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BP神经网络预测复合材料热压罐成型均匀性

林源 关志东

林源, 关志东. BP神经网络预测复合材料热压罐成型均匀性[J]. 北京航空航天大学学报, 2021, 47(6): 1271-1276. doi: 10.13700/j.bh.1001-5965.2020.0158
引用本文: 林源, 关志东. BP神经网络预测复合材料热压罐成型均匀性[J]. 北京航空航天大学学报, 2021, 47(6): 1271-1276. doi: 10.13700/j.bh.1001-5965.2020.0158
LIN Yuan, GUAN Zhidong. Predicting the formation uniformity of composite autoclave by BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1271-1276. doi: 10.13700/j.bh.1001-5965.2020.0158(in Chinese)
Citation: LIN Yuan, GUAN Zhidong. Predicting the formation uniformity of composite autoclave by BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1271-1276. doi: 10.13700/j.bh.1001-5965.2020.0158(in Chinese)

BP神经网络预测复合材料热压罐成型均匀性

doi: 10.13700/j.bh.1001-5965.2020.0158
详细信息
    通讯作者:

    关志东, E-mail: zdguan@buaa.edu.cn

  • 中图分类号: V258+.3;TB332

Predicting the formation uniformity of composite autoclave by BP neural network

More Information
  • 摘要:

    复合材料热压罐成型过程中的固化度差值是复合材料固化度均匀性的主要表征参数之一。基于3层BP神经网络,以复合材料双平台固化工艺曲线的加热速率、保温时间和保温温度为输入参数,建立了成型过程任一时刻最大固化度差值的快速估算模型。仿真复合材料热压罐成型过程,得到最大固化度差值作为试验样本数据,对BP神经网络进行训练,训练结束后对该模型的准确性进行验证。结果表明:该BP神经网络估算模型准确性和效率较高,为复合材料热压罐成型最大固化度差值的估算提供了一种快速有效的新方法。

     

  • 图 1  BP神经网络模型结构

    Figure 1.  Structure of BP neural network model

    图 2  BP神经网络估算模型训练均方误差曲线

    Figure 2.  Training MSE curves of BP neural network estimation model

    表  1  复合材料AS4/3501-6热力学及固化动力学模型参数

    Table  1.   Thermodynamics and cure kinetics model parameters for AS4/3501-6 composites

    参数 数值
    ρ/(kg·m-3) 1 578
    Cp/(J·(kg·K)-1) 862
    kT/(W·(m·K)-1) 0.413 5
    kL/(W·(m·K)-1) 12.83
    Hr/(J·kg-1) 198.6×103
    A1/min-1 2.102×109
    A2/min-1 -2.014×109
    A3/min-1 1.960×105
    ΔE1/(J·mol-1) 8.07×104
    ΔE2/(J·mol-1) 7.78×104
    ΔE3/(J·mol-1) 5.66×104
    注:ρ为复合材料的密度; Cp为比热; kTkL分别为复合材料的横向和纵向导热系数; Hr为最终反应热; Ai(i=1, 2, 3)为频率因子; ΔEi(i=1, 2, 3)为活化能。
    下载: 导出CSV

    表  2  工艺参数取值范围

    Table  2.   Range of process parameters

    参数 取值范围
    a1/(℃·min-1) [1,5]
    a2/(℃·min-1) [1,5]
    T1/℃ [115,155]
    T2/℃ [175,215]
    tp/min [0, 100]
    tg/min [0, 150]
    下载: 导出CSV

    表  3  固化数据

    Table  3.   Curing data

    a1/(℃·min-1) a2/(℃·min-1) T1/℃ T2/℃ tp/min tg/min Δα
    3 3 120 180 120 60 0.008 23
    3 3 121 180 120 60 0.008 23
    3 3 119 180 120 60 0.008 36
    4 3 120 179 120 60 0.008 04
    2 3 120 179 120 60 0.007 96
    2 4 120 179 120 60 0.008 50
    2 2 120 179 120 60 0.006 78
    2 2 120 179 123 60 0.006 78
    1 1 116 176 102 72 0.003 48
    1 1 116 175 93 78 0.003 42
    1 1 116 176 93 78 0.003 42
    1 2 116 175 93 78 0.005 89
    1 1 116 175 90 78 0.003 42
    1 1 116 175 96 78 0.003 41
    1 1 116 175 96 80 0.003 39
    1 1 115 177 66 100 0.003 26
    1 1 121 175 78 100 0.002 92
    1 1 121 177 78 100 0.002 92
    1 1 130 175 78 100 0.002 33
    1 1 130 176 78 100 0.002 34
    下载: 导出CSV

    表  4  Δα的估算

    Table  4.   Estimation of Δα

    a1/(℃·min-1) a2/(℃·min-1) T1/℃ T2/℃ tp/min tg/min Δα的试验值Δαs Δα的估算值Δαg 误差值Δαsαg 误差率Δ/Δs
    3 3 120 181 120 60 0.008 56 0.008 09 0.000 47 0.054
    2 2 120 179 123 60 0.006 78 0.006 53 0.000 25 0.036
    1 1 119 178 120 60 0.003 82 0.003 89 -0.000 07 -0.018
    1 1 117 117 114 64 0.003 69 0.003 66 0.000 03 0.008
    1 1 115 175 90 88 0.003 38 0.003 50 -0.000 12 -0.035
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-23
  • 录用日期:  2020-05-30
  • 刊出日期:  2021-06-20

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