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

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, an apparatus, and a program for identifying the type of plating applied to steel sheets for use in automobile body components. [Background technology]

[0002] The various types of steel sheets used as materials for the body components that make up an automobile body have continued to change over the years, with progress being made in increasing strength, thinning, and weight reduction. In particular, due to the demands of stricter automobile crash 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 continually increasing.

[0003] Automotive body components are often manufactured by cold press forming of steel sheets, which has high production efficiency and allows for 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 shapes and mold shapes of the automotive body components from the design stage onwards in order to avoid defects during press forming.

[0004] However, if the thickness of the body components is reduced in order to reduce the weight of the car body, the rigidity of the body components or the rigidity of the entire car body will decrease. Therefore, it is not possible to uniformly increase the strength and reduce the thickness of all the body components. Which parts of the car body are strengthened or thinned, and how to design the optimal shape of the car body components according to the steel plate strength, are decided based on the experience of the engineers who design the car body and the accumulated technology that the vehicle manufacturers have cultivated. For this reason, it is common to learn and absorb the ingenuity, innovative technologies, and new material usage and construction methods seen in the design of the car bodies manufactured by each manufacturer through research of the car bodies on sale.

[0005] In such an investigation, the automobile body is disassembled at the joints between the body components, separated into the body components, and a part of the steel plate, which is the material of the body components, is taken (sampled) to identify the characteristics of the steel plate by microscopic observation of hardness, plate thickness, and structure, and to perform comparative analysis. To identify the characteristics of the steel plate, for example, the steel plate taken from the body component is embedded in a resin molding material to prepare a test specimen, the cross section of the steel plate embedded in the test specimen is mirror-polished, and the thickness and hardness of the steel plate are measured by the micro-Vickers method. Furthermore, the tensile strength (TS) of the steel plate is calculated from the measured hardness using a conversion formula. Then, by comparing these values ​​with the manufacturing standards, it becomes possible to identify the characteristics (standards, etc.) of the steel plate used in the body components.

[0006] A single automobile body has 300 to 500 body components, and the materials of each body component for each vehicle are compiled and stored in a material database. This material 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 that is on sale is disassembled and data such as the materials of the parts used in the vehicle (strength, plate thickness, weight, dimensions, whether or not there is surface treatment, plating layer thickness, type of base (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 stored, the data is classified by group names given to parts classified according to a standard that is uniform among multiple vehicles of different vehicle manufacturers or models, and part function names given based on the functions of the parts and that are uniform among multiple vehicles. This makes it possible to quickly extract parts to be compared and easily compare multiple vehicles of different vehicle 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] For the body components of automobile bodies, steel sheets that have not been surface-treated (bare materials, JIS standards SPC, SPH, etc.) and plated surface-treated steel sheets (Japan Iron and Steel Federation standards SEC, SGC, etc.) are used. The plating applied to the surface-treated steel sheets includes zinc plating systems such as hot-dip galvanizing (GI), alloyed hot-dip galvanizing (GA), and electrolytic galvanizing (EG), and aluminum plating systems such as aluminum-silicon plating (Al-Si), which is a hot-dip aluminum-based plating. These surface-treated steel sheets are appropriately selected according to the required specifications such as rust prevention specifications, press formability, and painted appearance, and the adoption policy of each part or each vehicle manufacturer. In Japan, vehicle manufacturers mostly use GA for the majority of cold-pressed parts and Al-Si for hot-pressed parts, but overseas vehicle manufacturers often use GI or EG for cold pressing and Al-Si for hot pressing.

[0009] In creating a database for each vehicle, it is possible 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 so, it is necessary to take a sample of steel sheet for each vehicle body component and identify the type of plating applied to the steel sheet.

