Please wait a minute...
金属学报  2020, Vol. 56 Issue (5): 693-703    DOI: 10.11900/0412.1961.2019.00337
  本期目录 | 过刊浏览 |
超洁净轴承钢中夹杂物与滚动接触疲劳寿命的关系
孙飞龙1, 耿克2, 俞峰3, 罗海文1()
1.北京科技大学冶金与生态工程学院 北京 100083
2.江阴兴澄特钢有限公司 江阴 214400
3.钢铁研究总院 北京 100081
Relationship of Inclusions and Rolling Contact Fatigue Life for Ultra-Clean Bearing Steel
SUN Feilong1, GENG Ke2, YU Feng3, LUO Haiwen1()
1.Metallurgical Department of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.Jiangyin Xingcheng Special Steel Works Co. Ltd. , Jiangyin 214400, China
3.Central Iron and Steel Research Institute,Beijing 100081, China
全文: PDF(2358 KB)   HTML
摘要: 

以3种不同工艺工业生产的总O含量均≤6×10-6的超洁净GCr15轴承钢为研究对象,通过推力片实验测试这3种钢的滚动接触疲劳寿命并获得额定寿命(L10)和中值寿命(L50),通过ASPEX扫描电镜获得各工艺下的夹杂物样本数据并进行统计分析,使用极值法(SEV)和广义Pareto分布法(GPD)估算出样品中最大夹杂物特征尺(CSMI),然后将其与实测L10L50进行对比和分析。结果表明,SEV法仅检测每个样品的最大夹杂物,无法通过其获得的CSMI来合理解释3种钢L10L50的变化,2者之间相关性较差;而GPD法分析夹杂物时,需要对阈值尺寸以上的所有夹杂物进行表征和统计分析,可以获得夹杂物的数量密度以及不同类型夹杂物的CSMI,GPD法所预测出的最危险类型TiN夹杂物的CSMI可以合理解释L10的变化,2者之间有较好相关性,但无法据此解释L50的变化;但将总的夹杂物数量密度与TiN夹杂物最大特征尺寸相结合,能合理解释3种钢的L50差异,这是因为当更多样品失效时,裂纹萌生位置将不再仅仅局限于最危险类型夹杂物。因此,最危险类型夹杂物的CSMI与超纯净轴承钢中的早期疲劳失效的L10相关性最强,而夹杂物的数量密度对高概率的中值疲劳寿命L50也有重要影响。

关键词 轴承钢夹杂物滚动接触疲劳寿命统计极值法广义Pareto分布法    
Abstract

The cleanliness of bearing steels produced in China has been greatly improved due to the significant progress in the steelmaking technologies in the past decade, leading to their total oxygen (T.O.) contents lowered to no more than 6×10-6. Under such a high cleanliness, it is then expected that the influence of non-metallic inclusions on fatigue property should be different from the previous knowledge, because both the size and quantity of inclusions are reduced greatly. Therefore, both inclusions and fatigue properties for three ultra-clean GCr15 (100Cr6) bearing steels containing T.O. around 6×10-6, which were manufactured via different industrial production processes, were studied for this purpose. First, inclusions were characterized by ASPEX SEM and then statically analyzed by the statistics of extreme values (SEV) and the generalized Pareto distribution (GPD). Next, their rolling contact fatigue lives (RCF) L10 and L50 were measured by flat washer tests. Only the largest inclusion in each sample is required for predicting the characteristic sizes of maximum inclusion (CSMI) for the three steels using the SEV method. The calculated CSMIs, however, are not consistent with the variation of either L10 or L50, indicating they are not relevant. In contrast, the types of inclusions above threshold (u) size can be classified and their number density of inclusions quantified when the GPD method is employed. In particularly, the CSMIs of different types of inclusions can be determined. In this case, it has been found that the CSMI of TiN inclusion, which is the most dangerous for initiating cracking, is in a good agreement with the low probability rolling fatigue life (L10), suggesting that they are very correlated. This, however, cannot explain the variation of high-probability fatigue life (L50). Instead, the density of total inclusions also played an important role on the L50 of ultra-clean bearing steels in addition to the CSMI of TiN inclusions. This is reasonable because cracking shall be initiated at not only the most dangerous TiN inclusion during the early failure but also some other highly dense inclusions particularly during the late failure. Therefore, it is then concluded that the L10 is much more related to the CSMI of most dangerous TiN inclusion; whilst the L50 is strongly affected by the number density of total inclusions.

