超声滚压EA4T车轴钢疲劳性能及寿命预测

张继旺; 张浩楠; 杨冰; 苏凯新; 李行

doi:10.3969/j.issn.0258-2724.20220257

超声滚压EA4T车轴钢疲劳性能及寿命预测

doi: 10.3969/j.issn.0258-2724.20220257

1.
西南交通大学轨道交通运载系统全国重点实验室，四川成都 610036
2.
航天科工惯性技术有限公司，北京 100070

基金项目: 国家自然科学基金项目（51675445）

详细信息

作者简介:
张继旺（1983—），男，研究员，博士，研究方向为材料和结构的疲劳与断裂，E-amil：zhangjiwang@swjtu.edu.cn

中图分类号: U270.33
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- 被引次数: 0
出版历程
- 收稿日期: 2022-04-10
- 修回日期: 2022-05-18
- 网络出版日期: 2023-11-09

Fatigue Properties and Life Prediction of Ultrasonic Rolling EA4T Axle Steel

1.
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610036, China
2.
Aerospace Science and Industry Inertial Technology, Beijing 100070, China

摘要

摘要:
为研究表面超声滚压（SURP）处理对EA4T车轴钢疲劳性能的影响，首先，采用SURP技术对EA4T车轴钢试样进行表面处理，并对处理后的试样进行表面性能测试，分析表面三维形貌、粗糙度、硬度、残余应力、半高宽（FWHM）和晶粒尺寸的变化；然后，采用旋转弯曲疲劳试验机对EA4T车轴钢试样进行疲劳试验，获得应力-疲劳寿命（S-N）曲线，并分析裂纹扩展规律，研究SURP处理对EA4T车轴钢疲劳性能和裂纹扩展行为的影响；最后，采用BP（back propagation）神经网络建立了以加载应力幅值、表面粗糙度、表面半高宽、表面硬度、硬化层深度、表面残余应力和残余应力层深度为输入的超声滚压EA4T车轴钢疲劳寿命预测模型，并对超声滚压EA4T车轴钢试样进行寿命预测. 研究结果表明：SURP处理可以使试样表面粗糙度降低为0.17 μm，并去除表面梨沟形貌；试样表面硬度提升至420 HV，试样表面引入约 −500 MPa的残余应力以及约550 μm深的残余应力层；研磨试样和研磨抛光试样以及SURP处理试样均具有传统疲劳极限，研磨试样和研磨抛光试样的疲劳性能基本一致，且疲劳极限均为355 MPa，SURP处理试样疲劳性能显著提升，其疲劳极限为455 MPa，相比研磨试样提升了28%；疲劳断口观察表明，所有试样的疲劳裂纹均萌生自表面，SURP处理没有改变试样的疲劳破坏机制；SURP处理使试样的裂纹扩展门槛值从6.29 MPa·m^1/2增加到11.21 MPa·m^1/2，同时减缓了裂纹萌生以及短裂纹扩展，从而显著提高了EA4T车轴钢疲劳性能；超声滚压EA4T车轴钢疲劳寿命预测模型预测精度为88.5%.
- EA4T车轴钢 /
- 超声滚压 /
- 疲劳性能 /
- 裂纹扩展门槛值 /
- BP神经网络
Abstract:
In order to study the influence of surface ultrasonic rolling processing (SURP) on the fatigue properties of EA4T axle steel, the EA4T axle steel specimens were firstly treated by SURP technology, and the surface properties of the treated specimens were analyzed. The 3D surface morphologies, roughness, hardness, residual stress, full width at half maximum (FWHM), and crystal size were investigated. Then, the fatigue tests were carried out on the EA4T axle steel specimens using a rotary bending fatigue testing machine. The stress–fatigue life (S-N) curves and crack propagation behaviors were obtained, and the effect of SURP on fatigue properties and crack propagation behaviors of EA4T axle steels was analyzed. Finally, a back propagation (BP) neural network was used to establish the fatigue life prediction model of SURP EA4T axle steel, which took load stress amplitude, surface roughness, surface FWHM, surface hardness, hardened layer depth, surface residual stress, and depth of residual stress layer as input. In addition, the life of SURP EA4T axle steel specimens was predicted. The results show that SURP can reduce the surface roughness of the specimens to 0.17 μm and remove the groove morphology. Meanwhile, the surface hardness of the specimens is improved to 420 HV, and a surface residual stress of about −500 MPa and a residual stress layer with a depth of about 550 μm are introduced. There are conventional fatigue limits for grinding specimens, grinding with polishing specimens, as well as SURP specimens. The fatigue properties of grinding specimens and grinding with polishing specimens are basically the same, with a fatigue limit of 355 MPa. The fatigue property of SURP specimens is improved significantly, with a fatigue limit of 455 MPa, an increase of 28% compared with the grinding specimens. Fatigue fracture observations show that the fatigue cracks of all specimens initiate from the surface, and SURP cannot change the fatigue damage mechanism of the specimen. SURP can increase the threshold value of crack propagation of the specimen from 6.29 MPa·m^1/2 to 11.21 MPa·m^1/2, and it can slow down the crack initiation and propagation of short cracks, thus significantly improving the fatigue property of EA4T axle steel. The fatigue life prediction model of SURP EA4T axle steel has a prediction accuracy of 88.5%.
- EA4T axle steel /
- ultrasonic rolling /
- fatigue property /
- threshold value of crack propagation /
- BP neural network

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图 1 试样取样位置示意

Figure 1. Specimen location

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图 2 试样形状及尺寸

Figure 2. Shape and dimension of specimen

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图 3 试样表面三维形貌和轮廓

Figure 3. 3D surface morphologies and profiles of specimens

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图 4 试样剖面维氏硬度分布

Figure 4. Vickers hardness distributions of specimens

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图 5 试样剖面残余应力分布

Figure 5. Residual stress distributions of specimens

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图 6 试样剖面FWHM分布

Figure 6. FWHM distributions of specimens

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图 7 SURP处理和研磨试样的XRD

Figure 7. XRD patterns of SURP and grinding specimens

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图 8 S-N曲线

Figure 8. S-N curves

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图 9 研磨试样疲劳断口

Figure 9. Fatigue fracture of grinding specimen

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图 10 SURP处理试样疲劳断口

Figure 10. Fatigue fracture of SURP specimen

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图 11 研磨抛光和SURP处理试样的裂纹扩展速率

Figure 11. Crack propagation rates in grinding with polishing and SURP specimens

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图 12 研磨抛光和SURP处理试样裂纹扩展行为

Figure 12. Crack propagation behaviors of grinding with polishing and SURP specimens

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图 13 BP神经网络模型的结构

Figure 13. Structure of BP neural network model

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图 14 BP神经网络训练集结果

Figure 14. Training set results of BP neural network

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图 15 BP神经网络测试集结果

Figure 15. Testing set results of BP neural network

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表 1 EA4T化学成分（质量百分比）

Table 1. Chemical composition of EA4T axel steel %

化学成分 C Si Mn P S Cr Cu Ni Mo

质量百分比 0.2700 0.3900 0.7200 0.0075 0.0013 1.1100 0.0140 0.2500 0.2470

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表 2 试样的加工方式和试验内容

Table 2. Processing method and experimental content of specimens

试样种类加工方法试验内容

1 研磨疲劳试验
2 研磨抛光疲劳试验、裂纹扩展试验
3 SURP 疲劳试验、裂纹扩展试验

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