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轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测

徐钰淳 朱建辉 师超钰 王宁昌 赵延军 张高亮 乔帅 谷春青

徐钰淳, 朱建辉, 师超钰, 王宁昌, 赵延军, 张高亮, 乔帅, 谷春青. 轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测[J]. 金刚石与磨料磨具工程, 2024, 44(3): 346-353. doi: 10.13394/j.cnki.jgszz.2023-0118
引用本文: 徐钰淳, 朱建辉, 师超钰, 王宁昌, 赵延军, 张高亮, 乔帅, 谷春青. 轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测[J]. 金刚石与磨料磨具工程, 2024, 44(3): 346-353. doi: 10.13394/j.cnki.jgszz.2023-0118
XU Yuchun, ZHU Jianhui, SHI Chaoyu, WANG Ningchang, ZHAO Yanjun, ZHANG Gaoliang, QIAO Shuai, GU Chunqing. Roughness prediction of Al2O3-based ceramic insulation coating on bearing surface[J]. Diamond & Abrasives Engineering, 2024, 44(3): 346-353. doi: 10.13394/j.cnki.jgszz.2023-0118
Citation: XU Yuchun, ZHU Jianhui, SHI Chaoyu, WANG Ningchang, ZHAO Yanjun, ZHANG Gaoliang, QIAO Shuai, GU Chunqing. Roughness prediction of Al2O3-based ceramic insulation coating on bearing surface[J]. Diamond & Abrasives Engineering, 2024, 44(3): 346-353. doi: 10.13394/j.cnki.jgszz.2023-0118

轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测

doi: 10.13394/j.cnki.jgszz.2023-0118
基金项目: 国家重点研发计划(2020YFB2007900)
详细信息
    作者简介:

    徐钰淳,男,1995年生,硕士、工程师。主要研究方向:精密磨削加工及过程监测。E-mail:yuchun-xu@foxmail.com

  • 中图分类号: TG58; TG74; TQ164

Roughness prediction of Al2O3-based ceramic insulation coating on bearing surface

  • 摘要: 为了提升轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测精度,提出基于光谱共焦原理的砂轮表面测量及磨粒特征参数量化方法,以砂轮表面的磨粒特征参数K,砂轮线速度vs,工件进给速度f,切削深度ap及法向磨削力F为输入参数,建立能够直接反映砂轮表面时变状态的工件表面粗糙度BP神经网络预测模型,并通过已知磨削样本及砂轮磨损后的4组未知样本对网络预测模型性能进行验证。结果表明:已知样本的BP网络模型粗糙度预测结果与实际结果的规律及数值较为一致,其网络输出误差均 < ± 0.04 μm;4组未知样本的网络预测精度下降,但其相对误差最大值的绝对值不超过20.00%。建立的包含砂轮表面磨粒特征参数的神经网络预测模型,可以适应砂轮磨粒磨损时变状态下的轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测,且其对未知样本具有一定的泛化能力。

     

  • 图  1  砂轮表面的光谱共焦系统测试原理

    Figure  1.  Test principle of spectral confocal system of grinding wheel surface

    图  2  基于磨削参数和砂轮表面状态的BP神经网络结构

    Figure  2.  BP neural network structure based on grinding parameters and grinding wheel surface state

    图  3  磨削现场

    Figure  3.  Grinding site

    图  4  Al2O3基陶瓷涂层轴承钢样品

    Figure  4.  Al2O3-based ceramic coating bearing steel sample

    图  5  BP网络结构图

    Figure  5.  BP network structure diagram

    图  6  网络训练的收敛过程

    Figure  6.  Convergence process of network training

    图  7  预测值与实际值对比

    Figure  7.  Comparison between predicted and actual values

    图  8  粗糙度误差

    Figure  8.  Roughness error

    图  9  砂轮表面的磨粒磨损对比

    Figure  9.  Comparison of abrasive wear on grinding wheel surface

    图  10  磨损前后砂轮表面的形貌数据测量结果

    Figure  10.  Measurement results of surface morphology data of grinding wheels before and after wear