Key wordsbearing steel    inclusion    rolling contact fatigue life    statistics of extreme values method    generalized Pareto distribution method
收稿日期: 2019-10-10     
ZTFLH:  TF762.4  
基金资助:国家重点研发计划项目(2016YFB0300102);国家国际科技合作专项项目(2015DFG51950);中央高校基本科研业务费专项资金项目(FRF-TP-18-002C2)
通讯作者: 罗海文     E-mail: luohaiwen@ustb.edu.cn
Corresponding author: LUO Haiwen     E-mail: luohaiwen@ustb.edu.cn
作者简介: 孙飞龙,男,1993年生,硕士生

引用本文:

孙飞龙, 耿克, 俞峰, 罗海文. 超洁净轴承钢中夹杂物与滚动接触疲劳寿命的关系[J]. 金属学报, 2020, 56(5): 693-703.
Feilong SUN, Ke GENG, Feng YU, Haiwen LUO. Relationship of Inclusions and Rolling Contact Fatigue Life for Ultra-Clean Bearing Steel. Acta Metall Sin, 2020, 56(5): 693-703.

链接本文:

https://www.ams.org.cn/CN/10.11900/0412.1961.2019.00337      或      https://www.ams.org.cn/CN/Y2020/V56/I5/693

No.CSiMnCrNiMoCuAltAlsPSCaMgTiNOFe
10.950.270.411.550.020.010.020.0170.0150.0130.00210.00050.00070.00120.00220.0006Bal.
20.990.260.361.480.060.020.080.0150.0120.0120.00280.00050.00090.00120.00470.0005Bal.
30.950.240.361.440.020.010.020.0170.0160.0040.00140.00050.00080.00080.00220.0006Bal.
表1  GCr15轴承钢的化学成分 (mass fraction / %)
iNo.1No.2No.3
xi / μmTypeFigurexi / μmTypeFigurexi / μmTypeFigure
15.32Glo.-O4.95Glo.-O5.79TiN
25.85TiN5.26Al2O36.57TiN
36.65TiN5.90TiN6.97TiN
46.92Glo.-O6.08TiN7.02TiN
56.93TiN6.10Al2O37.14MnS
67.15Al2O36.99TiN7.79TiN
77.16Sul.-O7.89Sul.-O7.99Glo.-O
87.19Al2O37.90Al2O38.24TiN
97.69TiN7.93TiN9.01TiN
107.99TiN8.19Al2O39.37TiN
118.94TiN8.61TiN9.40TiN
129.48Glo.-O8.75TiN9.44TiN
1310.77Glo.-O8.98MnS10.10TiN
1410.96Glo.-O8.99Al2O310.63MnS
1511.35TiN9.03Glo.-O11.47TiN
1611.82TiN9.08Glo.-O11.53TiN
1712.43Glo.-O9.13Sul.-O11.83TiN
1812.85Glo.-O9.32TiN11.88TiN
1914.21Sul.-O10.58Glo.-O12.37TiN
2014.80TiN10.62Glo.-O12.49TiN
2118.42MnS10.75TiN13.87Sul.-O
2221.59Sul.-O11.53Glo.-O14.44Glo.-O
2321.83TiN12.07TiN17.10TiN
2429.10MnS15.68Glo.-O20.98Glo.-O
表2  ASPEX检测出的最大夹杂物尺寸和形貌
图1  统计极值(SEV)法估算的最大夹杂物特征尺寸结果
No.αλLinear fitxv / μm
15.558.62x=5.55y+8.6246.96
22.227.59x=2.22y+7.5922.92
33.268.83x=3.26y+8.8331.35
表3  SEV法最大夹杂物估算结果
图2  ASPEX检测出的No.1~No.3钢中1 μm以上夹杂物的统计分析结果
图3  No.1~No.3钢中不同类型和尺寸的夹杂物典型分布
图4  广义Pareto分布(GPD)法阈值u的确定
TypeNo.uξσxmax / μmxv / μmxlim / μm
Sulfide1---10.14--
22.60-0.170.715.256.306.80
33.80-0.131.307.7611.3413.96
Sul.-O12.00-0.130.764.546.637.87
22.60-0.300.824.645.245.31
31.90-0.370.923.844.334.36
Al2O311.80-0.320.934.324.704.75
22.40-0.310.995.005.525.58
32.00-0.050.625.358.0613.99
Glo.-O12.90-0.291.376.287.417.56
2---11.53--
3---14.44--
TiN13.10-0.293.8711.8216.0116.51
22.60-0.142.168.7514.8617.79
32.60-0.232.8411.8314.4015.12
