5 This is a graph showing the relationship between the grinding wheel rotation frequency and the vibration intensity. Figure 5 In the figure, the horizontal axis is the grinding wheel rotation frequency (Hz), and the vertical axis is the vibration intensity (m / sec 2 ). Figure 5 The graph shown is a graph in which the rigidity parameter is kept constant, the grinding wheel rotation speed N is changed, the peak value PNi (i=1 to 6) corresponding to the frequency of the integral multiple of the grinding wheel rotation speed is determined, and the peak value PNi is plotted for each multiple i. Figure 5 The graph shown is a graph in which the peak intensities at multiples i of the grinding wheel rotation speed are plotted, and this graph is described as a peak intensity map.

[0054] like Figure 5 As shown in FIG. 1 , when the rigidity parameter is kept constant and the grinding wheel speed N is changed, a peak value of the vibration intensity corresponding to a frequency that is an integer multiple of the grinding wheel speed N appears. Figure 5If the relationship between the grinding wheel rotation frequency and the vibration intensity is clearly defined, it is possible to determine how the peak value of the vibration intensity changes for each integer multiple i of the grinding wheel rotation frequency N when the grinding wheel rotation frequency N, which is a grinding wheel rotation parameter, changes. -1 m / s 2 ), the range of the grinding wheel rotation speed N that generates abnormal vibration in the roller grinder 10 and the multiple i corresponding thereto can be determined.

[0055] Furthermore, by changing the rigidity parameters Figure 5 The relationship shown enables the creation of an abnormal vibration map of the roller grinder 10 . Figure 6 is the abnormal vibration map associated with the rigidity parameters. Figure 6 In the figure, the horizontal axis is the total value of the grinding wheel diameter and the roller diameter (mm), and the vertical axis is the grinding wheel speed (rps).

[0056] Figure 6 The abnormal vibration map shown is a map in which the total value (4 points) of the diameter of the roll and the diameter of the grinding wheel, which are rigidity parameters, is used as the horizontal axis, and the range of the grinding wheel rotation speed N where the vibration intensity exceeds the threshold is represented by a bar shape for each multiple i. Figure 6 In this way, the abnormal vibration map represented by the discrete value of the variable represented by the horizontal axis is recorded as a discrete abnormal vibration map. The discrete abnormal vibration map is preferably created by changing the condition of the rigid parameter by more than 3 and less than 15. If the number of changes in the condition of the rigid parameter is set to less than 3 to create the discrete abnormal vibration map, it is difficult to predict the abnormal vibration generated under the conditions of different rigid parameters in the map, so it is not preferred. In addition, even if the number of changes in the condition of the rigid parameter is set to more than 15 to create the discrete abnormal vibration map, the prediction accuracy of the abnormal vibration will not be significantly improved, and the labor of creating the map increases, so it is not preferred.

[0057] like Figure 6 As shown, it can be seen that the range in which the abnormal vibration of the roller grinder 10 occurs is determined by the grinding wheel rotation parameter related to the rotation speed of the grinding wheel 14, that is, the grinding wheel rotation speed. Furthermore, it can be seen that the range in which the abnormal vibration occurs varies depending on the total value of the diameter of the roll and the diameter of the grinding wheel, which are the rigidity parameters.

[0058] according to Figure 6 When the sum of the diameter of the roll and the diameter of the grinding wheel is constant, the vibration intensity of the roll grinder 10 exceeds the threshold value within the range of the grinding wheel rotation speed shown by the bar shape in the figure, so it can be predicted that abnormal vibration of the roll grinder 10 will occur during the grinding process of the roll within this range. Figure 6 The threshold of abnormal vibration is set to 10 -1 m / s2 , the threshold value of the vibration intensity may be determined based on the actual value of the vibration data during roll grinding of the roll in which chattering occurs.

[0059] Figure 7 This is another example of abnormal vibration mapping related to stiffness parameters. Figure 7 The vibration map shown is a Figure 6 The abnormal vibration map shown is an abnormal vibration map in which the upper limit and lower limit of the grinding wheel rotation speed that generate abnormal vibrations of the same multiple are connected by continuous curves. By creating such an abnormal vibration map, it is possible to determine the range of abnormal vibrations of the roller grinder 10 for each multiple i of the grinding wheel rotation speed for the sum of the diameter of any roll and the diameter of the grinding wheel, thereby predicting the abnormal vibration of the roll. Figure 7 In this way, an abnormal vibration map that represents continuous changes in the variable represented by the horizontal axis is described as a continuous type abnormal vibration map.

[0060] In this way, we can know that if we create Figure 7 The continuous abnormal vibration map shown can predict the occurrence of abnormal vibration in the grinding process of the roll 12 using the roll grinder 10 by obtaining the total value of the roll diameter and the grinding wheel diameter as the rigidity parameter and the rotation speed of the grinding wheel 14 as the grinding wheel rotation parameter.

[0061] In the method for predicting abnormal vibration of a roller grinder according to the first embodiment, the acquisition step is performed to acquire the rigidity parameter and the grinding wheel rotation parameter, and then the prediction step is performed to use these parameters and the previously created Figure 7 The abnormal vibration map (continuous abnormal vibration map) shown predicts abnormal vibration in the roll grinding process, thereby predicting abnormal vibration of the roll grinder 10 corresponding to the natural frequency of the entire mechanical system including the grinding wheel 14 and the workpiece.