    图  11  Al2O3基陶瓷涂层工件的表面形貌及粗糙度测量结果

    Figure  11.  Surface morphology and roughness measurement results of Al2O3-based ceramic coating workpieces

    表  1  正交试验因素及水平

    Table  1.   Orthogonal test factors and levels

    水平因素
    砂轮线速度
    vs / (m·s−1)
    A
    切深
    ap / μm
    B
    工件进给速度
    f / (mm·min−1)
    C
    1155300
    22515900
    335251 500
    445352 100
    下载: 导出CSV

    表  2  正交试验表

    Table  2.   Orthogonal test table

    试验序号ABC
    11552 100
    215151 500
    31525900
    41535300
    52551 500
    62515900
    72525300
    825352 100
    9355900
    103515300
    1135252 100
    1235351 500
    13455300
    1445152 100
    1545251 500
    164535900
    下载: 导出CSV

    表  3  1#砂轮表面磨粒的特征参数与磨削试验结果

    Table  3.   Characteristic parameters of abrasive particles on the surface of grinding wheel 1# and grinding resuilts

    序号特征参数 K法向磨削力 F / N表面粗糙度 Sa / μm
    130.326.870.319
    230.429.730.373
    330.366.730.531
    430.187.530.599
    530.225.940.239
    629.921.880.361
    730.473.870.528
    829.888.790.593
    930.313.960.225
    1029.816.200.312
    1130.088.400.455
    1230.390.410.500
    1330.413.380.202
    1429.784.480.332
    1530.672.020.365
    1630.488.510.545
    下载: 导出CSV

    表  4  2#砂轮表面磨粒的特征参数与磨削试验结果

    Table  4.   Characteristic parameters of abrasive particles on the surface of grinding wheel 2# and grinding resuilts

    序号特征参数 K法向磨削力 F / N表面粗糙度 Sa / μm
    115.840.730.254
    215.834.050.312
    315.425.550.453
    415.573.240.507
    515.733.690.204
    615.439.630.309
    716.190.160.430
    815.486.580.486
    915.427.800.178
    1015.432.490.268
    1115.489.570.387
    1215.6103.640.423
    1315.538.410.151
    1416.138.370.287
    1515.6103.690.315
    1615.4104.420.467
    下载: 导出CSV

    表  5  磨削试验参数

    Table  5.   Grinding test parameters

    参数规格或取值
    砂轮编号1#
    砂轮线速度vs / (m·s−1)20,32,37,50
    工件进给速度 f / (mm·min−1)1400,900,600,200
    切削深度 ap / μm24,27,11,5
    下载: 导出CSV

    表  6  磨削试验网络输入向量的试验值与预测值对比

    Table  6.   Comparison between experimental and predicted values of input vectors in grinding test networks

    试验组网络输入向量矩阵
    q
    表面粗糙度
    Sa / μm
    相对误差
    δ / %
    实际预测
    1[20,24,1400,20.1,73.64]0.4060.3796.65
    2[32,27,900,20.5,59.19]0.4360.4184.13
    3[37,11,600,20.3,17.66]0.2030.241−18.72
    4[50,5,200,20.6,15.76]0.1700.198−16.47
    下载: 导出CSV
  • [1] 卜珍宇, 赵晓琴, 郭向东, 等. 电机轴承防护措施及Al2O3陶瓷绝缘涂层研究现状 [J]. 表面技术,2021,50(5):51-59.

    BU Zhenyu, ZHAO Xiaoqin, GUO Xiangdong, et al. Electromotor bearing protection measures and research status of Al2O3 ceramic coating [J]. Surface Technology,2021,50(5):51-59.
    [2] 余剑武, 胡其丰, 文丞, 等. 基于支持向量机的电火花加工8418钢表面粗糙度预测模型 [J]. 中国机械工程,2018,29(7):771-774.

