CN 41-1243/TG ISSN 1006-852X

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

基于CNN的金刚石砂轮激光修锐参数优化

高孟阳 陈根余 李玮 周伟 李杰

高孟阳, 陈根余, 李玮, 周伟, 李杰. 基于CNN的金刚石砂轮激光修锐参数优化[J]. 金刚石与磨料磨具工程, 2022, 42(5): 602-609. doi: 10.13394/j.cnki.jgszz.2022.0018
引用本文: 高孟阳, 陈根余, 李玮, 周伟, 李杰. 基于CNN的金刚石砂轮激光修锐参数优化[J]. 金刚石与磨料磨具工程, 2022, 42(5): 602-609. doi: 10.13394/j.cnki.jgszz.2022.0018
GAO Mengyang, CHEN Genyu, LI Wei, ZHOU Wei, LI Jie. Optimization of laser sharpening parameters for diamond grinding wheel based on CNN[J]. Diamond & Abrasives Engineering, 2022, 42(5): 602-609. doi: 10.13394/j.cnki.jgszz.2022.0018
Citation: GAO Mengyang, CHEN Genyu, LI Wei, ZHOU Wei, LI Jie. Optimization of laser sharpening parameters for diamond grinding wheel based on CNN[J]. Diamond & Abrasives Engineering, 2022, 42(5): 602-609. doi: 10.13394/j.cnki.jgszz.2022.0018

基于CNN的金刚石砂轮激光修锐参数优化

doi: 10.13394/j.cnki.jgszz.2022.0018
基金项目: 广东省重点领域研发计划(2020B090924005)。
详细信息
    作者简介:

    陈根余,男,1965年生,博士、教授、博士生导师。主要研究方向:激光制造及激光微细加工技术。E-mail:hdgychen@163.com

  • 中图分类号: TQ164;TG74

Optimization of laser sharpening parameters for diamond grinding wheel based on CNN

  • 摘要: 采用正交试验法对青铜金刚石砂轮进行激光修锐试验,并对其激光修锐参数进行优化。通过卷积神经网络(convolutional neural network,CNN)对砂轮表面图片进行像素级的金刚石磨粒识别,提取磨粒面积信息,求出磨粒突出高度,利用统计分布规律得到突出高度得分和最佳区间比率2个激光修锐质量评价指标。利用提出的评价指标对试验得到的砂轮激光修锐图片进行质量评价,并进行极差分析。结果表明:平均功率是影响修锐质量最大的因素。最优的修锐工艺参数为:平均功率,35 W;重复频率,100 kHz;转速,300 r/min;扫描速度,1.0 mm/min。

     

  • 图  1  不同磨粒分割算法的分割结果

    Figure  1.  Segmentation results of different abrasive particle segmentation algorithms

    图  2  改进U-Net神经网络结构

    Figure  2.  Improved U-Net neural network structure

    图  3  超景深显微镜

    Figure  3.  Super depth of field microscope

    图  4  激光砂轮修整机床

    Figure  4.  Machine of laser dressing wheels

    图  5  激光修锐金刚石砂轮原理图

    Figure  5.  Schematic diagram of laser sharpening diamond grinding wheel

    图  6  1~16组磨粒分割结果

    Figure  6.  Grains segmentation results in group 1 to group 16

    图  7  1~16组磨粒突出高度分布

    Figure  7.  Abrasive grain protrusion height distribution in group 1 to group 16

    图  8  17组试验结果对比

    Figure  8.  Comparison of test results for 17 groups

    表  1  神经网络评价结果

    Table  1.   Neural network evaluation results

    模型平均交并比
    ζ / %
    准确率
    η / %
    U-Net82.9790.29
    MobileNet v2
    SE Net + Focal Loss
    87.5393.05
    下载: 导出CSV

    表  2  正交试验因素与水平

    Table  2.   Orthogonal test factors and levels

    水平因素
    A
    平均功率
    Pm / W
    B
    重复频率
    f0 / kHz
    C
    转速
    vs / (r∙min−1)
    D
    扫描速度
    v / (mm∙min−1)
    125701501.0
    230802002.5
    335902504.0
    4401003005.5
    下载: 导出CSV

    表  3  正交试验表

    Table  3.   Orthogonal test table

    组号因素水平组合
    ABCD
    1 1 1 1 1
    2 1 2 2 2
    3 1 3 3 3
    4 1 4 4 4
    5 2 1 2 3
    6 2 2 1 4
    7 2 3 4 1
    8 2 4 3 2
    9 3 1 3 4
    10 3 2 4 3
    11 3 3 1 2
    12 3 4 2 1
    13 4 1 4 2
    14 4 2 3 1
    15 4 3 2 4
    16 4 4 1 3
    下载: 导出CSV