[0062] Next, the load parameter related to the load acting on the grinding wheel 14 is described. In the abnormal vibration prediction method of the roller grinder according to the first embodiment, the load parameter may be further added to predict the abnormal vibration of the roller grinder 10 using the rigidity parameter, the grinding wheel rotation parameter, and the load parameter.

[0063] The load parameter related to the load acting on the grinding wheel 14 is a parameter related to the load (load, tangential force) acting on the contact portion between the grinding wheel 14 and the roller 12 during the grinding process. As the load parameter, the load current value (hereinafter referred to as the grinding wheel load current value) of the motor that rotates the grinding wheel 14, i.e., the grinding wheel rotation motor 30, and the grinding wheel cut-in amount during the grinding process can be used.

[0064] The load parameter related to the load acting on the grinding wheel 14 affects the elastic deformation of the contact portion of the grinding wheel 14 with the roller 12, and affects the state of mechanical looseness of the grinding wheel support portion 66, thereby indirectly affecting the rigidity of the grinding machine. If the grinding wheel cut-in amount and the grinding wheel load current value as the load parameter are large, the reaction force received by the grinding wheel 14 from the roller 12 increases, and the force received by the grinding wheel support portion 66 also increases. As a result, the mechanical looseness of the grinding wheel support portion 66 is suppressed, and the apparent rigidity of the grinding wheel support portion 66 increases.

[0065] Figure 8 This is a graph showing the relationship between the grinding wheel load current value and the grinding machine rigidity. Figure 8 In the figure, the horizontal axis is the grinding wheel load current value (A), and the vertical axis is the grinding machine rigidity (N / mm). Figure 8 The graph shown is the result of investigating the relationship between the grinding wheel load current value and the grinding machine rigidity in the rough grinding process of the roll grinder 10, with the sum of the roll diameter and the grinding wheel diameter being constant. Figure 8 As shown in FIG. 1 , it was confirmed that as the grinding wheel load current value increases, the apparent rigidity of the grinding wheel support portion 66 changes, and there is a tendency for the rigidity of the roller grinder 10 to increase.

[0066] Fig. 9 It is an abnormal vibration map related to load parameters (continuous abnormal vibration map). Fig. 9 In the graph, the horizontal axis is the grinding wheel load current value (A), and the vertical axis is the grinding wheel speed (rps). Fig. 9 The abnormal vibration mapping shown can be achieved by comparing Figures 4 to 7 The method for creating the abnormal vibration map of the rigidity parameter described in is the same as that for creating the abnormal vibration map of the rigidity parameter described in . Specifically, the frequency spectrum for a specific grinding wheel speed is measured, and a peak intensity map is created in which the peak intensity at multiples i of the grinding wheel speed is plotted. Then, multiple peak intensity maps are created by changing the load parameter, and a continuous abnormal vibration map related to the load parameter is created based on the discrete abnormal vibration map related to the load parameter.

[0067] like Figure 8 , Fig. 9As shown, the load parameter affects the rigidity of the roller grinder 10, and thus affects the generation of abnormal vibration of the roller grinder 10. Therefore, it is preferable to further add the load parameter, and use the rigidity parameter, the grinding wheel rotation parameter and the load parameter to predict the abnormal vibration of the roller grinder 10. In particular, it is preferable to include the distance between the rotation center of the roller 12 and the rotation center of the grinding wheel 14 or the total value of the diameter of the roller 12 and the diameter of the grinding wheel 14 as the rigidity parameter, and include the grinding wheel load current value as the load parameter, and in addition, use the grinding wheel rotation parameter to predict the abnormal vibration of the roller grinder 10. This is because the prediction accuracy of the abnormal vibration of the roller grinder 10 is improved by using the rigidity and apparent rigidity of the structure based on the grinding wheel support part 66. Specifically, as long as the load parameters are created separately, the abnormal vibration of the roller grinder 10 can be predicted. Figure 7 The abnormal vibration map (continuous abnormal vibration map) related to the rigidity parameter shown in the figure can be used to predict the abnormal vibration of the roller grinder 10 using the abnormal vibration map related to the rigidity parameter corresponding to the acquired load parameter. This can improve the prediction accuracy of the generation of abnormal vibration in the roller grinder 10.

[0068] [Second embodiment]

[0069] Next, a second embodiment of the present invention will be described. Fig.10 Schematic diagram showing a roller grinding machine 110 capable of implementing the abnormal vibration prediction method of a roller grinding machine according to the second embodiment. Figure 1 The roller grinding equipment 100 shown in FIG. 1 is different in that it includes an abnormal vibration prediction device 44. Figure 1 The same reference numerals are used to denote the common elements of the roller grinding apparatus 100 shown, and duplicate descriptions thereof will be omitted.

[0070] In the abnormal vibration prediction method of the roller grinder involved in the second embodiment, the abnormal vibration in the grinding process of the rolling roller is predicted using an abnormal vibration prediction model. The abnormal vibration prediction model takes as input input data including rigidity parameters related to the rigidity of the roller grinder and grinding wheel rotation parameters related to the rotation speed of the grinding wheel 14, and outputs abnormal vibration information in the grinding process of the rolling roller.

