Plate-shaped object shape measuring method, plate-shaped object shape control method, plate-shaped object manufacturing method, plate-shaped object quality control method, plate-shaped object shape measuring apparatus, and plate-shaped object manufacturing facility

Abstract

To provide a shape measuring method of a plate-like object, a shape control method of the plate-like object, a manufacturing method of the plate-like object, a quality control method of the plate-like object, a shape measuring device of the plate-like object, and a manufacturing facility of the plate-like object, capable of surely and stably measuring a shape, by reducing measurement abnormality of the shape of an edge part of the plate-like object, regardless of a manufacturing environment or a measuring environment of the plate-like object.SOLUTION: A shape measurement method for a plate-shaped object includes a first profile extraction step of extracting a first profile indicating a contour of a plate-shaped object from an image of an edge portion of the plate-shaped object, a first calculation step of calculating an index of a shape of the edge portion of the plate-shaped object from the first profile, a second calculation step of calculating a degree of discontinuity of the contour of the plate-shaped object as a measurement abnormality degree from the first profile, and a determination step of determining whether or not the index is normal from the measurement abnormality degree.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a method for measuring the shape of a plate-like object, a method for controlling the shape of a plate-like object, a method for manufacturing a plate-like object, a method for quality control of a plate-like object, an apparatus for measuring the shape of a plate-like object, and a manufacturing facility for a plate-like object. [Background technology]

[0002] Shape control of sheet metal is important in the materials industry, and quantification of product shape has been required. For example, there is a high demand for measuring the shape of steel in steelmaking processes from the perspective of operational stability and product quality assurance. Shape measurement during rolling, which is used to produce products with the desired shape, is particularly important because it leads to improved product quality and operational stability by providing feedback for initial setting of rolling conditions and for rolling control during rolling.

[0003] For these shapes, methods have been proposed for measuring sheet elongation and bending by capturing images of the steel sheet and extracting its contours. For example, Patent Documents 1 and 2 disclose a method in which a steel sheet is imaged from directly below from above, thermal radiation is captured to obtain a radiation image from which contours are extracted, local contours are measured, and these contours are joined together in the longitudinal direction to calculate the bending of the entire length of the head and tail ends.

[0004] Furthermore, for example, Patent Documents 3 and 4 disclose a method of capturing an image of a steel plate at a low angle relative to the longitudinal direction, rather than capturing an image perpendicular to the surface, thereby capturing an image of the profile with a sufficient length in the longitudinal direction. In this disclosure, "plate elongation" refers to a state in which the shape of the plate changes in a wavy manner in the longitudinal direction, which occurs during rolling, and "bending" refers to a state in which the plate deforms gradually in a direction perpendicular to the longitudinal direction. Both of these are examples of undesirable states that should be detected in the shape of a plate-like object. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2023-7559 [Patent Document 2] Japanese Patent Application Laid-Open No. 2017-26280 [Patent Document 3] Japanese Patent Application Publication No. 6-147856 [Patent Document 4] US Patent Application Publication No. 2013 / 0004080 Summary of the Invention [Problem to be solved by the invention]

[0006] In the methods disclosed in Patent Documents 1 to 4, measurement anomalies caused by the manufacturing environment or measurement environment, such as water vapor, waterlogging, and local temperature drops at the edge, make it impossible to correctly calculate the first profile showing the contour, which can result in large errors when measuring plate elongation or bending.

[0007] The present invention has been made in consideration of the above, and aims to provide a method for measuring the shape of a plate-like object, a method for controlling the shape of a plate-like object, a method for manufacturing a plate-like object, a method for quality control of a plate-like object, an apparatus for measuring the shape of a plate-like object, and manufacturing equipment for a plate-like object, which can reduce measurement abnormalities in the shape of the edge portion of the plate-like object and measure the shape reliably and stably, regardless of the manufacturing environment or measurement environment of the plate-like object. [Means for solving the problem]

[0008] (1) A method for measuring the shape of a plate-like object according to the present invention includes: a first profile extraction step of extracting a first profile indicating a contour of the plate-like object from an image of an edge portion of the plate-like object; a first calculation step of calculating an index of the shape of an edge portion of the plate-like object from the first profile; a second calculation step of calculating a degree of discontinuity of the outline of the plate-like object from the first profile as a measurement abnormality degree; a determination step of determining whether the index is normal or not based on the degree of measurement abnormality; Includes:

[0009] (2) The method for measuring the shape of a plate-like object according to the present invention is the method for measuring the shape of a plate-like object described in (1) above, wherein the second calculation step applies a combination of one or more of a median filter, a low-pass filter, a high-pass filter, or a differential filter to the first profile to obtain a second profile, and calculates the degree of measurement anomaly based on the second profile.

[0010] (3) The method for measuring the shape of a plate-like object according to the present invention is the method for measuring the shape of a plate-like object described in (2) above, wherein the second calculation step takes the absolute value of the second profile and then calculates the maximum value, and calculates the calculated maximum value as the degree of measurement abnormality.

[0011] (4) The shape control method for a plate-like object according to the present invention measures the shape of the plate-like object using the shape measurement method for a plate-like object described in any one of (1) to (3) above, and controls the shape of the plate-like object based on the measurement results.

[0012] (5) The method for manufacturing a plate-like object according to the present invention measures the shape of the plate-like object using the method for measuring the shape of a plate-like object described in any one of (1) to (3) above, and manufactures the plate-like object based on the measurement results.

[0013] (6) The quality control method for a plate-like object according to the present invention measures the shape of the plate-like object using the shape measurement method for a plate-like object described in any one of (1) to (3) above, and controls the quality of the plate-like object based on the measurement results.

[0014] (7) The shape measuring device for a plate-like object according to the present invention is a first profile extraction unit that extracts a first profile that indicates a contour of the plate-like object from an image of an edge portion of the plate-like object; a first calculation unit that calculates an index of the shape of an edge portion of the plate-like object from the first profile; a second calculation unit that calculates a degree of discontinuity of the contour of the plate-like object from the first profile as a measurement abnormality degree; a determination unit that determines whether the index is normal or not based on the degree of measurement abnormality; Equipped with.

