Method, device and program for identifying plating type of automotive steel sheet

Abstract

To provide a method, a device, and a program for identifying the plating type of a steel sheet for automobile that identifies the type of plating applied to a steel sheet used for a body component of an automobile body.SOLUTION: A method for identifying the type of plating applied to a steel sheet used for a body component of an automobile body comprises a steel sheet section grayscale image acquisition step S1 in which a steel sheet is taken from the body component, a test piece including the steel sheet is prepared, an optical micrograph of the steel sheet section in the thickness direction of the prepared test piece is captured, and a grayscale image of the captured optical micrograph is acquired; a grayscale gradation change extraction step S3 in which a grayscale gradation change in the thickness direction of the steel sheet in the acquired grayscale image of the steel sheet section is extracted; and a plating type identification step S5 in which the type of plating applied to the steel sheet is identified based on the extracted grayscale gradation change in the thickness direction of the steel sheet.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] The various types of steel sheets used as materials for the body components that make up automobiles have continued to change over the years, with progress being made in increasing strength, thickness, and weight. In particular, due to stricter automobile collision safety standards, improved performance requirements, improved fuel efficiency, and reduced CO2 emissions, it is necessary to achieve the conflicting performance of increasing the strength of the body frame while reducing its mass, and the use of high-strength steel sheets in body components is continuously increasing.

[0003] Automotive body components are often manufactured by cold press forming of steel sheets, which allows for high production efficiency, mass production, and low-cost manufacturing. However, as the strength of steel sheets increases, their press formability decreases, making them more susceptible to defects such as cracks (fractures), wrinkles, and poor shape and dimensional accuracy during press forming. For this reason, it is necessary to incorporate technical innovations into the press forming method and the shape and mold configuration of automotive 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 reduce vehicle weight reduces the rigidity of the components and the entire vehicle body. Therefore, it is not possible to uniformly strengthen and thin all body components. The decisions about which parts of a vehicle body to strengthen or thin, as well as how to optimally design the body components based on the steel plate strength, are determined by the experience of the engineers who design the vehicle body and the accumulated technology of the vehicle manufacturer. For this reason, it is common to study the automobile bodies currently on the market to learn and absorb the ingenuity, innovative technologies, new material usage, and construction methods found in the designs of automobile bodies produced by each manufacturer.

[0005] In such investigations, automobile bodies are disassembled at the joints between body components and separated into individual body components. A portion of the steel plate from which the body components are made is sampled. The steel plate's hardness, thickness, and structure are measured under a microscope to identify its identity and for comparative analysis. To identify the steel plate's identity, for example, a steel plate sampled from a body component is embedded in a resin molding compound to create a test specimen. The cross section of the embedded steel plate is mirror-polished, and the steel plate's thickness and hardness are measured using the micro-Vickers method. Furthermore, the tensile strength (TS) of the steel plate is calculated using a conversion formula from the measured hardness. These values ​​are then compared with manufacturing standards to identify the identity (standards, etc.) of the steel plate used in the body components.

[0006] A single automobile body consists of 300 to 500 body components, and the materials of each body component for each vehicle are compiled and stored in a materials database. This materials database is then used as the basis for various analytical investigations of the body components. Regarding the creation of a database for each vehicle, for example, Patent Document 1 discloses a technology in which a vehicle being sold is disassembled and data such as the material of the parts used in the vehicle (strength, plate thickness, weight, dimensions, presence or absence of surface treatment, plating layer thickness, type of substrate (hot-rolled base or cold-rolled base), etc.), the number of spot welds, and images of the parts are stored. In this technology, when data on parts is saved, the data is classified by group names given to parts classified according to standards that are uniform across multiple vehicles of different manufacturers or models, and part function names that are given based on the function of the part and are uniform across multiple vehicles. This makes it possible to quickly extract parts to be compared and easily compare them, even across multiple vehicles of different manufacturers or models. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 5151380 Summary of the Invention [Problem to be solved by the invention]

[0008] The body components of automobiles are made of steel sheets that have not been surface-treated (bare materials, JIS standards SPC, SPH, etc.) or plated surface-treated steel sheets (Japan Iron and Steel Federation standards SEC, SGC, etc.).The plating applied to the surface-treated steel sheets includes zinc plating systems such as hot-dip galvanizing (GI), galvannealed hot-dip galvanizing (GA), and electrogalvanizing (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 appropriately according to the required specifications, such as rust prevention, press formability, and painted appearance, as well as the adoption policies of each part or vehicle manufacturer. While the majority of vehicle manufacturers in Japan use GA for the majority of cold-pressed parts and Al-Si for hot-pressed parts, overseas vehicle manufacturers often use GI or EG for cold-pressed parts and Al-Si for hot-pressed parts.

[0009] In creating a database for each vehicle, it is conceivable to store the type of plating on the surface-treated steel sheet as data related to the material of the part, as in the technology disclosed in Patent Document 1. 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 steel sheets, methods are used to quantify the components in the plating layer by wet analysis, or to identify the element distribution in the plating layer by spectroscopic analysis. As a method for wet analysis of a plating layer, a method in which the plating layer of a steel sheet is stripped and dissolved with an alkaline solution or the like and then analyzed can be given. Methods for spectroscopically analyzing the plating layer include X-ray spectroscopic analysis of the cross section of the plating layer using XPS (X-ray photoelectron spectroscopy) or EPMA (electron probe microanalyzer), or sputtering spectroscopic analysis such as GDS (glow discharge optical emission spectroscopy).

[0011] Identifying the elemental distribution of a coating layer using spectroscopic analysis has the advantage of being able to identify the type of coating on a steel sheet nondestructively. However, spectroscopic analysis has the disadvantages of high measurement costs and the laborious preparation of analytical samples because measurements are performed in a vacuum. In particular, since an automobile body typically has 300 to 500 body components, identifying the type of coating on the steel sheets used in these body components using the above-mentioned wet analysis or spectroscopic analysis requires enormous measurement costs and labor.

[0012] The present invention has been made to solve the above-mentioned problems, and has an object to provide a method, device, and program for identifying the type of plating applied to steel sheets for automotive use, which can identify the type of plating applied to steel sheets of vehicle body components without requiring enormous measurement costs or effort. [Means for solving the problem]

[0013] (1) The method for identifying the type of plating applied to a steel sheet for an automobile according to the present invention is for identifying the type of plating applied to a steel sheet used for a body component of an automobile body, and includes the steps of: a steel plate cross-section grayscale image acquisition step of collecting a steel plate from the vehicle body component to prepare a test specimen including the steel plate, taking an optical microscope photograph of a cross section of the steel plate in the plate thickness direction of the prepared test specimen, and acquiring a grayscale image of the taken optical microscope photograph; a grayscale gradation change extraction step of extracting a grayscale gradation change in the thickness direction of the steel plate from the acquired grayscale image of the steel plate cross section; and a plating type identification step of identifying the type of plating applied to the steel sheet based on the extracted grayscale gradation change in the thickness direction of the steel sheet.

[0014] (2) In the above (1), The plating type identifying step includes: a grayscale gradation change standard pattern acquisition step for acquiring a grayscale gradation change standard pattern for various platings, the grayscale gradation change in the plate thickness direction in a grayscale image of an optical microscope photograph obtained by capturing an image of a portion including a cross section of a standard steel plate to which a plating type is previously known; and a plating type comparison step in which the grayscale gradation change in the thickness direction of the steel plate extracted in the steel plate cross-section grayscale image acquisition step is compared with the standard patterns acquired for various platings to determine the type of plating applied to the steel plate.

[0015] (3) In the above (2), In the step of acquiring a standard pattern of grayscale gradation change, The standard pattern is obtained as a function having the position in the plate thickness direction as an independent variable and the grayscale gradation at each position in the plate thickness direction as a dependent variable; In the plating type collation determination step, The method is characterized in that the type of plating applied to the steel sheet is determined based on the similarity between the grayscale gradation change in the thickness direction of the steel sheet and the function of the standard pattern.

