CN112666199A - Method and device for predicting fatigue life of bearing steel - Google Patents
Method and device for predicting fatigue life of bearing steel Download PDFInfo
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- CN112666199A CN112666199A CN202110273209.5A CN202110273209A CN112666199A CN 112666199 A CN112666199 A CN 112666199A CN 202110273209 A CN202110273209 A CN 202110273209A CN 112666199 A CN112666199 A CN 112666199A
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Abstract
The invention discloses a method and a device for predicting the fatigue life of bearing steel, and relates to the technical field of steel materials. One embodiment includes: obtaining at least one group of metallographic samples from the end face of the bearing steel; grinding and polishing the metallographic surface of the metallographic sample; placing the metallographic samples in a full-automatic inclusion analyzer for inclusion detection to obtain detection result data of each group of metallographic samples; calculating the average inclusion distance of each group of metallographic samples based on the coordinate information; calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance; wherein the uniformity is used for predicting the fatigue life of the bearing steel. According to the embodiment, the fatigue life of the bearing steel can be predicted by predicting the uniformity of the distribution of the inclusions.
Description
Technical Field
The invention relates to the technical field of steel materials, in particular to a method for predicting the high cycle fatigue life of bearing steel by representing the distribution uniformity of inclusions in the bearing steel.
Background
The fatigue life is the most important index for inspecting whether the quality of the bearing steel is qualified, and researches show that more than 70 percent of high-cycle fatigue failure of the bearing steel is caused by inclusions, so that the characterization of the distribution uniformity of the inclusions is very necessary.
At present, the size distribution of inclusions, that is, the distribution of inclusions with the largest size, is predicted mainly by the weibull distribution. However, this method can predict only the maximum size inclusion distribution, and cannot predict the uniformity of the inclusion distribution. High quality bearing steel requires inclusions in the steel to be as small in size, as small in number and as uniform in distribution as possible. When the size of inclusions in steel is small to a certain extent, the influencing factor is the number and distribution. Therefore, the more uniformly the inclusions are distributed in the bearing steel, the more advantageous the fatigue life is for improvement.
Disclosure of Invention
The invention aims to solve the technical problems that the distribution of inclusions with the largest size can only be predicted, the uniformity of the distribution of the inclusions cannot be predicted, and the fatigue life of bearing steel cannot be further predicted. Aiming at the defects in the prior art, a method and a device for predicting the fatigue life of bearing steel are provided.
In order to solve the technical problem, the invention provides a method for predicting the fatigue life of bearing steel, which comprises the following steps:
obtaining at least one group of metallographic samples from the end surface of bearing steel, and grinding and polishing the metallographic surface of the metallographic samples;
placing the metallographic samples in ASPEX for inclusion detection to obtain detection result data of each group of metallographic samples; the detection result data comprises the number of inclusions and coordinate information of each inclusion;
calculating the average inclusion distance of each group of the metallographic samples based on the coordinate information;
calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance; wherein the uniformity is used for predicting the length of the fatigue life of the bearing steel.
Optionally, at least one set of metallographic sample is obtained from the end surface of the bearing steel, and the metallographic surface of the metallographic sample is polished, including:
sampling the end face center of the bearing steel to obtain at least one group of metallographic samples; the metallographic sample is a cube, and the end face of the bearing steel on the metallographic sample is a metallographic surface;
coarse grinding the gold phase surface by using 180-mesh sand paper, and fine grinding the gold phase surface by sequentially using 300-mesh, 600-mesh, 1000-mesh, 1500-mesh and 2000-mesh sand paper;
polishing the gold phase surface without a polishing agent;
washing the metallographic sample by using deionized water and slightly polishing;
and drying the metallographic sample by using a blower, and covering the metallographic surface with a cleaning adhesive tape.
Optionally, the metallographic sample is placed in a full-automatic inclusion analyzer for inclusion detection, and the method further includes:
setting the detection area and the size range of the inclusions.
