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基于Hopkinson杆和人工神经网络的三轴冲击力传感器同步解耦标定方法

王清华 郭伟国 徐丰 高猛 王志浩

王清华, 郭伟国, 徐丰, 高猛, 王志浩. 基于Hopkinson杆和人工神经网络的三轴冲击力传感器同步解耦标定方法[J]. 爆炸与冲击. doi: 10.11883/bzycj-2022-0015
引用本文: 王清华, 郭伟国, 徐丰, 高猛, 王志浩. 基于Hopkinson杆和人工神经网络的三轴冲击力传感器同步解耦标定方法[J]. 爆炸与冲击. doi: 10.11883/bzycj-2022-0015
WANG Qinghua, GUO Weiguo, XU Feng, GAO Meng, WANG Zhihao. Synchronous and decoupling calibration of tri-axial impact force transducers based on a Hopkinson bar and an artificial neural network[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2022-0015
Citation: WANG Qinghua, GUO Weiguo, XU Feng, GAO Meng, WANG Zhihao. Synchronous and decoupling calibration of tri-axial impact force transducers based on a Hopkinson bar and an artificial neural network[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2022-0015

基于Hopkinson杆和人工神经网络的三轴冲击力传感器同步解耦标定方法

doi: 10.11883/bzycj-2022-0015
基金项目: 国家自然科学基金(11702224, 11872051)
详细信息
    作者简介:

    王清华(1993- ),男,博士研究生,QinghuaWang@mail.nwpu.edu.cn

    通讯作者:

    郭伟国(1960- ),男,博士,教授,weiguo@nwpu.edu.cn

  • 中图分类号: O347; TP212; TH89

Synchronous and decoupling calibration of tri-axial impact force transducers based on a Hopkinson bar and an artificial neural network

  • 摘要: 三分量冲击力载荷的同步激励与输入输出间的精准建模是三轴冲击力传感器标定所面临的主要挑战。为了实现对三轴冲击力传感器的有效标定,使其能够准确测量空间中的三维冲击力载荷。首先,基于Hopkinson杆与矢量分解原理建立了一种高幅值(104 N量级)、窄脉宽(10-1 ms量级)可计量三分量冲击力载荷的同步激励方法,实现了对三轴冲击力传感器的同步加载。基于最小二乘原理与矩阵微分构建了三轴冲击力传感器的线性标定模型,并通过改变子弹结构与冲击气压揭示了线性解耦标定模型中传感器主灵敏度系数与轴间耦合灵敏度系数并非固定常数而均与冲击力载荷脉冲构型(幅值、脉宽)相关的冲击特性。将能够反映载荷构型信息的传感器各轴输出电压脉冲的幅值与脉宽作为影响因素,并以神经元的形式添加到人工神经网络(artificial neural network, ANN)的输入层,建立了基于ANN的三轴冲击力传感器输出电压与输入载荷间的代理模型,实现了数据驱动的三轴冲击力传感器非线性解耦标定。结果表明,ANN相对最小二乘模型标定精度更高,采用ANN进行三轴冲击力传感器标定具有可行性和有效性。
  • 图  1  三轴冲击力传感器同步加载原理

    Figure  1.  The synchronous loading principle for tri-axial impact force transducers

    图  2  基于ANN的非线性解耦标定原理图

    Figure  2.  Schematic diagram of the ANN-based nonlinear decoupling calibration model

    图  3  BP算法流程图

    Figure  3.  Flowchart of the BP algorithm

    图  4  B25B三轴冲击力传感器

    Figure  4.  B25B tri-axial impact force transducer

    图  5  实验中采用的标定装置

    Figure  5.  The calibration device used in experiments

    图  6  基于有限单元法建立的实验装置的数值模型

    Figure  6.  A numerical model built based on the finite element method for the experimental device

    图  7  各采样点处的轴向应力峰值随截面到杆端距离的变化

    Figure  7.  Variation of the axial stress peak at each sampling point with the distance from the cross-section to the front end of the bar

    图  8  不同型号子弹激励的典型冲击载荷

    Figure  8.  Typical loads excited by the different bullets

    图  9  传感器的典型输入与输出

    Figure  9.  Typical inputs and outputs of the tri-axial force transducer

    图  10  三轴冲击力传感器灵敏度系数随载荷幅值的变化

    Figure  10.  Variation of the sensitivity coefficients of the tri-axial impact force transducer with load amplitude

    图  11  三轴冲击力传感器灵敏度系数随载荷脉宽的变化

    Figure  11.  Variation of the sensitivity coefficients of the tri-axial impact force transducer with pulse width

    图  12  验证集及训练集的损失随迭代次数的变化

    Figure  12.  Variation of the losses of validation set and training set with the number of iterations

    图  13  不同加载情形下标定模型精度对比

    Figure  13.  Comparison of the accuracy of the calibration models under different loading situations

    表  1  B25B型三轴冲击力传感器的性能参数

    Table  1.   Parameters of a B25B tri-axial impact force transducer

    测量轴量程/
    kN
    激励电压/
    V
    一阶固有频率/
    kHz
    灵敏度系数
    mV/kN
    x151012.2750.480
    y151012.2750.480
    z301012.2730.223
    下载: 导出CSV
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  • 收稿日期:  2022-01-10
  • 修回日期:  2022-06-17
  • 网络出版日期:  2022-06-24

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