表4  GPD法估计最大夹杂物的结果
图5  3种钢的滚动接触疲劳寿命与Weibull曲线及95%置信区间
No.L10 / 107 cycL50 / 107 cycNAtxvxv (GPD) / μm
(95% confidence(95% confidencemm-2(SEV)SulSul.-OAl2O3Glo.-OTiN
intervals)intervals)μm
10.543.262.4446.96~6.634.707.4116.01
(0.25, 1.48)(1.98, 4.90)
20.702.415.6722.926.305.245.52~14.86
(0.35, 1.30)(1.68, 3.34)
31.073.343.8131.3511.344.338.06~14.40
(0.45, 1.90)(2.45, 5.15)
表5  3组钢滚动接触疲劳寿命与夹杂物数量密度及SEV和GPD法预测的最大夹杂物特征尺寸对比
1 Tomita Y. Improved fracture toughness of ultrahigh strength steel through control of non-metallic inclusions [J]. J. Mater. Sci., 1993, 28: 853
2 Tian C, Liu J H, Dong H. Inclusions evaluation and rolling contact fatigue life of high clean bearing steels [J]. Shanghai Met., 2018, 40(4): 1
2 田 超, 刘剑辉, 董 瀚. 高洁净轴承钢夹杂物评价与滚动接触疲劳寿命 [J]. 上海金属, 2018, 40(4): 1
3 Zhang L F, Yang W, Zhang X W, et al. Systematic analysis of non-metallic inclusions in steel [J]. Iron Steel, 2014, 49(2): 1
3 张立峰, 杨 文, 张学伟等. 钢中夹杂物的系统分析技术 [J]. 钢铁, 2014, 49(2): 1
4 Murakami Y. Inclusion rating by statistics of extreme values and its application to fatigue strength prediction and quality control of materials [J]. J. Res. Natl. Inst. Stand. Technol., 1994, 99: 345
5 Shi G, Atkinson H V, Sellars C M, et al. Application of the generalized Pareto distribution to the estimation of the size of the maximum inclusion in clean steels [J]. Acta Mater., 1999, 47: 1455
6 Scarf P A, Laycock P J. Applications of extreme value theory in corrosion engineering [J]. J. Res. Natl. Inst. Stand. Technol., 1994, 99: 313
7 Laycock P J, Scarf P A. Exceedances, extremes, extrapolation and order statistics for pits, pitting and other localized corrosion phenomena [J]. Corros. Sci., 1993, 35: 135
8 Hetzner D W. Developing ASTM E 2283: Standard practice for extreme value analysis of nonmetallic inclusions in steel and other microstructural features [J]. J. ASTM Int., 2006, 3: 1
9 Atkinson H V, Shi G. Characterization of inclusions in clean steels: A review including the statistics of extremes methods [J]. Prog. Mater. Sci., 2003, 48: 457
10 Ma C, Luo H W. Precipitation and evolution behavior of carbide during heat treatments of GCr15 bearing steel [J]. J. Mater. Eng., 2017, 45(6): 97
10 马 超, 罗海文. GCr15轴承钢热处理过程中碳化物的析出与演变行为 [J]. 材料工程, 2017, 45(6): 97
11 Gumbel E J. Statistics of Extremes [M]. New York: Columbia University Press, 1958: 247
12 Murakami Y, Toriyama T, Coudert E M. Instructions for a new method of inclusion rating and correlations with the fatigue limit [J]. J. Test. Eval., 1994, 22: 318
