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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
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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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