CN 41-1243/TG ISSN 1006-852X
Volume 44 Issue 3
Jun.  2024
Turn off MathJax
Article Contents
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

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

doi: 10.13394/j.cnki.jgszz.2023.0074
More Information
  • Received Date: 2023-03-25
  • Accepted Date: 2023-06-07
  • Rev Recd Date: 2023-06-07
  • Available Online: 2024-06-28
  • An intelligent tool wear prediction model has been proposed for the micro-grinding tool, optimized using a genetic algorithm (GA) based BP neural network. The GA-BP prediction model is applied with in-situ tool wear detection to obtain training set data and combines cluster analysis to divide the tool wear stages. To represent the uncertainty in wear characteristics, the loss of cross-sectional area of the micro-grinding tool has been selected as an index to evaluate tool wear loss. The K-means clustering algorithm is used to cluster and analyze the tool wear stages under different process parameters. The GA-BP neural network includes five neurons in the input layer: rotating speed, feed rate, cutting depth, grinding length, and the initial cross-sectional area of the tool. The output layer neuron predicts the loss of the tool's cross-sectional area. To validate the method, a series of micro-grinding experiments were performed under different parameters for the micro-groove array of monocrystalline silicon. The loss of the tool's cross-sectional area was measured by a self-made visual inspection system, providing learning samples for the prediction model. The predicted results of the GA-BP neural network model were compared with the traditional Gaussian process regression method. The results show that the GA-BP neural network model can correctly predict tool wear loss and identify wear stages under different process parameters and grinding lengths. It has higher prediction accuracy during the self-learning process, with an average error of 5% .

     

  • loading
  • [1]
    AURICH J C, KIRSCH B, SETTI D, et al. Abrasive processes for micro parts and structures [J]. CIRP Annals-Manufacturing Technology,2019,68:653-676. doi: 10.1016/j.cirp.2019.05.006
    [2]
    PARK H W, LIANG S Y. Force modeling of micro-grinding incorporating crystallographic effects [J]. International Journal of Machine Tools and Manufacture,2008,48(15):1658-1667. doi: 10.1016/j.ijmachtools.2008.07.004
    [3]
    李伟, 周志雄, 尹韶辉, 等. 微细磨削技术及微磨床设备研究现状分析与探讨[J]. 机械工程学报, 2016, 52(17): 10-19. doi: 10.3901/JME.2016.17.010

    LI Wei, ZHOU Zhixiong, YIN Shaohui, et al. Research status analysis and review of micro-grinding technology and micro-grinding machines[J]. Journal of Mechanical Engineering, 2016, 52(17): 10-19. doi: 10.3901/JME.2016.17.010
    [4]
    REN Y, LI C, LI W, et al. Study on micro-grinding quality in micro-grinding tool for single crystal silicon [J]. Journal of Manufacturing Processes,2019,42:246-256. doi: 10.1016/j.jmapro.2019.04.030
    [5]
    LI W, REN Y, LI C, et al. Investigation of machining and wear performance of various diamond micro-grinding tools [J]. International Journal of Advanced Manufacturing Technology,2020,106(3):921-935. doi: 10.1007/s00170-019-04610-4
    [6]
    温雪龙, 巩亚东, 程军, 等. 电镀金刚石微磨具磨损机理分析与试验研究 [J]. 机械工程学报,2015,51(11):177-185. doi: 10.3901/JME.2015.11.177

    WEN Xuelong, GONG Yadong, CHENG Jun, et al. Mechanism analysis and experimental research on wear of electroplated diamond micro-grinding tool [J]. Journal of Mechanical Engineering,2015,51(11):177-185. doi: 10.3901/JME.2015.11.177
    [7]
    KIRSCH B, BOHLEY M, ARRABIYEH P A, et al. Application of ultra-small micro grinding and micro milling tools [J]. Micromachines,2017,8(9):261. doi: 10.3390/mi8090261
    [8]
    LI X, LIU X, YUE C, et al. Systematic review on tool breakage monitoring techniques in machining operations [J]. International Journal of Machine Tools & Manufacture,2022,176:103882. doi: 10.1016/j.ijmachtools.2022.103882
    [9]
    刘献礼, 李雪冰, 丁明娜, 等. 面向智能制造的刀具全生命周期智能管控技术 [J]. 机械工程学报,2021,57(10):196-219. doi: 10.3901/JME.2021.10.196

