Evaluation method, evaluation device, and computer program

Abstract

The present application can quantitatively evaluate the blister of a coating film. An evaluation method for evaluating the blister of a coating film formed on an object includes: (a) a step of acquiring image data of a surface of the coating film on which light irradiation is performed by a coaxial illumination device with a camera device; (b) a step of performing at least a binarization process on the image data to detect a region of the blister present on the surface of the coating film; and (c) a step of obtaining an area ratio of the region of the blister in an evaluation target region of the image data.

Description

Technical Field

[0001] This invention relates to an evaluation method, evaluation apparatus, and computer program for evaluating bubbling in coatings. Background Technology

[0002] As a method for evaluating the condition of a coating surface (hereinafter also referred to as "coated surface" in this specification), there are methods that utilize the condition of defects, unevenness, etc., of the coated surface. The detection of defects and unevenness of the coated surface also varies depending on the method of light irradiation. For example, Patent Document 1 describes a method that adjusts the angle of the irradiation light irradiating the coated surface, so that even small defects of about 50 μm can be detected.

[0003] In addition, as one of the methods for evaluating the deterioration of coatings formed on objects (coated objects), the method for evaluating coating swelling ("blistering") as specified in JIS K5600-8-2 is known. In the evaluation method specified in JIS K5600-8-2, the surface condition of the coating is compared with graded samples based on multiple reference plates, and the grade sample is visually determined to be closest to, thereby rating the degree (density and size) of the blistering.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2006-105672 Summary of the Invention

[0007] The technical problem that the invention aims to solve

[0008] The above-mentioned visual evaluation method cannot quantitatively evaluate the bubbling of the coating.

[0009] Therefore, the object of the present invention is to provide a method and apparatus, as well as a computer program, capable of quantitatively evaluating bubbling of a coating.

[0010] Technical means for solving technical problems

[0011] The evaluation method of the present invention is an evaluation method for evaluating blistering of a coating film formed on an object, comprising: (a) acquiring image data of the surface of the coating film irradiated by a coaxial illumination device using a camera device; (b) performing at least binarization processing on the image data to detect regions of blistering present on the surface of the coating film; and (c) determining the area ratio of the blistering region to the area of ​​the evaluation object region of the image data.

[0012] In the above evaluation method, the first condition for acquiring the image data in (a) and the second condition for detecting the area of ​​the bubble in (b) can be selected based on at least one of the size of the bubble present on the surface of the coating film, the surface roughness of the coating film, and the gloss of the coating film.

[0013] In the above evaluation method, the first condition can include the shortest distance between the semi-transparent mirror of the coaxial illumination device and the effective field of view of the surface of the coating film, and the second condition includes the threshold setting method in the above binarization process.

[0014] In the above evaluation method, step (b) may include: (i) a step of trimming the image data to obtain trimmed image data corresponding to the region of the evaluation object; and (ii) a step of performing the above binarization process after smoothing the trimmed image data, or performing the above binarization process without performing smoothing.

[0015] In the above evaluation method, the second condition may include the kernel value used by the smoothing filter in the above smoothing process.

[0016] The evaluation method described above can include a step of conducting a corrosion resistance test on the object having the coating above before step (a) above.

[0017] The aforementioned evaluation apparatus is an evaluation apparatus for evaluating bubbles in a coating film formed on an object. It includes: an acquisition unit for acquiring image data of the surface of the coating film, which is illuminated by a coaxial illumination device and captured by a camera device; a detection unit for performing at least binarization processing on the image data to detect areas of bubbles present on the surface of the coating film; and a calculation unit for determining the area ratio of the bubble area to the area of ​​the evaluation object in which the image data is acquired.

[0018] The computer program enables the computer to perform the above evaluation methods.

[0019] These generalized specific approaches can be implemented by systems, methods, and computer programs, as well as combinations thereof.

[0020] Invention Effects

[0021] According to the evaluation method, evaluation device and computer program of the present invention, the bubbling of the coating film can be quantitatively evaluated. Attached Figure Description

[0022] Figure 1 This is a block diagram showing the structure of the evaluation device.

[0023] Figure 2 It is a conceptual diagram used to illustrate coaxial lighting, and it also shows the coaxial lighting device and the camera device.

[0024] Figure 3A This is a conceptual diagram illustrating an example of the relationship between incident and reflected light.

[0025] Figure 3B This is another conceptual diagram illustrating the relationship between incident and reflected light.

[0026] Figure 4 This is a conceptual diagram illustrating the relationship between the surface of a bubbly coating (workpiece) and the irradiated and reflected light.

[0027] Figure 5 It is a bifurcation diagram that shows the selection of evaluation methods based on bubble size, gloss, and surface roughness.

[0028] Figure 6A This is an example of image data captured on the surface of the coating.

[0029] Figure 6B It is Figure 6A An example of smoothed image data.

[0030] Figure 6C It is Figure 6B An example of image data after binarization.

[0031] Figure 7A This is another example of capturing image data of the coated surface.