13 Beretta S, Murakami Y. Statistical analysis of defects for fatigue strength prediction and quality control of materials [J]. Fatigue Fract. Eng. Mater. Struct., 1998, 21: 1049
14 Anderson C W, Shi G, Atkinson H V, et al. Interrelationship between statistical methods for estimating the size of the maximum inclusion in clean steels [J]. Acta Mater., 2003, 51: 2331
15 Anderson C W, Shi G, Atkinson H V, et al. The precision of methods using the statistics of extremes for the estimation of the maximum size of inclusions in clean steels [J]. Acta Mater., 2000, 48: 4235
16 Shi G, Atkinson H V, Sellars C M, et al. Maximum inclusion size in two clean steels Part 1 Comparison of maximum size estimates by statistics of extremes and generalised Pareto distribution methods [J]. Ironmak. Steelmak., 2000, 27: 355
17 Shi G, Atkinson H V, Sellars C M, et al. Computer simulation of the estimation of the maximum inclusion size in clean steels by the generalized Pareto distribution method [J]. Acta Mater., 2001, 49: 1813
18 Zhang J M, Zhang J F, Yang Z G, et al. Estimation of maximum inclusion size and fatigure strength in high strength steel [J]. Acta Metall. Sin., 2004, 40: 846
18 张继明, 张建锋, 杨振国等. 高强钢中最大夹杂物的尺寸估计与疲劳强度预测 [J]. 金属学报, 2004, 40: 846
19 Yates J R, Shi G, Atkinson H V, et al. Fatigue tolerant design of steel components based on the size of large inclusions [J]. Fatigue Fract. Eng. Mater. Struct., 2002, 25: 667
20 Shi Z Y, Xu H F, Xu D, et al. Characterization of inclusions in GCr15 bearing steel by ASPEX and rotary bending fatigue methods [J]. Iron Steel, 2019, 54(4): 55
20 史智越, 徐海峰, 许 达等. 采用ASPEX和旋弯疲劳法表征GCr15轴承钢夹杂物 [J]. 钢铁, 2019, 54(4): 55
21 Ma C, Luo H W. Inclusion particles of super-clean steel examined by both scanning electron microscope and electrolytic extraction [J]. Metall. Anal., 2017, 37(8): 1
21 马 超, 罗海文. 扫描电镜和电解萃取法用于超洁净钢中夹杂物的表征 [J]. 冶金分析, 2017, 37(8): 1
22 Monnot J, Heritier B, Cogne J Y. Relationship of melting practice, inclusion type, and size with fatigue resistance of bearing steels [A]. Proceedings of Effect of Steel Manufacturing Process on the Quality of Bearing Steels [C]. West Conshohocken, PA: ASTM Int., 1988: 149
23 Lund T B, Johansson S A, Ölund L J P. Nucleation of fatigue in very low oxygen bearing steels [A]. Proceedings of Bearing Steels: Into the 21st Century [C]. West Conshohocken, PA: ASTM Int., 1998: 124
24 Fu J, Wang P, Xu J H, et al. Effect and control of minor elements—Oxygen, nitrogen, titanium and calcium in bearing steel [J]. Spec. Steel, 1998, 19(6): 31
24 傅 杰, 王 平, 徐君浩等. 轴承钢中微量元素氧-氮-钛-钙的作用与控制 [J]. 特殊钢, 1998, 19(6): 31
