JP2023149011A - Accurate cleanliness evaluation method for inclusion identification accuracy - Google Patents

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

The present invention relates to a cleanliness evaluation method for accurately evaluating the maximum inclusion diameter of nonmetallic inclusions in steel materials. In particular, the present invention relates to a method for identifying oxide regions among non-metallic inclusions and evaluating the maximum oxide diameter in a steel material.

In high-strength steel used as bearing steel, stress concentration on non-metallic inclusions inevitably included in the steel may cause cracks, which may initiate fatigue fracture. Inclusions in steel are mainly generated unavoidably during the steel manufacturing process and remain without being removed. It is thought that the range of influence of stress concentration around these inclusions is correlated with the size of the nonmetallic inclusions. Therefore, it is important to understand the size of nonmetallic inclusions when estimating the life of a bearing. In particular, from the viewpoint of ensuring the reliability of steel, it is important to understand the diameter of a particularly large nonmetallic inclusion (maximum inclusion diameter) among the plurality of nonmetallic inclusions that exist.

Various methods have been proposed to evaluate non-metallic inclusions in steel, including direct observation of polished samples using a microscope as specified by Japanese Industrial Standards JIS G0555, ASTM E45, etc. microscopy, etc.

In addition, "Metal Fatigue: Effects of Micro-Defects and Inclusions" (Yokendo, Takayoshi Murakami) describes how to evaluate non-metallic inclusions in steel by measuring the maximum diameter of inclusions within a reference volume using an optical microscope. A method of predicting this using an extreme value statistical method is disclosed (see Non-Patent Document 1).

Japanese Unexamined Patent Publication No. 2000-214142 discloses a method for evaluating the cleanliness of a metal material using an optical microscope using an extreme value statistical method. Furthermore, as a method for evaluating nonmetallic inclusions in steel, there is also a method using ultrasonic flaw detection, which can evaluate a larger volume than conventional optical microscopy (see Patent Document 1).

Japanese Patent Application Publication No. 2000-214142

“Metal Fatigue: Effects of Micro-Defects and Inclusions” Yokendo, Takayoshi Murakami (1993)

However, although the method of Patent Document 1 is a patent that evaluates cleanliness using the extreme value statistical method using conventional optical microscopy and ultrasonic flaw detection, Among various inclusion systems such as substances, there is no specific mention of a method for identifying oxide systems, which are particularly important from the viewpoint of their influence on fatigue, and the identification of oxides has not been sufficient.

Now, in extreme value statistical evaluation, the evaluator photographs the largest inclusion within the standard observation area of the steel material using an optical microscope, and then performs the same process on multiple standard areas to obtain multiple standard areas. A method is adopted in which the inclusion diameter is predicted using the extreme value statistical method based on the maximum inclusion size within. However, the task of identifying regions for each type of inclusion system using this method relies heavily on the knowledge and experience of the evaluator, and the interpretation of the shape is likely to differ from person to person. In this way, since it is a so-called sensory evaluation, the size of the identified inclusion region may increase or decrease, and therefore, the results of the predicted values will vary depending on the evaluator who performed the work.

The present invention aims to solve the problem that variations tend to occur among evaluators, and to provide a method for accurately identifying regions for each type of inclusion system and accurately evaluating the predicted diameter of inclusions. The purpose is

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

In addition, we acquired backscattered electron images using a scanning electron microscope (SEM) and elemental mapping images using an electron probe microanalyzer (EPMA) for the largest oxide photographed using an optical microscope for each predetermined observation area. By comparing these images with the above-mentioned processed image, the image areas corresponding to the various inclusion systems identified are painted using image editing software, and based on the painted images, Based on the grasped area, extreme value statistical evaluation is performed.

