留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

碳钢试样拉伸过程磁信号多尺度熵特征提取及磁畴观测

刘涛 沈朝洋 雷经发 邬竞雄 孙虹

刘涛, 沈朝洋, 雷经发, 邬竞雄, 孙虹. 碳钢试样拉伸过程磁信号多尺度熵特征提取及磁畴观测[J]. 机械科学与技术, 2022, 41(5): 808-814. doi: 10.13433/j.cnki.1003-8728.20220069
引用本文: 刘涛, 沈朝洋, 雷经发, 邬竞雄, 孙虹. 碳钢试样拉伸过程磁信号多尺度熵特征提取及磁畴观测[J]. 机械科学与技术, 2022, 41(5): 808-814. doi: 10.13433/j.cnki.1003-8728.20220069
LIU Tao, SHEN Chaoyang, LEI Jingfa, WU Jingxiong, SUN Hong. Multi-scale Entropy Feature Extraction of Magnetic Signal and Observation of Magnetic Domain in Tension of Carbon Steel Specimen[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(5): 808-814. doi: 10.13433/j.cnki.1003-8728.20220069
Citation: LIU Tao, SHEN Chaoyang, LEI Jingfa, WU Jingxiong, SUN Hong. Multi-scale Entropy Feature Extraction of Magnetic Signal and Observation of Magnetic Domain in Tension of Carbon Steel Specimen[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(5): 808-814. doi: 10.13433/j.cnki.1003-8728.20220069

碳钢试样拉伸过程磁信号多尺度熵特征提取及磁畴观测

doi: 10.13433/j.cnki.1003-8728.20220069
基金项目: 

国家自然科学基金项目 51805003

安徽省高校优秀拔尖人才培育项目 gxyqZD2019057

安徽高校协同创新项目 GXXT-2019-022

详细信息
    作者简介:

    刘涛(1984-), 副教授, 硕士生导师, 研究方向为无损检测、可持续设计与制造等, tao.liu@ahjzu.edu.cn

  • 中图分类号: TH140.7

Multi-scale Entropy Feature Extraction of Magnetic Signal and Observation of Magnetic Domain in Tension of Carbon Steel Specimen

  • 摘要: 为揭示碳钢试样拉伸损伤过程磁信号多尺度特征, 选取45钢试样开展准静态拉伸实验, 提取磁场强度法向分量Hp(y)和磁场强度梯度K的多尺度熵特征, 结合上述特征, 选取支持向量机方法构建了损伤评估模型。利用原子力显微法观测各拉伸损伤阶段的磁畴形貌, 提取了磁畴相位角特征。结果表明: 各拉伸损伤阶段Hp(y)均存在对应于隐性损伤区域的零值点, 随着损伤程度增加, Hp(y)绝对值增大, K值曲线在Hp(y)零值点处出现峰值。各阶段Hp(y)多尺度熵值随尺度因子的增加呈上升趋势。随着损伤程度的增加, Hp(y)多尺度熵值降低, 而磁畴相位角呈逐渐上升趋势, 颈缩和断裂阶段Hp(y)多尺度熵值再次升高, 磁畴相位角则开始下降, 所构建的损伤评估模型具有83.3%的损伤识别精度。本文成果可为铁磁构件在役检测及损伤评估提供方法模型支撑。
  • 图  1  原子力显微镜轻敲原理

    图  2  试样尺寸(单位mm)

    图  3  各拉伸损伤阶段Hp(y)曲线

    图  4  各拉伸损伤阶段K值曲线

    图  5  各拉伸损伤阶段Hp(y)多尺度熵值

    图  6  各拉伸损伤阶段K多尺度熵值

    图  7  支持向量机损伤评估模型测试

    图  8  45钢样块表面形貌和磁畴图

    图  9  不同拉伸损伤阶段磁畴相位角

    图  10  各损伤阶段磁畴相位角熵和能量特征

    表  1  45钢试样元素质量分数

    ω(Mn) ω(Cr) ω(Co) ω(P) ω(S) ω(C) ω(Si) ω(Cu) ω(Ni) ω(Fe)
    0.66% 0.21% 0.081% 0.019% 0.025% 0.45% 0.22% 0.18% 0.21% Bal.
    下载: 导出CSV

    表  2  部分磁特征样本数据

    特征参数 初始阶段 弹性阶段 屈服阶段 强化阶段 颈缩阶段 断裂阶段
    |Hp(y)|max 43 134 163 194 339 394
    Kmax 0.387 88 0.703 03 0.903 03 1.018 18 1.521 21 13.787 88
    MSE(Hp(y)) MSE(τ=1) 0.030 4 0.013 5 0.008 3 0.007 4 0.005 4 0.027 8
    MSE(τ=2) 0.045 2 0.014 7 0.009 4 0.008 7 0.009 0.054 9
    MSE(τ=10) 0.135 1 0.029 2 0.013 2 0.02 0.042 4 0.064 5
    MSE(K)) MSE(τ=1) 0.860 3 0.971 8 0.717 8 0.584 2 0.461 9 0.057 1
    MSE(τ=2) 1.092 5 1.145 2 0.897 3 0.751 3 0.660 6 0.094 1
    MSE(τ=10) 0.851 7 0.752 7 0.727 4 0.557 7 0.676 0.191 2
    下载: 导出CSV
  • [1] SHI P P, SU S Q, CHEN Z M. Overview of researches on the nondestructive testing method of metal magnetic memory: status and challenges[J]. Journal of Nondestructive Evaluation, 2020, 39(2): 43 doi: 10.1007/s10921-020-00688-z
    [2] WANG H P, DONG L H, WANG H D, et al. Effect of tensile stress on metal magnetic memory signals during on-line measurement in ferromagnetic steel[J]. NDT & E International, 2021, 117: 102378
    [3] 冷建成, 李政达, 王玉洁, 等. 循环应力对磁记忆效应影响的试验研究[J]. 材料导报, 2019, 33(10): 1723-1727, 1733 doi: 10.11896/cldb.18020090