[0032] Figure 7B It is Figure 7A An example of image data that has been smoothed and then binarized.

[0033] Figure 8 This is a diagram showing an example of the illumination range on the surface of the coating.

[0034] Figure 9A This is yet another example of capturing image data of the coated surface.

[0035] Figure 9B It is Figure 9A An example of smoothed image data.

[0036] Figure 9C It is Figure 9B An example of image data after binarization.

[0037] Figure 10 This is a flowchart used to explain the processing of the evaluation method. Detailed Implementation

[0038] Hereinafter, the evaluation method, evaluation apparatus, and computer program involved in the embodiments will be described with reference to the accompanying drawings. The evaluation method, evaluation apparatus, and computer program of the present invention are used to evaluate blistering of coatings. Furthermore, in the following description, the same reference numerals are used to denote the same components, and descriptions are omitted.

[0039] In this invention, "coating" is a film formed on an object (the object to be coated), and refers to a film derived from paint.

[0040] "Paint" is a material used to cover the surface of an object for protection, decoration, and various other purposes.

[0041] There are no particular limitations on the materials of the coating and the coating film formed from the coating, the method of forming the coating film, the thickness of the coating film, the material and shape of the object constituting the coating film (at least its surface).

[0042] As for "objects", we can list objects in the shapes of flat plates, cylinders, rods, etc.

[0043] Materials that can be used to describe "objects" include, for example, metals, resins, rubber, and ceramics. Metals that can be used to describe objects include aluminum, stainless steel, iron-containing elemental metals, and alloys.

[0044] As a component of the coating film, it preferably contains a fluoropolymer. The fluoropolymer can be a fluororesin or a fluororubber, but is preferably a fluororesin.

[0045] Examples of the aforementioned fluoropolymers include polytetrafluoroethylene (PTFE), tetrafluoroethylene (TFE) / hexafluoropropylene (HFP) copolymer (FEP), TFE / perfluoro(alkyl vinyl ether) (PAVE) copolymer (PFA), TFE / HFP / PAVE copolymer (EPA), polychlorotrifluoroethylene (PCTFE), TFE / chlorotrifluoroethylene (CTFE) copolymer, TFE / ethylene (Et) copolymer (ETFE), TFE / CTFE / Et copolymer (ECTFE), and polyvinylidene fluoride (PVDF).

[0046] Examples of fluororubbers mentioned above include vinylidene fluoride [VdF] fluororubbers, tetrafluoroethylene [TFE] / propylene [Pr] fluororubbers, TFE / Pr / VdF fluororubbers, ethylene [Et] / hexafluoropropylene [HFP] fluororubbers, Et / HFP / VdF fluororubbers, Et / HFP / TFE fluororubbers, fluorosilicone fluororubbers, and fluorophosphazene fluororubbers.

[0047] Regarding the coating components, components other than the aforementioned fluoropolymers can include, for example, liquid media. Examples of such liquid media include water, organic solvents, and mixtures of water and organic solvents.

[0048] Other than the aforementioned fluoropolymers, adhesive resins may also be listed as components. The aforementioned adhesive resins are preferably heat-resistant resins (excluding fluoropolymers). "Heat resistance" refers to the property of continuous use at temperatures above 150°C. Examples of such heat-resistant resins include polyamide-imide resin (PAI), polyimide resin (PI), polyethersulfone resin (PES), polyetherimide resin, aromatic polyetherketone resin, aromatic polyester resin, and polyarylate sulfide resin.

[0049] Other components besides the aforementioned fluoropolymers may include surfactants, dispersants, viscosity modifiers, film-forming aids, film-forming agents, anti-bubbling agents, drying delay agents, thixotropic agents, pH adjusters, pigments, conductive agents, antistatic agents, leveling agents, anti-splatter agents, matting agents, anti-blocking agents, heat stabilizers, antioxidants, abrasion inhibitors, fillers, rust inhibitors, curing agents, acid acceptors, ultraviolet absorbers, light stabilizers, mildew inhibitors, antibacterial agents, and other additives.

[0050] Methods for forming coatings include, for example, spraying, dip coating, roller coating, curtain coating, screen printing, dispensing coating, electrodeposition coating, electrostatic coating, flow impregnation, rotary lining forming, and rotary forming.

[0051] The thickness of the coating is preferably 1 to 5000 μm.

[0052] "Blistering" is the expansion of the coating film. Blistering can occur for various reasons. In the case of coating film deterioration, it can occur due to the generation of gas between the coating film and the coated object (e.g., vaporization of components in the coating film and / or liquid components that have infiltrated from the outside). Blistering can occur, for example, due to the natural deterioration of the coating film, or it can occur due to corrosion resistance testing of the coating film to evaluate its corrosion resistance.

[0053] "Evaluation of bubbling on the coated surface" includes an evaluation of the current surface condition of the coating (which, if used after coating formation, has the same meaning as a degradation evaluation) and an evaluation of the corrosion resistance of the coating (if the coating has been formed but not yet used).