[0016] (4) In the above (1), The plating type identifying step includes: a grayscale gradation change function approximation step of approximating the grayscale gradation change in the thickness direction of the steel plate extracted in the grayscale gradation change extraction step as a function in which a position in the thickness direction is an independent variable and a grayscale gradation at each position in the thickness direction is a dependent variable; and a plating type determination step of determining the type of plating applied to the steel sheet based on the coefficients of the approximated function of grayscale gradation change in the thickness direction of the steel sheet.

[0017] (5) In the above (4), The grayscale gradation change function approximation step is characterized in that each of the grayscale gradation changes in the thickness direction of the steel plate extracted in the grayscale gradation change extraction step is function-approximated using the following equation (1) in which a sigmoid function is combined:

number

[0018] (6) In the above (5), In the grayscale gradation change extraction step, extracting grayscale gradation changes in the thickness direction of the steel plate at a plurality of locations of the steel plate in the grayscale image of the steel plate cross section; Each of the grayscale gradation changes in the thickness direction extracted at the plurality of locations is functionally approximated using the formula (1), In the plating type determination step, When the total number n of regions of the formula (1) obtained by functional approximation for each of the grayscale gradation changes in the sheet thickness direction extracted at the plurality of locations is 3, it is determined that the type of coating applied to the steel sheet is either electrogalvanized or hot-dip galvanized; The variation in the difference b3-b1 between the coefficient b3 and the coefficient b1 calculated by functionally approximating each of the grayscale gradation changes in the thickness direction extracted at the plurality of locations using Equation (1) is compared with the variation in the coefficient b1; If the variation of b3-b1 is greater than the variation of b1, the coating applied to the steel sheet is determined to be hot-dip galvanized; If the variation in b3-b1 is smaller than the variation in b1, the plating applied to the steel sheet is determined to be electrogalvanized.

[0019] (7) In the above (5) or (6), In the plating type determination step, When the total number n of regions of the formula (1) obtained by functionally approximating each of the grayscale gradation changes in the thickness direction extracted at a plurality of locations of the steel sheet is 3 or 5, it is determined that the type of coating applied to the steel sheet is galvannealed coating; When the total number n of regions of Equation (1) obtained by functionally approximating the grayscale gradation changes in the thickness direction extracted from a plurality of locations on the steel sheet is 5 or 7, the type of plating applied to the steel sheet is determined to be hot-dip aluminum-based plating.

[0020] (8) In any one of (1) to (5) above, The plating applied to the steel sheet is characterized by being any one of hot-dip galvanizing, electrogalvanizing, alloyed hot-dip galvanizing, and hot-dip aluminum-based plating.

[0021] (9) The plating type identification device for automotive steel sheets according to the present invention identifies the type of plating applied to steel sheets used for body components of an automobile body, and a steel plate cross-section grayscale image acquisition unit that acquires a grayscale image of an optical microscope photograph of a steel plate cross section in a plate thickness direction of a test specimen including the steel plate collected from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in the thickness direction of the steel plate from the acquired grayscale image of the steel plate cross section; and a plating type identification unit that identifies the type of plating applied to the steel plate based on the extracted grayscale gradation change in the thickness direction of the steel plate.

[0022] (10) A plating type identification program for automotive steel sheets according to the present invention identifies the type of plating applied to steel sheets used for body components of an automobile body, and includes: Computer, a steel plate cross-section grayscale image acquisition unit that acquires a grayscale image of an optical microscope photograph of a steel plate cross section in a plate thickness direction of a test specimen including the steel plate collected from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in the thickness direction of the steel plate from the acquired grayscale image of the steel plate cross section; The present invention is characterized in that it functions as a plating type identification unit that identifies the type of plating applied to the steel sheet based on the extracted grayscale gradation change in the thickness direction of the steel sheet. [Effects of the Invention]

[0023] According to the present invention, it is possible to extract grayscale gradation changes in the sheet thickness direction using grayscale images of optical microscope photographs of cross sections in the sheet thickness direction of steel sheets taken from vehicle body components, and to identify the type of plating based on the grayscale gradation changes. This makes it possible to collect material information for each vehicle body component that makes up an automobile body and efficiently build a material database without performing instrumental analysis, which requires enormous measurement costs and effort, to identify the type of plating applied to steel sheets. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a flowchart showing a process flow in a method for identifying the type of plating on an automotive steel sheet according to an embodiment of the present invention. FIG. [Figure 2] FIG. 1 is a diagram illustrating the background to the invention, showing items that can identify the type of plating based on an optical microscope photograph of a cross section of a steel sheet in the thickness direction of a test specimen in which a plated steel sheet is embedded and fixed in resin. [Figure 3] 1 is a graph showing a schematic representation of the grayscale gradation change in the thickness direction of a steel sheet extracted from grayscale images of optical microscope photographs of the cross sections of (a) galvannealed (GA) and (b) hot-dip galvanized (GI) steel sheets, which were obtained through the process leading to the present invention. [Figure 4]1 is a graph showing a schematic representation of the grayscale gradation change in the thickness direction of a steel sheet extracted from grayscale images of optical microscope photographs of the cross sections of steel sheets that have been subjected to (c) electrogalvanization (EG) and (c) aluminum silicon plating (Al-Si), which is a hot-dip aluminum-based plating, in the course of arriving at the present invention. [Figure 5] FIG. 1 is a flow chart illustrating two modes of a plating type identifying step in a method for identifying the plating type of automotive steel sheet according to an embodiment of the present invention. [Figure 6] 1A and 1B are diagrams illustrating differences depending on the location at which grayscale gradation changes in the thickness direction are extracted in grayscale images of optical microscope photographs of the cross section of a plated steel sheet in this embodiment ((a) Location at which grayscale gradation changes are extracted, (b) Graph showing differences in grayscale gradation changes extracted at multiple locations). [Figure 7] 1 is a graph showing grayscale gradation changes in the sheet thickness direction, approximated by a sigmoid function, for steel sheets that have been subjected to (a) electrogalvanization, (b) hot-dip galvanization, (c) galvannealed hot-dip galvanization, and (d) aluminum-silicon plating, which is a hot-dip aluminum-based coating, in a method for identifying the type of coating on automotive steel sheets according to an embodiment of the present invention. [Figure 8] 1 is a diagram showing an example of the configuration of an automotive steel sheet plating type identification device according to an embodiment of the present invention. [Figure 9] 1 is a diagram illustrating a specific configuration of a plating type identifying unit in an automotive steel sheet plating type identifying device according to an embodiment of the present invention. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0025] Before describing the method, device, and program for identifying the type of plating on automotive steel sheets according to the embodiment of the present invention, the background to the invention will be described.

[0026] <Background to the invention> In order to identify the material properties (strength and specifications) of steel sheets used in vehicle body components, it is essential to extract steel sheets from the vehicle body components and embed the extracted steel sheets in resin to prepare test specimens, which is an essential step for benchmarking vehicle body components and building a material database. In identifying the material properties of steel sheets using such test specimens, optical microscope photographs of a portion of the test specimen that includes a cross section of the steel sheet in the thickness direction (hereinafter referred to as "steel sheet cross section" in this application) have been taken, and the thickness of the steel sheet has been measured, the presence or absence of a plating layer has been observed, and the thickness of the plating layer has been measured, etc.

[0027] Therefore, the inventors realized that there would be benefits in terms of time and cost if it were possible to identify the type of plating from an optical microscope photograph of the cross section of a steel sheet.Furthermore, in order to identify the type of plating from an optical microscope photograph of the cross section of a steel sheet, they thought it would be important to clarify the relationship between the items that represent the physical and chemical properties of the plating layer in the optical microscope photograph and the type of plating.