Optionally, the metallographic sample is placed in a full-automatic inclusion analyzer for inclusion detection, and detection result data of each group of metallographic samples are obtained, wherein the detection result data comprises:
respectively placing each group of the metallographic samples in a full-automatic inclusion analyzer, tearing off a cleaning adhesive tape, blowing and wiping the metallographic surface by using compressed air, and closing a cabin door of the full-automatic inclusion analyzer;
based on the detection area and the size range of the inclusions, the full-automatic inclusion analyzer detects the inclusions in each group of metallographic samples and generates detection result data of each group of metallographic samples.
Optionally, calculating an average inclusion spacing for each set of the metallographic specimen based on the coordinate information includes:
reading all the coordinate information from the detection result data; wherein the coordinate information comprises positive abscissa values and positive ordinate values;
writing the coordinate information of all inclusions of each group of the metallographic sample into the same data table;
reading the coordinate information in the data table by utilizing a Python program, respectively calculating the linear distance between each inclusion and all adjacent inclusions, and selecting the minimum linear distance from the linear distances as the individual distance between the inclusion and the nearest inclusion;
and calculating the average inclusion distance of each group of metallographic samples according to the individual distance.
Optionally, calculating the uniformity of the bearing steel from the average inclusion spacing and the ideal inclusion spacing comprises:
calculating the ideal inclusion distance by using an ideal distance formula; wherein the ideal spacing formula is
In the formula (I), the compound is shown in the specification,
is the ideal inclusion spacing of the metallographic specimen, S is the detection area,
is the number of inclusions in the metallographic specimen;
calculating the uniformity of the bearing steel according to a uniformity formula; wherein the uniformity formula is
Wherein C is the degree of uniformity,
is the average inclusion spacing as described above,
is the number of said metallographic samples; and, the closer the value of the uniformity is to 1, the longer the fatigue life of the bearing steel.
In order to solve the above technical problems, the present invention provides an apparatus for predicting fatigue life of bearing steel, comprising:
the full-automatic inclusion analyzer is used for carrying out inclusion detection on at least one group of metallographic samples to obtain detection result data of each group of metallographic samples; the metallographic sample is obtained from the end face of the bearing steel, and the metallographic surface of the metallographic sample is covered by a cleaning adhesive tape after being polished, washed by deionized water and dried; the detection result data comprises the number of inclusions and coordinate information of each inclusion;
the calculation module is used for calculating the average inclusion distance of each group of metallographic samples based on the coordinate information;
the prediction module is used for calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance; wherein the homogeneity is used for predicting the fatigue life of the bearing steel.
Optionally, the device further comprises a setting module, wherein the setting module is used for setting the detection area and the size range of the inclusions, and
the fully automatic inclusion analyzer is further configured to:
carrying out inclusion detection on each group of metallographic samples based on the detection area and the size range of the inclusions, and generating detection result data of each group of metallographic samples; wherein, before the inclusion detection, the cleaning adhesive tape is torn off and the gold phase surface is blown and wiped by compressed air.
Optionally, the computing module is further configured to:
reading all the coordinate information from the detection result data; wherein the coordinate information comprises positive abscissa values and positive ordinate values;
writing the coordinate information of all inclusions of each group of the metallographic sample into the same data table;
reading the coordinate information in the data table by utilizing a Python program, respectively calculating the linear distance between each inclusion and all adjacent inclusions, and selecting the minimum linear distance from the linear distances as the individual distance between the inclusion and the nearest inclusion;
and calculating the average inclusion distance of each group of metallographic samples according to the individual distance.
Optionally, the prediction module is further configured to:
by usingCalculating the ideal inclusion distance by an ideal distance formula; wherein the ideal spacing formula is
In the formula (I), the compound is shown in the specification,
is the ideal inclusion spacing of the metallographic specimen, S is the detection area,
is the number of inclusions in the metallographic specimen;
calculating the uniformity of the bearing steel according to a uniformity formula; wherein the uniformity formula is
Wherein C is the degree of uniformity,
is the average inclusion spacing as described above,
is the number of said metallographic samples; and, the closer the value of the uniformity is to 1, the longer the fatigue life of the bearing steel.