[0010] To identify the type of plating applied to a steel sheet, methods are used in which the components in the plating layer are quantified by wet analysis, or the element distribution in the plating layer is identified by spectroscopic analysis. As a method for wet analysis of a plating layer, there is a method in which the plating layer of a steel sheet is peeled off and dissolved with an alkaline solution or the like and then analyzed. 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] The method of identifying the element distribution of the plating layer by spectroscopic analysis has the advantage of being able to identify the plating type of the steel sheet non-destructively. On the other hand, spectroscopic analysis has the disadvantages of being very costly to measure and being time-consuming to prepare the analytical samples because the measurement is performed in a vacuum. In particular, since an automobile body usually has 300 to 500 body components per vehicle, identifying the plating type of the steel sheet used in the body components by the above-mentioned wet analysis or spectroscopic analysis requires huge measurement costs and effort.

[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 components, which can identify the type of plating applied to steel sheets of vehicle body components without requiring enormous measurement costs and 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 obtaining a steel plate cross-section grayscale image of the steel plate by extracting the steel plate from the vehicle body component to prepare a test specimen including the steel plate, taking an optical microscope photograph of a steel plate cross-section in a 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 a thickness direction of the steel plate from the acquired grayscale image of the cross section of the steel plate; 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 identification step includes: a step of acquiring a standard pattern of grayscale gradation change for various platings, using a standard pattern of 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; 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 grayscale gradation change pattern, 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 a function of the standard pattern.

[0016] (4) In the above (1), The plating type identification 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 the position in the thickness direction is an independent variable and the 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 coefficient 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 a function approximation is performed for each of the grayscale gradation changes in the thickness direction of the steel plate extracted in the grayscale gradation change extraction step, using the following formula (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 a plate thickness direction at a plurality of locations of the steel plate in the grayscale image of the cross section of the steel plate; The grayscale gradation changes in the thickness direction extracted at the plurality of locations are each approximated by a function using the above formula (1), 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 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 of 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 the formula (1) is compared with the variation of 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; When the variation of b3-b1 is smaller than the variation of b1, the plating applied to the steel sheet is determined to be electrolytic zinc plating.

[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 sheet thickness direction extracted at a plurality of points of the steel sheet is 3 or 5, it is determined that the type of coating applied to the steel sheet is galvannealed, When the total number n of regions of the formula (1) that respectively approximates the grayscale gradation changes in the sheet thickness direction extracted at a plurality of points on the steel sheet as functions is 5 and 7, the type of plating applied to the steel sheet is determined to be a hot-dip aluminum-based plating.

[0020] (8) In any one of (1) to (5) above, The plating applied to the steel sheet is characterized in that it is any one of hot-dip galvanizing, electrolytic galvanizing, 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 is for identifying the type of plating applied to steel sheets used for vehicle body components of an automobile body, and is a steel plate cross-section grayscale image acquisition unit for acquiring 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 taken from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in a thickness direction of the steel plate from the acquired grayscale image of the cross section of the steel plate; 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 is for identifying the type of plating applied to a steel sheet used for a vehicle body component of an automobile body, comprising: Computer, a steel plate cross-section grayscale image acquisition unit for acquiring 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 taken from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in a thickness direction of the steel plate from the acquired grayscale image of the cross section of the steel plate; 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 plate based on the extracted grayscale gradation change in the thickness direction of the steel plate. Effect of the Invention

