JP2018072194A - Life prediction method and identification method for bearing material, and method of manufacturing bearing - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a life prediction method for quantitatively evaluating inclusion thereby predicting life of bearing material with a high degree of accuracy, and an identification method for identifying a bearing whose long life is guaranteed by life prediction.SOLUTION: The life prediction method comprises steps (S201 and 202) of applying elemental analysis and extreme value statistics to bearing material, thereby estimating the value of the maximum square measure of inclusion included in the bearing material, and a step (S203) of predicting the life of the bearing material using a preacquired life prediction formula and the maximum square measure of inclusion acquired in the estimation step. The life prediction formula is determined in advance from the square measure of the inclusion included in a test piece obtained by applying elemental analysis to a test piece of the same composition as the bearing material, the oxygen content level in the test piece composition and the result of rolling life test conducted on the test piece by using the result of estimating the maximum square measure of inclusion by the extreme value statistics method.SELECTED DRAWING: Figure 5

Description

  The present invention relates to a bearing material life prediction method, identification method, and bearing manufacturing method, and more particularly to a rolling bearing material life prediction method, identification method, and rolling bearing manufacturing method.

  The life of a rolling bearing is often determined by the likelihood of a peeling phenomenon occurring on the surface of the material for the rolling bearing, for example, a steel material. This peeling phenomenon is a kind of fatigue failure due to rolling contact of a steel material for a rolling bearing. The exfoliation phenomenon involves a strength factor that depends on the chemical composition and hardness of the material and an internal defect factor that depends on non-metallic inclusions inherent in the material. The former strength factor mainly affects the level of peel life. The latter internal defect factor greatly influences the variation in peeling life.

  Internal defect factors are thought to be influenced by the number of non-metallic inclusions present irreversibly and their size distribution. The internal defect factor may be greatly different for each of the plurality of steel materials even if the plurality of steel materials are the same steel type, that is, the strength factor is the same level. For this reason, the life of rolling bearings often varies with a life ratio of 10 times or more. Therefore, when manufacturing rolling bearings, bearing life varies, and ten or more test pieces are prepared for each lot and a rolling life test is performed.

In the rolling life test, a test piece is rolled with a counterpart test piece with a maximum contact stress of about several GPa, and the number of rolling cycles until the test piece is damaged due to peeling or the like is investigated. Some test pieces have a long life and the number of rolling cycles until breakage exceeds the order of 10 ⁸ . Usually, a cylindrical or disk-shaped specimen is used for the test piece, and a cylindrical or spherical specimen is used for the counterpart test piece. In this rolling life test, the life (number of rolling cycles) at which 10% of all the test pieces are damaged is defined as L10, and the bearing life for each lot is evaluated based on this L10.

  In recent years, various cleaning treatment techniques in the manufacturing process of steel materials have been developed, and non-metallic inclusions in the steel materials have been greatly reduced. For this reason, steel materials with less non-metallic inclusions are supplied as rolling bearing materials, and long-life rolling bearings are manufactured.

If the rolling bearing has a long life, the time required for the rolling life test to be performed until the material is damaged increases. Therefore, according to the conventional method for evaluating the bearing life described above, it is necessary to perform a rolling life test on each of the 10 or more test pieces for each lot until each of the test pieces is damaged. There's a problem. Further, as described above, steel materials with few non-metallic inclusions are used as rolling bearing materials. For this reason, the number of rolling cycles until each test piece is broken often exceeds 10 ^8, and the time required for the rolling life test is further increased.

  In order to shorten the time required for the rolling life test, it is conceivable to increase the load load (maximum contact stress) in the test and perform an accelerated test. However, when the load is increased, a failure phenomenon different from the failure mode in the normal use of the bearing appears, and the bearing life in accordance with the actual use may not be evaluated.

  Therefore, in Japanese Patent Application Laid-Open No. 2008-196622 (Patent Document 1), a rolling life test is performed by measuring non-inclusions in steel materials and creating a rolling life estimation formula for each steel material from the measurement results of inclusions. A method for predicting the life of a rolling bearing material in a short time without performing the method is disclosed.

