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AMPSO与SVR相结合的铣刀寿命预测研究

罗丹 惠记庄 丁凯 张泽宇 邵树军 杜超

罗丹,惠记庄,丁凯, 等. AMPSO与SVR相结合的铣刀寿命预测研究[J]. 机械科学与技术,2023,42(5):730-735 doi: 10.13433/j.cnki.1003-8728.20220036
引用本文: 罗丹,惠记庄,丁凯, 等. AMPSO与SVR相结合的铣刀寿命预测研究[J]. 机械科学与技术,2023,42(5):730-735 doi: 10.13433/j.cnki.1003-8728.20220036
LUO Dan, HUI Jizhuang, DING Kai, ZHANG Zeyu, SHAO Shujun, DU Chao. Life Prediction of Milling Cutters Combining AMPSO with SVR[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 730-735. doi: 10.13433/j.cnki.1003-8728.20220036
Citation: LUO Dan, HUI Jizhuang, DING Kai, ZHANG Zeyu, SHAO Shujun, DU Chao. Life Prediction of Milling Cutters Combining AMPSO with SVR[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 730-735. doi: 10.13433/j.cnki.1003-8728.20220036

AMPSO与SVR相结合的铣刀寿命预测研究

doi: 10.13433/j.cnki.1003-8728.20220036
基金项目: 陕西省科技重大专项(2018zdzx01-01-01)、中央高校基本科研业务费高新技术培育项目(300102250201)及陕西省高等教育教学改革重点攻关项目(19BG010)
详细信息
    作者简介:

    罗丹(1997−),硕士研究生,研究方向为刀具管控与寿命预测,luodan202105@163.com

    通讯作者:

    丁凯,副教授,硕士生导师,kding@chd.edu.cn

  • 中图分类号: TG71;TP183

Life Prediction of Milling Cutters Combining AMPSO with SVR

  • 摘要: 针对支持向量回归机在预测铣刀寿命时惩罚参数 和核函数参数 难确定、不同的参数设置对预测效果影响较大的问题,提出了自适应变异粒子群算法。在支持向量回归算法的基础上,引入AMPSO优化SVR参数,建立AMPSO与SVR相结合的数控铣刀寿命预测模型。通过硬质合金钢铣刀铣削的实验验证表明,相比于网格搜索法和神经网络算法,AMPSO-SVR算法在测试样本集的平均相对预测误差低至0.72%,相较前两者预测误差更小,可准确预测数控铣刀寿命,为数控加工过程中的换刀决策提供依据。
  • 图  1  训练样本集上的铣刀寿命预测结果

    表  1  实验样本数据(训练 + 测试)

    样本铣削速度/(r·min−1 )铣削深度/mm铣削宽度/mm铣刀直径/mm铣刀齿数/个每齿进给量/(mm·z−1)实际寿命/h
    11971406030.0880
    21822608040.1090
    316428010050.12105
    4124512016080.16155
    5115612018090.18170
    6908200250120.18245
    77812220300130.18290
    81068140200100.15200
    915048012060.14120
    10132410014070.15135
    下载: 导出CSV

    表  2  测试样本集上的铣刀寿命预测结果

    样本实际寿命/h预测寿命/h绝对误差/h相对误差/%
    8200198.82571.17430.5872
    9120119.19920.80080.6673
    10135136.22691.22690.9088
    下载: 导出CSV

    表  3  归一化方式对比

    样本不归一化[−1,1]归一化[0,1]归一化
    819.9984%0.5872%2.0883%
    945.8361%0.6673%2.0131%
    1018.5210%0.9088%4.1526%
    下载: 导出CSV

    表  4  算法预测结果对比


    实际
    寿命/h
    AMPSO-SVRGS-SVRBP神经网络
    预测
    寿命/h
    相对
    误差/%
    训练
    时间/s
    预测
    寿命/h
    相对
    误差/%
    训练
    时间/s
    预测
    寿命/h
    相对
    误差/%
    训练
    时间/s
    8200198.82570.58720.87202.03691.01850.43188.61005.69502.10
    9120119.19920.66730.87120.71460.59550.43112.22086.48272.10
    10135136.22690.90880.87137.88662.13820.43124.89387.48612.10
    下载: 导出CSV
  • [1] KONG D D, CHEN Y J, LI N, et al. Tool wear monitoring based on kernel principal component analysis and v-support vector regression[J]. The International Journal of Advanced Manufacturing Technology, 2017, 89(1-4): 175-190. doi: 10.1007/s00170-016-9070-x
    [2] 潘柏松, 俞铭杰, 项涌涌, 等. 考虑刀具磨损的铣削加工精度可靠性分析及工艺优化设计[J]. 计算机集成制造系统, 2020, 26(11): 2982-2991. doi: 10.13196/j.cims.2020.11.008

    PAN B S, YU M J, XIANG Y Y, et al. Accuracy reliability analysis and process optimization design of milling processing considering tool wear[J]. Computer Integrated Manufacturing Systems, 2020, 26(11): 2982-2991. (in Chinese) doi: 10.13196/j.cims.2020.11.008
    [3] Taylor F W. On the art of cutting metals[M]. American Society of Mechanical Engineers, 1906.
    [4] Hastings W F, Mathew P, Oxley L B. Estimated cutting temperatures their use as a predictor of tool performance when machining plain carbon steels[C]. Proceedings of thr 20th International MTDR Conference, 1979
    [5] 辛红敏, 吴华伟. 整体叶盘盘铣开槽加工刀具寿命预测[J]. 河南科技大学学报(自然科学版), 2020, 41(2): 16-20.