[0023] According to the present invention, a grayscale gradation change in the sheet thickness direction is extracted using a grayscale image of an optical microscope photograph of a cross section in the sheet thickness direction of a steel sheet taken from a vehicle body component, and the type of plating can be identified based on the grayscale gradation change. This makes it possible to collect material information of each vehicle body component that constitutes an automobile body and efficiently build a material database without performing instrumental analysis that requires huge measurement costs and labor to identify the type of plating applied to the steel sheet. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a flow chart showing a process flow in a method for identifying the type of plating on automotive steel sheets according to an embodiment of the present invention. [Diagram 2] FIG. 1 is a diagram illustrating the items that can identify the plating type based on an optical microscope photograph of a cross section of a steel sheet in the sheet thickness direction in a test specimen in which a plated steel sheet is embedded and fixed in resin, which explains how the present invention was arrived at. [Diagram 3] FIG. 1 is a graph showing a schematic representation of a grayscale gradation change in the sheet thickness direction extracted from grayscale images of optical microscope photographs of cross sections of steel sheets subjected to (a) galvannealed hot-dip plating (GA) and (b) hot-dip galvanized plating (GI) in the course of arriving at the present invention. [Figure 4]FIG. 1 is a graph showing a schematic representation of the grayscale gradation change in the sheet thickness direction extracted from grayscale images of optical microscope photographs of the cross sections of steel sheets treated with (c) electrolytic galvanization (EG) and (c) aluminum silicon plating (Al-Si), which is a molten aluminum-based plating, in the course of developing the present invention. [Diagram 5] FIG. 1 is a flow chart illustrating two aspects of a plating type identifying step in a plating type identifying method for automotive steel sheets according to an embodiment of the present invention. [Figure 6] FIG. 13 is a diagram illustrating the difference depending on the location where the grayscale gradation change in the sheet thickness direction is extracted in a grayscale image of an optical microscope photograph of a cross section of a plated steel sheet in this embodiment ((a) Location where the grayscale gradation change is extracted, (b) Graph showing the difference in the grayscale gradation change extracted at multiple locations). [Figure 7] FIG. 1 is a graph showing a grayscale gradation change in the sheet thickness direction, approximated by a sigmoid function, for steel sheets subjected to each of (a) electrolytic galvanization, (b) hot-dip galvanization, (c) alloyed hot-dip galvanization, and (d) aluminum-silicon plating, which is a hot-dip aluminum-based plating, in a method for identifying the type of plating of automotive steel sheets according to an embodiment of the present invention. [Figure 8] 1 is a diagram showing an example of the configuration of a plating type identification device for automotive steel sheets according to an embodiment of the present invention. FIG. [Figure 9] FIG. 2 is a diagram for explaining a specific configuration of a plating type identifying unit in the plating type identifying device for automotive steel sheets according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] Before describing the method, device, and program for identifying the type of plating on automotive steel sheets according to the embodiments 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 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 including a cross section of the steel sheet in the thickness direction (hereinafter referred to as "steel sheet cross section" in this application) were taken, and the thickness of the steel sheet was measured, the presence or absence of a plating layer was observed, and the thickness of the plating layer was measured, etc.

[0027] Therefore, the inventors conceived 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 considered it 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 indicate the characteristics of a coating layer in an optical microscope photograph of a steel sheet cross section, 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 steel sheet cross section shown in FIG.

[0029] (1) Morphology of the plating layer The morphological characteristics of the plating layer include multiple crystal compositions and eutectic structures found in heterogeneous plating layers, as well as 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, Fig. 2(d)), which is a molten aluminum-based plating after hot press forming, and cracks and voids are observed. In addition, the coating layer in which the cracks occurred is a morphology commonly seen in galvannealed coatings (GA, Figure 2(a)), which is caused by a decrease in the ductility of the Zn-Fe alloy crystals formed by 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 were observed in the coating layers, and the morphology of the coating layers was 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) is observed in the area corresponding to the boundary between the steel sheet substrate (base steel sheet) and the coating layer. Such a contrast change is presumed to be due to the presence or absence of a continuous component diffusion layer between the steel sheet substrate and the coating layer.

[0033] Since galvannealed (GA) coating has a continuous Fe-Al-Zn diffusion layer, 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 electro-galvanized (EG) coatings do not have a chemically-dispersed layer, and therefore have the characteristic that there is a clear change in contrast at the boundary between the base steel sheet and the coating layer, as shown in Figures 2(b) and (c). In addition, with aluminum-silicon plating (Al-Si), a molten aluminum-based coating, the color tones (grayscale gradation) of the base steel sheet and the coating layer are almost the same, as shown in Figure 2(d), so the change in contrast at the boundary is unclear.

[0034] (3) Ability to conform to the shape of the substrate steel The ability to conform to the shape of the substrate steel sheet refers to the effect of the surface shape (irregularities) of the substrate steel sheet on the irregularities of the coating layer surface. Hot-dip galvanizing (GI) is a process in which a steel sheet is immersed in molten zinc and then pulled up. As a result, 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 steel sheet. In contrast, in electrolytic galvanization (EG), electrodeposition occurs along the unevenness of the base steel sheet, so the thickness of the plating layer is almost constant, 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 plating layer surface.

