A cleanliness evaluation method with high accuracy for identifying inclusions

Abstract

To provide a method for correctly identifying a region for each inclusion system and accurately evaluating an inclusion prediction diameter.SOLUTION: An evaluation method comprises the steps of: acquiring a color photograph via a green filter of an optical microscope in addition to a gray scale photograph of an inclusion that is normally imaged by the optical microscope in the extreme value statistical evaluation work; creating a processed image by changing brightness and contrast with respect to the acquired color inclusion image with image editing software; acquiring a reflection electron image with a scan electron microscope (SEM) for a maximum oxide imaged by the optical microscope; acquiring an element mapping image with an electronic probe microanalyser (EPMA); comparing these images with the processed image; executing separate-coloring for each inclusion system on the image editing software; and executing extreme value statistical evaluation on the basis of the result thereof.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a cleanliness evaluation method for accurately evaluating the maximum diameter of non-metallic inclusions in steel material, and more particularly to a method for identifying oxide regions among non-metallic inclusions and evaluating the maximum oxide diameter in steel material. [Background technology]

[0002] In high-strength steels used for bearings, etc., stress concentration on unavoidably contained nonmetallic inclusions can cause cracks, which can then initiate fatigue failure. Inclusions in steel are primarily unavoidably generated during the steel manufacturing process and remain without being removed. The extent of stress concentration around these inclusions is thought to correlate with the size of the inclusions. Therefore, understanding the size of nonmetallic inclusions is important when estimating the life of bearings. In particular, understanding the diameter of the largest nonmetallic inclusion (the largest inclusion diameter) among the multiple nonmetallic inclusions present is important from the perspective of ensuring the reliability of the steel.

[0003] Various methods have been proposed for evaluating non-metallic inclusions in steel, including the microscopic method, which involves directly observing polished samples using a microscope, as specified in Japanese Industrial Standards JIS G0555 and ASTM E45.

[0004] Furthermore, "Metal Fatigue: The Effects of Minute Defects and Inclusions" (Yokendo, written by Murakami Takayoshi) discloses a method for evaluating non-metallic inclusions in steel, in which the maximum diameter of an inclusion within a reference volume is predicted using an optical microscope and extreme value statistics (see Non-Patent Document 1).

[0005] Japanese Patent Laid-Open Publication No. 2000-214142 discloses a method for evaluating the cleanliness of metallic materials using an optical microscope and the method of extreme value statistics. Another method for evaluating nonmetallic inclusions in steel is to use ultrasonic flaw detection, which can evaluate a larger volume than conventional optical microscopes (see Patent Document 1). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-214142 [Non-patent literature]

[0007] [Non-Patent Document 1] "Metal Fatigue: The Effects of Micro-Defects and Inclusions" Yokendo, by Takayoshi Murakami (1993) Summary of the Invention [Problem to be solved by the invention]

[0008] However, although the method of Patent Document 1 is a patent that evaluates cleanliness by the extreme value statistics method using conventional optical microscopy and ultrasonic flaw detection, it does not specifically mention a method for identifying oxide-based inclusions, which are particularly important from the perspective of their impact on fatigue among various types of non-metallic inclusions such as oxides, sulfides, and nitrides, and is therefore insufficient in identifying oxides.

[0009] In the extreme value statistical evaluation, an evaluator photographs the largest inclusion within a standard observation area of ​​a steel material using an optical microscope, and then performs the same process on multiple standard areas to obtain the largest inclusion size within the multiple standard areas. Based on the obtained size, the inclusion diameter is predicted using the extreme value statistical method. However, the task of identifying the regions of various inclusion types using this method is heavily dependent on the evaluator's knowledge and experience, and interpretation of the shape is likely to differ depending on the evaluator. As this is a so-called sensory evaluation, the size of the identified inclusion region may increase or decrease, resulting in variability in the predicted values ​​depending on the evaluator performing the task.

[0010] In order to solve the problem of variations that tend to occur depending on the evaluator, the present invention aims to provide a method for accurately identifying regions of various inclusion types and accurately evaluating the predicted diameter of inclusions. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems, in the extreme value statistical evaluation work of the present invention, first, in addition to the grayscale image of the inclusion that is normally photographed with an optical microscope, a color image is obtained with an optical microscope through a green filter, and then a new processed image is created by changing the brightness and contrast of the obtained color image of the inclusion using image editing software.