[0071] Fig.11: is a schematic diagram showing a configuration example of an abnormal vibration prediction device 44. The abnormal vibration prediction device 44 is, for example, a general-purpose computer such as a workstation or a personal computer. The abnormal vibration prediction device 44 includes a control unit 46, an input unit 48, an output unit 50, and a storage unit 52. The control unit 46 is, for example, a CPU, and by executing a program read from the storage unit 52, the control unit 46 functions as a data acquisition unit 54, an abnormal vibration prediction unit 56, a prediction model generation unit 58, and a guidance information acquisition unit 60.

[0072] The input unit 48 is, for example, a keyboard, a touch panel provided integrally with a display, etc. The output unit 50 is, for example, an LCD or CRT display, etc. The storage unit 52 is, for example, an information recording medium such as a flash memory that can update records, a hard disk built in or connected via a data communication terminal, a memory card, and a read / write device thereof. The storage unit 52 records programs for the database 62, the abnormal vibration prediction model 64, and the control unit 46 to execute various functions, data used by the programs, etc.

[0073] Next, the processing performed by the data acquisition unit 54, the abnormal vibration prediction unit 56, the guidance information acquisition unit 60, and the prediction model generation unit 58 will be described. The data acquisition unit 54 acquires a rigidity parameter related to the rigidity of the roller grinder 10 and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel 14 as input data.

[0074] The data acquisition unit 54 performs an acquisition step to acquire a rigidity parameter and a grinding wheel rotation parameter. The rigidity parameter acquired by the data acquisition unit 54 may be any operating parameter related to the rigidity of the grinding wheel support unit 66. The operating parameter related to the rigidity of the grinding wheel support unit 66 is, for example, at least one of the distance between the rotation center of the roll 12 and the rotation center of the grinding wheel 14 and the sum of the diameter of the roll 12 and the diameter of the grinding wheel 14. In addition, the grinding wheel rotation parameter acquired by the data acquisition unit 54 may be any operating parameter related to the rotation speed of the grinding wheel 14. The operating parameter related to the rotation speed of the grinding wheel 14 is, for example, at least one of the rotation speed, rotation frequency, and rotation angular velocity of the grinding wheel 14. As these values, the rotation speed, rotation frequency, and rotation angular velocity of the grinding wheel rotating motor 30 that drives the grinding wheel 14 can be used. These parameters affect the generation of abnormal vibration of the roller grinder 10 corresponding to the natural vibration frequency of the entire mechanical system including the grinding wheel and the workpiece. Therefore, by including these data in the input data, the abnormal vibration prediction model 64 becomes an abnormal vibration prediction model that outputs abnormal vibration information corresponding to the natural vibration frequency.

[0075] The data acquisition unit 54 may acquire these input data from the control calculation device 40 or may acquire them through an operator's input operation on the input unit 48. The data acquisition unit 54 outputs these input data to the abnormal vibration prediction unit 56.

[0076] Preferably, the data acquisition unit 54 performs an acquisition step to further acquire a load parameter related to the load acting on the grinding wheel 14 as input data. The load parameter acquired by the data acquisition unit 54 can be any operating parameter related to the load acting on the contact portion between the grinding wheel 14 and the roller 12, for example, at least one of a grinding wheel load current value and a grinding wheel cut-in amount in a grinding process. As described above, the load parameter affects the generation of abnormal vibration, so by including the rigidity parameter, the grinding wheel rotation parameter, and the load parameter in the input data, the prediction accuracy of the generation of abnormal vibration is improved.

[0077] In addition, the data acquisition unit 54 acquires a data set including the actual data of the rigidity parameter, the actual data of the grinding wheel rotation parameter, and the actual data of the abnormal vibration information as a set, and stores it in the database 62 of the storage unit 52. The data acquisition unit 54 may acquire the data set of these actual data stored in the control calculation device 40, or may acquire the data set of actual data through the input operation of the operator to the input unit 48. In addition, the data acquisition unit 54 may acquire each actual data, in which case, each actual data may be temporarily stored, and after acquiring the corresponding other actual data and preparing a set of data sets, it may be stored in the database 62.

[0078] It is preferred that more than 100 data sets are stored in the database 62. It is more preferred that more than 200 data sets are stored in the database 62, and it is further preferred that more than 500 data sets are stored. The data acquisition unit 54 may also filter the data sets stored in the database 62 according to a predetermined condition. In addition, an upper limit may be set for the number of data sets stored in the database 62, and when the data sets exceeding the upper limit are stored, the data acquisition unit 54 deletes the oldest data set to update the data sets stored in the database 62.

[0079] When the abnormal vibration prediction unit 56 obtains input data from the data acquisition unit 54, it executes a prediction step and predicts the abnormal vibration in the grinding process of the roll 12 using the abnormal vibration prediction model. The abnormal vibration prediction model is, for example, a machine learning model that has been learned to output abnormal vibration information in the grinding process of the roll 12 when input data including rigidity parameters and grinding wheel rotation parameters are input. The so-called abnormal vibration information is information related to the abnormal vibration in the grinding process of the roll 12, for example, it can be information indicating the presence or absence of abnormal vibration in the grinding process, or it can be information indicating the vibration intensity of the vibration generated in the grinding process. In the case where the abnormal vibration information is information indicating the vibration intensity, a threshold value of the vibration intensity that becomes abnormal vibration is predetermined and stored in the storage unit 52, and the abnormal vibration prediction unit 56 reads the threshold value from the storage unit 52 and compares it with the output information indicating the vibration intensity, thereby predicting the generation of abnormal vibration in the grinding process of the roll 12.