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

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

[0037] Graphs that show schematic grayscale gradation changes in the thickness direction of a steel plate in grayscale images of optical microscope photographs of the cross sections of the steel plate are shown in Figures 3 and 4. Note that in Figures 3 and 4, the grayscale gradation changes in the thickness direction of a steel plate extracted from multiple locations (three locations) on the steel plate are displayed by overlapping a solid line, a dashed line, and a dashed dotted line.

[0038] Here, Fig. 3(a) shows the grayscale gradation change in a steel sheet that has been subjected to galvannealed (GA) coating, Fig. 3(b) shows the grayscale gradation change in a steel sheet that has been subjected to hot-dip galvannealed (GI) coating, Fig. 4(c) shows the grayscale gradation change in a steel sheet that has been subjected to electrolytic galvanization (EG) coating, and Fig. 4(d) shows the grayscale gradation change in a steel sheet that has been subjected to aluminum-silicon plating (Al-Si), which is 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 plating layer is the lightest (white) and therefore has a large grayscale value, while the resin is the darkest (black) and therefore has a small grayscale value, with the grayscale value of the base steel sheet being intermediate between that of the plating layer and the resin. Note that there are two types of resin assumed: a paint film on the steel sheet, and a resin used to embed and fix the steel sheet, and in either case, the grayscale is clearly darker than that of the plating layer or the base steel sheet.

[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 hot-dip galvannealed (GA) or aluminum-silicon-coated (Al-Si), a hot-dip aluminum-based coating, tend to have cracks (defects in the depth direction or peeling of the coating) 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 tone change in the thickness direction is extracted in the grayscale image of the cross section of the steel sheet, the effects of cracks and voids in the coating layer can be seen in the distribution of grayscale tone change, as shown in Figures 3(a) and 4(d).

[0042] In contrast, in the case of hot-dip galvanized (GI) and electrogalvanized (EG) steel sheets, the coating layer is homogeneous without cracks or voids, as shown in Figures 2(b) and (c), and therefore, the grayscale gradation changes in the grayscale images of the cross section of the steel sheet are distributed almost uniformly in the coating layer, as shown in Figures 3(b) and 4(c).

[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 base steel sheet that is darker than the base steel sheet, as shown in Figures 2(b) and (c). Here, in the case of hot-dip galvanized (GI) steel, the boundary has a grayscale tone close to that of the base steel sheet (Figure 3(b)A), whereas in the case of electrogalvanized (EG) steel, the boundary has a grayscale tone close to that of the resin (Figure 4(c)B).

[0044] In a 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 between the plating layer and the base steel sheet is observed that has a different grayscale tone from that of the plating layer and the base steel sheet.

[0045] In addition, even in a grayscale image of the cross section of a steel sheet that has been coated with aluminum-silicon plating (Al-Si), a molten aluminum-based coating, the grayscale gradation of the coating layer and the base steel sheet are almost the same, as shown in Figure 4(d), making it 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), which is a molten aluminum-based plating, is characterized by the existence 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 change in the plate thickness direction of the plating layer (Figure 4(d)).

[0046] Regarding "(3) Ability to conform to the shape of the base steel sheet" In grayscale images of the cross sections 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 change in 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 changes in electrolytic galvanization (EG) show that the thickness of the plating layer is constant, while the surface position of the plating layer fluctuates in accordance with the surface shape of the base steel sheet, as shown in Figure 4(c).