The method and the device for predicting the fatigue life of the bearing steel have the following beneficial effects: the uniformity of the distribution of inclusions can be predicted, so that the fatigue life of the bearing steel is predicted; further, the performance of bearing steels of different heats in terms of fatigue life can also be compared.
Drawings
FIG. 1 is a schematic diagram of a method for predicting fatigue life of bearing steel according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an apparatus for predicting fatigue life of bearing steel according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
According to the embodiment of the invention, the fatigue life of the bearing steel is predicted according to the uniformity of the bearing steel by adopting a mode of representing the distribution uniformity of the inclusion of the high-quality bearing steel. Furthermore, the uniformity of the inclusions in the bearing steel with the same steel grade is compared, and the performance of the bearing steel with different heats in the aspect of fatigue life can be predicted.
As shown in fig. 1, a method for predicting fatigue life of bearing steel according to an embodiment of the present invention mainly includes:
and S101, obtaining at least one group of metallographic samples from the end face of the bearing steel, and grinding and polishing the metallographic surface of the metallographic samples.
When sampling from the bearing steel, samples (i.e., metallographic samples) are taken from the end face of the bearing steel, and one or more sets of metallographic samples can be taken for each bearing steel.
In a preferred embodiment, the metallographic sample is a cube. In the embodiment of the invention, the surface of the end face of the bearing steel on the metallographic sample is called a metallographic surface. Before the metallographic sample is detected, the metallographic surface needs to be polished.
In the embodiment of the present invention, step S101 may be implemented by: sampling the end face center of the bearing steel to obtain at least one group of metallographic samples; coarse grinding the metallographic surface by using 180-mesh abrasive paper, and fine grinding the metallographic surface by sequentially using 300-mesh, 600-mesh, 1000-mesh, 1500-mesh and 2000-mesh abrasive paper; polishing the metallographic surface without a polishing agent; washing and lightly polishing the metallographic sample by using deionized water; and (3) drying the metallographic sample by using a blower, and covering the metallographic surface with a cleaning adhesive tape.
For polishing the gold phase surface, firstly, 180-mesh abrasive paper is used for rough grinding, the surface is polished to ensure the smoothness of the gold phase surface, then 300-mesh, 600-mesh, 1000-mesh, 1500-mesh and 2000-mesh abrasive paper is used for fine grinding in sequence, and the last scratch is guaranteed to be polished off in the fine grinding process.
For polishing of the gold phase surface, polishing without a polishing agent was used in order to prevent the polishing agent from affecting the detection result. Polishing without polishing agent means: polishing the metallographic surface without using a polishing agent; or, a polishing agent is used for rough polishing, and finally, a piece of polishing cloth needs to be replaced to finely polish the metallographic surface.
It should be noted that the time of each stage of polishing is not too long.
And (4) washing the polished metallographic sample by using deionized water, and quickly performing light polishing on a polishing machine replaced with clean polishing cloth. The light sample of weathering with the hair-dryer after polishing is accomplished, and the purpose is got rid of the water stain of gluing on the metallography, prevents that the metallographic surface from oxidizing, influences the testing result. And finally, covering the metallographic surface with a clean adhesive tape to protect the metallographic surface.
And S102, placing the metallographic samples in a full-automatic inclusion analyzer for inclusion detection to obtain detection result data of each group of metallographic samples.
The detection result data of the embodiment of the invention may include the number of inclusions and coordinate information of each inclusion. The full-automatic inclusion Analyzer (ASPEX) is a product of a scanning electron microscope-energy spectrometer system and is also a steel inclusion analyzer with the highest analysis speed.
In the embodiment of the present invention, before step S102, the following steps may be implemented: setting the detection area and the size range of the inclusions.
The detection area may be set according to actual conditions. The size range of inclusions means the size of inclusions to be detected, and the size range of inclusions can be determined according to the steel grade.
In the embodiment of the present invention, step S102 may be implemented by: respectively placing each group of metallographic samples in a full-automatic inclusion analyzer, tearing off a cleaning adhesive tape, blowing and wiping a metallographic surface by using compressed air, and closing a cabin door of the full-automatic inclusion analyzer; based on the detection area and the size range of the inclusions, the full-automatic inclusion analyzer detects the inclusions in each group of metallographic samples and generates detection result data of each group of metallographic samples.