[0012] Furthermore, for the largest oxide photographed by the optical microscope for each predetermined observation area, a backscattered electron image is obtained by a scanning electron microscope (SEM), and an elemental mapping image is obtained by an electron probe microanalyzer (EPMA). These images are compared with the processed image described above, and the image regions corresponding to the various inclusion systems identified are colored in different colors using image editing software. Extreme value statistical evaluation is then performed based on the regions identified based on the colored images.

[0013] That is, the first means for solving the problems of the present invention is: A step of capturing an image of a predetermined observation area of ​​a steel material containing inclusions with an optical microscope through a monochromatic transparent color filter to obtain an image of the steel material containing the inclusions; creating an enhanced image by processing the image from the image so as to enhance the contours of the image; a step of imaging an observation region including the inclusions using a scanning electron microscope to obtain a backscattered electron image; creating an analytical image in which the component composition of the region containing the inclusion is identified using characteristic X-rays; creating an identification image in which the highlighted image is displayed based on the backscattered electron image and the analytical image, allowing the areas on the image occupied by each inclusion system to be identified; and a step of calculating the maximum inclusion diameter for each inclusion system from the obtained identification image. This is a method for identifying the inclusion system in an observation area containing inclusions and evaluating the maximum inclusion diameter.

[0014] The second means is the evaluation method according to the first means, characterized in that the colored transparent color filter is a green filter.

[0015] The third means is the evaluation method according to the first or second means, characterized in that the image processing for obtaining the emphasized image includes at least a step of color inversion processing.

[0016] The fourth means is the evaluation method according to any one of the first to third means, characterized in that the analytical device for identifying the component composition of each region using characteristic X-rays is an electron probe microanalyzer (EPMA).

[0017] The fifth means is the evaluation method according to any one of the first to fourth means, characterized in that the identification of inclusions includes identifying at least oxide-based inclusions.

[0018] The sixth means includes a step of obtaining an identification image of inclusions and a calculation result of a maximum inclusion diameter for each of a plurality of non-overlapping observation regions in a steel material by the evaluation method according to any one of the first to fifth means; a step of calculating a predicted value of the maximum inclusion diameter by statistically predicting the maximum inclusion diameter of the inclusion system in a steel region (prediction area) that is larger than the observation region (reference inspection area) based on the calculated value of the maximum inclusion diameter obtained; This is a method for predicting and evaluating the cleanliness of steel materials, which has excellent accuracy in identifying inclusions.

[0019] The seventh means is that the statistical method is an extreme value statistical method, and the inspection reference area is 100 mm 2 , the predicted area is 30,000 mm 2 The method for predicting and evaluating the cleanliness of a steel material with excellent accuracy in identifying inclusions according to the sixth means is characterized in that the method predicts the maximum diameter of inclusions in the steel material under the conditions above. [Effects of the Invention]

[0020] The method according to the present invention makes it possible to more accurately identify regions of various inclusions in an observation object, thereby identifying the inclusion type of non-metallic inclusions contained in a steel material and more accurately predicting the maximum inclusion diameter corresponding to the inclusion type.

[0021] By using a colored filter such as a green filter, it is possible to efficiently process the image by adjusting the color tone and gradation of the image, for example, to emphasize its contours. By comparing the resulting emphasized image with an SEM backscattered electron image or an EPMA analysis image, which have even stronger contrast and are less susceptible to the effects of charging, it becomes possible to accurately identify the regions of each inclusion type with unprecedented observation precision. This makes it easy to avoid confusion between oxides and sulfides, and enables more accurate prediction and evaluation of the properties of metal materials that differ depending on the type of inclusion, such as fatigue properties. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a flowchart showing an outline of the procedure of the steps of carrying out the present invention. [Figure 2] This is a backscattered electron image showing an example of an inclusion photographed by SEM. [Figure 3] These are elemental mapping images of the inclusions in Figure 2 analyzed by EPMA, where (a) is a backscattered electron image, (b) is a sulfur mapping image, and (c) is an oxygen mapping image. (Note that the original image in Figure 3 is a color image.) [Figure 4] This shows the procedure for creating images colored by type of inclusion in this process. (a) is a color image taken with an optical microscope through a green filter, (b) is an enhanced image in which the contours of the inclusions are emphasized, and (c) is an image colored by type of inclusion (note that the original image in Figure 4 is a color image). [Figure 5]This figure shows the difference in oxide area determination and the difference in the measurement results of the oxide size (√area: square root of the product of the minor axis and major axis of the inclusion) based on that. (a) is an example of a conventional method where the area was determined manually based on a monochrome image of an optical microscope photograph, and the sulfide system was included in the area determination. (b) is an example of an accurate determination of oxide size using the procedure of the present invention. [Figure 6] 1 is an extreme value statistics graph in which multiple measured oxide sizes are plotted and the predicted diameter of inclusions in the predicted area in steel is calculated based on the extreme value statistics method. Black circles represent predictions made using the method of the present invention, and open circles represent predictions made using conventional normal procedures. It shows that the results of the predicted inclusion diameters obtained using these methods are different. DETAILED DESCRIPTION OF THE INVENTION