[0047] In this way, the inventors have found that the characteristics of various platings applied to a steel sheet are expressed in the grayscale gradation change in the sheet thickness direction in a 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] <How to identify plating type> The plating type identifying method for automotive steel sheets according to this embodiment (hereinafter referred to as the "plating type identifying 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 identifying method according to this embodiment includes a steel sheet cross-section grayscale image acquiring step S1, a grayscale gradation change extracting step S3, and a plating type identifying 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, electrolytic galvanizing, 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 taken from a vehicle body component to prepare a test specimen containing 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 and fixing the collected steel plate in a resin for optical microscope observation. The resin for embedding and fixing the steel plate can be, for example, a heat-curing type epoxy resin or a room temperature curing type 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 structure of the steel sheet, 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] The aforementioned Figures 3 and 4 show graphs in which the grayscale gradation changes in the thickness direction of the steel sheet extracted at multiple locations (3 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 cross sections of the steel sheet for each of the plating types (a) to (d). As shown in Figures 3 and 4, the plating layer is inhomogeneous, 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 sheet 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] When cracks or peeling are observed in the area showing the coating layer, as in the distribution of grayscale gradation changes in the thickness direction of a steel plate shown in Figure 3(a), it can be identified as galvannealed (GA) (item (1) Morphological characteristics 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 case of 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 surface position of the plating layer fluctuates while the plating layer thickness is constant, as can be seen from the grayscale gradation change in the thickness direction of the steel plate. This enables the product to be identified as electrolytic galvanized (EG) (item (3) Ability to conform to the shape of the base steel plate).

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

[0061] In the plating type identification step S5, the plating type can be more specifically identified 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 a gray-scale gradation change standard pattern acquisition step S5a and a 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, grayscale gradation changes 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, for various platings.

[0064] The standard pattern of grayscale gradation change in the cross section of the standard steel plate can be obtained by the same procedure as 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 including a standard steel plate with a known type of plating is prepared, and an optical microscope photograph of a portion including the cross section of the standard steel plate in the prepared standard test piece (standard steel plate cross section) is taken, 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 carrying out these operations in the same manner for standard steel sheets with different types of plating, standard patterns of grayscale gradation changes in the sheet thickness direction can be obtained 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 photographed 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) showing a grayscale gradation change in the plate thickness direction in a grayscale image of a cross section of a 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 change, for example, a function that approximates the grayscale gradation change 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 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 a standard pattern similar to this may be visually compared to determine the plating type.

[0069] Alternatively, when a standard pattern function of grayscale gradation change is acquired in the grayscale gradation change standard pattern acquisition step S5a, the comparison may be performed by determining the similarity between the grayscale gradation change in the thickness direction of the steel plate and the standard pattern function. In this case, the similarity can be calculated by using the sum of squared residuals between the grayscale tone in the thickness direction in the grayscale image of the steel sheet 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] As the similarity, the grayscale gradation change in the thickness direction of the steel sheet cross section may be expressed as a function, and the correlation coefficient between the grayscale gradation change and a standard pattern function may be calculated. When the correlation coefficient is used as the similarity, it is determined that the closer the correlation coefficient value is to 1, the higher the similarity. Then, among the standard pattern functions of various platings, the plating identical to the standard pattern function having a high 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 change in the thickness direction of the steel sheet with a standard pattern, it is preferable 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 from 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 described above), it can be identified as plating of that standard pattern.

[0072] As shown in Fig. 3(a), steel sheets with galvannealed (GA) coating show the effects of cracks and peeling of 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 treated 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 depending on the location of the steel sheet where the grayscale gradation changes are extracted, as shown in Figure 3(d). Therefore, if any of the grayscale gradation changes extracted at multiple locations are 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] In addition, as shown in Fig. 3(b), for steel sheets that have been subjected to hot-dip galvanization (GI), the thickness of the plating layer changes 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 of 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, for electrolytic galvanized (EI) steel sheets, the position of the plating layer changes depending on the location where the grayscale gradation change is extracted, but the thickness of the plating layer is almost constant, as shown in Fig. 3(c). Therefore, even if the location of 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 electrolytic galvanized.

[0076] [Aspect B] In the embodiment B, as shown in FIG. 5(b), the type of plating is identified by a grayscale tone 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, by a sigmoid function, the grayscale change in region i (hereinafter simply referred to as "region") where the grayscale changes between the resin and the plating layer, between the plating layer and the base steel sheet, etc. By combining the grayscale changes in each region i in order from the resin side, a function is obtained that expresses the grayscale change in the entire sheet thickness direction.