And (3) after the metallographic samples are processed, carrying out inclusion detection on the metallographic samples by using ASPEX, thereby obtaining the number of inclusions in each group of metallographic samples and the coordinate information of each inclusion.
And S103, calculating the average inclusion distance of each group of metallographic samples based on the coordinate information.
According to the coordinate information of all inclusions in the detection result data, the average value of the distance between any two inclusions in each group of metallographic samples, namely the average inclusion distance, can be calculated. The coordinate information of the embodiment of the present invention may include an abscissa positive value and an ordinate positive value, wherein the abscissa positive value (X _ ABS) represents a coordinate value of the abscissa axis, the ordinate positive value (Y _ ABS) represents a coordinate value of the ordinate axis, and X _ ABS and Y _ ABS are positive values.
In the embodiment of the present invention, step S103 may be implemented by: reading all coordinate information from the detection result data; writing the coordinate information of all inclusions of each group of metallographic samples into the same data table; reading coordinate information in the data table by using a Python program, respectively calculating straight line distances between each inclusion and all adjacent inclusions, and selecting the minimum straight line distance from the straight line distances as an individual distance between the inclusion and the nearest inclusion; and calculating the average inclusion distance of each group of metallographic samples according to the individual distance.
For the average inclusion distance of each group of metallographic samples, Python (computer programming language) can be used for writing a program for calculation, namely, coordinate information of all inclusions of each group of metallographic samples is written into a data table; reading the coordinate information of each inclusion from the data table by using a Python program, and calculating the individual distance between each inclusion and the nearest inclusion; the average inclusion spacing of each group of metallographic samples can be calculated according to the individual distances. It should be noted that only coordinate information of all inclusions of a set of metallographic samples is recorded in each data table. The nearest inclusion is an inclusion closest to the currently calculated inclusion, that is, the nearest inclusion is relative to an inclusion.
In practical applications, when calculating the individual distance between an inclusion and the nearest inclusion, the linear distances between the inclusion and all the inclusions adjacent to the inclusion are calculated, and then the minimum value (i.e. the minimum linear distance) is selected from the linear distances, where the minimum value is the individual distance between the inclusion and the nearest inclusion, and the inclusion corresponding to the minimum value is the nearest inclusion of the inclusion.
As a preferred embodiment, the code of the Python program includes:
# load library
import pandas as pd
import numpy as np
# read data
df = pd.read _ excel (r 'C: \ Users \ cff \ Desktop \ coordinate. xlsx')
X = df['x'].values
Y = df['y'].values
# minimization
MINDIS = []
for i in range(len(df)):
Dis = []
for j in range(len(df)):
if j != i:
dis = np.sqrt((X[i]-X[j])**2 + (Y[i]-Y[j])**2)
Dis.append(round(dis,5))
mindis = min(Dis)
MINDIS.append(mindis)
# collation, output
df['mindis'] = MINDIS
To _ excel ('derived result. xlsx', index = None)
And step S104, calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance.
Wherein the uniformity is related to the fatigue life, so the uniformity can be used to predict the fatigue life of the bearing steel, and the closer the value of the uniformity is to 1, the longer the fatigue life of the bearing steel.
In the embodiment of the present invention, step S104 may be implemented by: calculating the ideal inclusion distance by using an ideal distance formula; calculating the uniformity of the bearing steel according to a uniformity formula; the length of the fatigue life of the bearing steel is predicted based on the uniformity.
Wherein the ideal spacing formula is
The uniformity formula is
In the formula (I), the compound is shown in the specification,
is the ideal inclusion space of the metallographic sample, S is the detection area,
the number of inclusions in the metallographic specimen; c is the degree of homogeneity,
is the average inclusion spacing of the metallographic specimen,
is the number of metallographic samples.