That is, the first means for solving the problems of the present invention is as follows:
Obtaining an image of the steel material containing the inclusions by imaging a predetermined observation area of the steel material containing the inclusions with an optical microscope through a monochromatic transparent color filter;
creating an enhanced processed image by processing the image from the image so as to emphasize the outline of the image;
a step of imaging the observation area including the inclusion with a scanning electron microscope to obtain a backscattered electron image;
creating an analysis image in which the component composition of the region containing the inclusions is identified using characteristic X-rays;
creating an identification image in which a region on the image occupied by each inclusion system is displayed in an identifiable manner based on the enhanced processed image based on the reflected electron image and the analysis image;
a step of calculating the maximum inclusion diameter for each inclusion system from the obtained identification image,
This is a method for identifying an inclusion system in an observation area containing inclusions and evaluating the maximum inclusion diameter.

The second means is the evaluation method described in the first means, characterized in that the colored transparent color filter is a green filter.

The third means is the evaluation method described in the first or second means, characterized in that the image processing for obtaining the enhanced processed image includes at least a step of color reversal processing.

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

The fifth means is the evaluation method according to any one of the first to fourth means, wherein the identification of the inclusion system identifies at least an oxide-based inclusion system.

The sixth means uses the evaluation method described in any one of the first to fifth means to obtain an identification image of the inclusion system and the maximum inclusion diameter for each observation region at a plurality of non-overlapping observation regions in the steel material. a step of obtaining a calculation result;
Based on the calculated value of the maximum inclusion diameter obtained, the maximum inclusion diameter of the inclusion system in the steel area (predicted area) that is larger than the area of the observation area (inspection reference area) was predicted using a statistical method. a step of calculating a predicted value of the maximum inclusion diameter;
This is a predictive evaluation method for the cleanliness of steel materials with excellent inclusion identification accuracy.

The seventh means is characterized in that the above-mentioned statistical method is an extreme value statistical method, and the maximum inclusion diameter in the steel material is predicted under the conditions that the inspection reference area is 100 mm ² and the predicted area is 30000 mm ² . This is a method for predicting and evaluating the cleanliness of steel materials that is excellent in inclusion identification accuracy as described in the sixth means.

By the method according to the present invention, it is possible to more accurately identify the regions of various inclusions in the observation target, so that the inclusion system of non-metallic inclusions contained in the steel material can be identified, and depending on the inclusion system. Therefore, the maximum inclusion diameter can be predicted with higher accuracy.

By using a colored filter such as a green filter, it is possible to efficiently enhance contours by adjusting the color tone and each gradation of the image. By comparing electronic images, EPMA analysis images, etc., it becomes possible to accurately identify regions of each type of inclusion with unprecedented observation accuracy. Therefore, confusion between oxides and sulfides can be easily avoided, and properties of metal materials, such as fatigue properties, which differ depending on the type of inclusion, can be more accurately predicted and evaluated.

1 is a flowchart showing an outline of a procedure for implementing the present invention. This is a backscattered electron image showing an example of inclusions photographed by SEM. These are elemental mapping images obtained by analyzing the inclusions in FIG. 2 using EPMA, in which (a) is a backscattered electron image, (b) is a mapping image of sulfur, and (c) is a mapping image of oxygen. (The original drawing in Figure 3 is a color image.) This is the procedure for creating separate images for each type of inclusion system in this implementation process. (a) is a color image taken through a green filter with an optical microscope, (b) is an enhanced processed image that emphasizes the outline of inclusions, ( c) is a colored image in which each type of inclusion system is colored separately (the original image in FIG. 4 is a color image). FIG. 4 is a diagram showing differences in oxide area determination and differences in measurement results of the oxide size (√area: the square root of the product of the minor axis and major axis of the inclusion) based on the determination. (a) is an example in which discrimination was performed by a person using a conventional method based on a monochrome image of an optical microscope photograph, and the area was determined including sulfide-based substances. (b) is an example in which the size of an oxide is correctly determined using the procedure of the present invention. This is an extreme value statistical graph in which a plurality of measured oxide sizes are plotted and the predicted diameter of inclusions in the predicted area in steel is calculated based on the extreme value statistical method. ● is a prediction made by the method of the present invention, and ◯ is a prediction made by the conventional normal procedure. It has been shown that the predicted inclusion diameter results derived from these methods are different.