JP 2008-196622 A

  In Japanese Patent Application Laid-Open No. 2008-196622, inclusions are qualitatively classified into manganese sulfide and aluminum oxide depending on the color and shape of a color image. For this reason, there is a possibility that erroneous inclusion classification may occur, and there is a possibility of erroneous determination in which scratches and dirt on the material surface are determined as inclusions. Moreover, the life prediction formula using only internal defect factors such as the number of non-metallic inclusions as explanatory variables has room for improvement in the prediction accuracy.

  The present invention has been made in view of the above problems, and its purpose is to quantitatively evaluate inclusions and accurately predict the life of a rolling bearing material in a short time, and a bearing material life prediction method, It is to provide a bearing material identifying method for identifying a bearing whose long life is guaranteed by life prediction, and a bearing manufacturing method using the identifying method.

  The life prediction method for a bearing material according to an aspect of the present invention performs the elemental analysis process and the extreme value statistical method on the bearing material, thereby obtaining the maximum area value of inclusions contained in the bearing material. A step of estimating, a step of predicting the life of the bearing material from the life prediction formula obtained in advance and the value of the maximum area of inclusions obtained in the step of estimating. The service life prediction formula uses the extreme value statistical method to determine the maximum area of inclusions from the area of inclusions contained in the specimens obtained by performing elemental analysis on the specimens with the same composition as the bearing material. It is determined using the correlation between the estimated result, the oxygen content in the composition of the test piece, and the result of the rolling life test performed on the test piece.

In the method for identifying a bearing material according to one aspect of the present invention, the life predicted in the step of predicting the life of the bearing material using the life prediction method and the step of predicting the life is 9 × 10 ^7. A step of identifying a bearing material based on whether or not the above is true.

  A method of manufacturing a bearing according to one aspect of the present invention includes a step of assembling parts constituting a bearing to constitute a bearing, and a bearing material constituting the part is identified using the bearing material identification method. And a step of selecting a bearing.

  According to the present invention, inclusions included in the test piece of the bearing material are classified for each composition by elemental analysis, and the maximum area of the inclusions is estimated by the extreme value statistical method. And, the above-mentioned obtained from the test piece of the bearing material which is the object to be predicted and the life prediction formula obtained in advance which is the relationship between the maximum area of inclusions, the oxygen content in the composition of the test piece and the life of the bearing The life of the bearing material, which is the object to be predicted, is predicted from the value of the maximum area and the oxygen content. For this reason, compared with the conventional life prediction method based on the inclusion measurement using color images, the influence of scratches and dirt on the test surface of the test piece on the measurement result can be reduced. By considering the oxygen content showing a good correlation, it is possible to predict the bearing life more accurately.

  According to the present invention, for example, for the inclusions in the test piece classified by composition by elemental analysis using SEM-EDX, the area of each inclusion is measured for each type, and the extreme value is determined from the measurement result. The maximum area is determined for each type of inclusion using statistical methods. And the life prediction formula is calculated | required from the result of having performed the life test about the test piece from which the maximum area was calculated | required for every kind of inclusion, and the oxygen content in the composition of the said maximum area and test piece. Since the elemental analysis process is used to detect each inclusion composition, the effect of scratches and dirt on the test surface on the measurement results can be reduced compared to the inclusion measurement method using color images. Furthermore, by considering the oxygen content showing a good correlation with the life, the bearing material and the bearing can be more accurately identified according to the life.