[0023] In the following, for the purpose of explaining embodiments of the present invention, a method for predictive evaluation of cleanliness according to the present invention, which has high accuracy in identifying inclusions, will be described in detail using the flowchart in Fig. 1. However, the cleanliness evaluation method and predictive evaluation method of the present invention are not limited to the following examples. Furthermore, although the following description focuses on oxides as a representative example, the method can be reasonably implemented even when focusing on sulfides, for example, rather than oxides, because it can identify types of inclusions and can efficiently distinguish between oxides and sulfides, for example.

[0024] (Process A: Collecting test specimens) When preparing test pieces to be used in extreme value statistical evaluation, the steel material to be evaluated is subjected to appropriate heat treatment as required, and then a test piece having a predetermined area and height is cut out from the steel material. In addition, if inclusions tend to fall off during the finish polishing in the subsequent process or polishing scratches tend to remain, this can sometimes be improved by increasing the hardness of the steel material in addition to adjusting the polishing conditions, so the hardness may be adjusted by performing heat treatment in advance as necessary. Next, the specimens are prepared for microscopic observation by polishing the observation surface of the specimen either as is or, if necessary, after embedding them in resin. Several such specimens are prepared; for example, about 30 specimens should be prepared.

[0025] (Process A: Specific example) The test specimens used in the examples were made from SUJ2 steel that had been hot forged to a diameter of 65 mm. The steel was then roughly machined to a test specimen shape for extreme value statistical evaluation, here 10 mm x 10 mm x 8 mm in size. The area of ​​the observation surface at this time was 100 mm. 2 The test specimens were taken from the central periphery of the forged and rolled material so that the surface parallel to the rolling direction was the observation surface of the sample. The collected test pieces were then embedded in resin, polished using an automatic wet polishing device or a manual wet polishing machine, and finally buffed with diamond abrasives to a mirror finish. Twelve such test pieces were prepared. Note that polishing with colloidal silica may also be performed following buffing.

[0026] (Process B: Imaging of non-metallic inclusions) Within the inspection reference area of ​​each test piece obtained in step A, an inclusion whose size, √area (the square root of the product of the minor axis and the major axis of the inclusion), is thought to be the largest is selected, and then the lens magnification of the optical microscope is set to a high enough magnification to enable the fine shape and color differences of the individual or compound inclusions that make up the inclusion to be recognized, and images of the inclusion are taken through a single, colored, transparent color filter. The number of images taken is the same as the number of test pieces prepared. For example, in the case of the example, 12 images are taken.

[0027] (Process B: Specific example) Figure 4(a) shows a color image of an inclusion photographed through a green color filter. Here, the observation area of ​​the test piece for extreme value statistical evaluation, i.e., the inspection reference area, is 100 mm as shown in the specific example of process A. 2The optical microscope was set at a magnification of 400x. A green filter was used as the monochromatic transparent color filter, which is suitable for enhancing the contrast of the target inclusions, and 12 such color images were obtained. Grayscale images of the inclusions were also taken separately from the color images for comparison in subsequent processes.

[0028] (Process C: Creating processed images using image editing software) Following step B, the images are processed using image editing software to enhance the contrast of each type of inclusion in the obtained color images. Twelve images are processed here.