Hereinafter, in order to explain the embodiments of the present invention, a predictive evaluation method of cleanliness with high inclusion identification accuracy according to the present invention will be described in detail using the flowchart of FIG. However, the cleanliness evaluation method and predictive evaluation method of the present invention are not limited to the following examples. In addition, although the following explanation focuses on oxides as a representative example, since the type of inclusion can be identified, for example, oxides and sulfides can be efficiently distinguished. This is a method that can be reasonably implemented even when focusing on

(Process A: Regarding collection of test pieces)
To prepare a test piece for use in extreme value statistical evaluation, the steel material to be evaluated is subjected to appropriate heat treatment as necessary, and then a test piece is cut out from the steel material to have a predetermined area and a predetermined height.
In addition, if inclusions tend to fall off or polishing scratches tend to remain during final polishing in the post-process, it may be improved by increasing the hardness of the steel material in addition to adjusting the polishing conditions. Therefore, if necessary, heat treatment may be performed in advance to adjust the hardness.
Next, either as a test piece or after embedding the test piece in resin as necessary, the observation surface of the sample is subjected to final polishing to prepare a test piece for microscopic observation. A plurality of such test pieces are prepared. For example, about 30 pieces may be manufactured.

(Process A: Specific example)
The test pieces used in the examples were prepared from SUJ2 steel hot-forged to φ65 mm as the steel material to be evaluated. From there, it was rough-processed into a test piece shape, here a size of 10 mm x 10 mm x 8 mm, for performing extreme value statistical evaluation. The area of the observation surface at this time was 100 mm ² . The test piece was taken from the middle circumference of the forged material so that the surface parallel to the rolling direction was the observation surface of the sample.
After embedding the collected test piece in resin, it is polished using an automatic wet polishing device or a manual wet polishing machine, and then finally buffed using diamond abrasive grains to achieve a mirror-like finish. processed. Twelve such test pieces were produced. Note that polishing with colloidal silica may be performed subsequent to buffing.

(Process B: Regarding imaging of nonmetallic inclusions)
Select the inclusion whose size √area (square root of the product of the short axis and long axis of the inclusion) is considered to be the largest within the inspection standard area of each test piece obtained in Step A, and then The lens magnification of the optical microscope is set to a high magnification to the extent that it is possible to recognize the minute shapes and differences in color of the single or composite inclusions that make up the inclusions. Take an image. The number of images to be taken is equal to the number of prepared test pieces. For example, in the case of the embodiment, the number of images to be photographed is 12.

(Process B: Specific example)
FIG. 4(a) shows a color image of the inclusion taken through a green color filter. Here, the observation area of the test piece for extreme value statistical evaluation, that is, the inspection reference area, was set to 100 mm ² as shown in the specific example of step A, and the photographing magnification of the optical microscope was set to 400 times. In addition, a green filter suitable for enhancing the contrast of target inclusions was used as the monochrome transparent color filter, and 12 such color images were obtained. Furthermore, at this time, a grayscale inclusion image was captured separately from the color image for comparison in the subsequent process.

(Process C: Regarding creation of processed images using image editing software)
Following step B, image editing software is used to process the image so that the contrast of each type of inclusion system in the obtained color image is further emphasized. The number of sheets processed here is 12.

(Process C: Specific example)
FIG. 4(b) shows a processed image processed using image editing software. For the processed image, use commercially available image editing software (for example, Adobe Photoshop (registered trademark), etc.) on the color image obtained in step B, and solarize it using the color curve correction function implemented in the software. Image processing was performed to emphasize inclusions by manipulating color tone and gradation through processing (color inversion processing). Furthermore, we implemented a conversion process that maximizes the brightness of midtones while minimizing the contrast of midtones. The image processing and conversion processing here are performed for the purpose of emphasizing the contours of each type of inclusion system, and therefore are not limited to the methods exemplified here.