[0054] "(Surface roughness of coating)" (or "coating roughness") is the roughness obtained by measuring the unevenness of the coating surface, such as the arithmetic mean roughness Ra.

[0055] "Values ​​characterizing the surface of the coating" refer to values ​​obtained about the surface of the coating of the evaluation object, such as the size of the bubbles, the surface roughness of the coating, and the gloss of the coating.

[0056] Coaxial Lighting

[0057] First, the illumination used by the evaluation device when acquiring image data will be explained. Using... Figure 1 In the evaluation device 1 described later, image data of the coating surface (coated surface) captured by the imaging device 2 is used. Here, the imaging device 2 uses coaxial illumination when capturing the coated surface. Coaxial illumination is an illumination method that illuminates light coaxially with the imaging axis (camera axis) of the imaging device, and can be as follows: Figure 2 One example illustrates an illumination method in which illumination light from a light source 31 is introduced into the optical path of the lens 21 of the imaging device 2. Specifically, coaxial illumination is as follows: Figure 2 The illumination method shown involves reflecting light from a light source 31 through a semi-transparent mirror 32 and directing it toward a workpiece (i.e., the evaluation object, which in this invention is an object with a coating, and the surface of the workpiece corresponds to the surface of the coating) W. Here, the shortest distance between the semi-transparent mirror 32 and the effective field of view of the workpiece W is defined as distance LWD (mm). The structure including the light source 31 and the semi-transparent mirror 32 used for this coaxial illumination is referred to as the coaxial illumination device 3.

[0058] like Figure 3A As shown in one example, suppose a flat workpiece W is opposite the lens 21 of the imaging device 2, and a light source 31 illuminates the workpiece W at an oblique angle with a light beam L1. At this time, the reflected light L2, obtained from the reflection of the light beam L1, travels in a direction different from that of the lens 21. Therefore, in such cases... Figure 3A When the lens 21 of the camera device 2 is positioned in a direction different from the optical path of the reflected light L2, the image of the workpiece W captured by the camera device 2 is formed by capturing the scattered reflected light.

[0059] In contrast, in such Figure 3B The usage shown is the same as Figure 3A In the example of the same workpiece W and lens 21, assume that the workpiece W is illuminated by illumination light L1 from the same direction as the optical axis of lens 21. At this time, the reflected light L2 travels towards the lens 21 of the imaging device 2. Therefore, in such cases... Figure 3B When the lens 21 of the camera device 2 is positioned in the same direction as the optical path of the reflected light L2, the camera device 2 is able to obtain an image that captures the positively reflected light.

[0060] like Figure 4 As shown, workpiece W, with bubbles B formed on its surface due to coaxial illumination, was photographed. Figure 4 In the example, the workpiece W is also opposite the camera device 2. Among them, in Figure 4 The diagram of the coating film formed on the surface of workpiece W is omitted. Figure 4In the case where the surface portion P1 of the coating on the workpiece W without bubbles B is irradiated with illumination light L1, its reflected light L2 is directed toward the lens 21 of the imaging device 2. Therefore, in the image data, the surface portion P1 of the coating is captured as brighter. Additionally, the reflected light L2 obtained from illumination light L1 irradiating the vertex portion P2 of bubble B is directed toward the lens 21. Therefore, in the image data, the vertex portion P2 of bubble B is captured as brighter. On the other hand, the reflected light L2 obtained from illumination light L1 irradiating other portions of bubble B (e.g., P3) is directed toward the direction corresponding to the inclined surface of the portion irradiated by illumination light L1 (e.g., P3). Therefore, in the image data, portions such as bubbles B with inclined surfaces on the workpiece W with the coating are captured as darker.

[0061] In this way, by taking pictures with coaxial illumination, image data can be obtained in which the flat parts of the coating are bright, and the parts with sloping surfaces, such as bubbles B, which are convex, are dark. That is, in the image data, the white parts are the surface of the coating, and the black parts are the parts where bubbles B have formed, and the overall bubble area can be extracted. However, in practice, the surface values ​​of the coating that characterize the object being evaluated using the properties of the coating and / or the state of the bubbles cannot be used to easily select the area of ​​bubble B using the same method. Therefore, as described later, image data is acquired using an evaluation method selected corresponding to the values ​​characterizing the coating surface, and the acquired image data is processed to select the area of ​​bubble B.

[0062] <Evaluation Device>

[0063] Next, refer to Figure 1 The evaluation apparatus 1 according to the embodiment will be described. The evaluation apparatus 1 is an evaluation device for evaluating bubbles in a coating film formed on an object, and includes:

[0064] Acquisition unit 112 acquires image data of the surface of the coating film obtained by the coaxial illumination device 3 and the image captured by the camera device 2;

[0065] The detection unit 113 performs at least binarization processing on the image data to detect areas of blistering on the surface of the coating; and

[0066] The calculation unit 114 calculates the area ratio of the bubbled region in the region of the evaluation object in the image data.