It should be noted that the fatigue life predicted by the embodiment of the present invention is not a quantification of the bearing steel life, but a rough estimation of the fatigue life of the bearing steel, and the fatigue life grade can be classified based on the uniformity in the application. In addition to predicting the fatigue life of the bearing steel, the embodiments of the present invention may also comparatively analyze the fatigue life performance of different bearing steels, for example, calculate the uniformity of two bearing steels, and analyze the fatigue life performance of the two bearing steels based on the uniformity of the two bearing steels.
In addition, as shown in fig. 2, an apparatus 200 for predicting the fatigue life of bearing steel according to an embodiment of the present invention mainly includes a fully automatic inclusion analyzer 201, a calculation module 202, and a prediction module 203.
The full-automatic inclusion analyzer 201 is used for carrying out inclusion detection on at least one group of metallographic samples to obtain detection result data of each group of metallographic samples; the metallographic sample is obtained from the end face of the bearing steel, and the metallographic surface of the metallographic sample is covered by a cleaning adhesive tape after being polished, washed by deionized water and dried; the detection result data comprises the number of inclusions and coordinate information of each inclusion;
a calculating module 202, configured to calculate an average inclusion distance of each group of the metallographic samples based on the coordinate information;
the prediction module 203 is used for calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance; wherein the uniformity is used for predicting the fatigue life of the bearing steel.
In an embodiment of the present invention, the apparatus 200 for predicting the fatigue life of bearing steel may further include a setting module (not shown) for setting the detection area and the inclusion size range.
In the embodiment of the present invention, the fully automatic inclusion analyzer 201 may further be configured to:
carrying out inclusion detection on each group of metallographic samples based on the detection area and the size range of the inclusions, and generating detection result data of each group of metallographic samples; wherein, before the inclusion detection, the cleaning adhesive tape is torn off and the gold phase surface is blown and wiped by compressed air.
In this embodiment of the present invention, the calculating module 202 may further be configured to:
reading all the coordinate information from the detection result data; wherein the coordinate information comprises positive abscissa values and positive ordinate values;
writing the coordinate information of all inclusions of each group of the metallographic sample into the same data table;
reading the coordinate information in the data table by utilizing a Python program, respectively calculating the linear distance between each inclusion and all adjacent inclusions, and selecting the minimum linear distance from the linear distances as the individual distance between the inclusion and the nearest inclusion;
and calculating the average inclusion distance of each group of metallographic samples according to the individual distance.
In this embodiment of the present invention, the prediction module 203 may further be configured to:
calculating the ideal inclusion distance by using an ideal distance formula; wherein the ideal spacing formula is
In the formula (I), the compound is shown in the specification,
is the ideal inclusion spacing of the metallographic specimen, S is the detection area,
is the number of inclusions in the metallographic specimen;
calculating the uniformity of the bearing steel according to a uniformity formula; wherein the uniformity formula is
Wherein C is the degree of uniformity,
is the average inclusion spacing as described above,
is the number of said metallographic samples; and, the closer the value of the uniformity is to 1, the longer the fatigue life of the bearing steel.
The present invention will be further described by way of examples in order to facilitate a more complete, accurate and thorough understanding of the concepts and solutions of the present invention and to facilitate its implementation by those skilled in the art, but the scope of the present invention is not limited to these examples.
Example one
1. Samples were taken from the center on the bearing steel end face, assuming three cubic metallographic samples of 10mm by 10 mm.
2. Coarse grinding three gold phase surfaces by 180-mesh sand paper, grinding off the surface to ensure the smooth finish of the gold phase surfaces, and then fine grinding by 300-mesh, 600-mesh, 1000-mesh, 1500-mesh and 2000-mesh sand papers in sequence, wherein the last scratch is ground off in the fine grinding process.
3. And (3) polishing the metallographic surface after the sand paper is used for fine grinding, wherein in order to prevent the influence of the polishing agent on the detection result, the polishing agent is not adopted, or the polishing agent is used for rough polishing firstly, and finally, a piece of polishing cloth needs to be replaced to perform fine polishing on the metallographic surface.