    YU Jianwu, HU Qifeng, WEN Chen, et al. Prediction model of surface roughness of 8418 steel by EDM based on SVM [J]. China Mechanical Engineering,2018,29(7):771-774.
    [3] PAN Y, WANG Y, ZhOU P, et al. Activation functions selection for BP neural network model of ground surface roughness [J]. Journal of Intelligent Manufacturing,2020(31):1825-1836. doi: 10.1007/s10845-020-01538-5
    [4] 任敬心, 华定安. 磨削原理[M]. 西安: 西北工业大学出版社, 1988: 206-612.

    REN Jingxin, HUA Anding. Grinding principle [M]. Xi'an: Northwestern Polytechnical University Press, 1988: 206-216.
    [5] NAMETALA C A L, SOUZA A M, JÚNIOR B R P, et al. A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels. [J]. CIRP Journal of Manufacturing Science and Technology,2020(30):157-173. doi: 10.1016/j.cirpj.2020.05.004
    [6] YANG J, ZhANG L, LIU G, et al. Sintered silicon carbide grinding surface roughness prediction based on deep learning and neural network [J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering,2022,44(7):1-13.
    [7] XIAO G. Prediction of surface roughness of abrasive belt grinding of superalloy material based on RLSOM-RBF [J]. Materials,2021,14(19):5701.
    [8] GUO W, WU C, DING Z, et al. Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding [J]. The International Journal of Advanced Manufacturing Technology,2021(112):2853-2871. doi: 10.1007/s00170-020-06523-z
    [9] LAI J Y, LIN P C. Grinded surface roughness prediction using data-driven models with contact force information: 2022 IEEE/ASME international conference on advanced intelligent mechatronics (AIM) [C]. Sapporo: IEEE, 2022: 983-989.
    [10] MIRIFAR S, KADIVAR M, AZARHOUSHANG B. First steps through intelligent grinding using machine learning via integrated acoustic emission sensors [J]. Journal of Manufacturing and Materials Processing, 2020, 4(2): 35.
    [11] 董志刚, 马槐遥, 康仁科, 等. SiCf/SiC复合材料超声辅助干式侧磨砂轮磨损研究 [J]. 机械工程学报,2022,58(15):134-143. doi: 10.3901/JME.2022.15.134

    DONG Zhigang, MA Huaiyao, KANG Renke, et al. Study on wear of grinding wheel in ultrasonic assisted dry side grinding of SiCf/SiC composites [J]. Journal of Mechanical Engineering,2022,58(15):134-143. doi: 10.3901/JME.2022.15.134
    [12] 胡天荣, 郑红伟, 戴士杰. 基于异类信息融合的砂轮磨损状态监测 [J]. 工具技术,2023,57(2):126-131.

    HU Tianrong, ZHENG Hongwei, DAI Shijie. Grinding wheel wear condition monitoring based on heterogeneous information fusion [J]. Tool Engineering,2023,57(2):126-131.
    [13] 王津楠, 陈凤东, 刘炳国, 等. 基于白光LED的光谱共焦位移传感器 [J]. 中国测试,2017,43(1):69-73.

    WANG Jinnan, CHEN Fengdong, LIU Bingguo, et al. White LED-based spectrum confocal displacement sensor [J]. China Measurment & Test,2017,43(1):69-73.
    [14] 王嵘冰, 徐红艳, 李波, 等. BP神经网络隐含层节点数确定方法研究 [J]. 计算机技术与发展,2018,28(4):31-35.

    WANG Rongbing, XU Hongyan, LI Bo, et al. Research on method of determining hidden layer nodes in BP neural network [J]. Computer Technology and Development,2018,28(4):31-35.
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出版历程
  • 收稿日期:  2023-05-22
  • 修回日期:  2023-08-01
  • 网络出版日期:  2024-06-28
  • 刊出日期:  2024-06-28

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