超洁净轴承钢中夹杂物与滚动接触疲劳寿命的关系

doi:10.11900/0412.1961.2019.00337

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

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[J]. Acta Metall Sin, 2020, 56(5): 693-703.

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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L₁₀和L₅₀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L₁₀和L₅₀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 words： bearing 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)

作者简介: 孙飞龙，男，1993年生，硕士生

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

表1 GCr15轴承钢的化学成分 (mass fraction / %)

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.	$α$	$λ$	Linear fit	x_v / μm
1	5.55	8.62	x=5.55y+8.62	46.96
2	2.22	7.59	x=2.22y+7.59	22.92
3	3.26	8.83	x=3.26y+8.83	31.35

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

Type	No.	u	$ξ$	$σ$	x_max / μm	x_v / μm	x_lim / μm
Sulfide	1	-	-	-	10.14	-	-
	2	2.60	-0.17	0.71	5.25	6.30	6.80
	3	3.80	-0.13	1.30	7.76	11.34	13.96
Sul.-O	1	2.00	-0.13	0.76	4.54	6.63	7.87
	2	2.60	-0.30	0.82	4.64	5.24	5.31
	3	1.90	-0.37	0.92	3.84	4.33	4.36
Al₂O₃	1	1.80	-0.32	0.93	4.32	4.70	4.75
	2	2.40	-0.31	0.99	5.00	5.52	5.58
	3	2.00	-0.05	0.62	5.35	8.06	13.99
Glo.-O	1	2.90	-0.29	1.37	6.28	7.41	7.56
	2	-	-	-	11.53	-	-
	3	-	-	-	14.44	-	-
TiN	1	3.10	-0.29	3.87	11.82	16.01	16.51
	2	2.60	-0.14	2.16	8.75	14.86	17.79
	3	2.60	-0.23	2.84	11.83	14.40	15.12

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

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

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