JP5392179B2 - Steel plate defect detection method and defect detection system - Google Patents

Description

The present invention relates to a steel sheet defect detection method and a defect detection system suitable for detecting surface defects and surface layer defects of materials.

  In general, in the process of manufacturing a thin steel plate, the surface or surface layer of the thin steel plate is formed by indentation by a roll or the like, or dross that is formed by dripping molten zinc bath into the galvanized layer. Various defects such as blowhole defects scattered in the steel sheet due to the trapping and rolling of argon gas in the slab during casting, surface flaws caused by non-uniform thickness of the galvanized layer, etc. May occur.

  Of these defects, those that appear optically different from the normal part have been detected by visual inspection by an operator.

  In addition, regarding the detection of defects in a steel plate, a technique is known in which the surface of a steel plate is imaged by a CCD camera or the like, and defect detection is performed based on the imaging data (for example, see Patent Document 1).

JP 2004-219177 A

R. Gonzalez and R. Woods Digital Image Processing, Addison-Wesley Publishing Company, 1992

  However, when visual inspection is performed, the line speed must be reduced, and the accuracy varies depending on the operator. Furthermore, in recent years, with the stricter quality control, it has been required to detect minute defects that are difficult to visually confirm.

  Further, in the technique of imaging the surface of a steel plate with a CCD camera or the like, defects present on the surface layer of the steel plate are often difficult to see from the surface, and it cannot be said that the detection ability is high.

  The present invention has been made in view of the above points, so that even when a material such as a steel plate is transported or moved, the surface defects and surface layer defects of the material can be detected with high accuracy. The purpose is to do.

The defect detection method for a steel sheet of the present invention is a steel sheet defect detection method for detecting surface defects and surface layer defects of a steel sheet , and a heating procedure for heating the surface of the steel sheet with a heating device, and heating or heating in the heating procedure. A thermal image data acquisition procedure for acquiring thermal image data indicating a thermal distribution of a surface region in the steel sheet immediately after being performed by a camera using infrared rays, and a surface temperature represented by the thermal image data acquired in the thermal image data acquisition procedure And a detection procedure for calculating an emissivity change amount Δε (x, y) based on the equation (15) and detecting at least the presence or absence of a defect based on the change amount Δε (x, y). And

The defect detection system for a steel sheet of the present invention is a defect detection system for a steel sheet that detects surface defects and surface layer defects of the steel sheet , and a heating device that heats the surface of the steel sheet , and a heating device that is being heated or immediately after being heated by the heating device. The amount of change in emissivity based on equation (15) from the camera that acquires thermal image data indicating the heat distribution of the surface region in the steel sheet using infrared rays and the surface temperature represented by the thermal image data acquired by the camera It has a detection device that calculates Δε (x, y) and detects at least the presence or absence of a defect based on the change amount Δε (x, y) .

  According to the present invention, the thermal image data of the surface region being cooled after being heated by the heating device is acquired, the amount of change in emissivity is obtained from the surface temperature represented by the thermal image data, and the defect is determined using the amount of change. By detecting the presence / absence of the surface defects, surface defects and surface layer defects of the material can be accurately detected even when the material is transported or moved.

It is a figure which shows the schematic structural example of the defect detection system of the thin steel plate which concerns on this embodiment. It is a figure which shows the image of the image process in the image process part of a detection apparatus. It is a figure which shows the defect which can become a detection target. It is a figure for demonstrating the nondestructive inspection using an infrared thermography camera. It is a figure which shows the relationship of the pixel used for numerical formula process calculation. It is a characteristic view which shows the surface temperature which the thermal image data acquired with the infrared thermography camera represents. It is a characteristic view which shows the variation | change_quantity of the emissivity corresponding to the temperature characteristic line of FIG. It is a characteristic view which shows the temperature distribution curve of a surface layer. It is a characteristic view which shows the effect which performed the Gaussian filter process to thermal image data. It is a figure which shows the hardware structural example of the computer system which functions as a detection apparatus. It is a figure which shows the image of the Example which performed the defect detection about the steel plate which a foreign material or a space | gap exists in a surface layer. It is a figure which shows the image of the Example which performed the defect detection about the steel plate in which the micro convex part was formed in the surface. It is a figure which shows the image of the Example which performed the defect detection about the steel plate in which the fine acute angle recessed part was formed in the surface. It is a figure which shows the image of the Example which performed the defect detection about the steel plate which the foreign material adhered to the surface. It is a figure which shows the image of the Example which performed the defect detection about the resin-made fuel tank for vehicles in which the foreign material has mixed in the surface layer.