[0067] Evaluation device 1 is an information processing device including a control unit 11, a storage unit 12, and a communication unit 13. The control unit 11 is a controller that implements overall control of the evaluation device 1. For example, the control unit 11 reads and executes the evaluation program P stored in the storage unit 12 to perform processing as a selection unit 111, an acquisition unit 112, a detection unit 113, a calculation unit 114, and a result processing unit 115. Furthermore, the control unit 11 is not limited to the part that implements the specified function through hardware and software cooperation; it can also be a hardware loop specifically designed to implement the specified function. That is, the control unit 11 can be implemented by various processors such as CPUs, MPUs, GPUs, FPGAs, DSPs, and ASICs.

[0068] Storage unit 12 is a recording medium for recording various types of information. Storage unit 12 can be implemented using, for example, RAM, ROM, flash memory, SSD (Solid State Drive), hard disk, other storage devices, or appropriate combinations thereof. In addition to storing the evaluation program P executed by control unit 11, storage unit 12 also stores various types of data. For example, storage unit 12 stores image data 121, result data 122, and evaluation program P.

[0069] The communication unit 13 is an interface loop (module) for enabling data communication with external devices via the network 4. For example, the communication unit 13 can enable data communication with the camera device 2 that captures image data. The communication unit 13 can also enable data communication with other external devices.

[0070] The evaluation device 1 may have an input unit 14 and an output unit 15. The input unit 14 is an input unit such as an operation button, mouse, or keyboard for inputting operation signals and data. The output unit 15 is an output unit such as a display for outputting processing results and data.

[0071] Here, the evaluation device 1 can be implemented by a single computer or by a combination of multiple computers connected via a network. Furthermore, although not shown in the figures, it is also possible, for example, that all or part of the data stored in the storage unit 12 is stored in an external recording medium connected via a network, and the evaluation device 1 uses the data stored in the external recording medium.

[0072] The selection unit 111 selects a first condition for the acquisition unit 112 to acquire image data and a second condition for the detection unit 113 to detect areas of bubbles, based on at least one of the following: the size of bubbles present on the surface of the coating film, the surface roughness of the coating film, and the gloss of the coating film. Specifically, the selection unit 111 receives at least one of the values ​​characterizing the surface of the coating film to be evaluated: "bubble size," "gloss," and "surface roughness." Furthermore, the selection unit 111 selects an "evaluation method" that specifies the first condition for image data acquisition and the second condition for image processing based on the received values ​​characterizing the surface of the coating film.

[0073] Regarding bubble size, for example, the value can be measured visually by the operator using a ruler. In this case, it is preferable to use the average bubble size of the evaluation area. For example, when measuring bubble size, five bubbles can be randomly measured from the evaluation area of ​​the coating, and their average value can be used. This prevents the selection of an unbalanced evaluation method based solely on the size of a single bubble.

[0074] Regarding the gloss of the coating, values ​​measured using a gloss meter can be used, for example.

[0075] Regarding the surface roughness of the coating, values ​​measured using a surface roughness measuring instrument can be used, for example.

[0076] For example, the operator inputs the values ​​of bubble size, gloss, and surface roughness, measured as described above, into the evaluation device 1 via the input unit 14, thereby the selection unit 111 selects an evaluation method. For example, after obtaining the bubble size, gloss, and surface roughness, the selection unit 111 selects an evaluation method based on the criteria shown in Table 1. Furthermore, Figure 5 The bifurcation diagram shows the judgment values ​​for the selection of the evaluation methods shown in Table 1. In Table 1 and... Figure 5 In the example, when the bubble size S is 0.5 mm or more, the gloss G (measured at 60°) is 55 or more, and the surface roughness Ra is 0.2 μm or more, the selection unit 111 selects evaluation method a. When the bubble size S is less than 0.5 mm, the selection unit 111 selects evaluation method h.

[0077] [Table 1]

[0078] Evaluation method a S≥0.5mm G≥55 Ra≥0.2μm Evaluation method b S≥0.5mm G≥55 Ra < 0.2 μm Evaluation method c S≥3mm G＜55 Ra≥0.4μm Evaluation method d S≥3mm G＜55 Ra < 0.4 μm Evaluation method e 0.5mm≤S<3mm G＜55 Ra≥0.6μm Evaluation method f 0.5mm≤S<3mm G＜55 0.4μm≤Ra<0.6μm Evaluation method g 0.5mm≤S<3mm G＜55 Ra < 0.4 μm Evaluation method h S < 0.5 mm

[0079] In the evaluation device 1, for each evaluation method selected by the selection unit 111, parameters are set as a first condition used in image acquisition (described later) and parameters as a second condition used in image processing. The first condition is a parameter for determining the shortest distance (LWD) between the semi-transparent mirror 32 of the coaxial illumination device 3 and the effective field of view of the coating surface. The second condition includes, as a parameter, the image size when trimming the area of ​​the evaluation object from the captured image data. Furthermore, the second condition includes a parameter for setting a threshold in the binarization process. Additionally, the second condition includes, as a parameter, the kernel value used by the median filter in the smoothing process.

