JP3300546B2 - Steel sheet flaw detection method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for detecting flaws on a steel sheet, and more particularly to a signal processing method for detecting roll flaws in a cold rolling mill with a flaw detector using a CCD camera.

[0002]

2. Description of the Related Art In a rolling process, monitoring roll flaws on the surface of a rolled steel sheet is important for maintaining product quality. When the roll flaw becomes large, take measures such as changing the roll.

Conventionally, the flaw inspection in the continuous tandem cold rolling process is a sampling inspection in which one coil is extracted for every several coils and inspected off-line. For this reason, once a flaw occurs, a situation occurs in which a few coils inevitably have flaws. In order to rescue these flawed coils, an additional step must be performed with respect to the normal process. Inconveniently, the operation efficiency was greatly reduced.

Therefore, an online flaw detection method has been developed. Conventional online roll flaw judgment is CC
The presence or absence of a flaw is determined by reflection and color shading from a single screen shot by a D camera or an image signal in the width direction of a steel sheet obtained by laser scanning or the like.

[0005]

This on-line flaw detection has been put to practical use in the pickling and refining process, but has not yet been put to practical use in the cold rolling process. The reason is that
In the cold rolling process, when an on-line flaw detection is performed using a sensor, as shown in FIG. 9, there are many disturbance elements, and among them, water droplets attached to the steel sheet are very large and many signals are generated. Therefore, it is difficult to discriminate from flaws.

On the other hand, a method for detecting roll flaws for performing online flaw detection in the cold rolling step has been filed by the present applicant as Japanese Patent Application Nos. 5-99805 and 5-111822.

The roll flaw detection method described in Japanese Patent Application No. 5-99805 detects a roll flaw from image data obtained by imaging a rolling roll, and then enlarges and images the roll flaw. However, since it is necessary to image the same place twice on the moving steel plate, there is a problem that the imaging apparatus becomes complicated. Further, it is difficult to reliably distinguish between a roll flaw and a water droplet only by enlarging an image.

In the roll transfer flaw detection method described in Japanese Patent Application No. 5-111822, image data taken in synchronization with the circumference of a rolling roll is stored in a storage means for a plurality of screens, and light-dark level data in a plate width direction is stored. Is added to all the images to increase the S / N ratio of the flaw detection, and the periodicity such as the roll transfer flaw is more effective by accumulating the signal level. On the contrary, since the S / N is lowered for a flaw, there is a problem that it is difficult to reliably distinguish water droplets.

In Japanese Patent Application Laid-Open No. 4-200820, an image corresponding to one rotation of a rolling roll is captured by capturing an image of a steel plate with an infrared camera, and a low-temperature portion is identified by image processing. A method is disclosed in which a logical product S and a logical sum N of image data between the image data and a screen are determined, and a roll flaw is determined when the ratio S / N is equal to or more than a predetermined value.
However, since the starting point of a roll flaw having a pitch property such as a roll flaw is a relatively large flaw and gradually decreases while having a pitch due to the rotation of the roll, the method described in the above-mentioned publication discloses noise, While the logical sum part representing the disturbance element does not change, the logical product part representing the flaw element becomes smaller, the S / N ratio becomes smaller for each judgment, and other sporadic flaws occur. There is a problem that it is difficult to distinguish the cooling water from water droplets or the like adhering to the steel plate surface.

Therefore, an object of the present invention is to remove water droplets, which are extremely large disturbances in signal processing, and to perform online flaw detection in a cold rolling process.

[0011]

According to the present invention, there is provided a method for detecting flaws on a steel sheet, wherein an image of a sheet being rolled is imaged so that a part of the image is overlapped with the preceding and subsequent images, and image signals for a plurality of sheets are obtained. The position of the abnormal light and dark point on the plate is detected based on the image signal, and the position of the abnormal light and dark point is corrected by the moving distance of the plate between each imaging time and compared with the position of the abnormal light and dark point of an adjacent image. When it is located at the same position, it is determined to be a flaw, and when it is not at the same position, it is determined to be a disturbance.