[0080] As machine learning models, in addition to neural networks (including deep learning, convolutional neural networks, etc.), decision tree learning, random forest, support vector regression, etc. can also be used. In addition, an integrated model composed of multiple models can be used, and classification models such as k-nearest neighbor method and logistic regression can also be used.

[0081] Fig.12 This is a diagram showing an example of using a neural network as an abnormal vibration prediction model. Fig.12 In the above, L1 is an input layer, L2 is an intermediate layer, and L3 is an output layer. By using such a machine learning model, it is possible to freely select parameters that are correlated with the abnormal vibration of the roller grinder 10 as input without considering the problem of multicollinearity, thereby improving the prediction accuracy of the abnormal vibration of the roller grinder 10. For example, a neural network with two intermediate layers and three nodes in each layer may be used, and a sigmoid function may be used as an activation function.

[0082] The prediction model generation unit 58 uses the data set stored in the database 62 to perform machine learning on the machine learning model to generate a machine learning model that has been learned. The prediction model generation unit 58 stores the generated machine learning model that has been learned in the storage unit 52 as an abnormal vibration prediction model. In addition, the prediction model generation unit 58 can also improve the estimation accuracy of abnormal vibration by dividing the data set accumulated in the database 62 into learning data and test data for learning. For example, the prediction model generation unit 58 can also use the learning data to perform machine learning on the weight coefficients of the neural network, and use the test data to appropriately change the structure of the neural network so that the correct answer rate of the abnormal vibration prediction of the roller grinder 10 becomes higher. The so-called changed structure of the neural network is, for example, the number of intermediate layers and the number of nodes. In this way, the prediction accuracy of the abnormal vibration of the roller grinder 10 can be improved.

[0083] In addition, the prediction model generation unit 58 may also use the error propagation method to update the weight coefficient of the machine learning model that has been learned. In addition, the prediction model generation unit 58 may also use the data set stored in the database 62 every 6 months to update the generated machine learning model that has been learned. The more data sets used for machine learning, the more accurately the abnormal vibration of the roller grinder 10 can be predicted. In addition, by using a new data set for machine learning, an abnormal vibration prediction model that reflects the state of the roller grinder 10 in the most recent period can be formed, so that the abnormal vibration of the roller grinder 10 can be predicted with higher accuracy.

[0084] The abnormal vibration prediction unit 56 inputs input data including the rigidity parameter and the grinding wheel rotation parameter into the abnormal vibration prediction model and outputs abnormal vibration information, thereby predicting the generation of abnormal vibration in the grinding process of the roll 12. When the abnormal vibration prediction unit 56 predicts the generation of abnormal vibration in the grinding process of the roll 12, the prediction result is displayed on the output unit 50. The operator can confirm whether abnormal vibration will occur in the grinding process of the roll 12 by visually recognizing the display.

[0085] The guidance information acquisition unit 60 performs the guidance information acquisition step when it is predicted that abnormal vibration will occur in the roller grinder 10 or when the operator inputs an instruction to execute the guidance information acquisition step through the input operation of the input unit 48. The guidance information acquisition unit 60 uses the abnormal vibration prediction model to determine input data that will not cause abnormal vibration of the roller grinder 10. When it is predicted that abnormal vibration will occur in the roller grinder 10, the guidance information acquisition unit 60 reads a table indicating a combination of input data from the storage unit 52. The guidance information acquisition unit 60 inputs the input data to the abnormal vibration prediction model according to the table and outputs abnormal vibration information.

[0086] The guidance information acquisition unit 60 determines a combination of input data that outputs abnormal vibration information that does not generate abnormal vibration. The guidance information acquisition unit 60 displays the determined combination of input data on the output unit 50. The operator can confirm the rigidity parameter and the grinding wheel rotation parameter that do not generate abnormal vibration in the roller grinding process by visually recognizing the display.

[0087] Thus, in the abnormal vibration prediction method of the roll grinder according to the second embodiment, first, the acquisition step is performed, and the data acquisition unit 54 acquires input data including the rigidity parameter and the grinding wheel rotation parameter. Then, the prediction step is performed, and the abnormal vibration prediction unit 56 inputs the input data including the rigidity parameter and the grinding wheel rotation parameter into the abnormal vibration prediction model, and outputs the abnormal vibration information in the grinding process of the roll 12. Thus, the generation of abnormal vibration information in the grinding process of the roll 12 can be predicted.

