Method, apparatus, and program for identifying the type of plating on automotive steel sheets.

Abstract

To provide a method, device, and program for identifying a type of plating of an automobile steel plate for identifying a type of plating applied onto a steel plate to be used for a vehicle body configuration component of an automobile body.SOLUTION: A method for identifying a type of plating of an automobile body is a method for identifying a type of plating applied onto a steel plate to be used for a vehicle body configuration component of the automobile body. The method comprises: a steel plate cross-section gray scale image acquisition process S1 of sampling a steel plate from the vehicle body configuration component so as to manufacture a test piece including the steel plate, and imaging an optical microscopic photograph of the steel plate cross-section in the manufactured test piece so as to acquire a gray scale image; a gray scale gradation histogram generation process S3 of generating a histogram of a frequency of a gray scale gradation in the acquired gray scale image; and a plating type identification process S5 of identifying a type of plating applied onto the steel plate based on a distribution form of the generated histogram.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method, apparatus, and program for identifying the type of plating applied to steel sheets used in automotive body components. [Background technology]

[0002] The various types of steel sheets used as materials for body components that make up automobile bodies have continued to change over time, with progress being made towards higher strength and thinner, lighter materials. In particular, due to the demands for stricter automobile collision safety standards, improved performance requirements, improved fuel efficiency, and reduced CO2 emissions, it is necessary to achieve the conflicting performances of increasing the structural strength of the vehicle body and reducing its mass, and the use of high-strength steel sheets in vehicle body components is continuously increasing.

[0003] Automobile body components are often manufactured by cold press forming of steel sheets, which offers high production efficiency and allows for mass production and low-cost manufacturing. However, as steel sheets become stronger, their press formability decreases, making them more susceptible to defects such as cracking (fracture), wrinkling, and poor shape and dimensional accuracy during press forming. Therefore, it is necessary to incorporate technical improvements into the press forming method and the shape and mold design of the body components from the design stage of the automobile body to avoid defects during press forming.

[0004] However, reducing the thickness of body components to lighten the vehicle body reduces the rigidity of the body components or the overall rigidity of the vehicle body. Therefore, it is not possible to uniformly increase the strength and thin down all body components. Which parts of the vehicle body to strengthen or thin down, and how to design the optimal shape of the body components according to the strength of the steel plates, are determined by the experience of the engineers who design the vehicle body and the accumulated technology cultivated by the vehicle manufacturer. For this reason, it is common practice to study and absorb the ingenuity, innovative technologies, new material usage methods, and manufacturing methods found in the design of vehicle bodies manufactured by each manufacturer by researching vehicle bodies that are currently on the market.

[0005] In such investigations, the automobile body is disassembled at the joints between its components, separating them into individual components. A portion of the steel sheet material from these components is then sampled, and its properties are identified and compared through microscopic observation of its hardness, thickness, and microstructure. For example, to identify the properties of the steel sheet, a sample of steel sheet taken from a component is embedded in a resin mold to create a test specimen. The cross-section of the embedded steel sheet is then mirror-polished, and its thickness and hardness are measured using the micro-Vickers method. Furthermore, the tensile strength (TS) of the steel sheet is calculated using a conversion formula based on the measured hardness. By comparing these values ​​with manufacturing specifications, it becomes possible to identify the properties (specifications, etc.) of the steel sheet used in the automobile body components.

[0006] A single automobile body consists of 300 to 500 components, and the materials used for each component are recorded in a database and stored in a materials database. This materials database is then used as the basis for various analyses and investigations of the automobile body components. Regarding the creation of a database for each vehicle, for example, Patent Document 1 discloses a technology for disassembling a vehicle that is being sold and saving data such as the material of the parts used in that vehicle (strength, plate thickness, weight, dimensions, presence or absence of surface treatment, plating thickness, type of base material (hot-rolled or cold-rolled)), the number of spot welds, and images of the parts. This technology classifies parts based on group names assigned to parts classified according to a unified standard across multiple vehicles from different manufacturers or models, and part function names assigned based on the function of the parts, also unified across multiple vehicles. This makes it possible to quickly extract parts to be compared and easily perform comparisons, even across multiple vehicles from different manufacturers or models. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Patent No. 5151380 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] Automobile body components utilize either untreated steel sheets (bare material, JIS standard SPC, SPH, etc.) or surface-treated steel sheets (Japan Iron and Steel Federation standard SEC, SGC, etc.). The plating applied to surface-treated steel sheets includes zinc plating systems such as hot-dip galvanizing (GI), alloyed hot-dip galvanizing (GA), and electro-galvanizing (EG), as well as aluminum plating systems such as aluminum-silicon plating (Al-Si), which is a hot-dip aluminum-based plating. These surface-treated steel sheets are selected as appropriate according to the required specifications such as rust prevention, press formability, and paint appearance, as well as the adoption policies of each part or vehicle manufacturer. In Japan, most vehicle manufacturers use GA for cold-pressed parts and Al-Si for hot-pressed parts, while overseas vehicle manufacturers often use GI or EG for cold-pressed parts and Al-Si for hot-pressed parts.

[0009] As with the technology disclosed in Patent Document 1, in creating a database for each vehicle, it is conceivable to store the type of plating on surface-treated steel sheets as data related to the material of the parts. To do this, it is necessary to take a sample of steel sheet for each vehicle body component and identify the type of plating applied to that steel sheet.

[0010] To identify the type of plating applied to a steel sheet, methods such as quantitative analysis of the components in the plating layer by wet analysis or identification of the elemental distribution in the plating layer by spectroscopic analysis are used. One method for wet analysis of the plating layer is to strip and dissolve the plating layer of the steel sheet using an alkaline solution or the like, and then analyze it. Furthermore, methods for spectroscopic analysis of the plating layer include X-ray spectroscopy such as XPS (X-ray photoelectron spectroscopy) and EPMA (electron probe microanalyzer), or sputtering spectroscopy such as GDS (glow discharge emission spectroscopy).

[0011] Spectroscopic analysis, a method for identifying the elemental distribution of a plating layer, has the advantage of being able to identify the type of plating on a steel sheet non-destructively. On the other hand, spectroscopic analysis has disadvantages, such as high measurement costs and the time-consuming preparation of analytical samples because measurements are taken in a vacuum. In particular, since an automobile body typically has 300 to 500 components, identifying the type of plating on the steel sheets used in these components using the above-mentioned wet analysis or spectroscopic analysis would be extremely costly and time-consuming.