It is a schematic sectional drawing which shows the structure of a deep groove ball bearing. It is a schematic sectional drawing which shows the structure of a tapered roller bearing. It is a flowchart explaining the outline | summary of the life prediction method of the steel materials in this Embodiment. It is a flowchart which shows the process (S100) in FIG. 3 in detail. It is a flowchart which shows the process (S200) in FIG. 3 in detail. It is a graph which shows the relationship between the oxygen content of the test piece calculated | required from the material mill sheet | seat, and the actual 10% life by a rolling life test. It is a graph which shows the relationship between the estimated 10% lifetime by the lifetime prediction formula shown in a linear form, and the actual 10% lifetime by an actual test. It is a graph which shows the relationship between the estimated 10% lifetime by the lifetime prediction formula shown by a logarithmic form, and the actual 10% lifetime by an actual test. It is a graph which shows the relationship between the estimated 10% lifetime by the lifetime prediction formula shown by an index | exponent form, and the actual 10% lifetime by an actual test.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following drawings, the same or corresponding parts are denoted by the same reference numerals, and description thereof will not be repeated.

<Configuration of bearing to which the demand prediction method according to the present embodiment is applied>
First, an example of a rolling bearing that is an object of the present embodiment will be described with reference to FIGS. 1 and 2.

  Referring to FIG. 1, a deep groove ball bearing 1 as a first example of a rolling bearing formed in the present embodiment mainly includes an outer ring 10, an inner ring 11, a plurality of balls 12, and a cage 13. I have. The outer ring 10 has an annular shape and has an outer ring rolling surface 10A on the inner peripheral surface. The inner ring 11 has an annular shape and has an inner ring rolling surface 11A on the outer peripheral surface. The inner ring 11 is disposed on the inner peripheral side of the outer ring 10 so that the inner ring rolling surface 11A faces the outer ring rolling surface 10A. The outer diameter of the outer ring 10 and the inner ring 11 is, for example, 150 mm or less.

  The ball 12 is disposed on the inner peripheral surface of the outer ring 10. The balls 12 are arranged at a predetermined pitch on an annular track along the circumferential direction of the outer ring 10 and the inner ring 11 by a cage 13 made of a synthetic resin, and are rotatably held on the track. Yes. The ball 12 has a ball rolling surface 12A and is in contact with the outer ring rolling surface 10A and the inner ring rolling surface 11A on the ball rolling surface 12A. With such a configuration, the outer ring 10 and the inner ring 11 of the deep groove ball bearing 1 are rotatable relative to each other.

  Referring to FIG. 2, a tapered roller bearing 2 as a second example of the rolling bearing formed in the present embodiment mainly includes an outer ring 20, an inner ring 21, a plurality of rollers 22, and a cage 23. I have. The outer ring 20 has an annular shape and has an outer ring rolling surface 20A on the inner peripheral surface. The inner ring 21 has an annular shape, and has an inner ring rolling surface 21A on the outer peripheral surface. The inner ring 21 is disposed on the inner peripheral side of the outer ring 20 so that the inner ring rolling surface 21A faces the outer ring rolling surface 20A.

  The rollers 22 are disposed on the inner peripheral surface of the outer ring 20. The roller 22 has a roller rolling surface 22A, which contacts the inner ring rolling surface 21A and the outer ring rolling surface 20A at the roller rolling surface 22A, and at a predetermined pitch in the circumferential direction by a cage 23 made of synthetic resin. Has been placed. As a result, the roller 22 is rotatably held on the annular raceway of the outer ring 20 and the inner ring 21. Further, in the tapered roller bearing 2, the apex of each of the cone including the outer ring rolling surface 20A, the cone including the inner ring rolling surface 21A, and the cone including the locus of the rotating shaft when the roller 22 rolls is the center of the bearing. It is configured to intersect at one point on the line. With such a configuration, the outer ring 20 and the inner ring 21 of the tapered roller bearing 2 are rotatable relative to each other.

  The outer ring 10 and the inner ring 11, and the outer ring 20 and the inner ring 21 are preferably made of a steel material that is an example of a bearing material, such as JIS standard SUJ2.

  The balls 12 and the rollers 22 are rolling elements and may be made of a steel material that is an example of a bearing material, but may be made of another material, for example, a sialon sintered body. The above-described bearing is a bearing that is identified as having a sufficient life by predicting the life of the steel material, which is a constituent material, using a life prediction method described below.