    表  4  各组突出高度得分和最佳高度区间比率

    Table  4.   Salient height scores and optimal height interval ratio for each group

    组名突出高度得分 S最佳高度区间比率 γ
    1 1.686 37.912[(69/182)×100]
    2 1.343 23.810[(50/210)×100]
    3 1.478 31.395[(54/172)×100]
    4 1.639 32.323[(96/297)×100]
    5 1.353 26.786[(60/224)×100]
    6 1.557 31.527[(64/203)×100]
    7 1.607 31.122[(61/196)×100]
    8 1.584 33.992[(86/253)×100]
    9 1.892 41.423[(99/239)×100]
    10 2.207 50.181[(139/277)×100]
    11 2.194 51.339[(115/224)×100]
    12 2.619 60.428[(113/187)×100]
    13 2.481 57.848[(129/223)×100]
    14 2.494 53.846[(70/130)×100]
    15 2.199 52.907[(91/172)×100]
    16 2.193 48.421[(92/190)×100]
    下载: 导出CSV

    表  5  各因素4水平下突出高度得分的平均响应和效应极差

    Table  5.   Average response and effect range of salient height scores under four levels of each factor

    水平ABCD
    k11.536 51.853 01.907 52.101 5
    k21.525 31.900 31.878 51.900 5
    k32.228 01.869 51.862 01.807 8
    k42.341 82.008 81.983 51.821 8
    R0.816 50.155 80.121 50.293 7
    排序1342
    下载: 导出CSV

    表  6  各因素4水平下最佳高度区间比率的平均响应和效应极差

    Table  6.   Average response and effect range of optimal height interval ratio under four levels of each factor

    水平ABCD
    k131.360 040.992 342.299 845.827 0
    k230.856 839.841 040.982 841.747 3
    k350.842 841.690 840.164 039.195 8
    k453.255 543.791 042.868 539.545 0
    R22.398 73.950 02.704 56.631 2
    排序1342
    下载: 导出CSV
  • [1] 贾云海, 卢学军, 邓福铭, 等. 金刚石砂轮精密修整工艺研究 [J]. 金刚石与磨料磨具工程,2009,29(2):31-35. doi: 10.3969/j.issn.1006-852X.2009.02.008

    JIA Yunhai, LU Xuejun, DENG Fuming, et al. Study on high accuracy crush dressing of diamond grinding wheel [J]. Diamond & Abrasives Engineering,2009,29(2):31-35. doi: 10.3969/j.issn.1006-852X.2009.02.008
    [2] SUZUKI K, UEMATSU T, NAKAGAWA T. On-machine trueing/dressing of metal bond grinding wheels by electro-discharge machining [J]. CIRP Annals,1987,36(1):115-118. doi: 10.1016/S0007-8506(07)62566-9
    [3] 尚振涛, 黄含, 王树启, 等. 粗粒度青铜结合剂金刚石砂轮电火花–机械复合整形试验研究 [J]. 金刚石与磨料磨具工程,2006,26(6):42-48. doi: 10.3969/j.issn.1006-852X.2006.06.011

    SHANG Zhentao, HUANG Han, WANG Shuqi, et al. Experimental study on truing of bronze-bonded diamond wheels with coarse abrasive grits using a novel hybrid method [J]. Diamond & Abrasives Engineering,2006,26(6):42-48. doi: 10.3969/j.issn.1006-852X.2006.06.011
    [4] CHEN G, CAI S, ZHOU C. On the laser -driven integrated dressing and truing of bronze-bonded grinding wheels [J]. Diamond & Related Materials,2015,60:99-110.
    [5] CHEN G, MEI L, ZHANG B, et al. Experiment and numerical simulation study on laser truing and dressing of bronze-bonded diamond wheel [J]. Optics and Lasers in Engineering,2010,48:295-304. doi: 10.1016/j.optlaseng.2009.11.006
    [6] 蔡颂, 熊彪, 陈根余, 等. 青铜金刚石砂轮的激光整形与修锐 [J]. 红外与激光工程,2017,46(4):66-75.

    CAI Song, XIONG Biao, CHEN Genyu, et al. Laser truing and sharpening of bronze-bond diamond grinding wheel [J]. Infrared and Laser Engineering,2017,46(4):66-75.
    [7] 周远航, 张健, 冯爱新, 等. 皮秒绿激光修锐青铜基金刚石砂轮损伤规律与机制 [J]. 中国激光,2021,48(6):197-206.

