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基于机器学习的短纤维增强复合材料弹性力学性能预测

王吉玲 金浩 郭瑞文 史晨曦 杨礼芳 李梅娥 周进雄

王吉玲, 金浩, 郭瑞文, 等. 基于机器学习的短纤维增强复合材料弹性力学性能预测[J]. 复合材料学报, 2024, 42(0): 1-12.
引用本文: 王吉玲, 金浩, 郭瑞文, 等. 基于机器学习的短纤维增强复合材料弹性力学性能预测[J]. 复合材料学报, 2024, 42(0): 1-12.
WANG Jiling, JIN Hao, GUO Ruiwen, et al. Prediction of elastic properties of short fiber reinforced composites based on machine learning[J]. Acta Materiae Compositae Sinica.
Citation: WANG Jiling, JIN Hao, GUO Ruiwen, et al. Prediction of elastic properties of short fiber reinforced composites based on machine learning[J]. Acta Materiae Compositae Sinica.

基于机器学习的短纤维增强复合材料弹性力学性能预测

详细信息
    通讯作者:

    李梅娥,博士,副教授,硕士生导师,研究方向为材料成型过程的模拟仿真 E-mail: limeie@mail.xjtu.edu.cn

  • 中图分类号: TB332;TB333

Prediction of elastic properties of short fiber reinforced composites based on machine learning

  • 摘要: 短纤维增强复合材料弹性力学性能受其内部结构和基础材料性能影响显著,参数化分析这些影响需要极高的实验或数值分析成本。针对这一问题,本文将基于周期性代表性体积单元(RVE)的数值均匀化方法与人工神经网络(ANN)进行结合,分别构建了空间随机分布、层内随机分布和定向排列三种形式的短纤维增强复合材料力学性能预测代理模型。每个代理模型均可以快速实现不同参数组合(纤维长度、长径比、体积分数以及纤维和基体材料属性)下复合材料的等效弹性性能预测,拟合优度R2均在0.98以上,计算所用时间与常规模拟计算相比可忽略不计,大大节省了实验和计算成本,为短纤维增强复合材料的设计定制创造了重要条件。

     

  • 图  1  基于RSA算法的周期性RVE生成流程图

    Figure  1.  Flow chart of the periodic RVE generation based on RSA algorithm

    图  2  长径比为15、体积分数为10%的空间随机分布纤维增强复合材料的RVE

    Figure  2.  RVE of composites reinforced by spatially randomly distributed fibers with aspect ratio of 15 and volume fraction of 10%

    图  3  基于纤维边界几何近似的体素网格生成算法流程图

    Figure  3.  Flow chart of voxel mesh generation algorithm based on fiber boundary geometry approximation

    图  4  不同单元尺寸下单根纤维体素网格和RVE体素网格

    Figure  4.  Voxel mesh of a single fiber with different element sizes and voxel mesh of the RVE.

    图  5  六种加载方式:(a) E11;(b) E22;(c) E33;(d) G12;(e) G13;(f) G23(箭头表示位移载荷施加方向,红色虚线表示变形后的形状)

    Figure  5.  Six loading methods: (a) E11; (b) E22; (c) E33; (d) G12; (e) G13; (f) G23 (Arrows indicate the direction in which the displacement load is applied, the red dotted line indicates the deformed shape)

    图  6  T300短碳纤维(Csf)/Mg复合材料等效弹性性能随单元尺寸减小的变化:(a)弹性模量;(b)泊松比

    Figure  6.  Variation of effective material properties of T300 short fiber (Csf)/Mg composite regarding the decrease of the element size: (a) Elastic Modulus; (b) Poisson’s ratio

    图  7  长径比为10、体积分数为10%的层内随机分布Csf/Mg复合材料的RVE

    Figure  7.  RVE of Csf/Mg composites reinforced by in-layer randomly distributed fibers with aspect ratios of 15 and volume fraction of 10%