[0029] (Process C: Specific example) Figure 4(b) shows a processed image processed using image editing software. The processed image was created by using commercially available image editing software (such as Adobe Photoshop (registered trademark)) to manipulate the color tone and gradation of the color image obtained in step B, using a solarization process (color inversion process) with the color curve correction function implemented in the software to emphasize the inclusions. In addition, a conversion process was performed to maximize the brightness of the intermediate tones while minimizing their contrast. The image processing and conversion process here is performed with the purpose of emphasizing the contours of each type of inclusion, and is not limited to the method exemplified here.

[0030] (Step D: Obtaining a backscattered electron image using an SEM) Detailed observation of each inclusion to be evaluated is performed using a scanning electron microscope, and a backscattered electron image at an appropriate magnification is obtained. While a typical SEM observes a secondary electron image, the present invention deliberately uses a backscattered electron image. The reason for using a backscattered electron image rather than a typical secondary electron image is that, as will be explained below, it is highly useful in that it provides contrast according to the chemical composition of the inclusion and is less susceptible to the effects of charge-up in inclusions with high electrical insulation.

[0031] The composition of each inclusion is analyzed based on characteristic X-rays obtained by an energy dispersive X-ray spectrometer (EDS). Although this method has inferior quantitative capabilities and detection limits compared to an electron probe microanalyzer (EPMA), which will be described later, it is suitable for determining the type and typical composition of inclusions such as oxides and sulfides.

[0032] (Process D: Specific example) Figure 2 is an example of a backscattered electron image of an inclusion obtained by SEM. Backscattered electron images are able to show the detailed morphology of the inclusion and have sufficient resolution to be compared with an optical microscope. Based on the results of composition analysis by EDS, the inclusion in the image in Figure 2 was found to be composed of MgO-Al2O3-based oxides and CaS-based sulfides.

[0033] The amount of backscattered electrons emitted in an SEM depends on the atomic number, with the amount of electrons emitted increasing as the atomic number increases. Therefore, if there are various differences in chemical composition on the sample surface, differences in contrast depending on the atomic number will be obtained. Therefore, when trying to distinguish between different types of inclusions, such as oxide inclusions and sulfide inclusions, backscattered electron images (COMPO images) are more suitable than general secondary electron images.

[0034] (Step E: Obtaining elemental mapping images using EPMA) To analyze the composition of each inclusion being evaluated, elemental mapping images are obtained based on the analysis results of characteristic X-rays obtained by elemental electron probe microanalyzer (EPMA).Since characteristic X-rays differ for each element, images for each element, such as S and O, can be obtained.

[0035] (Process E: Specific example) Figure 3 shows elemental mapping images of a certain inclusion analyzed by EPMA, where (a) is a backscattered electron image, (b) is a sulfur mapping image, and (c) is an oxygen mapping image. The results show that the contrast matches that of the backscattered electron image taken in step D.

[0036] (Process F: Creating a color-coded image of inclusions) For each inclusion to be evaluated, the processed image obtained in step C is compared with the backscattered electron image obtained in step D and the elemental mapping image obtained in step E, and image editing software is used to identify and color the inclusions by type. When a field emission electron probe microanalyzer (FE-EPMA), which has high spatial resolution and analytical accuracy, is used, it is possible to capture the morphology of the inclusions in detail and obtain a detailed elemental mapping image, so this image may be directly compared with the processed image obtained in step C.

[0037] (Process F: Specific example) Figure 4(c) shows an image in which the inclusions were colored according to their type. The coloring here was done using commercially available image editing software, just as in process C. The base image for creating the colored image was a grayscale image of the inclusions taken separately from the color image in process B. After converting the grayscale to RGB color, the processed image created in process C was compared with the backscattered electron image obtained in process D and the element mapping image obtained in process E to identify the inclusion types. These images were then overlaid using the layer function of the image editing software, and the oxide and sulfide regions were colored into different colors on the base image while checking each pixel. Here, as an example, oxides were colored red and sulfides were colored blue.

[0038] Furthermore, the image in which the regions are colored differently for each type of inclusion can be suitably used as training data for supervised learning in machine learning.

[0039] (Process G: Measuring inclusion size) Based on the color-coded images for each type of inclusion obtained in process F, attention is focused on oxides, and the size of the oxide, √area (the square root of the product of the minor axis and major axis of the oxide), is measured.