[0088] In addition, Fig.11 In the example shown, the abnormal vibration prediction device 44 is described using an example having a prediction model generation unit 58, but the abnormal vibration prediction device 44 may not have the prediction model generation unit 58. In the case where the abnormal vibration prediction device 44 does not have the prediction model generation unit 58, it is sufficient to save the abnormal vibration prediction model in the storage unit 52 from the outside through the input unit 48. In addition, the abnormal vibration prediction device 44 is described using an example having an instruction information acquisition unit 60, but the abnormal vibration prediction device 44 may not have the instruction information acquisition unit 60. However, by having the instruction information acquisition unit 60, it is possible to determine the rigidity parameters and grinding wheel rotation parameters that will not generate abnormal vibrations, and it is possible to set the grinding conditions that will not generate abnormal vibrations based on the parameters. Therefore, the abnormal vibration prediction device 44 preferably has the instruction information acquisition unit 60.

[0089] In addition, in the abnormal vibration prediction method of the roller grinder involved in the second embodiment, an example of using a machine learning model that has been learned as an abnormal vibration prediction model is described, but a multiple regression model can also be used as an abnormal vibration prediction model. In this case, the input in the machine learning model becomes an explanatory variable, and the output in the machine learning model becomes a target variable. In the case of using a multiple regression model, the data set stored in the database 62 is also used to pre-calculate the parameters of the multiple regression model and save them in the storage unit 52. Moreover, the abnormal vibration prediction device 44 can be composed of a control computing device 40 or a control controller 42 and a single computer, and the control computing device 40, the control controller 42 and the abnormal vibration prediction device 44 can also be composed of a single computer.

[0090] It has been described above that the generation of abnormal vibration in the grinding process of the roll 12 can be predicted by using the abnormal vibration prediction method of the roll grinder according to the first embodiment of the present invention and the abnormal vibration prediction method of the roll grinder according to the second embodiment of the present invention. By using the abnormal vibration prediction method of the roll grinder, a method for grinding the roll 12 that can suppress the generation of abnormal vibration during roll grinding can be realized. The method for grinding the roll 12 is a method for grinding the outer peripheral surface of the roll 12 that grinds the roll 12 by changing the grinding conditions of the roll grinder 10 to grinding conditions that are predicted not to generate abnormal vibration when abnormal vibration of the roll grinder 10 is predicted by implementing the abnormal vibration prediction method of the roll grinder.

[0091] For example, when the abnormal vibration prediction method for the roll grinder according to the first embodiment predicts the occurrence of abnormal vibration in the grinding process of the roll 12, it is only necessary to refer to Figure 7 The abnormal vibration map related to the rigidity parameter shown above can be used to reset the grinding conditions so that the grinding conditions of the roll 12 are not included in the dangerous area of ​​the abnormal vibration map.

[0092] In addition, the prediction of the occurrence of abnormal vibration based on the abnormal vibration prediction method of the roll grinder involved in the first embodiment may be performed before starting the grinding process of the roll 12. In this case, the acquisition step is performed before starting the grinding process of the roll 12, the setting value of the rigidity parameter and the setting value of the grinding wheel rotation parameter are acquired, and the prediction step is performed by acquiring the setting value of the load parameter as needed. Thus, the occurrence of abnormal vibration of the roll grinder 10 can be predicted before starting the grinding process of the roll 12, and thus, the appropriate grinding conditions predicted not to generate abnormal vibration can be set.

[0093] Furthermore, the generation of abnormal vibration may be predicted after the grinding process of the roll 12 is started. In this case, the acquisition step is performed after the grinding process of the roll 12 is started, the actual value of the rigidity parameter and the actual value of the grinding wheel rotation parameter are acquired, and the actual value of the load parameter is acquired as needed to perform the prediction step. Thus, the generation of abnormal vibration of the roll grinder 10 can be predicted in the middle of the grinding process of the roll 12, and the grinding condition can be reset to an appropriate grinding condition that is predicted not to generate abnormal vibration.

[0094] It is preferred that the roll 12 ground by the grinding method of the roll 12 reset to the appropriate grinding conditions is loaded into the rolling mill to roll the metal strip. By loading the roll 12 ground under the condition that does not cause abnormal vibration of the roll grinder 10, the metal strip can be rolled while suppressing the occurrence of chatter.

[0095] In addition, when the generation of abnormal vibration is predicted in the grinding process of the roll 12 by the abnormal vibration prediction method of the roll grinder involved in the second embodiment, the grinding conditions are changed, and the generation of abnormal vibration is predicted again by the abnormal vibration prediction method of the roll grinder. As long as this operation is repeated, the grinding conditions corresponding to the rigidity parameters and the grinding wheel rotation parameters predicted not to generate abnormal vibration can be reset. Moreover, in the case of having a guidance information acquisition unit 60, it is sufficient to reset the grinding conditions corresponding to the rigidity parameters and the grinding wheel rotation parameters determined by the guidance information acquisition unit 60 and predicted not to generate abnormal vibration. By setting the grinding conditions of the roll 12 in this way, the grinding of the roll 12 can be carried out while suppressing the generation of abnormal vibration corresponding to the natural vibration frequency of the entire mechanical system including the grinding wheel 14 and the workpiece.

[0096] In addition, the prediction of the occurrence of abnormal vibration based on the abnormal vibration prediction method of the roll grinder involved in the second embodiment may be performed before starting the grinding process of the roll 12. In this case, the acquisition step is performed before starting the grinding process of the roll 12, the setting value of the rigidity parameter and the setting value of the grinding wheel rotation parameter are acquired, and the prediction step is performed by acquiring the setting value of the load parameter as needed. Thus, the occurrence of abnormal vibration of the roll grinder 10 can be predicted before starting the grinding process of the roll 12, and appropriate grinding conditions that are predicted not to generate abnormal vibration can be set.