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

[0081] First, in the above-mentioned grayscale gradation change extraction step S3, grayscale gradation changes in the sheet thickness direction are extracted at multiple locations of the steel sheet in the grayscale image of the steel sheet 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 a location where a 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 advisable to extract 10 or more locations of grayscale gradation change (m≧10).

[0082] To approximate the grayscale gradation change using the function (1), the total number of regions in equation (1), n, is set to 3, and a is calculated by curve fitting (such as the least squares method). i , b i , c i (i=1 to 3) are calculated. Then, 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 formula (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 formula (1) is successively changed to 5 or 7, curve fitting is performed, and n that makes the sum of squares of the residual error equal to or less than the predetermined value is determined. 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 formula (1), the total number of regions n is set to 7.

[0084] FIG. 7 shows, as an example, the results of function approximation using Equation (1) of the grayscale gradation changes in the plate thickness direction extracted at three points 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 electrolytic galvanized (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] FIG. 7(c) shows a functional approximation of the grayscale gradation change in the sheet thickness direction of a galvannealed (GA) steel sheet using the following formulas (3-1) and (3-2), in which the total number of regions n in formula (1) is set to 3 and 5.

number

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

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 formula (1) for each plated steel sheet. i , b i , c i , s's average 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 formula (1) for hot-dip galvanizing (GI) and electrolytic galvanizing (EG) was 3. 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 for n=5. Furthermore, since the number of terms (total number of regions n) in formula (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 for 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 coefficient of the function of the 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 the gray scale 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 by formula (1), if n=3 is the predominant and n=5 is also present, this indicates that the coating layer contains cracks, etc. Therefore, the coating on the steel sheet can be identified as galvannealed (GA).

[0093] In addition, when the grayscale gradation changes at multiple locations on the steel sheet are approximated by formula (1), if n=5 is the predominant type with n=7 being mixed, this indicates that the plating layer contains voids, cracks, etc. Therefore, the plating applied to the steel sheet can be identified as aluminum silicon plating (Al-Si), which is a molten aluminum-based plating.

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

[0095] In this way, galvannealed coating (GA) and aluminum-silicon coating (Al-Si), which is a molten 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 electro-galvanized (EG), the total number of regions calculated by functional approximation of the grayscale gradation changes extracted at multiple locations using equation (1) is 3 in both cases, and therefore they cannot be distinguished based on the number of regions. As mentioned above, hot-dip galvanizing (GI) and electrolytic galvanizing (EG) have different conformance to the shape of the base steel sheet, so the coefficient b i Based on this, the following is identified:

[0097] Coefficient b in Equation (1) i is a coefficient that represents the position in the sheet thickness direction of the region where the grayscale gradation changes suddenly in the grayscale gradation change, 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 plating layer, with the standard deviation σ|b3-b1| of the plating layer thickness b3-b1.

number

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

[0100] Therefore, as shown in formula (5-1), when σ|b3-b1| is larger than σ|b1|, the variation in the coating layer thickness is large compared to the surface position of the coating layer, that is, the coating layer has low conformity to the shape of the base steel sheet. Therefore, when formula (5-1) is satisfied, the coating layer is identified as hot-dip galvanized (GI) (see Fig. 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 conformity to the shape of the base steel sheet. Therefore, when formula (5-2) is satisfied, the coating layer is identified as electrolytic galvanized (GI) (see Fig. 4(c)).

[0102] In this way, the discrimination between hot-dip galvanizing and electro-galvanizing 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), 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, which satisfies formula (5-1) (σ|b3-b1|>σ|b1|). On the other hand, in electrolytic galvanized (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 formula (5-2) (σ|b3-b1|≦σ|b1|).