4. And (3) washing a polished sample (namely a metallographic sample) by using deionized water, quickly performing light polishing (namely lightly polishing) on a polishing machine with clean polishing cloth, and drying the sample by using a blower, so as to remove water stains adhered to the metallographic sample and prevent the metallographic surface from being oxidized and influencing a detection result.
5. And after the steps are finished, covering the metallographic surface with a cleaning adhesive tape to protect the metallographic surface.
6. Assuming that the detection area is set to 32.363mm, the size range of the detected inclusions is determined according to different steel grades.
7. And placing the metallographic sample in an ASPEX cabin door, tearing off the cleaning adhesive tape before closing the cabin door, blowing and wiping the metallographic surface by using compressed air, closing the cabin door, and detecting inclusions in the metallographic sample. It should be noted that after completing the inclusion detection of the metallographic sample, the ASPEX can generate a detection result of the number of inclusions contained in each group of metallographic samples and the coordinate information of each inclusion, and the specific detection may refer to the working principle of the ASPEX, which is not described in detail in the embodiments of the present invention.
8. And opening detection result data generated by the ASPEX, and copying coordinate information (including X _ ABS and Y _ ABS) of each inclusion in the three groups of metallographic samples into different sheets of Excel (table).
9. Writing a program in Python, reading coordinate information in Excel, calculating the individual distance between each inclusion and the nearest inclusion, deriving result data (namely the individual distance) after the calculation is finished, and calculating the average inclusion distance of each group of metallographic samples.
10. Calculating an ideal inclusion spacing as followsSolving the following formula:
wherein the content of the first and second substances,
representing the ideal inclusion spacing of the metallographic specimen, i.e. first
Forming the inclusion space under the ideal condition corresponding to the metallographic sample; s represents the detection area;
indicating the number of inclusions, i.e. the first
The number of inclusions in the metallographic sample is formed;
the number of metallographic samples is 1, 2 and 3.
11. And (3) calculating the uniformity of the bearing steel, wherein the uniformity is calculated by the following formula:
wherein C represents uniformity.
12. By comparing the C values of different metallographic samples of the same steel grade, the fatigue life can be predicted, and the smaller the C value is, the more uniform the distribution of inclusions is, and the longer the fatigue life is.
Example two
In the implementation of the present invention, the purpose of predicting the fatigue life of bearing steel of different heats can be achieved by comparing the uniformity of inclusions in bearing steel of the same steel grade, specifically referring to the following example, the second embodiment is basically the same as the first embodiment, and the same parts are not repeated, but the differences are as follows:
1. three metallographic samples of 10mm by 10mm are respectively taken from the same positions of the end parts of bearing steel BG1 and bearing steel BG2 of a certain mark, and the surface of the end part is selected to be a gold phase surface.
2. After the metallographic surface of each metallographic sample is subjected to the steps of rough grinding, fine grinding, rough polishing, fine polishing and the like, all samples (namely metallographic samples) are detected by using Aspex, wherein the detection area is set to be 32.363mm, and the size range of inclusions is set to be 0.5-12 μm.
3. Data of all samples BG1 and BG2 were derived, coordinate information was processed using Python program to determine individual distances, and the individual distances were averaged, assuming that the obtained data are shown in table 1. The Python program may use the example of step S103.
TABLE 1
4. Based on the formula for calculating the ideal inclusion spacing given in step S104, the spacing at which the inclusions are uniformly distributed in an ideal case (i.e., the average inclusion spacing) in each sample can be determined from the detection area and the obtained number of inclusions, and specific numerical values are shown in table 2.
TABLE 2
5. Based on the formula for calculating the uniformity given in step S104, it can be calculated from the data in tables 1 and 2 that the uniformity C1=0.459 of the distribution of inclusions in the bearing steel BG 1; the uniformity of the distribution of inclusions in the bearing steel BG2 was C2= 0.430. Since C1 is larger than C2 and C1 is closer to 1, it can be predicted that bearing steel BG1 has a longer fatigue life than bearing steel BG 2.