  Hereinafter, a preferred embodiment of the present invention will be described with reference to the accompanying drawings by taking a case where a material is a thin steel plate as an example.

  First, a defect that can be a detection target in the present embodiment will be described with reference to FIG. There are various forms of defects in the thin steel plate, for example, forms such as sand dust, willow leaves, and spots.

  The example shown in FIG. 3 (a) is a defect 101a caused by a foreign object biting into the surface layer of the thin steel plate 100 or a void being formed, and a dross of a molten zinc bath biting into the plating layer is formed. For example, a dross soot or a blow hole defect in which a gap is formed in the steel sheet when argon gas is trapped in the slab during rolling and rolled. In this type, since the thermal conductivity of the defect 101a formed by foreign matter or voids is lower than that of the steel plate itself, the part corresponding to the defect 101a on the steel sheet surface is more easily heated and cooled than the normal part.

  The example shown in FIG. 3 (b) is a defect in which a minute convex portion 101b is formed on the surface of the thin steel plate 100, such as a defect in which a part of the galvanized layer is thick. In this type, since the surface area is large, the convex portion 101b is easily heated and cooled as compared with the normal portion. Further, since the defective portion is convex, the radiation amount at the convex portion 101b is smaller than the radiation amount at the normal region having the same area.

  The example shown in FIG. 3C is a defect in which a minute acute angle recess 101c is formed on the surface of the thin steel plate 100, and corresponds to an indentation flaw formed due to a foreign matter adhering to the roll. In this type, since the defect part has an acute shape, the radiation amount at the acute angle recess 101c is smaller than the radiation amount at the normal part having the same area.

  The example shown in FIG. 3D is a defect in which foreign matter 101 d such as dust adheres to the surface of the thin steel plate 100. In this type, since the emissivity of the foreign matter 101d is smaller than that of the steel plate itself, the radiation amount of the foreign matter 101d is smaller than the radiation amount at a normal part having the same area.

FIG. 1 is a diagram illustrating a schematic configuration example of a thin steel plate defect detection system according to the present embodiment.
In the defect detection system for a thin steel plate according to the present embodiment, as shown in FIG. 1A, the surface defect of the thin steel plate 100 having a thickness of about several millimeters using the heating device 1, the infrared thermography camera 2, and the detection device 3. And surface layer defects (hereinafter referred to as defects 101) are detected.

  In the present embodiment, the emissivity change amount Δε (x, y) is obtained from the surface temperature represented by the thermal image data acquired by the infrared thermography camera 2 based on the following equation (1). Then, the absolute value of the change amount Δε (x, y) of the emissivity becomes larger at the defect 101 site than at the normal site. Further, the type of the defect 101 is expressed by the positive / negative of the emissivity change amount Δε (x, y).

  In FIG. 1A, reference numeral 1 denotes a heating device (heater), which heats the surface of a thin steel plate 100 on an inspection line. In this case, it is preferable that the temperature of the thin steel plate 100 is higher than room temperature and is preferably less than about 100 ° C. (more preferably about 60 ° C.) in order to avoid the influence on the material. The thin steel plate 100 on the inspection line is conveyed in the arrow direction shown in FIG. 1A at a predetermined line speed (about 0 to 300 mpm).

  Reference numeral 2 denotes an infrared thermography camera, which is disposed on the opposite side of the heating device 1 with respect to the thin steel plate 100, images a surface area being heated by the heating device 1 as an inspection area S, and a two-dimensional thermal image of the inspection area S. Get the data. Here, the thermal image is an image representing a distribution of radiant heat released from the surface of the thin steel plate 100 as an inspection object, that is, a surface temperature distribution. The infrared thermography camera 2 includes an imaging unit having an infrared sensor and a signal processing unit (not shown), and converts thermal information assigned to each pixel into color information to obtain thermal image data.