[0040] (Process G: Specific example) Figure 5(b) shows the results of measuring the √area of ​​oxides. For comparison, (a) shows an example of a manual evaluation in which sulfides were mistakenly identified as oxides based on a grayscale photograph of inclusions taken with an optical microscope. However, this type of discrimination is difficult for anyone other than an expert. When oxides and sulfides are correctly identified using the method of the present invention, the √area of ​​the oxide is 12.6 μm, whereas the √area of ​​the incorrectly identified oxide is measured at 14.2 μm, which is slightly overestimated.

[0041] When measuring the size of oxides, there are cases where inclusions are separated from one another, as shown in Figure 5. When measuring the size of inclusions to be used as data for extreme value statistical evaluation, even if the inclusions are separated from one another, they must be judged as a single unit if they are close to one another. The judgment results in Figure 5 take into account this judgment method; for example, if the distance between two inclusions is greater than the √area (the square root of the product of the minor axis and major axis of the inclusion) of the smaller inclusion, the existence of the smaller inclusion is ignored; conversely, if the distance between the two inclusions is smaller than the √area of ​​the smaller inclusion (i.e., they are close), the two inclusions are considered to be a single inclusion. Furthermore, when two or more inclusions are dispersed closely together, the integrity is judged in order from the distance between the closest inclusions and the particle size of the smallest inclusion. This type of judgment takes into account the assumption that when inclusions are close to each other, they act as a single unit, increasing their harmful effect on fatigue. Note that the judgment criteria for regarding inclusions as a single unit shown here are only an example, and the present invention is not limited to this method alone.

[0042] (Process H: Implementation of extreme value statistics) A plurality of test pieces made from the steel material to be evaluated are evaluated using the extreme value statistics method based on the data on the maximum inclusion diameter for each test piece obtained through the above process, and the maximum inclusion diameter that can be contained in any predicted volume of the steel material is predicted.

[0043] (Process H: Specific example) Figure 6 shows an extreme value statistics graph obtained by the extreme value statistics method based on the obtained data on the maximum inclusion diameter. The approximation line showing the slope of the extreme value statistics plot was obtained by least squares approximation.

[0044] Here, we will explain the extreme value statistics method used to predict inclusion diameter. In this case, the extreme value statistics method involves taking a number of test pieces (j = 1, n) from a certain population, photographing the largest inclusion present within a predetermined observation range of each test piece with an optical microscope, plotting the size of the largest inclusion in each test piece in order from smallest to largest on an extreme value statistics graph, and then, based on the approximation line of these plots, calculating the maximum inclusion diameter in any area to be predicted (i.e., √area max ) is predicted. max It can be used to inspect product quality, as a comparative index of inclusion cleanliness between steels under evaluation, and as an index for improving cleanliness through improvements in steelmaking refining methods.

[0045] In particular, in this example, the observation area of ​​one field of view (inspection reference area: S o mm 2 ) for example, S o =100mm 2 (length 10mm x width 10mm) and the area S o The size of the largest inclusion (√area) was measured for each of the 12 fields of view, with no overlapping, and the predicted area S = 30,000 mm 2 The maximum size of the inclusion is calculated by the extreme value statistics method as √area max predicted.

[0046] The cumulative distribution function F (unit: %) shown on the vertical axis of the extreme value statistics graph is F j =j / (n+1)×100. Here, n is the total number of test pieces for which √area was evaluated, and j indicates the jth piece when arranged in order from smallest to largest √area. However, in this case, it is necessary to use probability paper to determine the plot position on the vertical axis. Therefore, instead of F, we use a normalized variable y, y = y j=-ln[-ln{j / (n+1)}] j The vertical axis can be plotted using the value of √area. The plotting method is to plot the largest inclusion in the observation field in order of size, √area, from smallest to largest, with the horizontal axis value being √area and the vertical axis value being the normalized variable y j Next, the maximum diameter of the inclusion that can exist in an arbitrary area (√area max ), the cumulative distribution function F and the normalized variable y for the area to be predicted are calculated from the recursion period T. In this case, T is the ratio of the predicted area S to the observed area S of each test piece. o Using T=(S+S o ) / S o Using this T, the cumulative distribution function F can be calculated from the formula F=(T-1) / T×100. Also, the vertical axis can be expressed using the value of y calculated from the formula y=-ln[-ln{(T-1) / T}] as the normalized variable y. Also, if S≫S o In this case, y=lnT, T=S / S o holds and can be easily calculated.