[0015] (8) A manufacturing facility for a plate-like object according to the present invention includes the shape measuring device for a plate-like object described in (7) above. [Effects of the Invention]

[0016] In the method for measuring the shape of a plate-like object, the method for controlling the shape of a plate-like object, the method for manufacturing a plate-like object, the method for quality control of a plate-like object, the shape measuring device for a plate-like object, and the manufacturing equipment for a plate-like object according to the present invention, the degree of discontinuity is calculated from the first profile as a measurement anomaly, and an index obtained from the calculated measurement anomaly is determined to be normal or not. This reduces measurement anomalies in the shape of the edge portion of the plate-like object, and enables reliable and stable shape measurement, regardless of the manufacturing environment or measurement environment of the plate-like object. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a diagram showing an example of the influence that a disturbance occurring in a steel plate manufacturing process has on the first profile. [Figure 2] FIG. 2 is a diagram showing an example of an image of a steel sheet and a first profile when sheet elongation occurs but no disturbance occurs. [Figure 3] FIG. 3 is a diagram showing an example of an image of a steel sheet and a first profile when sheet elongation occurs and a disturbance occurs (influence of water vapor). [Figure 4] FIG. 4 is a diagram for explaining the discontinuity of the first profile. [Figure 5] FIG. 5 is a diagram showing a schematic configuration of an apparatus for measuring the shape of a plate-like object according to an embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart showing a specific process flow of a shape measuring method executed by an apparatus for measuring the shape of a plate-like object according to an embodiment of the present invention. [Figure 7] FIG. 7 shows examples of images of a steel plate in a normal state and in a state of a defective shape. [Figure 8] FIG. 8 is a diagram for explaining the binarization process in the first profile extraction step of the method for measuring the shape of a plate-like object according to the embodiment of the present invention. [Figure 9] FIG. 9 is a diagram for explaining the extraction process of the first profile in the first profile extraction step of the method for measuring the shape of a plate-like object according to the embodiment of the present invention. [Figure 10] FIG. 10 is a diagram showing a first profile of a steel sheet when sheet elongation occurs but no disturbance occurs, and a second profile obtained by applying a differential filter to the first profile and taking the absolute value. [Figure 11] FIG. 11 is a diagram showing a first profile of a steel sheet when sheet elongation occurs and a disturbance occurs (influence of water vapor), and a second profile obtained by applying a differential filter to the first profile and taking the absolute value. [Figure 12] FIG. 12 shows a first profile of a steel sheet when sheet elongation occurs but no disturbance occurs, and a second profile obtained by applying a median filter to the first profile, and then applying a differential filter to obtain the absolute value. [Figure 13] FIG. 13 shows a first profile of a steel sheet when sheet elongation occurs and a disturbance occurs (influence of water vapor), and a second profile obtained by applying a median filter to the first profile, and then applying a differential filter to obtain the absolute value. [Figure 14] Figure 14 shows a second profile obtained by applying a median filter to the first profile of steel sheet S when sheet elongation occurs and a disturbance occurs (affected by blurred edges), and then applying a differential filter to obtain the absolute value. [Figure 15] Figure 15 shows a second profile obtained by applying a median filter to the first profile of steel sheet S when sheet elongation occurs and a disturbance occurs (there is the influence of roll reflection), and then applying a differential filter to obtain the absolute value. [Figure 16] FIG. 16 shows a second profile obtained by applying a median filter to the first profile of steel sheet S when sheet elongation occurs and a disturbance occurs (the effect of water riding is present), and then applying a differential filter to obtain the absolute value. [Figure 17] FIG. 17 is a diagram showing a first profile of a steel sheet when sheet elongation occurs and no disturbance occurs, and a second profile obtained by applying a band-pass filter to the first profile. [Figure 18] FIG. 18 is a diagram showing a first profile of a steel sheet when sheet elongation occurs and a disturbance occurs (there is an influence of water vapor), and a second profile obtained by applying a band-pass filter to the first profile. [Figure 19] FIG. 19 is a diagram showing an example of a case where the first profile of a steel plate is divided into groups in front of and behind a candidate discontinuity point, and an approximate straight line is fitted to each group. [Figure 20] FIG. 20 is a diagram showing an example of a configuration in which both edge portions of a steel plate are illuminated by a pair of light sources. [Figure 21] FIG. 21 is a diagram showing an example of a configuration in which a surface light source is arranged below the steel plate for illumination. [Figure 22] FIG. 22 shows an example of the positional relationship between the conveying direction of the steel plate and the camera, where (a) is a diagram of the positional relationship viewed from an angle, (b) is a diagram of the positional relationship viewed from above, and (c) is a diagram of the positional relationship viewed from the direction δ of (b). [Figure 23] FIG. 23 is a diagram showing an example of the positional relationship between the conveying direction of the steel plate and the camera, in which the optical axis of the camera is disposed perpendicular to the conveying direction of the steel plate. [Figure 24] FIG. 24 is a diagram showing an example of the positional relationship between the conveying direction of the steel plate and the camera, in which the optical axis of the camera is disposed at an angle to the conveying direction of the steel plate. DETAILED DESCRIPTION OF THE INVENTION

[0018] A method for measuring the shape of a plate-like object, a method for controlling the shape of a plate-like object, a method for manufacturing a plate-like object, a quality control method for a plate-like object, a shape measuring device for a plate-like object, and a manufacturing facility for a plate-like object according to embodiments of the present invention will be described with reference to the drawings. Note that the components in the following embodiments include those that are easily replaceable by a person skilled in the art, or those that are substantially identical.

[0019] (Findings leading to the present invention) The present inventors have investigated a technology for detecting the shape of an edge portion of a steel sheet from thermal radiation light emitted from the surface of the steel sheet during hot rolling. Specifically, the present inventors have investigated a technology for capturing images of the thermal radiation light of the steel sheet in a time series using a camera, extracting the outline of the steel sheet from the captured images as a first profile, and quantifying the shape change of the edge portion of the steel sheet as a "shape index" using this first profile.

[0020] Here, when calculating the shape of the edge portion of the steel sheet, it is preferable that the first profile indicating the outline is clear. However, in an actual manufacturing process, disturbances such as those shown by ovals in Fig. 1 may occur.

[0021] Figure 1(a) shows a disturbance caused by the water-riding effect. In this disturbance, water sprayed to cool the steel sheet S lands on the steel sheet S, absorbing thermal radiation light and causing the first profile to become unclear. Figure 1(b) shows a disturbance caused by edge blurring. In this disturbance, the temperature of the edge of the steel sheet S drops, causing the first profile to become unclear.

[0022] Also, (c) in Figure 1 shows disturbance caused by roll reflection. In this disturbance, the thermal radiation light from the steel sheet S is reflected by structures such as transport rolls, causing the structures to shine brightly and hindering edge detection. Also, (d) in Figure 1 shows disturbance caused by water vapor. In this disturbance, water heated to a high temperature evaporates, and the resulting steam absorbs the thermal radiation light, making the first profile unclear.

[0023] When disturbances such as those shown in Fig. 1 occur, it becomes difficult to extract a true first profile from an image of the steel sheet S, and errors may occur in the calculation results of shape indices such as steepness and elongation. Therefore, in order to accurately calculate shape indices, it is necessary to extract a first profile in a state where the above-mentioned disturbances have been removed. However, as shown in Fig. 1, there are various types of disturbances, and the degree of influence can be large in some cases, making it difficult to stably restore the true first profile.

[0024] Therefore, the inventors decided to aim for stable operation by determining whether the first profile obtained from the image of the steel plate S is normal or not, and if there is a concern that a large error may occur due to the influence of external disturbances and the reliability of the measurement results is low, not utilizing the data at that time.

