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Method and device for predicting fatigue life of bearing steel

A fatigue life and bearing steel technology, applied in the field of steel materials, can solve the problems such as the inability to further predict the long fatigue life of bearing steel and the inability to predict the uniformity of inclusion distribution.

Inactive Publication Date: 2021-04-16
UNIV OF SCI & TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is that only the distribution of inclusions with the largest size can be predicted, but the uniformity of inclusion distribution cannot be predicted, and the fatigue life of bearing steel cannot be further predicted

Method used

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  • Method and device for predicting fatigue life of bearing steel
  • Method and device for predicting fatigue life of bearing steel
  • Method and device for predicting fatigue life of bearing steel

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0069] As a preferred implementation, the code of the Python program includes:

[0070] # load library

[0071] import pandas as pd

[0072] import numpy as np

[0073] # read data

[0074] df = pd.read_excel(r'C:\Users\cff\Desktop\coordinates.xlsx')

[0075] X = df['x'].values

[0076] Y = df['y'].values

[0077] # Find the minimum value

[0078] MINDIS = []

[0079] for i in range(len(df)):

[0080] Dis = []

[0081] for j in range(len(df)):

[0082] if j != i:

[0083] dis = np. sqrt((X[i]-X[j])**2 + (Y[i]-Y[j])**2)

[0084] Dis.append(round(dis,5))

[0085] mindis = min(Dis)

[0086] MINDIS.append(mindis)

[0087] # tidy up, output

[0088] df['mindis'] = MINDIS

[0089] df.to_excel('export results.xlsx', index = None)

[0090] Step S104, calculating the uniformity of the bearing steel according to the average inclusion spacing and the ideal inclusion spacing.

[0091] Among them, the uniformity is related to the fatigue life, so the uniformity can be use...

Embodiment 1

[0112] 1. Sampling from the center of the end face of the bearing steel, assuming three metallographic samples of a cube of 10mm*10mm*10mm.

[0113] 2. Roughly grind the three metallographic surfaces with 180-mesh sandpaper to remove the skin to ensure the smoothness of the metallographic surface, and then use 300-mesh, 600-mesh, 1000-mesh, 1500-mesh and 2000-mesh sandpaper to finely grind in turn. The fine grinding process It is necessary to ensure that the last scratches are worn off.

[0114] 3. After finely grinding with the above-mentioned sandpaper, polish the metallographic surface. In order to prevent the polishing agent from affecting the test results, do not use the polishing agent, or first use the polishing agent for rough polishing, and finally need to change a polishing cloth. Thin face to face.

[0115] 4. Rinse the polished sample (i.e. metallographic sample) with deionized water, and quickly perform light polishing on a polishing machine with a clean polishin...

Embodiment 2

[0125] When implementing the present invention, the purpose of predicting the fatigue life of different heats of bearing steels can also be achieved by comparing the uniformity of inclusions in bearing steels of the same steel grade. For details, see the following examples. The second embodiment is basically the same as the first embodiment. The same, the similarities will not be repeated, the difference is:

[0126] 1. Take three metallographic samples of 10mm*10mm*10mm from the same part of the end of bearing steel BG1 and bearing steel BG2 of a certain brand, and select the surface where the end is located as the metallographic surface.

[0127] 2. After the metallographic surface of each metallographic sample undergoes steps such as rough grinding, fine grinding, rough polishing, and fine polishing, use Aspex to detect all samples (ie, metallographic samples), where the detection area is set to 32.363mm², the inclusion size range is set from 0.5μm to 12μm.

[0128] 3. Exp...

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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 specific embodiment comprises the following steps: obtaining at least one group of metallographic samples from the end surface of bearing steel; grinding and polishing the metallographic surface of the metallographic samples; putting the metallographic samples into 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 distribution uniformity of the inclusions.

Description

Technical field [0001] The present invention relates to the technical field of steel materials, and in particular to predicting the high-cycle fatigue life of bearing steel by characterizing the distribution uniformity of inclusions in bearing steel. Background technique [0002] Fatigue life is the most important indicator for testing the quality of bearing steel. Research shows that more than 70% of high-cycle fatigue failures of bearing steel are caused by inclusions. Therefore, it is very necessary to characterize the uniformity of inclusion distribution. [0003] At present, the inclusion size distribution is mainly predicted through the Weibull distribution, that is, the distribution of the largest size inclusions is predicted. However, this method can only predict the maximum size inclusion distribution and cannot predict the uniformity of inclusion distribution. The requirements for high-quality bearing steel include inclusions in the steel being as small in size, a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N23/2251G01N23/2202
Inventor 杨树峰曹方刘威赵朋杨曙磊李京社
Owner UNIV OF SCI & TECH BEIJING
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