[0012] The present invention was made to solve the above-mentioned problems, and aims to provide a method, apparatus, and program for identifying the type of plating applied to steel sheets of automotive body components that can identify the type of plating applied to the steel sheets of automotive body components without requiring enormous measurement costs or effort. [Means for solving the problem]

[0013] (1) The present invention relates to a method for identifying the type of plating applied to a steel sheet used in a vehicle body component of an automobile, A steel plate cross-section grayscale image acquisition step involves taking a steel plate from the aforementioned vehicle body component to prepare a test specimen containing the steel plate, taking an optical microscope image of the cross-section of the steel plate in the prepared test specimen, and acquiring a grayscale image of the acquired optical microscope image, A grayscale tone histogram creation step, which creates a histogram of the frequency of grayscale tones for each pixel in the acquired grayscale image, The present invention is characterized by including a plating type identification step, which identifies the type of plating applied to the steel plate based on the distribution pattern of the created histogram.

[0014] (2) In the items described in (1) above, The grayscale image of the cross-section of the steel plate includes a resin layer showing the resin that embeds and fixes the steel plate in the test specimen and / or the resin painted on the surface of the steel plate. The plating applied to the steel sheet is any one of hot-dip galvanizing, alloyed hot-dip galvanizing, electro-galvanizing, and hot-dip aluminum-based plating, In the plating type identification step, Select three peaks with large peak areas in the distribution of the histogram, The three selected peaks are used as the first peak, the second peak, and the third peak representing the following regions a, region b, and region c in ascending order of the peak gradation value, In the distribution of the histogram, determine the presence or absence of a fourth peak representing the following region d with a relatively small peak area between the first peak and the second peak, When it is determined that the fourth peak exists, If the relative position of the fourth peak is closer to the first peak than to the second peak, it is identified that the plating applied to the steel sheet is electro-galvanizing, If the relative position of the fourth peak is closer to the second peak than to the first peak, it is identified that the plating applied to the steel sheet is hot-dip galvanizing, When it is determined that the fourth peak does not exist, If the second peak and the third peak overlap, it is identified that the plating applied to the steel sheet is hot-dip aluminum-based plating, If the second peak and the third peak are separated, it is identified that the plating applied to the steel sheet is alloyed hot-dip galvanizing, which is characterized by this. Region a: Region mainly composed of the gray scale gradation of cracks in the resin layer and the plating layer Region b: Region mainly composed of the gray scale gradation of the base steel sheet Region c: Region mainly composed of the gray scale gradation of the plating layer Region d: Region mainly composed of the gray scale gradation of the boundary between the plating layer and the base steel sheet

[0015] (3) In the case of the above (2), In the plating type identification step, The distribution of the histogram is divided into multiple regions using grayscale tones where the frequency of the histogram is minimized, thereby dividing the distribution of the histogram into peaks. The distribution of the histograms separated for each peak is approximated by a Gaussian function represented by the following equation (1): The coefficient a in the approximated equation (1) i and c i By calculating the product of these values ​​as the peak area of ​​the peaks in each divided histogram, the three peaks with the largest peak areas are selected. The presence or absence of the fourth peak is determined by the presence or absence of a histogram divided by each peak between the first peak and the second peak. The relative positional relationship between the fourth peak and the first and second peaks is given by the coefficient b in equation (1). i Determined by, The relative positional relationship between the second and third peaks is given by the coefficient b in equation (1). i and c i This invention is characterized by its ability to distinguish between the two.

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[0016] (4) The plating type identification device for automotive steel sheets according to the present invention identifies the type of plating applied to steel sheets used in the body components of an automobile body, A grayscale image acquisition unit for steel plate cross-sections acquires a grayscale image of an optical microscope photograph of a cross-section of a steel plate in a test specimen including a steel plate taken from the aforementioned vehicle body component. A grayscale tone histogram creation unit creates a histogram of the frequency of grayscale tones for each pixel in the acquired grayscale image, The present invention is characterized by comprising a plating type identification unit that identifies the type of plating applied to the steel plate based on the distribution pattern of the created histogram.

[0017] (5) The automotive steel sheet plating type identification program according to the present invention identifies the type of plating applied to a steel sheet used in a vehicle body component of an automobile body, Computers, A grayscale image acquisition unit for steel plate cross-sections acquires a grayscale image of an optical microscope photograph of a cross-section of a steel plate in a test specimen including a steel plate taken from the aforementioned vehicle body component. A grayscale tone histogram creation unit creates a histogram of the frequency of grayscale tones for each pixel in the acquired grayscale image, The present invention is characterized by functioning as a plating type identification unit that identifies the type of plating applied to the steel plate based on the distribution pattern of the created histogram. [Effects of the Invention]

[0018] According to the present invention, it is possible to identify the type of plating by taking a steel plate from a vehicle body component and using a grayscale image of an optical microscope photograph of the cross-section of the steel plate. This makes it possible to efficiently collect material information for each vehicle body component and build a material database without having to perform instrumental analysis, which requires enormous measurement costs and effort, to identify the type of plating applied to the steel plate. [Brief explanation of the drawing]

[0019] [Figure 1] This is a flowchart showing the processing flow in a method for identifying the type of plating on automotive steel sheets according to an embodiment of the present invention. [Figure 2] This diagram illustrates the process leading to the present invention and shows items representing the physical and chemical properties of the plating layer in an optical microscope image of a cross-section of a plated steel sheet. [Figure 3]This figure illustrates the relationship between the distribution of the grayscale gradation histogram created from grayscale digital image data of an optical microscope photograph of a steel plate cross-section and the type of plating applied to the steel plate (dashed line: distribution approximated by a Gaussian function for each peak, solid line: distribution of the histogram with peaks approximated by a Gaussian function superimposed for each region). [Figure 4] This figure illustrates the configuration of a plating type identification device and program for automotive steel sheets according to an embodiment of the present invention. [Figure 5] This diagram illustrates the flow of identifying the type of plating applied to a steel plate taken from a vehicle body component by elemental analysis in the example. [Figure 6] In the examples, the distribution of grayscale histograms created using the plating type identification method according to the present invention is shown for steel sheets whose plating type was determined by conventional instrumental analysis ((a) hot-dip galvanizing, (b) electro-galvanizing, (c) alloyed hot-dip galvanizing, (d) aluminum silicon plating, which is a hot-dip aluminum-based plating). [Modes for carrying out the invention]

[0020] Before describing the method, apparatus, and program for identifying the type of plating on automotive steel sheets according to embodiments of the present invention, the background leading to the present invention will be stated.