[0028] Therefore, the inventors have conducted extensive research into the factors that represent the characteristics of the coating layer in an optical microscope photograph of the cross section of a steel sheet, and have identified the following four factors (1) to (4). Each of factors (1) to (4) will be explained below in relation to the optical microscope photograph of the cross section of the steel sheet shown in Figure 2.

[0029] (1) Morphology of the plating layer The morphological characteristics of the plating layer include multiple crystalline compositions and eutectic structures found in heterogeneous plating layers, cracks and peeling of the plating that occur in the plating layer due to press forming, and voids, which are air gap defects.

[0030] The inhomogeneous plating layer has a morphology characteristic of the eutectic structure of aluminum-silicon plating (Al-Si, Figure 2(d)), which is a molten aluminum-based plating after hot press forming, and cracks and voids can be seen. Furthermore, the coating layer in which cracks have occurred is a common form seen in galvannealed coatings (GA, Figure 2(a)), which is caused by a decrease in the ductility of the Zn-Fe alloy crystals formed during the alloying process.

[0031] In contrast, in the coating layers of hot-dip galvanized (GI) and electrogalvanized (EG) steel sheets, as shown in Figures 2(b) and (c), no eutectic structure or cracks are observed in the coating layers, and the morphology of the coating layers is found to be homogeneous.

[0032] (2) Change in contrast at the boundary between the base steel sheet and the coating layer In optical microscope photographs of the cross section of a steel sheet, a contrast change (a change in grayscale gradation in the case of a grayscale image) can be observed in the area corresponding to the boundary between the substrate steel sheet (base steel sheet) and the coating layer. This contrast change is presumed to be due to the presence or absence of a continuous diffusion layer between the substrate steel sheet and the coating layer.

[0033] Galvannealed (GA) coatings have a continuous Fe-Al-Zn diffusion layer, and as shown in Figure 2(a), although there is a difference in the contrast (grayscale gradation) between the base steel sheet and the coating layer, the boundary between the coating layer and the base steel sheet is unclear. In contrast, hot-dip galvanized (GI) and electrogalvanized (EG) coatings do not have a component-specific diffusion layer, and are therefore characterized by a clear contrast change at the boundary between the base steel sheet and the coating layer, as shown in Figures 2(b) and (c). Furthermore, as shown in Figure 2(d), aluminum-silicon plating (Al-Si), which is a molten aluminum-based coating, has almost the same color tone (grayscale gradation) of the base steel sheet and the coating layer, so the change in contrast at the boundary is unclear.

[0034] (3) Ability to conform to the shape of the substrate steel sheet The ability to conform to the shape of the substrate steel sheet refers to the effect of the surface shape (unevenness) of the substrate steel sheet on the unevenness of the coating layer surface. Hot-dip galvanizing (GI) is a process in which a steel substrate is immersed in molten zinc and then pulled up. As shown in Figure 2(b), the surface tension of the liquid molten zinc before solidification makes the surface of the coating layer smooth, and it is less affected by the surface shape of the substrate steel. In contrast, with electrolytic galvanization (EG), electrodeposition occurs along the unevenness of the base steel sheet, resulting in a nearly constant thickness of the coating layer, as shown in Figure 2(c), and the influence of the surface shape of the base steel sheet is reflected in the unevenness of the coating layer surface.

[0035] (4) Combination of surface roughness and shape As shown in Figure 2(c), the surface of electrogalvanized (EG) plating is covered with a step-like (step-like) pattern of electro-deposited Zn crystals. Therefore, when observing the cross section of an electrogalvanized steel sheet, a characteristic step-like shape can be seen at the outermost surface.

[0036] Thus, the inventors discovered the above four items that represent the physical and chemical properties of the plating layer in grayscale images of optical microscope photographs of the cross section of plated steel sheet. They then further investigated a method for identifying the type of plating from optical microscope photographs of the cross section of steel sheet based on these items. They then came to the conclusion that the characteristics of the plating applied to steel sheet might be expressed in the change in color tone (grayscale gradation) along the sheet thickness direction in the grayscale image of an optical microscope photograph of the steel sheet cross section.

[0037] Figures 3 and 4 show graphs that schematically show the grayscale gradation changes in the thickness direction of a steel plate in grayscale images of optical microscope photographs of the cross section of the steel plate. In Figures 3 and 4, the grayscale gradation changes in the thickness direction of the steel plate extracted from multiple locations (three locations) on the steel plate are displayed by overlapping solid lines, dashed lines, and dashed dotted lines.

[0038] Here, Figure 3(a) shows the grayscale gradation change in a steel sheet that has been subjected to galvannealed (GA) coating, Figure 3(b) shows the grayscale gradation change in a steel sheet that has been subjected to hot-dip galvannealed (GI) coating, Figure 4(c) shows the grayscale gradation change in a steel sheet that has been subjected to electrogalvanized (EG) coating, and Figure 4(d) shows the grayscale gradation change in a steel sheet that has been subjected to aluminum-silicon plating (Al-Si), a hot-dip aluminum-based coating. In each graph shown in Figures 3 and 4, the horizontal axis represents the positions (coordinates) of pixels aligned in the thickness direction of the steel plate in the grayscale image of the steel plate cross section, and the vertical axis represents the grayscale gradation of the pixels at each position in the thickness direction.

[0039] 3 and 4, the coating layer is the brightest (white) and therefore has a large grayscale value, while the resin is the darkest (black) and therefore has a small grayscale value, resulting in the grayscale of the steel substrate being intermediate between that of the coating layer and the resin. Note that the resin is assumed to be either a paint film on the steel substrate or a resin used to embed and fix the steel substrate, and in either case, the grayscale is clearly darker than that of the coating layer or the steel substrate.

[0040] The grayscale gradation changes for each plating type shown in Figures 3 and 4 (a) to (d) show the characteristics of items (1) to (3) that represent the physical and chemical properties of the plating layer described above.

[0041] Regarding "(1) Morphology of the plating layer" Steel sheets coated with galvannealed (GA) or aluminum-silicon (Al-Si) plating, a hot-dip aluminum-based coating, tend to have cracks (depth-direction cracks or peeling defects) and voids (void-like defects) in the coating layer, as shown in Figures 2(a) and 2(d). Therefore, depending on the location where the grayscale gradation change in the thickness direction is extracted in the grayscale image of the steel sheet cross section, the influence of cracks and voids in the coating layer can be seen in the distribution of grayscale gradation change, as shown in Figures 3(a) and 4(d).

[0042] In contrast, in hot-dip galvanized (GI) and electrogalvanized (EG) steel sheets, the coating layer is homogeneous and free of cracks and voids, as shown in Figures 2(b) and 2(c). Therefore, as shown in Figures 3(b) and 4(c), the grayscale gradation changes in the grayscale images of the steel sheet cross section are distributed almost uniformly in the coating layer.

[0043] Regarding "(2) Change in contrast at the boundary between the base steel sheet and the coating layer" In grayscale images of the cross section of hot-dip galvanized (GI) or electrogalvanized (EG) steel sheets, there is a boundary between the coating layer and the substrate steel sheet that is darker than the substrate steel sheet, as shown in Figures 2(b) and (c). Here, in the case of hot-dip galvanized (GI), the boundary has a grayscale tone similar to that of the substrate steel sheet (Figure 3(b)A), whereas in the case of electrogalvanized (EG), the boundary has a grayscale tone similar to that of the resin (Figure 4(c)B).

[0044] In the grayscale image of the cross section of a steel sheet that has been subjected to galvannealed (GA) plating, as shown in Figure 3(a), no boundary with a different grayscale tone from that of the plating layer and the base steel sheet is observed between the plating layer and the base steel sheet.