[0080] The parameters for each evaluation method selected based on the values ​​representing the surface of the coating will be explained below. For simplicity, examples of how each parameter is determined for each surface value representing the coating are provided below, but the explanation is not limited to these examples. Specifically, the selection unit 111 selects an evaluation method by considering various surface values ​​representing the coating. Furthermore, in the evaluation apparatus 1, for each evaluation method selected by the selection unit 111, image data acquisition and image processing are performed using pre-set parameters.

[0081] Adjustments corresponding to bubble size

[0082] Figure 6A This is an example of capturing image data of the coating surface. Additionally, Figure 6B Yes Figure 6A Image data obtained by smoothing image data. Furthermore, Figure 6C Yes Figure 6B Image data is obtained by binarizing the image data. Smoothing and binarization are image processing steps performed by the detection unit 113 (described later) to detect the region of the bubble with good accuracy. For example, in cases where the bubble size is large, such as... Figure 6C As shown, near the center of the bubble, the portion reflecting strong reflected light is enlarged and sometimes appears white in the image data (e.g., Figure 6C (A1 part in the image). In cases where a portion of the bubble turns white, the whitened portion is considered not to be part of the bubble region and cannot be accurately evaluated. Therefore, when the bubble size is larger than a specified value, for example, in the smoothing filter, compared to cases where the bubble size is smaller than the specified value, by applying smoothing processing to pixels with a large area for each pixel, it is easier to extract the bubble B portion. In the evaluation device 1, by adjusting the kernel value used in the smoothing filter as a parameter corresponding to the size of bubble B, the extraction accuracy of bubble B can be improved.

[0083] On the other hand, when the size of bubble B is small, for example, by shortening the shortest distance (LWD) between the semi-transparent mirror 32 (which serves as coaxial illumination) and the effective field of view of the coating surface, the bubble B portion can be easily extracted. That is, when the size of bubble B is small, there is a tendency for the contrast between the coating surface and the bubble B portion in the image data to decrease, making it difficult to detect the area of ​​bubble B. Therefore, when the bubble is small, the detection of bubble B is difficult. Therefore, for example, when the bubble size is smaller than a specified size, by reducing the distance LWD as a parameter, the bubble B portion can be easily extracted. In the evaluation device 1, by adjusting the distance LWD parameter accordingly to the size of bubble B, the extraction accuracy of bubble B can be improved.

[0084] Adjustments corresponding to surface roughness

[0085] For example, when evaluating a coating surface with high surface roughness and protrusions other than bubbles, binarization sometimes causes the areas other than bubbles to appear black as well (e.g., Figure 6C (Part A2 in the image). In such cases, parts other than bubbles may sometimes be detected as bubbles. Once these protrusions other than bubbles on the surface of the coating are falsely detected as bubbles, accurate evaluation is impossible. Therefore, when the surface roughness is greater than a specified value, for example, in the smoothing filter, by blurring each pixel using pixels with a large surrounding area, it is easy to remove the protrusions other than bubble B. This is because such protrusions other than bubble B are usually smaller than bubble B itself. Thus, in the evaluation device 1, by adjusting the kernel value used in the smoothing filter as a parameter corresponding to the surface roughness, the extraction accuracy of bubble B can be improved.

[0086] Adjustments corresponding to the gloss of the coating

[0087] Figure 7A This is an example of capturing image data of a coating surface with high gloss. For instance, in cases of high gloss coatings, the reflected light L2 is sometimes weakened due to scattering at surface defects. Therefore, smoothing and binarization are performed, such as... Figure 7B As shown, in image data, such defects, etc., are present in certain parts ( Figure 7BThe A3 portion (in the image) sometimes appears dark. In this case, when extracting the region of bubble B using the same binarization process as usual, the defective portion other than bubble B may also appear dark and be detected as bubble B. Therefore, in the case of a high-gloss coating where defects are easily displayed in such binarized image data, the defects can be made less noticeable by setting the threshold to the darker side during binarization. Specifically, in 256 gray levels, "0" represents a value representing black, and "255" represents a value representing white. Thus, to shift the threshold towards the darker side, the threshold set according to the prescribed method is subtracted from the value of the parameter set according to the degree of gloss, resulting in a new threshold, thereby making the defects less noticeable.

[0088] The acquisition unit 112 acquires image data 121 containing the evaluation object area using parameters determined by the evaluation method selected by the selection unit 111. Specifically, the acquisition unit 112 acquires image data 121 using the distance (LWD) parameter determined by the evaluation method. Furthermore, the acquisition unit 112 stores the acquired image data 121 in the storage unit 12. For example, the acquisition unit 112 is connected to the camera device 2, transmits a camera operation signal to the camera device 2, and acquires image data 121 captured by the camera device 2.