The uniformity of inclusions in the two bearing steels is calculated, the bearing steel BG1 is predicted to have longer fatigue life than the bearing steel BG2, and the prediction is verified:
1.5 parts of rolling contact fatigue blanks are taken from the same parts of the bearing steel BG1 and the bearing steel BG2 respectively, and heat treatment is carried out. And after the rolling contact fatigue blank is processed by the same process heat treatment system, the rolling contact fatigue blank is finely processed into a rolling contact fatigue sample. The dimensions of the rolling contact fatigue test specimens can be referred to as follows: the rolling contact fatigue test piece was processed into a cylindrical shape by removing materials, and had a surface roughness of 24 μm, a diameter of 10mm (diameter tolerance of-0.002 mm), a length of 78 mm (length tolerance of. + -. 0.05 mm), a roundness tolerance of 0.00025mm, and a cylindricity tolerance of 0.00005 mm.
2. The rolling contact fatigue is subjected to related experiments by adopting BG-M10 (a bearing material contact fatigue testing machine), the rotating speed of a main shaft of the equipment is set to be 8000r/min, the experiment temperature is room temperature, the lubricating mode adopts oil lubrication, and the contact stress of 4.0GPa is adopted. And (4) after a long-time cyclic contact stress action, until the rolling contact fatigue test sample fails.
3. The rolling contact fatigue test piece fatigue life values were collated to obtain the data shown in table 3.
TABLE 3
4. The data in table 3 are collated to obtain: bearing steel BG1 average rolling contact fatigue life 2.72X 108The average rolling contact fatigue life of the bearing steel BG2 is 1.51 multiplied by 10 in cycle8Circulating for a week. The fatigue life of the bearing steel BG1 is longer than that of the bearing steel BG2 in actual conditions, so the prediction is accurate.
In summary, the method and the device for predicting the fatigue life of the bearing steel in the embodiments of the present invention have at least the following beneficial effects: the fatigue life of the bearing steel can be predicted; further, the performance of bearing steels of different heats in terms of fatigue life can also be compared.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of predicting fatigue life of bearing steel, comprising:
obtaining at least one group of metallographic samples from the end surface of bearing steel, and grinding and polishing the metallographic surface of the metallographic samples;
placing the metallographic samples in a full-automatic inclusion analyzer for inclusion detection to obtain detection result data of each group of metallographic samples; the detection result data comprises the number of inclusions and coordinate information of each inclusion;
calculating the average inclusion distance of each group of the metallographic samples based on the coordinate information;
calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance; wherein the uniformity is used for predicting the length of the fatigue life of the bearing steel.
2. The method of claim 1, wherein taking at least one set of metallographic samples from the end faces of the bearing steel and grinding and polishing the metallographic surfaces of the metallographic samples comprises:
sampling the end face center of the bearing steel to obtain at least one group of metallographic samples; the metallographic sample is a cube, and the end face of the bearing steel on the metallographic sample is a metallographic surface;
coarse grinding the gold phase surface by using 180-mesh sand paper, and fine grinding the gold phase surface by sequentially using 300-mesh, 600-mesh, 1000-mesh, 1500-mesh and 2000-mesh sand paper;
polishing the gold phase surface without a polishing agent;
washing the metallographic sample by using deionized water and slightly polishing;
and drying the metallographic sample by using a blower, and covering the metallographic surface with a cleaning adhesive tape.
3. The method of claim 1, wherein the metallographic sample is placed in a fully automated inclusion analyzer for inclusion detection, and further comprising:
setting the detection area and the size range of the inclusions.
4. The method of claim 3, wherein the inclusion detection of the metallographic samples is performed in a fully automatic inclusion analyzer to obtain detection result data of each group of metallographic samples, and the method comprises the following steps:
respectively placing each group of the metallographic samples in a full-automatic inclusion analyzer, tearing off a cleaning adhesive tape, blowing and wiping the metallographic surface by using compressed air, and closing a cabin door of the full-automatic inclusion analyzer;
based on the detection area and the size range of the inclusions, the full-automatic inclusion analyzer detects the inclusions in each group of metallographic samples and generates detection result data of each group of metallographic samples.