  Here, although the infrared thermography camera 2 may be arranged on the same surface side as the heating device 1, the thermal energy radiated by the heating device 1 when acquiring the thermal image data of the inspection area S by the infrared thermography camera 2. Must not enter the infrared thermography camera 2 due to reflection on the steel plate 100 or the like. For this purpose, the infrared thermography camera 2 is arranged on the downstream side of the heating device 1, and a heat shielding member (not shown) is arranged between the heating device 1 and the infrared thermography camera 2. For example, even if the heating device 1 is turned off after the steel plate 100 is heated, residual heat is generated in the heating device 1, so that thermal energy due to the residual heat is not incident on the infrared thermography camera 2. It is desirable to do. FIG. 1B shows an example in which the infrared thermography camera 2 is attached at an angle different from the example in FIG. 1A, but the attachment angle and installation location are not limited. Absent.

  Further, the frame rate, integration time, etc. of the infrared thermography camera 2 are appropriately set according to the line speed. For example, the integration time of a commercially available infrared thermography camera is about 0.01 ms, and even with a steel plate traveling at 150 mpm, the moving amount of the steel plate during one frame shooting is about 0.025 mm. The influence on the image quality is 10% or less for a pixel of 0.25 mm or more, and can be almost ignored.

  3 is a detection device composed of a personal computer or the like, obtains an emissivity change Δε (x, y) from the surface temperature represented by the thermal image data acquired by the infrared thermography camera 2, detects the presence or absence of the defect 101, Further, the type of the defect 101 is determined. As described above, the absolute value of the emissivity change amount Δε (x, y) is larger in the defect 101 portion than in the normal portion 102. Further, the type of the defect 101 is expressed by the positive / negative of the change amount Δε (x, y) of the emissivity. Therefore, when the defect 101 exists in the inspection area S, the absolute value of the emissivity change amount Δε (x, y) of the portion is the absolute value of the emissivity change amount Δε (x, y) of the normal portion 102. The type of the defect 101 can be determined based on whether the amount of change in emissivity Δε (x, y) is positive or negative.

  In the detection device 3, reference numeral 301 denotes an input unit that inputs thermal image data acquired by the infrared thermography camera 2.

  An image processing unit 302 performs predetermined image processing on the thermal image data input to the input unit 301. FIG. 2 is a diagram illustrating an example of an image subjected to image processing by the image processing unit 302. For example, when thermal image data as shown in FIG. 2A is input to the input unit 301, mathematical processing is performed based on the formula (1) to generate image data as shown in FIG. 2B. Then, the image data after the mathematical formula processing is binarized to generate image data as shown in FIG. The mathematical expression processing is performed to suppress the influence due to the temperature unevenness indicated by the thermal image data in FIG. 2A and to grasp the heat balance on the surface and the surface layer of the thin steel plate 100 as described later. The surface layer means a range that is close to the surface and can be approximated to the surface, for example, when the plate thickness is about 1 to 2 mm, a depth range of about 1/4 of the plate thickness from the surface.

  As described above, since the absolute value of the change amount Δε (x, y) of the emissivity is detected largely at the defect 101 site in the inspection area S by the mathematical processing, the image processing, for example, FIG. As shown in the black part of FIG. 5, an image in which the defect 101 is extracted is generated. Further, the type of the defect 101 can be determined based on whether the emissivity change amount Δε (x, y) is positive or negative.

  An output unit 303 outputs, for example, the image data processed by the image processing unit 302 to a monitor (not shown) and displays the image.

  Hereinafter, the principle of the method for detecting surface defects and surface layer defects of the thin steel plate according to the present embodiment will be described in detail.