[0021] <Background leading to the invention> In order to identify the material (strength and specifications) of steel plates used in vehicle body components, it is essential to take steel plates from vehicle body components and embed the taken steel plates in resin to create test specimens. This is an essential process for benchmarking vehicle body components and building a materials database. In identifying the material of steel plates using such test specimens, optical microscope images of the portion of the test specimen including the cross-section of the steel plate (hereinafter referred to as "steel plate cross-section" in this application) were taken, and the thickness of the steel plate was measured, the presence or absence of a plating layer was observed, and the thickness of the plating layer was measured, etc.

[0022] Therefore, the inventor believed that being able to identify the type of plating from an optical microscope image of a steel plate cross-section would be advantageous in terms of both time and cost. Furthermore, in order to identify the type of plating from an optical microscope image of a steel plate cross-section, it was considered important to clarify the relationship between the items representing the physical and chemical properties of the plating layer in the optical microscope image and the type of plating.

[0023] Therefore, the inventors diligently examined the items that represent the characteristics of the plating layer in optical microscope images of the cross-section of the steel plate, and as a result, identified the following four items (1) to (4). Below, each of items (1) to (4) will be explained in correspondence with the optical microscope image of the cross-section of the steel plate shown in Figure 2.

[0024] (1) Morphological characteristics of the plated layer Morphological features of a plated layer include multiple crystal compositions and eutectic structures found in heterogeneous plated layers, as well as cracks that occur in the plated layer due to press forming processes.

[0025] The heterogeneous plating layer is a characteristic morphology of the eutectic structure of aluminum-silicon plating (Al-Si, Figure 2(d)), which is a molten aluminum-based plating applied after hot press forming, and cracks and voids are observed. Furthermore, the cracked plating layer is a common morphology in alloyed hot-dip galvanizing (GA, Figure 2(a)), which results from a decrease in the ductility of the Zn-Fe alloy crystals formed by the alloying treatment.

[0026] In contrast, in the plating layers of steel sheets that have been hot-dip galvanized (GI) and electro-galvanized (EG), as shown in Figures 2(b) and (c), no eutectic structure or crack initiation is observed in the plating layer, indicating that the morphology of the plating layer is homogeneous.

[0027] (2) Contrast change at the boundary between the base steel plate and the plating layer In optical microscope images of steel plate cross-sections, some exhibit a change in contrast (a change in grayscale gradation in grayscale images) at the boundary between the base steel plate and the plating layer. This change in contrast is presumed to be due to the presence or absence of a continuous component diffusion layer between the base steel plate and the plating layer.

[0028] Since alloyed hot-dip galvanizing (GA) has a continuous diffusion layer of Fe-Al-Zn, as shown in Figure 2(a), although there is a difference in contrast (grayscale gradation) between the base steel sheet and the plated layer, the boundary between the plated layer and the base steel sheet is indistinct. In contrast, hot-dip galvanizing (GI) and electro-galvanizing (EG) do not have a component diffusion layer, and therefore, as shown in Figures 2(b) and (c), they are characterized by a clear contrast change at the boundary between the base steel sheet and the plating layer. Furthermore, in the case of aluminum-silicon plating (Al-Si), which is a molten aluminum-based plating, the color tone (grayscale gradation) of the base steel plate and the plating layer are almost the same, as shown in Figure 2(d), so the change in contrast at the boundary is not clear.

[0029] (3) Adaptability to the shape of the underlying steel plate The ability to conform to the shape of the underlying steel plate refers to the influence of the surface shape (irregularities) of the underlying steel plate on the irregularities of the surface of the plating layer. Hot-dip galvanizing (GI) is a process that involves inserting a base steel plate into molten zinc and then pulling it out. As shown in Figure 2(b), the surface tension of the liquid molten zinc before solidification makes the surface of the plated layer smooth, and it is less affected by the surface shape of the base steel plate. In contrast, with electrogalvanizing (EG), electrodeposition occurs along the irregularities of the underlying steel sheet, resulting in a nearly constant plating layer thickness, as shown in Figure 2(c). This means that the surface shape of the underlying steel sheet is reflected in the surface irregularities of the plating layer.

[0030] (4) Combinations of roughness and shape of the outermost layer As shown in Figure 2(c), electro-galvanized zinc (EG) has a plating layer whose surface is covered with step-shaped (staircase-shaped) Zn electrodeposited crystals. Therefore, when observing a cross-section of an electro-galvanized steel sheet, a characteristic shape of a step-shaped outermost layer can be seen.

[0031] In this study, the inventor focused on item (2) (contrast change at the boundary between the base steel sheet and the plating layer), noting that differences in plating types resulted in characteristic differences in the contrast change of grayscale images of optical microscope photographs of the cross-section of the steel sheet. The inventor then conceived the idea of ​​evaluating the contrast change of the grayscale image as a difference in the distribution pattern of the histogram of the occurrence frequency of grayscale tones.

[0032] Figure 3 shows histograms created by accumulating the frequency of occurrence of each grayscale tone in the grayscale images of optical microscope photographs of cross-sections of steel plates with various plating processes.

[0033] In Figure 3, (a) is a histogram of grayscale tones created from grayscale images of steel sheet cross-sections coated with alloy hot-dip galvanizing (GA), (b) is hot-dip galvanizing (GI), (c) is electro-galvanizing (EG), and (d) is aluminum-silicon plating (Al-Si), which is a hot-dip aluminum-based plating.

[0034] In the histograms for alloyed hot-dip galvanizing (GA), hot-dip galvanizing (GI), and electro-galvanizing (EG), as shown in Figures 3(a) to (c), the plated layer has the highest grayscale gradation (white), while the resin has the lowest grayscale gradation (black). Furthermore, the grayscale gradation of the underlying steel sheet is intermediate between that of the plated layer and the resin.