[0104] As described above, according to the method for identifying the type of plating on automotive steel sheets according to the present 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 on each of the automotive body components that make up the automotive body and efficiently build a material database without performing instrumental analysis that requires huge measurement costs and labor 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 taken from the vehicle body component is any one of the four types of plating, i.e., alloyed hot-dip galvanizing, aluminum-silicon plating which is a hot-dip aluminum-based plating, hot-dip galvanizing, and electrolytic galvanizing. Therefore, in the plating type determination step S5d according to aspect B of the plating type identification step 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 the steel sheet sampled from the vehicle body component is one of two types, namely, 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 above two types of plating depending on whether the total number n of regions in formula (1) is 3 and 5, or 5 and 7.

[0107] Furthermore, a case may be envisaged in which it is necessary to identify whether the plating applied to the steel sheet sampled from the vehicle body component is either hot-dip galvanized or electrolytic 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] 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 vehicle body components of automobile bodies. As an example, as shown in Fig. 8, 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 as a result of the CPU of the computer executing 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 tone 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 of configuration A carries out aspect A (Figure 5(a)) of the plating type identification process S5, and has a grayscale gradation change standard pattern acquisition unit 15a and a plating type comparison determination unit 15b, as shown in Figure 9(a).

[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 step 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 portions including cross sections of standard steel plates with plating of known types in advance. The standard patterns are acquired as standard pattern functions that approximate grayscale gradation changes extracted from the grayscale images of the cross sections of the standard steel plates.

[0117] (Plating type verification and judgment section) 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 compares the plating type based on the similarity between the grayscale gradation change in the thickness direction of the steel sheet and a standard pattern function. Then, among the standard pattern functions of various platings, the plating identical to the standard pattern function having a high 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 plate 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 relating to configuration B carries out aspect B (Figure 5(b)) of the plating type identification process S5, and has a grayscale gradation change function approximation unit 15c and a plating type comparison determination unit 15b, as shown in Figure 9(b).

[0121] (Grayscale tone change function approximation part) The grayscale gradation change function approximation unit 15c implements 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) will differ 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 a 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 coefficient 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 function approximation of the grayscale gradation change in the plate thickness direction with (1), the plating type can be determined in the same manner as in the above-mentioned plating type determination step S15d.

[0125] <Plating type identification program> An 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 the present embodiment identifies the type of plating applied to a steel sheet used for a vehicle body component of an automobile body. The plating type identification program according to the present 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] In addition, 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 the present embodiment can also identify the type of plating applied to a steel sheet by 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 constitutes an automobile body and efficiently build a material database without performing instrumental analysis that requires huge measurement costs and labor to identify the type of plating applied to a 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, electrolytic galvanizing, alloyed hot-dip galvanizing, and aluminum-silicon plating, which is a hot-dip aluminum-based plating. This is because steel sheets plated with the above four types of plating are used for almost all of the vehicle body components on the market. Therefore, in identifying the type of plating of 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 such a plated steel sheet has a different distribution pattern of grayscale gradation in an optical microscope photograph of the cross section of the steel sheet 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 include a resin that is painted on the surface of the steel sheet. Even in such a case, since the resin is black, it does not cause any 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, whereby the taken optical microscope photograph can 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 with a digital camera is obtained as digital data of a color image, and then the digital data is converted into digital data of a grayscale image.

[0133] The grayscale image may have a grayscale gradation of 8 bits (256 gradations) or more, and may be digital data of a high-definition grayscale image such as 16 bits (65,536 gradations). If the grayscale gradation is smaller than 8 bits, there is a risk that it will be impossible to identify, for example, aluminum silicon plating, which is a molten aluminum-based plating after hot press forming.

[0134] In addition, the optical microscope photograph of the cross section of the steel sheet taken with a digital camera preferably has a magnification of about 1000 to 2000. This is because, although the resolution improves with a higher magnification, the range (field of view) of the image is reduced, and there is a concern that the image may not provide average information of the entire plating layer on the cross section of the steel sheet.

[0135] Furthermore, the resolution of the digital camera is important along with 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. Usually, in a grayscale image of an optical microscope photograph in which a plated steel sheet is embedded in black resin and photographed, the area corresponding to the resin is the darkest (black), the plating layer is the lightest (white), and the base steel sheet is intermediate (gray). However, aluminum-silicon plating, which is a molten aluminum-based plating, is different from other platings in that the grayscale gradation of the plating layer is an intermediate color with little difference from the base steel sheet. 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 base steel sheet.