(Process D: Regarding acquisition of backscattered electron image by SEM)
Detailed observation of each inclusion to be evaluated is performed using a scanning electron microscope, and a backscattered electron image is obtained at an appropriate magnification. In a general SEM, a secondary electron image is observed, but in the present invention, a backscattered electron image is intentionally taken and used. The reason for using a backscattered electron image rather than a general secondary electron image is that, as explained below, it provides contrast depending on the chemical composition of the inclusion, and the effect of charge-up on inclusions with high electrical insulation properties. This is because it is highly useful in that it is difficult to produce.

Further, the composition of each inclusion is analyzed based on characteristic X-rays obtained by an energy dispersive X-ray spectrometer (EDS). In this case, although it is inferior in quantitative performance and detection limit compared to the electron probe microanalyzer (EPMA) described later, it is suitable for understanding the types and representative compositions of inclusions such as oxides and sulfides.

(Process D: Specific example)
FIG. 2 is an example of a backscattered electron image of an inclusion obtained by SEM. The backscattered electron image can express the fine morphology of inclusions, and has sufficient resolution to be compared with an optical microscope. The inclusions in the image of FIG. 2 were composed of MgO--Al ₂ O ₃ -based oxides and CaS-based sulfides, based on the results of compositional analysis by EDS.

Here, the amount of backscattered electrons emitted in SEM depends on the atomic number, and the larger the atomic number, the larger the amount emitted. Therefore, if there are various chemical composition differences on the sample surface, each atomic number will A dependent contrast difference will be obtained. Therefore, when trying to distinguish between different types of inclusions such as oxide inclusions and sulfide inclusions, a backscattered electron image (COMPO image) is more suitable than a general secondary electron image. I can say that.

(Process E: Regarding acquisition of elemental mapping image by EPMA)
In order to analyze the composition of each inclusion to be evaluated, elemental mapping images are obtained based on the analysis results of characteristic X-rays obtained by electron probe microanalyzer (EPMA) analysis for each element. Since characteristic X-rays differ for each element, images for each element such as S and O can be obtained.

(Process E: Specific example)
FIG. 3 shows elemental mapping images of certain inclusions analyzed by EPMA, in which (a) shows a backscattered electron image, (b) shows a mapping image of sulfur, and (c) shows a mapping image of oxygen. This result matched the contrast of the backscattered electron image taken in step D.

(Process F: Creating a separate color 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 the type of inclusion is determined using image editing software. Identify and color the inclusions separately. In addition, when using a field emission type electron probe microanalyzer (FE-EPMA) with high spatial resolution and analysis accuracy, it is possible to capture the morphology of inclusions in detail and to obtain detailed elemental mapping images. Therefore, this image may be directly compared with the processed image obtained in step C.

(Process F: Specific example)
FIG. 4C is an image in which inclusions are colored differently for each type of inclusion. For coloring here, commercially available image editing software was used as in Step C. The base image for creating the painted image was a grayscale inclusion image that had been captured separately from the color image in step B, and after converting the grayscale to RGB color, it was created in step C. The types of inclusions were identified by comparing the processed image with the backscattered electron image obtained in Step D and the elemental mapping image obtained in Step E. The images were superimposed using the layer function of the image editing software, and each pixel was While checking, the oxide and sulfide areas were painted separately on the base image. As an example, oxides are colored red and sulfides are colored blue.

In addition, the image in which areas are colored differently for each type of inclusion can also be suitably used as teacher data used in supervised learning of machine learning.

(Process G: Measurement of inclusion size)
Based on the colored image for each type of inclusion obtained in step F, focusing on the oxide, the size of the oxide, √area (square root of the product of the minor axis and major axis of the oxide), is measured.

(Process G: Specific example)
FIG. 5(b) shows the results of √area measurement of the oxide. For comparison, (a) shows an example in which human evaluation misidentified sulfides as oxides and measured them based on grayscale photographs of inclusions taken with an optical microscope. However, such identification is difficult unless one is an expert. When oxides and sulfides are correctly identified by the method of the present invention, the size √area of the oxide is 12.6 μm, whereas the size √area of the incorrectly identified oxide is 14.2 μm. Overmeasured.