[0035] Furthermore, the histograms for hot-dip galvanizing (GI) and electro-galvanizing (EG) show, as illustrated in Figures 3(b) and 3(c), that there is a boundary between the plating layer and the underlying steel sheet (A in Figure 3(b) and B in Figure 3(c)). However, in hot-dip galvanizing (GI), the boundary between the plating layer and the underlying steel sheet has a grayscale tone similar to that of the underlying steel sheet, as shown in Figure 3(b). In contrast, in electro-galvanizing (EG), the boundary between the plating layer and the underlying steel sheet has a grayscale tone similar to that of resin, as shown in Figure 3(c).

[0036] As shown in Figure 2(a) above, in alloyed zinc hot-dip galvanizing (GA), there is no clear boundary between the galvanized layer and the underlying steel sheet. Therefore, in the histogram of alloyed zinc hot-dip galvanizing (GA), as shown in Figure 3(a), there is no small peak between the grayscale peak representing the galvanized layer and the grayscale peak representing the underlying steel sheet.

[0037] In aluminum-silicon plating (Al-Si), a type of molten aluminum plating, the grayscale gradation of the base steel sheet and the plating layer are almost identical, as shown in Figure 2(d), making it difficult to distinguish the boundary between the two. Therefore, the histogram of aluminum-silicon plating shows a distribution in which the grayscale gradations representing the plating layer and the base steel sheet overlap, as shown in Figure 3(d). However, in the grayscale gradation representing the plating layer, there is a characteristic feature of having two peaks with different grayscale gradations (E and O in Figure 3(d)).

[0038] Furthermore, when optical microscope images taken at the same magnification were made identical in field of view and size, and the contrast was adjusted so that the grayscale tones of the resin and plating layers were at opposite extremes, it was found that the histogram of grayscale tones exhibited a characteristic distribution pattern depending on the type of plating.

[0039] This invention is based on the above findings, and its specific configuration will be described below.

[0040] <Method for identifying plating type> The plating type identification method for automotive steel sheets according to this embodiment (hereinafter referred to as the "plating type identification method") identifies the type of plating applied to steel sheets used in the body components of an automobile body. As shown in Figure 1, the plating type identification method according to this embodiment includes a steel sheet cross-section grayscale image acquisition step S1, a grayscale gradation histogram creation step S3, and a plating type identification step S5. The following describes each of the above processes assuming that the plating applied to the steel plate is one of the following: hot-dip galvanizing, alloyed hot-dip galvanizing, electro-galvanizing, or aluminum-silicon plating (a hot-dip aluminum-based plating).

[0041] ≪Process for acquiring grayscale images of steel plate cross-sections≫ Step S1, which involves acquiring a grayscale image of the cross-section of a steel plate, involves taking a steel plate from a vehicle body component to create a test specimen containing the steel plate, taking an optical microscope image of the cross-section of the steel plate in the created test specimen, and acquiring a grayscale image of the captured optical microscope image.

[0042] The test specimens can be prepared by embedding and fixing a collected steel plate in a resin suitable for observation under an optical microscope. As the resin, for example, a heat-curing epoxy resin or a room-temperature curing carbon-added acrylic resin can be used; the type of resin is not particularly limited as long as it is black after curing.

[0043] After the resin in which the steel plate is embedded has hardened, the test specimen should be mirror-polished on the cross-section of the steel plate before being observed under an optical microscope. It is advisable not to apply etching, a common procedure for observing the steel plate structure, to the test specimens of this invention, as it may affect the plating layer applied to the steel plate.

[0044] ≪Grayscale Histogram Creation Process≫ The grayscale tone histogram creation step S3 is a step in which a histogram of the frequency of grayscale tones for each pixel in the grayscale image acquired in the steel plate cross-section grayscale image acquisition step S1 is created.

[0045] <<Plating type identification process S5>> The plating type identification step S5 is a step in which the type of plating applied to the steel plate is identified based on the distribution pattern of the histogram created in the grayscale gradation histogram creation step S3.

[0046] Specific methods for identifying the type of plating in the plating type identification process S5 include the following methods A and B.

[0047] (Aspect A) In embodiment A, first, three peaks with large peak areas are selected from the histogram distribution. A histogram peak is a bell-shaped distribution curve containing a single local maximum, and the peak area is the area enclosed by that bell-shaped distribution curve. The selection of the three peaks with large peak areas can be done, for example, by visual inspection by a worker, or by calculating the area enclosed by the bell-shaped distribution curves of each peak in the histogram distribution.

[0048] Next, the three selected peaks are designated as the first, second, and third peaks, respectively, in ascending order of peak grayscale values, representing regions a, b, and c. Here, peak grayscale value refers to the grayscale value at the center of each peak. Area a: Area mainly consisting of grayscale gradations of cracks and voids in resin and plating layers. Area b: Area mainly consisting of grayscale gradations of the underlying steel plate. Area c: Area mainly consisting of grayscale gradations of the plating layer

[0049] Next, we determine whether there is a fourth peak in the histogram distribution that represents the region d below, where the peak area is relatively small between the first and second peaks. Area d: An area mainly consisting of grayscale gradations at the boundary between the plated layer and the underlying steel sheet.

[0050] (A-1) If a fourth peak is determined to exist In this case, the plating applied to the steel sheet is identified as hot-dip galvanizing or electro-galvanizing based on the relative positions of the fourth peak and the first or second peak. Furthermore, if the relative position of the fourth peak is closer to the first peak than to the second peak, the plating applied to the steel sheet is identified as electro-galvanized. In contrast, if the relative position of the fourth peak is closer to the second peak than to the first peak, the plating applied to the steel sheet is identified as hot-dip galvanizing.

[0051] Thus, the ability to identify the type of plating based on the relative positions of the fourth peak and the first or second peak is due to the aforementioned plating type identification item "(2) Contrast change at the boundary between the base steel sheet and the plating layer". In other words, as shown in Figure 3(c), the fact that the boundary between the plating layer and the underlying steel sheet in electro-galvanized plating is a grayscale gradation similar to that of resin means that the relative position of the fourth peak is close to that of the first peak. In contrast, as shown in Figure 3(b), the boundary between the galvanized layer and the underlying steel sheet in hot-dip galvanizing (GI) has a grayscale tone similar to that of the underlying steel sheet, which means that the relative position of the fourth peak is close to that of the second peak.