[0025] FIG. 2 shows an image and a first profile of the steel sheet S when sheet elongation occurs but no disturbance occurs. In FIG. 2(a), the left side is an image of the steel sheet S, and the right side is an image (binarized image) obtained by binarizing the image of the steel sheet S. FIG. 2(b) shows the first profile of the steel sheet S extracted from the binarized image of FIG. 2(a). The vertical axis of FIG. 2(b) is a number indicating a pixel in the image of the steel sheet S, and corresponds to the position in the width direction of the steel sheet S (this also applies to the subsequent figures). The horizontal axis of FIG. 2(b) is a number indicating a pixel in the image of the steel sheet S, and corresponds to the position in the longitudinal direction of the steel sheet S (this also applies to the subsequent figures).

[0026] Figure 3 shows an image and a first profile of steel sheet S when sheet elongation occurs and a disturbance occurs (water vapor influence). In Figure 3(a), the left side is an image of steel sheet S, and the right side is an image (binarized image) obtained by binarizing the image of steel sheet S. Also, Figure 3(b) shows the first profile of steel sheet S extracted from the binarized image of Figure 3(a).

[0027] As shown in Figure 2(b), the first profile, which is affected only by sheet elongation, exhibits a relatively gradual change. On the other hand, as shown in Figure 3(b), the first profile, which is affected by both sheet elongation and disturbance, exhibits localized, sudden changes in shape and discontinuities. This discontinuity in the first profile is a characteristic of all disturbances, including the water-riding effect, blurred edges, roll reflection, and steam. Therefore, it is possible to determine whether a disturbance has occurred based on the presence or absence of such discontinuities. Note that the "discontinuities" described here do not refer to randomly occurring high-frequency components, such as those shown in Figure 4(a), but rather to discontinuities that occur as a trend change due to the presence of edges, such as those shown in Figure 4(b).

[0028] Therefore, by calculating the degree of such discontinuity from the first profile, it is possible to evaluate the reliability of the shape indicator, which is the measurement result. Then, by using the obtained measurement anomaly degree to select only highly reliable shape indicators and use them for measurement, it is possible to improve measurement accuracy. Furthermore, by using the measurement results with improved accuracy to set manufacturing conditions or control the manufacturing process, it is possible to improve the reliability and stability of the manufacturing process. Note that the above-mentioned "measurement anomaly degree" is merely a value for evaluating the reliability of the shape indicator, and is not a value that indicates an abnormality in the shape itself.

[0029] (shape measuring device) A shape measuring device for a plate-like object according to an embodiment will be described with reference to FIG. 5. The shape measuring device is a device for measuring the shape of a plate-like object. Below, a case where the shape measuring device is applied to hot finish rolling will be described. Note that the shape measuring device is particularly suitable for hot finish rolling, although it can of course be applied to processes other than hot finish rolling.

[0030] In the following, a case will be described in which the plate-like object to be measured is a steel plate S. Note that the plate-like object includes not only the steel plate S but also a strip-shaped steel strip (i.e., a coil). In the following, an explanation will be given assuming the sheet elongation of the edge portion of the steel plate S (specifically, a shape called edge elongation) as an example of the shape measured by the shape measuring device.

[0031] 5, the shape measuring device according to the embodiment includes a camera 2 and an image processing device 3. In the shape measuring device according to the embodiment, the camera 2 captures an image of a plate-like object, and the obtained image data is sent to the image processing device 3, which then performs calculations to calculate a shape index and a degree of measurement abnormality.

[0032] It is preferable to use an area camera as the camera 2. When capturing an image with the camera 2, it is preferable to capture an image so that an area of ​​the steel sheet S where changes in the shape of the steel sheet S can be seen is included in the field of view. The camera 2 may be installed in an appropriately selected location where changes in brightness can be clearly captured. Next, the image processing device 3 will be described.

[0033] The image processing device 3 is realized by, for example, a general-purpose computer such as a workstation or a personal computer. The image processing device 3 may be installed near the camera 2, or if high speed is not required, it may be installed on the cloud.

[0034] As will be described later, the image processing device 3 functions as a first profile extraction unit that extracts a first profile that indicates the outline of the steel sheet S from an image of the edge portion of the steel sheet S captured by the camera 2. The image processing device 3 also functions as a first calculation unit that calculates an index of the shape of the edge portion of the steel sheet S from the first profile. In doing so, the image processing device 3 appropriately selects one or more of the steepness, wave height, wave pitch, elongation amount, and elongation rate of the edge portion of the steel sheet S, and calculates them as the above indexes.

[0035] The image processing device 3 also functions as a second calculation unit that calculates the degree of discontinuity of the contour of the steel sheet S as a measurement abnormality degree from the first profile. The image processing device 3 also functions as a determination unit that determines whether the index of the shape of the edge portion of the steel sheet S is normal or not from the calculated measurement abnormality degree. Below, a process will be described in which the image processing device 3 calculates the index of the shape of the edge portion of the steel sheet S from the image obtained from the camera 2.

[0036] (Shape measurement method) A method for measuring the shape of a plate-like object according to the embodiment will be described with reference to Figures 6 to 21. The method for measuring the shape of a plate-like object according to the embodiment is mainly performed by an image processing device 3. The method for measuring the shape of a plate-like object according to the embodiment includes a first profile extraction step (steps S1 to S4), a first calculation step (step S6), a second calculation step (step S7), and a determination step (step S8).

[0037] FIG. 7(a) shows an image of the steel sheet S in a normal state, and FIG. 7(b) shows an image of the steel sheet S when a shape defect has occurred. As shown in FIG. 7, the state of sheet elongation can be determined visually. The image processing device 3 calculates an index of the edge portion of the steel sheet S from the acquired image of the steel sheet S using the procedure described below. Note that, for ease of explanation, the axis that is closer to being parallel to the conveyance direction of the steel sheet S is taken as the horizontal axis with respect to the vertical and horizontal axes of the image.

[0038] <First profile extraction step> In the first profile extraction step, first, as shown in Fig. 8, only the plate region of the steel plate S is extracted by binarization processing (step S1 in Fig. 6). Fig. 8(a) is an image before binarization processing, and Fig. 8(b) is an image after binarization processing.

[0039] At this time, disturbances may occur, such as water riding on the plate surface or splashes of cooling water causing localized reductions in brightness on the plate surface, or splashes of water in the space above the plate scattering the thermal radiation light from the steel plate S and causing it to glow brightly. Because these disturbances are often minute and in the high frequency range relative to the pitch of the shape to be calculated, it is desirable to perform expansion and / or contraction processing or removal of connected and / or isolated points using a median filter or the like on the plate region extracted in the binarization processing step (step S2 in Fig. 6).

[0040] Furthermore, after the connection and / or removal of isolated points, multiple blobs (lumps recognized by connecting surrounding pixels during binarization) that are candidates for plate regions may be generated. In this case, the plate region may be determined by extracting the blob (step S3 in FIG. 6) or by determining whether it is a plate region based on the size, orientation, etc. of the steel plate S.

[0041] Next, a first profile indicating the outlines of both edge portions is calculated from the plate region of the steel plate S obtained as described above (step S4 in FIG. 6). There are various methods for calculating the first profile of the edge portion, but as an example, a method for extracting the first profile by search is shown in FIG. 9. (a) of FIG. 9 is a diagram showing how the first profile is searched, and (b) of FIG. 9 is a diagram showing the extracted first profile.