  Nondestructive inspection using an infrared thermography camera is performed in various fields. As shown in FIG. 4, for example, it is possible to detect the presence or absence of a defect 402 inside the inspection object 401 such as mixing of foreign matters or formation of voids. Has been done. In this case, the amount of radiant heat on the surface 401 a of the inspection object 401 varies depending on the difference in thermal conductivity between the inspection object 401 and the defect 402. Therefore, it is possible to detect the presence or absence of the defect 402 inside the inspection target 401 by grasping the radiant heat distribution on the surface 401a of the inspection target 401 with time using an infrared thermography camera.

  In this case, if the thermal conductivity of the inspection object 401 and the defect 402 are greatly different, it is possible to detect the defect 402 with high accuracy, but when the thermal conductivity is comparable, for example, FIG. As in the example shown in (b) and FIG. 3 (c), it cannot be applied when there is no difference in the thermal conductivity of the inspection object (thin steel plate 100) and the defect.

  Further, in the existing method, the temperature abnormality of the defective part is determined from the radiant heat distribution, but since it is affected by the thermal diffusion in the two-dimensional surface direction (xy direction) of the inspection object 401, the radiant heat quantity of the defective part. Is attenuated, and there is a problem that the determination accuracy of the defective part is lowered.

  On the other hand, in the defect detection method according to the present embodiment, the radiant heat distribution on the surface of the inspection object (thin steel plate 100) is captured by the infrared thermography camera 2, but as described above, it is obtained by the equation (1). Since the absolute value of the emissivity change amount Δε (x, y) is larger in the defect 101 portion than in the normal portion 102, if the defect 101 exists in the inspection area S, the emissivity change amount Δε ( A large absolute value of x, y) is detected.

  Here, the Laplacian ΔT (x, y) of the steel sheet surface temperature in the pixel (pixel of interest) used for calculating the emissivity variation Δε (x, y) can be described by the following equation (2).

  As shown in FIG. 5, the right side of Expression (2) indicates the surface temperature T (x, y) at the pixel of interest among the surface temperatures represented by the thermal image data acquired by the infrared thermography camera 2, and the upper, lower, left, and right sides thereof. Can be calculated using the surface temperatures T (x + 1, y), T (x, y + 1), T (x-1, y), and T (x, y-1). In the present embodiment, an example in which one pixel is calculated as the pixel of interest has been described. However, a block including a plurality of pixels (such as a 2 × 2 block) may be calculated as a unit.

  On the other hand, the heat transfer phenomenon inside the thin steel plate 100 can be approximated by a three-dimensional steady heat conduction equation described by the following equations (3) to (5).

Furthermore, when the emissivity of the normal part is ε, and ε ₁ = ε + Δε (x, y) and ε ₂ = ε are substituted into the equations (4) and (5) and the difference between both sides is taken, the following equation ( 6) is obtained.

  Further, when the plate thickness h is very small (when the plate thickness h is 5 mm or less), the plate temperature T can be approximated by the following equation (7).

  As a result, the equation (3) can be rewritten as the following equation (8).

  Further, from the equation (7), the following equations (9) and (10) are obtained. At this time, since the plate thickness h is very small, the right side of the equation (10) ignores the higher-order term of h.

  Further, when Expression (9) and Expression (10) are substituted into Expression (6), the following Expression (11) is obtained.

  On the other hand, the following equation (12) is obtained from the heat transfer equilibrium condition (balance of radiant heat transfer).

  Then, using Expression (12), Expression (11) can be rewritten as the following Expression (13).

  Furthermore, the following equation (14) is obtained from the equations (8) and (13). Moreover, Formula (1) can be derived | led-out by deform | transforming Formula (14).

Here, the thermal image data of the inspection area S being heated is acquired from the back side of the observation surface without conveying the thin steel plate 100 by the infrared thermography camera 2, and the temperature characteristic lines T _{1 to} T ₃ as shown in FIG. Consider the resulting case. At this time, the pixel size of the infrared thermography camera 2 is set to 0.4 mm, which is about twice the size of the defect 101.

The temperature characteristic line T ₁ represents the temperature characteristic at the normal part 102, and the change amount Δε (x, y) of the emissivity at this position is shown as the characteristic line E ₁ in FIG. As shown by the characteristic line E ₁ in FIG. 7, the change amount Δε (x, y) of the emissivity becomes a value in the vicinity of zero while including noise.