[0047] The process carried out this time correctly distinguished the oxide region from other inclusions, and the observation range (i.e., S o , here 100mm 2 As an example of 12 fields of view where the maximum oxide diameter per area was measured, √area, when the recursion period T was set to 300, i.e., the maximum inclusion diameter that could exist in steel with an area equivalent to 300 test pieces, was estimated to be 35.6 μm, as shown in Figure 6.

[0048] This predicted area (area of ​​the steel material to be predicted) is selected depending on the purpose, and is not limited to the area equivalent to 300 test pieces as shown here. In addition, the evaluation method described here is based on two-dimensional observation and does not take into account the three-dimensional shape and distribution of inclusions, so a more accurate √area max Three-dimensional observation may be performed to predict the area S on a two-dimensional plane. oHowever, by virtually considering the thickness of the inclusions, a pseudo three-dimensional evaluation may be performed.

[0049] For comparison, FIG. 6 also shows extreme value statistical evaluation results obtained from the normal extreme value statistical evaluation procedure steps shown in steps A, B and G in FIG. 1 as a comparative example with the method of the present invention.

[0050] As shown in Figure 6, the present invention precisely identifies the oxide region based on the comparison of the original inclusion image taken with an optical microscope, processed with image editing software, with the analysis results of the inclusion composition by analytical equipment such as SEM or EPMA, in order to measure the size of the oxide more accurately than in the conventional procedure. max This method can be applied to inclusions other than oxides as well.

[0051] As shown in Figure 6, the maximum predicted inclusion diameter using the conventional procedure is 31.5 µm, which is smaller than the more accurate predicted inclusion diameter of 35.6 µm according to the present invention, and the prediction does not take into account the risk of inclusion contamination. In particular, considering that there are situations in which it is necessary to place importance on the effect of oxide-based inclusions on fatigue life, if it were possible to easily distinguish between inclusion types and make accurate predictions, this would be extremely useful for making predictions in situations in which the effect of each inclusion varies, such as fatigue properties.

Claims

1. A step of capturing an image of a predetermined observation area of ​​a steel material containing inclusions with an optical microscope through a monochromatic transparent color filter to obtain an image of the steel material containing the inclusions; creating an enhanced image by processing the image from the image so as to enhance the contours of the image; a step of imaging an observation region including the inclusions using a scanning electron microscope to obtain a backscattered electron image; creating an analytical image in which the component composition of the region containing the inclusions is identified using characteristic X-rays; creating an identification image in which the highlighted image is displayed based on the backscattered electron image and the analytical image, allowing the areas on the image occupied by each inclusion system to be identified; and a step of calculating the maximum inclusion diameter for each inclusion system from the obtained identification image. A method for identifying the inclusion system in an observation area containing inclusions and evaluating the maximum inclusion diameter.

2. 2. The evaluation method according to claim 1, wherein the colored transparent color filter is a green filter.

3. 3. The evaluation method according to claim 1, wherein the image processing for obtaining the enhanced image includes at least a step of color inversion.

4. 4. The evaluation method according to claim 1, wherein the analytical device for identifying the component composition of each region using characteristic X-rays is an electron probe microanalyzer (EPMA).

5. 5. The evaluation method according to claim 1, wherein the identification of inclusions comprises identifying at least oxide-based inclusions.

6. a step of obtaining an identification image of inclusions and a calculation result of a maximum inclusion diameter for each of a plurality of non-overlapping observation regions in a steel material by the evaluation method according to any one of claims 1 to 5; a step of calculating a predicted value of the maximum inclusion diameter by statistically predicting the maximum inclusion diameter of the inclusion system in a steel region (prediction area) that is larger than the observation region (reference inspection area) based on the calculated value of the maximum inclusion diameter obtained; This method for predicting and evaluating the cleanliness of steel materials has excellent accuracy in identifying inclusions.

7. The statistical method is an extreme value statistical method, and the inspection area is 100 mm 2 , the predicted area is 30,000 mm 2 7. The method for predicting and evaluating the cleanliness of steel material with excellent accuracy in identifying inclusions according to claim 6, characterized in that the maximum diameter of inclusions in the steel material is predicted under the condition of

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