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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

张继旺, 张浩楠, 杨冰, 苏凯新, 李行. 超声滚压EA4T车轴钢疲劳性能及寿命预测[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20220257
引用本文: 张继旺, 张浩楠, 杨冰, 苏凯新, 李行. 超声滚压EA4T车轴钢疲劳性能及寿命预测[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20220257
ZHANG Jiwang, ZHANG Haonan, YANG Bing, SU Kaixin, LI Hang. Fatigue Properties and Life Prediction of Ultrasonic Rolling EA4T Axle Steel[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220257
Citation: ZHANG Jiwang, ZHANG Haonan, YANG Bing, SU Kaixin, LI Hang. Fatigue Properties and Life Prediction of Ultrasonic Rolling EA4T Axle Steel[J]. Journal of Southwest Jiaotong University. 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
基金项目: 国家自然科学基金(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,zhangjiwang@swjtu.edu.cn

  • 中图分类号: U270.33

Fatigue Properties and Life Prediction of Ultrasonic Rolling EA4T Axle Steel

  • 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,并分析了裂纹扩展规律,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处理后可以使试样表面粗糙度降低为0.17 μm,并去除了表面梨沟形貌;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$ \sqrt{\mathrm{m}} $增加到11.21 MPa$ \sqrt{\mathrm{m}} $,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%.

     

  • 图 1  试样取样位置示意

    Figure 1.  Specimen location

    图 2  试样形状及尺寸

    Figure 2.  Shape and dimension of specimen

    图 3  试样表面三维形貌和轮廓

    Figure 3.  3D surface morphologies and profiles of specimens

    图 4  试样剖面维氏硬度分布

    Figure 4.  Vickers hardness distributions of specimens

    图 5  试样剖面残余应力分布

    Figure 5.  Residual stress distributions of specimens

    图 6  试样剖面FWHM分布

    Figure 6.  FWHM distributions of specimens

    图 7  SURP处理和研磨试样的XRD

    Figure 7.  XRD patterns of SURP and grinding specimens

    图 8  S-N曲线

    Figure 8.  S-N curves

    图 9  研磨试样疲劳断口

    Figure 9.  Fatigue fracture of grinding specimen

    图 10  SURP处理试样疲劳断口

    Figure 10.  Fatigue fracture of SURP specimen

    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

    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

    图 13  BP神经网络模型的结构

    Figure 13.  Structure of BP neural network model

    图 14  BP神经网络训练集结果

    Figure 14.  Training set results of BP neural network

    图 15  BP神经网络测试集结果

    Figure 15.  Testing set results of BP neural network

    表  1  EA4T化学成分(质量百分比)

    Table  1.   Chemical composition of EA4T axel steel %

    化学成分CSiMnPSCrCuNiMo
    质量百分比0.270.390.720.00750.00131.110.0140.250.247
    下载: 导出CSV

    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
    3SURP疲劳试验、裂纹扩展试验
    下载: 导出CSV
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  • 收稿日期:  2021-04-10
  • 修回日期:  2022-05-18
  • 网络出版日期:  2023-11-09

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