[0045] Furthermore, in grayscale images of the cross section of a steel sheet coated with aluminum-silicon (Al-Si) plating, a type of molten aluminum-based coating, the grayscale gradation of the coating layer and the base steel sheet is almost the same, as shown in Figure 4(d). Therefore, it is difficult to recognize the boundary between the coating layer and the base steel sheet, which have different grayscale gradations. However, aluminum-silicon plating (Al-Si), a type of molten aluminum-based plating, is characterized by the presence of two phases with different grayscale gradations in the plating layer, and as a result, unevenness corresponding to these two phases can be seen in the grayscale gradation changes in the thickness direction of the plating layer (Figure 4(d)).

[0046] Regarding "(3) Ability to conform to the shape of the substrate steel sheet" In grayscale images of the cross section of hot-dip galvanized (GI) and electrogalvanized (EG) steel sheets, there is a difference in the ability of the coating layer to follow the surface shape (unevenness) of the underlying steel sheet. As shown in Figure 3(b), the grayscale gradation change of hot-dip galvanized (GI) coating indicates that the surface position of the coating layer does not follow the surface shape of the base steel sheet, and that the surface position of the coating layer is constant, while the local thickness of the coating layer (coating layer thickness) changes. In contrast, the grayscale gradation changes of electrogalvanized (EG) coatings, as shown in Figure 4(c), indicate that the thickness of the coating layer is constant, but the surface position of the coating layer fluctuates in accordance with the surface shape of the base steel sheet.

[0047] In this way, the inventors have found that the characteristics of various platings applied to steel sheets are expressed in the grayscale gradation changes in the sheet thickness direction in the grayscale image of the cross section of the steel sheet. The present invention has been made based on the above findings, and the specific configuration thereof will be described below.

[0048] <Method for identifying plating types> 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 vehicle body components of automobile bodies. As shown in Fig. 1, the plating type identification method according to this embodiment includes a steel sheet cross-section grayscale image acquisition step S1, a grayscale gradation change extraction step S3, and a plating type identification step S5. Hereinafter, each of the above steps will be described for the case where the plating applied to the steel sheet is any one of hot-dip galvanizing, alloyed hot-dip galvanizing, electrogalvanizing, and aluminum-silicon plating, which is a hot-dip aluminum-based plating.

[0049] <Steel plate cross-section grayscale image acquisition process> The steel plate cross-section grayscale image acquisition process S1 is a process in which a steel plate is collected from a vehicle body component to prepare a test specimen including the steel plate, an optical microscope photograph of the steel plate cross section in the plate thickness direction of the prepared test specimen is taken, and a grayscale image of the taken optical microscope photograph is obtained.

[0050] The test specimen can be prepared by embedding the collected steel plate in a resin suitable for optical microscope observation. The resin for embedding the steel plate can be, for example, a heat-curing epoxy resin or a room-temperature-curing carbon-added acrylic resin, and the type of resin is not particularly limited as long as it is black in color after curing.

[0051] After the resin has hardened, the cross section of the steel sheet of the test specimen may be mirror-polished and then imaged using an optical microscope. Note that etching, which is commonly performed for the purpose of observing the steel sheet structure, is desirably not applied to the test specimen of the present invention because it may affect the plating layer applied to the steel sheet.

[0052] <Grayscale gradation change extraction process> The grayscale gradation change extraction step S3 is a step of extracting a grayscale gradation change in the thickness direction of the steel plate in the grayscale image of the cross section of the steel plate acquired in the steel plate cross-section grayscale image acquisition step S1.

[0053] In the grayscale gradation change extraction step S3, a graph (discrete data) or function representing the relationship between the position in the thickness direction of the steel plate in the grayscale image of the steel plate cross section and the grayscale gradation at each position in the thickness direction is extracted as the grayscale gradation change. In the grayscale gradation change extraction step S3, the relationship between the positions (coordinates) of consecutive pixels in the plate thickness direction and the grayscale gradation of each pixel may be extracted as the grayscale gradation change.

[0054] 3 and 4, which have been described above, show graphs in which the grayscale gradation changes in the thickness direction of the steel sheet, extracted at multiple locations (three locations) on the steel sheet, are represented by solid lines, dashed lines, and dashed dotted lines, using grayscale images of optical microscope photographs of the steel sheet cross section for each of the plating types (a) to (d). As shown in Figures 3 and 4, the plating layer is heterogeneous, so differences in the grayscale gradation changes can be seen depending on the location where the grayscale gradation changes are extracted.

[0055] <Plating type identification process> The plating type identifying step S5 is a step of identifying the type of plating applied to the steel sheet based on the grayscale gradation change in the thickness direction of the steel sheet extracted in the grayscale gradation change extracting step S3.

[0056] In the grayscale gradation changes in the thickness direction of the steel sheet cross section shown in Figures 3 and 4, the type of plating can be identified as follows based on the above-mentioned items (1) or (3) that represent the plating characteristics.

[0057] If cracks or peeling are evident in the area showing the coating layer, as shown in the distribution of grayscale changes in the thickness direction of the steel sheet in Figure 3(a), it can be identified as galvannealed (GA) (item (1) Morphology of the coating layer).

[0058] When the surface position of the coating layer is constant and the thickness of the coating layer varies, as in the grayscale gradation change in the thickness direction of the steel sheet shown in Figure 3(b), it can be identified as hot-dip galvanized (GI) (item (3) Ability to follow the shape of the base steel sheet).

[0059] As shown in Figure 4(c), the grayscale gradation changes in the thickness direction of the steel sheet, and the surface position of the plating layer fluctuates, but the thickness of the plating layer is constant, so it can be identified as electrogalvanized (EG) (item (3) Ability to follow the shape of the base steel sheet).

[0060] As shown in Figure 4(d), the grayscale change in the thickness direction of the steel plate shows the effects of cracks and voids in the area showing the plating layer, which allows it to be identified as aluminum silicon plating (Al-Si), a molten aluminum-based plating (item (1) Morphology of the plating layer).

[0061] In the plating type identifying step S5, the plating type can be identified more specifically according to the following mode A or mode B.

[0062] [Aspect A] In mode A, as shown in FIG. 5(a), the type of plating is identified by grayscale gradation change standard pattern acquisition step S5a and plating type collation determination step S5b.

[0063] (Grayscale gradation change standard pattern acquisition step) The grayscale gradation change standard pattern acquisition step S5a is a step of acquiring, as a standard pattern for various platings, the grayscale gradation change in the plate thickness direction in a grayscale image of an optical microscope photograph of a portion including a cross section of a standard steel plate to which a plating of a known type has been applied.

[0064] The standard pattern of grayscale gradation changes in the cross section of a standard steel plate can be obtained by the same procedures as those in the steel plate cross-section grayscale image acquisition step S1 and grayscale gradation change extraction step S3 described above. That is, first, a standard test piece is prepared including a standard steel plate to which a known type of plating has been applied, and an optical microscope photograph is taken of a portion of the prepared standard test piece including the cross section of the standard steel plate (standard steel plate cross section), and a grayscale image of the portion is obtained. Next, the grayscale gradation change in the thickness direction of the standard steel plate is extracted from the acquired grayscale image of the cross section of the standard steel plate. By performing these operations in the same way on standard steel sheets with different types of plating, it is possible to obtain standard patterns of grayscale gradation changes in the sheet thickness direction for various platings.

[0065] It is preferable that the grayscale image of the optical microscope photograph of the cross section of the standard steel plate has the same magnification and contrast adjusted in the same way as the grayscale image of the optical microscope photograph of the cross section of the steel plate taken in the steel plate cross section grayscale image acquisition step S1.

[0066] In the grayscale gradation change standard pattern acquisition step S5a, a graph (discrete data) representing the grayscale gradation change in the plate thickness direction in the grayscale image of the cross section of the standard steel plate may be acquired as the grayscale gradation change standard pattern. Alternatively, a function (hereinafter referred to as a "standard pattern function") may be obtained in which the position (coordinate) in the plate thickness direction of the standard steel plate is an independent variable and the grayscale gradation at each position in the plate thickness direction is a dependent variable. When obtaining a standard pattern function of grayscale gradation changes, for example, a function that approximates the grayscale gradation changes extracted from a grayscale image of the cross section of the standard steel plate may be obtained.