[0052] Furthermore, the absolute value of the difference in peak grayscale values ​​between each peak can be used as the relative position between the fourth peak and the first or second peak. If the absolute difference between the peak tone value of the fourth peak and the peak tone value of the first peak is smaller than the absolute difference between the peak tone values ​​of the fourth peak and the second peak, it indicates that the relative position of the fourth peak is closer to the first peak than to the second peak. Conversely, if the absolute value of the difference between the peak tone value of the fourth peak and the peak tone value of the first peak is greater than the absolute value of the difference between the peak tone values ​​of the fourth peak and the second peak, it indicates that the relative position of the fourth peak is closer to the second peak than to the first peak.

[0053] (A-2) If it is determined that there is no fourth peak In this case, based on the relative positions of the second and third peaks, the plating applied to the steel plate is identified as alloyed hot-dip galvanizing or aluminum-silicon plating, which is a hot-dip aluminum-based plating. As the relative position between the second and third peaks, the absolute value of the difference in peak grayscale values ​​of each peak can be used, similar to the relative position between the fourth peak and the first or second peak described above.

[0054] When the absolute value of the difference between the peak grayscale value of the second peak and the peak grayscale value of the third peak is small, it indicates that the second and third peaks are close together and overlapping, thus identifying the plating applied to the steel sheet as a molten aluminum-based plating, specifically aluminum-silicon plating (Al-Si). This is because, in aluminum-silicon plating (Al-Si), the grayscale grayscale of the base steel sheet and the plating layer are almost equal, and the distribution of grayscale grayscale representing the plating layer and the distribution of grayscale grayscale representing the base steel sheet overlap (Figure 3(d)).

[0055] In contrast, if the absolute value of the difference between the peak grayscale value of the second peak and the peak grayscale value of the third peak is large, the second and third peaks are far apart and separated, so the plating applied to the steel sheet is identified as alloyed hot-dip galvanizing (GA). This is because in alloyed hot-dip galvanizing (GA), a difference in contrast (grayscale grayscale) is observed between the underlying steel sheet and the plating layer (Figure 3(a)).

[0056] [Pattern B] In embodiment B, first, the histogram distribution is divided into multiple regions using grayscale tones where the frequency of histograms is minimal, thereby dividing the histogram distribution into peaks. Then, the distribution of the histograms, divided by peak, is approximated by a Gaussian function represented by the following equation (1).

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[0057] Next, the product of the coefficients a i and c i in the approximated formula (1) is calculated as the peak area of peak i in the divided histogram. Thus, by calculating the peak areas for all the peaks in the histogram, three peaks with large peak areas are selected. Then, in the same manner as in the above-described aspect A, for the three selected peaks, they are set as the first peak, the second peak, and the third peak in ascending order of the peak gradation values. Furthermore, the presence or absence of a fourth peak with a small peak area between the first peak and the second peak is determined.

[0058] (B-1) When it is determined that the fourth peak is present In this case, in the same manner as in the above-described aspect A, based on the relative positions of the fourth peak and the first peak or the second peak, it is discriminated that the plating applied to the steel sheet is hot-dip galvanizing or electro-galvanizing. Here, the coefficient b i in the above-described formula (1) represents the center position of the peak. Therefore, the relative positional relationship between the fourth peak and the first peak and the second peak can be discriminated by the coefficient b i as shown in the following formulas (2-1) and (2-2).

Equation

[0059] When formula (2-1) is satisfied, since the relative position of the fourth peak is closer to the first peak than the second peak, it can be discriminated that the type of plating is electro-galvanizing (EG) (see Fig. 3(c)).

[0060] When formula (2-2) is satisfied, since the relative position of the fourth peak is closer to the second peak than the first peak, it can be discriminated that the type of plating is hot-dip galvanizing (GI) (see Fig. 3(b)).

[0061] (B-2) If it is determined that there is no fourth peak In this case, based on the relative positions of the second and third peaks, the plating applied to the steel plate is identified as alloyed hot-dip galvanizing or aluminum-silicon plating, which is a hot-dip aluminum-based plating. Here, the relative positional relationship between the second and third peaks is given by the coefficient b in equation (1), as shown in equation (3) below. i and c i This can be used to distinguish between them.

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[0062] Equation (3) represents the peak separation between the second and third peaks. If equation (3) is satisfied, that is, if the peak separation between the second and third peaks is low, it indicates that the second and third peaks are close together and overlapping, and therefore the type of plating is identified as molten aluminum-based plating, specifically aluminum-silicon plating (Al-Si) (see Figure 3(d)). In contrast, if equation (3) is not satisfied, that is, if the peak separation between the second and third peaks is high, it indicates that the second and third peaks are far apart and separated, and therefore the type of plating is identified as alloyed hot-dip galvanizing (GA) (see Figure 3(d)).

[0063] In equation (3), the coefficient on the right-hand side (=1.1774) is a value given as an example of an indicator of peak separation, and can be appropriately given depending on the contrast of the grayscale image.

[0064] As described above, according to the method for identifying the type of plating on automotive steel sheets according to this embodiment, the type of plating applied to a steel sheet can be identified by taking a steel sheet from a vehicle body component and using a grayscale image of an optical microscope photograph of the cross-section of the steel sheet. This makes it possible to collect material information for each vehicle body component that makes up an automobile body and to efficiently build a material database without having to perform instrumental analysis which requires enormous measurement costs and effort to identify the type of plating applied to a steel sheet.

[0065] <Plating type identification device> The plating type identification device 1 for automotive steel sheets according to an embodiment of the present invention (hereinafter referred to as "plating type identification device 1") identifies the type of plating applied to steel sheets used in the body components of an automobile body. As an example, as shown in Figure 4, the plating type identification device 1 comprises a steel sheet cross-section grayscale image acquisition unit 11, a grayscale gradation histogram creation unit 13, and a plating type identification unit 15. The plating type identification device 1 may be composed of a CPU (Central Processing Unit) of a computer (PC, etc.). In this case, each of the above parts functions when the computer's CPU executes a predetermined program.