[0012]

The present invention distinguishes flaws and water droplets by focusing on the fact that the relative position of the flaws on the steel plate with respect to the steel plate is constant, while the position of the water droplet that disturbs moves with respect to the steel plate. .

A plurality of images of the plate being rolled are taken so as to partially overlap the preceding and succeeding images, and the position of the abnormal light / dark point on the plate is detected in each image. If the abnormal light and dark points are flaws, the relative position on the plate is constant, so if the position of the abnormal light and dark points is corrected by the moving distance of the plate between each imaging time, the abnormal light and dark points of the adjacent image are corrected. Matches the position. However, when the abnormal light / dark point is a water drop, the position of the water drop moves due to purging or the like during each imaging time, so that even if the moving distance is corrected, the position of the abnormal light / dark point of the adjacent image matches. do not do. This makes it possible to distinguish between flaws and water drops.

[0014]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be specifically described below with reference to embodiments.

FIG. 1 shows an example of the configuration of a system for carrying out a method for detecting a roll defect according to the present invention.
In this embodiment, the rolling stand 2 is controlled by the CCD camera 1.
The surface of the steel sheet 3 rolled from is imaged. Further, a strobe 4 for illuminating an imaging range (shown by oblique lines) of the steel plate 3 is provided. FIG. 2 shows C
FIG. 2 is a block diagram illustrating a signal processing system for an image signal from the CD camera 1. The image signal from the CCD camera 1 is
The image processing device 5 receives various types of signal processing to be described later, and is supplied to the image memory 6, the arithmetic device 7, and the monitor 8.

First, the principle of the flaw detection method of the present invention will be described.

[Step 1] As shown in FIG. 3A, the steel sheet 3 rolled from the rolling stand 2 by the CCD camera 1 so that there is a portion where images before and after in the rolling direction of the steel sheet 3 overlap. An image of the surface is taken, and an image signal from the CCD camera 1 is written into the image memory 6. FIG. 4 (a)
(N-1) th, nth, (n + 1) th consecutive 3
The imaging part of the image for one sheet is relatively shown. Now, assuming that a flaw occurs at the position P shown in FIG. 3 on the surface of the steel plate 3, the flaw signal positions P _n−1 and P _{n in the} (n−1) th, nth, and (n + 1) th images. , P _{n + 1} are shown in FIG.
(A), (b) and (c) are obtained. FIG.
The dashed circles in (b) and (c) virtually indicate the position P _n-1 of the flaw signal in the (n-1) th image.

Then, the image processing device 5 reads the contents (256 gradations) of the image memory 6, calculates the change rate of the light and dark level of an arbitrary pixel and its surrounding pixels in all the pixel areas, and obtains a predetermined threshold or more. A flaw is determined by regarding the signal output as a flaw.

[Step 2] The position of each image is corrected by the current passing speed, and it is determined whether or not the position of the flaw signal has changed in the preceding and following images.

[Step 3] If the relative position of the flaw signal is not moving, it is regarded as a flaw or flaw origin, and if the relative position of the flaw signal is moving, it is regarded as a disturbance such as a water drop. That is, when the flaw signal is caused by the flaw of the steel sheet, the flaw signal is the same distance as the distance the steel sheet has moved as shown in FIGS. 4 (a), (b) and (c). Just move. Therefore, FIG.
As shown in (b) and (c), the progresses D _n , D
generating position of the _{n + 1} only flaw signal P _n, when corrected for P n _{+ 1,} n-th, the position P _n, P n _{+ 1} of the flaw signal in the (n-1) th image is, (n-1 ) -Th image has the same position as the position P _n-1 of the flaw signal. That is, it is understood that the relative position with respect to the steel plate 3 does not change. On the other hand, when water droplets are generated as a cause of the flaw signal, FIG.
As shown in (a), (b), and (c), the moving distance of the flaw signal is different from the moving distance of the steel sheet due to the influence of the purge. Therefore, progress fraction D _n of the steel plate 3, the generation position of D n _{+ 1} only flaw signal Q _n, even when corrected Q n _{+ 1,}
(N-1) -th not a position Q _n-1 at the same position of the flaw signal in the image. Note that the broken circles in FIGS. 5B and 5C indicate (n) when it is assumed that the position of the water droplet does not move.
-1) Indicates the position Q _n-1 of the flaw signal in the first image.