On the other hand, the temperature characteristic line T ₂ represents the temperature characteristic at a site where a defect of the type shown in FIG. 3A or FIG. 3B is formed, and the emissivity change amount Δε (x, y) is shown by the characteristic line E ₂ in FIG. When a defect of the type shown in FIG. 3A or FIG. 3B exists, the change amount Δε (x, y) of the emissivity becomes a positive value as shown by the characteristic line E ₂ in FIG. This is because, as shown in FIG. 8, with respect to the temperature distribution of the surface layer of the normal site shown in the temperature characteristic lines t _1, a large heat radiation amount in the defect site, than normal site in the surface layer as shown in the temperature characteristic curve t ₂ This means that the temperature is low and the temperature distribution has a concave shape (Δ _xy T is a positive value).

The temperature characteristic line T ₃ represents the temperature characteristic at the site where the type of defect shown in FIG. 3C or FIG. 3D is formed, and the emissivity variation Δε (x, y) is shown by the characteristic line E ₃ in FIG. When a defect of the type shown in FIG. 3 (c) or FIG. 3 (d) are present, as shown by the characteristic line E ₃ in FIG. 7, the change in emissivity amount [Delta] [epsilon] (x, y) is a negative value. This is because, as shown in FIG. 8, the amount of heat radiation is smaller at the defective portion than the temperature distribution of the surface portion of the normal portion indicated by the temperature characteristic line t _1, and at the surface layer as compared with the normal portion as indicated by the temperature characteristic line t _3. This also means that the temperature is high and has a convex temperature distribution (Δ _xy T is a negative value).

FIG. 9 is a characteristic diagram showing the effect of applying the Gaussian filter processing to the thermal image data.
When noise is mixed in the thermal image data acquired by the infrared thermography camera 2, Gaussian filter processing is performed to remove the noise. The characteristic line E shown in FIG. 9 shows the result of applying mathematical processing to the thermal image data with noise mixed, and the change in emissivity is greatly oscillated due to the influence of noise. On the other hand, a characteristic line E ′ shown in FIG. 9 shows the result of applying mathematical processing after applying the Gaussian filter processing to the thermal image data, and the vibration observed in the characteristic line E is suppressed. Note that filter processing for noise removal is not limited to Gaussian filter processing as long as smoothing is performed. For the Gaussian filter processing exemplified here, for example, the method disclosed in Non-Patent Document 1 is used.

  As described above, when the emissivity change amount Δε (x, y) is obtained from the surface temperature represented by the thermal image data acquired by the infrared thermography camera 2, the absolute value of the emissivity change amount Δε (x, y) is obtained. The value is larger at the defect 101 site than at the normal site. Thereby, since the defect 101 was detected, the surface defect and the surface layer defect of the steel sheet can be detected with high accuracy without reducing the line speed.

  In addition, since the region where the temperature is affected by the defect 101 site is enlarged due to the thermal diffusion effect, the pixel size can be set larger than that of an optical scissors inspection device using a CCD camera.

FIG. 10 is a block diagram illustrating a hardware configuration example of the detection apparatus 3 according to the present embodiment.
As shown in FIG. 10, the detection device 3 according to this embodiment includes a CPU 51, an input device 52, a display device 53, a storage device 54, and a communication device 55, and each unit is connected via a bus 56. Has been. The storage device 54 includes a ROM, a RAM, an HD, and the like, and stores a computer program that controls the operation of the detection device 3 described above. The function or processing of the detection device 3 is realized by the CPU 51 executing the computer program. In addition, the detection apparatus 3 does not need to be one apparatus, and may be comprised from several apparatus. Moreover, in this embodiment, although the heating apparatus 1, the infrared thermography camera 2, and the detection apparatus 3 are each comprised by a different apparatus, the said 3 apparatus may be comprised by one apparatus and one CPU. It is also possible to control the heating device or the like.