[0067] (Plating type verification step) The plating type collation and determination step S5b is a step of collating the grayscale gradation change in the thickness direction of the steel sheet with standard patterns obtained for various platings, and determining the type of plating applied to the steel sheet.

[0068] In the plating type comparison and determination step S5b, for example, a graph of the grayscale gradation change in the thickness direction of the steel plate (FIGS. 3 and 4) may be displayed, and the plating type may be determined by visually comparing it with a standard pattern similar to this.

[0069] Alternatively, when a standard pattern function of grayscale gradation change is acquired in grayscale gradation change standard pattern acquisition step S5a, the grayscale gradation change in the thickness direction of the steel plate may be compared by determining the similarity between the standard pattern function and the standard pattern function. In this case, the similarity can be calculated by using the residual sum of squares between the grayscale tone in the thickness direction of the grayscale image of the steel plate cross section and the grayscale tone given by the standard pattern function. Alternatively, the difference between the values ​​obtained by integrating (accumulating) the grayscale gradation change in the steel sheet cross section and the standard pattern of grayscale gradation change in the sheet thickness direction may be used as the similarity. In this way, when the residual sum of squares of grayscale gradations or the difference between integrated values ​​is used as the similarity, the smaller these values ​​are, the higher the similarity is determined to be.

[0070] The similarity may be expressed as a function of the grayscale gradation change in the thickness direction of the steel sheet cross section, and the correlation coefficient with a standard pattern function of the grayscale gradation change may be calculated. When the correlation coefficient is used as the similarity, the closer the correlation coefficient value is to 1, the higher the similarity is determined to be. Then, among the standard pattern functions of various platings, the plating identical to the standard pattern function with the highest similarity is determined to be the plating applied to the steel sheet.

[0071] In order to identify the type of plating by comparing the grayscale gradation changes in the thickness direction of the steel sheet with a standard pattern, it is advisable to extract grayscale gradation changes at multiple locations on the cross section of the steel sheet in the grayscale gradation change extraction step S3. The grayscale gradation changes extracted at multiple locations are then compared with a standard pattern, and if there is a grayscale gradation change that is close to the standard pattern (for example, has a high degree of similarity as mentioned above), it can be identified as plating of that standard pattern.

[0072] As shown in Figure 3(a), steel sheets that have been subjected to galvannealed (GA) coating can show the effects of cracks and peeling in the coating layer in the distribution of grayscale gradation changes depending on the location where they are extracted. Therefore, if any of the grayscale gradation changes extracted at multiple locations is close to the standard pattern of galvannealed coating, it can be identified as galvannealed coating.

[0073] Similarly, for steel sheets coated with aluminum-silicon plating (Al-Si), a type of molten aluminum-based plating, the effects of cracks and peeling in the plating layer can be seen in the distribution of grayscale gradation changes depending on the location on the steel sheet where they are extracted, as shown in Figure 3(d). Therefore, if any of the grayscale gradation changes extracted at multiple locations is close to the standard pattern of aluminum-silicon plating, a type of molten aluminum-based plating, it can be identified as aluminum-silicon plating.

[0074] Furthermore, as shown in Figure 3(b), for hot-dip galvanized (GI) steel sheets, the thickness of the plating layer varies depending on the location where the grayscale gradation change is extracted, but the surface position of the plating layer does not change. Therefore, even if the location on the steel sheet where the grayscale gradation change is extracted is different, if the surface position of the plating layer is close to the surface position of the plating layer in the standard pattern, it can be identified as hot-dip galvanized.

[0075] Furthermore, as shown in Figure 3(c), the position of the plating layer on electrogalvanized (EI) steel sheets varies depending on the location where the grayscale gradation change is extracted, but the thickness of the plating layer is almost constant. Therefore, even if the location on the steel sheet where the grayscale gradation change is extracted is different, if the thickness of the plating layer is close to that of the standard pattern, it can be identified as electrogalvanized.

[0076] [Aspect B] In mode B, as shown in FIG. 5(b), the type of plating is identified by a grayscale gradation change function approximation step S5c and a plating type determination step S5d.

[0077] (Grayscale Tone Change Function Approximation Step) The grayscale gradation change function approximation step S5c is a step of approximating the grayscale gradation change in the thickness direction of the steel plate extracted in the grayscale gradation change extraction step S3 with a function. Here, the function approximating the grayscale gradation change has the position in the thickness direction of the steel plate as an independent variable and the grayscale gradation at each position in the thickness direction as a dependent variable.

[0078] An example of the function that approximates the grayscale gradation change in the grayscale gradation change function approximation step S5c is a function f m It is recommended to use (x).

number

[0079] Equation (1) expresses, as a sigmoid function, the change in grayscale tone in region i (hereinafter simply referred to as "region") where the grayscale tone changes between the resin and the plating layer, between the plating layer and the base steel sheet, etc. By combining the grayscale tone changes in each region i in order from the resin side, a function is obtained that represents the change in grayscale tone throughout the entire thickness direction.

[0080] The function f shown in equation (1) m The procedure for functionally approximating the grayscale gradation change in the thickness direction of the steel plate using (x) is as follows.

[0081] First, in the grayscale gradation change extraction step S3 described above, grayscale gradation changes in the thickness direction are extracted at multiple locations on the steel plate in the grayscale image of the steel plate cross section. Then, for each of the grayscale gradation changes in the thickness direction extracted at multiple locations, a function approximation is performed using equation (1), and each coefficient a i , b i , c i , S is calculated. The subscript m in formula (1) is an identification number indicating the location where the grayscale gradation change in the plate thickness direction is extracted in the grayscale image of the steel plate cross section, as shown in Fig. 6. It is recommended that the grayscale gradation change be extracted at 10 or more locations (m ≥ 10).

[0082] To approximate the grayscale gradation change using the formula (1), the total number of regions in formula (1), n, is set to 3, and by curve fitting (such as the least squares method), a i , b i , c i (i=1 to 3) is calculated. If the sum of squares of the residual error in the curve fitting is equal to or less than a predetermined value, the total number n of regions in equation (1) is determined to be 3.

[0083] If the sum of squares of the residual error in the curve fitting exceeds a predetermined value, n in equation (1) is changed to 5 or 7 in turn, and curve fitting is performed to determine n such that the sum of squares of the residual error is equal to or less than the predetermined value. In addition, if the sum of squares of the residual error exceeds a predetermined value even when curve fitting the grayscale gradation change in the plate thickness direction with n=7 in Equation (1), the total number of regions n is set to 7.

[0084] As an example, FIG. 7 shows the results of functional approximation using Equation (1) of the grayscale gradation changes in the thickness direction extracted at three locations in a grayscale image of a steel plate cross section.

[0085] Figures 7(a) and (b) show the results of functional approximation of the grayscale gradation change in the thickness direction of electrogalvanized (EG) and hot-dip galvanized (GI) steel sheets using the following equation (2), in which the total number of regions in equation (1) is set to 3.

number

[0086] Figure 7(c) shows a functional approximation of the grayscale gradation change in the thickness direction of a galvannealed (GA) steel sheet using the following equations (3-1) and (3-2), where the total number of regions n in equation (1) is set to 3 and 5, respectively.

number

[0087] FIG. 7(d) is a graph showing functional approximation of the grayscale gradation change in the thickness direction of a steel sheet coated with aluminum silicon plating (Al-Si), which is a hot-dip aluminum-based coating, using the following formulas (4-1) and (4-2) in which the total number of regions n in formula (1) is set to 5 and 7, respectively.

number

[0088] Table 1 shows the coefficient a calculated by functional approximation of the grayscale gradation change in the thickness direction of the steel sheet extracted at 10 points in the grayscale image of the steel sheet cross section using Equation (1). i , b i , c i , s, the mean value u and standard deviation σ are shown. [Table 1]

[0089] As shown in Table 1, the number of terms (total number of regions n) in equation (1) for hot-dip galvanizing (GI) and electrogalvanizing (EG) was three. In addition, since the number of terms (total number of regions n) in formula (1) for alloy hot-dip galvanized (GA) was 3 and 5, the average value u and standard deviation σ of each coefficient are shown assuming n=5. Furthermore, since the number of terms (total number of regions n) in equation (1) for aluminum silicon plating (Al-Si), which is a molten aluminum-based plating, was 5 and 7, the average value u and standard deviation σ of each coefficient are shown assuming n = 7.