[0066] ≪Grayscale image acquisition unit for steel plate cross-section≫ The steel plate cross-section grayscale image acquisition unit 11 acquires a grayscale image of an optical microscope photograph of a steel plate cross-section taken from a test specimen, including a steel plate taken from a vehicle body component.

[0067] ≪Grayscale Histogram Creation Section≫ The grayscale tone histogram creation unit 13 creates a histogram of the frequency of grayscale tones for each pixel in the grayscale image acquired by the steel plate cross-section grayscale image acquisition unit 11.

[0068] <<Plating type identification section>> The plating type identification unit 15 identifies the type of plating applied to the steel plate based on the distribution pattern of the histogram created by the grayscale gradation histogram creation unit 13.

[0069] Specifically, the plating type identification unit 15 should identify the plating type using a procedure similar to that of aspect B of the plating type identification step S5 of the plating type identification method according to the embodiment described above.

[0070] First, the histogram distribution is divided into multiple regions using grayscale tones where the frequency of histograms is minimal, thereby separating the histogram distribution by peak. The distribution of the histograms separated by peaks is then approximated by a Gaussian function represented by equation (1) mentioned above.

[0071] Next, the coefficient a in equation (1) approximates the distribution of the histogram divided by peak. i and c i Three peaks with the largest product (=peak area) are selected, and these three selected peaks are designated as the first peak, second peak, and third peak in order of increasing peak grayscale value.

[0072] Next, the presence or absence of a fourth peak is determined by checking the distribution of histograms separated by each peak between the first and second peaks.

[0073] If a fourth peak is determined to be present, the plating applied to the steel sheet is identified as hot-dip galvanizing or electro-galvanizing based on the relative position of the fourth peak with the first or second peak. The relative positional relationship between the fourth peak with the first peak and the second peak is determined by the aforementioned equations (2-1) and (2-1).

[0074] If it is determined that there is no fourth peak, the plating applied to the steel plate is identified as alloyed hot-dip galvanizing or aluminum-silicon plating, which is a hot-dip aluminum-based plating, based on the relative positions of the second and third peaks. The relative positional relationship between the second and third peaks is determined by equation (3) described above.

[0075] <Plating type identification program> Embodiments of the present invention can be configured as a plating type identification program for automotive steel sheets (hereinafter referred to as the "plating type identification program"). In other words, the plating type identification program according to this embodiment identifies the type of plating applied to steel plates used in the body components of an automobile body. The plating type identification program has the function of causing a computer to operate as a steel plate cross-section grayscale image acquisition unit 11, a grayscale gradation histogram creation unit 13, and a plating type identification unit 15, as shown in Figure 4 as an example.

[0076] As described above, the plating type identification device and program for automotive steel sheets according to this embodiment can also identify the type of plating using a grayscale image of an optical microscope photograph of a cross-section of a steel sheet taken from a vehicle body component. This makes it possible to collect material information for each vehicle body component that makes up an automobile body and to efficiently build a material database without performing instrumental analysis which requires enormous measurement costs and effort.

[0077] Furthermore, in the grayscale image of the cross-section of the steel plate underlayment, if there are differences in the grayscale tones of the resin, the underlying steel plate, and the plating layer, three distinct peaks representing the resin, the underlying steel plate, and the plating layer can be clearly seen in the histogram of the frequency of grayscale tones in the grayscale image.

[0078] However, depending on the contrast of the grayscale image, it may be possible to obtain a histogram distribution in which the peak representing the base steel sheet and the peak representing the plating layer overlap. In such cases, in order to select the peaks representing the base steel sheet and the plating layer from the overlapping peaks, the two peaks can be selected by approximating them as two superpositions of the Gaussian function described above.

[0079] Furthermore, in this embodiment, the type of plating applied to the steel sheet used for the body components of an automobile was assumed to be one of the following: hot-dip galvanizing, electro-galvanizing, alloyed hot-dip galvanizing, or aluminum-silicon plating, which is a hot-dip aluminum-based plating. This is because commercially available automobile body components are almost always made of steel sheets with the above four types of plating applied. Therefore, in identifying the type of plating for automobile steel sheets according to the present invention, there is no particular problem even if it is assumed to be one of the above four types of plating.

[0080] However, even if steel plates with plating other than the four types described above are used in vehicle body components, it has been confirmed that the distribution pattern of grayscale tones in the cross-section of such plated steel plates differs from that of steel plates with the four types described above. Therefore, according to the present invention, it is possible to identify plating types other than the four types described above based on the distribution pattern of the grayscale tones histogram created from grayscale images of optical microscope photographs of the cross-section of the steel plate.

[0081] Furthermore, in the above description, the optical microscope image of the steel plate cross-section included the resin used to embed and fix the steel plate during the preparation of the test specimen. However, in the present invention, the resin may also include the resin painted on the surface of the steel plate. Even in such cases, since the resin is black, this does not hinder the identification of the type of plating applied to the steel plate according to the present invention.

[0082] In this invention, a digital camera such as a CCD camera can be suitably used to capture optical microscope images of the cross-section of a steel plate. This allows the captured optical microscope image to be obtained as digital data of a grayscale image.

[0083] However, the present invention may also involve acquiring an optical microscope image of a steel plate cross-section captured using a digital camera as digital data of a color image, and then converting it to digital data of a grayscale image.

[0084] The grayscale gradation of a grayscale image only needs to be 8 bits (256 gradations) or higher, and high-resolution grayscale digital data such as 16 bits (65,536 gradations) is also acceptable. If the grayscale gradation is less than 8 bits, there is a risk that it may not be possible to distinguish, for example, aluminum silicon plating, which is a molten aluminum-based plating after hot press forming.

[0085] Furthermore, for optical microscope images of steel plate cross-sections captured by a digital camera, a magnification of around 1000 to 2000 times is preferable. This is because, although higher magnification improves resolution, it reduces the imaged area (field of view), raising concerns that it may not provide average information about the entire plating layer on the steel plate cross-section.

[0086] Furthermore, while the resolution of the digital camera is important, along with the magnification of the optical microscope image, there are no particular limitations as long as it is suitable for the magnification of the optical microscope image. Digital cameras with approximately 1.0M to 10.0M pixels can be suitably used.