(Example)
FIG. 11 shows an example in which defect detection is performed using a steel plate having foreign matters or voids in the surface layer as shown in FIG. Here, a heater was arranged on the back side of the observation surface, the power of the heater was adjusted so that the steel plate temperature was maintained at about 50 ° C., and thermal image data was acquired with an infrared thermography camera during heating. The number of pixels of the infrared thermography camera is 200 × 200, and the pixel size is 0.25 mm. Further, the distance between the infrared thermography camera and the steel plate is 25 cm, and the infrared thermography camera is arranged perpendicular to the steel plate surface.

  FIG. 11A is a diagram showing a thermal image acquired by an infrared thermography camera, and FIG. 11B is a diagram showing an image obtained by mathematically processing thermal image data. FIG. 11C is a diagram illustrating an image obtained by binarizing image data after mathematical expression processing. 11A to 11C schematically show each actually obtained image (zoom-up image). As shown in FIG. 11C, an image in which the defect (foreign matter or void) 1101 was taken out was generated, and good results were obtained.

  FIG. 12 shows an example in which defect detection is performed using a steel plate having a minute projection on the surface as shown in FIG. Here, a heater was arranged on the back side of the observation surface, the power of the heater was adjusted so that the steel plate temperature was maintained at about 50 ° C., and thermal image data was acquired with an infrared thermography camera during heating. The number of pixels of the infrared thermography camera is 200 × 200, and the pixel size is 0.25 mm. Further, the distance between the infrared thermography camera and the steel plate is 25 cm, and the infrared thermography camera is arranged perpendicular to the steel plate surface.

  FIG. 12A is a diagram illustrating a thermal image acquired by an infrared thermography camera, and FIG. 12B is a diagram illustrating an image obtained by mathematically processing thermal image data. FIG. 12C is a diagram illustrating an image obtained by binarizing image data after mathematical expression processing. FIGS. 12A to 12C schematically show each actually obtained image (zoom-up image). As shown in FIG. 12C, an image in which the defect (small convex portion) 1201 was taken out was generated, and good results were obtained. Note that the line-shaped pattern 1202 appearing in FIGS. 12A to 12C is obtained by checking the position of the foreign object in advance and marking the defect so as to surround the defect, and is not a false detection. .

  FIG. 13 shows an example in which defect detection is performed using a steel plate having a minute acute angle recess formed on the surface as shown in FIG. Here, a heater was arranged on the back side of the observation surface, the power of the heater was adjusted so that the steel plate temperature was maintained at about 65 ° C., and each thermal image data was acquired by an infrared thermography camera during heating. The number of pixels of the infrared thermography camera is 200 × 200, and the pixel size is 0.25 mm. Further, the distance between the infrared thermography camera and the steel plate is 25 cm, and the infrared thermography camera is arranged perpendicular to the steel plate surface.

  FIG. 13A is a diagram showing a thermal image acquired by an infrared thermography camera, and FIG. 13B is a diagram showing an image obtained by mathematically processing thermal image data. FIG. 13C is a diagram illustrating an image obtained by binarizing image data after mathematical expression processing. FIG. 13A to FIG. 13C schematically show each actually obtained image (zoom-up image). As shown in FIG. 13C, an image in which a defect (a minute acute-angle concave portion) 1301 was taken out was generated, and a good result was obtained.

  FIG. 14 shows an example in which defect detection is performed using a steel plate with foreign matter attached to the surface as shown in FIG. Here, a heater was arranged on the back side of the observation surface, the power of the heater was adjusted so that the steel plate temperature was maintained at about 45 ° C., and thermal image data was acquired with an infrared thermography camera during heating. The number of pixels of the infrared thermography camera is 200 × 200, and the pixel size is 0.25 mm. Further, the distance between the infrared thermography camera and the steel plate is 25 cm, and the infrared thermography camera is arranged perpendicular to the steel plate surface.

  FIG. 14A is a diagram showing a thermal image acquired by an infrared thermography camera, and FIG. 14B is a diagram showing an image obtained by mathematically processing thermal image data. FIG. 14C is a diagram illustrating an image obtained by binarizing image data after mathematical expression processing. FIGS. 14A to 14C schematically show each actually obtained image (zoom-up image). As shown in FIG. 14C, an image in which the defect (foreign matter) 1401 was taken out was generated, and good results were obtained.