[0042] As shown in (a) of Figure 9, a search may be performed from outside the plate region in the image toward the plate region and the coordinates at which the plate region is approached may be recorded, or conversely, a search may be performed from inside the plate region to outside the plate region. In this embodiment, the first profiles of the upper edge portion and the lower edge portion are calculated as one-dimensional vectors by searching the contour in the vertical direction at each point on the horizontal axis on the image.

[0043] Here, the image obtained in the imaging step is captured obliquely with respect to the conveyance direction of the steel sheet S, and therefore the resolution differs between the wave height direction and the wave pitch direction. Therefore, the resolution is corrected separately in the wave height direction and the wave pitch direction for the first profile on the obtained image (step S5 in FIG. 6). Note that step S5 does not have to be performed after step S4 as in the example of FIG. 9, and may be performed on the shape index calculated after step S6 (described later).

[0044] Specifically, in step S5, first, the tilt of the optical axis of camera 2 is corrected by a rotation process so that the longitudinal direction and the horizontal axis of the steel plate S coincide. Since the resolution in the wave height direction, i.e., the vertical axis, is "rn (mm / pixel)" and the resolution in the wave pitch direction, i.e., the horizontal axis, is "rp (mm / pixel)", it is possible to convert into a first profile of the steel plate S.

[0045] Furthermore, the resolution actually changes because the distance from the camera 2 differs between the front edge portion and the rear edge portion. Therefore, the resolution may be calculated and corrected separately for the position of the front edge portion and the position of the rear edge portion according to the distance from the camera 2. In other words, the resolution in the wave height direction and the wave pitch direction may be calculated from the positional relationship between the camera 2 and the steel plate S, and converted into actual dimensions.

[0046] Alternatively, instead of performing the above-described processing on the obtained first profile of the edge portion, the first profile may be calculated by performing coordinate transformation using the attitude parameters of camera 2 and finding the orthogonal projection onto plane β (see FIG. 22). This makes it possible to perform strict geometric correction over the entire field of view of camera 2.

[0047] Furthermore, since the pitch and period of plate elongation are almost fixed, noise that does not contribute to the shape may be removed from the first profile of the edge portion by applying a low-pass filter or band-pass filter that removes frequency components other than these.

[0048] <First calculation step> In the first calculation step, an index of the shape of the edge portion of the steel sheet S is calculated from the first profile extracted in the first profile extraction step (step S6 in Fig. 6). The index of the shape of the edge portion is often discussed in terms of a parameter called steepness, which is the ratio of wave height to wave pitch, but the index may be one or more appropriately selected from wave height, wave pitch, elongation amount, elongation rate, etc. There are various methods for calculating the index of the shape of the edge portion of the steel sheet S, but the following (1) to (3) can be mentioned as representative methods.

[0049] (1) The maximum and minimum points are calculated, and the vertical distance of the image is taken as the wave height, and the horizontal distance as the wave pitch. The steepness is calculated as the ratio of the wave height to the wave pitch. (2) A sine curve is fitted, and the wave height and wave pitch are calculated from the amplitude and period. The steepness is calculated as the ratio of the wave height to the wave pitch. (3) The amount and rate of sheet elongation are calculated from the contour length, and the steepness is directly calculated by the method of Patent Document 2.

[0050] By using the index of the shape of the edge portion of the steel sheet S obtained in this manner, it becomes possible to control parameters and feedback control during rolling, and further to judge the pass / fail of the sheet shape of the coil. Note that instead of calculating the index of the shape of the edge portion from the first profile of the steel sheet S, for example, it is also possible to calculate the wave height, wave period, steepness, elongation amount, elongation rate, etc. from the first profile on an image, and then correct them later using the resolution rn (mm / pixel) in the wave height direction and the resolution rp (mm / pixel) in the wave pitch direction.

[0051] <Second calculation step> In the second calculation step, the degree of discontinuity in the contour of the steel sheet S is calculated as the measurement abnormality degree from the first profile extracted in the first profile extraction step (step S7 in FIG. 6). That is, in the second calculation step, the measurement abnormality degree is calculated from the discontinuity in the first profile indicating the contour in order to evaluate the reliability of the shape index obtained in the first calculation step.

[0052] In the second calculation step, a second profile can be obtained by applying a combination of one or more of a median filter, a low-pass filter, a high-pass filter, and a differential filter to the first profile, and the degree of measurement anomaly can be calculated based on the second profile. In the second calculation step, an absolute value can be taken for the second profile, and then the maximum value can be calculated, and the calculated maximum value can be calculated as the degree of measurement anomaly. Details of the second calculation step will be described below.

[0053] A specific method for calculating discontinuities is to index the changes before and after the target position, and a typical method is to use a combination of one or more of a differential filter, a low-pass filter, or a high-pass filter.

[0054] First, we will explain the method using a differential filter. The differential filter described here calculates the amount of change between previous and next pixels as an index, and the filter width can be arbitrary as long as it has this function. When using a differential filter, for example, it is possible to simply output the difference between previous and next positions as in the following equation (1), or to extract the difference between distant points as in the following equation (2). It is also possible to weight the position as in the following equation (3). Furthermore, the sum of the positive coefficients and the sum of the negative coefficients do not necessarily have to be 1.

[0055]

number

number

number

[0056] Figure 10(a) shows the first profile of the steel sheet S when sheet elongation occurs but no disturbance occurs. Figure 10(b) shows the result of applying a differential filter to the first profile of Figure 10(a) and taking the absolute value (hereinafter referred to as the "second profile"). Figure 11(a) shows the first profile of the steel sheet S when sheet elongation occurs but a disturbance occurs (with the influence of water vapor). Figure 11(b) shows the second profile of the first profile of Figure 11(a) when a differential filter is applied and the absolute value is taken. Here, the ellipses in Figures 11(a) and (b) indicate the location of the disturbance on each profile. The differential filter applied in Figures 10(b) and 11(b) was [1, 0, 0, 0, 0, -1].

[0057] The second profile shown in Figure 10(b) always has small values, whereas the second profile shown in Figure 11(b) has large values ​​only at points where disturbances occur (see the ovals). Therefore, by applying a differential filter to the first profile, it is possible to more clearly identify the degree of discontinuity at each position. Note that whether the value is positive or negative, it represents a discontinuity, so it is preferable to take the absolute value.

[0058] Furthermore, if even one discontinuous point occurs in the second profile, it will have a significant impact on the calculation of the ratio of the contour length, wave height, and wave pitch, and therefore the degree of measurement anomaly is considered to be large. In other words, the second profile emphasizes the degree of discontinuity in the contour of the steel sheet S in the first profile. Therefore, in the second calculation step, the absolute value of the second profile is taken and then the maximum value is calculated, and the calculated maximum value can be calculated as the degree of measurement anomaly.