[0087] In this invention, in order to accurately identify the type of plating using a grayscale image of an optical microscope photograph including a cross-section of a steel plate, it is preferable to uniformly adjust the contrast of the grayscale image. Typically, in a grayscale image of an optical microscope photograph taken of a plated steel plate embedded in black resin, the area corresponding to the resin is the darkest (black), the plating layer is the brightest (white), and the underlying steel plate is intermediate (gray). However, unlike other platings, aluminum silicon plating, which is a molten aluminum-based plating, has an intermediate color in the grayscale gradation of the plating layer that is not significantly different from that of the underlying steel plate. Considering this, it is preferable to adjust the contrast of the grayscale image based on the black part of the resin and the intermediate color part of the underlying steel plate.

[0088] When adjusting the contrast of a grayscale image, it is desirable to set the black areas, which correspond to resin, to 5-10% and the intermediate color areas of the underlying steel plate to 50%, assuming a full grayscale scale of 100% (0% = black, 100% = white).

[0089] The reason for not setting the grayscale gradation of the black areas corresponding to resin to 0% is to prevent excessive contrast adjustment, which can cause so-called "black clipping" and reduce the accuracy of identifying the type of plating. Furthermore, by adjusting the contrast as described above, the degree of magnification of intermediate colors and black areas is less likely to be excessive, so "white clipping" in white areas can also be suppressed at the same time.

[0090] Based on the above, in the present invention, it is preferable to adjust the contrast so that the distribution of the histogram of the grayscale image falls within 3% to 97% of the full scale when taking an optical microscope image of the cross-section of the steel plate. [Examples]

[0091] We conducted experiments to confirm the effects of the present invention, and these will be described below.

[0092] In the experiment, five passenger cars were procured, disassembled, and their body components were examined. For 200 major components weighing 100g or more per vehicle, which constitute the white body structure of the automobile, steel plates were taken and embedded in epoxy resin to create test specimens. After polishing the cross-sections of the embedded steel plates, optical microscope images of the steel plate cross-sections were taken using a 4-megapixel CCD camera with an optical magnification of 1500x.

[0093] Next, optical microscope images were acquired as digital data (8-bit, 256 gradations) of grayscale images and stored in a memory device along with identification codes assigned to each vehicle body component. Furthermore, the type of plating applied to the steel plates used in the target vehicle body components was identified by conventional instrumental analysis (EPMA elemental analysis) as described in the problem of this application, following the procedure shown in Figure 5. In this embodiment, the vehicle body components investigated included all four types of plating: electrogalvanizing (EG), hot-dip galvanizing (GI), alloyed hot-dip galvanizing (GA), and aluminum-silicon plating (Al-Si, hot-formed parts), which is a hot-dip aluminum-based plating. No other types of plating were used.

[0094] From the 1,000 vehicle body components surveyed (equivalent to 5 passenger cars), steel plates were collected from 730 components determined to be made of steel. Each steel plate was then embedded and fixed in resin to create test specimens. Then, using the prepared test specimen, a grayscale image of an optical microscope photograph of the steel plate cross-section was obtained.

[0095] Furthermore, for each acquired grayscale image, the grayscale gradation for each pixel was collected in the cross-sectional depth direction of the steel plate, centered on the plating layer, within a range of 410 to 620 pixels, and in the direction perpendicular to the cross-sectional depth direction, within a range of 2048 pixels, and a histogram of the frequency of grayscale gradations was created.

[0096] Figure 6 shows the distribution of grayscale gradation histograms created from grayscale images of optical microscope photographs of cross-sections of steel plates whose plating types were determined by conventional instrumental analysis. The dashed lines in Figure 6 represent the distributions approximated by equation (1) for the first peak (resin), second peak (underlying steel plate), third peak (plating layer), and fourth peak (boundary), which were selected from the histogram distribution. The solid gray line in Figure 6 represents the distribution of grayscale gradation histograms in the grayscale image by superimposing the first, second, third, and fourth peaks.

[0097] Furthermore, Table 1 shows the coefficients a obtained by approximating the first, second, third, and fourth peaks, selected from the histogram distributions created for each plating type, with the Gaussian function of equation (1) mentioned above. i , b i and c i The values ​​are shown. In Table 1, the resin is represented by the first peak, the base steel plate by the second peak, the plating layer by the third peak, and the boundary area by the fourth peak.

[0098] [Table 1]

[0099] Furthermore, Table 2 shows the results of determining the presence or absence of a fourth peak in the distribution pattern of the histograms for each plating type shown in Figures 6(a) to (d), the degree of separation between the second and third peaks calculated by the aforementioned equation (3), and the relative position of the fourth and first peaks.

[0100] [Table 2]

[0101] The peak separation values ​​for the second and third peaks shown in Table 2 were calculated using the formula (b3-b2)-1.174(c3+c2) according to equation (3) mentioned above. A positive peak separation value indicates that the second and third peaks are separated, while a negative peak separation value indicates that the second and third peaks overlap and are not separated.

[0102] Furthermore, the relative position of the fourth peak shown in Table 2 is the value calculated by |b2-b4|-|b4-b1| according to the aforementioned equations (2-1) and (2-2). When the relative position of the fourth peak is positive, it indicates that the relative position of the fourth peak is close to the first peak, and when the relative position of the fourth peak is negative, it indicates that the relative position of the fourth peak is close to the second peak.

[0103] The histogram shown in Figure 6(a) has a fourth peak between the first and second peaks, and the relative position of the fourth peak is negative, indicating that the relative position of the fourth peak is close to the second peak. Therefore, the distribution pattern of this histogram indicates that the type of plating applied to the steel plate is hot-dip galvanizing (GI), which is consistent with the type of plating identified by instrumental analysis.

[0104] The histogram shown in Figure 6(b) has a fourth peak between the first and second peaks, and the relative position of the fourth peak is positive, indicating that the relative position of the fourth peak is close to the first peak. Therefore, the distribution pattern of this histogram indicates that the type of plating applied to the steel sheet is electro-galvanized (EG), which is consistent with the type of plating identified by instrumental analysis.

[0105] The histogram shown in Figure 6(c) shows that there is no fourth peak between the first and second peaks, and the separation between the second and third peaks is positive, indicating that the second and third peaks are separated. Therefore, the distribution pattern of this histogram indicates that the type of plating applied to the steel plate is alloyed hot-dip galvanizing (GA), which is consistent with the type of plating identified by instrumental analysis.

