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金刚石滚轮修整时的径向圆跳动状态在线判别

付庭斌 朱振伟 张瑞 赵华东

付庭斌, 朱振伟, 张瑞, 赵华东. 金刚石滚轮修整时的径向圆跳动状态在线判别[J]. 金刚石与磨料磨具工程, 2022, 42(2): 233-239. doi: 10.13394/j.cnki.jgszz.2021.0119
引用本文: 付庭斌, 朱振伟, 张瑞, 赵华东. 金刚石滚轮修整时的径向圆跳动状态在线判别[J]. 金刚石与磨料磨具工程, 2022, 42(2): 233-239. doi: 10.13394/j.cnki.jgszz.2021.0119
FU Tingbin, ZHU Zhenwei, ZHANG Rui, ZHAO Huadong. On-line discrimination of radial runout state during diamond roller trimming[J]. Diamond & Abrasives Engineering, 2022, 42(2): 233-239. doi: 10.13394/j.cnki.jgszz.2021.0119
Citation: FU Tingbin, ZHU Zhenwei, ZHANG Rui, ZHAO Huadong. On-line discrimination of radial runout state during diamond roller trimming[J]. Diamond & Abrasives Engineering, 2022, 42(2): 233-239. doi: 10.13394/j.cnki.jgszz.2021.0119

金刚石滚轮修整时的径向圆跳动状态在线判别

doi: 10.13394/j.cnki.jgszz.2021.0119
基金项目: 郑州市协同创新项目(18XTZX12006)。
详细信息
    通讯作者:

    赵华东,男,1978年生,博士生导师。主要研究方向:智能制造。E-mail: 82662906@qq.com

  • 中图分类号: TQ164

On-line discrimination of radial runout state during diamond roller trimming

  • 摘要: 金刚石滚轮修整砂轮时的性能受其径向圆跳动的影响,而其径向圆跳动状态判别的智能化程度较低。为此,对金刚石滚轮修整状态下的径向圆跳动磨削声发射信号,提出一种基于小波分解和SVM的在线检测方法。将磨削声发射信号通过小波变换并分解,提取小波分解系数的有效值、方差及能谱系数3种特征参数。结果表明:将3种特征参数彼此组合输入到SVM中进行状态识别时的准确率都在96.0%以上;3种特征参数同时输入时的准确率最高,达到了98.3%。该检测方法具有实际应用价值。

     

  • 图  1  金刚石滚轮轮廓

    Figure  1.  Diamond roller profile

    图  2  金刚石滚轮实物

    Figure  2.  Real diamond roller

    图  3  CVD金刚石模型

    Figure  3.  CVD diamond model

    图  4  修整试验平台

    Figure  4.  Trimming test platform

    图  5  粗修状态下的AE信号

    Figure  5.  AE signal in rough trimming state

    图  6  精修状态下的AE信号

    Figure  6.  AE signal in finishing state

    图  7  修整完成状态下的AE信号

    Figure  7.  AE signal in trimming completed state

    图  8  粗修状态下的AE信号频谱

    Figure  8.  AE signal spectrum in rough trimming state

    图  9  精修状态下的AE信号频谱

    Figure  9.  AE signal spectrum in finishing state

    图  10  修整完成时的AE信号频谱

    Figure  10.  AE signal spectrum in trimming completed state

    图  13  不同修整状态下的AE信号小波能谱系数

    Figure  13.  Wavelet energy spectrum coefficients of AE signals under different trimming states

    图  11  不同修整状态下的AE信号小波系数有效值

    Figure  11.  Effective values of wavelet coefficients of AE signals under different trimming states

    图  12  不同修整状态下的AE信号小波系数方差

    Figure  12.  Variances of AE signal wavelet coefficients under different trimming states

    表  1  修整试验参数

    Table  1.   Trimming test parameters

    参数类型或取值
    滚轮直径 D1 / mm 130
    滚轮宽度 W1 / mm 16
    砂轮直径 D2 / mm 200
    砂轮中金刚石粒度代号 120/140
    砂轮宽度 W2 / mm 3
    砂轮中金刚石浓度 C / % 120
    砂轮结合剂 V
    砂轮转速 n1 / (r·min−1) 4 000
    滚轮转速 n2 / (r·min−1) 70
    纵向走刀速度 n3 / (mm·min−1) 2.4
    粗修时的进给量 s1 / μm 8
    精修时的进给量 s2 / μm 2
    精修完成时的进给量 s3 / μm 2
    下载: 导出CSV

    表  2  小波系数有效值

    Table  2.   Effective values of wavelet coefficients

    序号a5d5d4d3d2d1
    10.1770.2100.0510.0260.0090.005
    20.2550.2010.0470.0240.0080.004
    30.2150.1360.0370.0210.0080.004
    40.0230.1220.0290.0160.0060.004
    $ \vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $
    5970.1970.1950.0460.0270.0090.004
    5980.2070.1600.0410.0210.0710.004
    5990.2380.1240.0320.0180.0610.004
    6000.2070.1600.0410.0210.0710.004
    下载: 导出CSV

    表  3  小波系数方差

    Table  3.   Variances of wavelet coefficients

    序号a5d5d4d3d2d1
    13.1×10−24.4×10−20.3×10−26.8×10−48.1×10−52.0×10−5
    23.5×10−23.1×10−20.1×10−25.0×10−45.9×10−51.8×10−5
    33.6×10−22.4×10−20.2×10−23.6×10−46.2×10−52.3×10−5
    43.5×10−21.6×10−20.1×10−23.9×10−44.4×10−51.7×10−5
    $\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $
    5973.6×10−23.6×10−22.2×10−26.1×10−45.6×10−51.9×10−5
    5982.8×10−21.8×10−21.3×10−25.5×10−46.5×10−51.8×10−5
    5993.4×10−23.2×10−21.9×10−25.2×10−45.8×10−52.0×10−5
    6003.7×10−22.3×10−21.2×10−23.4×10−43.8×10−51.9×10−5
    下载: 导出CSV

    表  4  小波能谱系数

    Table  4.   Wavelet energy spectrum coefficients

    序号a5d5d4d3d2d1
    167.230.2580.3941.7492.55227.82
    281.040.3120.2971.2831.80415.26
    371.530.2960.2661.0822.49224.33
    467.070.6320.5562.2193.25726.26
    $\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $
    59752.970.7481.0313.9564.89536.61
    59861.740.2560.4191.8753.10132.61
    59953.270.2390.2420.9831.32343.94
    60053.940.3290.3571.6202.44741.31
    下载: 导出CSV

    表  5  修整状态分类测试的准确率

    Table  5.   Accuracy of trimming state classification test

    输入的AE信号
    特征参数
    输入特征个数mSVM的准确率
    Ac / %
    $ {X}_{rms}^{m} $和$ {V}_{var}^{m} $1296.8
    $ {X}_{rms}^{m} $和$ {\eta }^{k} $1296.5
    $ {V}_{var}^{m} $和$ {\eta }^{k} $1297.2
    $ {X}_{rms}^{m} $、$ {V}_{var}^{m} $和$ {\eta }^{k} $1898.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-21
  • 修回日期:  2021-09-24
  • 刊出日期:  2022-05-27

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