[0097] Furthermore, the generation of abnormal vibration may be predicted after the grinding process of the roll 12 is started. In this case, the acquisition step is performed after the grinding process of the roll 12 is started, the actual value of the rigidity parameter and the actual value of the grinding wheel rotation parameter are acquired, and the actual value of the load parameter is acquired as needed to perform the prediction step. Thus, the generation of abnormal vibration of the roll grinder 10 can be predicted in the middle of the grinding process of the roll 12, and the grinding condition can be reset to an appropriate grinding condition that is predicted not to generate abnormal vibration.

[0098] It is preferred that the roll 12 ground by the grinding method of the roll 12 reset to the appropriate grinding conditions is loaded into the rolling mill to roll the metal strip. By loading the roll 12 ground under the condition that does not cause abnormal vibration of the roll grinder 10, the metal strip can be rolled while suppressing the occurrence of chatter.

[0099] Example

[0100] [Example 1]

[0101] Next, Example 1 is described in which the rolls used in the tandem cold rolling mill are ground while predicting the occurrence of abnormal vibration using the abnormal vibration prediction method of the roll grinder according to the first embodiment. In this example, a vibration meter installed in the roll grinder is used to obtain vibration data of the roll grinder under various grinding conditions to create a Figure 7 The abnormal vibration map (continuous abnormal vibration map) shown in FIG. 1 is used to determine the relationship between the range of grinding wheel rotation speeds that produces abnormal vibration of the roll and the rigidity parameter of the roll grinder. The threshold value of abnormal vibration at this time is set to 10 -1 m / s 2 For each grinding wheel load current value as load parameter, a Figure 7 Abnormal vibration mapping related to rigidity parameters shown. The generation of abnormal vibration of the roller grinder is predicted using the abnormal vibration mapping related to the rigidity parameters. The grinding wheel used for roller grinding is a cylindrical alumina grinding wheel. The current grinding wheel diameter of the grinding wheel is 635mm. The diameter of the roller as a workpiece is 1370mm.

[0102] As the traverse of the roller grinding process, rough grinding was selected, and as the grinding conditions, the load current value was set to 120A, the grinding wheel speed was set to 750rpm, the roller speed was set to 10rpm, and the traverse speed was set to 2mm / pass. However, if the abnormal vibration map (continuous abnormal vibration map) related to the rigidity parameter created in advance is referred to, the vibration intensity of 3 times the grinding wheel speed N becomes larger, and the generation of abnormal vibration of the roller grinder is predicted. Therefore, the grinding wheel speed is changed to 630rpm, and the grinding conditions are reset to prevent abnormal vibration of the roller grinder to grind the roller. Then, the roller ground by the reset grinding conditions is used for the tandem cold rolling mill to roll the steel strip, and the steel strip can be rolled without vibration.

[0103] In order to confirm the effect of the example, the roll was ground under the above-mentioned grinding conditions, with the grinding wheel rotation speed changed to 630 rpm and with the grinding wheel rotation speed maintained at 750 rpm, and the vibration intensity in each grinding step was measured.

[0104] Fig.13 This is a graph showing the results of vibration intensity measurements as a frequency spectrum. Fig.13 The horizontal axis is the vibration frequency (Hz), and the vertical axis is the vibration intensity (m / sec 2 ). Fig.13 The solid line in the graph represents the vibration intensity when the grinding wheel rotates at 630 rpm, and the dotted line represents the vibration intensity when the grinding wheel rotates at 750 rpm.

[0105] As predicted by the abnormal vibration map, in the grinding process where the wheel speed was 750 rpm, a vibration exceeding the threshold value (0.1 m / s 2 ). In contrast, in the grinding process at a grinding wheel speed of 630 rpm where abnormal vibration was not predicted, no large abnormal vibration exceeding the threshold value was generated. Based on this result, it was confirmed that abnormal vibration can be predicted by implementing the abnormal vibration prediction method for the roller grinder involved in this embodiment. In addition, it was confirmed that when such abnormal vibration is predicted, the abnormal vibration of the roller grinder can be suppressed by changing the grinding conditions to grinding conditions that do not generate abnormal vibration using the abnormal vibration map.

[0106] [Example 2]

[0107] Next, Example 2 is described in which the abnormal vibration of the roller grinder is predicted using an abnormal vibration map created by the same method as in Example 1 described above. In Example 2, as an abnormal vibration map, the rigidity parameter is changed by four conditions to create an abnormal vibration map related to the rigidity parameter (discrete abnormal vibration map). Moreover, for the abnormal vibration map related to the rigidity parameter, the load parameter is changed by five conditions, and an abnormal vibration map related to the rigidity parameter is created for each load parameter (discrete abnormal vibration map). Then, the X-axis is taken as the sum of the diameter of the roller and the diameter of the grinding wheel, and the Y-axis is taken as the load parameter, and a three-dimensional abnormal vibration map (continuous abnormal vibration map) is generated by connecting the upper limit and lower limit of the grinding wheel rotation speed that generates abnormal vibration using continuous curved surfaces.