When measuring the size of oxides, there are cases where inclusions are separated from each other as shown in FIG. In measuring the size of inclusions to be used as data for extreme value statistical evaluation, even if inclusions are far apart, if they are close to each other, it is necessary to judge them as one. The determination results in Figure 5 take into account the determination method. For example, if the distance between two inclusions is smaller than the √area (the square root of the product of the minor axis and major axis of the inclusion). If they are far apart, the existence of the smaller inclusion is ignored; on the other hand, if the distance between the two is smaller (i.e. closer) than the √area of the smaller inclusion, the two are considered to be a single inclusion. is taken.
Further, when two or more inclusions are closely dispersed, the integrity is determined in order from the distance between the closest inclusions and the particle size of the smaller inclusion.
Such a determination takes into account the assumption that when inclusions are close together, they act as a unit and become more harmful to fatigue. Note that the criteria for determining inclusions shown here are only one example, and the present invention is not limited to this method.

(Process H: Regarding implementation of extreme value statistical method)
Evaluation Using the extreme value statistical method, multiple test pieces made from the steel material are evaluated based on the maximum inclusion diameter data for each test piece obtained through the above process, and an arbitrary predicted volume of the steel material is evaluated. Predict the maximum diameter of inclusions that can be included.

(Process H: Specific example)
FIG. 6 shows an extreme value statistical graph obtained by the extreme value statistical method based on the data of the maximum inclusion diameter obtained. Note that the approximate straight line representing the slope of the extreme value statistical plot is obtained by least squares approximation.

Here, the extreme value statistical method used to predict the inclusion diameter will be explained. In this case, the extreme value statistical method is to collect multiple test pieces (j = 1, n) from a certain population and use an optical microscope to find the largest inclusion that exists within a predetermined observation range of each test piece. After photographing, the size of the largest inclusion in each test piece is plotted in order from smallest to largest on an extreme value statistical graph, and based on the approximate straight line of those plots, the maximum size in any area to be predicted is calculated. This is to predict the inclusion diameter (i.e., √area _max ). The obtained √area _max can be used for product quality inspection, as an index for comparison of inclusion cleanliness between evaluated steel materials, or as an index for improving cleanliness by improving steelmaking and refining methods.

In particular, in this example, the observation area of one field of view (inspection reference area: S _o mm ² ) is set to, for example, S _o = 100 mm ² (10 mm long x 10 mm wide), and the areas S _o are made to not overlap. Then, the size (√area) of the largest inclusion was measured for each of the 12 visual fields, and the size √area _max of the largest inclusion was predicted by the extreme value statistical method with the predicted area S=30000 mm ² .

The value of the cumulative distribution function F (unit: %) shown on the vertical axis in the extreme value statistical graph can be determined from the formula F _j =j/(n+1)×100. Here, n indicates the total number of test pieces for which √area was evaluated, and j indicates the jth number when √area is arranged in descending order. However, in this case, it is necessary to use probability paper to determine the plot position on the vertical axis. Therefore, instead of F, the value of y _j obtained from the equation y _j =-ln [-ln{j/(n+1)}] may be used to represent the vertical axis as the scaling variable y. The method of plotting is to sequentially plot the largest inclusion size √area in the observation field in descending order, with the value on the horizontal axis set as √area, and the value on the vertical axis set as the j-th standardization variable y _j . shall be carried out. Next, in order to predict the maximum inclusion diameter (√area _max ) that can exist in an arbitrary area, the cumulative distribution function F and scaling variable y in the area to be predicted are determined from the recurrence period T. At this time, T is calculated using the predicted area S and the observed area S _o of each test piece using the formula T = (S + S _o )/S _o , and using this T, the cumulative distribution function F is calculated as F = (T - 1)/T×100. Further, the vertical axis may be expressed using the value of y obtained from the equation y=-ln[-ln{(T-1)/T}] as the standardization variable y. Further, in the case of S≫S _o , y=lnT and T=S/S _o hold, which can be easily calculated.