5. The method of claim 1, wherein calculating an average inclusion spacing for each set of the metallographic samples based on the coordinate information comprises:
reading all the coordinate information from the detection result data; wherein the coordinate information comprises positive abscissa values and positive ordinate values;
writing the coordinate information of all inclusions of each group of the metallographic sample into the same data table;
reading the coordinate information in the data table by utilizing a Python program, respectively calculating the linear distance between each inclusion and all adjacent inclusions, and selecting the minimum linear distance from the linear distances as the individual distance between the inclusion and the nearest inclusion;
and calculating the average inclusion distance of each group of metallographic samples according to the individual distance.
6. The method of claim 3, wherein calculating a uniformity of the bearing steel from the average inclusion spacing and ideal inclusion spacing, and predicting a fatigue life of the bearing steel based on the uniformity comprises:
calculating the ideal inclusion distance by using an ideal distance formula; wherein the ideal spacing formula is
In the formula (I), the compound is shown in the specification,
is the ideal inclusion spacing of the metallographic specimen, S is the detection area,
is the number of inclusions in the metallographic specimen;
calculating the uniformity of the bearing steel according to a uniformity formula; wherein the uniformity formula is
Wherein C is the degree of uniformity,
is the average inclusion spacing as described above,
is the number of said metallographic samples; and, the closer the value of the uniformity is to 1, the longer the fatigue life of the bearing steel.
7. An apparatus for predicting fatigue life of bearing steel, comprising:
the full-automatic inclusion analyzer is used for carrying out inclusion detection on at least one group of metallographic samples to obtain detection result data of each group of metallographic samples; the metallographic sample is obtained from the end face of the bearing steel, and the metallographic surface of the metallographic sample is covered by a cleaning adhesive tape after being polished, washed by deionized water and dried; the detection result data comprises the number of inclusions and coordinate information of each inclusion;
the calculation module is used for calculating the average inclusion distance of each group of metallographic samples based on the coordinate information;
the prediction module is used for calculating the uniformity of the bearing steel according to the average inclusion distance and the ideal inclusion distance; wherein the uniformity is used for predicting the length of the fatigue life of the bearing steel.
8. The apparatus of claim 7,
the device also comprises a setting module, wherein the setting module is used for setting the detection area and the size range of the inclusions, and
the fully automatic inclusion analyzer is further configured to:
carrying out inclusion detection on each group of metallographic samples based on the detection area and the size range of the inclusions, and generating detection result data of each group of metallographic samples; wherein, before the inclusion detection, the cleaning adhesive tape is torn off and the gold phase surface is blown and wiped by compressed air.
9. The apparatus of claim 7, wherein the computing module is further configured to:
reading all the coordinate information from the detection result data; wherein the coordinate information comprises positive abscissa values and positive ordinate values;
writing the coordinate information of all inclusions of each group of the metallographic sample into the same data table;
reading the coordinate information in the data table by utilizing a Python program, respectively calculating the linear distance between each inclusion and all adjacent inclusions, and selecting the minimum linear distance from the linear distances as the individual distance between the inclusion and the nearest inclusion;
and calculating the average inclusion distance of each group of metallographic samples according to the individual distance.
10. The apparatus of claim 8, wherein the prediction module is further configured to:
calculating the ideal inclusion distance by using an ideal distance formula; wherein the idealThe formula of the spacing is
In the formula (I), the compound is shown in the specification,
is the ideal inclusion spacing of the metallographic specimen, S is the detection area,
is the number of inclusions in the metallographic specimen;
calculating the uniformity of the bearing steel according to a uniformity formula; wherein the uniformity formula is
Wherein C is the degree of uniformity,
is the average inclusion spacing as described above,
is the number of said metallographic samples; and, the closer the value of the uniformity is to 1, the longer the fatigue life of the bearing steel.
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| US20060048576A1 (en) * | 2002-01-17 | 2006-03-09 | Akihiro Kiuchi | Bearing steel,method for evaluating large-sized inclusions in the steel and rolling bearing |
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| US20060048576A1 (en) * | 2002-01-17 | 2006-03-09 | Akihiro Kiuchi | Bearing steel,method for evaluating large-sized inclusions in the steel and rolling bearing |
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