[0108] The abnormal vibration map of Example 2 uses the sum of the diameter of the rolling roller and the diameter of the grinding wheel as the rigidity parameter. The sum of the diameter of the rolling roller and the diameter of the grinding wheel is changed in the range of 1800 to 2500 mm to create an abnormal vibration map related to the rigidity parameter. The load parameter uses the grinding wheel load current value. The grinding wheel load current value is changed in the range of 100 to 140A, and an abnormal vibration map related to the rigidity parameter is created for each load parameter, and the abnormal vibration map is used to generate a three-dimensional abnormal vibration map (continuous abnormal vibration map).

[0109] In Example 2, the abnormal vibration of the roll grinder is predicted using the three-dimensional abnormal vibration map created in this way. A cylindrical alumina-based grinding wheel is used for roll grinding. The current grinding wheel diameter of the grinding wheel is 640 mm. The diameter of the roll is 1302 mm.

[0110] Rough grinding is selected as the traverse motion of the roller grinding process. As roller grinding conditions, the grinding wheel speed is set to 730 rpm, the roller speed is set to 10 rpm, and the traverse speed is set to 2 mm / pass. If the three-dimensional abnormal vibration map created as described above is referred to, it is predicted that abnormal vibration will occur when the grinding wheel load current value is set to 140A. On the other hand, for the same rigidity parameter value, if the grinding wheel load current value is set to 100A, it is predicted that no abnormal vibration will occur. Therefore, the grinding wheel load current value is set to 100A and 140A, roller grinding is performed under two conditions, and the vibration intensity measured by the vibration meter 32 is measured.

[0111] Fig.14 This is a graph showing the results of vibration intensity measurements. Fig.14 The horizontal axis is the vibration frequency (Hz), and the vertical axis is the vibration intensity (m / sec 2 ). Fig.14 The solid line in the graph represents the vibration intensity when the grinding wheel load current value is 100A, and the dotted line represents the vibration intensity when the grinding wheel load current value is 140A.

[0112] As predicted by the three-dimensional abnormal vibration map, the vibration intensity exceeded the threshold value (0.1 m / s) at the grinding wheel load current value of 140 A, which predicted the occurrence of abnormal vibration. 2 ). In contrast, in roller grinding under the condition of a grinding wheel load current value of 100A where abnormal vibration was not predicted, no large abnormal vibration exceeding the threshold value was generated. Based on this result, it was confirmed that abnormal vibration can be predicted by implementing the abnormal vibration prediction method for a roller grinder according to the present embodiment. In addition, it was confirmed that when such abnormal vibration is predicted, the abnormal vibration of the roller grinder can be suppressed by changing the grinding conditions to grinding conditions that do not generate abnormal vibration using an abnormal vibration map.

[0113] [Example 3]

[0114] Next, a third embodiment is described in which the generation of abnormal vibration in the grinding process of a roll is predicted using an abnormal vibration prediction model, which takes as input the rigidity parameter of a roll grinder, the grinding wheel rotation parameter of a grinding wheel, and the load parameter acting on the grinding wheel, and outputs abnormal vibration information in the grinding process of a roll. In the third embodiment, actual data obtained when the roll is ground in advance under various grinding conditions is obtained as learning data. The learning data obtained as actual data is as follows.

[0115] Rigidity parameters of roller grinder: the sum of the diameter of the roller and the diameter of the grinding wheel

[0116] Grinding wheel rotation parameters of grinding wheel: Grinding wheel speed

[0117] Load parameters acting on the grinding wheel: Grinding wheel load current value

[0118] Under various grinding conditions, the vibration intensity exceeded the threshold (0.1m / s 2 ) is considered unqualified, and the case where the vibration intensity is below the threshold is considered qualified, and the actual value of the abnormal vibration information is obtained. Regarding the range of grinding conditions of the collected learning data, the total value of the roller diameter and the grinding wheel diameter is in the range of 1800 to 2500 mm, the grinding wheel speed is in the range of 8.5 to 13.5 rpm, and the grinding wheel load current value is in the range of 100 to 140 A.

[0119] The abnormal vibration prediction model is generated by using a data set that is a set of actual input data of these rigidity parameters, grinding wheel rotation parameters, and load parameters and actual output data of the occurrence or non-occurrence of abnormal vibration (pass or fail) to perform machine learning on a machine learning model. The machine learning model used is a neural network with two layers of three nodes in each layer and a sigmoid function as the activation function.

[0120] In Example 3, the abnormal vibration prediction model generated in this way is used to predict the occurrence of abnormal vibration in the grinding of a newly performed roll. The number of rolls that were ground was 10. In addition, in Example 3, in the rough grinding process of the roll, when the abnormal vibration prediction model predicts that abnormal vibration will occur, the grinding conditions of the roll grinder are changed to grinding conditions that are predicted not to generate abnormal vibration and determined by the guidance information acquisition unit, and the roll grinding is performed. As a result, when the ground rolls are loaded into the cold rolling mill to roll the steel plate, no chatter occurs during rolling. In contrast, when the steel plate is rolled using the rolls that were ground without changing the grinding conditions of the roll grinder even when the abnormal vibration prediction model predicts that abnormal vibration will occur, chatter occurs at an incidence rate of 60%.