<Explanation of life prediction method>
Next, the life prediction method of the steel material which is the bearing material which comprises the said deep groove ball bearing 1 and the tapered roller bearing 2 in this Embodiment is demonstrated using FIGS. Referring to FIG. 3, the steel material life prediction method roughly includes a step of obtaining a mathematical formula for predicting the life of the steel material (S100) and a step of predicting the life of the prediction object (S200). Yes.

Referring to FIG. 4, in the process of predicting the life of steel material, first, elemental analysis processing is performed on the test pieces of steel material constituting deep groove ball bearing 1 and tapered roller bearing 2 (S101). That is, first, a test piece made of the same material as the steel constituting the deep groove ball bearing 1 and the tapered roller bearing 2 is prepared, and a backscattered electron image is observed on the test piece, for example, by an SEM (scanning electron microscope). As a result, the test surface which is a part of the steel material is observed, and each inclusion existing in the steel material is extracted from the difference in contrast. Each inclusion is extracted by utilizing the fact that the contrast differs for each material of the inclusion in the observation image of the test surface of the steel material. In addition, elemental analysis is sequentially performed on each extracted inclusion by EDX (energy dispersive X-ray spectroscopy). Thereby, manganese sulfide (MnS) and aluminum oxide (Al ₂ O ₃ ) as non-metallic inclusions contained in the test piece of the steel material are quantitatively classified. Here, the size (area) and the number of non-metallic inclusions made of, for example, manganese sulfide and aluminum oxide are measured.

  Next, the maximum areas of manganese sulfide and aluminum oxide as non-metallic inclusions contained in the test piece classified in the step (S101) are estimated by a generally known extreme value statistical method (S102). Here, based on the distribution of the manganese sulfide and aluminum oxide, which are non-metallic inclusions detected from the test piece in the step (S101), on the test surface of the test piece, the test object is subjected to the extreme value statistical method. The maximum area of inclusions (manganese sulfide and aluminum oxide) on the surface is estimated. More specifically, based on the distribution of the size of inclusions on the test surface of a predetermined area that is a part of the steel material constituting the rolling bearing, the steel material including the test surface is in a predetermined cross-sectional area or volume. The maximum area of inclusions at is estimated.

  On the other hand, a rolling life test (S103) using a steel specimen is performed. This is a test for measuring the L10 life by investigating the number of rolling cycles until failure using a steel specimen of a rolling bearing in order to obtain a life prediction formula.

  Note that the order of the steps (S101) and (S102) and the step (S103) is not limited, and the steps (S101) and (S102) may be performed after the step (S103) is performed first.

  And the maximum area of the inclusion of the test piece estimated in the steps (S101) and (S102), the oxygen content in the composition of each test piece obtained from the material mill sheet of each test piece, and the step (S103) Correlation with the result of the rolling life test of the test piece derived in the above is obtained, and a life prediction formula is obtained from the correlation (S104). That is, the life prediction formula obtained in the step (S104) is to perform elemental analysis processing (measurement by SEM and EDX analysis) and extreme value statistical method on a test piece having the same composition as the bearing material for which life prediction is performed. Is determined using the correlation between the result of estimating the maximum area of inclusions, the oxygen content in the composition of each test piece, and the result of performing a rolling life test on the test piece.