  The present invention can also be applied to other materials. FIG. 15 shows an example in which defect detection is performed using a resin automobile fuel tank in which foreign matter is mixed in the surface layer instead of a thin steel plate as a test material. Here, a heater is arranged on the back side of the observation surface, the power of the heater is adjusted so that the fuel tank temperature is maintained at about 70 ° C., and thermal image data is acquired by an infrared thermography camera during heating. The number of pixels of the infrared thermography camera is 80 × 80, and the pixel size is 0.25 mm. Further, the distance between the infrared thermography camera and the fuel tank is 25 cm, and the infrared thermography camera is arranged perpendicular to the fuel tank surface.

  FIG. 15A is a diagram showing a thermal image acquired by an infrared thermography camera, and FIG. 15B is a diagram showing an image obtained by mathematically processing thermal image data. FIG. 15C is a diagram illustrating an image obtained by binarizing image data after mathematical expression processing. FIG. 15A to FIG. 15C schematically show each actually obtained image (zoom-up image). As shown in FIG. 15C, an image in which the defect (surface layer foreign matter) 1501 was taken out was generated, and good results were obtained.

  In principle, the present invention detects a singular point of the heat transfer phenomenon on the material surface and the surface layer. The heat transfer phenomenon occurs in any material, and any shape as long as it is a defect on the surface and the surface layer. However, since it can be detected by thermography measurement, it is applicable not only to steel plates and resins but also to any other material.

  Moreover, in the Example shown in FIGS. 11-15, although the steel plate or the fuel tank is fixed and the defect detection is performed, it is as having mentioned above that the defect of the steel plate or fuel tank currently conveyed can be detected. For example, the integration time of a commercially available infrared thermography camera is about 0.01 ms, and even when a defect in a steel plate traveling at 150 mpm is detected, the moving amount of the steel plate during one frame shooting is about 0.025 mm. The influence on the image quality is 10% or less for a pixel of 0.25 mm or more, and can be almost ignored.

(Other embodiments according to the present invention)
The processing in the above-described embodiment of the present invention can be realized by operating a program stored in a RAM or ROM of a computer. This program and a computer-readable recording medium recording the program are included in the present invention.

  Further, the present invention can be implemented as, for example, a system, apparatus, method, program, or recording medium. Specifically, the present invention may be applied to a system including a plurality of devices. The present invention may be applied to an apparatus composed of a single device.

  Note that the present invention includes a case where a software program that realizes the functions of the above-described embodiments is supplied directly or remotely to a system or apparatus. This includes the case where the system or the computer of the apparatus is also achieved by reading and executing the supplied program code.

  Accordingly, since the functions of the present invention are implemented by computer, the program code installed in the computer also implements the present invention. In other words, the present invention includes a computer program itself for realizing the functional processing of the present invention.

  In that case, as long as it has the function of a program, it may be in the form of object code, a program executed by an interpreter, script data supplied to the OS, and the like.

  Examples of the recording medium for supplying the program include a flexible disk, a hard disk, an optical disk, and a magneto-optical disk. Further, there are MO, CD-ROM, CD-R, CD-RW, magnetic tape, nonvolatile memory card, ROM, DVD (DVD-ROM, DVD-R) and the like.

  As another program supply method, there is a method of connecting to a homepage on the Internet using a browser of a client computer. The computer program itself of the present invention or a compressed file including an automatic installation function can be downloaded from the homepage by downloading it to a recording medium such as a hard disk.

  As another method, the program read from the recording medium is first written in a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer. Then, based on the instructions of the program, the CPU or the like provided in the function expansion board or function expansion unit performs part or all of the actual processing, and the functions of the above-described embodiments are also realized by the processing.

