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基于神经网络的数据挖掘模型在吸能装置上的应用

车全伟 雷成 李玉如 朱涛 唐兆 姚曙光

车全伟, 雷成, 李玉如, 朱涛, 唐兆, 姚曙光. 基于神经网络的数据挖掘模型在吸能装置上的应用[J]. 西南交通大学学报, 2021, 56(5): 995-1001. doi: 10.3969/j.issn.0258-2724.20200266
引用本文: 车全伟, 雷成, 李玉如, 朱涛, 唐兆, 姚曙光. 基于神经网络的数据挖掘模型在吸能装置上的应用[J]. 西南交通大学学报, 2021, 56(5): 995-1001. doi: 10.3969/j.issn.0258-2724.20200266
CHE Quanwei, LEI Cheng, LI Yuru, ZHU Tao, TANG Zhao, YAO Shuguang. Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 995-1001. doi: 10.3969/j.issn.0258-2724.20200266
Citation: CHE Quanwei, LEI Cheng, LI Yuru, ZHU Tao, TANG Zhao, YAO Shuguang. Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 995-1001. doi: 10.3969/j.issn.0258-2724.20200266

基于神经网络的数据挖掘模型在吸能装置上的应用

doi: 10.3969/j.issn.0258-2724.20200266
基金项目: 国家重点研发计划课题(2016YFB1200404);四川省科技计划(2019YJ0216)
详细信息
    作者简介:

    车全伟(1984—),男,博士研究生,研究方向为机车车辆结构设计,E-mail:chequanwei.sf@crrcgc.cc

    通讯作者:

    朱涛(1984—),男,副研究员,研究方向为机车车辆结构强度、碰撞动力学,E-mail:zhutao034@swjtu.cn

  • 中图分类号: U270.1

Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device

  • 摘要: 针对传统有限元分析方法对机车车辆结构耐撞性计算效率低的问题,在已有仿真分析数据基础上,引入机器学习方法,对车辆关键结构的耐撞性以及碰撞安全性进行分析预测. 首先,建立基于神经网络的数据挖掘模型,在此基础上构建车辆关键结构的碰撞响应预测方法;其次,通过试验验证了防爬吸能装置有限元模型的正确性,以此模型为基础获得不同壁厚防爬吸能装置的碰撞响应仿真数据;然后,以吸能装置壁厚作为模型输入,不同壁厚所对应的位移、速度、界面力和内能等碰撞响应作为模型输出,将有限元仿真数据用于模型训练,优化后的数据挖掘模型的拟合优度在0.922以上;最后,为验证模型预测的准确性,将碰撞数学模型的预测结果与有限元仿真结果进行对比,速度、位移、界面力和内能的平均相对误差分别为7.10%、4.51%、6.20%和2.50%. 研究结果表明:基于神经网络构建的数据挖掘模型在保证精度的情况下,能很好地反映防爬吸能装置的碰撞特性,大幅降低了计算时间,提高了计算效率.

     

  • 图 1  多层神经网络架构

    Figure 1.  Multi-layer neural network architecture

    图 2  数据挖掘模型训练过程

    Figure 2.  Training process of data mining model

    图 3  基于神经网络的碰撞响应预测框架

    Figure 3.  Collision response prediction framework based on neural network

    图 4  防爬吸能装置碰撞有限元模型

    Figure 4.  Collision finite element model of energy absorbing device

    图 5  吸能装置的水平位移随时间变化

    Figure 5.  Horizontal displacement of energy absorption device changing with time

    图 6  吸能装置内能随位移变化

    Figure 6.  Internalenergy of energy absorption device changing with displacement

    图 7  不同壁厚下位移结果

    Figure 7.  Displacement results under different wall thicknesses

    图 8  不同壁厚下速度结果

    Figure 8.  Speed results at different wall thicknesses

    图 9  不同壁厚下内能结果

    Figure 9.  Internal energy results at different wall thicknesses

    图 10  不同壁厚下界面力结果

    Figure 10.  Interfacial force results at different wall thicknesses

    图 11  速度仿真-预测对比

    Figure 11.  Comparison of speed simulation prediction

    图 12  位移仿真-预测对比

    Figure 12.  Comparison of displacement simulation prediction

    图 13  内能仿真-预测对比

    Figure 13.  Comparison of internal energy simulation prediction

    图 14  界面力仿真-预测对比

    Figure 14.  Comparison of interfacial force simulation prediction

    表  1  吸能装置材料参数

    Table  1.   Material parameters of energy absorption device

    部件名材料壁厚/mm单元质量/kg
    吸能梁 Q310 2.5 shell 14.75
    导向梁 Q235A solid 11.90
    筋板 SUS304 3.0 shell 12.41
    防爬齿 Q345 solid 14.86
    安装座 Q355 16.0 solid 12.35
    安装板 Q355 4.0 solid 2.00
    下载: 导出CSV

    表  2  数据挖掘模型的训练拟合优度

    Table  2.   Training fitting-goodness of data mining model

    训练输出位移/mm速度/(m•s−1内能/kJ界面力/kN
    拟合优度0.9920.9970.9990.922
    下载: 导出CSV

    表  3  数据挖掘模型的计算效率与精度对比

    Table  3.   Comparison of computational efficiency and accuracy of data mining models

    方法速度平均
    误差/%
    位移平均
    误差/%
    界面力平均
    误差/%
    内能平均
    误差/%
    训练时间/
    min
    预测时间/
    min
    计算时间/
    min
    有限元仿真300.00
    数据挖掘模型7.104.516.202.502.250.122.37
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-06
  • 修回日期:  2020-06-21
  • 网络出版日期:  2020-08-25
  • 刊出日期:  2021-10-15

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