[0090] (Plating type determination step) The plating type determination step S5d is a step of determining the type of plating applied to the steel sheet based on the coefficients of the function of grayscale gradation change in the thickness direction of the steel sheet approximated in the grayscale gradation change function approximation step S5c.

[0091] When function approximation is performed using equation (1) in grayscale gradation change function approximation step S5c, the type of plating can be determined as follows based on the coefficients in equation (1).

[0092] When the grayscale gradation changes at multiple locations on a steel sheet are approximated using formula (1), if n=3 predominates and n=5 is mixed, this indicates that the coating layer contains cracks, etc. Therefore, the coating on the steel sheet can be identified as galvannealed (GA).

[0093] Furthermore, when the grayscale gradation changes at multiple locations on a steel sheet are approximated using formula (1), if n=5 predominates with n=7 mixed in, this indicates that the coating layer contains voids, cracks, etc. Therefore, the coating on the steel sheet can be identified as aluminum-silicon coating (Al-Si), which is a hot-dip aluminum-based coating.

[0094] Furthermore, when the grayscale gradation changes at multiple locations on the steel sheet are approximated using equation (1), if n=3, it indicates that the coating layer is sound and free of cracks, and therefore the coating on the steel sheet can be identified as either hot-dip galvanized (GI) or electrogalvanized (EG).

[0095] In this way, alloyed hot-dip galvannealed (GA) coating and aluminum-silicon coating (Al-Si), which is a hot-dip aluminum-based coating, can be distinguished based on the area n calculated by functionally approximating the grayscale gradation changes at multiple locations using equation (1).

[0096] However, for hot-dip galvanized (GI) and electrogalvanized (EG), the total number of regions calculated by functionally approximating the grayscale gradation changes extracted at multiple locations using equation (1) is 3, so they cannot be distinguished based on the number of regions. Therefore, as mentioned above, there is a difference between hot-dip galvanizing (GI) and electrogalvanizing (EG) in the ability of the coating layer to follow the shape of the base steel sheet. Therefore, the coefficient b i Based on this, the following distinctions are made:

[0097] Coefficient b in Equation (1) i are coefficients that represent the position in the sheet thickness direction of the area where the grayscale gradation changes suddenly in the grayscale gradation change, where coefficient b1 represents the depth direction position of the boundary between the resin and the coating layer, and coefficient b3 represents the depth direction position of the boundary between the coating layer and the base steel sheet. Therefore, the difference between b3 and b1, b3 - b1, represents the distance from the boundary between the resin and the coating layer to the boundary between the coating layer and the base steel sheet, i.e., the coating layer thickness.

[0098] Therefore, as shown in the following equations (5-1) and (5-2), hot-dip galvanizing and electro-galvanizing are distinguished by comparing the standard deviation σ|b1| of the coefficient b1, which represents the surface position of the coating layer, with the standard deviation σ|b3-b1| of the coating layer thickness b3-b1.

number

[0099] Here, the standard deviation σ|b1| represents the variation in the surface position of the coating layer in the grayscale gradation changes extracted at multiple locations in the grayscale image of the steel sheet cross section, and the standard deviation σ|b3-b1| represents the variation in the coating layer thickness in the grayscale gradation changes extracted at multiple locations in the grayscale image of the steel sheet cross section.

[0100] Therefore, as shown in formula (5-1), when σ|b3-b1| is greater than σ|b1|, the coating layer thickness varies more significantly than at the surface of the coating layer, i.e., the coating layer has poor conformability to the shape of the substrate steel sheet. Therefore, when formula (5-1) is satisfied, the coating layer is identified as hot-dip galvanized (GI) (see Figure 3(b)).

[0101] On the other hand, as shown in formula (5-2), when σ|b3-b1| is smaller than σ|b1|, the variation in the surface position of the coating layer is large compared to the coating layer thickness, that is, the coating layer has high conformability to the shape of the substrate steel sheet. Therefore, when formula (5-2) is satisfied, the coating layer is identified as electrogalvanized (GI) (see Figure 4(c)).

[0102] In this way, the distinction between hot-dip galvanized and electro-galvanized using equations (5-1) and (5-2) can be confirmed by the standard deviation σ|b1| of coefficient b1 and the standard deviation σ|b3-b1| of coefficient b3-b1 shown in Table 1 above.

[0103] As shown in Table 1, in hot-dip galvanized (GI) coating, the standard deviation σ|b1| of b1, which represents the surface position of the coating layer, is 0.18, and the standard deviation σ|b3-b1| of the coating layer thickness b3-b1 is 3.06, satisfying formula (5-1) (σ|b3-b1|>σ|b1|). On the other hand, in electrogalvanized (EG), the standard deviation σ|b1| of the coefficient b1, which represents the surface position of the plating layer, is 1.91, and the standard deviation σ|b3-b1| of the plating layer thickness b3-b1 is 0.03, satisfying equation (5-2) (σ|b3-b1|≦σ|b1|).

[0104] As described above, according to the method for identifying the type of plating applied to automotive steel sheets according to this embodiment, a steel sheet is sampled from an automotive body component, and the type of plating applied to the steel sheet can be identified 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 automotive body component that makes up the automotive body and efficiently build a material database without performing instrumental analysis, which requires enormous measurement costs and effort, to identify the type of plating applied to the steel sheet.

[0105] The above explanation is based on the assumption that the plating applied to the steel sheet sampled from the vehicle body component is one of four types: alloyed hot-dip galvannealing, aluminum-silicon plating which is a hot-dip aluminum-based plating, hot-dip galvanizing, and electrogalvanizing. Therefore, in the plating type determination step S5d according to aspect B of the plating type identification process S5, the four types of plating are identified based on the value of the total number n of regions in formula (1), the standard deviation σ|b1| of the surface position of the plating layer, and the standard deviation σ|b3-b1| of the plating layer thickness.

[0106] However, it is also possible to identify whether the plating applied to a steel sheet sampled from a vehicle body component is one of two types: alloyed hot-dip galvannealing or aluminum-silicon plating, which is a hot-dip aluminum-based plating. In this case, in the plating type determination step S5d, the present invention may identify the two types of plating based on whether the total number n of regions in formula (1) is 3 and 5, or 5 and 7.

[0107] Furthermore, it is also conceivable to identify whether the plating applied to a steel sheet sampled from a vehicle body component is one of two types, hot-dip galvanized or electro-galvanized. In this case, in the plating type determination step S5d, the present invention may check whether the total number n of regions in formula (1) is 3 in both cases, and identify the two types of plating based on the standard deviation σ|b1| of the surface position of the plating layer and the standard deviation σ|b3-b1| of the plating layer thickness.

[0108] <Plating type identification device>

[0109] An automotive steel sheet plating type identification device 1 (hereinafter referred to as "plating type identification device 1") according to an embodiment of the present invention identifies the type of plating applied to steel sheets used in vehicle body components of automobile bodies. As shown in Fig. 8 as an example, the plating type identification device 1 includes a steel sheet cross-section grayscale image acquisition unit 11, a grayscale gradation change extraction unit 13, and a plating type identification unit 15. The plating type identification device 1 may be configured by a CPU (Central Processing Unit) of a computer (such as a PC). In this case, the above-mentioned units function when the CPU of the computer executes a predetermined program.