[0089] The detection unit 113 includes: (i) a step of trimming image data to obtain trimmed image data corresponding to the region of the evaluation object; and (ii) a step of performing smoothing processing on the trimmed image data followed by binarization processing, or performing binarization processing without smoothing processing. Specifically, the detection unit 113 uses parameters determined by the evaluation method selected by the selection unit 111 to perform image processing on the image data 121 acquired by the acquisition unit 112, and detects the bubble region included in the evaluation object region. Specifically, the detection unit 113 performs "grayscale conversion", "trimming", "smoothing" and "binarization" as image processing. Specifically, the image data 121 acquired by the acquisition unit 112 as color image data is used as first image data. The detection unit 113 performs grayscale conversion on the first image data, and uses the processed image data as second image data. Furthermore, the detection unit 113 extracts (trimmes) the evaluation object region from the second image data. Next, the detection unit 113 performs smoothing (blurring) processing on the second image data of the trimmed object region, and uses the resulting smoothed image data as the third image data. The detection unit 113 further performs binarization processing on the third image data, and uses the resulting binarized image data as the fourth image data. Then, the detection unit 113 detects bubbly regions from the fourth image data. However, assuming that the image data 121 captured by the imaging device 2 is not color image data but grayscale image data, grayscale processing is not required, and the detection unit 113 can simply trim the acquired image data 121. Furthermore, regarding grayscale conversion, it does not use processing with parameters specified for each evaluation method, but rather a general conversion from a color image to a grayscale image.

[0090] The following describes the processing performed by evaluation device 1, which involves adjusting, smoothing, and binarizing the parameters specified for each evaluation method.

[0091] Repair

[0092] Trimming involves extracting the object region for evaluation. The detection unit 113 trims the object region for image processing from the second image data, based on the trimmed image size, which is a parameter specified by the evaluation method selected in the selection unit 111 corresponding to at least one of the values ​​representing the surface of the coating. At this time, the image data 121 captured by the imaging device 2 is, for example, as shown in... Figure 8 As shown, only the area illuminated by the light from light source 31 becomes brighter, while the area not illuminated by the light becomes darker. Therefore, during the correction process, correction must be performed only on the bright areas of the second image data. When the corrected second image data is enlarged, for example... Figure 9A The image data shown. The size of the trimmed image is determined by extracting the range usable for image resolution based on the diffusion of light when it is irradiated onto the coating.

[0093] Smoothing

[0094] Next, we will illustrate an example of smoothing. Figure 9B Yes Figure 9A The image data is obtained by smoothing the image data. The detection unit 113 performs smoothing processing on the modified second image data using a smoothing filter that uses parameters corresponding to the kernel value set according to the evaluation method. The image data obtained by smoothing processing is the third image data. By performing smoothing processing, for example, when the bubble size is large, it is easier to extract the bubble area, and the influence caused by the roughness of the coating film can be reduced.

[0095] The detection unit 113 can, for example, use a Gaussian filter as a smoothing filter. A Gaussian filter is a filter that uses a Gaussian distribution to increase the weight of the central part of the observed pixel. In this case, the parameter set by the selection unit 111 is the kernel value used by the Gaussian filter. In addition to using a Gaussian filter, the detection unit 113 can also use a smoothing filter, a median filter, a bilateral filter, etc., as a smoothing filter.

[0096] Binarization

[0097] Next, we will explain an example of binarization. Figure 9C Yes Figure 9B Image data obtained by binarizing image data. Figure 9C The image data is an example of an area capable of detecting bubbles on a substrate. Specifically, Figure 9C The black areas in the image represent the bubbly regions. However, depending on the parameters used in image acquisition and processing, it is not limited to... Figure 9C The image data shown is capable of detecting blister regions. Therefore, in order to accurately detect blister regions, the detection unit 113 performs binarization processing on the smoothed third image data using a threshold value adjusted according to the parameter set corresponding to the evaluation method selected by the selection unit 111. The image data obtained by binarization processing is the fourth image data. By performing binarization processing, for example, the influence of the surface state of the coating can be removed to detect blister regions.

[0098] The detection unit 113 employs appropriate binarization processing, such as calculating a threshold for each small region in the image, during binarization. Here, the parameter set by the selection unit 111 is a value used to adjust the threshold set for binarization processing. Specifically, as described above, the threshold is set between 0 and 255, but the parameter is a value representing the number of gray levels that shifts the set threshold, for example, towards the whiter side (closer to "255"). This eliminates the influence of surface conditions, resulting in binarized image data that can distinguish regions of bubbles. Furthermore, the detection unit can also utilize various methods in binarization processing, such as discriminant analysis (Otsu binarization), repeated threshold selection, and percentage methods.

[0099] In this way, in the evaluation device 1, the evaluation method is selected based on the value of at least one of the following: "bubble size," "roughness," and "gloss" of the coating surface, as described above. Furthermore, by performing "trimming," "smoothing," and "binarization" processes using the parameters specified by the selected evaluation method, the area of ​​the bubble can be accurately detected. Therefore, in the evaluation device 1, the bubbling of the coating can be quantitatively evaluated using this bubbling area.