An example of 12 fields of view in which the oxide region was correctly distinguished from other inclusion systems by the process carried out this time, and the maximum oxide diameter √area for each observation range (i.e. S _o , here 100 mm ² ) was measured. If the recurrence period T is 300, the maximum diameter of an inclusion that can exist in steel with an area equivalent to 300 test pieces is estimated to be 35.6 μm, as shown in FIG. Ta.

This predicted area (the 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 exemplified here. In addition, the evaluation method described here is a result based on two-dimensional observation, and does not take into account the three-dimensional shape or distribution state of inclusions. Therefore, in order to predict √area _max with higher accuracy, Original observations may be made. Alternatively, the evaluation may be performed in a pseudo three-dimensional manner by virtually considering the thickness of the inclusion with respect to the observation area S _o on the two-dimensional plane.

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.

As shown in Figure 6, in order to more accurately measure the size of oxides than in the conventional procedure, the present invention combines an original image of inclusions taken with an optical microscope with an image processed using image editing software and an SEM. The maximum oxide diameter (√area _max ) can be evaluated with higher precision by precisely specifying the oxide region based on comparison with the analysis results of the inclusion composition using an analytical instrument such as EPMA. This method can also be applied to inclusions other than oxides.

As shown in FIG. 6, the maximum predicted inclusion diameter using the conventional procedure is 31.5 μm, which is a smaller value than the more accurate predicted inclusion diameter of 35.6 μm according to the present invention. This results in predictions that do not take into account the risk of inclusions. In particular, considering that there are situations in which it is necessary to emphasize the influence of oxide inclusions on fatigue life, it would be possible to easily identify each type of inclusion and accurately predict fatigue properties. This is extremely useful when making predictions in situations where different inclusions have different effects.

Claims (7)

Obtaining an image of the steel material containing the inclusions by imaging a predetermined observation area of the steel material containing the inclusions with an optical microscope through a monochromatic transparent color filter;
creating an enhanced processed image by processing the image from the image so as to emphasize the outline of the image;
a step of imaging the observation area including the inclusion with a scanning electron microscope to obtain a backscattered electron image;
creating an analysis image in which the component composition of the region containing the inclusions is identified using characteristic X-rays;
creating an identification image in which a region on the image occupied by each inclusion system is displayed in an identifiable manner based on the enhanced processed image based on the reflected electron image and the analysis image;
a step of calculating the maximum inclusion diameter for each inclusion system from the obtained identification image,
A method for identifying inclusion systems in observation areas containing inclusions and evaluating the maximum inclusion diameter. 2. The evaluation method according to claim 1, wherein the colored transparent color filter is a green filter. 3. The evaluation method according to claim 1, wherein the image processing for obtaining the emphasized processed image includes at least a step of performing color inversion processing. The evaluation method according to any one of claims 1 to 3, wherein the analysis device for identifying the component composition of each region using characteristic X-rays is an electron probe microanalyzer (EPMA). The evaluation method according to any one of claims 1 to 4, wherein the identification of the inclusion system identifies at least an oxide-based inclusion system. A step of obtaining an identification image of the inclusion system and a calculation result of the maximum inclusion diameter for each observation area for a plurality of non-overlapping observation areas in the steel material using the evaluation method according to any one of claims 1 to 5. and,
Based on the calculated value of the maximum inclusion diameter obtained, the maximum inclusion diameter of the inclusion system in the steel area (predicted area) that is larger than the area of the observation area (inspection reference area) was predicted using a statistical method. a step of calculating a predicted value of the maximum inclusion diameter;
A predictive evaluation method for the cleanliness of steel materials with excellent inclusion identification accuracy. 7. The statistical method is an extreme value statistical method, and the maximum inclusion diameter in the steel material is predicted under conditions of an inspection reference area of 100 mm ² and a predicted area of 30000 mm ² . A predictive evaluation method for the cleanliness of steel materials with excellent inclusion identification accuracy.

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