[0059] However, in a method of simply calculating the degree of discontinuity from the first profile using only a differential filter, if random high-frequency components are present in the second profile itself, there is a risk that the value will be large even if there is no change in the trend. Therefore, by removing these high-frequency components using a filter, it is possible to improve the accuracy of calculating the degree of discontinuity. The filter used here may be a low-pass filter such as a moving average filter, but it is preferable to use a median filter that can remove high-frequency components while leaving edges intact.

[0060] Figure 12(a) shows the first profile of the steel sheet S when sheet elongation occurs but no disturbance occurs. Figure 12(b) shows the second profile obtained by applying a median filter to the first profile of Figure 12(a) and then applying a differential filter to obtain the absolute value. Figure 13(a) shows the first profile of the steel sheet S when sheet elongation occurs but a disturbance occurs (with steam influence). Figure 13(b) shows the second profile obtained by applying a median filter to the first profile of Figure 13(a) and then applying a differential filter to obtain the absolute value. Here, the ellipses in Figures 13(a) and 13(b) indicate the location of the disturbance on each profile. The differential filter applied in Figures 12(b) and 13(b) was [1, 0, 0, 0, 0, -1].

[0061] In this way, by applying a median filter to the first profile in advance, the degree of discontinuity at positions where no disturbance occurs is reduced, making it possible to detect only discontinuous points under conditions with lower noise. Furthermore, even when the disturbance is blurred edges, roll reflections, or water riding, it becomes possible to detect discontinuous points, as shown in Figures 14 to 16.

[0062] Figure 14 shows a second profile obtained by applying a median filter to a first profile of the steel sheet S when sheet elongation occurs and a disturbance occurs (edge ​​blurring), and then applying a differential filter to obtain the absolute value. Figure 15 shows a second profile obtained by applying a median filter to a first profile of the steel sheet S when sheet elongation occurs and a disturbance occurs (roll reflection), and then applying a differential filter to obtain the absolute value. Figure 16 shows a second profile obtained by applying a median filter to a first profile of the steel sheet S when sheet elongation occurs and a disturbance occurs (water riding), and then applying a differential filter to obtain the absolute value. Here, ellipses in Figures 14 to 16 indicate the location of the disturbance on each profile. The differential filter applied in Figures 14 to 16 was [1, 0, 0, 0, 0, -1].

[0063] In the explanation so far, we have described a method of calculating the degree of discontinuity using a differential filter, but it is also possible to calculate the degree of discontinuity in a similar manner using a band-pass filter that combines a high-pass filter and a low-pass filter.

[0064] FIG. 17(a) shows a first profile of the steel sheet S when sheet elongation occurs but no disturbance occurs. FIG. 17(b) shows a second profile obtained by applying a band-pass filter to the first profile of FIG. 17(a). FIG. 18(a) shows a first profile of the steel sheet S when sheet elongation occurs but a disturbance occurs (influence of water vapor). FIG. 18(b) shows a second profile obtained by applying a band-pass filter to the first profile of FIG. 18(a). Here, the ellipses in FIGS. 18(a) and 18(b) indicate the locations of the disturbances on each profile.

[0065] 17 and 18, even when a band-pass filter is applied, it can be seen that, as with the differential filter, high values ​​are obtained at positions where the degree of discontinuity is high due to disturbances. Therefore, it is possible to calculate the degree of measurement anomaly and determine whether the calculated index is normal or not in the determination step described below.

[0066] As described above, by appropriately using one-dimensional profile information and filtering, the degree of discontinuity can be quickly calculated as an index of the degree of measurement anomaly. On the other hand, if fast response is not required, other methods may be used. For example, if the obtained shape index is used to immediately control a rolling mill in use, delayed anomaly detection can be problematic because it can lead to equipment trouble or the production of defective products. This is because, if an anomaly actually occurs, production will continue under incorrect rolling control. On the other hand, if the obtained shape index is used in a setup model for the next material or to determine the pass / fail status of the product, immediate anomaly detection is not necessarily required. In this case, methods that require a long processing time can also be applied.

[0067] As an example of another method that requires a lot of processing time, the area before and after the candidate discontinuity point may be divided into groups, and an approximate straight line or an approximate curve may be fitted to each group, and the degree of deviation at the candidate discontinuity point may be used as an index. An example of this is shown in FIG. 19. In FIG. 19, the vertical axis represents the width direction of the image, and the horizontal axis represents the length direction of the image. Also, in FIG. 19, the point cloud in the graph represents the contour profile (first profile) of the steel sheet S extracted in the first profile extraction step, and the arrow X indicates the location where the contour profile is discontinuous.

[0068] Figure 19(a) shows an example where data is divided into data before and after a discontinuity point and an approximate line is fitted to each. On the other hand, Figure 19(b) shows an example where data is divided into data before and after a location that is not a discontinuity point and an approximate line is fitted to each in the same way. The degree of deviation mentioned here refers to the difference in the predicted values ​​of two models at a candidate discontinuity point, or the difference between the model predicted values ​​of a group to which a point near the discontinuity does not belong. The degree of deviation can be used as an index of the degree of discontinuity.

[0069] For (a) and (b) of Figure 19, we performed straight-line approximation on the point clouds before and after the candidate discontinuity point. As a result, in (a) of Figure 19, the distance A between the two lines at the candidate position is large (i.e., the deviation is large), so it is highly likely that it is a discontinuity point. On the other hand, in (b) of Figure 19, the distance B between the two lines is small (i.e., the deviation is small), so it is low likely that it is a discontinuity point.

[0070] By performing this process on all points on the contour profile, the index of the maximum degree of discontinuity can be used as the degree of measurement abnormality. Furthermore, although a regression model such as an approximate straight line or approximate curve is used for prediction here, any method that can extrapolate the vicinity of candidate discontinuities, such as clustering regarded as a point group, can also be used.

[0071] Furthermore, anomaly determination may be performed using a convolutional neural network model using deep learning, etc. In this case, the image obtained by imaging may be used directly, but it is more preferable to use an image to which binarization processing has been applied in order to extract the contour, because this makes it possible to directly determine whether the contour of the steel sheet S has been extracted.

[0072] When performing anomaly detection using a convolutional neural network model using deep learning, first, from among the images to be used for detection, images in which the contour of the steel plate S is captured normally and images in which the contour is abnormal due to disturbance are visually selected and used as learning input values. Furthermore, learning output values ​​corresponding to these learning input values, for example, labels such as "contour is normal" or "contour is abnormal," are combined with the learning input values ​​to create a learning dataset. Next, using these learning datasets, an inference model that determines whether something is normal or abnormal is generated by machine learning. The confidence level obtained when this inference model is used to infer normality or abnormality may be used as the measurement anomaly level.

[0073] <Determination step> In the determination step, it is determined whether or not the shape index of the edge portion of the steel sheet S is normal based on the measurement abnormality degree calculated in the second calculation step (step S8 in FIG. 6), thereby improving the accuracy of the shape index.