[0100] The calculation unit 114 calculates the area ratio of the detected blistering areas within the object area of ​​the image size adjusted by the detection unit 113 as an evaluation value. Specifically, a smaller area ratio calculated by the calculation unit 114 indicates higher coating performance. Conversely, a larger area ratio calculated by the calculation unit 114 indicates lower coating performance. Thus, in the evaluation device 1, by using the evaluation value calculated by the calculation unit 114 as a numerical value representing corrosion resistance, the blistering of the coating can be quantitatively evaluated compared to the method where an evaluator visually evaluates the corrosion resistance of the coating. In this way, Figure 9C In the example, the area ratio of the bubbling region was calculated to be 42.63%.

[0101] The result processing unit 115 stores the area ratio of the bubbling region calculated by the calculation unit 114 as result data 122 in the storage unit 12 in association with the image data 121. Alternatively, the result processing unit 115 may output an evaluation value to the output unit 15. The result processing unit 115 records the obtained evaluation value as result data 122 in the storage unit 12. At this time, the result processing unit 115 may also associate various image data obtained by the acquisition unit 112 and various binarized image data obtained by the detection unit 113 with the original image data 121 and store them in the storage unit 12. Furthermore, the result processing unit 115 may also associate various data associated with the image data 121 with the image data 121 and store them in the storage unit 12.

[0102] Evaluation device 1 performs processing using the parameters of each determined evaluation method, as described above. This eliminates influences in the image data that are irrelevant to the coating performance, and allows for quantitative evaluation of coating bubbling based on numerical calculations of the bubbling area ratio. For example, although coatings sometimes use a variety of resins and pigments, the above-described processing allows for quantitative evaluation of coating bubbling.

[0103] Evaluation Methods

[0104] The evaluation method involved in the implementation method is an evaluation method for evaluating blistering of a coating film formed on an object.

[0105] (a) Acquire image data of the surface of the coating film illuminated by a coaxial lighting device using a camera device;

[0106] (b) The image data is at least binarized to detect areas of blistering on the surface of the coating; and...

[0107] (c) Calculate the area ratio of the bubbled region in the evaluation area of ​​the image data.

[0108] Below, refer to Figure 10 The flowchart shown illustrates the evaluation method using evaluation device 1.

[0109] First, the evaluation device 1 receives input values ​​characterizing the surface of the coating film as data for selecting an evaluation method (S01). For example, after the user inputs values ​​of bubble size, gloss, and surface roughness as characterizing the surface of the coating film through the operation of the input unit 14, the evaluation device 1 selects an evaluation method for evaluation.

[0110] The evaluation device 1 acquires image data of the surface of the coating on the workpiece W to be evaluated using parameters set by the first condition of the evaluation method selected in step S01 (S02). Specifically, the evaluation device 1 acquires image data by setting the shortest distance LWD between the semi-transparent mirror 32 of the coaxial illumination device and the effective field of view of the surface of the workpiece W using parameters specified by the first condition of the selected evaluation method. The image data acquired in step S02 is first image data. Furthermore, the evaluation device 1 stores the acquired first image data in the storage unit 12.

[0111] The evaluation device 1 grayscales the first image data acquired in step S02 (S03). The grayscaled image data in step S03 is the second image data. Furthermore, the evaluation device 1 stores the grayscaled second image data in the storage unit 12.

[0112] The evaluation device 1 extracts (trims) the region to be evaluated based on the second image data after grayscale scaling in step S03, using the image size corresponding to the parameters set by the second condition of the evaluation method selected in step S01 (S04). Specifically, the evaluation device 1 sets the position of the object region based on the position of the light irradiating the workpiece W when it is acquired in step S02.

[0113] Evaluation device 1 performs smoothing processing on the second image data of the object region extracted in step S04 (S05). At this time, evaluation device 1 uses a smoothing filter that utilizes the kernel value of the parameter set by the second condition of the evaluation method selected in step S01 to perform smoothing processing on the second image data of the object region.

[0114] Evaluation device 1 performs binarization processing (S06) on the third image data obtained through the smoothing process in step S05. At this time, evaluation device 1 adjusts the threshold for binarization of the parameters set by the second condition of the evaluation method selected in step S01, and performs binarization processing on pixels of the third image data that are white if their luminance values ​​are above the adjusted threshold and black if they are below the threshold.

[0115] Evaluation device 1 detects the blistering region based on the fourth image data obtained in the binarization process of step S06 (S07). As described above, in the image data, flat areas become brighter and convex areas become darker. Therefore, in the binarized image data, the original workpiece W portion becomes flat and white, while the blistering B portion, which is a convex portion, becomes black. Therefore, the black area detected from the binarized image data is taken as the blistering region.

[0116] The evaluation device 1 uses the bubbling regions detected in step S07 to calculate the area ratio of the bubbling regions in the object region being evaluated (S08). Specifically, the evaluation device 1 calculates the number of pixels in the bubbling regions relative to the number of pixels in the binarized image data of the modified object region. Furthermore, the evaluation device 1 stores the calculated area ratio as an evaluation result in the storage unit 12.