DESCRIPTION OF SYMBOLS 1 Heating apparatus 2 Infrared thermography camera 3 Detection apparatus 301 Input part 302 Image processing part 303 Output part 100 Sheet steel 101 Defect 102 Normal part

Claims (8)

A steel sheet defect detection method for detecting surface defects and surface layer defects of a steel sheet ,
A heating procedure for heating the surface of the steel sheet with a heating device;
Thermal image data acquisition procedure for acquiring thermal image data indicating the heat distribution of the surface area of the steel sheet during heating or immediately after heating in the heating procedure, by a camera using infrared rays,
Based on the surface temperature represented by the thermal image data acquired in the thermal image data acquisition procedure, an emissivity change amount Δε (x, y) is calculated based on Equation (1), and the change amount Δε (x, y) is calculated. defect detection method of steel sheet and having a detection procedure for detecting the presence or absence of at least defect based.

A steel sheet defect detection method for detecting surface defects and surface layer defects of a steel sheet,
A heating procedure for heating the surface of the steel sheet with a heating device;
Thermal image data acquisition procedure for acquiring thermal image data indicating the heat distribution of the surface area of the steel sheet during heating or immediately after heating in the heating procedure, by a camera using infrared rays,
Based on the surface temperature represented by the thermal image data acquired in the thermal image data acquisition procedure, the absolute value of the emissivity change amount Δε (x, y) is calculated based on the equation (2), and based on the calculated absolute value defect detection method of steel sheet and having a detection procedure for determining the presence or absence of a defect Te.

A steel sheet defect detection method for detecting surface defects and surface layer defects of a steel sheet,
A heating procedure for heating the surface of the steel sheet with a heating device;
Thermal image data acquisition procedure for acquiring thermal image data indicating the heat distribution of the surface area of the steel sheet during heating or immediately after heating in the heating procedure, by a camera using infrared rays,
A change in emissivity is calculated from the surface temperature represented by the thermal image data acquired in the thermal image data acquisition procedure, and at least the presence or absence of a defect is detected based on the calculated change in emissivity, and the thermal image data The sign of the emissivity change amount Δε (x, y) is calculated from the surface temperature represented by the thermal image data acquired in the acquisition procedure based on the equation (3), and the defect type is further determined based on the calculated sign. A method for detecting a defect in a steel sheet, comprising: a detection procedure.

In the thermal image data acquisition procedure, when acquiring thermal image data of a surface region by the camera, the thermal image data is acquired so that thermal energy radiated by the heating device does not enter the camera. The steel sheet defect detection method according to any one of claims 1 to 3 . A steel sheet defect detection system for detecting surface defects and surface layer defects of a steel sheet ,
A heating device for heating the surface of the steel sheet ;
A thermal image data indicating the heat distribution of the surface area in the steel sheet during heating or immediately after heating by the heating device, a camera that acquires infrared rays,
An emissivity change amount Δε (x, y) is calculated from the surface temperature represented by the thermal image data acquired by the camera based on the equation (4), and at least a defect is determined based on the change amount Δε (x, y). And a detection device for detecting the presence or absence of a steel sheet .

A steel sheet defect detection system for detecting surface defects and surface layer defects of a steel sheet,
A heating device for heating the surface of the steel sheet;
A thermal image data indicating the heat distribution of the surface area in the steel sheet during heating or immediately after heating by the heating device, a camera that acquires infrared rays,
The absolute value of the emissivity variation Δε (x, y) is calculated from the surface temperature represented by the thermal image data acquired by the camera based on the equation (5), and the presence or absence of a defect is determined based on the calculated absolute value. defect detection system of steel sheet characterized by having a determining detection device.

A steel sheet defect detection system for detecting surface defects and surface layer defects of a steel sheet,
A heating device for heating the surface of the steel sheet;
A thermal image data indicating the heat distribution of the surface area in the steel sheet during heating or immediately after heating by the heating device, a camera that acquires infrared rays,
The amount of change in emissivity is calculated from the surface temperature represented by the thermal image data acquired by the camera, and at least the presence or absence of a defect is detected based on the calculated amount of change in emissivity, and the thermal image acquired by the camera A defect in a steel sheet , characterized in that the sign of the emissivity change Δε (x, y) is calculated from the surface temperature represented by the data based on the equation (6), and the defect type is further determined by the calculated sign. Detection system.

The steel sheet defect detection system according to any one of claims 5 to 7 , further comprising incident prevention means for preventing thermal energy radiated from the heating device from entering the camera.

Images