  Next, using the life prediction formula obtained as described above, a step of predicting the life of a steel material that is a bearing material constituting the deep groove ball bearing 1 that is an actual prediction object is performed. Specifically, referring to FIG. 5, first, in the step (S101), the steel material constituting the bearing (for example, the deep groove ball bearing 1 and the tapered roller bearing 2) as a prediction object whose life is to be actually predicted is actually processed. Then, an element analysis process similar to the element analysis process performed on the test piece is performed (S201). Next, using the result of the step (S201), for the steel material of the prediction target, as in the estimation performed on the test piece in the step (S102), inclusions such as manganese sulfide and The maximum area of aluminum oxide is estimated by the extreme value statistical method (S202). This step (S201) and step (S202) are steps for estimating the maximum area value of inclusions contained in the bearing material by performing elemental analysis processing and extreme value statistical method on the bearing material. Correspond. Further, the oxygen content of the bearing material can be obtained from the steel material mill sheet that is the bearing material that is the prediction target. Then, the estimation result of the maximum area and the oxygen content are substituted into the life prediction formula obtained in step (S104) (S203). Thereby, the lifetime of the steel material as the said prediction target object can be estimated. In this step (S203), the life prediction formula obtained in advance, the maximum area value of inclusions obtained in steps (S201) and (S202), which are estimation steps, and the oxygen content of the bearing material It corresponds to the process of predicting the life of bearing materials from

As the format of the life prediction formula, for example, three types of linear, logarithmic, and exponential can be considered. Then, a multiple regression analysis is performed on the square root of the maximum area area _MAX of each inclusion and the oxygen content of the bearing material as numerical values to be substituted into the life prediction formula, that is, as explanatory variables. Thereby, for example, the following mathematical formula can be obtained.

The above (formula 1) represents the life prediction formula as a linear formula. The above (Equation 2) is a logarithmic mathematical expression, and the above (Equation 3) is an exponential mathematical expression. In the above formulas, L ₁₀ est. Means an estimated 10% life, that is, a life estimated that 10% of the test pieces of the actual product are damaged. Also

Represents the square root √area _MAX of the estimated maximum area of the inclusion X. That is, √area _{MAX, MnS} is the square root of the estimation result of the maximum area of manganese sulfide, and √area _{MAX, Al2O3} is the square root of the estimation result of the maximum area of aluminum oxide. [O] is the oxygen content of the test piece. Furthermore, γ, ε, κ, a, b, c and d in the above formulas are constants.

  Even if any of the above (formula 1), (formula 2) and (formula 3) is used, the value of the square root and the oxygen content of the estimation result of the maximum area to be substituted into the formula, and the actual lifetime value Correlation can be shown.

<Method for identifying bearing material and method for producing bearing>
And if the lifetime of the steel material which is a bearing material is estimated using the above life prediction formulas, the bearing material can be identified based on the predicted lifetime. For example, for a steel material that is a bearing material constituting a bearing component such as a bearing ring, a predicted life is obtained by using the life prediction method as described above. And the bearing using the steel materials with the estimated life L10 of 9 × 10 ⁷ or more can be identified from the lot as a non-defective product. Thereby, a highly reliable rolling bearing using a high-quality steel material can be identified.

Moreover, a rolling bearing having high reliability can be provided by incorporating the above-described identification method into a rolling bearing manufacturing process, particularly an inspection process. For example, in the method for manufacturing a rolling bearing, a step of preparing components for the rolling bearing is performed. In the preparation step, a bearing ring, a rolling element, a cage, and the like constituting the rolling bearing are prepared. Furthermore, the process which assembles the prepared components and comprises a bearing is implemented. Further, the bearings are selected by identifying the steel material, which is a material for the bearing constituting the components of the rolling bearing, using the identification method described above. Specifically, it is possible to determine a bearing whose predicted life exceeds a predetermined reference value (for example, the predicted life L10 is 9 × 10 ⁷ or more) as a non-defective product that has passed the inspection. In this way, it is possible to manufacture a highly reliable rolling bearing whose predicted life exceeds a predetermined reference value as shown in FIGS.

<Effect>
Next, the function and effect of this embodiment will be described.

  In this embodiment, when investigating the life of a rolling bearing, it is possible to save labor and cost for preparing a large number of test pieces for each lot and performing a rolling life test. That is, the estimated maximum area of the nonmetallic inclusions in the steel showing the internal defect factor is estimated using the extreme value statistical method using the result of the elemental analysis process as described above. Moreover, the oxygen content data is obtained from the material mill sheet of the test piece. Then, the estimated maximum area and oxygen content are substituted into the life prediction formulas exemplified in the above (1 formula) to (3 formula). In this way, the life of the rolling bearing material can be predicted with high accuracy without performing a rolling life test on the predicted object.