    ZHOU Yuanhang, ZHANG Jian, FENG Aixin, et al. Damage law and mechanism of bronze-based diamond grinding wheel sharpening with picosecond green laser [J]. Chinese Journal of Lasers,2021,48(6):197-206.
    [8] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C]// IEEE. Proceedings of the IEEE conference on computer vision and pattern recognition(2015). San Juan: IEEE: 3431-3440.
    [9] RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation [C]// MICCIA. International conference on medical image computing and computer-assisted intervention(2015). Cham Switzerland: Springer: 234-241.
    [10] BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495. doi: 10.1109/TPAMI.2016.2644615
    [11] CHEN L, PAPANDREOU G, KOKKINOS I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(4):834-848.
    [12] 李江昀, 杨志方, 郑俊锋, 等. 深度学习技术在钢铁工业中的应用 [J]. 钢铁,2021,56(9):43-49. doi: 10.13228/j.boyuan.issn0449-749x.20210296

    LI Jiangyun, YANG Zhifang, ZHENG Junfeng, et al. Applications of iron and steel industry with deep learning technologies [J]. Iron & Steel,2021,56(9):43-49. doi: 10.13228/j.boyuan.issn0449-749x.20210296
    [13] SANDLER M, HOWARD A, ZHU M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks [C]// CVPR. Proceedings of the IEEE conference on computer vision and pattern recognition(2018). San Juan: IEEE: 4510-4520.
    [14] HU J, SHEN L, SUN G. Squeeze-and-excitation networks [C]// IEEE. Proceedings of the IEEE conference on computer vision and pattern recognition(2018). San Juan: IEEE: 7132-7141.
    [15] LIN T, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [C]// IEEE. Proceedings of the IEEE international conference on computer vision(2017). San Juan: IEEE: 2980-2988.
    [16] 段念, 王文珊, 于怡青, 等. 不同形状磨粒随机分布磨料表面的三维建模仿真 [J]. 东华大学学报(自然科学版),2016,42(4):500-505.

    DUAN Nian, WANG Wenshan, YU Yiqing, et al. 3D-parametric modeling and programming of abrasive surface with grains of different shape on random distribution [J]. Journal of Donghua University (Natural Science),2016,42(4):500-505.
    [17] 赵金坠, 冯克明, 朱建辉, 等. 不同修锐工具对树脂结合剂CBN砂轮修锐效果的影响 [J]. 工具技术,2016,50(12):82-85. doi: 10.3969/j.issn.1000-7008.2016.12.019

    ZHAO Jinzhui, FENG Keming, ZHU Jianhui, et al. Impact of different dressing tool upon dressing effect of resin bonded CBN grinding wheel [J]. Tool Engineering,2016,50(12):82-85. doi: 10.3969/j.issn.1000-7008.2016.12.019
    [18] 苏玲玲, 黄辉, 徐西鹏. 金刚石磨具表面磨粒分布形态的定量评价 [J]. 中国机械工程,2014,25(10):1290-1294. doi: 10.3969/j.issn.1004-132X.2014.10.003

    SU Lingling, HUANG Hui, XU Xipeng. Quantitative measurement of grit distribution of diamond abrasive tools [J]. China Mechanical Engineering,2014,25(10):1290-1294. doi: 10.3969/j.issn.1004-132X.2014.10.003
    [19] 蔡颂, 陈根余, 何杰. 脉冲光纤激光修锐青铜金刚石砂轮相爆炸研究 [J]. 中国激光, 2015(9): 203-210.

    CAI Song, CHEN Genyu, HE Jie, Studies on the phase explosion of laser dressing bronze-bond diamond grinding wheel by a pulsed fiber laser [J]. Chinese Journal of Lasers, 2015 (9): 203-210.
    [20] YOUNG H, CHEN D. Online dressing of profile grinding wheels [J]. International Journal of Advanced Manufacturing Technology,2006,27(9/10):883-888. doi: 10.1007/s00170-004-2271-8
    [21] DOMAN D, WARKENTIN A, BAUER R. A survey of recent grinding wheel topography models [J]. International Journal of Machine Tools and Manufacture,2006,46(3/4):343-352.
    [22] YAN L, RONG Y, JIANG F, et al. Three-dimension surface characterization of grinding wheel using white light interferometer [J]. International Journal of Advanced Manufacturing Technology,2011,55(1/2/3/4):133-141.
  • 加载中
图(8) / 表(6)
计量
  • 文章访问数:  370
  • HTML全文浏览量:  116
  • PDF下载量:  50
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-07
  • 修回日期:  2022-04-21
  • 录用日期:  2022-04-26
  • 刊出日期:  2022-10-10

目录

    /

    返回文章
    返回