[0106] The histogram shown in Figure 6(d) shows that there is no fourth peak between the first and second peaks, and the separation between the second and third peaks is negative, indicating that the second and third peaks overlap rather than separate. Therefore, the distribution pattern of this histogram indicates that the type of plating applied to the steel plate is a hot-dip aluminum-based plating, specifically aluminum-silicon plating (Al-Si), which is consistent with the type of plating identified by instrumental analysis.

[0107] In this way, the type of plating was identified using grayscale images of optical microscope photographs of cross-sections of steel plates taken from vehicle body components, with the instrumental analysis and identification information of the plating type masked. As a result, the type of plating could be accurately identified for all the steel plates taken. Therefore, it has been demonstrated that, according to the present invention, the type of plating applied to steel plates used in vehicle body components can be accurately identified without performing instrumental analysis, which is costly and time-consuming. [Explanation of Symbols]

[0108] 1. Plating type identification device 11. Steel plate cross-section grayscale image acquisition unit 13. Grayscale Histogram Creation Section 15 Plating type identification section

Claims

1. A method for identifying the type of plating applied to a steel sheet used in a vehicle body component of an automobile, the method being used for identifying the type of plating applied to a steel sheet for an automobile body, A steel plate cross-section grayscale image acquisition step involves taking a steel plate from the aforementioned vehicle body component to prepare a test specimen containing the steel plate, taking an optical microscope image of the cross-section of the steel plate in the prepared test specimen, and acquiring a grayscale image of the acquired optical microscope image, A grayscale tone histogram creation step, which creates a histogram of the frequency of grayscale tones for each pixel in the acquired grayscale image, The system includes a plating type identification step that identifies the type of plating applied to the steel plate based on the distribution pattern of the created histogram, The grayscale image of the cross-section of the steel plate includes a resin layer showing the resin that embeds and fixes the steel plate in the test specimen and / or the resin painted on the surface of the steel plate. The plating applied to the steel plate is one of the following: hot-dip galvanizing, alloyed hot-dip galvanizing, electro-galvanizing, or hot-dip aluminum-based plating. In the aforementioned plating type identification step, Select the three peaks with the largest peak areas in the distribution of the histogram mentioned above. The three selected peaks, in ascending order of peak grayscale value, are designated as the first peak, second peak, and third peak, representing the following regions a, b, and c, respectively. In the distribution of the histogram, it is determined whether there is a fourth peak representing the following region d with a relatively small peak area between the first peak and the second peak. If it is determined that the fourth peak is present, If the relative position of the fourth peak is closer to the first peak than to the second peak, the plating applied to the steel plate is identified as electro-galvanized. If the relative position of the fourth peak is closer to the second peak than to the first peak, the plating applied to the steel plate is identified as hot-dip galvanizing. If it is determined that there is no fourth peak, If the second peak and the third peak overlap, it is identified that the plating applied to the steel plate is a hot-dip aluminum-based plating. A method for identifying the type of plating on an automotive steel sheet, characterized in that if the second peak and the third peak are separated, the plating applied to the steel sheet is identified as alloyed hot-dip galvanizing. Area a: Area mainly consisting of grayscale gradations of cracks in the resin layer and plating layer. Area b: Area mainly consisting of grayscale gradations of the underlying steel plate. Area c: Area mainly consisting of grayscale gradations of the plated layer Area d: An area mainly consisting of grayscale gradations at the boundary between the plating layer and the underlying steel sheet.

2. In the aforementioned plating type identification step, The distribution of the histogram is divided into multiple regions using grayscale tones where the frequency of the histogram is minimized, thereby dividing the distribution of the histogram into peaks. The distribution of the histograms separated for each peak is approximated by a Gaussian function represented by the following equation (1): The coefficient a in the approximated equation (1) i and c i By calculating the product of these values ​​as the peak area of ​​the peaks in each divided histogram, the three peaks with the largest peak areas are selected. The presence or absence of the fourth peak is determined by the presence or absence of a histogram divided into peaks between the first peak and the second peak. The relative positional relationship between the fourth peak and the first and second peaks is given by the coefficient b in equation (1). i Determined by, The relative positional relationship between the second peak and the third peak is given by the coefficient b in equation (1). i and c i A method for identifying the type of plating on an automobile steel sheet according to claim 1, characterized by determining by the above. [Math 1]

3. A device for identifying the type of plating applied to steel sheets used in the body components of an automobile, A grayscale image acquisition unit for steel plate cross-sections acquires a grayscale image of an optical microscope photograph of a cross-section of a steel plate in a test specimen including a steel plate taken from the aforementioned vehicle body component. A grayscale tone histogram creation unit creates a histogram of the frequency of grayscale tones for each pixel in the acquired grayscale image, The system includes a plating type identification unit that identifies the type of plating applied to the steel plate based on the distribution pattern of the created histogram, The aforementioned plating type identification unit is By dividing the histogram distribution into multiple regions using grayscale tones where the frequency of histograms is minimal, the histogram distribution is divided into peaks, and the distribution of the histograms divided into peaks is approximated by a Gaussian function represented by the following equation (1). Three peaks with large product values ​​of the coefficients ai and ci (= peak area) in equation (1), which approximates the distribution of the histogram divided by peak, are selected, and these three selected peaks are designated as the first peak, second peak, and third peak in order of increasing peak grayscale value. The presence or absence of a fourth peak is determined by the presence or absence of a histogram distribution separated by each peak between the first and second peaks. If a fourth peak is determined to be present, the plating applied to the steel plate is identified as hot-dip galvanizing or electro-galvanizing based on the relative position of the fourth peak and the first or second peak. A plating type identification device for automotive steel sheets, characterized in that, if it is determined that there is no fourth peak, it identifies the plating applied to the steel sheet as alloyed hot-dip galvanizing or aluminum-silicon plating, which is a hot-dip aluminum-based plating, based on the relative positions of the second and third peaks. [Math 2]

4. A program for identifying the type of plating applied to steel sheets used in the body components of an automobile, Computers, A plating type identification program for automotive steel sheets, characterized in that it functions as a steel sheet cross-section grayscale image acquisition unit, a grayscale gradation histogram creation unit, and a plating type identification unit, in the plating type identification device for automotive steel sheets according to claim 3.

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