  The above formula is obtained from the correlation between the result of the rolling life test using the test piece and the maximum area and oxygen content of inclusions, which are explanatory variables. In this embodiment, the composition of the inclusion is determined. Is not performed by a color image but by elemental analysis processing such as SEM-EDX. For this reason, it is possible to suppress misclassification of inclusions and noise such as scratches on the material surface, which may occur when a qualitative evaluation of inclusions is performed using a color image, as inclusions. Also from this viewpoint, in the present embodiment, the accuracy of the inclusion estimation formula can be further increased. That is, it is possible to reduce the influence of the difference in the state of the test surface caused by the difference in the sample preparation skill of the inspector and the variation for each measurement on the evaluation result.

  Further, in the present embodiment, not only the maximum area of inclusions but also the oxygen content is included as an explanatory variable of the life prediction formula. For this reason, the life of the rolling bearing can be predicted with higher accuracy than in the case of using the life prediction formula based only on the maximum area of inclusions.

Next, it demonstrates more concretely in the following examples.
(Example)
Eighteen types of steel specimens used for rolling bearings with different forms and sizes of non-metallic inclusions were prepared. These test pieces were subjected to a rolling life test under the test conditions shown in Table 1 below to obtain a life L10. This process corresponds to the step (S103) of FIG.

Moreover, the nonmetallic inclusions using SEM-EDX were investigated for each test piece. In the investigation of nonmetallic inclusions by SEM-EDX, the area of the test surface of each test piece was set to 307.2 mm ² , and manganese sulfide and aluminum oxide as inclusions were classified by elemental analysis treatment by EDX. This process corresponds to the step (S101) of FIG. And the area of each inclusion was measured with respect to each test piece. The maximum area area _MAX of inclusions contained in a region of about 2.4 mm ³ minutes of each test piece was estimated by an extreme value statistical method. The reason why the inclusion contained in the region of about 2.4 mm ³ minutes is estimated here is because the size of the region corresponds to the product of the area through which the contact ellipse of the test piece having a diameter of 12 mm passes and the load depth. . This process corresponds to the step (S102) of FIG.

  Moreover, about the material which comprises each said test piece, the oxygen content described in a material mill sheet | seat was investigated. FIG. 6 shows the relationship between the oxygen content of each test piece and the life L10 obtained by the rolling life test described above. Referring to FIG. 6, the horizontal axis in this figure indicates the oxygen content of the test piece, and the vertical axis indicates the life L10 of the test piece. FIG. 6 shows that there is a good correlation between the oxygen content of the test piece and the results of the rolling life test. For this reason, oxygen content is adopted as an explanatory variable of the life prediction formula.

Then, from the correlation between the estimated value of the maximum area area _MAX by the composition of inclusions of each test piece and the data of the oxygen content and the measurement result of the life L10, the above-mentioned, which shows an estimated 10% life by multiple regression analysis

The constants were derived. This corresponds to the step (S104) of FIG.
The constants included in (Expression 1) to (Expression 3) so that the correlation between the result of the rolling life test in the step (S104), the maximum area area _{MAX of} inclusions, and the oxygen content is the highest. The values of γ, ε, κ, a, b, c and d were determined. As a result, the correlation between the explanatory variable (here, the square root of the maximum area area _MAX of the inclusion and the oxygen content) and the lifetime is obtained by making (Equation 1) to (Equation 3) shown in Table 2 below. Became the highest. In addition, R ^{** 2} in a table ^| surface shows a freedom degree double adjustment contribution rate, and it shows that the correlation with an explanatory variable and a lifetime is so high that this figure is large. That is, the larger the value of R ^{** 2,} the higher the correlation between the estimated lifetime obtained from the above formula and the lifetime obtained from the actual test. Table 2 shows the value of R ^{** 2} in each equation.