[0137] It is desirable to adjust the contrast of a greyscale image so that, assuming the full scale of the greyscale gradation is 100% (0% = black, 100% = white), the black areas corresponding to the resin are 5 to 10% and the intermediate colour areas of the underlying steel sheet are 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 identifying the plating type. Furthermore, by adjusting the contrast as described above, it is difficult to expand the width of the intermediate color and black parts too much, so "whiteout" of the white parts can also be suppressed at the same time.

[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 distribution of the histogram 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 tone change extraction section 15 Plating type identification section 15a Grayscale gradation change standard pattern acquisition section 15b Plating type verification and judgment unit 15c Grayscale tone change function approximation section 15d Plating type determination section

Claims

1. A method for identifying the type of plating applied to a steel sheet used for a vehicle body component of an automobile body, comprising: a steel plate cross-section grayscale image acquisition step of obtaining a steel plate cross-section grayscale image of the steel plate by extracting the steel plate from the vehicle body component to prepare a test specimen including the steel plate, taking an optical microscope photograph of a steel plate cross-section in a 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 a thickness direction of the steel plate from the acquired grayscale image of the cross section of the steel plate; 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 thickness direction of the steel sheet.

2. The plating type identification step includes: a step of acquiring a standard pattern of grayscale gradation change for various platings, using a standard pattern of 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; 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 to determine the type of plating applied to the steel sheet.

3. In the step of acquiring a standard grayscale gradation change pattern, 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 a type of plating applied to an automotive steel sheet according to claim 2, further comprising determining a type of plating applied to the steel sheet based on a similarity between a grayscale gradation change in a thickness direction of the steel sheet and a function of the standard pattern.

4. The plating type identification 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 the position in the thickness direction is an independent variable and the 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 a coefficient 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, characterized in that, in the grayscale gradation change function approximation step, function approximation is performed using the following formula (1) in which a sigmoid type 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: [0010]

6. In the grayscale gradation change extraction step, Extracting grayscale gradation changes in a plate thickness direction at a plurality of locations of the steel plate in the grayscale image of the cross section of the steel plate; Each of the grayscale gradation changes in the thickness direction extracted at the multiple 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 functionally approximating 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 plating applied to the steel sheet is either electrogalvanized or hot-dip galvanized; The coefficient b calculated by function approximating each of the grayscale gradation changes in the thickness direction extracted at the multiple locations using formula (1) 3 and coefficient b 1 Difference from b 3 -b 1 The variation of and coefficient b 1 Compare the variation of with b 3 -b 1 The variation of b 1 If the variation is greater 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 The method for identifying the type of plating applied to an automotive steel sheet according to claim 5, characterized in that if the variation in the plating applied to the steel sheet is smaller than the variation in the plating applied to the steel sheet, the plating applied to the steel sheet is determined to be electrolytic zinc plating.

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 sheet thickness direction extracted at a plurality of points of the steel sheet is 3 or 5, it is determined that the type of coating applied to the steel sheet is galvannealed, 7. The method for identifying a type of plating on a steel sheet for an automobile 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 at a plurality of points 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 a steel sheet for an automobile according to claim 1, wherein the plating applied to the steel sheet is any one of hot-dip galvanizing, electrolytic galvanizing, 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 for vehicle body components of an automobile body, comprising: a steel plate cross-section grayscale image acquisition unit for acquiring 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 taken from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in a thickness direction of the steel plate from the acquired grayscale image of the cross section of the steel plate; 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 an automotive steel sheet for identifying a type of plating applied to a steel sheet used for a body component of an automobile body, comprising: Computer, a steel plate cross-section grayscale image acquisition unit for acquiring 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 taken from the vehicle body component; a grayscale gradation change extraction unit that extracts a grayscale gradation change in a thickness direction of the steel plate from the acquired grayscale image of the cross section of the steel plate; 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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