[0074] As a method for determining whether a shape index is normal or not from the degree of measurement abnormality, for example, a threshold value may simply be set, and an abnormality may be determined when the degree of measurement abnormality exceeds the threshold value. This threshold value may be experimentally determined in advance based on, for example, an image of the actual steel sheet S, a second profile extracted therefrom, and the presence or absence of disturbances in the steel sheet S. Furthermore, when an abnormality determination is performed using a convolutional neural network model using deep learning, the normality or abnormality may be determined directly.

[0075] The threshold value may be varied depending on information relating to the manufacture of the plate-like object (e.g., parameters selected from one or more of plate thickness, plate width, type, temperature, etc.) and the use of shape indicators, and may be combined with other information for a composite judgment. Furthermore, the cause of the abnormality may be classified from the image used to calculate the indicator determined to be abnormal. By identifying the cause of the abnormality, it becomes possible to take measures in the actual process (e.g., hot finish rolling process), such as blowing off water vapor or changing the water cooling conditions.

[0076] In the explanation so far, a method for determining whether or not an index calculated when indexing the sheet elongation of the steel sheet S is normal has been described, but bending can also be indexed by extracting a first profile showing the outline from an image of the steel sheet S. Similarly, for bending, it is also possible to determine whether or not a calculated index is normal by calculating the degree of discontinuity.

[0077] Furthermore, it is not necessary to use the thermal radiation light of the target as long as a first profile indicating the outline of the edge portion can be extracted from an image of the steel sheet S. For example, the steel sheet S may be imaged under normal conditions, and the first profile may be extracted by image processing such as a convolutional neural network.

[0078] Alternatively, imaging may be performed by the camera 2 while irradiating the top surface of the steel sheet S with light from a predetermined light source. That is, the present invention is also applicable to cases where light from a predetermined light source is applied to the steel sheet S and the reflected light of that light is imaged by the camera 2. In this case, a brightness difference due to the reflected light is generated between the surface of the steel sheet S and the background, and the outline of the steel sheet S can be extracted by performing image processing similar to that when thermal radiation light is used. Furthermore, for example, as shown in FIG. 20 , imaging may be performed by the camera 2 while illuminating the edge portion of the steel sheet S with a pair of light sources 11.

[0079] Furthermore, as shown in Figure 21, a camera 2 may be placed above the steel plate S and a surface light source 12 may be placed below the steel plate S, and the backlight from the surface light source 12 may be used to cast a shadow (silhouette) of the steel plate S and to cast backlight from the surface light source 12 in areas where the steel plate S is not present, thereby obtaining an image with a contrast opposite to that obtained when thermal radiation light or reflected light is used.

[0080] Furthermore, two or more of these techniques may be combined depending on the conditions of the plate-like object and its manufacturing equipment. For example, the accuracy of contour extraction can be improved by capturing separate images of thermal radiation and reflected light or thermal radiation and backlight, and then combining the resulting images. Generally, thermal radiation only contains components longer than the wavelength determined by the temperature of the target plate-like object. Therefore, by irradiating light with a wavelength shorter than the wavelength of the thermal radiation from a light source and using two types of light-receiving elements with sensitivity to each, it is possible to capture separate images of thermal radiation and reflected light or thermal radiation and backlight. It is desirable that the two wavelength ranges and the sensitivity characteristics of the two light-receiving elements do not interfere with each other. Furthermore, by designing the optical system so that the optical axes of the two images are aligned, perfect alignment is achieved, allowing for easy and accurate image processing for contour extraction. As described above, it is possible to reduce disturbances when obtaining the shape contour, such as by making it easier to extract the plate portion and background of the steel plate S.

[0081] (Example of improving shape index accuracy using judgment) An example of improving the reliability of a shape index using the results of determining whether or not there is an abnormality in the shape index will be described. First, when capturing images of the target surface with camera 2 to extract the first profile, multiple images are captured consecutively and the presence or absence of an abnormality is determined for each. At this time, by using only shape indexes determined to be normal, it is possible to avoid the influence of disturbances. Furthermore, multiple shape indexes determined to be normal may be used as the average value, median, or index with the lowest degree of measurement abnormality. In this way, a highly accurate shape index with minimal disturbance is transmitted to the pass / fail determination device.

[0082] The pass / fail judgment device judges whether the shape is pass / fail based on information about the production of plate-like objects (for example, parameters selected from one or more of plate thickness, plate width, type, or temperature) obtained from a host system and an indicator of the shape of the edge of the steel plate S. The pass / fail judgment result is sent to the host system. Based on the pass / fail judgment result, the host system makes decisions such as whether correction is necessary, whether to cut off any defective shapes, and whether rolling can be carried out in the next process. In this way, by utilizing indicators of the shape of the edge of the steel plate S to determine actions to be taken in the next process, it is possible to prevent problems and improve product quality.

[0083] (Example of rolling feedback control using judgment) An example of rolling feedback control using a shape measuring device according to the embodiment will be described. First, shape indices are calculated by an image processing device 3 from an image captured by a camera 2 and transmitted to a rolling control device. At this time, by adopting only shape indices determined to be normal, it becomes possible to avoid the influence of disturbances. Furthermore, if a certain degree of time delay is allowed, a plurality of shape indices determined to be normal may be adopted as the average value, median, or index with the lowest degree of measurement abnormality.

[0084] The rolling control device calculates control parameters using information about the production of plate-like objects obtained from a higher-level system (for example, parameters selected from one or more of plate thickness, plate width, type, or temperature), other measurement data such as the plate threading position, and shape indicators, and then sends control signals to the actual rolling rolls to perform feedback control such as leveling. Feedback control stabilizes the shape of the product and reduces shape defects. Furthermore, if a high percentage of images are judged to be abnormal or if almost all indicators are judged to be abnormal, the feedback control is temporarily stopped. Alternatively, the stability of the feedback control itself can be improved by providing a function to switch to manual operation or to a different logic.

[0085] Furthermore, when feedback control is judged to be abnormal using the degree of measurement abnormality output by the present invention and, as a result, automatic control is switched to another control, the control may become unstable if the judgment of normality and that of abnormality are switched rapidly. If control using a shape index is set as the normal control mode and control with many abnormal judgments and unreliable shape indexes is set as the abnormal control mode, it is desirable to impose constraints on the switching logic so that the control mode does not switch again immediately after each switch.

[0086] The simplest method is to prevent the control mode from being switched again for a predetermined time immediately after it has been switched. Alternatively, a method of applying a low-pass filter such as a moving average filter or median filter to the measurement anomaly level itself in the time direction may be used. Alternatively, a method of creating an evaluation function that imposes some kind of penalty on returning to the control mode before the switch immediately after the control mode has been switched, and making a judgment based on that evaluation function may be used.

[0087] In particular, in the case of hot-rolled steel sheets, the leading and trailing ends that are rolled first are unsteady parts in the process, and there is a high possibility that a stable profile cannot be extracted compared to the middle part of the steel sheet. Therefore, the abnormal control mode may be set when measurement of the leading and trailing ends begins, and then switched to the normal control mode as the number of normal judgments increases. Furthermore, the control responsiveness of the leading edge part may be adjusted by setting the switching conditions different from those at the center of the steel sheet only at the first switching.

