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基于GA-BP神经网络的微磨具磨损预测研究

田苗 于康宁 任莹晖 佘程熙 易峦

田苗, 于康宁, 任莹晖, 佘程熙, 易峦. 基于GA-BP神经网络的微磨具磨损预测研究[J]. 金刚石与磨料磨具工程, 2024, 44(3): 363-373. doi: 10.13394/j.cnki.jgszz.2023.0074
引用本文: 田苗, 于康宁, 任莹晖, 佘程熙, 易峦. 基于GA-BP神经网络的微磨具磨损预测研究[J]. 金刚石与磨料磨具工程, 2024, 44(3): 363-373. doi: 10.13394/j.cnki.jgszz.2023.0074
TIAN Miao, YU Kangning, REN Yinghui, SHE Chengxi, YI Luan. Wear prediction of micro-grinding tool based on GA-BP neural network[J]. Diamond & Abrasives Engineering, 2024, 44(3): 363-373. doi: 10.13394/j.cnki.jgszz.2023.0074
Citation: TIAN Miao, YU Kangning, REN Yinghui, SHE Chengxi, YI Luan. Wear prediction of micro-grinding tool based on GA-BP neural network[J]. Diamond & Abrasives Engineering, 2024, 44(3): 363-373. doi: 10.13394/j.cnki.jgszz.2023.0074

基于GA-BP神经网络的微磨具磨损预测研究

doi: 10.13394/j.cnki.jgszz.2023.0074
基金项目: 国家自然科学基金(52075161)
详细信息
    通讯作者:

    任莹晖,女,1979年出生,博士、副教授、博士研究生导师。主要研究方向为智能制造信息系统研究及开发、多能场复合微细加工技术及装备、难加工材料精密/超精密加工技术。E-mail:rebecca_ryh@163.com

  • 中图分类号: TH162; TG58

Wear prediction of micro-grinding tool based on GA-BP neural network

  • 摘要: 为提高硬脆材料微结构的加工效率和精度,需要预测微磨具的不确定性磨损。基于微磨具在位视觉磨损检测和聚类分析,提出基于遗传算法的反向神经网络(genetic algorithm back propagation,GA-BP)模型。选取微磨具磨头截面面积损失量为指标,以表征微磨具不确定性磨损特征。利用K-均值聚类算法划分微磨具磨损状态阶段。最后构建以主轴转速、进给率、微槽深度、磨削长度和微磨具初始截面面积为输入层神经元,以磨头截面面积损失量预测值为输出层的GA-BP神经网络模型。设计不同工艺参数条件下的单晶硅微槽微细磨削实验,基于自搭建的机器视觉系统在位测量微磨具的磨头截面面积磨损量。将实验测得的微磨具磨损量作为训练数据,与传统高斯过程回归预测模型对比,验证GA-BP神经网络模型的有效性和准确性。结果表明,GA-BP神经网络模型能够实现不同工艺参数和不同磨削长度下的微磨具磨损预测,比传统高斯过程回归预测模型具有更高预测精度,平均误差精度达到5%,可以实现微磨具磨损阶段状态预测。

     

  • 图  1  微磨具不确定性磨损

    Figure  1.  Uncertain wear of micro-grinding tools

    图  2  微磨具磨头截面面积损失量计算示意图

    Figure  2.  Schematic diagram for calculating loss of cross-sectional area of grinding head

    图  3  基于GA-BP网络模型的微磨具磨损预测方法流程

    Figure  3.  Sketch of technological process for micro-grinding tool wear prediction method based on GA-BP neural network

    图  4  单晶硅微槽微细磨削实验平台

    Figure  4.  Experimental platform for micro-grinding of monocrystalline silicon microgrooves

    图  5  微磨具磨头直径损失量随磨削长度的变化趋势

    Figure  5.  Variation trend of diameter loss of grinding head with grinding length

    图  6  微磨具磨头截面面积损失量随磨削长度的变化趋势

    Figure  6.  Variation trend of section area loss of micro-grinding head with grinding length

    图  7  单因素实验测得磨头截面面积损失量与磨削长度关系

    Figure  7.  Relationship between loss of cross-sectional area of grinding head measured by single factor experiment

    图  8  磨头截面面积损失量聚类图

    Figure  8.  Cluster diagram of loss of cross-sectional area of tool

    图  9  磨头截面面积损失量实验值与GA-BP预测值

    Figure  9.  Experimental value and GA-BP model predicted value of cross-sectional area loss of tool

    图  10  相同工况下的磨头截面面积损失量

    Figure  10.  Cross-sectional area loss of tool under same working condition

    图  11  变工况下的磨头截面面积损失量

    Figure  11.  Cross-sectional area loss of tool under variable working conditions

    表  1  微磨具磨头直径

    Table  1.   Diameter of micro-grinders

    微槽数预实验A dA / μm预实验B dB / μm预实验C dC / μm
    0414423413
    1411421410
    2407416404
    3409414403
    4405411402
    5406412404
    6404412404
    7411402
    8411402
    9403
    10401
    下载: 导出CSV

    表  2  微磨具磨头截面面积

    Table  2.   Cross-sectional area of micro-grinders

    微槽数预实验A SA / μm2预实验B SB / μm2预实验C SC / μm2
    035099.9036334.7235005.37
    134911.5536150.3734790.47
    234787.2036019.8334690.84
    334760.2935969.6634646.12
    434699.2135934.7534586.85
    534642.4835875.1234538.49
    634591.2135832.9434494.86
    735780.9434454.13
    835720.2234409.41
    934275.97
    1034096.34
    下载: 导出CSV

    表  3  工艺实验参数表

    Table  3.   Processing parameters

    组号 主轴转速n / (r·min−1) 进给率vf / (mm·min−1) 微槽深度h / μm
    1 20000 1.0 100
    2 40000 1.0 100
    3 60000 1.0 100
    4 80000 1.0 100
    5 100000 1.0 100
    6 60000 0.4 100
    7 60000 0.7 100
    8 60000 1.3 100
    9 60000 1.6 100
    10 60000 1.0 50
    11 60000 1.0 80
    12 60000 1.0 150
    13 60000 1.0 200
    14 60000 1.0 250
    15 60000 0.5 200
    下载: 导出CSV

    表  4  不同隐含层节点数的网络模型误差精度

    Table  4.   Error accuracy of network model with different number of hidden layer nodes

    隐含层节点数 N均方误差
    4 0.0126
    5 0.0111
    6 0.0070
    7 0.0045
    8 0.0020
    9 0.0061
    10 0.0085
    11 0.0062
    12 0.0031
    下载: 导出CSV
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  • 收稿日期:  2023-03-25
  • 修回日期:  2023-06-07
  • 录用日期:  2023-06-07
  • 网络出版日期:  2024-06-28
  • 刊出日期:  2024-06-28

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