[Step 4] When a flaw or flaw origin is detected, the flaw or flaw origin is displayed on a monitor 8 to determine whether or not the flaw or flaw origin is visually observed by an operator. Use as information.

Next, a method of detecting a flaw origin according to the present embodiment will be described in order according to a flowchart of FIG.

(1) The speed of the steel plate 3 is reduced to V _mpm or less (step 100). Incidentally, V _{mp m} is the rate that can be captured as there is a portion overlapping before and after the image with respect to the rolling direction is a value determined by the camera's field of view (imaging range). For example, when the visual field is 300 mm × 300 mm, the speed V _mpm is 180 m / min.

(2) By emitting a strobe light 4 from the inclined direction on the surface of the steel plate 3, the brightness of the flaw portion is raised, and the image is displayed every short fixed period so that a substantially continuous image can be obtained. Then, for example, a plurality of screens every 1/30 second, in this example, 64 screens, are picked up by the CCD camera 1 and taken into the image memory 6 via the image processing device 5 (steps 110 and 130). At this time, it keeps the steel sheet speed V _n during the image capture, for example, is measured and stored in the laser Doppler system speed meter provided on the delivery side of the rolling stand (Step 120). FIG. 7 shows the n-th screen and (n +
It is an explanatory view schematically showing the relationship with the 1) th screen,
The corresponding portion of the steel plate 3 indicated by oblique lines moves by the distance D during 1/30 second. Note that A
k and Bk are positions of flaws or flaw candidates on the n-th screen and the (n + 1) -th screen.

(3) In the n-th screen (see FIG. 7), 1 /
The length of the steel plate in the length direction (Y direction) of the distance advanced in 30 seconds is
As shown by the arrows, a search is performed for each pixel row in the width direction (X direction) of the steel sheet (see FIGS. 8A and 8B), and a portion (abnormal light and dark coordinates) where the brightness is particularly remarkable due to a flaw or the like is determined. Is extracted from the brightness change rate of The brightness change rate ΔL is, for example,
It is calculated by the following equation.

ΔL = √ ((L (x (n + 5)) − L (x
(N))) ² ) where L is the density level of each point, x is the coordinate in the X direction, and n is the offset address of x.

If the brightness change rate ΔL is equal to or larger than the set parameter, that position is determined as the abnormality occurrence position. Here, the abnormal occurrence position is referred to as a temporary abnormal point. The coordinates of the provisional abnormal point Ap are represented by, for example, Ap = (X _i , Y _j ) where 1 ≦ i, i ≦ 256 1 ≦ j, j ≦ 240.

Next, a concentration analysis near the temporary abnormal point Ap is performed.

Assuming that the coordinates of the temporary abnormal point Ap are (X _i , Y _j ), the average density D _Ak of the H × V dots centered on the coordinates (X _i , X _j +1) is calculated (FIG. 8 ( c)). Here, H is the number of dots in the Y direction, and V is the number of dots in the X direction.

If the difference between the calculated average density D _Ak and the average density obtained in advance is equal to or greater than the set parameter, the center coordinates (X, Y) are stored as starting point flaw candidates (step 14).
0). (4) If there is no flaw origin candidate, (3) is repeated (the number of captured images -1) times (step 160).

[0031] (5) If there is a starting point defect candidate, proceeding to the next screen (n + 1) th screen calls the coordinate position A _k of the origin flaw candidate in the n-th screen 1/30 seconds The coordinates of the position _Bk are obtained (step 170). The coordinates of B _k are given by the following equation.

B _k = (X _i , Y _j + Y _v ) where Y _v is a dot on the Y-axis obtained by multiplying the steel plate speed V _{n + 1} at the time of (n + 1) -th image capture by 1/30. This is a value converted to a number.

(6) The inside of the set area centered on the obtained B _k coordinates (indicated by hatching in FIG. 8E) is processed by the same method as in (3), and the density analysis near the abnormal light and dark coordinates is performed. Then, a flaw origin candidate is extracted, and coordinates C _k = (X ′, Y ′) starting from the upper left end advanced in 1/30 seconds are stored (step 180). Further, the average density D _Ck of the flaw origin candidate is calculated.