[0088] (Example of rolling preset control using machine learning) An example of rolling control using machine learning with a shape measuring device according to the embodiment will be described. First, when capturing an image of the target surface with camera 2 to extract a first profile, multiple images are captured consecutively and the presence or absence of an abnormality is determined for each. At this time, by adopting only the shape indices determined to be free of abnormalities, it becomes possible to avoid the influence of disturbances. Furthermore, multiple shape indices determined to be free of abnormalities may be adopted as the average value, median, or the index with the lowest degree of measurement abnormality. In this way, highly accurate shape indices with little disturbance are transmitted to the data server.

[0089] The data server stores shape indicators linked to information about the production of plate-like objects obtained from a host system (for example, parameters selected from one or more of plate thickness, plate width, product type, temperature, etc.) and control parameters during rolling. This stored data is transmitted to a machine learning device, which uses machine learning to construct a model that estimates control parameters for rolling while suppressing shape defects. The estimated model is transmitted to a control parameter estimation device, which estimates control parameters based on information about the production of plate-like objects obtained from the host system. The control parameters are transmitted to a rolling control device, which transmits control signals to the actual rolling rolls to perform preset control such as leveling. By performing such preset control, it is possible to stabilize the shape of the product and reduce shape defects themselves.

[0090] (Example of manufacturing equipment anomaly detection using measurement anomaly degree) An example of detecting an abnormality in a measuring device or manufacturing device using the degree of measurement abnormality calculated by the shape measuring device according to the embodiment will be described below. When the degree of measurement abnormality remains at a large value, it is highly likely that the edge of the steel sheet S is not being captured clearly for some reason. In this case, there are two possible causes: a cause on the measuring device side, such as the imaging conditions of the camera 2 or the soundness of the optical system (contamination, etc.), and a cause on the manufacturing device side, such as water vapor, waterlogging, uneven cooling or scratches on the edge of the steel sheet S.

[0091] Therefore, by extracting images with a high degree of measurement abnormality, discovering abnormalities from those images, and immediately correcting them, it is possible to maintain the integrity of the measuring device or manufacturing equipment. Furthermore, after accumulating a certain number of images with a high degree of measurement abnormality, the type of abnormality and the degree of harmfulness may be automatically determined using machine learning or the like.

[0092] (Example of suitable camera placement) An example of a suitable placement of the camera 2 of a shape measuring device will be described below with reference to Figures 22 to 24, based on an example in which the contour of the edge of a plate-like object is extracted using thermal radiation light. Note that the following placement conditions can be applied not only when thermal radiation light is used, but also when reflected light or backlight is used.

[0093] As will be described later, the camera 2 is preferably positioned so that the angle θ between the "optical axis of the camera 2" and the "plane (plane α) that is the reference plane of the plate-like object (steel sheet S)" is not 90 degrees (see FIG. 22). Furthermore, as will be described later, the camera 2 is preferably positioned so that the angle φ between the "orthogonal projection of the optical axis of the camera 2 onto the plane α" and the "conveyance direction p of the plate-like object (steel sheet S)" is not 0 degrees (see FIG. 22). By using the camera 2 positioned in this manner to capture an image of the thermal radiation light of the steel sheet S at the delivery side of hot finish rolling that has been rolled by the rolling rolls 1, it is possible to more clearly capture changes in the brightness of the thermal radiation light.

[0094] If a commercially available digital camera or the like is used as the camera 2, problems such as blurring of the image or graininess may occur, making it difficult to clearly capture the shape of the steel sheet S. Therefore, the following describes technical points for accurately capturing the shape of the steel sheet S using the camera 2.

[0095] In order to clearly capture the shape of the steel sheet S, it is necessary to consider the positional relationship between the steel sheet S to be measured and the camera 2. Here, we focus on the edge elongation of the steel sheet S and consider capturing the change in shape of the edge as the outline of the steel sheet S. Figure 22 shows an example of the positional relationship between the conveyance direction p of the steel sheet S and the camera 2. In Figure 22, (a) is a diagram showing this positional relationship from an oblique angle, (b) is a diagram showing this positional relationship from above, and (c) is a diagram showing this positional relationship from the direction δ of (b). Here, the direction δ is the direction in which the optical axis of the camera 2 can be seen from the side. Therefore, the magnitude of the angle θ, which will be described later, can be accurately seen.

[0096] 22(a), (b), and (c), the plane parallel to the conveying table for the steel plate S is designated as α, and the plane including the conveying direction p of the steel plate S and the normal n of the conveying table is designated as β. Furthermore, the angle between the optical axis of the camera 2 and the plane α is designated as θ (light-receiving angle θ), and the angle between the orthogonal projection of the optical axis of the camera 2 onto the plane α and the conveying direction p is designated as φ. Note that the plane α includes the surface of the plate-like object (steel plate S) when the plate-like object (steel plate S) is stably conveyed in a substantially flat shape. Therefore, the plane α is also referred to as the reference plane of the surface of the plate-like object (steel plate S).

[0097] Furthermore, the direction of the normal n of the conveying table is the same as the normal direction of the surface of the steel plate S when the steel plate S is conveyed stably in a substantially flat shape. Therefore, the plane α includes the conveying direction p of the plate-like object (steel plate S) and is perpendicular to the normal direction of the surface of the plate-like object (steel plate S). The plane β also includes the conveying direction p of the plate-like object (steel plate S) and the normal direction of the surface of the plate-like object (steel plate S). In FIG. 22, the intersection of the plane α and the optical axis is defined as point 0. The plane perpendicular to the conveying direction of the steel plate S is defined as plane γ. In other words, as the angle φ approaches 90 degrees, the plane γ and the optical axis become more parallel to each other.

[0098] In Figure 22, if the resolution of the camera 2 at the position of the steel plate S to be measured is "r (mm / pixel)", the resolution rn (mm / pixel) in the wave height direction and the resolution rp (mm / pixel) in the wave pitch direction can be expressed as in the following formulas (4) and (5). Note that the smaller the numerical value of the resolution, the higher the resolution.

[0099] rn=r / cosθ (4) rp=r / sinφ (5)

[0100] When the acceptance angle θ is close to 90 degrees, i.e., when the optical axis of the camera 2 is nearly perpendicular to the plane α parallel to the conveying table of the steel sheet S, the resolution rn in the wave height direction decreases, making it difficult to capture changes in the contour of the edge portion. Therefore, the acceptance angle θ is preferably as close to 0 degrees as possible. Similarly, when the angle φ is close to 0 degrees, i.e., when the optical axis of the camera 2 is nearly parallel to the conveying direction p of the steel sheet S, the resolution rp in the wave pitch direction decreases. Therefore, the angle φ is preferably as close to 90 degrees as possible. In particular, since the wave height to be measured is very small compared to the wave pitch, in order to capture the wave height with high accuracy, it is more preferable to make the acceptance angle θ as small as possible and to image the steel sheet S at a low angle.