    图  8  长径比为40、体积分数为30%的定向分布Csf/Mg复合材料的RVE

    Figure  8.  RVE of Csf/Mg composites reinforced by aligned distributed fibers with aspect ratios of 40 and volume fraction of 30%

    图  9  有限元分析与代理模型相结合的全局框架示意图

    Figure  9.  Global framework diagram of finite element analysis combined with surrogate model

    图  10  三层人工神经网络结构(ANN)

    Figure  10.  Three layer artificial neural network structure(ANN)

    图  11  Csf/Mg复合材料弹性力学性能预测的拟合回归图:(a) 纤维在空间随机分布;(b) 纤维在层内随机分布;(c) 纤维定向分布

    Figure  11.  Fitting regression diagram for prediction of elastic mechanical properties of Csf/Mg composites: (a) The fibers are randomly distributed in space; (b) The fibers are randomly distributed in layer; (c) The fibers are aligned in Z direction

    图  12  随机纤维生成的不均匀性:(a) 位置不均匀;(b) 取向不均匀

    Figure  12.  Nonuniformity in random fiber generation: (a) Uneven position; (b) Uneven orientation

    表  1  数值均匀化、Digimat和拉伸试验[20]得到的Csf/Mg复合材料等效弹性性能比较

    Table  1.   Comparison of effective elastic properties of Csf/Mg composite obtained from the numerical homogenization, Digimat and tensile experiment[20]

    Properties Numerical homogenization Digimat Tensile experiment ErrorN-T ErrorD-T
    E/GPa 49.42 52.98 50.45 2.0% 5.0%
    G/GPa 18.12 19.44 19.02 4.7% 2.2%
    ν 0.3430 0.3384 0.3425 0.1% 1.2%
    下载: 导出CSV

    表  2  计算结果与Liu[10]的工作和Digimat的比较

    Table  2.   Comparison of the computational results with Liu’s[10] work and Digimat

    Properties Random distribution in layer Aligned distribution
    Present work Liu’s work Digimat Present work Liu’s work Digimat
    E11/GPa 2.8648 2.8176 2.7382 3.0210 3.4347 2.9302
    E22/GPa 2.6166 2.7453 2.5915 3.1762 3.4951 2.9302
    E33/GPa 2.1908 2.1695 2.0903 17.933 19.712 20.540
    G12/GPa 1.0134 1.0467 0.99472 1.0272 1.1591 0.98722
    G13/GPa 0.74077 0.74028 0.71036 1.1158 1.3543 1.0744
    G23/GPa 0.73648 0.73819 0.70854 1.2524 1.3049 1.0744
    ν12 0.34793 0.33629 0.34558 0.4407 0.42266 0.48406
    ν21 0.31873 0.32766 0.32706 0.4634 0.43010 0.48406
    ν13 0.33177 0.34107 0.34547 0.053278 0.055075 0.045703
    ν31 0.25533 0.26263 0.26372 0.31627 0.31608 0.32037
    ν23 0.34807 0.34568 0.35505 0.054968 0.054838 0.045703
    ν32 0.29293 0.27318 0.28638 0.31035 0.30928 0.32037
    下载: 导出CSV

    表  3  选定变量及其取值范围

    Table  3.   Input variables and their value ranges

    lf/mmafVf/%Ef/GPaνfEm/GPaνm
    Random distribution in space1-55-155-1070-2300.20-0.350.1-60.30-0.45
    Random distribution in layer1-510-155-1070-2300.20-0.350.1-60.30-0.45
    Aligned distribution1-1020-4020-3070-2300.20-0.350.1-60.30-0.45
    Notes: lf—Length of fiber; af—Aspect ratio of fiber; Vf—Volume fraction of fiber; Ef—Young’s modulus of fiber; νf—Poisson’s ratio of fiber; Em—Young’s modulus of matrix; νm—Poisson’s ratio of matrix.
    下载: 导出CSV
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  • 收稿日期:  2023-12-18
  • 修回日期:  2024-01-24
  • 录用日期:  2024-02-02
  • 网络出版日期:  2024-03-19

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