[0117] The evaluation device 1 outputs the result obtained in step S08 to the output unit 15 (S09).

[0118] For example, the coating being evaluated in evaluation device 1 can be evaluated for blistering after a corrosion resistance test has been performed to assess its corrosion resistance. Therefore, when evaluating a coating after a corrosion resistance test, the corrosion resistance test is performed before the evaluation process in evaluation device 1 begins. The conditions for the corrosion resistance test can be appropriately selected according to the coating and the evaluation purpose.

[0119] For example, the corrosion resistance test of a coating can be exemplified by the liner tester (trade name: Yamazaki LA-15 liner tester, manufactured by Yamazaki Seiki Research Institute Co., Ltd.) commonly used in the corrosion resistance test of resin liners. In the liner tester, a temperature gradient is applied to the inner and outer surfaces of the test piece to conduct an accelerated test. Specifically, for... Figure 9A The coating shown was applied to a SUS304 substrate with a fluoropolymer coating to achieve a film thickness of 300 μm. Water at 100°C was used as the ambient liquid on the inside of the test piece, while water at 30°C was circulated on the outside. The liquid phase was photographed after a 24-hour test.

[0120] In this way, image acquisition and image processing are performed using parameters for each evaluation method selected corresponding to the values ​​characterizing the surface of the coating. This allows for accurate calculation of the blister area ratio and quantitative evaluation of coating blistering.

[0121] <Effects and Supplements>

[0122] As described above, the embodiments have been illustrated as examples of the technology disclosed in this application. However, the technology of the present invention is not limited thereto, and can also be applied to embodiments with appropriate modifications, substitutions, additions, omissions, etc.

[0123] The evaluation methods, evaluation apparatus, and computer programs described in the full scope of this invention can be implemented using hardware resources, such as processors, memory, and program collaboration.

[0124] Industrial availability

[0125] The evaluation method, evaluation device, and computer program of the present invention can be used for quantitative evaluation of the surface condition of coatings.

[0126] This application claims priority based on Japanese Patent Application No. 2021-094610 filed on June 4, 2021, the entire contents of which are incorporated herein by reference.

[0127] Explanation of reference numerals in the attached figures

[0128] 1 Evaluation device

[0129] 11 Control Department

[0130] 111 Selection Department

[0131] 112 Acquisition Department

[0132] 113 Testing Department

[0133] 114 Computing Department

[0134] 115 Results Processing Department

[0135] 12 Storage Department

[0136] 121 Image Data

[0137] 122 Result Data

[0138] P Evaluation Procedure

[0139] 13 Ministry of Communications

[0140] 14 Input Section

[0141] 15 Output Section

[0142] 2. Camera device

[0143] 3. Coaxial lighting device

[0144] W: Workpiece (object with a coating).

Claims

1. An evaluation method for assessing blistering of a coating film formed on an object, characterized in that, include: (a) The step of acquiring image data of the surface of the coating film that has been illuminated by a coaxial illumination device using a camera device; (b) The step of performing at least binarization processing on the image data to detect areas of blistering on the surface of the coating; and (c) The step of determining the area ratio of the bubble region to the area of ​​the evaluation object in the image data. The first condition for acquiring the image data in (a) and the second condition for detecting the region of the bubbles in (b) are selected based on at least one of the following: the size of the bubbles present on the surface of the coating, the gloss of the coating, and the surface roughness of the coating. The first condition includes the shortest distance between the semi-transparent mirror of the coaxial illumination device and the effective field of view of the surface of the coating film, and the second condition includes the threshold setting method in the binarization process.

2. The evaluation method as described in claim 1, characterized in that, The (b) includes: (i) the step of trimming the image data to obtain trimmed image data corresponding to the region of the evaluation object; and (ii) The step of performing the binarization process after smoothing the modified image data, or performing the binarization process without smoothing the image data.

3. The evaluation method as described in claim 2, characterized in that, The second condition includes the kernel value used by the smoothing filter in the smoothing process.

4. The evaluation method according to any one of claims 1 to 3, characterized in that, Prior to (a), the method further includes a step of conducting a corrosion resistance test on the object having the coating.

5. An evaluation device for evaluating blistering of a coating film formed on an object, characterized in that, include: An acquisition unit that acquires image data of the surface of the coating film as obtained by a coaxial illumination device and a camera device; A detection unit that performs at least binarization processing on the image data to detect areas of bubbles present on the surface of the coating; and The calculation unit determines the area ratio of the bubbly region to the area of ​​the evaluation object in the image data. A first condition for acquiring the image data and a second condition for the detection unit to detect the area of ​​the bubble are selected based on at least one of the following: the size of the bubble present on the surface of the coating film, the gloss of the coating film, and the surface roughness of the coating film. The first condition includes the shortest distance between the semi-transparent mirror of the coaxial illumination device and the effective field of view of the surface of the coating film, and the second condition includes the threshold setting method in the binarization process.

6. A computer program product, characterized in that, Used to enable a computer to execute the evaluation method according to any one of claims 1 to 3.

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