[0101] Furthermore, by imaging the steel sheet S by looking at it at a low angle relative to the light-receiving angle θ as shown in Figure 22, the distance between both ends of the steel sheet S in the image becomes smaller, making it possible to acquire images of both edge portions in a smaller field of view. In addition, since the image size also becomes smaller when the target field of view becomes smaller, this is advantageous when the transport speed of the steel sheet S to be measured is high and high-speed imaging and image processing are required.

[0102] Furthermore, since the wave pitch of the steel sheet S is sufficiently larger than the wave height, the resolution rp in the wave pitch direction may be smaller than the resolution rn in the wave height direction. Therefore, as shown in Fig. 23, for example, it is preferable to set the angle φ to 90 degrees, i.e., to position the camera 2 perpendicular to the conveyance direction of the steel sheet S and view it from the side. However, if the angle φ is not extremely small, the camera 2 may be positioned at an angle to the conveyance direction of the steel sheet S, as shown in Fig. 24, for example.

[0103] (Plate-shaped object manufacturing equipment) The plate-like object shape measuring device according to the embodiment can also be applied to a plate-like object manufacturing facility. In this case, the plate-like object manufacturing facility is configured to include the plate-like object shape measuring device. In the plate-like object manufacturing facility according to the embodiment, the plate-like object shape measuring method can be used to manufacture plate-like objects while reliably and stably measuring the shape of the plate-like object regardless of the manufacturing environment or measurement environment.

[0104] (Method for controlling the shape of a plate-like object) The method for measuring the shape of a plate-like object according to the embodiment can also be applied to a method for controlling the shape of a plate-like object. In this case, the method for rolling a plate-like object measures the shape of the plate-like object using the method for measuring the shape of a plate-like object, and controls the shape of the plate-like object based on the measurement results. In the manufacturing equipment for plate-like objects according to the embodiment, the method for measuring the shape of a plate-like object can reliably and stably measure the shape of the plate-like object while controlling the shape of the plate-like object, regardless of the manufacturing environment or measurement environment.

[0105] (Method of manufacturing a plate-shaped object) The rolling method for a plate-like object according to the embodiment can also be applied to a method for manufacturing a plate-like object. In this case, the method for manufacturing a plate-like object measures the shape of the plate-like object using a method for measuring the shape of a plate-like object, and manufactures the plate-like object based on the measurement results. In the method for manufacturing a plate-like object according to the embodiment, the method for measuring the shape of a plate-like object can reliably and stably measure the shape of the plate-like object regardless of the manufacturing environment or measurement environment, while manufacturing the plate-like object.

[0106] (Quality control method for plate-shaped objects) The method for measuring the shape of a plate-like object according to the embodiment can also be applied to a method for controlling the quality of a plate-like object. In this case, the method for controlling the quality of a plate-like object measures the shape of the plate-like object using the method for measuring the shape of a plate-like object, and controls the quality of the plate-like object based on the measurement results. In the method for controlling the quality of a plate-like object according to the embodiment, the shape measurement method can reliably and stably measure the shape of the plate-like object, regardless of the manufacturing environment or the measurement environment, while controlling the quality of the plate-like object.

[0107] The above-described method for measuring the shape of a plate-like object, method for controlling the shape of a plate-like object, method for manufacturing a plate-like object, method for quality control of a plate-like object, device for measuring the shape of a plate-like object, and manufacturing equipment for a plate-like object calculate the degree of discontinuity from the profile as a measurement anomaly, and determine whether the index obtained from the calculated measurement anomaly is normal. This reduces measurement anomalies in the shape of the edge portion of the plate-like object, enabling reliable and stable shape measurement, regardless of the manufacturing environment or measurement environment of the plate-like object.

[0108] The method for measuring the shape of a plate-like object, the method for controlling the shape of a plate-like object, the method for manufacturing a plate-like object, the quality control method for a plate-like object, the shape measuring device for a plate-like object, and the manufacturing equipment for a plate-like object according to the present invention have been specifically described above using the preferred embodiments of the invention, but the scope of the present invention is not limited to these descriptions and should be broadly interpreted based on the claims. Furthermore, it goes without saying that various changes and modifications based on these descriptions are also included in the scope of the present invention.

[0109] For example, the present invention has been described with reference to the steel plate S at the finish rolling exit side of the hot rolling process, but it goes without saying that it can also be applied to other plate-shaped steel materials such as thick steel plates and slabs. Furthermore, it goes without saying that the present invention can be applied not only to steel materials and steel processes, but also to various plate-shaped (including sheet-shaped or strip-shaped) objects. Furthermore, the above description has been given using plate-shaped steel materials as an example of plate-shaped objects, but this is not limiting. It can also be applied to other metal materials, including steel. Furthermore, it is not limited to metal materials, but can also be applied to plate-shaped objects such as resins and synthetic chemical materials. [Explanation of symbols]

[0110] 1. Rolling mill 2 Cameras 3. Image processing device 11 Light source 12 surface light source S steel plate

Claims

1. a first profile extraction step of extracting a first profile indicating a contour of the plate-like object from an image of an edge portion of the plate-like object; a first calculation step of calculating an index of the shape of an edge portion of the plate-like object from the first profile; a second calculation step of calculating a degree of discontinuity of the outline of the plate-like object from the first profile as a measurement abnormality degree; a determination step of determining whether the index is normal or not based on the degree of measurement abnormality; A method for measuring the shape of a plate-like object, comprising:

2. 2. The method for measuring the shape of a plate-like object according to claim 1, wherein the second calculation step applies a combination of one or more of a median filter, a low-pass filter, a high-pass filter, and a differential filter to the first profile to obtain a second profile, and calculates the degree of measurement anomaly based on the second profile.

3. 3. The method for measuring the shape of a plate-like object according to claim 2, wherein the second calculation step calculates the maximum value after taking the absolute value of the second profile, and calculates the calculated maximum value as the degree of measurement abnormality.

4. A method for controlling the shape of a plate-like object, comprising measuring the shape of the plate-like object by the method for measuring the shape of the plate-like object according to any one of claims 1 to 3, and controlling the shape of the plate-like object based on the measurement result.

5. A method for manufacturing a plate-like object, comprising measuring the shape of the plate-like object by the method for measuring the shape of the plate-like object according to any one of claims 1 to 3, and manufacturing the plate-like object based on the measurement results.

6. A quality control method for a plate-like object, comprising measuring the shape of the plate-like object by the method for measuring the shape of the plate-like object according to any one of claims 1 to 3, and controlling the quality of the plate-like object based on the measurement results.

7. a first profile extraction unit that extracts a first profile that indicates a contour of the plate-like object from an image of an edge portion of the plate-like object; a first calculation unit that calculates an index of the shape of an edge portion of the plate-like object from the first profile; a second calculation unit that calculates a degree of discontinuity of the contour of the plate-like object from the first profile as a measurement abnormality degree; a determination unit that determines whether the index is normal or not based on the degree of measurement abnormality; A shape measuring device for a plate-like object comprising:

8. A manufacturing facility for plate-like objects, comprising the plate-like object shape measuring device according to claim 7.

Images