[0110] <Steel plate cross-section grayscale image acquisition unit> 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 in the plate thickness direction of a test specimen including a steel plate taken from a vehicle body component.

[0111] <Grayscale gradation change extraction section> The grayscale gradation change extraction unit 13 extracts grayscale gradation changes in the thickness direction of the steel plate in the grayscale image of the cross section of the steel plate acquired by the steel plate cross-section grayscale image acquisition unit 11.

[0112] The grayscale gradation change extraction unit 13 may extract the grayscale gradation change in the plate thickness direction, similar to the grayscale gradation change extraction step S3 of the plating type identification method according to the present embodiment described above.

[0113] <Plating type identification section> The plating type identifying unit 15 identifies the type of plating applied to the steel sheet based on the grayscale gradation change in the thickness direction of the steel sheet extracted by the grayscale gradation change extracting unit 13.

[0114] The plating type identification unit 15 may implement aspect A or aspect B (FIG. 6) of the plating type identification step S5 of the plating type identification method according to the present embodiment described above, and specific configurations thereof include configuration A and configuration B shown in FIG. 9.

[0115] [Configuration A] The plating type identification unit 15 according to configuration A performs aspect A (FIG. 5(a)) of the plating type identification step S5, and as shown in FIG. 9(a), has a grayscale gradation change standard pattern acquisition unit 15a and a plating type matching determination unit 15b.

[0116] (Grayscale gradation change standard pattern acquisition unit) The grayscale gradation change standard pattern acquisition unit 15a executes the grayscale gradation change standard pattern acquisition step S5a of the plating type identification process S5. That is, the grayscale gradation change standard pattern acquisition unit 15a acquires, as standard patterns for various platings, grayscale gradation changes in the plate thickness direction in grayscale images of optical microscope photographs of a portion including a cross section of a standard steel plate to which a plating of a known type has been applied. The standard patterns are acquired as standard pattern functions that approximate the grayscale gradation changes extracted from the grayscale images of the cross sections of the standard steel plate.

[0117] (Plating type verification and judgment unit) The plating type collation and determination unit 15b performs the plating type collation and determination step S5b of the plating type identification step S5. That is, the plating type collation and determination unit 15b compares the grayscale gradation change in the thickness direction of the steel sheet with standard patterns obtained for various platings, and determines the type of plating applied to the steel sheet.

[0118] The plating type collation determination unit 15b performs the collation of the plating type based on the similarity between the grayscale gradation change in the thickness direction of the steel sheet and the standard pattern function. Then, among the standard pattern functions of various platings, the plating identical to the standard pattern function with the highest similarity is determined to be the plating applied to the steel sheet.

[0119] As described above, the similarity can be determined by the sum of squares of the residuals between the grayscale gradation change in the thickness direction in the grayscale image of the steel sheet cross section and the grayscale gradation given by the standard pattern function, the difference between the value integrated in the thickness direction, or the correlation coefficient.

[0120] [Configuration B] The plating type identification unit 15 according to configuration B performs aspect B (FIG. 5(b)) of the plating type identification step S5, and as shown in FIG. 9(b), has a grayscale gradation change function approximation unit 15c and a plating type matching determination unit 15b.

[0121] (Grayscale tone change function approximation part) The grayscale gradation change function approximation unit 15c performs the grayscale gradation change function approximation step S5c of the plating type identification step S5. That is, the grayscale gradation change function approximation unit 15c approximates the grayscale gradation change in the thickness direction of the steel sheet extracted by the grayscale gradation change extraction unit 13 with a function.

[0122] The grayscale gradation change function approximation unit 15c may use the above-mentioned formula (1) to function-approximate the grayscale gradation change in the thickness direction of the steel plate.i , b i , c i ,S,n are calculated. When function approximation is performed using equation (1), the number of terms in equation (1) (total number of regions n) varies depending on the type of plating applied to the steel sheet and the location where the grayscale gradation change in the sheet thickness direction is extracted.

[0123] (Plating type determination section) The plating type determination unit 15d performs the plating type determination step S5d of the plating type identification step S5. That is, the plating type determination unit 15d determines the type of plating applied to the steel sheet based on the coefficients of the function of the grayscale gradation change in the thickness direction of the steel sheet approximated by the grayscale gradation change function approximation unit 15c.

[0124] When the grayscale gradation change function approximation unit 15c performs a function approximation of the grayscale gradation change in the plate thickness direction using (1), the plating type can be determined in the same manner as in the plating type determination step S15d described above.

[0125] <Plating type identification program> The embodiment of the present invention can be configured as a plating type identification program that causes the plating type identification device 1 configured by a computer to function. That is, the plating type identification program according to this embodiment identifies the type of plating applied to steel sheets used in vehicle body components of automobile bodies. The plating type identification program according to this embodiment causes a computer to function as a steel sheet cross-section grayscale image acquisition unit 11, a grayscale gradation change extraction unit 13, and a plating type identification unit 15.

[0126] The plating type identification program according to this embodiment may cause the plating type identification unit 15 to function as a grayscale gradation change standard pattern acquisition unit 15a and a plating type comparison determination unit 15b, as shown in FIG. 9(a). Alternatively, the plating type identification unit 15 may function as a grayscale gradation change function approximation unit 15c and a plating type determination unit 15d, as shown in FIG. 9(b).

[0127] 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 applied to a steel sheet using a grayscale image of an optical microscope photograph of the cross section of a steel sheet taken from a steel sheet extracted from a vehicle body component. This makes it possible to collect material information for each vehicle body component that makes up the automobile body and efficiently build a material database without performing instrumental analysis, which requires enormous measurement costs and effort, to identify the type of plating applied to the steel sheet.

[0128] In this embodiment, the type of plating applied to steel sheets used for vehicle body components of automobile bodies is any one of hot-dip galvanizing, electrogalvanizing, galvannealed hot-dip galvanizing, and aluminum-silicon plating, which is a hot-dip aluminum-based plating. This is because most commercially available automobile body components use steel sheets that have been plated with any of the above four types of plating. Therefore, in identifying the type of plating applied to automotive steel sheets according to the present invention, there is no particular problem even if it is any one of the above four types of plating.

[0129] However, even if a steel sheet plated with a plating other than the above four types is used for a vehicle body component, it has been confirmed that the distribution pattern of grayscale gradations in an optical microscope photograph of the cross section of the steel sheet differs from that of a steel sheet plated with the above four types of plating. Therefore, according to the present invention, it is possible to identify types of plating other than the above four types based on the distribution pattern of a grayscale gradation histogram created from a grayscale image of an optical microscope photograph of the cross section of the steel sheet.

[0130] In the above explanation, the optical microscope photograph of the cross section of the steel sheet includes the resin in which the steel sheet was embedded and fixed when the test specimen was prepared, but in the present invention, the photograph may also include a resin that has been painted on the surface of the steel sheet. Even in such a case, since the resin is black, it does not pose a problem in identifying the type of plating applied to the steel sheet according to the present invention.

[0131] In the present invention, a digital camera such as a CCD camera can be suitably used to take an optical microscope photograph of the cross section of the steel sheet, thereby enabling the taken optical microscope photograph to be obtained as digital data of a grayscale image.

[0132] However, the present invention may also be such that an optical microscope photograph of a steel sheet cross section taken using a digital camera is acquired as digital data of a color image, and then converted into digital data of a grayscale image.

[0133] The grayscale gradation of the grayscale image needs to be 8 bits (256 gradations) or more, and digital data of a high-resolution grayscale image 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 will not be possible to distinguish, for example, aluminum silicon plating, which is a molten aluminum-based plating after hot press forming.

[0134] Furthermore, optical microscope photographs of the cross section of a steel sheet taken with a digital camera are preferably taken at a magnification of about 1000 to 2000. This is because, although higher magnification improves resolution, the captured area (field of view) is smaller, and there is a concern that the photograph may not provide average information about the entire coating layer on the cross section of the steel sheet.