In addition, FIG. 7 to FIG. 9 show the relationship between the estimated 10% life L ₁₀ est. And the actual 10% life L ₁₀ exp. The horizontal axis of each figure shows the estimated 10% life L ₁₀ est., And the vertical axis of each figure shows the actual 10% life L ₁₀ exp. Note that the actual 10% life L ₁₀ exp. Is not an estimate obtained from the above formula, but is a value of a life at which 10% of actual products are damaged by the rolling life test (above described). Equivalent to the life L10). The data plotted in each figure shows the estimated 10% life L ₁₀ est. And the actual 10% life L ₁₀ exp. For each of the 18 types of test pieces. Note that the residual sum of squares as the sum of squares of the error between the estimated 10% life and the actual 10% life obtained from the equation (1) shown in FIG. 7 was 1.24 × 10 ⁸ . Similarly, the residual sum of squares of the estimated 10% life and the actual 10% life obtained from the two formulas shown in FIG. 8 is 1.17 × 10 ⁸ , and is obtained from the three formulas shown in FIG. The residual sum of squares between the estimated 10% life and the actual 10% life was 1.56 × 10 ⁸ .

  By using the mathematical formulas shown in Table 2 above, it is possible to predict the rolling life with high accuracy without performing an actual rolling life test on a rolling bearing steel material as an arbitrary prediction target. it can.

  Each embodiment and example disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

  1 deep groove ball bearing, 2 tapered roller bearing, 10, 20 outer ring, 10A, 20A outer ring rolling surface, 11, 21 inner ring, 11A, 21A inner ring rolling surface, 12 balls, 13, 23 cage, 22 rollers.

Claims (6)

A method for predicting the life of a bearing material,
Estimating the value of the maximum area of inclusions contained in the bearing material by performing elemental analysis processing and extreme value statistical method on the bearing material;
A step of predicting the life of the bearing material from a life prediction formula obtained in advance and the value of the maximum area of inclusions obtained in the step of estimating,
The life prediction formula is the result of estimating the maximum area of the inclusion by performing the elemental analysis process and the extreme value statistical method on the test piece having the same composition as the bearing material, and the composition of the test piece. The life prediction method of the material for bearings determined using the correlation between the oxygen content in and the result of the rolling life test performed on the test piece. In the elemental analysis treatment, manganese sulfide and aluminum oxide are classified as the inclusions contained in the test piece,
In the life prediction formula, the square root of the maximum area value of the manganese sulfide is √area _{MAX, MnS,} the square root of the maximum area value of the aluminum oxide is √area _{MAX, Al2O3} , and the oxygen content is [ O]

The life prediction method of the material for bearings of Claim 1 represented by these. In the elemental analysis treatment, manganese sulfide and aluminum oxide are classified as the inclusions contained in the test piece,
In the life prediction formula, the square root of the maximum area value of the manganese sulfide is √area _{MAX, MnS,} the square root of the maximum area value of the aluminum oxide is √area _{MAX, Al2O3} , and the oxygen content is [ O]

The life prediction method of the material for bearings of Claim 1 represented by these. In the elemental analysis treatment, manganese sulfide and aluminum oxide are classified as the inclusions contained in the test piece,
In the life prediction formula, the square root of the maximum area value of the manganese sulfide is √area _{MAX, MnS,} the square root of the maximum area value of the aluminum oxide is √area _{MAX, Al2O3} , and the oxygen content is [ O]

The life prediction method of the material for bearings of Claim 1 represented by these. A method for identifying a bearing material constituting a bearing,
The step of predicting the life of the bearing material using the life prediction method according to any one of claims 1 to 4,
A method of identifying a bearing material, comprising: identifying the bearing material based on whether or not the predicted life in the step of predicting the life is 9 × 10 ⁷ or more. A bearing manufacturing method comprising:
Assembling parts constituting the bearing to constitute the bearing;
A method for manufacturing a bearing, comprising: identifying a bearing material constituting the component by using the bearing material identification method according to claim 5, and selecting the bearing.

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