    LENG J C, LI Z D, WANG Y J, et al. Experimental study on the effect of cyclic stress on magnetic memory effect[J]. Materials Reports, 2019, 33(10): 1723-1727, 1733 (in Chinese) doi: 10.11896/cldb.18020090
    [4] 邸新杰, 李午申, 白世武, 等. 金属磁记忆检测信号的二维谱熵特征[J]. 焊接学报, 2006, 27(11): 69-72 doi: 10.3321/j.issn:0253-360X.2006.11.018

    DI X J, LI W S, BAI S W, et al. Two-dimension spectrum entropy feature for metal magnetic memory signal[J]. Transactions of the China Welding Institution, 2006, 27(11): 69-72 (in Chinese) doi: 10.3321/j.issn:0253-360X.2006.11.018
    [5] 白鹭, 任吉林, 范振中. 基于小波包能量谱的磁记忆检测方法[J]. 无损检测, 2008, 30(10): 744-746 https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC200810024.htm

    BAI L, REN J L, FAN Z Z. The metal magnetic memory testing based on wavelet packet energy spectrum[J]. Nondestructive Testing, 2008, 30(10): 744-746 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC200810024.htm
    [6] MOONESAN M, KASHEFI M. Effect of sample initial magnetic field on the metal magnetic memory NDT result[J]. Journal of Magnetism and Magnetic Materials, 2018, 460: 285-291 doi: 10.1016/j.jmmm.2018.04.006
    [7] 樊清泉, 任尚坤, 任仙芝, 等. 外加磁场对Q235钢力磁效应影响试验研究[J]. 中国测试, 2019, 45(6): 46-53 https://www.cnki.com.cn/Article/CJFDTOTAL-SYCS201906009.htm

    FAN Q Q, REN S K, REN X Z, et al. Experimental study on the influence of external magnetic field on magneto-mechanical effect for Q235 steel[J]. China Measurement & Test, 2019, 45(6): 46-53 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SYCS201906009.htm
    [8] HUANG H H, QIAN Z C. Effect of temperature and stress on residual magnetic signals in ferromagnetic structural steel[J]. IEEE Transactions on Magnetics, 2017, 53(1): 6200108
    [9] SHI P P, ZHANG P C, JIN K, et al. Thermo-magneto-elastoplastic coupling model of metal magnetic memory testing method for ferromagnetic materials[J]. Journal of Applied Physics, 2018, 123(14): 145102 doi: 10.1063/1.5022534
    [10] 任文坚, 孙金立, 陈曦, 等. 地磁场中应力对磁畴组织结构影响的试验研究[J]. 机械工程学报, 2013, 49(2): 8-13 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201302003.htm

    REN W J, SUN J L, CHEN X, et al. Experimental study on effect of stress on magnetic domain structure under geomagnetic condition[J]. Journal of Mechanical Engineering, 2013, 49(2): 8-13 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201302003.htm
    [11] ZHU W, MA Z W, YAN J, et al. Magnetic domain structures and their evolution in quasi-two-dimensional ferromagnet Cr5Te8[J]. Journal of Magnetism and Magnetic Materials, 2020, 512: 167019 doi: 10.1016/j.jmmm.2020.167019
    [12] SALAZAR W, MORENO-ALDANA L C, DEL BUSTO J W S. Magnetic domain structures of Sr3Co2Z hexaferrite by TEM[J]. Journal of Magnetism and Magnetic Materials, 2020, 501: 166423 doi: 10.1016/j.jmmm.2020.166423
    [13] 刘涛, 鲍宏, 朱达荣, 等. 基于磁记忆和表面纹理特征融合的再制造毛坯疲劳损伤评估[J]. 中国机械工程, 2018, 29(13): 1615-1621 doi: 10.3969/j.issn.1004-132X.2018.13.016

    LIU T, BAO H, ZHU D R, et al. Fatigue damage evaluation of remanufacturing cores using feature fusion of magnetic memory and surface texture[J]. China Mechanical Engineering, 2018, 29(13): 1615-1621 (in Chinese) doi: 10.3969/j.issn.1004-132X.2018.13.016
    [14] 李思岐, 俞洋, 党永斌, 等. 基于改进的支持向量回归机算法的磁记忆定量化缺陷反演[J]. 工程科学学报, 2018, 40(9): 1123-1130 https://www.cnki.com.cn/Article/CJFDTOTAL-BJKD201809014.htm

    LI S Q, YU Y, DANG Y B, et al. Metal magnetic memory quantitative inversion of defects based on optimized support vector machine regression[J]. Chinese Journal of Engineering, 2018, 40(9): 1123-1130 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJKD201809014.htm
    [15] CHEN L F, CUI X L, LI Z H, et al. A new deep learning algorithm for SAR scene classification based on spatial statistical modeling and features re-calibration[J]. Sensors, 2019, 19(11): 2479 doi: 10.3390/s19112479
  • 加载中
图(10) / 表(2)
计量
  • 文章访问数:  125
  • HTML全文浏览量:  37
  • PDF下载量:  10
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-07-31
  • 刊出日期:  2022-05-01

目录

    /

    返回文章
    返回