25 University of Science and Technology Beijing. Generalized Pareto method rating software for inclusions in steel GPD. Model V1.0 [CP]. Copyright Registration No. 2018SR904406
25 (北京科技大学. 钢中夹杂物的帕累托评级软件. GPDModelV1.0 [CP]. 著作权登记号: 2018SR904406
26 Shi G, Atkinson H V, Sellars C M, et al. Comparison of extreme value statistics methods for predicting maximum inclusion size in clean steels [J]. Ironmak. Steelmak., 1999, 26: 239
27 Brooksbank D, Andrews K W. Thermal expansion of some inclusions found in steels and relation to tessellated stresses [J]. J. Iron. Steel. Inst., 1968, 206: 595
28 Brooksbank D, Andrews K W. Stress fields around inclusions and their relation to mechanical properties [J]. J. Iron. Steel. Inst., 1972, 210: 246
29 Walker P F F. Improving the reliability of highly loaded rolling bearings: The effect of upstream processing on inclusions [J]. Mater. Sci. Technol., 2014, 30: 385
30 Hashimoto K, Hiraoka K, Kida K, et al. Effect of sulphide inclusions on rolling contact fatigue life of bearing steels [J]. Mater. Sci. Technol., 2013, 28: 39
31 Jung I H. Overview of the applications of thermodynamic databases to steelmaking processes [J]. Calphad, 2010, 34: 332
32 Neishi Y, Makino T, Matsui N, et al. Influence of the inclusion shape on the rolling contact fatigue life of carburized steels [J]. Metall. Mater. Trans., 2013, 44A: 2131
[1] 俞峰,陈兴品,徐海峰,董瀚,翁宇庆,曹文全. 滚动轴承钢冶金质量与疲劳性能现状及高端轴承钢发展方向[J]. 金属学报, 2020, 56(4): 513-522.
[2] 张新房, 闫龙格. 脉冲电流调控金属熔体中的非金属夹杂物[J]. 金属学报, 2020, 56(3): 257-277.
[3] 冯业飞,周晓明,邹金文,王超渊,田高峰,宋晓俊,曾维虎. 粉末高温合金中SiO2夹杂物与基体的界面反应机理及对其变形行为的影响[J]. 金属学报, 2019, 55(11): 1437-1447.
[4] 黄宇, 成国光, 谢有. 稀土Ce对钎具钢中夹杂物的改质机理研究[J]. 金属学报, 2018, 54(9): 1253-1261.
[5] 侯渊, 任忠鸣, 王江, 张振强, 李霞. 纵向静磁场对定向凝固GCr15轴承钢柱状晶向等轴晶转变的影响[J]. 金属学报, 2018, 54(5): 801-808.
[6] 马歌, 左秀荣, 洪良, 姬颖伦, 董俊媛, 王慧慧. 深海用X70管线钢焊接接头腐蚀行为研究[J]. 金属学报, 2018, 54(4): 527-536.
[7] 王新华,李秀刚,李强,黄福祥,李海波,杨建. X80管线钢板中条串状CaO-Al2O3系非金属夹杂物的控制[J]. 金属学报, 2013, 49(5): 553-561.
[8] 马跃 苏航 潘涛 余音宏 杨才福 张永权 彭云. 中高碳钢中复合延性夹杂物控制研究[J]. 金属学报, 2012, 48(11): 1321-1328.
[9] 童文辉 王杰 周吉学 杨院生. 气体保护熔炼条件下Mg-Gd-Y-Zr合金的夹杂物[J]. 金属学报, 2012, 48(1): 63-69.
[10] 邵肖静 王新华 姜敏 王万军 黄福祥 冀云卿. 加热过程中硫系易切削钢中MnS夹杂物行为的动态原位观察[J]. 金属学报, 2011, 47(9): 1210-1215.
[11] 马跃 潘涛 江波 崔银会 苏航 彭云. S含量对高速车轮钢断裂韧性影响的研究[J]. 金属学报, 2011, 47(8): 978-983.
[12] 胡志勇 杨成威 姜敏 杨光维 王万军 王新华. Ti脱氧钢含Ti复合夹杂物诱导晶内针状铁素体的原位观察[J]. 金属学报, 2011, 47(8): 971-977.
[13] 郦晓慧 黄发 王俭秋 韩恩厚 柯伟. TiN夹杂物对690合金管在高温高压水中的腐蚀和应力腐蚀行为的影响[J]. 金属学报, 2011, 47(7): 847-852.
[14] 张永健 惠卫军 项金钟 董瀚 翁宇庆. 晶粒尺寸对42CrMoVNb钢超高周疲劳性能的影响[J]. 金属学报, 2009, 45(7): 880-886.
[15] 徐匡迪. 关于洁净钢的若干基本问题[J]. 金属学报, 2009, 45(3): 257-269.