[0135] Furthermore, the resolution of the digital camera is as important as the magnification of the optical microscope photograph, but there are no particular limitations as long as it is suitable for the magnification of the optical microscope photograph, and a digital camera with a pixel count of approximately 1.0M to 10.0M pixels can be suitably used.

[0136] In the present invention, in order to accurately identify the type of plating using a grayscale image of an optical microscope photograph including a steel sheet cross section, it is preferable to make the contrast of the grayscale image uniform. Typically, in a grayscale image of an optical microscope photograph taken after a plated steel sheet is embedded in black resin, the area corresponding to the resin is darkest (black), the plating layer is lightest (white), and the base steel sheet is intermediate (gray). However, aluminum-silicon plating, which is a hot-dip aluminum-based plating, differs from other platings in that the grayscale gradation of the plating layer is an intermediate color that is not significantly different from the base steel sheet. Taking this into consideration, it is advisable to adjust the contrast of the grayscale image based on the black part of the resin and the intermediate color part of the base steel sheet.

[0137] When adjusting the contrast of a grayscale image, if the full scale of the grayscale gradation is 100% (0% = black, 100% = white), it is desirable to adjust the black area corresponding to the resin to 5 to 10% and the intermediate color area of ​​the base steel sheet to 50%.

[0138] The reason why the grayscale gradation of the black parts corresponding to the resin is not set to 0% is to prevent excessive contrast expansion adjustment, which can cause so-called "black crush" and reduce the accuracy of plating type identification. Furthermore, by adjusting the contrast as described above, it is difficult to expand the width of the intermediate color and black parts too much, which also makes it possible to suppress "whiteout" in the white parts.

[0139] For these reasons, in the present invention, it is advisable to take an optical microscope photograph of the cross section of a steel sheet by adjusting the contrast so that the histogram distribution of the grayscale image falls within the range of 3% to 97% of the full scale. [Explanation of symbols]

[0140] 1. Plating type identification device 11 Steel plate cross-section grayscale image acquisition unit 13 Grayscale gradation change extraction section 15 Plating type identification section 15a Grayscale gradation change standard pattern acquisition unit 15b Plating type verification and judgment unit 15c Grayscale gradation change function approximation part 15d Plating type determination section

Claims

1. A method for identifying the type of plating applied to a steel sheet used for a body component of an automobile body, comprising: a steel plate cross-section grayscale image acquisition step of collecting a steel plate from the vehicle body component to prepare a test specimen including the steel plate, taking an optical microscope photograph of a cross section of the steel plate in the plate thickness direction of the prepared test specimen, and acquiring a grayscale image of the taken optical microscope photograph; a grayscale gradation change extraction step of extracting a grayscale gradation change in the thickness direction of the steel plate from the acquired grayscale image of the steel plate cross section; and a plating type identifying step of identifying the type of plating applied to the steel sheet based on the extracted grayscale gradation change in the sheet thickness direction of the steel sheet.

2. The plating type identifying step includes: a grayscale gradation change standard pattern acquisition step for acquiring a grayscale gradation change standard pattern for various platings, the grayscale gradation change in the plate thickness direction in a grayscale image of an optical microscope photograph obtained by capturing an image of a portion including a cross section of a standard steel plate to which a plating type is previously known; and a plating type comparison step of comparing the grayscale gradation change in the thickness direction of the steel sheet extracted in the steel sheet cross-section grayscale image acquisition step with the standard patterns acquired for various platings, and determining the type of plating applied to the steel sheet.

3. In the step of acquiring a standard pattern of grayscale gradation change, The standard pattern is obtained as a function having the position in the plate thickness direction as an independent variable and the grayscale gradation at each position in the plate thickness direction as a dependent variable; In the plating type collation determination step, 3. The method for identifying the type of plating applied to an automotive steel sheet according to claim 2, further comprising determining the type of plating applied to the steel sheet based on the degree of similarity between a grayscale gradation change in the thickness direction of the steel sheet and a function of the standard pattern.

4. The plating type identifying step includes: a grayscale gradation change function approximation step of approximating the grayscale gradation change in the thickness direction of the steel plate extracted in the grayscale gradation change extraction step as a function in which a position in the thickness direction is an independent variable and a grayscale gradation at each position in the thickness direction is a dependent variable; and a plating type determination step of determining the type of plating applied to the steel sheet based on coefficients of the approximated function of grayscale gradation change in the sheet thickness direction of the steel sheet.

5. 5. The method for identifying a plating type of an automotive steel sheet according to claim 4, wherein in the grayscale gradation change function approximation step, function approximation is performed using the following formula (1) in which a sigmoid function is combined with each of the grayscale gradation changes in the thickness direction of the steel sheet extracted in the grayscale gradation change extraction step: [Equation 1]

6. In the grayscale gradation change extraction step, extracting grayscale gradation changes in the thickness direction of the steel plate at a plurality of locations of the steel plate in the grayscale image of the steel plate cross section; Each of the grayscale gradation changes in the thickness direction extracted at the plurality of locations is functionally approximated using the formula (1), In the plating type determination step, When the total number n of regions of the formula (1) obtained by functional approximation for each of the grayscale gradation changes in the sheet thickness direction extracted at the plurality of locations is 3, it is determined that the type of coating applied to the steel sheet is either electrogalvanized or hot-dip galvanized; The coefficient b calculated by functionally approximating each of the grayscale gradation changes in the thickness direction extracted at the plurality of locations using Equation (1) 3 and coefficient b 1 Difference from b 3 -b 1 The variation of and coefficient b 1 Compare the variation of and b 3 -b 1 The variation of b 1 If the variation is larger than the above, it is determined that the coating applied to the steel sheet is hot-dip galvanized, b 3 -b 1 The variation of b 1 6. The method for identifying the type of plating applied to an automotive steel sheet according to claim 5, wherein the plating applied to the steel sheet is determined to be electrogalvanized if the variation in the plating type is smaller than the variation in the plating type applied to the steel sheet.

7. In the plating type determination step, When the total number n of regions of the formula (1) obtained by functionally approximating each of the grayscale gradation changes in the thickness direction extracted at a plurality of locations of the steel sheet is 3 or 5, it is determined that the type of coating applied to the steel sheet is galvannealed coating; 7. The method for identifying the type of plating applied to an automotive steel sheet according to claim 5 or 6, characterized in that, when a total number n of regions of Equation (1) obtained by functionally approximating the grayscale gradation changes in the sheet thickness direction extracted from a plurality of locations on the steel sheet is 5 or 7, the type of plating applied to the steel sheet is determined to be a hot-dip aluminum-based plating.

8. 6. The method for identifying the type of plating applied to automotive steel sheet according to claim 1, wherein the plating applied to the steel sheet is any one of hot-dip galvanizing, electrogalvanizing, alloyed hot-dip galvanizing, and hot-dip aluminum-based plating.

9. A plating type identification device for automotive steel sheets that identifies the type of plating applied to steel sheets used in body components of an automobile body, a steel plate cross-section grayscale image acquisition unit that acquires a grayscale image of an optical microscope photograph of a steel plate cross section in a plate thickness direction of a test specimen including the steel plate collected from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in the thickness direction of the steel plate from the acquired grayscale image of the steel plate cross section; and a plating type identification unit that identifies the type of plating applied to the steel sheet based on the extracted grayscale gradation change in the thickness direction of the steel sheet.

10. A plating type identification program for automotive steel sheets that identifies the type of plating applied to steel sheets used in body components of an automobile body, comprising: Computer, a steel plate cross-section grayscale image acquisition unit that acquires a grayscale image of an optical microscope photograph of a steel plate cross section in a plate thickness direction of a test specimen including the steel plate collected from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in the thickness direction of the steel plate from the acquired grayscale image of the steel plate cross section; and a plating type identification unit that identifies the type of plating applied to the steel sheet based on the extracted grayscale gradation change in the thickness direction of the steel sheet.

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