[0121] Description of symbols 10 Roller Grinding Machine 12 Rollers 14 Grinding wheel 16 Grinding head 18 Supporting table 20 Roller Chuck 22 Roller Rotation Motor 24 Tailstock 26 Guide 28 Guide 30 Grinding wheel rotation motor 32 Vibration meter 40. Computing device for control 42 Control Controller 44 Abnormal vibration prediction device 46 Control Department 48 Input section 50 Output 52 Storage 54 Data Acquisition Department 56 Abnormal Vibration Prediction Department 58 Prediction Model Generation Department 60 Guidance Information Acquisition Department 62 Database 64 Abnormal vibration prediction model 66 Grinding wheel support 68 Roller support 70 Roller Grinding Machine 80 Roller Grinding Machine 100 Roller Grinding Equipment 110 Roller grinding equipment.

Claims

1. A method for predicting abnormal vibration of a roller grinder, wherein the roller grinder grinds the outer peripheral surface of a rotating roller using a grinding wheel, wherein: The abnormal vibration prediction method of the roller grinder comprises: an acquisition step of acquiring a rigidity parameter related to the rigidity of the roller grinder and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel; and The prediction step predicts the occurrence of abnormal vibration in the grinding process of the roll using the rigidity parameter and the grinding wheel rotation parameter.

2. The method for predicting abnormal vibration of a roller grinder according to claim 1, wherein: The rigidity parameter includes a distance between a rotation center of the roll and a rotation center of the grinding wheel or a total value of a diameter of the roll and a diameter of the grinding wheel.

3. The method for predicting abnormal vibration of a roller grinder according to claim 1, wherein: In the obtaining step, a load parameter related to the load acting on the grinding wheel is also obtained. In the prediction step, the occurrence of abnormal vibration in the grinding process of the roll is predicted using the rigidity parameter, the grinding wheel rotation parameter, and the load parameter.

4. The method for predicting abnormal vibration of a roller grinder according to claim 3, wherein: The load parameter includes a load current value of a motor that rotates the grinding wheel.

5. A method for predicting abnormal vibration of a roller grinder, wherein the roller grinder grinds the outer peripheral surface of a rotating roller using a grinding wheel, wherein: The abnormal vibration prediction method of the roller grinder comprises: an acquisition step of acquiring input data, the input data including a rigidity parameter related to the rigidity of the roller grinder and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel; and The prediction step predicts the occurrence of abnormal vibration in the grinding process of the roll using an abnormal vibration prediction model, wherein the abnormal vibration prediction model takes the input data as input and outputs abnormal vibration information in the grinding process of the roll.

6. The method for predicting abnormal vibration of a roller grinder according to claim 5, wherein: The rigidity parameter includes a distance between a rotation center of the roll and a rotation center of the grinding wheel or a total value of a diameter of the roll and a diameter of the grinding wheel.

7. The method for predicting abnormal vibration of a roller grinder according to claim 5, wherein: In the acquisition step, input data including the rigidity parameter, the grinding wheel rotation parameter, and a load parameter related to a load acting on the grinding wheel is acquired.

8. The method for predicting abnormal vibration of a roller grinder according to claim 7, wherein: The load parameter includes a load current value of a motor that rotates the grinding wheel.

9. A method for grinding a roll, using the method for predicting abnormal vibration of a roll grinder according to any one of claims 1 to 8, wherein: When the occurrence of the abnormal vibration is predicted in the prediction step, the grinding conditions of the roller grinder are changed to grinding conditions in which the occurrence of the abnormal vibration is predicted not to occur.

10. A method for rolling a metal strip, comprising: inserting a roll ground by the roll grinding method according to claim 9 into a rolling mill to roll the metal strip.

11. An abnormal vibration prediction device for a roller grinder, wherein the roller grinder grinds the outer peripheral surface of a rotating roller using a grinding wheel, wherein: The abnormal vibration prediction device of the roller grinder comprises: a data acquisition unit that acquires input data including a rigidity parameter related to the rigidity of the roller grinder and a grinding wheel rotation parameter related to the rotation speed of the grinding wheel; and The abnormal vibration prediction unit predicts the occurrence of abnormal vibration of the roll grinder using an abnormal vibration prediction model that takes the input data as an input and outputs abnormal vibration information.

12. The abnormal vibration prediction device for a roller grinder according to claim 11, wherein: The data acquisition unit acquires input data including the rigidity parameter, the grinding wheel rotation parameter, and a load parameter related to a load acting on the grinding wheel.

13. The abnormal vibration prediction device for a roller grinder according to claim 11 or 12, wherein: The abnormal vibration prediction device for a roller grinder further includes a guidance information acquisition unit that uses the abnormal vibration prediction model to determine the input data that will not cause the abnormal vibration.

14. A roller grinding device comprising: A roller grinder that grinds the outer peripheral surface of a rotating roller using a grinding wheel; and An abnormal vibration prediction device for a roller grinder according to claim 11 or 12.

15. A roller grinding device comprising: A roller grinder that grinds the outer peripheral surface of a rotating roller using a grinding wheel; and The abnormal vibration prediction device for a roller grinder according to claim 13.

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