    LIU Xianli, LI Xuebing, DING Mingna, et al. Intelligent management and control technology of cutting tool life-cycle for intelligent manufacturing [J]. Journal of Mechanical Engineering,2021,57(10):196-219. doi: 10.3901/JME.2021.10.196
    [10]
    NOURI M, FUSSELL B K, ZINITI B L, et al. Real-time tool wear monitoring in milling using a cutting condition independent method [J]. International Journal of Machine Tools and Manufacture,2015,89:1-13. doi: 10.1016/j.ijmachtools.2014.10.011
    [11]
    MEI Y, YU Z, YANG Z. Experimental investigation of correlation between attrition wear and features of acoustic emission signals in single-grit grinding [J]. The International Journal of Advanced Manufacturing Technology,2017,93(5):2275-2287. doi: 10.1007/s00170-017-0687-1
    [12]
    XIE Z, LI J, LU Y. An integrated wireless vibration sensing tool holder for milling tool condition monitoring [J]. International Journal of Advanced Manufacturing Technology,2018,95(5):2885-2896. doi: 10.1016/j.measurement.2021.109038
    [13]
    GARCIA-ORDAS M T, ALEGRE E, GONZALEZ-CASTRO V, et al. A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques [J]. International Journal of Advanced Manufacturing Technology,2017,90(5):1947-1961. doi: 10.1007/s00170-016-9541-0
    [14]
    MIKOLAJCZYK T, NOWICKI K, KLODOWSKI A, et al. Neural network approach for automatic image analysis of cutting edges wear [J]. Mechanical Systems and Signal Processing,2017,88(5):100-110. doi: 10.1016/j.ymssp.2016.11.026
    [15]
    XU L, NIU M, ZHAO D, et al. Methodology for the immediate detection and treatment of wheel wear in contour grinding [J]. Precision Engineering,2019,60:405-412. doi: 10.1016/j.precisioneng.2019.09.006
    [16]
    LI L, AN Q. An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis [J]. Measurement,2016,79:44-52. doi: 10.1016/j.measurement.2015.10.029
    [17]
    KONG D, CHEN Y, LI N. Gaussian process regression for tool wear prediction [J]. Mechanical systems and signal processing,2018,104:556-574. doi: 10.1016/j.ymssp.2017.11.021
    [18]
    刘强. 刀具磨损的偏最小二乘回归分析与建模 [J]. 北京航空航天大学学报,2000,26(4):457-460. doi: 10.3969/j.issn.1001-5965.2000.04.023

    LIU Qiang. Pratial least squares regressive analysis and modeling for tool wear [J]. Journal of Beijing University of Aeronautics and Astronautics,2000,26(4):457-460. doi: 10.3969/j.issn.1001-5965.2000.04.023
    [19]
    张锴锋, 袁惠群, 聂鹏. 基于广义维数与优化BP神经网络的刀具磨损量预测 [J]. 东北大学学报(自然科学版),2013,34(9):1292-1295. doi: 10.3969/j.issn.1005-3026.2013.09.018

    ZHANG Kaifeng, YUAN Huiqun, NIE Peng. Prediction of tool wear based on generalized dimensions and optimized BP neural network [J]. Journal of Northeastern University (Natural Science),2013,34(9):1292-1295. doi: 10.3969/j.issn.1005-3026.2013.09.018
    [20]
    LI J, LU J, CHEN C, et al. Tool wear state prediction based on feature-based transfer learning [J]. International Journal of Advanced Manufacturing Technology,2021,113(11):3283-3301. doi: 10.1007/s00170-021-06780-6
    [21]
    史珂铭, 邹益胜, 刘永志, 等. 一种不同工艺条件下刀具磨损状态多类域适应迁移辨识方法 [J]. 中国机械工程,2022,33(15):1841-1849. doi: 10.3969/j.issn.1004-132X.2022.15.010

    SHI Keming, ZOU Yisheng, LIU Yongzhi, et al. A multi class domain adaptive transfer identification method for tool wear states under different processing conditions [J]. China Mechanical Engineering,2022,33(15):1841-1849. doi: 10.3969/j.issn.1004-132X.2022.15.010
    [22]
    何彦, 凌俊杰, 王禹林, 等. 基于长短时记忆卷积神经网络的刀具磨损在线监测模型 [J]. 中国机械工程,2020,31(16):1959-1967. doi: 10.3969/j.issn.1004-132X.2020.16.008

    HE Yan, LING Junjie, WANG Yulin, et al. In-process tool wear monitoring model based on LSTM-CNN [J]. China Mechanical Engineering,2020,31(16):1959-1967. doi: 10.3969/j.issn.1004-132X.2020.16.008
    [23]
    万鹏, 李迎光, 刘长青, 等. 基于域对抗门控网络的变工况刀具磨损精确预测方法 [J]. 航空学报,2021,42(10):524879.

    WAN Peng, LI Yingguang, LIU Changqing, et al. Method for accurate prediction of tool wear under varying cutting conditions based on domain adversarial gating neural network [J]. Acta Aeronautica et Astronautica Sinica,2021,42(10):524879.
    [24]
    MINH H L, SANG T T, ABDEL W M, et al. A new metaheuristic optimization based on K-means clustering algorithm and its application to structural damage identification [J]. Knowledge-Based Systems,2022,251:109189. doi: 10.1016/j.knosys.2022.109189
    [25]
    YANG Y, LIAO Q, WANG J, et a. Application of multi-objective particle swarm optimization based on short-term memory and K-means clustering in multi-modal multi-objective optimization [J]. Engineering Applications of Artificial Intelligence,2022,112:104866. doi: 10.1016/j.engappai.2022.104866
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(4)

    Article Metrics

    Article views (31) PDF downloads(5) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return