(7) It is determined whether there is a flaw candidate (step 1).
90) If there is no starting point flaw candidate, it is determined whether or not to end the search (step 200). If the search is to be continued, the process returns to step 180, and if to end the search, the process returns to step 140.

(8) If there are starting point flaw candidates, the starting point flaw candidate coordinates and average density value found in (3) are compared with the flaw origin point candidate coordinates and average density obtained in (6) (step 2).
10).

(9) If the result is within the set threshold value, that is, if the difference between the starting point flaw candidate coordinates is small and the change in density is small, an alarm is output as a true starting point flaw (step). 220).

(10) If the result is other than the set threshold value, it is determined that the droplet is a water drop, and the processing from (3) is repeated until (the number of captured images-1) is reached (step 23).
0).

In the above-described flaw detection, FIG.
As shown in (d) and (e), the purging is performed in the direction perpendicular to the rolling direction of the steel sheet, and the water droplets are positively moved, so that the water droplets, which are disturbances, can be more reliably moved.

When detecting flaws, as shown in FIG. 9, welding lines and welding recognition holes exist as disturbances, but these disturbances are eliminated by software because the timing of passing through the camera imaging area is known in advance. It is possible to That is, when a disturbance having a pattern corresponding to a welding line or a welding recognition hole is detected, a program may be created so as to ignore the detected disturbance.

[0040]

According to the method for detecting flaws on a steel sheet of the present invention, the following effects can be obtained.

(1) Disturbance caused by water droplets, which is a bottleneck of online flaw detection in the cold rolling process, can be eliminated, and online flaw detection in the cold rolling process becomes possible.

[Brief description of the drawings]

FIG. 1 is an explanatory diagram showing a configuration example of a system for implementing a flaw detection method according to the present invention.

FIG. 2 is a block diagram showing a signal processing system for an image signal from a CCD camera.

FIG. 3 is an explanatory diagram showing an imaging position with respect to a steel plate.

FIG. 4 is an explanatory diagram illustrating a captured image when a steel plate has a flaw.

FIG. 5 is an explanatory diagram showing a captured image when a water drop is present on a steel plate.

FIG. 6 is a flowchart illustrating a method of detecting a flaw origin according to the present embodiment.

FIG. 7 is an explanatory diagram schematically showing a relationship between an n-th screen and an (n + 1) -th screen.

FIG. 8 is an explanatory diagram illustrating a method of detecting a flaw origin according to the present embodiment.

FIG. 9 is an explanatory diagram showing signal levels of disturbance and flaws online.

[Explanation of symbols]

1. CCD camera, 2. Rolling stand, 3. Steel plate, 4.
Strobe, 5 ... Image processing device, 6 ... Image memory, 7 ... Computing device, 8 ... Monitor

──────────────────────────────────────────────────続 き Continuation of the front page (72) Inventor Mitsutoshi Kubota 1-1, Hibata-cho, Tobata-ku, Kitakyushu-shi, Fukuoka Prefecture Nippon Steel Corporation Yawata Works (56) References JP-A-4-81918 (JP) JP-A-63-153679 (JP, A) JP-A-1-13655 (JP, A) JP-A-4-2311851 (JP, A) JP-A-6-201348 (JP, A) 6-102197 (JP, A) JP-A-4-200820 (JP, A) JP-A-6-258250 (JP, A) JP-A-62-181081 (JP, U) (58) Fields investigated (Int. Cl ^7, DB name) G01B 11/00 -. 11/30 G01N 21/89

Claims (1)

(57) [Claims] 1. An image of a plate being rolled is picked up so as to partially overlap the preceding and following images to obtain a plurality of image signals, and the positions of abnormal light and dark points on the plate are determined based on the image signals. The position of the abnormal light and dark point is corrected by the moving distance of the plate between each imaging time and compared with the position of the abnormal light and dark point of an adjacent image. A method for detecting flaws on a steel sheet, wherein when it is not at a position, a disturbance is determined.