    XIN H M, WU H W. Tool life prediction in disc milling grooving of blisk[J]. Journal of Henan University of Science and Technology (Natural Science), 2020, 41(2): 16-20. (in Chinese)
    [6] WU D Z, JENNINGS C, TERPENNY J, et al. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests[J]. Journal of Manufacturing Science and Engineering, 2017, 139(7): 071018. doi: 10.1115/1.4036350
    [7] 曾晓雪, 吉卫喜, 徐杰. 基于CPSO-BP的刀具寿命预测算法[J]. 组合机床与自动化加工技术, 2020(8): 57-59.

    ZENG X X, JI W X, XU J. Research on tool life prediction method based on CPSO-BP[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2020(8): 57-59. (in Chinese)
    [8] 李鑫, 史振宇, 蒋森河, 等. 人工神经网络预测刀具磨损和切削力[J]. 控制理论与应用, 2018, 35(12): 1731-1737.

    LI X, SHI Z Y, JIANG S H, et al. Artificial neural network predicts tool wear and cutting force[J]. Control Theory & Applications, 2018, 35(12): 1731-1737. (in Chinese)
    [9] CHENG M H, JIAO L, SHI X C, et al. An intelligent prediction model of the tool wear based on machine learning in turning high strength steel[J]. Proceedings of the Institution of Mechanical Engineers, Part B:Journal of Engineering Manufacture, 2020, 234(13): 1580-1597. doi: 10.1177/0954405420935787
    [10] 程灿, 李建勇, 徐文胜, 等. 基于支持向量机与粒子滤波的刀具磨损状态识别[J]. 振动与冲击, 2018, 37(17): 48-55.

    CHENG C, LI J Y, XU W S, et al. Tools' wear state recognition based on support vector machine and particle filtering[J]. Journal of Vibration and Shock, 2018, 37(17): 48-55. (in Chinese)
    [11] BENKEDJOUH T, MEDJAHER K, ZERHOUNI N, et al. Health assessment and life prediction of cutting tools based on support vector regression[J]. Journal of Intelligent Manufacturing, 2015, 26(2): 213-223. doi: 10.1007/s10845-013-0774-6
    [12] 江鸿怀, 金晓怡, 邢亚飞, 等. 基于粒子群优化算法的五自由度机械臂轨迹规划[J]. 机械设计与研究, 2020, 36(1): 107-110. doi: 10.13952/j.cnki.jofmdr.2020.0022

    JIANG H H, JIN X Y, XING Y F, et al. Five-degree-of-freedom manipulator trajectory planning based on PSO particle algorithm[J]. Machine Design & Research, 2020, 36(1): 107-110. (in Chinese) doi: 10.13952/j.cnki.jofmdr.2020.0022
    [13] 杨旭, 邱明, 陈立海, 等. 基于PSO-RWE的自适应小波阈值函数滚动轴承振动信号去噪方法[J]. 航空动力学报, 2020, 35(11): 2339-2347.

    YANG X, QIU M, CHEN L H, et al. Adaptive wavelet threshold function based on PSO-RWE for vibration signal denoising of rolling bearing[J]. Journal of Aerospace Power, 2020, 35(11): 2339-2347. (in Chinese)
    [14] DENG W, YAO R, ZHAO H M, et al. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm[J]. Soft Computing, 2019, 23(7): 2445-2462. doi: 10.1007/s00500-017-2940-9
    [15] 薛宏. 企业级刀具全生命周期管理系统研发[D]. 重庆: 重庆大学, 2015

    XUE H. The research on enterprise tool lifecycle management system[D]. Chongqing: Chongqing University, 2015. (in Chinese)
    [16] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016

    ZHOU Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016. (in Chinese)
    [17] 黄媛, 孙树栋, 李兢尧. 基于ACO-BP神经网络的刀具寿命预测[J]. 机械科学与技术, 2009, 28(11): 1517-1521. doi: 10.3321/j.issn:1003-8728.2009.11.027

    HUANG Y, SUN S D, LI J Y. Prediction of cutting tool life based on ACO-BP neural network[J]. Mechanical Science and Technology for Aerospace Engineering, 2009, 28(11): 1517-1521. (in Chinese) doi: 10.3321/j.issn:1003-8728.2009.11.027
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出版历程
  • 收稿日期:  2021-05-30
  • 网络出版日期:  2023-05-29
  • 刊出日期:  2023-05-25

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